var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://api.zotero.org/users/605836/collections/98W6NZ9F/items?key=Py0EWgS20Ou10SSxCEyfvr4L&format=bibtex&limit=100\",\"jsonp\":\"1\",\"host\":\"bibbase.org\",\"ssl\":false},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data\",\"issn\":\"1612-5118\",\"shorttitle\":\"ML-CASCADE\",\"url\":\"https://doi.org/10.1007/s10346-024-02360-3\",\"doi\":\"10.1007/s10346-024-02360-3\",\"abstract\":\"Landslides pose a significant threat to humans as well as the environment. Rapid and precise mapping of landslide extent is necessary for understanding their spatial distribution, assessing susceptibility, and developing early warning systems. Traditional landslide mapping methods rely on labor-intensive field studies and manual mapping using high-resolution imagery, which are both costly and time-consuming. While existing machine learning-based automated mapping methods exist, they have limited transferability due to low availability of training data and the inability to handle out-of-distribution scenarios. This study introduces ML-CASCADE, a user-friendly open-source tool designed for real-time landslide mapping. It is a semi-automated tool that requires the user to create landslide and non-landslide samples using pre- and post-landslide Sentinel-2 imagery to train a machine learning model. The model training features include Sentinel-2 data, terrain data, vegetation indices, and bare soil index. ML-CASCADE is developed as an easy-to-use application on top of Google Earth Engine and supports both pixel and object-based classification methods. We validate the landslide extent developed using ML-CASCADE with independent expert-developed inventories. ML-CASCADE is not only able to identify the landslide extent accurately but can also map a complex cluster of landslides within 5 min and a simple landslide within 2 min. Due to its ease of use, speed, and accuracy, ML-CASCADE will serve as a critical operational asset for landslide risk management.\",\"language\":\"en\",\"urldate\":\"2024-09-11\",\"journal\":\"Landslides\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Sharma\"],\"firstnames\":[\"Nirdesh\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2024\",\"keywords\":\"Artificial Intelligence, Cloud computing, Google earth engine, Landslide extent mapping, Machine learning, Sentinel-2\",\"bibtex\":\"@article{sharma_ml-cascade_2024,\\n\\ttitle = {{ML}-{CASCADE}: {A} machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data},\\n\\tissn = {1612-5118},\\n\\tshorttitle = {{ML}-{CASCADE}},\\n\\turl = {https://doi.org/10.1007/s10346-024-02360-3},\\n\\tdoi = {10.1007/s10346-024-02360-3},\\n\\tabstract = {Landslides pose a significant threat to humans as well as the environment. Rapid and precise mapping of landslide extent is necessary for understanding their spatial distribution, assessing susceptibility, and developing early warning systems. Traditional landslide mapping methods rely on labor-intensive field studies and manual mapping using high-resolution imagery, which are both costly and time-consuming. While existing machine learning-based automated mapping methods exist, they have limited transferability due to low availability of training data and the inability to handle out-of-distribution scenarios. This study introduces ML-CASCADE, a user-friendly open-source tool designed for real-time landslide mapping. It is a semi-automated tool that requires the user to create landslide and non-landslide samples using pre- and post-landslide Sentinel-2 imagery to train a machine learning model. The model training features include Sentinel-2 data, terrain data, vegetation indices, and bare soil index. ML-CASCADE is developed as an easy-to-use application on top of Google Earth Engine and supports both pixel and object-based classification methods. We validate the landslide extent developed using ML-CASCADE with independent expert-developed inventories. ML-CASCADE is not only able to identify the landslide extent accurately but can also map a complex cluster of landslides within 5 min and a simple landslide within 2 min. Due to its ease of use, speed, and accuracy, ML-CASCADE will serve as a critical operational asset for landslide risk management.},\\n\\tlanguage = {en},\\n\\turldate = {2024-09-11},\\n\\tjournal = {Landslides},\\n\\tauthor = {Sharma, Nirdesh and Saharia, Manabendra},\\n\\tmonth = sep,\\n\\tyear = {2024},\\n\\tkeywords = {Artificial Intelligence, Cloud computing, Google earth engine, Landslide extent mapping, Machine learning, Sentinel-2},\\n}\\n\\n\",\"author_short\":[\"Sharma, N.\",\"Saharia, M.\"],\"key\":\"sharma_ml-cascade_2024\",\"id\":\"sharma_ml-cascade_2024\",\"bibbaseid\":\"sharma-saharia-mlcascadeamachinelearningandcloudcomputingbasedtoolforrapidandautomatedmappingoflandslidesusingearthobservationdata-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/s10346-024-02360-3\"},\"keyword\":[\"Artificial Intelligence\",\"Cloud computing\",\"Google earth engine\",\"Landslide extent mapping\",\"Machine learning\",\"Sentinel-2\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Geospatial modeling and mapping of soil erosion in India\",\"volume\":\"240\",\"issn\":\"0341-8162\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0341816224001930\",\"doi\":\"10.1016/j.catena.2024.107996\",\"abstract\":\"Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classification system currently doesn’t exist over India. Given the resource-intensive and time-consuming nature of field experiments required for the measurement of soil loss across a vast country, the Revised Universal Soil Loss Equation (RUSLE) was extensively utilized for soil erosion calculations due to its simplicity, streamlined data requirements, and accuracy. This study estimates the yearly potential soil loss throughout India at a spatial resolution of 250 m and quantifies its variability considering districts, soil texture, soil type, land use and land cover, and basins. The relative importance of individual and combined impact of multiple parameters on quantified soil loss has been assessed using a random forest model. Rainfall erosivity (R-factor) emerges as the most crucial feature in estimating soil erosion in Indian conditions while rainfall intensity, combined with the topographic factor, demonstrated the highest influence on soil erosion in Indian conditions when the combined impact was assessed. Further, we mapped the Sediment Delivery Ratio (SDR) and Specific Sediment Yield (SSY) to assess the actual soil loss reaching downstream of basins across the national boundary. The national mean values for Sediment Delivery Ratio (SDR) and Sediment Yield (SSY) stand at 0.11 and 2.61 tons per hectare per year, respectively. The yearly potential soil loss for India is calculated at 21 tons per hectare per year, and nine out of the twenty districts with the highest susceptibility to soil erosion are in the state of Assam. Finally, a novel impact-based erosion-severity classification system has been introduced which finds that 29.46% of the landmass is prone to minor erosion while 3.17% experiences catastrophic erosion. This is the first comprehensive national-scale assessment of both soil erosion and sediment yield mapping over India, and the consequent classification system will enable the planning and implementation of soil conservation strategies locally as well as nationally.\",\"urldate\":\"2024-09-01\",\"journal\":\"CATENA\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Raj\"],\"firstnames\":[\"Ravi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chakma\"],\"firstnames\":[\"Sumedha\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2024\",\"keywords\":\"India, Sediment Delivery Ratio (SDR), Soil Conservation, Soil erosion, Specific Sediment Yield (SSY)\",\"pages\":\"107996\",\"bibtex\":\"@article{raj_geospatial_2024,\\n\\ttitle = {Geospatial modeling and mapping of soil erosion in {India}},\\n\\tvolume = {240},\\n\\tissn = {0341-8162},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S0341816224001930},\\n\\tdoi = {10.1016/j.catena.2024.107996},\\n\\tabstract = {Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classification system currently doesn’t exist over India. Given the resource-intensive and time-consuming nature of field experiments required for the measurement of soil loss across a vast country, the Revised Universal Soil Loss Equation (RUSLE) was extensively utilized for soil erosion calculations due to its simplicity, streamlined data requirements, and accuracy. This study estimates the yearly potential soil loss throughout India at a spatial resolution of 250 m and quantifies its variability considering districts, soil texture, soil type, land use and land cover, and basins. The relative importance of individual and combined impact of multiple parameters on quantified soil loss has been assessed using a random forest model. Rainfall erosivity (R-factor) emerges as the most crucial feature in estimating soil erosion in Indian conditions while rainfall intensity, combined with the topographic factor, demonstrated the highest influence on soil erosion in Indian conditions when the combined impact was assessed. Further, we mapped the Sediment Delivery Ratio (SDR) and Specific Sediment Yield (SSY) to assess the actual soil loss reaching downstream of basins across the national boundary. The national mean values for Sediment Delivery Ratio (SDR) and Sediment Yield (SSY) stand at 0.11 and 2.61 tons per hectare per year, respectively. The yearly potential soil loss for India is calculated at 21 tons per hectare per year, and nine out of the twenty districts with the highest susceptibility to soil erosion are in the state of Assam. Finally, a novel impact-based erosion-severity classification system has been introduced which finds that 29.46\\\\% of the landmass is prone to minor erosion while 3.17\\\\% experiences catastrophic erosion. This is the first comprehensive national-scale assessment of both soil erosion and sediment yield mapping over India, and the consequent classification system will enable the planning and implementation of soil conservation strategies locally as well as nationally.},\\n\\turldate = {2024-09-01},\\n\\tjournal = {CATENA},\\n\\tauthor = {Raj, Ravi and Saharia, Manabendra and Chakma, Sumedha},\\n\\tmonth = may,\\n\\tyear = {2024},\\n\\tkeywords = {India, Sediment Delivery Ratio (SDR), Soil Conservation, Soil erosion, Specific Sediment Yield (SSY)},\\n\\tpages = {107996},\\n}\\n\\n\",\"author_short\":[\"Raj, R.\",\"Saharia, M.\",\"Chakma, S.\"],\"key\":\"raj_geospatial_2024\",\"id\":\"raj_geospatial_2024\",\"bibbaseid\":\"raj-saharia-chakma-geospatialmodelingandmappingofsoilerosioninindia-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0341816224001930\"},\"keyword\":[\"India\",\"Sediment Delivery Ratio (SDR)\",\"Soil Conservation\",\"Soil erosion\",\"Specific Sediment Yield (SSY)\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning\",\"volume\":\"-1\",\"issn\":\"1525-7541, 1525-755X\",\"url\":\"https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-23-0196.1/JHM-D-23-0196.1.xml\",\"doi\":\"10.1175/JHM-D-23-0196.1\",\"abstract\":\"Flash floods are one of the most devastating natural disasters, yet many aspects of their severity and impact are poorly understood. The recession limb is related to post-flood recovery and its impact on communities, yet it remains less documented than the rising limb of the hydrograph to predict the peak discharge and timing of floods. This work introduces a new metric called the flash flood recovery or recoveriness, which is the potential for recovery of a watershed to pre-flood conditions. Using a comprehensive database of 78 years and supervised machine learning algorithms, flash flood recovery is mapped in the conterminous United States. A suite of geomorphological and climatological variables is used as predictors to provide probabilistic estimates of recoveriness. Slope index, river basin area and river length are found to be the most significant predictors to predict recoveriness. Several new localized hotspots were identified, such as the western slopes of the Appalachians consisting of Kentucky, Tennessee, and West Virginia and the interlinked areas of western Montana and northern Idaho. This new metric can be useful for prioritizing relief and rehabilitation efforts as well as precautionary measures for disaster risk reduction.\",\"language\":\"EN\",\"number\":\"aop\",\"urldate\":\"2024-08-31\",\"journal\":\"Journal of Hydrometeorology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kumar\"],\"firstnames\":[\"Anil\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirstetter\"],\"firstnames\":[\"Pierre\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2024\",\"note\":\"Publisher: American Meteorological Society Section: Journal of Hydrometeorology\",\"bibtex\":\"@article{kumar_mapping_2024,\\n\\ttitle = {Mapping a novel metric for {Flash} {Flood} {Recovery} using {Interpretable} {Machine} {Learning}},\\n\\tvolume = {-1},\\n\\tissn = {1525-7541, 1525-755X},\\n\\turl = {https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-23-0196.1/JHM-D-23-0196.1.xml},\\n\\tdoi = {10.1175/JHM-D-23-0196.1},\\n\\tabstract = {Flash floods are one of the most devastating natural disasters, yet many aspects of their severity and impact are poorly understood. The recession limb is related to post-flood recovery and its impact on communities, yet it remains less documented than the rising limb of the hydrograph to predict the peak discharge and timing of floods. This work introduces a new metric called the flash flood recovery or recoveriness, which is the potential for recovery of a watershed to pre-flood conditions. Using a comprehensive database of 78 years and supervised machine learning algorithms, flash flood recovery is mapped in the conterminous United States. A suite of geomorphological and climatological variables is used as predictors to provide probabilistic estimates of recoveriness. Slope index, river basin area and river length are found to be the most significant predictors to predict recoveriness. Several new localized hotspots were identified, such as the western slopes of the Appalachians consisting of Kentucky, Tennessee, and West Virginia and the interlinked areas of western Montana and northern Idaho. This new metric can be useful for prioritizing relief and rehabilitation efforts as well as precautionary measures for disaster risk reduction.},\\n\\tlanguage = {EN},\\n\\tnumber = {aop},\\n\\turldate = {2024-08-31},\\n\\tjournal = {Journal of Hydrometeorology},\\n\\tauthor = {Kumar, Anil and Saharia, Manabendra and Kirstetter, Pierre},\\n\\tmonth = aug,\\n\\tyear = {2024},\\n\\tnote = {Publisher: American Meteorological Society\\nSection: Journal of Hydrometeorology},\\n}\\n\\n\",\"author_short\":[\"Kumar, A.\",\"Saharia, M.\",\"Kirstetter, P.\"],\"key\":\"kumar_mapping_2024\",\"id\":\"kumar_mapping_2024\",\"bibbaseid\":\"kumar-saharia-kirstetter-mappinganovelmetricforflashfloodrecoveryusinginterpretablemachinelearning-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-23-0196.1/JHM-D-23-0196.1.xml\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Improved soil moisture estimation and detection of irrigation signal by incorporating SMAP soil moisture into the Indian Land Data Assimilation System (ILDAS)\",\"volume\":\"638\",\"issn\":\"00221694\",\"url\":\"https://linkinghub.elsevier.com/retrieve/pii/S0022169424009776\",\"doi\":\"10.1016/j.jhydrol.2024.131581\",\"abstract\":\"Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).\",\"language\":\"en\",\"urldate\":\"2024-06-28\",\"journal\":\"Journal of Hydrology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Chakraborty\"],\"firstnames\":[\"Arijit\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chakma\"],\"firstnames\":[\"Sumedha\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kumar\",\"Pandey\"],\"firstnames\":[\"Dharmendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Niranjan\",\"Kumar\"],\"firstnames\":[\"Kondapalli\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Thakur\"],\"firstnames\":[\"Praveen\",\"K.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kumar\"],\"firstnames\":[\"Sujay\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Getirana\"],\"firstnames\":[\"Augusto\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2024\",\"pages\":\"131581\",\"bibtex\":\"@article{chakraborty_improved_2024,\\n\\ttitle = {Improved soil moisture estimation and detection of irrigation signal by incorporating {SMAP} soil moisture into the {Indian} {Land} {Data} {Assimilation} {System} ({ILDAS})},\\n\\tvolume = {638},\\n\\tissn = {00221694},\\n\\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169424009776},\\n\\tdoi = {10.1016/j.jhydrol.2024.131581},\\n\\tabstract = {Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).},\\n\\tlanguage = {en},\\n\\turldate = {2024-06-28},\\n\\tjournal = {Journal of Hydrology},\\n\\tauthor = {Chakraborty, Arijit and Saharia, Manabendra and Chakma, Sumedha and Kumar Pandey, Dharmendra and Niranjan Kumar, Kondapalli and Thakur, Praveen K. and Kumar, Sujay and Getirana, Augusto},\\n\\tmonth = jul,\\n\\tyear = {2024},\\n\\tpages = {131581},\\n}\\n\\n\",\"author_short\":[\"Chakraborty, A.\",\"Saharia, M.\",\"Chakma, S.\",\"Kumar Pandey, D.\",\"Niranjan Kumar, K.\",\"Thakur, P. K.\",\"Kumar, S.\",\"Getirana, A.\"],\"key\":\"chakraborty_improved_2024\",\"id\":\"chakraborty_improved_2024\",\"bibbaseid\":\"chakraborty-saharia-chakma-kumarpandey-niranjankumar-thakur-kumar-getirana-improvedsoilmoistureestimationanddetectionofirrigationsignalbyincorporatingsmapsoilmoistureintotheindianlanddataassimilationsystemildas-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://linkinghub.elsevier.com/retrieve/pii/S0022169424009776\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Irrigation-driven groundwater depletion in the Ganges-Brahmaputra basin decreases the streamflow in the Bay of Bengal\",\"volume\":\"5\",\"issn\":\"2662-4435\",\"url\":\"https://www.nature.com/articles/s43247-024-01348-0\",\"doi\":\"10.1038/s43247-024-01348-0\",\"abstract\":\"Abstract Ganges and Brahmaputra, two of Asia’s most prominent rivers, have a crucial role in Southeast Asia’s geopolitics and economy and are home to one of the world’s biggest marine ecosystems. Irrigation-driven groundwater depletion and climate change affect the Ganges-Brahmaputra’s hydrology, threatening the stability of the Bay of Bengal. Here, we quantify, using results from a land reanalysis, the impacts of a changing climate and intensive irrigation on the surface water flowing into the Bay of Bengal. The effects of such activities mostly occurring in the Ganges basin, either intensified or lessened depending on the area by the climatic conditions, decrease freshwater flow into the bay by up to 1200 m 3 /s/year. While the increase in precipitation in the Ganges basin reduces the effects of groundwater depletion on the streamflow, the decrease in precipitation and the snowmelt decline in the Brahmaputra basin exacerbate streamflow reduction due to groundwater depletion at the delta.\",\"language\":\"en\",\"number\":\"1\",\"urldate\":\"2024-04-04\",\"journal\":\"Communications Earth & Environment\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Maina\"],\"firstnames\":[\"Fadji\",\"Z.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Getirana\"],\"firstnames\":[\"Augusto\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kumar\"],\"firstnames\":[\"Sujay\",\"V.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Biswas\"],\"firstnames\":[\"Nishan\",\"Kumar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McLarty\"],\"firstnames\":[\"Sasha\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Appana\"],\"firstnames\":[\"Ravi\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2024\",\"pages\":\"169\",\"bibtex\":\"@article{maina_irrigation-driven_2024,\\n\\ttitle = {Irrigation-driven groundwater depletion in the {Ganges}-{Brahmaputra} basin decreases the streamflow in the {Bay} of {Bengal}},\\n\\tvolume = {5},\\n\\tissn = {2662-4435},\\n\\turl = {https://www.nature.com/articles/s43247-024-01348-0},\\n\\tdoi = {10.1038/s43247-024-01348-0},\\n\\tabstract = {Abstract\\n            \\n              Ganges and Brahmaputra, two of Asia’s most prominent rivers, have a crucial role in Southeast Asia’s geopolitics and economy and are home to one of the world’s biggest marine ecosystems. Irrigation-driven groundwater depletion and climate change affect the Ganges-Brahmaputra’s hydrology, threatening the stability of the Bay of Bengal. Here, we quantify, using results from a land reanalysis, the impacts of a changing climate and intensive irrigation on the surface water flowing into the Bay of Bengal. The effects of such activities mostly occurring in the Ganges basin, either intensified or lessened depending on the area by the climatic conditions, decrease freshwater flow into the bay by up to 1200 m\\n              3\\n              /s/year. While the increase in precipitation in the Ganges basin reduces the effects of groundwater depletion on the streamflow, the decrease in precipitation and the snowmelt decline in the Brahmaputra basin exacerbate streamflow reduction due to groundwater depletion at the delta.},\\n\\tlanguage = {en},\\n\\tnumber = {1},\\n\\turldate = {2024-04-04},\\n\\tjournal = {Communications Earth \\\\& Environment},\\n\\tauthor = {Maina, Fadji Z. and Getirana, Augusto and Kumar, Sujay V. and Saharia, Manabendra and Biswas, Nishan Kumar and McLarty, Sasha and Appana, Ravi},\\n\\tmonth = apr,\\n\\tyear = {2024},\\n\\tpages = {169},\\n}\\n\\n\",\"author_short\":[\"Maina, F. Z.\",\"Getirana, A.\",\"Kumar, S. V.\",\"Saharia, M.\",\"Biswas, N. K.\",\"McLarty, S.\",\"Appana, R.\"],\"key\":\"maina_irrigation-driven_2024\",\"id\":\"maina_irrigation-driven_2024\",\"bibbaseid\":\"maina-getirana-kumar-saharia-biswas-mclarty-appana-irrigationdrivengroundwaterdepletioninthegangesbrahmaputrabasindecreasesthestreamflowinthebayofbengal-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.nature.com/articles/s43247-024-01348-0\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Towards an Indian land data assimilation system (ILDAS): A coupled hydrologic-hydraulic system for water balance assessments\",\"volume\":\"629\",\"issn\":\"0022-1694\",\"shorttitle\":\"Towards an Indian land data assimilation system (ILDAS)\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0022169423015469\",\"doi\":\"10.1016/j.jhydrol.2023.130604\",\"abstract\":\"Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.\",\"urldate\":\"2023-12-15\",\"journal\":\"Journal of Hydrology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Magotra\"],\"firstnames\":[\"Bhanu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Prakash\"],\"firstnames\":[\"Ved\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Getirana\"],\"firstnames\":[\"Augusto\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kumar\"],\"firstnames\":[\"Sujay\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pradhan\"],\"firstnames\":[\"Rohit\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dhanya\"],\"firstnames\":[\"C.\",\"T.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rajagopalan\"],\"firstnames\":[\"Balaji\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Singh\"],\"firstnames\":[\"Raghavendra\",\"P.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pandey\"],\"firstnames\":[\"Ayush\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mohapatra\"],\"firstnames\":[\"Mrutyunjay\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2024\",\"keywords\":\"Indian Land Data Assimilation System (ILDAS), Streamflow, Water balance assessments, south Asia\",\"pages\":\"130604\",\"bibtex\":\"@article{magotra_towards_2024,\\n\\ttitle = {Towards an {Indian} land data assimilation system ({ILDAS}): {A} coupled hydrologic-hydraulic system for water balance assessments},\\n\\tvolume = {629},\\n\\tissn = {0022-1694},\\n\\tshorttitle = {Towards an {Indian} land data assimilation system ({ILDAS})},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S0022169423015469},\\n\\tdoi = {10.1016/j.jhydrol.2023.130604},\\n\\tabstract = {Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.},\\n\\turldate = {2023-12-15},\\n\\tjournal = {Journal of Hydrology},\\n\\tauthor = {Magotra, Bhanu and Prakash, Ved and Saharia, Manabendra and Getirana, Augusto and Kumar, Sujay and Pradhan, Rohit and Dhanya, C. T. and Rajagopalan, Balaji and Singh, Raghavendra P. and Pandey, Ayush and Mohapatra, Mrutyunjay},\\n\\tmonth = feb,\\n\\tyear = {2024},\\n\\tkeywords = {Indian Land Data Assimilation System (ILDAS), Streamflow, Water balance assessments, south Asia},\\n\\tpages = {130604},\\n}\\n\\n\",\"author_short\":[\"Magotra, B.\",\"Prakash, V.\",\"Saharia, M.\",\"Getirana, A.\",\"Kumar, S.\",\"Pradhan, R.\",\"Dhanya, C. T.\",\"Rajagopalan, B.\",\"Singh, R. P.\",\"Pandey, A.\",\"Mohapatra, M.\"],\"key\":\"magotra_towards_2024\",\"id\":\"magotra_towards_2024\",\"bibbaseid\":\"magotra-prakash-saharia-getirana-kumar-pradhan-dhanya-rajagopalan-etal-towardsanindianlanddataassimilationsystemildasacoupledhydrologichydraulicsystemforwaterbalanceassessments-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0022169423015469\"},\"keyword\":[\"Indian Land Data Assimilation System (ILDAS)\",\"Streamflow\",\"Water balance assessments\",\"south Asia\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":6,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Precipitation inequality exacerbates streamflow inequality, but dams moderate it\",\"volume\":\"912\",\"issn\":\"0048-9697\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0048969723077288\",\"doi\":\"10.1016/j.scitotenv.2023.169098\",\"abstract\":\"Access to clean water is a fundamental human right, yet millions worldwide face the dire consequences of water scarcity and inadequate sanitation. Water inequality, characterized by disparities in access and availability of water resources, has emerged as a critical global challenge with far-reaching social, economic, and environmental implications. Using a globally representative observational streamflow dataset and Gini coefficients, this study investigates how streamflow inequality, which has a large impact on inequality of water availability, varies spatially and temporally, and its relationship with different underlying catchment characteristics. This study finds that watersheds in arid climates exhibit a higher degree of streamflow inequality than polar and equatorial ones. Africa experiences the highest streamflow inequality, followed by Australia, while South America experiences relatively lower streamflow inequality. Around 19.6 % of the catchments in Australia display an increasing trend in streamflow inequality, pointing to worsening conditions. Conversely, South America experiences a decreasing trend in streamflow inequality in 18.3 % of its catchments during the same period. It is also found that a more evenly distributed precipitation within the catchment and higher dam storage capacity corresponds to more evenly distributed streamflow availability throughout the year. This study enhances our understanding of streamflow inequality worldwide, which will aid policy formulation to foster sustainable development.\",\"urldate\":\"2023-12-09\",\"journal\":\"Science of The Total Environment\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kuntla\"],\"firstnames\":[\"Sai\",\"Kiran\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Prakash\"],\"firstnames\":[\"Samar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Villarini\"],\"firstnames\":[\"Gabriele\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2024\",\"keywords\":\"Gini coefficient, Streamflow, Sustainable development, Water, Water scarcity\",\"pages\":\"169098\",\"bibtex\":\"@article{kuntla_precipitation_2024,\\n\\ttitle = {Precipitation inequality exacerbates streamflow inequality, but dams moderate it},\\n\\tvolume = {912},\\n\\tissn = {0048-9697},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S0048969723077288},\\n\\tdoi = {10.1016/j.scitotenv.2023.169098},\\n\\tabstract = {Access to clean water is a fundamental human right, yet millions worldwide face the dire consequences of water scarcity and inadequate sanitation. Water inequality, characterized by disparities in access and availability of water resources, has emerged as a critical global challenge with far-reaching social, economic, and environmental implications. Using a globally representative observational streamflow dataset and Gini coefficients, this study investigates how streamflow inequality, which has a large impact on inequality of water availability, varies spatially and temporally, and its relationship with different underlying catchment characteristics. This study finds that watersheds in arid climates exhibit a higher degree of streamflow inequality than polar and equatorial ones. Africa experiences the highest streamflow inequality, followed by Australia, while South America experiences relatively lower streamflow inequality. Around 19.6 \\\\% of the catchments in Australia display an increasing trend in streamflow inequality, pointing to worsening conditions. Conversely, South America experiences a decreasing trend in streamflow inequality in 18.3 \\\\% of its catchments during the same period. It is also found that a more evenly distributed precipitation within the catchment and higher dam storage capacity corresponds to more evenly distributed streamflow availability throughout the year. This study enhances our understanding of streamflow inequality worldwide, which will aid policy formulation to foster sustainable development.},\\n\\turldate = {2023-12-09},\\n\\tjournal = {Science of The Total Environment},\\n\\tauthor = {Kuntla, Sai Kiran and Saharia, Manabendra and Prakash, Samar and Villarini, Gabriele},\\n\\tmonth = feb,\\n\\tyear = {2024},\\n\\tkeywords = {Gini coefficient, Streamflow, Sustainable development, Water, Water scarcity},\\n\\tpages = {169098},\\n}\\n\\n\",\"author_short\":[\"Kuntla, S. K.\",\"Saharia, M.\",\"Prakash, S.\",\"Villarini, G.\"],\"key\":\"kuntla_precipitation_2024\",\"id\":\"kuntla_precipitation_2024\",\"bibbaseid\":\"kuntla-saharia-prakash-villarini-precipitationinequalityexacerbatesstreamflowinequalitybutdamsmoderateit-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0048969723077288\"},\"keyword\":[\"Gini coefficient\",\"Streamflow\",\"Sustainable development\",\"Water\",\"Water scarcity\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data\",\"volume\":\"235\",\"issn\":\"0341-8162\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0341816223007440\",\"doi\":\"10.1016/j.catena.2023.107653\",\"abstract\":\"Landslide susceptibility represents the potential of slope failure for given geo-environmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome all these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of One -Sided Selection and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India's first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001° (∼100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73 %, sensitivity of 97.08 %, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75 % area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records. The ILSM is expected to aid policymaking in disaster risk reduction and developing landslide prediction models.\",\"urldate\":\"2023-11-16\",\"journal\":\"CATENA\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Sharma\"],\"firstnames\":[\"Nirdesh\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ramana\"],\"firstnames\":[\"G.\",\"V.\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2024\",\"keywords\":\"Big data, Ensemble learning, High resolution landslide susceptibility, One-sided selection (OSS), Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE)\",\"pages\":\"107653\",\"bibtex\":\"@article{sharma_high_2024,\\n\\ttitle = {High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data},\\n\\tvolume = {235},\\n\\tissn = {0341-8162},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S0341816223007440},\\n\\tdoi = {10.1016/j.catena.2023.107653},\\n\\tabstract = {Landslide susceptibility represents the potential of slope failure for given geo-environmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome all these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of One -Sided Selection and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India's first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001° (∼100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73 \\\\%, sensitivity of 97.08 \\\\%, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75 \\\\% area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records. The ILSM is expected to aid policymaking in disaster risk reduction and developing landslide prediction models.},\\n\\turldate = {2023-11-16},\\n\\tjournal = {CATENA},\\n\\tauthor = {Sharma, Nirdesh and Saharia, Manabendra and Ramana, G. V.},\\n\\tmonth = feb,\\n\\tyear = {2024},\\n\\tkeywords = {Big data, Ensemble learning, High resolution landslide susceptibility, One-sided selection (OSS), Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE)},\\n\\tpages = {107653},\\n}\\n\\n\",\"author_short\":[\"Sharma, N.\",\"Saharia, M.\",\"Ramana, G. V.\"],\"key\":\"sharma_high_2024\",\"id\":\"sharma_high_2024\",\"bibbaseid\":\"sharma-saharia-ramana-highresolutionlandslidesusceptibilitymappingusingensemblemachinelearningandgeospatialbigdata-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0341816223007440\"},\"keyword\":[\"Big data\",\"Ensemble learning\",\"High resolution landslide susceptibility\",\"One-sided selection (OSS)\",\"Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE)\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":13,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"LoRa-Based Communication System for Monitoring Water Quality of Lakes and Reservoirs\",\"volume\":\"1\",\"doi\":\"10.1109/MIGARS57353.2023.10064590\",\"abstract\":\"This paper describes a conceptual framework for the establishment of a Long-Range Wide Area Network (LoRaWAN)based communication system for monitoring water quality of lakes and reservoirs. The proposed system allows connection of multiple sensors to the LoRa transceivers and enables data transmission to a cloud server. In a proof-of-concept test, the LoRa gateway was set up, a temperature sensor node was connected and used to monitor lake water temperature, and data was transferred to LoRa gateway which serves as the central monitoring system and is visualized on a centralized dashboard. In this work, a coverage test of the proposed system is also carried out to test the LoRAWAN communication range in the urban environment. We find that the range of the system varies from 150-2,500 m and it is affected by the antenna positioning placement and obstacles in between the gateway and the sensor node. The maximum range was found to be 2.5 km when there was clear line of sight between the gateway and sensor node.\",\"booktitle\":\"2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pandey\"],\"firstnames\":[\"Rajdeep\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sharma\"],\"firstnames\":[\"Nirdesh\",\"Kumar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2023\",\"keywords\":\"Lake Monitoring, Lakes, LoRaWAN, Logic gates, Reservoirs, Temperature measurement, Temperature sensors, Water Quality, Water quality, Wide area networks\",\"pages\":\"1–4\",\"bibtex\":\"@inproceedings{pandey_lora-based_2023,\\n\\ttitle = {{LoRa}-{Based} {Communication} {System} for {Monitoring} {Water} {Quality} of {Lakes} and {Reservoirs}},\\n\\tvolume = {1},\\n\\tdoi = {10.1109/MIGARS57353.2023.10064590},\\n\\tabstract = {This paper describes a conceptual framework for the establishment of a Long-Range Wide Area Network (LoRaWAN)based communication system for monitoring water quality of lakes and reservoirs. The proposed system allows connection of multiple sensors to the LoRa transceivers and enables data transmission to a cloud server. In a proof-of-concept test, the LoRa gateway was set up, a temperature sensor node was connected and used to monitor lake water temperature, and data was transferred to LoRa gateway which serves as the central monitoring system and is visualized on a centralized dashboard. In this work, a coverage test of the proposed system is also carried out to test the LoRAWAN communication range in the urban environment. We find that the range of the system varies from 150-2,500 m and it is affected by the antenna positioning placement and obstacles in between the gateway and the sensor node. The maximum range was found to be 2.5 km when there was clear line of sight between the gateway and sensor node.},\\n\\tbooktitle = {2023 {International} {Conference} on {Machine} {Intelligence} for {GeoAnalytics} and {Remote} {Sensing} ({MIGARS})},\\n\\tauthor = {Pandey, Rajdeep and Sharma, Nirdesh Kumar and Saharia, Manabendra},\\n\\tmonth = jan,\\n\\tyear = {2023},\\n\\tkeywords = {Lake Monitoring, Lakes, LoRaWAN, Logic gates, Reservoirs, Temperature measurement, Temperature sensors, Water Quality, Water quality, Wide area networks},\\n\\tpages = {1--4},\\n}\\n\\n\",\"author_short\":[\"Pandey, R.\",\"Sharma, N. K.\",\"Saharia, M.\"],\"key\":\"pandey_lora-based_2023\",\"id\":\"pandey_lora-based_2023\",\"bibbaseid\":\"pandey-sharma-saharia-lorabasedcommunicationsystemformonitoringwaterqualityoflakesandreservoirs-2023\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Lake Monitoring\",\"Lakes\",\"LoRaWAN\",\"Logic gates\",\"Reservoirs\",\"Temperature measurement\",\"Temperature sensors\",\"Water Quality\",\"Water quality\",\"Wide area networks\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Mapping soil erodibility over India\",\"volume\":\"230\",\"issn\":\"0341-8162\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0341816223003624\",\"doi\":\"10.1016/j.catena.2023.107271\",\"abstract\":\"Soil erosion is a major environmental problem worldwide, and almost half of India’s total geographical area is susceptible to it. The Revised Universal Soil Loss Equation (RUSLE) has been widely used globally to estimate soil erosion, and the Soil erodibility factor, denoted by the K-factor, is an essential component of RUSLE. Although previous studies have assessed soil erodibility in India, they have been limited to small scales such as watersheds or districts. A national-scale assessment of soil erodibility doesn’t exist and is critical to developing a systematic understanding of soil erosion over India. In this study, we estimated soil erodibility factors over India using RUSLE Nomograph and Environmental Policy Integrated Climate (EPIC) model approaches at a high resolution of 250 m. Our results showed that the K-factor estimated using the Nomograph approach was more accurate than the observed soil erodibility factors. Additionally, we developed erodibility indices such as CR (Clay Ratio), MCR (Modified Clay Ratio), and CLOM (Critical Level of Organic Matter) to assess their sensitivity with respect to soil erodibility factors. Finally, we created a susceptibility to erosion map over India using CLOM index classification. The national average soil erodibility factor for India is estimated to be 0.028 t-ha-h/ha/MJ/mm. Histosols soil type is the least susceptible to erosion, while the Xerosols soil type is most susceptible among the prevalent soil classes in India. This is the first national-scale mapping of soil erodibility over India, providing an essential asset for soil conservation and erosion management planning by experts.\",\"urldate\":\"2023-08-16\",\"journal\":\"CATENA\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Raj\"],\"firstnames\":[\"Ravi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chakma\"],\"firstnames\":[\"Sumedha\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2023\",\"keywords\":\"Clay Ratio, Critical Level of Organic Matter, India, Modified Clay Ratio, Soil erodibility factor, Soil erosion\",\"pages\":\"107271\",\"bibtex\":\"@article{raj_mapping_2023,\\n\\ttitle = {Mapping soil erodibility over {India}},\\n\\tvolume = {230},\\n\\tissn = {0341-8162},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S0341816223003624},\\n\\tdoi = {10.1016/j.catena.2023.107271},\\n\\tabstract = {Soil erosion is a major environmental problem worldwide, and almost half of India’s total geographical area is susceptible to it. The Revised Universal Soil Loss Equation (RUSLE) has been widely used globally to estimate soil erosion, and the Soil erodibility factor, denoted by the K-factor, is an essential component of RUSLE. Although previous studies have assessed soil erodibility in India, they have been limited to small scales such as watersheds or districts. A national-scale assessment of soil erodibility doesn’t exist and is critical to developing a systematic understanding of soil erosion over India. In this study, we estimated soil erodibility factors over India using RUSLE Nomograph and Environmental Policy Integrated Climate (EPIC) model approaches at a high resolution of 250 m. Our results showed that the K-factor estimated using the Nomograph approach was more accurate than the observed soil erodibility factors. Additionally, we developed erodibility indices such as CR (Clay Ratio), MCR (Modified Clay Ratio), and CLOM (Critical Level of Organic Matter) to assess their sensitivity with respect to soil erodibility factors. Finally, we created a susceptibility to erosion map over India using CLOM index classification. The national average soil erodibility factor for India is estimated to be 0.028 t-ha-h/ha/MJ/mm. Histosols soil type is the least susceptible to erosion, while the Xerosols soil type is most susceptible among the prevalent soil classes in India. This is the first national-scale mapping of soil erodibility over India, providing an essential asset for soil conservation and erosion management planning by experts.},\\n\\turldate = {2023-08-16},\\n\\tjournal = {CATENA},\\n\\tauthor = {Raj, Ravi and Saharia, Manabendra and Chakma, Sumedha},\\n\\tmonth = sep,\\n\\tyear = {2023},\\n\\tkeywords = {Clay Ratio, Critical Level of Organic Matter, India, Modified Clay Ratio, Soil erodibility factor, Soil erosion},\\n\\tpages = {107271},\\n}\\n\\n\",\"author_short\":[\"Raj, R.\",\"Saharia, M.\",\"Chakma, S.\"],\"key\":\"raj_mapping_2023\",\"id\":\"raj_mapping_2023\",\"bibbaseid\":\"raj-saharia-chakma-mappingsoilerodibilityoverindia-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0341816223003624\"},\"keyword\":[\"Clay Ratio\",\"Critical Level of Organic Matter\",\"India\",\"Modified Clay Ratio\",\"Soil erodibility factor\",\"Soil erosion\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Spatio-temporal analysis of air quality and its relationship with COVID-19 lockdown over Dublin\",\"volume\":\"28\",\"issn\":\"2352-9385\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S2352938522001434\",\"doi\":\"10.1016/j.rsase.2022.100835\",\"abstract\":\"Air pollution has become one of the biggest challenges for human and environmental health. Major pollutants such as Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), Carbon Monoxide (CO), and Particulate matter (PM10 and PM2.5) are being ejected in a large quantity every day. Initially, authorities did not implement the strictest mitigation policies due to pressures of balancing the economic needs of people and public safety. Still, after realizing the effect of the COVID-19 pandemic, countries around the world imposed a complete lockdown to contain the outbreak, which had the unexpected benefit of causing a drastic improvement in air quality. The present study investigates the air pollution scenarios over the Dublin city through satellites (Sentinel-5P and Moderate Resolution Imaging Spectroradiometer) and ground-based observations. An average of 28% reduction in average NO2 level and a 27.7% improvement in AQI (Air Quality Index) was experienced in 2020 compared to 2019 during the lockdown period (27 March–05 June). We found that PM10 and PM2.5 are the most dominating factor in the AQI over Dublin.\",\"urldate\":\"2023-08-16\",\"journal\":\"Remote Sensing Applications: Society and Environment\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kumari\"],\"firstnames\":[\"Sushma\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yadav\"],\"firstnames\":[\"Avinash\",\"Chand\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dev\"],\"firstnames\":[\"Soumyabrata\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2022\",\"keywords\":\"AQI, Air pollution, COVID-19 pandemic, Dublin, MODIS, Sentinel-5P\",\"pages\":\"100835\",\"bibtex\":\"@article{kumari_spatio-temporal_2022,\\n\\ttitle = {Spatio-temporal analysis of air quality and its relationship with {COVID}-19 lockdown over {Dublin}},\\n\\tvolume = {28},\\n\\tissn = {2352-9385},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S2352938522001434},\\n\\tdoi = {10.1016/j.rsase.2022.100835},\\n\\tabstract = {Air pollution has become one of the biggest challenges for human and environmental health. Major pollutants such as Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), Carbon Monoxide (CO), and Particulate matter (PM10 and PM2.5) are being ejected in a large quantity every day. Initially, authorities did not implement the strictest mitigation policies due to pressures of balancing the economic needs of people and public safety. Still, after realizing the effect of the COVID-19 pandemic, countries around the world imposed a complete lockdown to contain the outbreak, which had the unexpected benefit of causing a drastic improvement in air quality. The present study investigates the air pollution scenarios over the Dublin city through satellites (Sentinel-5P and Moderate Resolution Imaging Spectroradiometer) and ground-based observations. An average of 28\\\\% reduction in average NO2 level and a 27.7\\\\% improvement in AQI (Air Quality Index) was experienced in 2020 compared to 2019 during the lockdown period (27 March–05 June). We found that PM10 and PM2.5 are the most dominating factor in the AQI over Dublin.},\\n\\turldate = {2023-08-16},\\n\\tjournal = {Remote Sensing Applications: Society and Environment},\\n\\tauthor = {Kumari, Sushma and Yadav, Avinash Chand and Saharia, Manabendra and Dev, Soumyabrata},\\n\\tmonth = nov,\\n\\tyear = {2022},\\n\\tkeywords = {AQI, Air pollution, COVID-19 pandemic, Dublin, MODIS, Sentinel-5P},\\n\\tpages = {100835},\\n}\\n\\n\",\"author_short\":[\"Kumari, S.\",\"Yadav, A. C.\",\"Saharia, M.\",\"Dev, S.\"],\"key\":\"kumari_spatio-temporal_2022\",\"id\":\"kumari_spatio-temporal_2022\",\"bibbaseid\":\"kumari-yadav-saharia-dev-spatiotemporalanalysisofairqualityanditsrelationshipwithcovid19lockdownoverdublin-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S2352938522001434\"},\"keyword\":[\"AQI\",\"Air pollution\",\"COVID-19 pandemic\",\"Dublin\",\"MODIS\",\"Sentinel-5P\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Global-scale characterization of streamflow extremes\",\"volume\":\"615\",\"issn\":\"00221694\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0022169422012380?via%3Dihub\",\"doi\":\"10.1016/j.jhydrol.2022.128668\",\"abstract\":\"The increasing risk of floods across the globe needs focused attention because of the extensive damage to human lives and economy. A comprehensive understanding of its causative factors is of vital importance. Yet catchment characterization studies are generally limited to case studies or regional domains. A comprehensive global characterization is currently unavailable, which requires collecting and collating a large number of datasets over vast areas. This study embraces large-sample data-driven science as a new paradigm to characterize streamflow extremes by utilizing global datasets of physiographic explanatory variables that could explain various facets of extreme streamflows. Along with the spatial and temporal variations of high streamflow extremes, their corre­ lation with various catchment characteristics such as geomorphology, meteorology, climatology, landcover, li­ thology, etc. were examined. The multidimensional relationships between the streamflow extremes and catchment characteristics were modeled using a Random Forest approach and combined with an interpretable machine learning framework to identify the most dominant factors in varying climate classes. Interpretation with SHAP (SHapley Additive exPlanations) reveals that meteorological variables are the most influential variables across the climatic classes. However, the variables and their influences change among different climatic classes. Moreover, different geomorphological variables come into dominance across climatic classes (such as basin relief in warm temperate and drainage texture in arid climates). Overall, the insights from the study could play a crucial role in predicting the unit peak discharge at ungauged stations from the known catchment characteristics. Moreover, these findings can also play a crucial role in formulating risk management strategy.\",\"language\":\"en\",\"urldate\":\"2022-12-11\",\"journal\":\"Journal of Hydrology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kuntla\"],\"firstnames\":[\"Sai\",\"Kiran\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirstetter\"],\"firstnames\":[\"Pierre\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2022\",\"pages\":\"128668\",\"bibtex\":\"@article{kuntla_global-scale_2022,\\n\\ttitle = {Global-scale characterization of streamflow extremes},\\n\\tvolume = {615},\\n\\tissn = {00221694},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S0022169422012380?via%3Dihub},\\n\\tdoi = {10.1016/j.jhydrol.2022.128668},\\n\\tabstract = {The increasing risk of floods across the globe needs focused attention because of the extensive damage to human lives and economy. A comprehensive understanding of its causative factors is of vital importance. Yet catchment characterization studies are generally limited to case studies or regional domains. A comprehensive global characterization is currently unavailable, which requires collecting and collating a large number of datasets over vast areas. This study embraces large-sample data-driven science as a new paradigm to characterize streamflow extremes by utilizing global datasets of physiographic explanatory variables that could explain various facets of extreme streamflows. Along with the spatial and temporal variations of high streamflow extremes, their corre­ lation with various catchment characteristics such as geomorphology, meteorology, climatology, landcover, li­ thology, etc. were examined. The multidimensional relationships between the streamflow extremes and catchment characteristics were modeled using a Random Forest approach and combined with an interpretable machine learning framework to identify the most dominant factors in varying climate classes. Interpretation with SHAP (SHapley Additive exPlanations) reveals that meteorological variables are the most influential variables across the climatic classes. However, the variables and their influences change among different climatic classes. Moreover, different geomorphological variables come into dominance across climatic classes (such as basin relief in warm temperate and drainage texture in arid climates). Overall, the insights from the study could play a crucial role in predicting the unit peak discharge at ungauged stations from the known catchment characteristics. Moreover, these findings can also play a crucial role in formulating risk management strategy.},\\n\\tlanguage = {en},\\n\\turldate = {2022-12-11},\\n\\tjournal = {Journal of Hydrology},\\n\\tauthor = {Kuntla, Sai Kiran and Saharia, Manabendra and Kirstetter, Pierre},\\n\\tmonth = dec,\\n\\tyear = {2022},\\n\\tpages = {128668},\\n}\\n\\n\",\"author_short\":[\"Kuntla, S. K.\",\"Saharia, M.\",\"Kirstetter, P.\"],\"key\":\"kuntla_global-scale_2022\",\"id\":\"kuntla_global-scale_2022\",\"bibbaseid\":\"kuntla-saharia-kirstetter-globalscalecharacterizationofstreamflowextremes-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0022169422012380?via%3Dihub\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau\",\"volume\":\"35\",\"issn\":\"0143-1161\",\"url\":\"https://doi.org/10.1080/01431161.2014.960612\",\"doi\":\"10.1080/01431161.2014.960612\",\"abstract\":\"This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.\",\"number\":\"19\",\"urldate\":\"2021-10-30\",\"journal\":\"International Journal of Remote Sensing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shen\"],\"firstnames\":[\"Yan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Xiong\"],\"firstnames\":[\"Anyuan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hong\"],\"firstnames\":[\"Yang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yu\"],\"firstnames\":[\"Jingjing\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pan\"],\"firstnames\":[\"Yang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chen\"],\"firstnames\":[\"Zhuoqi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2014\",\"note\":\"Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/01431161.2014.960612\",\"pages\":\"6843–6858\",\"bibtex\":\"@article{shen_uncertainty_2014,\\n\\ttitle = {Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the {Tibetan} {Plateau}},\\n\\tvolume = {35},\\n\\tissn = {0143-1161},\\n\\turl = {https://doi.org/10.1080/01431161.2014.960612},\\n\\tdoi = {10.1080/01431161.2014.960612},\\n\\tabstract = {This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.},\\n\\tnumber = {19},\\n\\turldate = {2021-10-30},\\n\\tjournal = {International Journal of Remote Sensing},\\n\\tauthor = {Shen, Yan and Xiong, Anyuan and Hong, Yang and Yu, Jingjing and Pan, Yang and Chen, Zhuoqi and Saharia, Manabendra},\\n\\tmonth = oct,\\n\\tyear = {2014},\\n\\tnote = {Publisher: Taylor \\\\& Francis\\n\\\\_eprint: https://doi.org/10.1080/01431161.2014.960612},\\n\\tpages = {6843--6858},\\n}\\n\\n\",\"author_short\":[\"Shen, Y.\",\"Xiong, A.\",\"Hong, Y.\",\"Yu, J.\",\"Pan, Y.\",\"Chen, Z.\",\"Saharia, M.\"],\"key\":\"shen_uncertainty_2014\",\"id\":\"shen_uncertainty_2014\",\"bibbaseid\":\"shen-xiong-hong-yu-pan-chen-saharia-uncertaintyanalysisoffivesatellitebasedprecipitationproductsandevaluationofthreeoptimallymergedmultialgorithmproductsoverthetibetanplateau-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1080/01431161.2014.960612\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas\",\"volume\":\"19\",\"issn\":\"1525-7541, 1525-755X\",\"url\":\"https://journals.ametsoc.org/view/journals/hydr/19/9/jhm-d-18-0027_1.xml\",\"doi\":\"10.1175/JHM-D-18-0027.1\",\"abstract\":\"Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.\",\"language\":\"EN\",\"number\":\"9\",\"urldate\":\"2022-09-27\",\"journal\":\"Journal of Hydrometeorology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kim\"],\"firstnames\":[\"Sunghee\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sadeghi\"],\"firstnames\":[\"Hossein\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Limon\"],\"firstnames\":[\"Reza\",\"Ahmad\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Seo\"],\"firstnames\":[\"Dong-Jun\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Philpott\"],\"firstnames\":[\"Andrew\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bell\"],\"firstnames\":[\"Frank\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brown\"],\"firstnames\":[\"James\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"He\"],\"firstnames\":[\"Minxue\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2018\",\"note\":\"Publisher: American Meteorological Society Section: Journal of Hydrometeorology\",\"pages\":\"1467–1483\",\"bibtex\":\"@article{kim_assessing_2018,\\n\\ttitle = {Assessing the {Skill} of {Medium}-{Range} {Ensemble} {Precipitation} and {Streamflow} {Forecasts} from the {Hydrologic} {Ensemble} {Forecast} {Service} ({HEFS}) for the {Upper} {Trinity} {River} {Basin} in {North} {Texas}},\\n\\tvolume = {19},\\n\\tissn = {1525-7541, 1525-755X},\\n\\turl = {https://journals.ametsoc.org/view/journals/hydr/19/9/jhm-d-18-0027_1.xml},\\n\\tdoi = {10.1175/JHM-D-18-0027.1},\\n\\tabstract = {Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20\\\\% over climatological forecast for the largest 1\\\\% of the observed biweekly flow.},\\n\\tlanguage = {EN},\\n\\tnumber = {9},\\n\\turldate = {2022-09-27},\\n\\tjournal = {Journal of Hydrometeorology},\\n\\tauthor = {Kim, Sunghee and Sadeghi, Hossein and Limon, Reza Ahmad and Saharia, Manabendra and Seo, Dong-Jun and Philpott, Andrew and Bell, Frank and Brown, James and He, Minxue},\\n\\tmonth = sep,\\n\\tyear = {2018},\\n\\tnote = {Publisher: American Meteorological Society\\nSection: Journal of Hydrometeorology},\\n\\tpages = {1467--1483},\\n}\\n\\n\",\"author_short\":[\"Kim, S.\",\"Sadeghi, H.\",\"Limon, R. A.\",\"Saharia, M.\",\"Seo, D.\",\"Philpott, A.\",\"Bell, F.\",\"Brown, J.\",\"He, M.\"],\"key\":\"kim_assessing_2018\",\"id\":\"kim_assessing_2018\",\"bibbaseid\":\"kim-sadeghi-limon-saharia-seo-philpott-bell-brown-etal-assessingtheskillofmediumrangeensembleprecipitationandstreamflowforecastsfromthehydrologicensembleforecastservicehefsfortheuppertrinityriverbasininnorthtexas-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://journals.ametsoc.org/view/journals/hydr/19/9/jhm-d-18-0027_1.xml\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system\",\"volume\":\"25\",\"issn\":\"1027-5606\",\"url\":\"https://hess.copernicus.org/articles/25/5603/2021/\",\"doi\":\"10.5194/hess-25-5603-2021\",\"abstract\":\"\\\\textlessp\\\\textgreater\\\\textlessstrong class=\\\"journal-contentHeaderColor\\\"\\\\textgreaterAbstract.\\\\textless/strong\\\\textgreater This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.\\\\textless/p\\\\textgreater\",\"language\":\"English\",\"number\":\"10\",\"urldate\":\"2022-04-02\",\"journal\":\"Hydrology and Earth System Sciences\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Newman\"],\"firstnames\":[\"Andrew\",\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stone\"],\"firstnames\":[\"Amanda\",\"G.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Holman\"],\"firstnames\":[\"Kathleen\",\"D.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Addor\"],\"firstnames\":[\"Nans\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Clark\"],\"firstnames\":[\"Martyn\",\"P.\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2021\",\"note\":\"Publisher: Copernicus GmbH\",\"pages\":\"5603–5621\",\"bibtex\":\"@article{newman_identifying_2021,\\n\\ttitle = {Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system},\\n\\tvolume = {25},\\n\\tissn = {1027-5606},\\n\\turl = {https://hess.copernicus.org/articles/25/5603/2021/},\\n\\tdoi = {10.5194/hess-25-5603-2021},\\n\\tabstract = {{\\\\textless}p{\\\\textgreater}{\\\\textless}strong class=\\\"journal-contentHeaderColor\\\"{\\\\textgreater}Abstract.{\\\\textless}/strong{\\\\textgreater} This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.{\\\\textless}/p{\\\\textgreater}},\\n\\tlanguage = {English},\\n\\tnumber = {10},\\n\\turldate = {2022-04-02},\\n\\tjournal = {Hydrology and Earth System Sciences},\\n\\tauthor = {Newman, Andrew J. and Stone, Amanda G. and Saharia, Manabendra and Holman, Kathleen D. and Addor, Nans and Clark, Martyn P.},\\n\\tmonth = oct,\\n\\tyear = {2021},\\n\\tnote = {Publisher: Copernicus GmbH},\\n\\tpages = {5603--5621},\\n}\\n\\n\",\"author_short\":[\"Newman, A. J.\",\"Stone, A. G.\",\"Saharia, M.\",\"Holman, K. D.\",\"Addor, N.\",\"Clark, M. P.\"],\"key\":\"newman_identifying_2021\",\"id\":\"newman_identifying_2021\",\"bibbaseid\":\"newman-stone-saharia-holman-addor-clark-identifyingsensitivitiesinfloodfrequencyanalysesusingastochastichydrologicmodelingsystem-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://hess.copernicus.org/articles/25/5603/2021/\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Mapping rainfall erosivity over India using multiple precipitation datasets\",\"volume\":\"214\",\"issn\":\"0341-8162\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0341816222002429\",\"doi\":\"10.1016/j.catena.2022.106256\",\"abstract\":\"Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water, and rainfall erosivity is one of the major components. The current assessments of rainfall erosivity in India are however largely based on rain-gauge recordings and surveys which hinders its estimation and understanding over large areas. Growing availability of remotely-sensed gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based estimations. In this study, multiple national and global gridded precipitation datasets were utilized to develop a high-resolution rainfall erosivity factor (R-factor) map to highlight areas prone to rainfall-induced erosion. Further, a large selection of empirical equations from literature were employed for estimating rainfall erosivity to provide a comparative analysis of these commonly adopted methods. The calculated rainfall erosivity is also compared with alternative methods to estimate R-factor such as Fournier Index (FI) and Modified Fournier Index (MFI). It was observed that MFI is highly correlated with rainfall erosivity, and an equation was finally proposed to estimate R-factor using MFI. This is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will aid in understanding and mitigating rainfall-induced erosion.\",\"language\":\"en\",\"urldate\":\"2022-08-01\",\"journal\":\"CATENA\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Raj\"],\"firstnames\":[\"Ravi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chakma\"],\"firstnames\":[\"Sumedha\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rafieinasab\"],\"firstnames\":[\"Arezoo\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2022\",\"keywords\":\"Fournier Index, India, Modified Fournier Index, Rainfall erosivity factor, Rainfall-kinetic energy\",\"pages\":\"106256\",\"bibtex\":\"@article{raj_mapping_2022,\\n\\ttitle = {Mapping rainfall erosivity over {India} using multiple precipitation datasets},\\n\\tvolume = {214},\\n\\tissn = {0341-8162},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S0341816222002429},\\n\\tdoi = {10.1016/j.catena.2022.106256},\\n\\tabstract = {Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water, and rainfall erosivity is one of the major components. The current assessments of rainfall erosivity in India are however largely based on rain-gauge recordings and surveys which hinders its estimation and understanding over large areas. Growing availability of remotely-sensed gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based estimations. In this study, multiple national and global gridded precipitation datasets were utilized to develop a high-resolution rainfall erosivity factor (R-factor) map to highlight areas prone to rainfall-induced erosion. Further, a large selection of empirical equations from literature were employed for estimating rainfall erosivity to provide a comparative analysis of these commonly adopted methods. The calculated rainfall erosivity is also compared with alternative methods to estimate R-factor such as Fournier Index (FI) and Modified Fournier Index (MFI). It was observed that MFI is highly correlated with rainfall erosivity, and an equation was finally proposed to estimate R-factor using MFI. This is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will aid in understanding and mitigating rainfall-induced erosion.},\\n\\tlanguage = {en},\\n\\turldate = {2022-08-01},\\n\\tjournal = {CATENA},\\n\\tauthor = {Raj, Ravi and Saharia, Manabendra and Chakma, Sumedha and Rafieinasab, Arezoo},\\n\\tmonth = jul,\\n\\tyear = {2022},\\n\\tkeywords = {Fournier Index, India, Modified Fournier Index, Rainfall erosivity factor, Rainfall-kinetic energy},\\n\\tpages = {106256},\\n}\\n\\n\",\"author_short\":[\"Raj, R.\",\"Saharia, M.\",\"Chakma, S.\",\"Rafieinasab, A.\"],\"key\":\"raj_mapping_2022\",\"id\":\"raj_mapping_2022\",\"bibbaseid\":\"raj-saharia-chakma-rafieinasab-mappingrainfallerosivityoverindiausingmultipleprecipitationdatasets-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0341816222002429\"},\"keyword\":[\"Fournier Index\",\"India\",\"Modified Fournier Index\",\"Rainfall erosivity factor\",\"Rainfall-kinetic energy\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Toward Predicting Flood Event Peak Discharge in Ungauged Basins by Learning Universal Hydrological Behaviors with Machine Learning\",\"volume\":\"22\",\"issn\":\"1525-7541, 1525-755X\",\"url\":\"https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-20-0302.1.xml\",\"doi\":\"10.1175/JHM-D-20-0302.1\",\"abstract\":\"Abstract In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine-learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multidimensional statistical modeling approach. Among different machine-learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created that can be deployed in the future for flash flood forecasting. The results confirm that, although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of subbasin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins that could most benefit from distributed hydrologic modeling.\",\"language\":\"EN\",\"number\":\"11\",\"urldate\":\"2022-04-02\",\"journal\":\"Journal of Hydrometeorology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Potdar\"],\"firstnames\":[\"Akhil\",\"Sanjay\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirstetter\"],\"firstnames\":[\"Pierre-Emmanuel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Woods\"],\"firstnames\":[\"Devon\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2021\",\"note\":\"Publisher: American Meteorological Society Section: Journal of Hydrometeorology\",\"pages\":\"2971–2982\",\"bibtex\":\"@article{potdar_toward_2021,\\n\\ttitle = {Toward {Predicting} {Flood} {Event} {Peak} {Discharge} in {Ungauged} {Basins} by {Learning} {Universal} {Hydrological} {Behaviors} with {Machine} {Learning}},\\n\\tvolume = {22},\\n\\tissn = {1525-7541, 1525-755X},\\n\\turl = {https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-20-0302.1.xml},\\n\\tdoi = {10.1175/JHM-D-20-0302.1},\\n\\tabstract = {Abstract In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine-learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multidimensional statistical modeling approach. Among different machine-learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created that can be deployed in the future for flash flood forecasting. The results confirm that, although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of subbasin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins that could most benefit from distributed hydrologic modeling.},\\n\\tlanguage = {EN},\\n\\tnumber = {11},\\n\\turldate = {2022-04-02},\\n\\tjournal = {Journal of Hydrometeorology},\\n\\tauthor = {Potdar, Akhil Sanjay and Kirstetter, Pierre-Emmanuel and Woods, Devon and Saharia, Manabendra},\\n\\tmonth = nov,\\n\\tyear = {2021},\\n\\tnote = {Publisher: American Meteorological Society\\nSection: Journal of Hydrometeorology},\\n\\tpages = {2971--2982},\\n}\\n\\n\",\"author_short\":[\"Potdar, A. S.\",\"Kirstetter, P.\",\"Woods, D.\",\"Saharia, M.\"],\"key\":\"potdar_toward_2021\",\"id\":\"potdar_toward_2021\",\"bibbaseid\":\"potdar-kirstetter-woods-saharia-towardpredictingfloodeventpeakdischargeinungaugedbasinsbylearninguniversalhydrologicalbehaviorswithmachinelearning-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-20-0302.1.xml\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"On the Impact of Rainfall Spatial Variability, Geomorphology, and Climatology on Flash Floods\",\"volume\":\"57\",\"issn\":\"1944-7973\",\"url\":\"https://onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029124\",\"doi\":\"10.1029/2020WR029124\",\"abstract\":\"AbstractThe effects of spatial variability of rainfall, geomorphology, and climatology of precipitation and temperature on the hydrologic response remain poorly understood. This study characterizes the catchment response in terms of a variable called flashiness, that describes the severity of the flood response as the rate of rise of the unit discharge. It overcomes limitations of prior works based on limited case studies or simulations by gathering information on basins of widely varying characteristics and by using a high-resolution rainfall and flooding event data set spanning 10 years over the Continental United States. The objective is to develop a robust understanding of how rainfall spatial variability influences flash flood severity and to assess its contribution relative to basin physiography and climatology. This study explores the first-order dependencies as well as the variability in these relationships and investigates the complex interactions using a multi-dimensional statistical modeling approach. The results confirm that the spatial organization of rainfall influences the basin response on par with geomorphology and climatology. Basin physiography dampens the effect of lower rainfall intensities, while higher rainfall overwhelms other factors and primarily contributes to flashiness. Dispersion of precipitation with respect to the flow path decreases flood severity. An improved understanding of sub-basin scale rainfall spatial variability aids in developing a robust flash flood severity index to identify and mitigate flash flooding situations as well as identifying basins which could most benefit from distributed hydrologic modeling.\",\"language\":\"en\",\"number\":\"9\",\"urldate\":\"2021-09-24\",\"journal\":\"Water Resources Research\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirstetter\"],\"firstnames\":[\"Pierre-Emmanuel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vergara\"],\"firstnames\":[\"Humberto\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gourley\"],\"firstnames\":[\"Jonathan\",\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Emmanuel\"],\"firstnames\":[\"Isabelle\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Andrieu\"],\"firstnames\":[\"Hervé\"],\"suffixes\":[]}],\"year\":\"2021\",\"note\":\"_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029124\",\"keywords\":\"flash flood, geomorphology, rainfall spatial variability\",\"pages\":\"e2020WR029124\",\"bibtex\":\"@article{saharia_impact_2021,\\n\\ttitle = {On the {Impact} of {Rainfall} {Spatial} {Variability}, {Geomorphology}, and {Climatology} on {Flash} {Floods}},\\n\\tvolume = {57},\\n\\tissn = {1944-7973},\\n\\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029124},\\n\\tdoi = {10.1029/2020WR029124},\\n\\tabstract = {AbstractThe effects of spatial variability of rainfall, geomorphology, and climatology of precipitation and temperature on the hydrologic response remain poorly understood. This study characterizes the catchment response in terms of a variable called flashiness, that describes the severity of the flood response as the rate of rise of the unit discharge. It overcomes limitations of prior works based on limited case studies or simulations by gathering information on basins of widely varying characteristics and by using a high-resolution rainfall and flooding event data set spanning 10 years over the Continental United States. The objective is to develop a robust understanding of how rainfall spatial variability influences flash flood severity and to assess its contribution relative to basin physiography and climatology. This study explores the first-order dependencies as well as the variability in these relationships and investigates the complex interactions using a multi-dimensional statistical modeling approach. The results confirm that the spatial organization of rainfall influences the basin response on par with geomorphology and climatology. Basin physiography dampens the effect of lower rainfall intensities, while higher rainfall overwhelms other factors and primarily contributes to flashiness. Dispersion of precipitation with respect to the flow path decreases flood severity. An improved understanding of sub-basin scale rainfall spatial variability aids in developing a robust flash flood severity index to identify and mitigate flash flooding situations as well as identifying basins which could most benefit from distributed hydrologic modeling.},\\n\\tlanguage = {en},\\n\\tnumber = {9},\\n\\turldate = {2021-09-24},\\n\\tjournal = {Water Resources Research},\\n\\tauthor = {Saharia, Manabendra and Kirstetter, Pierre-Emmanuel and Vergara, Humberto and Gourley, Jonathan J. and Emmanuel, Isabelle and Andrieu, Hervé},\\n\\tyear = {2021},\\n\\tnote = {\\\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029124},\\n\\tkeywords = {flash flood, geomorphology, rainfall spatial variability},\\n\\tpages = {e2020WR029124},\\n}\\n\\n\",\"author_short\":[\"Saharia, M.\",\"Kirstetter, P.\",\"Vergara, H.\",\"Gourley, J. J.\",\"Emmanuel, I.\",\"Andrieu, H.\"],\"key\":\"saharia_impact_2021\",\"id\":\"saharia_impact_2021\",\"bibbaseid\":\"saharia-kirstetter-vergara-gourley-emmanuel-andrieu-ontheimpactofrainfallspatialvariabilitygeomorphologyandclimatologyonflashfloods-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029124\"},\"keyword\":[\"flash flood\",\"geomorphology\",\"rainfall spatial variability\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Mapping Flash Flood Severity in the United States\",\"volume\":\"18\",\"issn\":\"1525-7541, 1525-755X\",\"url\":\"https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0082_1.xml\",\"doi\":\"10.1175/JHM-D-16-0082.1\",\"abstract\":\"\\\\textlesssection class=\\\"abstract\\\"\\\\textgreater\\\\textlessh2 class=\\\"abstractTitle text-title my-1\\\" id=\\\"d3028e2\\\"\\\\textgreaterAbstract\\\\textless/h2\\\\textgreater\\\\textlessp\\\\textgreaterFlash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.\\\\textless/p\\\\textgreater\\\\textless/section\\\\textgreater\",\"language\":\"EN\",\"number\":\"2\",\"urldate\":\"2021-04-09\",\"journal\":\"Journal of Hydrometeorology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirstetter\"],\"firstnames\":[\"Pierre-Emmanuel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vergara\"],\"firstnames\":[\"Humberto\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gourley\"],\"firstnames\":[\"Jonathan\",\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hong\"],\"firstnames\":[\"Yang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Giroud\"],\"firstnames\":[\"Marine\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2017\",\"note\":\"Publisher: American Meteorological Society Section: Journal of Hydrometeorology\",\"pages\":\"397–411\",\"bibtex\":\"@article{saharia_mapping_2017,\\n\\ttitle = {Mapping {Flash} {Flood} {Severity} in the {United} {States}},\\n\\tvolume = {18},\\n\\tissn = {1525-7541, 1525-755X},\\n\\turl = {https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0082_1.xml},\\n\\tdoi = {10.1175/JHM-D-16-0082.1},\\n\\tabstract = {{\\\\textless}section class=\\\"abstract\\\"{\\\\textgreater}{\\\\textless}h2 class=\\\"abstractTitle text-title my-1\\\" id=\\\"d3028e2\\\"{\\\\textgreater}Abstract{\\\\textless}/h2{\\\\textgreater}{\\\\textless}p{\\\\textgreater}Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.{\\\\textless}/p{\\\\textgreater}{\\\\textless}/section{\\\\textgreater}},\\n\\tlanguage = {EN},\\n\\tnumber = {2},\\n\\turldate = {2021-04-09},\\n\\tjournal = {Journal of Hydrometeorology},\\n\\tauthor = {Saharia, Manabendra and Kirstetter, Pierre-Emmanuel and Vergara, Humberto and Gourley, Jonathan J. and Hong, Yang and Giroud, Marine},\\n\\tmonth = feb,\\n\\tyear = {2017},\\n\\tnote = {Publisher: American Meteorological Society\\nSection: Journal of Hydrometeorology},\\n\\tpages = {397--411},\\n}\\n\\n\",\"author_short\":[\"Saharia, M.\",\"Kirstetter, P.\",\"Vergara, H.\",\"Gourley, J. J.\",\"Hong, Y.\",\"Giroud, M.\"],\"key\":\"saharia_mapping_2017\",\"id\":\"saharia_mapping_2017\",\"bibbaseid\":\"saharia-kirstetter-vergara-gourley-hong-giroud-mappingflashfloodseverityintheunitedstates-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0082_1.xml\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting\",\"volume\":\"16\",\"issn\":\"1976-3808\",\"url\":\"https://doi.org/10.1007/s12205-012-1463-2\",\"doi\":\"10.1007/s12205-012-1463-2\",\"abstract\":\"Artificial Neural Networks have been widely used to develop effective runoff-forecasting models. An overwhelming majority of networks are static in nature and also developed without incorporating geomorphologic information of the watershed. The objective of this study is to develop an efficient dynamic neural network model which also accounts for morphometric characteristics of the catchment. The model developed using Time-Lagged Recurrent Neural Networks (TLRNs) is used to estimate runoff for river Dikrong, a tributary of river Brahmaputra in India. Comparisons with traditional static models, with and without integration of geomorphologic data, reveal the proposed model to be a promising tool in operational hydrology.\",\"language\":\"en\",\"number\":\"5\",\"urldate\":\"2021-03-31\",\"journal\":\"KSCE Journal of Civil Engineering\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bhattacharjya\"],\"firstnames\":[\"Rajib\",\"Kumar\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2012\",\"pages\":\"862–869\",\"bibtex\":\"@article{saharia_geomorphology-based_2012,\\n\\ttitle = {Geomorphology-based {Time}-{Lagged} {Recurrent} {Neural} {Networks} for runoff forecasting},\\n\\tvolume = {16},\\n\\tissn = {1976-3808},\\n\\turl = {https://doi.org/10.1007/s12205-012-1463-2},\\n\\tdoi = {10.1007/s12205-012-1463-2},\\n\\tabstract = {Artificial Neural Networks have been widely used to develop effective runoff-forecasting models. An overwhelming majority of networks are static in nature and also developed without incorporating geomorphologic information of the watershed. The objective of this study is to develop an efficient dynamic neural network model which also accounts for morphometric characteristics of the catchment. The model developed using Time-Lagged Recurrent Neural Networks (TLRNs) is used to estimate runoff for river Dikrong, a tributary of river Brahmaputra in India. Comparisons with traditional static models, with and without integration of geomorphologic data, reveal the proposed model to be a promising tool in operational hydrology.},\\n\\tlanguage = {en},\\n\\tnumber = {5},\\n\\turldate = {2021-03-31},\\n\\tjournal = {KSCE Journal of Civil Engineering},\\n\\tauthor = {Saharia, Manabendra and Bhattacharjya, Rajib Kumar},\\n\\tmonth = jul,\\n\\tyear = {2012},\\n\\tpages = {862--869},\\n}\\n\\n\",\"author_short\":[\"Saharia, M.\",\"Bhattacharjya, R. K.\"],\"key\":\"saharia_geomorphology-based_2012\",\"id\":\"saharia_geomorphology-based_2012\",\"bibbaseid\":\"saharia-bhattacharjya-geomorphologybasedtimelaggedrecurrentneuralnetworksforrunoffforecasting-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/s12205-012-1463-2\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Analysis of rainfall and temperature trends in northeast India\",\"volume\":\"33\",\"copyright\":\"Copyright © 2012 Royal Meteorological Society\",\"issn\":\"1097-0088\",\"url\":\"https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3483\",\"doi\":\"https://doi.org/10.1002/joc.3483\",\"abstract\":\"The northeast region (NER) of India covers an area of 0.26 million km2. This region is one of the highest rainfall-receiving regions on the planet. Consequently, it has huge water and hydropower potential and analysis of rainfall and temperature trends would be of interest to water and energy planners. Trends in monthly, seasonal, and annual rainfall and temperature on the subdivision and regional scale for the NER were examined in this study. Trend analysis of rainfall data series for 1871–2008 did not show any clear trend for the region as a whole, although there are seasonal trends for some seasons and for some hydro-meteorological subdivisions. Similar analysis for temperature data showed that all the four temperature variables (maximum, minimum, and mean temperatures and temperature range) had rising trend. Notably for the post-monsoon season, the Sen's estimator of slope ( °C/year) was 0.019, 0.011, and 0.015 for the maximum, minimum, and mean temperature, respectively. Copyright © 2012 Royal Meteorological Society\",\"language\":\"en\",\"number\":\"4\",\"urldate\":\"2021-03-31\",\"journal\":\"International Journal of Climatology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Jain\"],\"firstnames\":[\"S.\",\"K.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kumar\"],\"firstnames\":[\"Vijay\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"M.\"],\"suffixes\":[]}],\"year\":\"2013\",\"note\":\"_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.3483\",\"keywords\":\"northeast India, rainfall, temperature, trend analysis\",\"pages\":\"968–978\",\"bibtex\":\"@article{jain_analysis_2013,\\n\\ttitle = {Analysis of rainfall and temperature trends in northeast {India}},\\n\\tvolume = {33},\\n\\tcopyright = {Copyright © 2012 Royal Meteorological Society},\\n\\tissn = {1097-0088},\\n\\turl = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3483},\\n\\tdoi = {https://doi.org/10.1002/joc.3483},\\n\\tabstract = {The northeast region (NER) of India covers an area of 0.26 million km2. This region is one of the highest rainfall-receiving regions on the planet. Consequently, it has huge water and hydropower potential and analysis of rainfall and temperature trends would be of interest to water and energy planners. Trends in monthly, seasonal, and annual rainfall and temperature on the subdivision and regional scale for the NER were examined in this study. Trend analysis of rainfall data series for 1871–2008 did not show any clear trend for the region as a whole, although there are seasonal trends for some seasons and for some hydro-meteorological subdivisions. Similar analysis for temperature data showed that all the four temperature variables (maximum, minimum, and mean temperatures and temperature range) had rising trend. Notably for the post-monsoon season, the Sen's estimator of slope ( °C/year) was 0.019, 0.011, and 0.015 for the maximum, minimum, and mean temperature, respectively. Copyright © 2012 Royal Meteorological Society},\\n\\tlanguage = {en},\\n\\tnumber = {4},\\n\\turldate = {2021-03-31},\\n\\tjournal = {International Journal of Climatology},\\n\\tauthor = {Jain, S. K. and Kumar, Vijay and Saharia, M.},\\n\\tyear = {2013},\\n\\tnote = {\\\\_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.3483},\\n\\tkeywords = {northeast India, rainfall, temperature, trend analysis},\\n\\tpages = {968--978},\\n}\\n\\n\",\"author_short\":[\"Jain, S. K.\",\"Kumar, V.\",\"Saharia, M.\"],\"key\":\"jain_analysis_2013\",\"id\":\"jain_analysis_2013\",\"bibbaseid\":\"jain-kumar-saharia-analysisofrainfallandtemperaturetrendsinnortheastindia-2013\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3483\"},\"keyword\":[\"northeast India\",\"rainfall\",\"temperature\",\"trend analysis\"],\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"A public Cloud-based China’s Landslide Inventory Database (CsLID): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011\",\"volume\":\"13\",\"issn\":\"1993-0321\",\"shorttitle\":\"A public Cloud-based China’s Landslide Inventory Database (CsLID)\",\"url\":\"https://doi.org/10.1007/s11629-015-3659-7\",\"doi\":\"10.1007/s11629-015-3659-7\",\"abstract\":\"Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the CsLID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the CsLID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.\",\"language\":\"en\",\"number\":\"7\",\"urldate\":\"2021-03-31\",\"journal\":\"Journal of Mountain Science\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Li\"],\"firstnames\":[\"Wei-yue\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Liu\"],\"firstnames\":[\"Chun\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hong\"],\"firstnames\":[\"Yang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"Xin-hua\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wan\"],\"firstnames\":[\"Zhan-ming\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sun\"],\"firstnames\":[\"Wei-wei\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yao\"],\"firstnames\":[\"Dong-jing\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chen\"],\"firstnames\":[\"Wen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chen\"],\"firstnames\":[\"Sheng\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yang\"],\"firstnames\":[\"Xiu-qin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yue\"],\"firstnames\":[\"Yue\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2016\",\"pages\":\"1275–1285\",\"bibtex\":\"@article{li_public_2016,\\n\\ttitle = {A public {Cloud}-based {China}’s {Landslide} {Inventory} {Database} ({CsLID}): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011},\\n\\tvolume = {13},\\n\\tissn = {1993-0321},\\n\\tshorttitle = {A public {Cloud}-based {China}’s {Landslide} {Inventory} {Database} ({CsLID})},\\n\\turl = {https://doi.org/10.1007/s11629-015-3659-7},\\n\\tdoi = {10.1007/s11629-015-3659-7},\\n\\tabstract = {Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the CsLID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the CsLID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.},\\n\\tlanguage = {en},\\n\\tnumber = {7},\\n\\turldate = {2021-03-31},\\n\\tjournal = {Journal of Mountain Science},\\n\\tauthor = {Li, Wei-yue and Liu, Chun and Hong, Yang and Zhang, Xin-hua and Wan, Zhan-ming and Saharia, Manabendra and Sun, Wei-wei and Yao, Dong-jing and Chen, Wen and Chen, Sheng and Yang, Xiu-qin and Yue, Yue},\\n\\tmonth = jul,\\n\\tyear = {2016},\\n\\tpages = {1275--1285},\\n}\\n\\n\",\"author_short\":[\"Li, W.\",\"Liu, C.\",\"Hong, Y.\",\"Zhang, X.\",\"Wan, Z.\",\"Saharia, M.\",\"Sun, W.\",\"Yao, D.\",\"Chen, W.\",\"Chen, S.\",\"Yang, X.\",\"Yue, Y.\"],\"key\":\"li_public_2016\",\"id\":\"li_public_2016\",\"bibbaseid\":\"li-liu-hong-zhang-wan-saharia-sun-yao-etal-apubliccloudbasedchinaslandslideinventorydatabasecsliddevelopmentzoneandspatiotemporalanalysisforsignificanthistoricalevents19492011-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/s11629-015-3659-7\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research\",\"issn\":\"1573-0840\",\"shorttitle\":\"India flood inventory\",\"url\":\"https://doi.org/10.1007/s11069-021-04698-6\",\"doi\":\"10.1007/s11069-021-04698-6\",\"abstract\":\"Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the existence of a historical inventory of floods is imperative to promote such research endeavors. Though, a few global inventories exist, they lack the spatio-temporal fidelity necessary to make them useful for computational research due to reasons such as concentrating exclusively on large floods, limited temporal scope, non-standard data formats. Therefore, there is an urgent need for developing a new database that combines data from global and hitherto-underutilized local datasets using an extensible and common schema. This paper describes the ongoing effort of building the India Flood Inventory (IFI), which is the first freely available, analysis-ready geospatial dataset over the region with detailed qualitative and quantitative information regarding floods, including spatial extents. The paper outlines the methodology that has been adopted as well as some preliminary findings using the data contained in this inventory. This dataset is expected to advance the understanding of flood processes in the worst affected region of the world.\",\"language\":\"en\",\"urldate\":\"2021-03-31\",\"journal\":\"Natural Hazards\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jain\"],\"firstnames\":[\"Avish\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Baishya\"],\"firstnames\":[\"Ronit\",\"Raj\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Haobam\"],\"firstnames\":[\"Saagar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sreejith\"],\"firstnames\":[\"O.\",\"P.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pai\"],\"firstnames\":[\"D.\",\"S.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rafieeinasab\"],\"firstnames\":[\"Arezoo\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2021\",\"bibtex\":\"@article{saharia_india_2021,\\n\\ttitle = {India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research},\\n\\tissn = {1573-0840},\\n\\tshorttitle = {India flood inventory},\\n\\turl = {https://doi.org/10.1007/s11069-021-04698-6},\\n\\tdoi = {10.1007/s11069-021-04698-6},\\n\\tabstract = {Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the existence of a historical inventory of floods is imperative to promote such research endeavors. Though, a few global inventories exist, they lack the spatio-temporal fidelity necessary to make them useful for computational research due to reasons such as concentrating exclusively on large floods, limited temporal scope, non-standard data formats. Therefore, there is an urgent need for developing a new database that combines data from global and hitherto-underutilized local datasets using an extensible and common schema. This paper describes the ongoing effort of building the India Flood Inventory (IFI), which is the first freely available, analysis-ready geospatial dataset over the region with detailed qualitative and quantitative information regarding floods, including spatial extents. The paper outlines the methodology that has been adopted as well as some preliminary findings using the data contained in this inventory. This dataset is expected to advance the understanding of flood processes in the worst affected region of the world.},\\n\\tlanguage = {en},\\n\\turldate = {2021-03-31},\\n\\tjournal = {Natural Hazards},\\n\\tauthor = {Saharia, Manabendra and Jain, Avish and Baishya, Ronit Raj and Haobam, Saagar and Sreejith, O. P. and Pai, D. S. and Rafieeinasab, Arezoo},\\n\\tmonth = mar,\\n\\tyear = {2021},\\n}\\n\\n\",\"author_short\":[\"Saharia, M.\",\"Jain, A.\",\"Baishya, R. R.\",\"Haobam, S.\",\"Sreejith, O. P.\",\"Pai, D. S.\",\"Rafieeinasab, A.\"],\"key\":\"saharia_india_2021\",\"id\":\"saharia_india_2021\",\"bibbaseid\":\"saharia-jain-baishya-haobam-sreejith-pai-rafieeinasab-indiafloodinventorycreationofamultisourcenationalgeospatialdatabasetofacilitatecomprehensivefloodresearch-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/s11069-021-04698-6\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems?\",\"volume\":\"16\",\"copyright\":\"CC0 1.0 Universal Public Domain Dedication\",\"issn\":\"1525-755X\",\"shorttitle\":\"Hydrometeorological Analysis and Remote Sensing of Extremes\",\"url\":\"https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-14-0048.1\",\"doi\":\"10.1175/JHM-D-14-0048.1\",\"abstract\":\"Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.\",\"number\":\"1\",\"urldate\":\"2018-07-27\",\"journal\":\"Journal of Hydrometeorology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"Yu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hong\"],\"firstnames\":[\"Yang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Xuguang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gourley\"],\"firstnames\":[\"Jonathan\",\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Xue\"],\"firstnames\":[\"Xianwu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ni\"],\"firstnames\":[\"Guangheng\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Gaili\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Huang\"],\"firstnames\":[\"Yong\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chen\"],\"firstnames\":[\"Sheng\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tang\"],\"firstnames\":[\"Guoqiang\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2014\",\"pages\":\"381–395\",\"bibtex\":\"@article{zhang_hydrometeorological_2014,\\n\\ttitle = {Hydrometeorological {Analysis} and {Remote} {Sensing} of {Extremes}: {Was} the {July} 2012 {Beijing} {Flood} {Event} {Detectable} and {Predictable} by {Global} {Satellite} {Observing} and {Global} {Weather} {Modeling} {Systems}?},\\n\\tvolume = {16},\\n\\tcopyright = {CC0 1.0 Universal Public Domain Dedication},\\n\\tissn = {1525-755X},\\n\\tshorttitle = {Hydrometeorological {Analysis} and {Remote} {Sensing} of {Extremes}},\\n\\turl = {https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-14-0048.1},\\n\\tdoi = {10.1175/JHM-D-14-0048.1},\\n\\tabstract = {Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of \\\\$1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.},\\n\\tnumber = {1},\\n\\turldate = {2018-07-27},\\n\\tjournal = {Journal of Hydrometeorology},\\n\\tauthor = {Zhang, Yu and Hong, Yang and Wang, Xuguang and Gourley, Jonathan J. and Xue, Xianwu and Saharia, Manabendra and Ni, Guangheng and Wang, Gaili and Huang, Yong and Chen, Sheng and Tang, Guoqiang},\\n\\tmonth = oct,\\n\\tyear = {2014},\\n\\tpages = {381--395},\\n}\\n\\n\",\"author_short\":[\"Zhang, Y.\",\"Hong, Y.\",\"Wang, X.\",\"Gourley, J. J.\",\"Xue, X.\",\"Saharia, M.\",\"Ni, G.\",\"Wang, G.\",\"Huang, Y.\",\"Chen, S.\",\"Tang, G.\"],\"key\":\"zhang_hydrometeorological_2014\",\"id\":\"zhang_hydrometeorological_2014\",\"bibbaseid\":\"zhang-hong-wang-gourley-xue-saharia-ni-wang-etal-hydrometeorologicalanalysisandremotesensingofextremeswasthejuly2012beijingfloodeventdetectableandpredictablebyglobalsatelliteobservingandglobalweathermodelingsystems-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-14-0048.1\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Rainstorm-induced shallow landslides process and evaluation – a case study from three hot spots, China\",\"volume\":\"7\",\"issn\":\"1947-5705\",\"url\":\"https://doi.org/10.1080/19475705.2016.1179685\",\"doi\":\"10.1080/19475705.2016.1179685\",\"abstract\":\"The critical stage in the evaluation of rainfall-induced landslide failure is in formulating reasonable models to better simulate spatiotemporal changes of slopes in the hilly terrains. A physically based model can take into account the contribution of rainfall infiltration and shear strength of saturated soil layer, and therefore help revealing the landslide formation mechanisms. This paper presents a physically based approach to simulate the landslide process triggered by rainstorm. On the basis of previous solutions, we select the simplified infiltration model Slope-Infiltration-Distributed Equilibrium (SLIDE) to illustrate the dynamical relations between factor of safety (FS) and accumulation of rainfall over time. This model is tested with three representative landslide events in the southwest, southeast, and south central of China during rainstorm. Results show that the time of landslide failure predicted from the SLIDE model is consistent with the reality. Meanwhile, this paper illustrates the differences of FS among the different slope gradients in the vicinity of same soil texture and relationship between FS and rainfall accumulation. This work formulates a methodology of rainstorm-induced landslide evaluation and improves upon the existing landslide prediction methods.\",\"number\":\"6\",\"urldate\":\"2018-07-27\",\"journal\":\"Geomatics, Natural Hazards and Risk\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Li\"],\"firstnames\":[\"Weiyue\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Liu\"],\"firstnames\":[\"Chun\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hong\"],\"firstnames\":[\"Yang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sun\"],\"firstnames\":[\"Weiwei\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yao\"],\"firstnames\":[\"Dongjing\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chen\"],\"firstnames\":[\"Wen\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2016\",\"pages\":\"1908–1918\",\"bibtex\":\"@article{li_rainstorm-induced_2016,\\n\\ttitle = {Rainstorm-induced shallow landslides process and evaluation – a case study from three hot spots, {China}},\\n\\tvolume = {7},\\n\\tissn = {1947-5705},\\n\\turl = {https://doi.org/10.1080/19475705.2016.1179685},\\n\\tdoi = {10.1080/19475705.2016.1179685},\\n\\tabstract = {The critical stage in the evaluation of rainfall-induced landslide failure is in formulating reasonable models to better simulate spatiotemporal changes of slopes in the hilly terrains. A physically based model can take into account the contribution of rainfall infiltration and shear strength of saturated soil layer, and therefore help revealing the landslide formation mechanisms. This paper presents a physically based approach to simulate the landslide process triggered by rainstorm. On the basis of previous solutions, we select the simplified infiltration model Slope-Infiltration-Distributed Equilibrium (SLIDE) to illustrate the dynamical relations between factor of safety (FS) and accumulation of rainfall over time. This model is tested with three representative landslide events in the southwest, southeast, and south central of China during rainstorm. Results show that the time of landslide failure predicted from the SLIDE model is consistent with the reality. Meanwhile, this paper illustrates the differences of FS among the different slope gradients in the vicinity of same soil texture and relationship between FS and rainfall accumulation. This work formulates a methodology of rainstorm-induced landslide evaluation and improves upon the existing landslide prediction methods.},\\n\\tnumber = {6},\\n\\turldate = {2018-07-27},\\n\\tjournal = {Geomatics, Natural Hazards and Risk},\\n\\tauthor = {Li, Weiyue and Liu, Chun and Hong, Yang and Saharia, Manabendra and Sun, Weiwei and Yao, Dongjing and Chen, Wen},\\n\\tmonth = nov,\\n\\tyear = {2016},\\n\\tpages = {1908--1918},\\n}\\n\\n\",\"author_short\":[\"Li, W.\",\"Liu, C.\",\"Hong, Y.\",\"Saharia, M.\",\"Sun, W.\",\"Yao, D.\",\"Chen, W.\"],\"key\":\"li_rainstorm-induced_2016\",\"id\":\"li_rainstorm-induced_2016\",\"bibbaseid\":\"li-liu-hong-saharia-sun-yao-chen-rainstorminducedshallowlandslidesprocessandevaluationacasestudyfromthreehotspotschina-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1080/19475705.2016.1179685\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Characterization of floods in the United States\",\"volume\":\"548\",\"copyright\":\"CC0 1.0 Universal Public Domain Dedication\",\"issn\":\"0022-1694\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S0022169417301476\",\"doi\":\"10.1016/j.jhydrol.2017.03.010\",\"abstract\":\"Floods have gained increasing global significance in the recent past due to their devastating nature and potential for causing significant economic and human losses. Until now, flood characterization studies in the United States have been limited due to the lack of a comprehensive database matching flood characteristics such as peak discharges and flood duration with geospatial and geomorphologic information. The availability of a representative and long archive of flooding events spanning 78 years over a variety of hydroclimatic regions results in a spatially and temporally comprehensive flood characterization over the continental U.S. This study, for the first time, employs a large-event database that is based on actual National Weather Service (NWS) definitions of floods instead of the frequently-adopted case study or frequentist approach, allowing us to base our findings on real definitions of floods. It examines flooding characteristics to identify how space and time scales of floods vary with climatic regimes and geomorphology. Flood events were characterized by linking flood response variables in gauged basins to spatially distributed variables describing climatology, geomorphology, and topography. The primary findings of this study are that the magnitude of flooding is highest is regions such as West Coast and southeastern U.S. which experience the most extraordinary precipitation. The seasonality of flooding varies greatly from maxima during the cool season on the West Coast, warm season in the desert Southwest, and early spring in the Southeast. The fastest responding events tend to be in steep basins of the arid Southwest caused by intense monsoon thunderstorms and steep terrain. The envelope curves of unit peak discharge are consistent with those reported for Europe and worldwide. But significant seasonal variability was observed in floods of the U.S. compared to Europe that is attributed to the diversity of causative rainfall ranging from synoptic scales with orographic enhancements in the West Coast, monsoon thunderstorms in the desert Southwest, to land-falling tropical storms and localized, intense thunderstorms in the Southeast.\",\"urldate\":\"2017-03-29\",\"journal\":\"Journal of Hydrology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirstetter\"],\"firstnames\":[\"Pierre-Emmanuel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vergara\"],\"firstnames\":[\"Humberto\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gourley\"],\"firstnames\":[\"Jonathan\",\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hong\"],\"firstnames\":[\"Yang\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2017\",\"pages\":\"524–535\",\"bibtex\":\"@article{saharia_characterization_2017,\\n\\ttitle = {Characterization of floods in the {United} {States}},\\n\\tvolume = {548},\\n\\tcopyright = {CC0 1.0 Universal Public Domain Dedication},\\n\\tissn = {0022-1694},\\n\\turl = {http://www.sciencedirect.com/science/article/pii/S0022169417301476},\\n\\tdoi = {10.1016/j.jhydrol.2017.03.010},\\n\\tabstract = {Floods have gained increasing global significance in the recent past due to their devastating nature and potential for causing significant economic and human losses. Until now, flood characterization studies in the United States have been limited due to the lack of a comprehensive database matching flood characteristics such as peak discharges and flood duration with geospatial and geomorphologic information. The availability of a representative and long archive of flooding events spanning 78 years over a variety of hydroclimatic regions results in a spatially and temporally comprehensive flood characterization over the continental U.S. This study, for the first time, employs a large-event database that is based on actual National Weather Service (NWS) definitions of floods instead of the frequently-adopted case study or frequentist approach, allowing us to base our findings on real definitions of floods. It examines flooding characteristics to identify how space and time scales of floods vary with climatic regimes and geomorphology. Flood events were characterized by linking flood response variables in gauged basins to spatially distributed variables describing climatology, geomorphology, and topography. The primary findings of this study are that the magnitude of flooding is highest is regions such as West Coast and southeastern U.S. which experience the most extraordinary precipitation. The seasonality of flooding varies greatly from maxima during the cool season on the West Coast, warm season in the desert Southwest, and early spring in the Southeast. The fastest responding events tend to be in steep basins of the arid Southwest caused by intense monsoon thunderstorms and steep terrain. The envelope curves of unit peak discharge are consistent with those reported for Europe and worldwide. But significant seasonal variability was observed in floods of the U.S. compared to Europe that is attributed to the diversity of causative rainfall ranging from synoptic scales with orographic enhancements in the West Coast, monsoon thunderstorms in the desert Southwest, to land-falling tropical storms and localized, intense thunderstorms in the Southeast.},\\n\\turldate = {2017-03-29},\\n\\tjournal = {Journal of Hydrology},\\n\\tauthor = {Saharia, Manabendra and Kirstetter, Pierre-Emmanuel and Vergara, Humberto and Gourley, Jonathan J. and Hong, Yang},\\n\\tmonth = may,\\n\\tyear = {2017},\\n\\tpages = {524--535},\\n}\\n\\n\",\"author_short\":[\"Saharia, M.\",\"Kirstetter, P.\",\"Vergara, H.\",\"Gourley, J. J.\",\"Hong, Y.\"],\"key\":\"saharia_characterization_2017\",\"id\":\"saharia_characterization_2017\",\"bibbaseid\":\"saharia-kirstetter-vergara-gourley-hong-characterizationoffloodsintheunitedstates-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S0022169417301476\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"River Reaches Flood Flow Prediction using PRNN Models\",\"volume\":\"1\",\"copyright\":\"CC0 1.0 Universal Public Domain Dedication\",\"issn\":\"2249-6866\",\"url\":\"http://journals.indexcopernicus.com/abstract.php?icid=1144099\",\"number\":\"4\",\"urldate\":\"2016-10-05\",\"journal\":\"International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development (IJCSEIERD)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Roy\"],\"firstnames\":[\"Parthajit\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Saharia\"],\"firstnames\":[\"Manabendra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Choudhury\"],\"firstnames\":[\"P.\"],\"suffixes\":[]}],\"year\":\"2014\",\"pages\":\"119–126\",\"bibtex\":\"@article{roy_river_2014,\\n\\ttitle = {River {Reaches} {Flood} {Flow} {Prediction} using {PRNN} {Models}},\\n\\tvolume = {1},\\n\\tcopyright = {CC0 1.0 Universal Public Domain Dedication},\\n\\tissn = {2249-6866},\\n\\turl = {http://journals.indexcopernicus.com/abstract.php?icid=1144099},\\n\\tnumber = {4},\\n\\turldate = {2016-10-05},\\n\\tjournal = {International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development (IJCSEIERD)},\\n\\tauthor = {Roy, Parthajit and Saharia, Manabendra and Choudhury, P.},\\n\\tyear = {2014},\\n\\tpages = {119--126},\\n}\\n\",\"author_short\":[\"Roy, P.\",\"Saharia, M.\",\"Choudhury, P.\"],\"key\":\"roy_river_2014\",\"id\":\"roy_river_2014\",\"bibbaseid\":\"roy-saharia-choudhury-riverreachesfloodflowpredictionusingprnnmodels-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://journals.indexcopernicus.com/abstract.php?icid=1144099\"},\"metadata\":{\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\"downloads\":0,\"html\":\"\"}],\n      metadata: {\"owner\":\"saharia, m\",\"authorlinks\":{\"saharia, m\":\"https://hydrosense.iitd.ac.in/publications/\"}},\n      ownerID: \"oRvNXfrs2hLx6Sfbj\"\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"items\" href=\"data:text/bibtex;base64,@article{sharma_ml-cascade_2024,
	title = {{ML}-{CASCADE}: {A} machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data},
	issn = {1612-5118},
	shorttitle = {{ML}-{CASCADE}},
	url = {https://doi.org/10.1007/s10346-024-02360-3},
	doi = {10.1007/s10346-024-02360-3},
	abstract = {Landslides pose a significant threat to humans as well as the environment. Rapid and precise mapping of landslide extent is necessary for understanding their spatial distribution, assessing susceptibility, and developing early warning systems. Traditional landslide mapping methods rely on labor-intensive field studies and manual mapping using high-resolution imagery, which are both costly and time-consuming. While existing machine learning-based automated mapping methods exist, they have limited transferability due to low availability of training data and the inability to handle out-of-distribution scenarios. This study introduces ML-CASCADE, a user-friendly open-source tool designed for real-time landslide mapping. It is a semi-automated tool that requires the user to create landslide and non-landslide samples using pre- and post-landslide Sentinel-2 imagery to train a machine learning model. The model training features include Sentinel-2 data, terrain data, vegetation indices, and bare soil index. ML-CASCADE is developed as an easy-to-use application on top of Google Earth Engine and supports both pixel and object-based classification methods. We validate the landslide extent developed using ML-CASCADE with independent expert-developed inventories. ML-CASCADE is not only able to identify the landslide extent accurately but can also map a complex cluster of landslides within 5 min and a simple landslide within 2 min. Due to its ease of use, speed, and accuracy, ML-CASCADE will serve as a critical operational asset for landslide risk management.},
	language = {en},
	urldate = {2024-09-11},
	journal = {Landslides},
	author = {Sharma, Nirdesh and Saharia, Manabendra},
	month = sep,
	year = {2024},
	keywords = {Artificial Intelligence, Cloud computing, Google earth engine, Landslide extent mapping, Machine learning, Sentinel-2},
}



@article{raj_geospatial_2024,
	title = {Geospatial modeling and mapping of soil erosion in {India}},
	volume = {240},
	issn = {0341-8162},
	url = {https://www.sciencedirect.com/science/article/pii/S0341816224001930},
	doi = {10.1016/j.catena.2024.107996},
	abstract = {Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classification system currently doesn’t exist over India. Given the resource-intensive and time-consuming nature of field experiments required for the measurement of soil loss across a vast country, the Revised Universal Soil Loss Equation (RUSLE) was extensively utilized for soil erosion calculations due to its simplicity, streamlined data requirements, and accuracy. This study estimates the yearly potential soil loss throughout India at a spatial resolution of 250 m and quantifies its variability considering districts, soil texture, soil type, land use and land cover, and basins. The relative importance of individual and combined impact of multiple parameters on quantified soil loss has been assessed using a random forest model. Rainfall erosivity (R-factor) emerges as the most crucial feature in estimating soil erosion in Indian conditions while rainfall intensity, combined with the topographic factor, demonstrated the highest influence on soil erosion in Indian conditions when the combined impact was assessed. Further, we mapped the Sediment Delivery Ratio (SDR) and Specific Sediment Yield (SSY) to assess the actual soil loss reaching downstream of basins across the national boundary. The national mean values for Sediment Delivery Ratio (SDR) and Sediment Yield (SSY) stand at 0.11 and 2.61 tons per hectare per year, respectively. The yearly potential soil loss for India is calculated at 21 tons per hectare per year, and nine out of the twenty districts with the highest susceptibility to soil erosion are in the state of Assam. Finally, a novel impact-based erosion-severity classification system has been introduced which finds that 29.46\% of the landmass is prone to minor erosion while 3.17\% experiences catastrophic erosion. This is the first comprehensive national-scale assessment of both soil erosion and sediment yield mapping over India, and the consequent classification system will enable the planning and implementation of soil conservation strategies locally as well as nationally.},
	urldate = {2024-09-01},
	journal = {CATENA},
	author = {Raj, Ravi and Saharia, Manabendra and Chakma, Sumedha},
	month = may,
	year = {2024},
	keywords = {India, Sediment Delivery Ratio (SDR), Soil Conservation, Soil erosion, Specific Sediment Yield (SSY)},
	pages = {107996},
}



@article{kumar_mapping_2024,
	title = {Mapping a novel metric for {Flash} {Flood} {Recovery} using {Interpretable} {Machine} {Learning}},
	volume = {-1},
	issn = {1525-7541, 1525-755X},
	url = {https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-23-0196.1/JHM-D-23-0196.1.xml},
	doi = {10.1175/JHM-D-23-0196.1},
	abstract = {Flash floods are one of the most devastating natural disasters, yet many aspects of their severity and impact are poorly understood. The recession limb is related to post-flood recovery and its impact on communities, yet it remains less documented than the rising limb of the hydrograph to predict the peak discharge and timing of floods. This work introduces a new metric called the flash flood recovery or recoveriness, which is the potential for recovery of a watershed to pre-flood conditions. Using a comprehensive database of 78 years and supervised machine learning algorithms, flash flood recovery is mapped in the conterminous United States. A suite of geomorphological and climatological variables is used as predictors to provide probabilistic estimates of recoveriness. Slope index, river basin area and river length are found to be the most significant predictors to predict recoveriness. Several new localized hotspots were identified, such as the western slopes of the Appalachians consisting of Kentucky, Tennessee, and West Virginia and the interlinked areas of western Montana and northern Idaho. This new metric can be useful for prioritizing relief and rehabilitation efforts as well as precautionary measures for disaster risk reduction.},
	language = {EN},
	number = {aop},
	urldate = {2024-08-31},
	journal = {Journal of Hydrometeorology},
	author = {Kumar, Anil and Saharia, Manabendra and Kirstetter, Pierre},
	month = aug,
	year = {2024},
	note = {Publisher: American Meteorological Society
Section: Journal of Hydrometeorology},
}



@article{chakraborty_improved_2024,
	title = {Improved soil moisture estimation and detection of irrigation signal by incorporating {SMAP} soil moisture into the {Indian} {Land} {Data} {Assimilation} {System} ({ILDAS})},
	volume = {638},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169424009776},
	doi = {10.1016/j.jhydrol.2024.131581},
	abstract = {Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).},
	language = {en},
	urldate = {2024-06-28},
	journal = {Journal of Hydrology},
	author = {Chakraborty, Arijit and Saharia, Manabendra and Chakma, Sumedha and Kumar Pandey, Dharmendra and Niranjan Kumar, Kondapalli and Thakur, Praveen K. and Kumar, Sujay and Getirana, Augusto},
	month = jul,
	year = {2024},
	pages = {131581},
}



@article{maina_irrigation-driven_2024,
	title = {Irrigation-driven groundwater depletion in the {Ganges}-{Brahmaputra} basin decreases the streamflow in the {Bay} of {Bengal}},
	volume = {5},
	issn = {2662-4435},
	url = {https://www.nature.com/articles/s43247-024-01348-0},
	doi = {10.1038/s43247-024-01348-0},
	abstract = {Abstract
            
              Ganges and Brahmaputra, two of Asia’s most prominent rivers, have a crucial role in Southeast Asia’s geopolitics and economy and are home to one of the world’s biggest marine ecosystems. Irrigation-driven groundwater depletion and climate change affect the Ganges-Brahmaputra’s hydrology, threatening the stability of the Bay of Bengal. Here, we quantify, using results from a land reanalysis, the impacts of a changing climate and intensive irrigation on the surface water flowing into the Bay of Bengal. The effects of such activities mostly occurring in the Ganges basin, either intensified or lessened depending on the area by the climatic conditions, decrease freshwater flow into the bay by up to 1200 m
              3
              /s/year. While the increase in precipitation in the Ganges basin reduces the effects of groundwater depletion on the streamflow, the decrease in precipitation and the snowmelt decline in the Brahmaputra basin exacerbate streamflow reduction due to groundwater depletion at the delta.},
	language = {en},
	number = {1},
	urldate = {2024-04-04},
	journal = {Communications Earth \& Environment},
	author = {Maina, Fadji Z. and Getirana, Augusto and Kumar, Sujay V. and Saharia, Manabendra and Biswas, Nishan Kumar and McLarty, Sasha and Appana, Ravi},
	month = apr,
	year = {2024},
	pages = {169},
}



@article{magotra_towards_2024,
	title = {Towards an {Indian} land data assimilation system ({ILDAS}): {A} coupled hydrologic-hydraulic system for water balance assessments},
	volume = {629},
	issn = {0022-1694},
	shorttitle = {Towards an {Indian} land data assimilation system ({ILDAS})},
	url = {https://www.sciencedirect.com/science/article/pii/S0022169423015469},
	doi = {10.1016/j.jhydrol.2023.130604},
	abstract = {Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.},
	urldate = {2023-12-15},
	journal = {Journal of Hydrology},
	author = {Magotra, Bhanu and Prakash, Ved and Saharia, Manabendra and Getirana, Augusto and Kumar, Sujay and Pradhan, Rohit and Dhanya, C. T. and Rajagopalan, Balaji and Singh, Raghavendra P. and Pandey, Ayush and Mohapatra, Mrutyunjay},
	month = feb,
	year = {2024},
	keywords = {Indian Land Data Assimilation System (ILDAS), Streamflow, Water balance assessments, south Asia},
	pages = {130604},
}



@article{kuntla_precipitation_2024,
	title = {Precipitation inequality exacerbates streamflow inequality, but dams moderate it},
	volume = {912},
	issn = {0048-9697},
	url = {https://www.sciencedirect.com/science/article/pii/S0048969723077288},
	doi = {10.1016/j.scitotenv.2023.169098},
	abstract = {Access to clean water is a fundamental human right, yet millions worldwide face the dire consequences of water scarcity and inadequate sanitation. Water inequality, characterized by disparities in access and availability of water resources, has emerged as a critical global challenge with far-reaching social, economic, and environmental implications. Using a globally representative observational streamflow dataset and Gini coefficients, this study investigates how streamflow inequality, which has a large impact on inequality of water availability, varies spatially and temporally, and its relationship with different underlying catchment characteristics. This study finds that watersheds in arid climates exhibit a higher degree of streamflow inequality than polar and equatorial ones. Africa experiences the highest streamflow inequality, followed by Australia, while South America experiences relatively lower streamflow inequality. Around 19.6 \% of the catchments in Australia display an increasing trend in streamflow inequality, pointing to worsening conditions. Conversely, South America experiences a decreasing trend in streamflow inequality in 18.3 \% of its catchments during the same period. It is also found that a more evenly distributed precipitation within the catchment and higher dam storage capacity corresponds to more evenly distributed streamflow availability throughout the year. This study enhances our understanding of streamflow inequality worldwide, which will aid policy formulation to foster sustainable development.},
	urldate = {2023-12-09},
	journal = {Science of The Total Environment},
	author = {Kuntla, Sai Kiran and Saharia, Manabendra and Prakash, Samar and Villarini, Gabriele},
	month = feb,
	year = {2024},
	keywords = {Gini coefficient, Streamflow, Sustainable development, Water, Water scarcity},
	pages = {169098},
}



@article{sharma_high_2024,
	title = {High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data},
	volume = {235},
	issn = {0341-8162},
	url = {https://www.sciencedirect.com/science/article/pii/S0341816223007440},
	doi = {10.1016/j.catena.2023.107653},
	abstract = {Landslide susceptibility represents the potential of slope failure for given geo-environmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome all these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of One -Sided Selection and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India's first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001° (∼100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73 \%, sensitivity of 97.08 \%, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75 \% area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records. The ILSM is expected to aid policymaking in disaster risk reduction and developing landslide prediction models.},
	urldate = {2023-11-16},
	journal = {CATENA},
	author = {Sharma, Nirdesh and Saharia, Manabendra and Ramana, G. V.},
	month = feb,
	year = {2024},
	keywords = {Big data, Ensemble learning, High resolution landslide susceptibility, One-sided selection (OSS), Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE)},
	pages = {107653},
}



@inproceedings{pandey_lora-based_2023,
	title = {{LoRa}-{Based} {Communication} {System} for {Monitoring} {Water} {Quality} of {Lakes} and {Reservoirs}},
	volume = {1},
	doi = {10.1109/MIGARS57353.2023.10064590},
	abstract = {This paper describes a conceptual framework for the establishment of a Long-Range Wide Area Network (LoRaWAN)based communication system for monitoring water quality of lakes and reservoirs. The proposed system allows connection of multiple sensors to the LoRa transceivers and enables data transmission to a cloud server. In a proof-of-concept test, the LoRa gateway was set up, a temperature sensor node was connected and used to monitor lake water temperature, and data was transferred to LoRa gateway which serves as the central monitoring system and is visualized on a centralized dashboard. In this work, a coverage test of the proposed system is also carried out to test the LoRAWAN communication range in the urban environment. We find that the range of the system varies from 150-2,500 m and it is affected by the antenna positioning placement and obstacles in between the gateway and the sensor node. The maximum range was found to be 2.5 km when there was clear line of sight between the gateway and sensor node.},
	booktitle = {2023 {International} {Conference} on {Machine} {Intelligence} for {GeoAnalytics} and {Remote} {Sensing} ({MIGARS})},
	author = {Pandey, Rajdeep and Sharma, Nirdesh Kumar and Saharia, Manabendra},
	month = jan,
	year = {2023},
	keywords = {Lake Monitoring, Lakes, LoRaWAN, Logic gates, Reservoirs, Temperature measurement, Temperature sensors, Water Quality, Water quality, Wide area networks},
	pages = {1--4},
}



@article{raj_mapping_2023,
	title = {Mapping soil erodibility over {India}},
	volume = {230},
	issn = {0341-8162},
	url = {https://www.sciencedirect.com/science/article/pii/S0341816223003624},
	doi = {10.1016/j.catena.2023.107271},
	abstract = {Soil erosion is a major environmental problem worldwide, and almost half of India’s total geographical area is susceptible to it. The Revised Universal Soil Loss Equation (RUSLE) has been widely used globally to estimate soil erosion, and the Soil erodibility factor, denoted by the K-factor, is an essential component of RUSLE. Although previous studies have assessed soil erodibility in India, they have been limited to small scales such as watersheds or districts. A national-scale assessment of soil erodibility doesn’t exist and is critical to developing a systematic understanding of soil erosion over India. In this study, we estimated soil erodibility factors over India using RUSLE Nomograph and Environmental Policy Integrated Climate (EPIC) model approaches at a high resolution of 250 m. Our results showed that the K-factor estimated using the Nomograph approach was more accurate than the observed soil erodibility factors. Additionally, we developed erodibility indices such as CR (Clay Ratio), MCR (Modified Clay Ratio), and CLOM (Critical Level of Organic Matter) to assess their sensitivity with respect to soil erodibility factors. Finally, we created a susceptibility to erosion map over India using CLOM index classification. The national average soil erodibility factor for India is estimated to be 0.028 t-ha-h/ha/MJ/mm. Histosols soil type is the least susceptible to erosion, while the Xerosols soil type is most susceptible among the prevalent soil classes in India. This is the first national-scale mapping of soil erodibility over India, providing an essential asset for soil conservation and erosion management planning by experts.},
	urldate = {2023-08-16},
	journal = {CATENA},
	author = {Raj, Ravi and Saharia, Manabendra and Chakma, Sumedha},
	month = sep,
	year = {2023},
	keywords = {Clay Ratio, Critical Level of Organic Matter, India, Modified Clay Ratio, Soil erodibility factor, Soil erosion},
	pages = {107271},
}



@article{kumari_spatio-temporal_2022,
	title = {Spatio-temporal analysis of air quality and its relationship with {COVID}-19 lockdown over {Dublin}},
	volume = {28},
	issn = {2352-9385},
	url = {https://www.sciencedirect.com/science/article/pii/S2352938522001434},
	doi = {10.1016/j.rsase.2022.100835},
	abstract = {Air pollution has become one of the biggest challenges for human and environmental health. Major pollutants such as Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), Carbon Monoxide (CO), and Particulate matter (PM10 and PM2.5) are being ejected in a large quantity every day. Initially, authorities did not implement the strictest mitigation policies due to pressures of balancing the economic needs of people and public safety. Still, after realizing the effect of the COVID-19 pandemic, countries around the world imposed a complete lockdown to contain the outbreak, which had the unexpected benefit of causing a drastic improvement in air quality. The present study investigates the air pollution scenarios over the Dublin city through satellites (Sentinel-5P and Moderate Resolution Imaging Spectroradiometer) and ground-based observations. An average of 28\% reduction in average NO2 level and a 27.7\% improvement in AQI (Air Quality Index) was experienced in 2020 compared to 2019 during the lockdown period (27 March–05 June). We found that PM10 and PM2.5 are the most dominating factor in the AQI over Dublin.},
	urldate = {2023-08-16},
	journal = {Remote Sensing Applications: Society and Environment},
	author = {Kumari, Sushma and Yadav, Avinash Chand and Saharia, Manabendra and Dev, Soumyabrata},
	month = nov,
	year = {2022},
	keywords = {AQI, Air pollution, COVID-19 pandemic, Dublin, MODIS, Sentinel-5P},
	pages = {100835},
}



@article{kuntla_global-scale_2022,
	title = {Global-scale characterization of streamflow extremes},
	volume = {615},
	issn = {00221694},
	url = {https://www.sciencedirect.com/science/article/pii/S0022169422012380?via%3Dihub},
	doi = {10.1016/j.jhydrol.2022.128668},
	abstract = {The increasing risk of floods across the globe needs focused attention because of the extensive damage to human lives and economy. A comprehensive understanding of its causative factors is of vital importance. Yet catchment characterization studies are generally limited to case studies or regional domains. A comprehensive global characterization is currently unavailable, which requires collecting and collating a large number of datasets over vast areas. This study embraces large-sample data-driven science as a new paradigm to characterize streamflow extremes by utilizing global datasets of physiographic explanatory variables that could explain various facets of extreme streamflows. Along with the spatial and temporal variations of high streamflow extremes, their corre­ lation with various catchment characteristics such as geomorphology, meteorology, climatology, landcover, li­ thology, etc. were examined. The multidimensional relationships between the streamflow extremes and catchment characteristics were modeled using a Random Forest approach and combined with an interpretable machine learning framework to identify the most dominant factors in varying climate classes. Interpretation with SHAP (SHapley Additive exPlanations) reveals that meteorological variables are the most influential variables across the climatic classes. However, the variables and their influences change among different climatic classes. Moreover, different geomorphological variables come into dominance across climatic classes (such as basin relief in warm temperate and drainage texture in arid climates). Overall, the insights from the study could play a crucial role in predicting the unit peak discharge at ungauged stations from the known catchment characteristics. Moreover, these findings can also play a crucial role in formulating risk management strategy.},
	language = {en},
	urldate = {2022-12-11},
	journal = {Journal of Hydrology},
	author = {Kuntla, Sai Kiran and Saharia, Manabendra and Kirstetter, Pierre},
	month = dec,
	year = {2022},
	pages = {128668},
}



@article{shen_uncertainty_2014,
	title = {Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the {Tibetan} {Plateau}},
	volume = {35},
	issn = {0143-1161},
	url = {https://doi.org/10.1080/01431161.2014.960612},
	doi = {10.1080/01431161.2014.960612},
	abstract = {This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.},
	number = {19},
	urldate = {2021-10-30},
	journal = {International Journal of Remote Sensing},
	author = {Shen, Yan and Xiong, Anyuan and Hong, Yang and Yu, Jingjing and Pan, Yang and Chen, Zhuoqi and Saharia, Manabendra},
	month = oct,
	year = {2014},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/01431161.2014.960612},
	pages = {6843--6858},
}



@article{kim_assessing_2018,
	title = {Assessing the {Skill} of {Medium}-{Range} {Ensemble} {Precipitation} and {Streamflow} {Forecasts} from the {Hydrologic} {Ensemble} {Forecast} {Service} ({HEFS}) for the {Upper} {Trinity} {River} {Basin} in {North} {Texas}},
	volume = {19},
	issn = {1525-7541, 1525-755X},
	url = {https://journals.ametsoc.org/view/journals/hydr/19/9/jhm-d-18-0027_1.xml},
	doi = {10.1175/JHM-D-18-0027.1},
	abstract = {Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20\% over climatological forecast for the largest 1\% of the observed biweekly flow.},
	language = {EN},
	number = {9},
	urldate = {2022-09-27},
	journal = {Journal of Hydrometeorology},
	author = {Kim, Sunghee and Sadeghi, Hossein and Limon, Reza Ahmad and Saharia, Manabendra and Seo, Dong-Jun and Philpott, Andrew and Bell, Frank and Brown, James and He, Minxue},
	month = sep,
	year = {2018},
	note = {Publisher: American Meteorological Society
Section: Journal of Hydrometeorology},
	pages = {1467--1483},
}



@article{newman_identifying_2021,
	title = {Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system},
	volume = {25},
	issn = {1027-5606},
	url = {https://hess.copernicus.org/articles/25/5603/2021/},
	doi = {10.5194/hess-25-5603-2021},
	abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.{\textless}/p{\textgreater}},
	language = {English},
	number = {10},
	urldate = {2022-04-02},
	journal = {Hydrology and Earth System Sciences},
	author = {Newman, Andrew J. and Stone, Amanda G. and Saharia, Manabendra and Holman, Kathleen D. and Addor, Nans and Clark, Martyn P.},
	month = oct,
	year = {2021},
	note = {Publisher: Copernicus GmbH},
	pages = {5603--5621},
}



@article{raj_mapping_2022,
	title = {Mapping rainfall erosivity over {India} using multiple precipitation datasets},
	volume = {214},
	issn = {0341-8162},
	url = {https://www.sciencedirect.com/science/article/pii/S0341816222002429},
	doi = {10.1016/j.catena.2022.106256},
	abstract = {Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water, and rainfall erosivity is one of the major components. The current assessments of rainfall erosivity in India are however largely based on rain-gauge recordings and surveys which hinders its estimation and understanding over large areas. Growing availability of remotely-sensed gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based estimations. In this study, multiple national and global gridded precipitation datasets were utilized to develop a high-resolution rainfall erosivity factor (R-factor) map to highlight areas prone to rainfall-induced erosion. Further, a large selection of empirical equations from literature were employed for estimating rainfall erosivity to provide a comparative analysis of these commonly adopted methods. The calculated rainfall erosivity is also compared with alternative methods to estimate R-factor such as Fournier Index (FI) and Modified Fournier Index (MFI). It was observed that MFI is highly correlated with rainfall erosivity, and an equation was finally proposed to estimate R-factor using MFI. This is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will aid in understanding and mitigating rainfall-induced erosion.},
	language = {en},
	urldate = {2022-08-01},
	journal = {CATENA},
	author = {Raj, Ravi and Saharia, Manabendra and Chakma, Sumedha and Rafieinasab, Arezoo},
	month = jul,
	year = {2022},
	keywords = {Fournier Index, India, Modified Fournier Index, Rainfall erosivity factor, Rainfall-kinetic energy},
	pages = {106256},
}



@article{potdar_toward_2021,
	title = {Toward {Predicting} {Flood} {Event} {Peak} {Discharge} in {Ungauged} {Basins} by {Learning} {Universal} {Hydrological} {Behaviors} with {Machine} {Learning}},
	volume = {22},
	issn = {1525-7541, 1525-755X},
	url = {https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-20-0302.1.xml},
	doi = {10.1175/JHM-D-20-0302.1},
	abstract = {Abstract In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine-learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multidimensional statistical modeling approach. Among different machine-learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created that can be deployed in the future for flash flood forecasting. The results confirm that, although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of subbasin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins that could most benefit from distributed hydrologic modeling.},
	language = {EN},
	number = {11},
	urldate = {2022-04-02},
	journal = {Journal of Hydrometeorology},
	author = {Potdar, Akhil Sanjay and Kirstetter, Pierre-Emmanuel and Woods, Devon and Saharia, Manabendra},
	month = nov,
	year = {2021},
	note = {Publisher: American Meteorological Society
Section: Journal of Hydrometeorology},
	pages = {2971--2982},
}



@article{saharia_impact_2021,
	title = {On the {Impact} of {Rainfall} {Spatial} {Variability}, {Geomorphology}, and {Climatology} on {Flash} {Floods}},
	volume = {57},
	issn = {1944-7973},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029124},
	doi = {10.1029/2020WR029124},
	abstract = {AbstractThe effects of spatial variability of rainfall, geomorphology, and climatology of precipitation and temperature on the hydrologic response remain poorly understood. This study characterizes the catchment response in terms of a variable called flashiness, that describes the severity of the flood response as the rate of rise of the unit discharge. It overcomes limitations of prior works based on limited case studies or simulations by gathering information on basins of widely varying characteristics and by using a high-resolution rainfall and flooding event data set spanning 10 years over the Continental United States. The objective is to develop a robust understanding of how rainfall spatial variability influences flash flood severity and to assess its contribution relative to basin physiography and climatology. This study explores the first-order dependencies as well as the variability in these relationships and investigates the complex interactions using a multi-dimensional statistical modeling approach. The results confirm that the spatial organization of rainfall influences the basin response on par with geomorphology and climatology. Basin physiography dampens the effect of lower rainfall intensities, while higher rainfall overwhelms other factors and primarily contributes to flashiness. Dispersion of precipitation with respect to the flow path decreases flood severity. An improved understanding of sub-basin scale rainfall spatial variability aids in developing a robust flash flood severity index to identify and mitigate flash flooding situations as well as identifying basins which could most benefit from distributed hydrologic modeling.},
	language = {en},
	number = {9},
	urldate = {2021-09-24},
	journal = {Water Resources Research},
	author = {Saharia, Manabendra and Kirstetter, Pierre-Emmanuel and Vergara, Humberto and Gourley, Jonathan J. and Emmanuel, Isabelle and Andrieu, Hervé},
	year = {2021},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029124},
	keywords = {flash flood, geomorphology, rainfall spatial variability},
	pages = {e2020WR029124},
}



@article{saharia_mapping_2017,
	title = {Mapping {Flash} {Flood} {Severity} in the {United} {States}},
	volume = {18},
	issn = {1525-7541, 1525-755X},
	url = {https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0082_1.xml},
	doi = {10.1175/JHM-D-16-0082.1},
	abstract = {{\textless}section class="abstract"{\textgreater}{\textless}h2 class="abstractTitle text-title my-1" id="d3028e2"{\textgreater}Abstract{\textless}/h2{\textgreater}{\textless}p{\textgreater}Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.{\textless}/p{\textgreater}{\textless}/section{\textgreater}},
	language = {EN},
	number = {2},
	urldate = {2021-04-09},
	journal = {Journal of Hydrometeorology},
	author = {Saharia, Manabendra and Kirstetter, Pierre-Emmanuel and Vergara, Humberto and Gourley, Jonathan J. and Hong, Yang and Giroud, Marine},
	month = feb,
	year = {2017},
	note = {Publisher: American Meteorological Society
Section: Journal of Hydrometeorology},
	pages = {397--411},
}



@article{saharia_geomorphology-based_2012,
	title = {Geomorphology-based {Time}-{Lagged} {Recurrent} {Neural} {Networks} for runoff forecasting},
	volume = {16},
	issn = {1976-3808},
	url = {https://doi.org/10.1007/s12205-012-1463-2},
	doi = {10.1007/s12205-012-1463-2},
	abstract = {Artificial Neural Networks have been widely used to develop effective runoff-forecasting models. An overwhelming majority of networks are static in nature and also developed without incorporating geomorphologic information of the watershed. The objective of this study is to develop an efficient dynamic neural network model which also accounts for morphometric characteristics of the catchment. The model developed using Time-Lagged Recurrent Neural Networks (TLRNs) is used to estimate runoff for river Dikrong, a tributary of river Brahmaputra in India. Comparisons with traditional static models, with and without integration of geomorphologic data, reveal the proposed model to be a promising tool in operational hydrology.},
	language = {en},
	number = {5},
	urldate = {2021-03-31},
	journal = {KSCE Journal of Civil Engineering},
	author = {Saharia, Manabendra and Bhattacharjya, Rajib Kumar},
	month = jul,
	year = {2012},
	pages = {862--869},
}



@article{jain_analysis_2013,
	title = {Analysis of rainfall and temperature trends in northeast {India}},
	volume = {33},
	copyright = {Copyright © 2012 Royal Meteorological Society},
	issn = {1097-0088},
	url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3483},
	doi = {https://doi.org/10.1002/joc.3483},
	abstract = {The northeast region (NER) of India covers an area of 0.26 million km2. This region is one of the highest rainfall-receiving regions on the planet. Consequently, it has huge water and hydropower potential and analysis of rainfall and temperature trends would be of interest to water and energy planners. Trends in monthly, seasonal, and annual rainfall and temperature on the subdivision and regional scale for the NER were examined in this study. Trend analysis of rainfall data series for 1871–2008 did not show any clear trend for the region as a whole, although there are seasonal trends for some seasons and for some hydro-meteorological subdivisions. Similar analysis for temperature data showed that all the four temperature variables (maximum, minimum, and mean temperatures and temperature range) had rising trend. Notably for the post-monsoon season, the Sen's estimator of slope ( °C/year) was 0.019, 0.011, and 0.015 for the maximum, minimum, and mean temperature, respectively. Copyright © 2012 Royal Meteorological Society},
	language = {en},
	number = {4},
	urldate = {2021-03-31},
	journal = {International Journal of Climatology},
	author = {Jain, S. K. and Kumar, Vijay and Saharia, M.},
	year = {2013},
	note = {\_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.3483},
	keywords = {northeast India, rainfall, temperature, trend analysis},
	pages = {968--978},
}



@article{li_public_2016,
	title = {A public {Cloud}-based {China}’s {Landslide} {Inventory} {Database} ({CsLID}): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011},
	volume = {13},
	issn = {1993-0321},
	shorttitle = {A public {Cloud}-based {China}’s {Landslide} {Inventory} {Database} ({CsLID})},
	url = {https://doi.org/10.1007/s11629-015-3659-7},
	doi = {10.1007/s11629-015-3659-7},
	abstract = {Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the CsLID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the CsLID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.},
	language = {en},
	number = {7},
	urldate = {2021-03-31},
	journal = {Journal of Mountain Science},
	author = {Li, Wei-yue and Liu, Chun and Hong, Yang and Zhang, Xin-hua and Wan, Zhan-ming and Saharia, Manabendra and Sun, Wei-wei and Yao, Dong-jing and Chen, Wen and Chen, Sheng and Yang, Xiu-qin and Yue, Yue},
	month = jul,
	year = {2016},
	pages = {1275--1285},
}



@article{saharia_india_2021,
	title = {India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research},
	issn = {1573-0840},
	shorttitle = {India flood inventory},
	url = {https://doi.org/10.1007/s11069-021-04698-6},
	doi = {10.1007/s11069-021-04698-6},
	abstract = {Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the existence of a historical inventory of floods is imperative to promote such research endeavors. Though, a few global inventories exist, they lack the spatio-temporal fidelity necessary to make them useful for computational research due to reasons such as concentrating exclusively on large floods, limited temporal scope, non-standard data formats. Therefore, there is an urgent need for developing a new database that combines data from global and hitherto-underutilized local datasets using an extensible and common schema. This paper describes the ongoing effort of building the India Flood Inventory (IFI), which is the first freely available, analysis-ready geospatial dataset over the region with detailed qualitative and quantitative information regarding floods, including spatial extents. The paper outlines the methodology that has been adopted as well as some preliminary findings using the data contained in this inventory. This dataset is expected to advance the understanding of flood processes in the worst affected region of the world.},
	language = {en},
	urldate = {2021-03-31},
	journal = {Natural Hazards},
	author = {Saharia, Manabendra and Jain, Avish and Baishya, Ronit Raj and Haobam, Saagar and Sreejith, O. P. and Pai, D. S. and Rafieeinasab, Arezoo},
	month = mar,
	year = {2021},
}



@article{zhang_hydrometeorological_2014,
	title = {Hydrometeorological {Analysis} and {Remote} {Sensing} of {Extremes}: {Was} the {July} 2012 {Beijing} {Flood} {Event} {Detectable} and {Predictable} by {Global} {Satellite} {Observing} and {Global} {Weather} {Modeling} {Systems}?},
	volume = {16},
	copyright = {CC0 1.0 Universal Public Domain Dedication},
	issn = {1525-755X},
	shorttitle = {Hydrometeorological {Analysis} and {Remote} {Sensing} of {Extremes}},
	url = {https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-14-0048.1},
	doi = {10.1175/JHM-D-14-0048.1},
	abstract = {Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of \$1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.},
	number = {1},
	urldate = {2018-07-27},
	journal = {Journal of Hydrometeorology},
	author = {Zhang, Yu and Hong, Yang and Wang, Xuguang and Gourley, Jonathan J. and Xue, Xianwu and Saharia, Manabendra and Ni, Guangheng and Wang, Gaili and Huang, Yong and Chen, Sheng and Tang, Guoqiang},
	month = oct,
	year = {2014},
	pages = {381--395},
}



@article{li_rainstorm-induced_2016,
	title = {Rainstorm-induced shallow landslides process and evaluation – a case study from three hot spots, {China}},
	volume = {7},
	issn = {1947-5705},
	url = {https://doi.org/10.1080/19475705.2016.1179685},
	doi = {10.1080/19475705.2016.1179685},
	abstract = {The critical stage in the evaluation of rainfall-induced landslide failure is in formulating reasonable models to better simulate spatiotemporal changes of slopes in the hilly terrains. A physically based model can take into account the contribution of rainfall infiltration and shear strength of saturated soil layer, and therefore help revealing the landslide formation mechanisms. This paper presents a physically based approach to simulate the landslide process triggered by rainstorm. On the basis of previous solutions, we select the simplified infiltration model Slope-Infiltration-Distributed Equilibrium (SLIDE) to illustrate the dynamical relations between factor of safety (FS) and accumulation of rainfall over time. This model is tested with three representative landslide events in the southwest, southeast, and south central of China during rainstorm. Results show that the time of landslide failure predicted from the SLIDE model is consistent with the reality. Meanwhile, this paper illustrates the differences of FS among the different slope gradients in the vicinity of same soil texture and relationship between FS and rainfall accumulation. This work formulates a methodology of rainstorm-induced landslide evaluation and improves upon the existing landslide prediction methods.},
	number = {6},
	urldate = {2018-07-27},
	journal = {Geomatics, Natural Hazards and Risk},
	author = {Li, Weiyue and Liu, Chun and Hong, Yang and Saharia, Manabendra and Sun, Weiwei and Yao, Dongjing and Chen, Wen},
	month = nov,
	year = {2016},
	pages = {1908--1918},
}



@article{saharia_characterization_2017,
	title = {Characterization of floods in the {United} {States}},
	volume = {548},
	copyright = {CC0 1.0 Universal Public Domain Dedication},
	issn = {0022-1694},
	url = {http://www.sciencedirect.com/science/article/pii/S0022169417301476},
	doi = {10.1016/j.jhydrol.2017.03.010},
	abstract = {Floods have gained increasing global significance in the recent past due to their devastating nature and potential for causing significant economic and human losses. Until now, flood characterization studies in the United States have been limited due to the lack of a comprehensive database matching flood characteristics such as peak discharges and flood duration with geospatial and geomorphologic information. The availability of a representative and long archive of flooding events spanning 78 years over a variety of hydroclimatic regions results in a spatially and temporally comprehensive flood characterization over the continental U.S. This study, for the first time, employs a large-event database that is based on actual National Weather Service (NWS) definitions of floods instead of the frequently-adopted case study or frequentist approach, allowing us to base our findings on real definitions of floods. It examines flooding characteristics to identify how space and time scales of floods vary with climatic regimes and geomorphology. Flood events were characterized by linking flood response variables in gauged basins to spatially distributed variables describing climatology, geomorphology, and topography. The primary findings of this study are that the magnitude of flooding is highest is regions such as West Coast and southeastern U.S. which experience the most extraordinary precipitation. The seasonality of flooding varies greatly from maxima during the cool season on the West Coast, warm season in the desert Southwest, and early spring in the Southeast. The fastest responding events tend to be in steep basins of the arid Southwest caused by intense monsoon thunderstorms and steep terrain. The envelope curves of unit peak discharge are consistent with those reported for Europe and worldwide. But significant seasonal variability was observed in floods of the U.S. compared to Europe that is attributed to the diversity of causative rainfall ranging from synoptic scales with orographic enhancements in the West Coast, monsoon thunderstorms in the desert Southwest, to land-falling tropical storms and localized, intense thunderstorms in the Southeast.},
	urldate = {2017-03-29},
	journal = {Journal of Hydrology},
	author = {Saharia, Manabendra and Kirstetter, Pierre-Emmanuel and Vergara, Humberto and Gourley, Jonathan J. and Hong, Yang},
	month = may,
	year = {2017},
	pages = {524--535},
}



@article{roy_river_2014,
	title = {River {Reaches} {Flood} {Flow} {Prediction} using {PRNN} {Models}},
	volume = {1},
	copyright = {CC0 1.0 Universal Public Domain Dedication},
	issn = {2249-6866},
	url = {http://journals.indexcopernicus.com/abstract.php?icid=1144099},
	number = {4},
	urldate = {2016-10-05},
	journal = {International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development (IJCSEIERD)},
	author = {Roy, Parthajit and Saharia, Manabendra and Choudhury, P.},
	year = {2014},
	pages = {119--126},
}
\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=https://api.zotero.org/users/605836/collections/98W6NZ9F/items?key=Py0EWgS20Ou10SSxCEyfvr4L&amp;format=bibtex&amp;limit=100\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=https://api.zotero.org/users/605836/collections/98W6NZ9F/items?key=Py0EWgS20Ou10SSxCEyfvr4L&amp;format=bibtex&amp;limit=100\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/show?bib=https%3A%2F%2Fapi.zotero.org%2Fusers%2F605836%2Fcollections%2F98W6NZ9F%2Fitems%3Fkey%3DPy0EWgS20Ou10SSxCEyfvr4L%26format%3Dbibtex%26limit%3D100&amp;jsonp=1&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/show?bib=https%3A%2F%2Fapi.zotero.org%2Fusers%2F605836%2Fcollections%2F98W6NZ9F%2Fitems%3Fkey%3DPy0EWgS20Ou10SSxCEyfvr4L%26format%3Dbibtex%26limit%3D100&amp;jsonp=1\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/show?bib=https%3A%2F%2Fapi.zotero.org%2Fusers%2F605836%2Fcollections%2F98W6NZ9F%2Fitems%3Fkey%3DPy0EWgS20Ou10SSxCEyfvr4L%26format%3Dbibtex%26limit%3D100&amp;jsonp=1\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2024\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2024')\"\n      onkeypress=\"toggleGroup('group_2024')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2024</span>\n      <span class=\"bibbase_group_count\">\n        \n        (8)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"sharma-saharia-mlcascadeamachinelearningandcloudcomputingbasedtoolforrapidandautomatedmappingoflandslidesusingearthobservationdata-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1007/s10346-024-02360-3\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sharma-saharia-mlcascadeamachinelearningandcloudcomputingbasedtoolforrapidandautomatedmappingoflandslidesusingearthobservationdata-2024', 'https://doi.org/10.1007/s10346-024-02360-3', event);\">\n    ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sharma, N.;  and <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>\n</span>\n\n  \n\n</span>\n\n  <i>Landslides</i>. September 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1007/s10346-024-02360-3\"\n     onclick=\"log_download('sharma-saharia-mlcascadeamachinelearningandcloudcomputingbasedtoolforrapidandautomatedmappingoflandslidesusingearthobservationdata-2024', 'https://doi.org/10.1007/s10346-024-02360-3', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s10346-024-02360-3\"\n     onclick=\"log_download('sharma-saharia-mlcascadeamachinelearningandcloudcomputingbasedtoolforrapidandautomatedmappingoflandslidesusingearthobservationdata-2024', 'http://doi.org/10.1007/s10346-024-02360-3', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sharma-saharia-mlcascadeamachinelearningandcloudcomputingbasedtoolforrapidandautomatedmappingoflandslidesusingearthobservationdata-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sharma-saharia-mlcascadeamachinelearningandcloudcomputingbasedtoolforrapidandautomatedmappingoflandslidesusingearthobservationdata-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{sharma_ml-cascade_2024,\n\ttitle = {{ML}-{CASCADE}: {A} machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data},\n\tissn = {1612-5118},\n\tshorttitle = {{ML}-{CASCADE}},\n\turl = {https://doi.org/10.1007/s10346-024-02360-3},\n\tdoi = {10.1007/s10346-024-02360-3},\n\tabstract = {Landslides pose a significant threat to humans as well as the environment. Rapid and precise mapping of landslide extent is necessary for understanding their spatial distribution, assessing susceptibility, and developing early warning systems. Traditional landslide mapping methods rely on labor-intensive field studies and manual mapping using high-resolution imagery, which are both costly and time-consuming. While existing machine learning-based automated mapping methods exist, they have limited transferability due to low availability of training data and the inability to handle out-of-distribution scenarios. This study introduces ML-CASCADE, a user-friendly open-source tool designed for real-time landslide mapping. It is a semi-automated tool that requires the user to create landslide and non-landslide samples using pre- and post-landslide Sentinel-2 imagery to train a machine learning model. The model training features include Sentinel-2 data, terrain data, vegetation indices, and bare soil index. ML-CASCADE is developed as an easy-to-use application on top of Google Earth Engine and supports both pixel and object-based classification methods. We validate the landslide extent developed using ML-CASCADE with independent expert-developed inventories. ML-CASCADE is not only able to identify the landslide extent accurately but can also map a complex cluster of landslides within 5 min and a simple landslide within 2 min. Due to its ease of use, speed, and accuracy, ML-CASCADE will serve as a critical operational asset for landslide risk management.},\n\tlanguage = {en},\n\turldate = {2024-09-11},\n\tjournal = {Landslides},\n\tauthor = {Sharma, Nirdesh and Saharia, Manabendra},\n\tmonth = sep,\n\tyear = {2024},\n\tkeywords = {Artificial Intelligence, Cloud computing, Google earth engine, Landslide extent mapping, Machine learning, Sentinel-2},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Landslides pose a significant threat to humans as well as the environment. Rapid and precise mapping of landslide extent is necessary for understanding their spatial distribution, assessing susceptibility, and developing early warning systems. Traditional landslide mapping methods rely on labor-intensive field studies and manual mapping using high-resolution imagery, which are both costly and time-consuming. While existing machine learning-based automated mapping methods exist, they have limited transferability due to low availability of training data and the inability to handle out-of-distribution scenarios. This study introduces ML-CASCADE, a user-friendly open-source tool designed for real-time landslide mapping. It is a semi-automated tool that requires the user to create landslide and non-landslide samples using pre- and post-landslide Sentinel-2 imagery to train a machine learning model. The model training features include Sentinel-2 data, terrain data, vegetation indices, and bare soil index. ML-CASCADE is developed as an easy-to-use application on top of Google Earth Engine and supports both pixel and object-based classification methods. We validate the landslide extent developed using ML-CASCADE with independent expert-developed inventories. ML-CASCADE is not only able to identify the landslide extent accurately but can also map a complex cluster of landslides within 5 min and a simple landslide within 2 min. Due to its ease of use, speed, and accuracy, ML-CASCADE will serve as a critical operational asset for landslide risk management.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"raj-saharia-chakma-geospatialmodelingandmappingofsoilerosioninindia-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0341816224001930\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'raj-saharia-chakma-geospatialmodelingandmappingofsoilerosioninindia-2024', 'https://www.sciencedirect.com/science/article/pii/S0341816224001930', event);\">\n    Geospatial modeling and mapping of soil erosion in India.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Raj, R.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>;  and Chakma, S.\n</span>\n\n  \n\n</span>\n\n  <i>CATENA</i>, 240: 107996. May 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0341816224001930\"\n     onclick=\"log_download('raj-saharia-chakma-geospatialmodelingandmappingofsoilerosioninindia-2024', 'https://www.sciencedirect.com/science/article/pii/S0341816224001930', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Geospatial modeling and mapping of soil erosion in India [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.catena.2024.107996\"\n     onclick=\"log_download('raj-saharia-chakma-geospatialmodelingandmappingofsoilerosioninindia-2024', 'http://doi.org/10.1016/j.catena.2024.107996', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/raj-saharia-chakma-geospatialmodelingandmappingofsoilerosioninindia-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('raj-saharia-chakma-geospatialmodelingandmappingofsoilerosioninindia-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{raj_geospatial_2024,\n\ttitle = {Geospatial modeling and mapping of soil erosion in {India}},\n\tvolume = {240},\n\tissn = {0341-8162},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0341816224001930},\n\tdoi = {10.1016/j.catena.2024.107996},\n\tabstract = {Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classification system currently doesn’t exist over India. Given the resource-intensive and time-consuming nature of field experiments required for the measurement of soil loss across a vast country, the Revised Universal Soil Loss Equation (RUSLE) was extensively utilized for soil erosion calculations due to its simplicity, streamlined data requirements, and accuracy. This study estimates the yearly potential soil loss throughout India at a spatial resolution of 250 m and quantifies its variability considering districts, soil texture, soil type, land use and land cover, and basins. The relative importance of individual and combined impact of multiple parameters on quantified soil loss has been assessed using a random forest model. Rainfall erosivity (R-factor) emerges as the most crucial feature in estimating soil erosion in Indian conditions while rainfall intensity, combined with the topographic factor, demonstrated the highest influence on soil erosion in Indian conditions when the combined impact was assessed. Further, we mapped the Sediment Delivery Ratio (SDR) and Specific Sediment Yield (SSY) to assess the actual soil loss reaching downstream of basins across the national boundary. The national mean values for Sediment Delivery Ratio (SDR) and Sediment Yield (SSY) stand at 0.11 and 2.61 tons per hectare per year, respectively. The yearly potential soil loss for India is calculated at 21 tons per hectare per year, and nine out of the twenty districts with the highest susceptibility to soil erosion are in the state of Assam. Finally, a novel impact-based erosion-severity classification system has been introduced which finds that 29.46\\% of the landmass is prone to minor erosion while 3.17\\% experiences catastrophic erosion. This is the first comprehensive national-scale assessment of both soil erosion and sediment yield mapping over India, and the consequent classification system will enable the planning and implementation of soil conservation strategies locally as well as nationally.},\n\turldate = {2024-09-01},\n\tjournal = {CATENA},\n\tauthor = {Raj, Ravi and Saharia, Manabendra and Chakma, Sumedha},\n\tmonth = may,\n\tyear = {2024},\n\tkeywords = {India, Sediment Delivery Ratio (SDR), Soil Conservation, Soil erosion, Specific Sediment Yield (SSY)},\n\tpages = {107996},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classification system currently doesn’t exist over India. Given the resource-intensive and time-consuming nature of field experiments required for the measurement of soil loss across a vast country, the Revised Universal Soil Loss Equation (RUSLE) was extensively utilized for soil erosion calculations due to its simplicity, streamlined data requirements, and accuracy. This study estimates the yearly potential soil loss throughout India at a spatial resolution of 250 m and quantifies its variability considering districts, soil texture, soil type, land use and land cover, and basins. The relative importance of individual and combined impact of multiple parameters on quantified soil loss has been assessed using a random forest model. Rainfall erosivity (R-factor) emerges as the most crucial feature in estimating soil erosion in Indian conditions while rainfall intensity, combined with the topographic factor, demonstrated the highest influence on soil erosion in Indian conditions when the combined impact was assessed. Further, we mapped the Sediment Delivery Ratio (SDR) and Specific Sediment Yield (SSY) to assess the actual soil loss reaching downstream of basins across the national boundary. The national mean values for Sediment Delivery Ratio (SDR) and Sediment Yield (SSY) stand at 0.11 and 2.61 tons per hectare per year, respectively. The yearly potential soil loss for India is calculated at 21 tons per hectare per year, and nine out of the twenty districts with the highest susceptibility to soil erosion are in the state of Assam. Finally, a novel impact-based erosion-severity classification system has been introduced which finds that 29.46% of the landmass is prone to minor erosion while 3.17% experiences catastrophic erosion. This is the first comprehensive national-scale assessment of both soil erosion and sediment yield mapping over India, and the consequent classification system will enable the planning and implementation of soil conservation strategies locally as well as nationally.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kumar-saharia-kirstetter-mappinganovelmetricforflashfloodrecoveryusinginterpretablemachinelearning-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-23-0196.1/JHM-D-23-0196.1.xml\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kumar-saharia-kirstetter-mappinganovelmetricforflashfloodrecoveryusinginterpretablemachinelearning-2024', 'https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-23-0196.1/JHM-D-23-0196.1.xml', event);\">\n    Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kumar, A.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>;  and Kirstetter, P.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrometeorology</i>, -1(aop). August 2024.\n  <span class=\"note\">Publisher: American Meteorological Society Section: Journal of Hydrometeorology</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-23-0196.1/JHM-D-23-0196.1.xml\"\n     onclick=\"log_download('kumar-saharia-kirstetter-mappinganovelmetricforflashfloodrecoveryusinginterpretablemachinelearning-2024', 'https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-23-0196.1/JHM-D-23-0196.1.xml', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1175/JHM-D-23-0196.1\"\n     onclick=\"log_download('kumar-saharia-kirstetter-mappinganovelmetricforflashfloodrecoveryusinginterpretablemachinelearning-2024', 'http://doi.org/10.1175/JHM-D-23-0196.1', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kumar-saharia-kirstetter-mappinganovelmetricforflashfloodrecoveryusinginterpretablemachinelearning-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kumar-saharia-kirstetter-mappinganovelmetricforflashfloodrecoveryusinginterpretablemachinelearning-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{kumar_mapping_2024,\n\ttitle = {Mapping a novel metric for {Flash} {Flood} {Recovery} using {Interpretable} {Machine} {Learning}},\n\tvolume = {-1},\n\tissn = {1525-7541, 1525-755X},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-23-0196.1/JHM-D-23-0196.1.xml},\n\tdoi = {10.1175/JHM-D-23-0196.1},\n\tabstract = {Flash floods are one of the most devastating natural disasters, yet many aspects of their severity and impact are poorly understood. The recession limb is related to post-flood recovery and its impact on communities, yet it remains less documented than the rising limb of the hydrograph to predict the peak discharge and timing of floods. This work introduces a new metric called the flash flood recovery or recoveriness, which is the potential for recovery of a watershed to pre-flood conditions. Using a comprehensive database of 78 years and supervised machine learning algorithms, flash flood recovery is mapped in the conterminous United States. A suite of geomorphological and climatological variables is used as predictors to provide probabilistic estimates of recoveriness. Slope index, river basin area and river length are found to be the most significant predictors to predict recoveriness. Several new localized hotspots were identified, such as the western slopes of the Appalachians consisting of Kentucky, Tennessee, and West Virginia and the interlinked areas of western Montana and northern Idaho. This new metric can be useful for prioritizing relief and rehabilitation efforts as well as precautionary measures for disaster risk reduction.},\n\tlanguage = {EN},\n\tnumber = {aop},\n\turldate = {2024-08-31},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Kumar, Anil and Saharia, Manabendra and Kirstetter, Pierre},\n\tmonth = aug,\n\tyear = {2024},\n\tnote = {Publisher: American Meteorological Society\nSection: Journal of Hydrometeorology},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Flash floods are one of the most devastating natural disasters, yet many aspects of their severity and impact are poorly understood. The recession limb is related to post-flood recovery and its impact on communities, yet it remains less documented than the rising limb of the hydrograph to predict the peak discharge and timing of floods. This work introduces a new metric called the flash flood recovery or recoveriness, which is the potential for recovery of a watershed to pre-flood conditions. Using a comprehensive database of 78 years and supervised machine learning algorithms, flash flood recovery is mapped in the conterminous United States. A suite of geomorphological and climatological variables is used as predictors to provide probabilistic estimates of recoveriness. Slope index, river basin area and river length are found to be the most significant predictors to predict recoveriness. Several new localized hotspots were identified, such as the western slopes of the Appalachians consisting of Kentucky, Tennessee, and West Virginia and the interlinked areas of western Montana and northern Idaho. This new metric can be useful for prioritizing relief and rehabilitation efforts as well as precautionary measures for disaster risk reduction.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chakraborty-saharia-chakma-kumarpandey-niranjankumar-thakur-kumar-getirana-improvedsoilmoistureestimationanddetectionofirrigationsignalbyincorporatingsmapsoilmoistureintotheindianlanddataassimilationsystemildas-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0022169424009776\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chakraborty-saharia-chakma-kumarpandey-niranjankumar-thakur-kumar-getirana-improvedsoilmoistureestimationanddetectionofirrigationsignalbyincorporatingsmapsoilmoistureintotheindianlanddataassimilationsystemildas-2024', 'https://linkinghub.elsevier.com/retrieve/pii/S0022169424009776', event);\">\n    Improved soil moisture estimation and detection of irrigation signal by incorporating SMAP soil moisture into the Indian Land Data Assimilation System (ILDAS).\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chakraborty, A.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Chakma, S.; Kumar Pandey, D.; Niranjan Kumar, K.; Thakur, P. K.; Kumar, S.;  and Getirana, A.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrology</i>, 638: 131581. July 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0022169424009776\"\n     onclick=\"log_download('chakraborty-saharia-chakma-kumarpandey-niranjankumar-thakur-kumar-getirana-improvedsoilmoistureestimationanddetectionofirrigationsignalbyincorporatingsmapsoilmoistureintotheindianlanddataassimilationsystemildas-2024', 'https://linkinghub.elsevier.com/retrieve/pii/S0022169424009776', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Improved soil moisture estimation and detection of irrigation signal by incorporating SMAP soil moisture into the Indian Land Data Assimilation System (ILDAS) [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jhydrol.2024.131581\"\n     onclick=\"log_download('chakraborty-saharia-chakma-kumarpandey-niranjankumar-thakur-kumar-getirana-improvedsoilmoistureestimationanddetectionofirrigationsignalbyincorporatingsmapsoilmoistureintotheindianlanddataassimilationsystemildas-2024', 'http://doi.org/10.1016/j.jhydrol.2024.131581', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chakraborty-saharia-chakma-kumarpandey-niranjankumar-thakur-kumar-getirana-improvedsoilmoistureestimationanddetectionofirrigationsignalbyincorporatingsmapsoilmoistureintotheindianlanddataassimilationsystemildas-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chakraborty-saharia-chakma-kumarpandey-niranjankumar-thakur-kumar-getirana-improvedsoilmoistureestimationanddetectionofirrigationsignalbyincorporatingsmapsoilmoistureintotheindianlanddataassimilationsystemildas-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{chakraborty_improved_2024,\n\ttitle = {Improved soil moisture estimation and detection of irrigation signal by incorporating {SMAP} soil moisture into the {Indian} {Land} {Data} {Assimilation} {System} ({ILDAS})},\n\tvolume = {638},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169424009776},\n\tdoi = {10.1016/j.jhydrol.2024.131581},\n\tabstract = {Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).},\n\tlanguage = {en},\n\turldate = {2024-06-28},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Chakraborty, Arijit and Saharia, Manabendra and Chakma, Sumedha and Kumar Pandey, Dharmendra and Niranjan Kumar, Kondapalli and Thakur, Praveen K. and Kumar, Sujay and Getirana, Augusto},\n\tmonth = jul,\n\tyear = {2024},\n\tpages = {131581},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"maina-getirana-kumar-saharia-biswas-mclarty-appana-irrigationdrivengroundwaterdepletioninthegangesbrahmaputrabasindecreasesthestreamflowinthebayofbengal-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.nature.com/articles/s43247-024-01348-0\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'maina-getirana-kumar-saharia-biswas-mclarty-appana-irrigationdrivengroundwaterdepletioninthegangesbrahmaputrabasindecreasesthestreamflowinthebayofbengal-2024', 'https://www.nature.com/articles/s43247-024-01348-0', event);\">\n    Irrigation-driven groundwater depletion in the Ganges-Brahmaputra basin decreases the streamflow in the Bay of Bengal.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Maina, F. Z.; Getirana, A.; Kumar, S. V.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Biswas, N. K.; McLarty, S.;  and Appana, R.\n</span>\n\n  \n\n</span>\n\n  <i>Communications Earth & Environment</i>, 5(1): 169. April 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.nature.com/articles/s43247-024-01348-0\"\n     onclick=\"log_download('maina-getirana-kumar-saharia-biswas-mclarty-appana-irrigationdrivengroundwaterdepletioninthegangesbrahmaputrabasindecreasesthestreamflowinthebayofbengal-2024', 'https://www.nature.com/articles/s43247-024-01348-0', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Irrigation-driven groundwater depletion in the Ganges-Brahmaputra basin decreases the streamflow in the Bay of Bengal [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1038/s43247-024-01348-0\"\n     onclick=\"log_download('maina-getirana-kumar-saharia-biswas-mclarty-appana-irrigationdrivengroundwaterdepletioninthegangesbrahmaputrabasindecreasesthestreamflowinthebayofbengal-2024', 'http://doi.org/10.1038/s43247-024-01348-0', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/maina-getirana-kumar-saharia-biswas-mclarty-appana-irrigationdrivengroundwaterdepletioninthegangesbrahmaputrabasindecreasesthestreamflowinthebayofbengal-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('maina-getirana-kumar-saharia-biswas-mclarty-appana-irrigationdrivengroundwaterdepletioninthegangesbrahmaputrabasindecreasesthestreamflowinthebayofbengal-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{maina_irrigation-driven_2024,\n\ttitle = {Irrigation-driven groundwater depletion in the {Ganges}-{Brahmaputra} basin decreases the streamflow in the {Bay} of {Bengal}},\n\tvolume = {5},\n\tissn = {2662-4435},\n\turl = {https://www.nature.com/articles/s43247-024-01348-0},\n\tdoi = {10.1038/s43247-024-01348-0},\n\tabstract = {Abstract\n            \n              Ganges and Brahmaputra, two of Asia’s most prominent rivers, have a crucial role in Southeast Asia’s geopolitics and economy and are home to one of the world’s biggest marine ecosystems. Irrigation-driven groundwater depletion and climate change affect the Ganges-Brahmaputra’s hydrology, threatening the stability of the Bay of Bengal. Here, we quantify, using results from a land reanalysis, the impacts of a changing climate and intensive irrigation on the surface water flowing into the Bay of Bengal. The effects of such activities mostly occurring in the Ganges basin, either intensified or lessened depending on the area by the climatic conditions, decrease freshwater flow into the bay by up to 1200 m\n              3\n              /s/year. While the increase in precipitation in the Ganges basin reduces the effects of groundwater depletion on the streamflow, the decrease in precipitation and the snowmelt decline in the Brahmaputra basin exacerbate streamflow reduction due to groundwater depletion at the delta.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-04-04},\n\tjournal = {Communications Earth \\&amp; Environment},\n\tauthor = {Maina, Fadji Z. and Getirana, Augusto and Kumar, Sujay V. and Saharia, Manabendra and Biswas, Nishan Kumar and McLarty, Sasha and Appana, Ravi},\n\tmonth = apr,\n\tyear = {2024},\n\tpages = {169},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract Ganges and Brahmaputra, two of Asia’s most prominent rivers, have a crucial role in Southeast Asia’s geopolitics and economy and are home to one of the world’s biggest marine ecosystems. Irrigation-driven groundwater depletion and climate change affect the Ganges-Brahmaputra’s hydrology, threatening the stability of the Bay of Bengal. Here, we quantify, using results from a land reanalysis, the impacts of a changing climate and intensive irrigation on the surface water flowing into the Bay of Bengal. The effects of such activities mostly occurring in the Ganges basin, either intensified or lessened depending on the area by the climatic conditions, decrease freshwater flow into the bay by up to 1200 m 3 /s/year. While the increase in precipitation in the Ganges basin reduces the effects of groundwater depletion on the streamflow, the decrease in precipitation and the snowmelt decline in the Brahmaputra basin exacerbate streamflow reduction due to groundwater depletion at the delta.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"magotra-prakash-saharia-getirana-kumar-pradhan-dhanya-rajagopalan-etal-towardsanindianlanddataassimilationsystemildasacoupledhydrologichydraulicsystemforwaterbalanceassessments-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0022169423015469\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'magotra-prakash-saharia-getirana-kumar-pradhan-dhanya-rajagopalan-etal-towardsanindianlanddataassimilationsystemildasacoupledhydrologichydraulicsystemforwaterbalanceassessments-2024', 'https://www.sciencedirect.com/science/article/pii/S0022169423015469', event);\">\n    Towards an Indian land data assimilation system (ILDAS): A coupled hydrologic-hydraulic system for water balance assessments.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Magotra, B.; Prakash, V.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Getirana, A.; Kumar, S.; Pradhan, R.; Dhanya, C. T.; Rajagopalan, B.; Singh, R. P.; Pandey, A.;  and Mohapatra, M.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrology</i>, 629: 130604. February 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0022169423015469\"\n     onclick=\"log_download('magotra-prakash-saharia-getirana-kumar-pradhan-dhanya-rajagopalan-etal-towardsanindianlanddataassimilationsystemildasacoupledhydrologichydraulicsystemforwaterbalanceassessments-2024', 'https://www.sciencedirect.com/science/article/pii/S0022169423015469', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Towards an Indian land data assimilation system (ILDAS): A coupled hydrologic-hydraulic system for water balance assessments [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jhydrol.2023.130604\"\n     onclick=\"log_download('magotra-prakash-saharia-getirana-kumar-pradhan-dhanya-rajagopalan-etal-towardsanindianlanddataassimilationsystemildasacoupledhydrologichydraulicsystemforwaterbalanceassessments-2024', 'http://doi.org/10.1016/j.jhydrol.2023.130604', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/magotra-prakash-saharia-getirana-kumar-pradhan-dhanya-rajagopalan-etal-towardsanindianlanddataassimilationsystemildasacoupledhydrologichydraulicsystemforwaterbalanceassessments-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>6 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('magotra-prakash-saharia-getirana-kumar-pradhan-dhanya-rajagopalan-etal-towardsanindianlanddataassimilationsystemildasacoupledhydrologichydraulicsystemforwaterbalanceassessments-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{magotra_towards_2024,\n\ttitle = {Towards an {Indian} land data assimilation system ({ILDAS}): {A} coupled hydrologic-hydraulic system for water balance assessments},\n\tvolume = {629},\n\tissn = {0022-1694},\n\tshorttitle = {Towards an {Indian} land data assimilation system ({ILDAS})},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0022169423015469},\n\tdoi = {10.1016/j.jhydrol.2023.130604},\n\tabstract = {Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.},\n\turldate = {2023-12-15},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Magotra, Bhanu and Prakash, Ved and Saharia, Manabendra and Getirana, Augusto and Kumar, Sujay and Pradhan, Rohit and Dhanya, C. T. and Rajagopalan, Balaji and Singh, Raghavendra P. and Pandey, Ayush and Mohapatra, Mrutyunjay},\n\tmonth = feb,\n\tyear = {2024},\n\tkeywords = {Indian Land Data Assimilation System (ILDAS), Streamflow, Water balance assessments, south Asia},\n\tpages = {130604},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kuntla-saharia-prakash-villarini-precipitationinequalityexacerbatesstreamflowinequalitybutdamsmoderateit-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0048969723077288\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kuntla-saharia-prakash-villarini-precipitationinequalityexacerbatesstreamflowinequalitybutdamsmoderateit-2024', 'https://www.sciencedirect.com/science/article/pii/S0048969723077288', event);\">\n    Precipitation inequality exacerbates streamflow inequality, but dams moderate it.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kuntla, S. K.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Prakash, S.;  and Villarini, G.\n</span>\n\n  \n\n</span>\n\n  <i>Science of The Total Environment</i>, 912: 169098. February 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0048969723077288\"\n     onclick=\"log_download('kuntla-saharia-prakash-villarini-precipitationinequalityexacerbatesstreamflowinequalitybutdamsmoderateit-2024', 'https://www.sciencedirect.com/science/article/pii/S0048969723077288', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Precipitation inequality exacerbates streamflow inequality, but dams moderate it [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.scitotenv.2023.169098\"\n     onclick=\"log_download('kuntla-saharia-prakash-villarini-precipitationinequalityexacerbatesstreamflowinequalitybutdamsmoderateit-2024', 'http://doi.org/10.1016/j.scitotenv.2023.169098', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kuntla-saharia-prakash-villarini-precipitationinequalityexacerbatesstreamflowinequalitybutdamsmoderateit-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kuntla-saharia-prakash-villarini-precipitationinequalityexacerbatesstreamflowinequalitybutdamsmoderateit-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{kuntla_precipitation_2024,\n\ttitle = {Precipitation inequality exacerbates streamflow inequality, but dams moderate it},\n\tvolume = {912},\n\tissn = {0048-9697},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0048969723077288},\n\tdoi = {10.1016/j.scitotenv.2023.169098},\n\tabstract = {Access to clean water is a fundamental human right, yet millions worldwide face the dire consequences of water scarcity and inadequate sanitation. Water inequality, characterized by disparities in access and availability of water resources, has emerged as a critical global challenge with far-reaching social, economic, and environmental implications. Using a globally representative observational streamflow dataset and Gini coefficients, this study investigates how streamflow inequality, which has a large impact on inequality of water availability, varies spatially and temporally, and its relationship with different underlying catchment characteristics. This study finds that watersheds in arid climates exhibit a higher degree of streamflow inequality than polar and equatorial ones. Africa experiences the highest streamflow inequality, followed by Australia, while South America experiences relatively lower streamflow inequality. Around 19.6 \\% of the catchments in Australia display an increasing trend in streamflow inequality, pointing to worsening conditions. Conversely, South America experiences a decreasing trend in streamflow inequality in 18.3 \\% of its catchments during the same period. It is also found that a more evenly distributed precipitation within the catchment and higher dam storage capacity corresponds to more evenly distributed streamflow availability throughout the year. This study enhances our understanding of streamflow inequality worldwide, which will aid policy formulation to foster sustainable development.},\n\turldate = {2023-12-09},\n\tjournal = {Science of The Total Environment},\n\tauthor = {Kuntla, Sai Kiran and Saharia, Manabendra and Prakash, Samar and Villarini, Gabriele},\n\tmonth = feb,\n\tyear = {2024},\n\tkeywords = {Gini coefficient, Streamflow, Sustainable development, Water, Water scarcity},\n\tpages = {169098},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Access to clean water is a fundamental human right, yet millions worldwide face the dire consequences of water scarcity and inadequate sanitation. Water inequality, characterized by disparities in access and availability of water resources, has emerged as a critical global challenge with far-reaching social, economic, and environmental implications. Using a globally representative observational streamflow dataset and Gini coefficients, this study investigates how streamflow inequality, which has a large impact on inequality of water availability, varies spatially and temporally, and its relationship with different underlying catchment characteristics. This study finds that watersheds in arid climates exhibit a higher degree of streamflow inequality than polar and equatorial ones. Africa experiences the highest streamflow inequality, followed by Australia, while South America experiences relatively lower streamflow inequality. Around 19.6 % of the catchments in Australia display an increasing trend in streamflow inequality, pointing to worsening conditions. Conversely, South America experiences a decreasing trend in streamflow inequality in 18.3 % of its catchments during the same period. It is also found that a more evenly distributed precipitation within the catchment and higher dam storage capacity corresponds to more evenly distributed streamflow availability throughout the year. This study enhances our understanding of streamflow inequality worldwide, which will aid policy formulation to foster sustainable development.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sharma-saharia-ramana-highresolutionlandslidesusceptibilitymappingusingensemblemachinelearningandgeospatialbigdata-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0341816223007440\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sharma-saharia-ramana-highresolutionlandslidesusceptibilitymappingusingensemblemachinelearningandgeospatialbigdata-2024', 'https://www.sciencedirect.com/science/article/pii/S0341816223007440', event);\">\n    High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sharma, N.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>;  and Ramana, G. V.\n</span>\n\n  \n\n</span>\n\n  <i>CATENA</i>, 235: 107653. February 2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0341816223007440\"\n     onclick=\"log_download('sharma-saharia-ramana-highresolutionlandslidesusceptibilitymappingusingensemblemachinelearningandgeospatialbigdata-2024', 'https://www.sciencedirect.com/science/article/pii/S0341816223007440', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.catena.2023.107653\"\n     onclick=\"log_download('sharma-saharia-ramana-highresolutionlandslidesusceptibilitymappingusingensemblemachinelearningandgeospatialbigdata-2024', 'http://doi.org/10.1016/j.catena.2023.107653', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sharma-saharia-ramana-highresolutionlandslidesusceptibilitymappingusingensemblemachinelearningandgeospatialbigdata-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>13 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sharma-saharia-ramana-highresolutionlandslidesusceptibilitymappingusingensemblemachinelearningandgeospatialbigdata-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{sharma_high_2024,\n\ttitle = {High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data},\n\tvolume = {235},\n\tissn = {0341-8162},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0341816223007440},\n\tdoi = {10.1016/j.catena.2023.107653},\n\tabstract = {Landslide susceptibility represents the potential of slope failure for given geo-environmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome all these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of One -Sided Selection and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India&#x27;s first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001° (∼100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73 \\%, sensitivity of 97.08 \\%, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75 \\% area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records. The ILSM is expected to aid policymaking in disaster risk reduction and developing landslide prediction models.},\n\turldate = {2023-11-16},\n\tjournal = {CATENA},\n\tauthor = {Sharma, Nirdesh and Saharia, Manabendra and Ramana, G. V.},\n\tmonth = feb,\n\tyear = {2024},\n\tkeywords = {Big data, Ensemble learning, High resolution landslide susceptibility, One-sided selection (OSS), Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE)},\n\tpages = {107653},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Landslide susceptibility represents the potential of slope failure for given geo-environmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome all these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of One -Sided Selection and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India's first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001° (∼100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73 %, sensitivity of 97.08 %, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75 % area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records. The ILSM is expected to aid policymaking in disaster risk reduction and developing landslide prediction models.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2023\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2023')\"\n      onkeypress=\"toggleGroup('group_2023')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2023</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"pandey-sharma-saharia-lorabasedcommunicationsystemformonitoringwaterqualityoflakesandreservoirs-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  LoRa-Based Communication System for Monitoring Water Quality of Lakes and Reservoirs.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pandey, R.; Sharma, N. K.;  and <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)</i>, volume 1, pages 1–4, January 2023. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/MIGARS57353.2023.10064590\"\n     onclick=\"log_download('pandey-sharma-saharia-lorabasedcommunicationsystemformonitoringwaterqualityoflakesandreservoirs-2023', 'http://doi.org/10.1109/MIGARS57353.2023.10064590', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pandey-sharma-saharia-lorabasedcommunicationsystemformonitoringwaterqualityoflakesandreservoirs-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pandey-sharma-saharia-lorabasedcommunicationsystemformonitoringwaterqualityoflakesandreservoirs-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{pandey_lora-based_2023,\n\ttitle = {{LoRa}-{Based} {Communication} {System} for {Monitoring} {Water} {Quality} of {Lakes} and {Reservoirs}},\n\tvolume = {1},\n\tdoi = {10.1109/MIGARS57353.2023.10064590},\n\tabstract = {This paper describes a conceptual framework for the establishment of a Long-Range Wide Area Network (LoRaWAN)based communication system for monitoring water quality of lakes and reservoirs. The proposed system allows connection of multiple sensors to the LoRa transceivers and enables data transmission to a cloud server. In a proof-of-concept test, the LoRa gateway was set up, a temperature sensor node was connected and used to monitor lake water temperature, and data was transferred to LoRa gateway which serves as the central monitoring system and is visualized on a centralized dashboard. In this work, a coverage test of the proposed system is also carried out to test the LoRAWAN communication range in the urban environment. We find that the range of the system varies from 150-2,500 m and it is affected by the antenna positioning placement and obstacles in between the gateway and the sensor node. The maximum range was found to be 2.5 km when there was clear line of sight between the gateway and sensor node.},\n\tbooktitle = {2023 {International} {Conference} on {Machine} {Intelligence} for {GeoAnalytics} and {Remote} {Sensing} ({MIGARS})},\n\tauthor = {Pandey, Rajdeep and Sharma, Nirdesh Kumar and Saharia, Manabendra},\n\tmonth = jan,\n\tyear = {2023},\n\tkeywords = {Lake Monitoring, Lakes, LoRaWAN, Logic gates, Reservoirs, Temperature measurement, Temperature sensors, Water Quality, Water quality, Wide area networks},\n\tpages = {1--4},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper describes a conceptual framework for the establishment of a Long-Range Wide Area Network (LoRaWAN)based communication system for monitoring water quality of lakes and reservoirs. The proposed system allows connection of multiple sensors to the LoRa transceivers and enables data transmission to a cloud server. In a proof-of-concept test, the LoRa gateway was set up, a temperature sensor node was connected and used to monitor lake water temperature, and data was transferred to LoRa gateway which serves as the central monitoring system and is visualized on a centralized dashboard. In this work, a coverage test of the proposed system is also carried out to test the LoRAWAN communication range in the urban environment. We find that the range of the system varies from 150-2,500 m and it is affected by the antenna positioning placement and obstacles in between the gateway and the sensor node. The maximum range was found to be 2.5 km when there was clear line of sight between the gateway and sensor node.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"raj-saharia-chakma-mappingsoilerodibilityoverindia-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0341816223003624\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'raj-saharia-chakma-mappingsoilerodibilityoverindia-2023', 'https://www.sciencedirect.com/science/article/pii/S0341816223003624', event);\">\n    Mapping soil erodibility over India.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Raj, R.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>;  and Chakma, S.\n</span>\n\n  \n\n</span>\n\n  <i>CATENA</i>, 230: 107271. September 2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0341816223003624\"\n     onclick=\"log_download('raj-saharia-chakma-mappingsoilerodibilityoverindia-2023', 'https://www.sciencedirect.com/science/article/pii/S0341816223003624', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Mapping soil erodibility over India [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.catena.2023.107271\"\n     onclick=\"log_download('raj-saharia-chakma-mappingsoilerodibilityoverindia-2023', 'http://doi.org/10.1016/j.catena.2023.107271', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/raj-saharia-chakma-mappingsoilerodibilityoverindia-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('raj-saharia-chakma-mappingsoilerodibilityoverindia-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{raj_mapping_2023,\n\ttitle = {Mapping soil erodibility over {India}},\n\tvolume = {230},\n\tissn = {0341-8162},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0341816223003624},\n\tdoi = {10.1016/j.catena.2023.107271},\n\tabstract = {Soil erosion is a major environmental problem worldwide, and almost half of India’s total geographical area is susceptible to it. The Revised Universal Soil Loss Equation (RUSLE) has been widely used globally to estimate soil erosion, and the Soil erodibility factor, denoted by the K-factor, is an essential component of RUSLE. Although previous studies have assessed soil erodibility in India, they have been limited to small scales such as watersheds or districts. A national-scale assessment of soil erodibility doesn’t exist and is critical to developing a systematic understanding of soil erosion over India. In this study, we estimated soil erodibility factors over India using RUSLE Nomograph and Environmental Policy Integrated Climate (EPIC) model approaches at a high resolution of 250 m. Our results showed that the K-factor estimated using the Nomograph approach was more accurate than the observed soil erodibility factors. Additionally, we developed erodibility indices such as CR (Clay Ratio), MCR (Modified Clay Ratio), and CLOM (Critical Level of Organic Matter) to assess their sensitivity with respect to soil erodibility factors. Finally, we created a susceptibility to erosion map over India using CLOM index classification. The national average soil erodibility factor for India is estimated to be 0.028 t-ha-h/ha/MJ/mm. Histosols soil type is the least susceptible to erosion, while the Xerosols soil type is most susceptible among the prevalent soil classes in India. This is the first national-scale mapping of soil erodibility over India, providing an essential asset for soil conservation and erosion management planning by experts.},\n\turldate = {2023-08-16},\n\tjournal = {CATENA},\n\tauthor = {Raj, Ravi and Saharia, Manabendra and Chakma, Sumedha},\n\tmonth = sep,\n\tyear = {2023},\n\tkeywords = {Clay Ratio, Critical Level of Organic Matter, India, Modified Clay Ratio, Soil erodibility factor, Soil erosion},\n\tpages = {107271},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Soil erosion is a major environmental problem worldwide, and almost half of India’s total geographical area is susceptible to it. The Revised Universal Soil Loss Equation (RUSLE) has been widely used globally to estimate soil erosion, and the Soil erodibility factor, denoted by the K-factor, is an essential component of RUSLE. Although previous studies have assessed soil erodibility in India, they have been limited to small scales such as watersheds or districts. A national-scale assessment of soil erodibility doesn’t exist and is critical to developing a systematic understanding of soil erosion over India. In this study, we estimated soil erodibility factors over India using RUSLE Nomograph and Environmental Policy Integrated Climate (EPIC) model approaches at a high resolution of 250 m. Our results showed that the K-factor estimated using the Nomograph approach was more accurate than the observed soil erodibility factors. Additionally, we developed erodibility indices such as CR (Clay Ratio), MCR (Modified Clay Ratio), and CLOM (Critical Level of Organic Matter) to assess their sensitivity with respect to soil erodibility factors. Finally, we created a susceptibility to erosion map over India using CLOM index classification. The national average soil erodibility factor for India is estimated to be 0.028 t-ha-h/ha/MJ/mm. Histosols soil type is the least susceptible to erosion, while the Xerosols soil type is most susceptible among the prevalent soil classes in India. This is the first national-scale mapping of soil erodibility over India, providing an essential asset for soil conservation and erosion management planning by experts.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"kumari-yadav-saharia-dev-spatiotemporalanalysisofairqualityanditsrelationshipwithcovid19lockdownoverdublin-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2352938522001434\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kumari-yadav-saharia-dev-spatiotemporalanalysisofairqualityanditsrelationshipwithcovid19lockdownoverdublin-2022', 'https://www.sciencedirect.com/science/article/pii/S2352938522001434', event);\">\n    Spatio-temporal analysis of air quality and its relationship with COVID-19 lockdown over Dublin.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kumari, S.; Yadav, A. C.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>;  and Dev, S.\n</span>\n\n  \n\n</span>\n\n  <i>Remote Sensing Applications: Society and Environment</i>, 28: 100835. November 2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2352938522001434\"\n     onclick=\"log_download('kumari-yadav-saharia-dev-spatiotemporalanalysisofairqualityanditsrelationshipwithcovid19lockdownoverdublin-2022', 'https://www.sciencedirect.com/science/article/pii/S2352938522001434', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Spatio-temporal analysis of air quality and its relationship with COVID-19 lockdown over Dublin [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.rsase.2022.100835\"\n     onclick=\"log_download('kumari-yadav-saharia-dev-spatiotemporalanalysisofairqualityanditsrelationshipwithcovid19lockdownoverdublin-2022', 'http://doi.org/10.1016/j.rsase.2022.100835', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kumari-yadav-saharia-dev-spatiotemporalanalysisofairqualityanditsrelationshipwithcovid19lockdownoverdublin-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kumari-yadav-saharia-dev-spatiotemporalanalysisofairqualityanditsrelationshipwithcovid19lockdownoverdublin-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{kumari_spatio-temporal_2022,\n\ttitle = {Spatio-temporal analysis of air quality and its relationship with {COVID}-19 lockdown over {Dublin}},\n\tvolume = {28},\n\tissn = {2352-9385},\n\turl = {https://www.sciencedirect.com/science/article/pii/S2352938522001434},\n\tdoi = {10.1016/j.rsase.2022.100835},\n\tabstract = {Air pollution has become one of the biggest challenges for human and environmental health. Major pollutants such as Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), Carbon Monoxide (CO), and Particulate matter (PM10 and PM2.5) are being ejected in a large quantity every day. Initially, authorities did not implement the strictest mitigation policies due to pressures of balancing the economic needs of people and public safety. Still, after realizing the effect of the COVID-19 pandemic, countries around the world imposed a complete lockdown to contain the outbreak, which had the unexpected benefit of causing a drastic improvement in air quality. The present study investigates the air pollution scenarios over the Dublin city through satellites (Sentinel-5P and Moderate Resolution Imaging Spectroradiometer) and ground-based observations. An average of 28\\% reduction in average NO2 level and a 27.7\\% improvement in AQI (Air Quality Index) was experienced in 2020 compared to 2019 during the lockdown period (27 March–05 June). We found that PM10 and PM2.5 are the most dominating factor in the AQI over Dublin.},\n\turldate = {2023-08-16},\n\tjournal = {Remote Sensing Applications: Society and Environment},\n\tauthor = {Kumari, Sushma and Yadav, Avinash Chand and Saharia, Manabendra and Dev, Soumyabrata},\n\tmonth = nov,\n\tyear = {2022},\n\tkeywords = {AQI, Air pollution, COVID-19 pandemic, Dublin, MODIS, Sentinel-5P},\n\tpages = {100835},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Air pollution has become one of the biggest challenges for human and environmental health. Major pollutants such as Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), Carbon Monoxide (CO), and Particulate matter (PM10 and PM2.5) are being ejected in a large quantity every day. Initially, authorities did not implement the strictest mitigation policies due to pressures of balancing the economic needs of people and public safety. Still, after realizing the effect of the COVID-19 pandemic, countries around the world imposed a complete lockdown to contain the outbreak, which had the unexpected benefit of causing a drastic improvement in air quality. The present study investigates the air pollution scenarios over the Dublin city through satellites (Sentinel-5P and Moderate Resolution Imaging Spectroradiometer) and ground-based observations. An average of 28% reduction in average NO2 level and a 27.7% improvement in AQI (Air Quality Index) was experienced in 2020 compared to 2019 during the lockdown period (27 March–05 June). We found that PM10 and PM2.5 are the most dominating factor in the AQI over Dublin.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kuntla-saharia-kirstetter-globalscalecharacterizationofstreamflowextremes-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0022169422012380?via%3Dihub\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kuntla-saharia-kirstetter-globalscalecharacterizationofstreamflowextremes-2022', 'https://www.sciencedirect.com/science/article/pii/S0022169422012380?via%3Dihub', event);\">\n    Global-scale characterization of streamflow extremes.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kuntla, S. K.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>;  and Kirstetter, P.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrology</i>, 615: 128668. December 2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0022169422012380?via%3Dihub\"\n     onclick=\"log_download('kuntla-saharia-kirstetter-globalscalecharacterizationofstreamflowextremes-2022', 'https://www.sciencedirect.com/science/article/pii/S0022169422012380?via%3Dihub', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Global-scale characterization of streamflow extremes [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jhydrol.2022.128668\"\n     onclick=\"log_download('kuntla-saharia-kirstetter-globalscalecharacterizationofstreamflowextremes-2022', 'http://doi.org/10.1016/j.jhydrol.2022.128668', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kuntla-saharia-kirstetter-globalscalecharacterizationofstreamflowextremes-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kuntla-saharia-kirstetter-globalscalecharacterizationofstreamflowextremes-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{kuntla_global-scale_2022,\n\ttitle = {Global-scale characterization of streamflow extremes},\n\tvolume = {615},\n\tissn = {00221694},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0022169422012380?via%3Dihub},\n\tdoi = {10.1016/j.jhydrol.2022.128668},\n\tabstract = {The increasing risk of floods across the globe needs focused attention because of the extensive damage to human lives and economy. A comprehensive understanding of its causative factors is of vital importance. Yet catchment characterization studies are generally limited to case studies or regional domains. A comprehensive global characterization is currently unavailable, which requires collecting and collating a large number of datasets over vast areas. This study embraces large-sample data-driven science as a new paradigm to characterize streamflow extremes by utilizing global datasets of physiographic explanatory variables that could explain various facets of extreme streamflows. Along with the spatial and temporal variations of high streamflow extremes, their corre­ lation with various catchment characteristics such as geomorphology, meteorology, climatology, landcover, li­ thology, etc. were examined. The multidimensional relationships between the streamflow extremes and catchment characteristics were modeled using a Random Forest approach and combined with an interpretable machine learning framework to identify the most dominant factors in varying climate classes. Interpretation with SHAP (SHapley Additive exPlanations) reveals that meteorological variables are the most influential variables across the climatic classes. However, the variables and their influences change among different climatic classes. Moreover, different geomorphological variables come into dominance across climatic classes (such as basin relief in warm temperate and drainage texture in arid climates). Overall, the insights from the study could play a crucial role in predicting the unit peak discharge at ungauged stations from the known catchment characteristics. Moreover, these findings can also play a crucial role in formulating risk management strategy.},\n\tlanguage = {en},\n\turldate = {2022-12-11},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Kuntla, Sai Kiran and Saharia, Manabendra and Kirstetter, Pierre},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {128668},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The increasing risk of floods across the globe needs focused attention because of the extensive damage to human lives and economy. A comprehensive understanding of its causative factors is of vital importance. Yet catchment characterization studies are generally limited to case studies or regional domains. A comprehensive global characterization is currently unavailable, which requires collecting and collating a large number of datasets over vast areas. This study embraces large-sample data-driven science as a new paradigm to characterize streamflow extremes by utilizing global datasets of physiographic explanatory variables that could explain various facets of extreme streamflows. Along with the spatial and temporal variations of high streamflow extremes, their corre­ lation with various catchment characteristics such as geomorphology, meteorology, climatology, landcover, li­ thology, etc. were examined. The multidimensional relationships between the streamflow extremes and catchment characteristics were modeled using a Random Forest approach and combined with an interpretable machine learning framework to identify the most dominant factors in varying climate classes. Interpretation with SHAP (SHapley Additive exPlanations) reveals that meteorological variables are the most influential variables across the climatic classes. However, the variables and their influences change among different climatic classes. Moreover, different geomorphological variables come into dominance across climatic classes (such as basin relief in warm temperate and drainage texture in arid climates). Overall, the insights from the study could play a crucial role in predicting the unit peak discharge at ungauged stations from the known catchment characteristics. Moreover, these findings can also play a crucial role in formulating risk management strategy.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"raj-saharia-chakma-rafieinasab-mappingrainfallerosivityoverindiausingmultipleprecipitationdatasets-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0341816222002429\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'raj-saharia-chakma-rafieinasab-mappingrainfallerosivityoverindiausingmultipleprecipitationdatasets-2022', 'https://www.sciencedirect.com/science/article/pii/S0341816222002429', event);\">\n    Mapping rainfall erosivity over India using multiple precipitation datasets.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Raj, R.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Chakma, S.;  and Rafieinasab, A.\n</span>\n\n  \n\n</span>\n\n  <i>CATENA</i>, 214: 106256. July 2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0341816222002429\"\n     onclick=\"log_download('raj-saharia-chakma-rafieinasab-mappingrainfallerosivityoverindiausingmultipleprecipitationdatasets-2022', 'https://www.sciencedirect.com/science/article/pii/S0341816222002429', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Mapping rainfall erosivity over India using multiple precipitation datasets [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.catena.2022.106256\"\n     onclick=\"log_download('raj-saharia-chakma-rafieinasab-mappingrainfallerosivityoverindiausingmultipleprecipitationdatasets-2022', 'http://doi.org/10.1016/j.catena.2022.106256', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/raj-saharia-chakma-rafieinasab-mappingrainfallerosivityoverindiausingmultipleprecipitationdatasets-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('raj-saharia-chakma-rafieinasab-mappingrainfallerosivityoverindiausingmultipleprecipitationdatasets-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{raj_mapping_2022,\n\ttitle = {Mapping rainfall erosivity over {India} using multiple precipitation datasets},\n\tvolume = {214},\n\tissn = {0341-8162},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0341816222002429},\n\tdoi = {10.1016/j.catena.2022.106256},\n\tabstract = {Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water, and rainfall erosivity is one of the major components. The current assessments of rainfall erosivity in India are however largely based on rain-gauge recordings and surveys which hinders its estimation and understanding over large areas. Growing availability of remotely-sensed gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based estimations. In this study, multiple national and global gridded precipitation datasets were utilized to develop a high-resolution rainfall erosivity factor (R-factor) map to highlight areas prone to rainfall-induced erosion. Further, a large selection of empirical equations from literature were employed for estimating rainfall erosivity to provide a comparative analysis of these commonly adopted methods. The calculated rainfall erosivity is also compared with alternative methods to estimate R-factor such as Fournier Index (FI) and Modified Fournier Index (MFI). It was observed that MFI is highly correlated with rainfall erosivity, and an equation was finally proposed to estimate R-factor using MFI. This is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will aid in understanding and mitigating rainfall-induced erosion.},\n\tlanguage = {en},\n\turldate = {2022-08-01},\n\tjournal = {CATENA},\n\tauthor = {Raj, Ravi and Saharia, Manabendra and Chakma, Sumedha and Rafieinasab, Arezoo},\n\tmonth = jul,\n\tyear = {2022},\n\tkeywords = {Fournier Index, India, Modified Fournier Index, Rainfall erosivity factor, Rainfall-kinetic energy},\n\tpages = {106256},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water, and rainfall erosivity is one of the major components. The current assessments of rainfall erosivity in India are however largely based on rain-gauge recordings and surveys which hinders its estimation and understanding over large areas. Growing availability of remotely-sensed gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based estimations. In this study, multiple national and global gridded precipitation datasets were utilized to develop a high-resolution rainfall erosivity factor (R-factor) map to highlight areas prone to rainfall-induced erosion. Further, a large selection of empirical equations from literature were employed for estimating rainfall erosivity to provide a comparative analysis of these commonly adopted methods. The calculated rainfall erosivity is also compared with alternative methods to estimate R-factor such as Fournier Index (FI) and Modified Fournier Index (MFI). It was observed that MFI is highly correlated with rainfall erosivity, and an equation was finally proposed to estimate R-factor using MFI. This is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will aid in understanding and mitigating rainfall-induced erosion.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"newman-stone-saharia-holman-addor-clark-identifyingsensitivitiesinfloodfrequencyanalysesusingastochastichydrologicmodelingsystem-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://hess.copernicus.org/articles/25/5603/2021/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'newman-stone-saharia-holman-addor-clark-identifyingsensitivitiesinfloodfrequencyanalysesusingastochastichydrologicmodelingsystem-2021', 'https://hess.copernicus.org/articles/25/5603/2021/', event);\">\n    Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Newman, A. J.; Stone, A. G.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Holman, K. D.; Addor, N.;  and Clark, M. P.\n</span>\n\n  \n\n</span>\n\n  <i>Hydrology and Earth System Sciences</i>, 25(10): 5603–5621. October 2021.\n  <span class=\"note\">Publisher: Copernicus GmbH</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://hess.copernicus.org/articles/25/5603/2021/\"\n     onclick=\"log_download('newman-stone-saharia-holman-addor-clark-identifyingsensitivitiesinfloodfrequencyanalysesusingastochastichydrologicmodelingsystem-2021', 'https://hess.copernicus.org/articles/25/5603/2021/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.5194/hess-25-5603-2021\"\n     onclick=\"log_download('newman-stone-saharia-holman-addor-clark-identifyingsensitivitiesinfloodfrequencyanalysesusingastochastichydrologicmodelingsystem-2021', 'http://doi.org/10.5194/hess-25-5603-2021', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/newman-stone-saharia-holman-addor-clark-identifyingsensitivitiesinfloodfrequencyanalysesusingastochastichydrologicmodelingsystem-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('newman-stone-saharia-holman-addor-clark-identifyingsensitivitiesinfloodfrequencyanalysesusingastochastichydrologicmodelingsystem-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{newman_identifying_2021,\n\ttitle = {Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system},\n\tvolume = {25},\n\tissn = {1027-5606},\n\turl = {https://hess.copernicus.org/articles/25/5603/2021/},\n\tdoi = {10.5194/hess-25-5603-2021},\n\tabstract = {{\\textless}p{\\textgreater}{\\textless}strong class=&quot;journal-contentHeaderColor&quot;{\\textgreater}Abstract.{\\textless}/strong{\\textgreater} This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.{\\textless}/p{\\textgreater}},\n\tlanguage = {English},\n\tnumber = {10},\n\turldate = {2022-04-02},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Newman, Andrew J. and Stone, Amanda G. and Saharia, Manabendra and Holman, Kathleen D. and Addor, Nans and Clark, Martyn P.},\n\tmonth = oct,\n\tyear = {2021},\n\tnote = {Publisher: Copernicus GmbH},\n\tpages = {5603--5621},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  \\textlessp\\textgreater\\textlessstrong class=\"journal-contentHeaderColor\"\\textgreaterAbstract.\\textless/strong\\textgreater This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.\\textless/p\\textgreater\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"potdar-kirstetter-woods-saharia-towardpredictingfloodeventpeakdischargeinungaugedbasinsbylearninguniversalhydrologicalbehaviorswithmachinelearning-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-20-0302.1.xml\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'potdar-kirstetter-woods-saharia-towardpredictingfloodeventpeakdischargeinungaugedbasinsbylearninguniversalhydrologicalbehaviorswithmachinelearning-2021', 'https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-20-0302.1.xml', event);\">\n    Toward Predicting Flood Event Peak Discharge in Ungauged Basins by Learning Universal Hydrological Behaviors with Machine Learning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Potdar, A. S.; Kirstetter, P.; Woods, D.;  and <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrometeorology</i>, 22(11): 2971–2982. November 2021.\n  <span class=\"note\">Publisher: American Meteorological Society Section: Journal of Hydrometeorology</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-20-0302.1.xml\"\n     onclick=\"log_download('potdar-kirstetter-woods-saharia-towardpredictingfloodeventpeakdischargeinungaugedbasinsbylearninguniversalhydrologicalbehaviorswithmachinelearning-2021', 'https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-20-0302.1.xml', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Toward Predicting Flood Event Peak Discharge in Ungauged Basins by Learning Universal Hydrological Behaviors with Machine Learning [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1175/JHM-D-20-0302.1\"\n     onclick=\"log_download('potdar-kirstetter-woods-saharia-towardpredictingfloodeventpeakdischargeinungaugedbasinsbylearninguniversalhydrologicalbehaviorswithmachinelearning-2021', 'http://doi.org/10.1175/JHM-D-20-0302.1', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/potdar-kirstetter-woods-saharia-towardpredictingfloodeventpeakdischargeinungaugedbasinsbylearninguniversalhydrologicalbehaviorswithmachinelearning-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('potdar-kirstetter-woods-saharia-towardpredictingfloodeventpeakdischargeinungaugedbasinsbylearninguniversalhydrologicalbehaviorswithmachinelearning-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{potdar_toward_2021,\n\ttitle = {Toward {Predicting} {Flood} {Event} {Peak} {Discharge} in {Ungauged} {Basins} by {Learning} {Universal} {Hydrological} {Behaviors} with {Machine} {Learning}},\n\tvolume = {22},\n\tissn = {1525-7541, 1525-755X},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/22/11/JHM-D-20-0302.1.xml},\n\tdoi = {10.1175/JHM-D-20-0302.1},\n\tabstract = {Abstract In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine-learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multidimensional statistical modeling approach. Among different machine-learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created that can be deployed in the future for flash flood forecasting. The results confirm that, although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of subbasin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins that could most benefit from distributed hydrologic modeling.},\n\tlanguage = {EN},\n\tnumber = {11},\n\turldate = {2022-04-02},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Potdar, Akhil Sanjay and Kirstetter, Pierre-Emmanuel and Woods, Devon and Saharia, Manabendra},\n\tmonth = nov,\n\tyear = {2021},\n\tnote = {Publisher: American Meteorological Society\nSection: Journal of Hydrometeorology},\n\tpages = {2971--2982},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine-learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multidimensional statistical modeling approach. Among different machine-learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created that can be deployed in the future for flash flood forecasting. The results confirm that, although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of subbasin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins that could most benefit from distributed hydrologic modeling.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"saharia-kirstetter-vergara-gourley-emmanuel-andrieu-ontheimpactofrainfallspatialvariabilitygeomorphologyandclimatologyonflashfloods-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029124\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'saharia-kirstetter-vergara-gourley-emmanuel-andrieu-ontheimpactofrainfallspatialvariabilitygeomorphologyandclimatologyonflashfloods-2021', 'https://onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029124', event);\">\n    On the Impact of Rainfall Spatial Variability, Geomorphology, and Climatology on Flash Floods.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Kirstetter, P.; Vergara, H.; Gourley, J. J.; Emmanuel, I.;  and Andrieu, H.\n</span>\n\n  \n\n</span>\n\n  <i>Water Resources Research</i>, 57(9): e2020WR029124.  2021.\n  <span class=\"note\">_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029124</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029124\"\n     onclick=\"log_download('saharia-kirstetter-vergara-gourley-emmanuel-andrieu-ontheimpactofrainfallspatialvariabilitygeomorphologyandclimatologyonflashfloods-2021', 'https://onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029124', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"On the Impact of Rainfall Spatial Variability, Geomorphology, and Climatology on Flash Floods [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1029/2020WR029124\"\n     onclick=\"log_download('saharia-kirstetter-vergara-gourley-emmanuel-andrieu-ontheimpactofrainfallspatialvariabilitygeomorphologyandclimatologyonflashfloods-2021', 'http://doi.org/10.1029/2020WR029124', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/saharia-kirstetter-vergara-gourley-emmanuel-andrieu-ontheimpactofrainfallspatialvariabilitygeomorphologyandclimatologyonflashfloods-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('saharia-kirstetter-vergara-gourley-emmanuel-andrieu-ontheimpactofrainfallspatialvariabilitygeomorphologyandclimatologyonflashfloods-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{saharia_impact_2021,\n\ttitle = {On the {Impact} of {Rainfall} {Spatial} {Variability}, {Geomorphology}, and {Climatology} on {Flash} {Floods}},\n\tvolume = {57},\n\tissn = {1944-7973},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2020WR029124},\n\tdoi = {10.1029/2020WR029124},\n\tabstract = {AbstractThe effects of spatial variability of rainfall, geomorphology, and climatology of precipitation and temperature on the hydrologic response remain poorly understood. This study characterizes the catchment response in terms of a variable called flashiness, that describes the severity of the flood response as the rate of rise of the unit discharge. It overcomes limitations of prior works based on limited case studies or simulations by gathering information on basins of widely varying characteristics and by using a high-resolution rainfall and flooding event data set spanning 10 years over the Continental United States. The objective is to develop a robust understanding of how rainfall spatial variability influences flash flood severity and to assess its contribution relative to basin physiography and climatology. This study explores the first-order dependencies as well as the variability in these relationships and investigates the complex interactions using a multi-dimensional statistical modeling approach. The results confirm that the spatial organization of rainfall influences the basin response on par with geomorphology and climatology. Basin physiography dampens the effect of lower rainfall intensities, while higher rainfall overwhelms other factors and primarily contributes to flashiness. Dispersion of precipitation with respect to the flow path decreases flood severity. An improved understanding of sub-basin scale rainfall spatial variability aids in developing a robust flash flood severity index to identify and mitigate flash flooding situations as well as identifying basins which could most benefit from distributed hydrologic modeling.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2021-09-24},\n\tjournal = {Water Resources Research},\n\tauthor = {Saharia, Manabendra and Kirstetter, Pierre-Emmanuel and Vergara, Humberto and Gourley, Jonathan J. and Emmanuel, Isabelle and Andrieu, Hervé},\n\tyear = {2021},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029124},\n\tkeywords = {flash flood, geomorphology, rainfall spatial variability},\n\tpages = {e2020WR029124},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  AbstractThe effects of spatial variability of rainfall, geomorphology, and climatology of precipitation and temperature on the hydrologic response remain poorly understood. This study characterizes the catchment response in terms of a variable called flashiness, that describes the severity of the flood response as the rate of rise of the unit discharge. It overcomes limitations of prior works based on limited case studies or simulations by gathering information on basins of widely varying characteristics and by using a high-resolution rainfall and flooding event data set spanning 10 years over the Continental United States. The objective is to develop a robust understanding of how rainfall spatial variability influences flash flood severity and to assess its contribution relative to basin physiography and climatology. This study explores the first-order dependencies as well as the variability in these relationships and investigates the complex interactions using a multi-dimensional statistical modeling approach. The results confirm that the spatial organization of rainfall influences the basin response on par with geomorphology and climatology. Basin physiography dampens the effect of lower rainfall intensities, while higher rainfall overwhelms other factors and primarily contributes to flashiness. Dispersion of precipitation with respect to the flow path decreases flood severity. An improved understanding of sub-basin scale rainfall spatial variability aids in developing a robust flash flood severity index to identify and mitigate flash flooding situations as well as identifying basins which could most benefit from distributed hydrologic modeling.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"saharia-jain-baishya-haobam-sreejith-pai-rafieeinasab-indiafloodinventorycreationofamultisourcenationalgeospatialdatabasetofacilitatecomprehensivefloodresearch-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1007/s11069-021-04698-6\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'saharia-jain-baishya-haobam-sreejith-pai-rafieeinasab-indiafloodinventorycreationofamultisourcenationalgeospatialdatabasetofacilitatecomprehensivefloodresearch-2021', 'https://doi.org/10.1007/s11069-021-04698-6', event);\">\n    India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Jain, A.; Baishya, R. R.; Haobam, S.; Sreejith, O. P.; Pai, D. S.;  and Rafieeinasab, A.\n</span>\n\n  \n\n</span>\n\n  <i>Natural Hazards</i>. March 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1007/s11069-021-04698-6\"\n     onclick=\"log_download('saharia-jain-baishya-haobam-sreejith-pai-rafieeinasab-indiafloodinventorycreationofamultisourcenationalgeospatialdatabasetofacilitatecomprehensivefloodresearch-2021', 'https://doi.org/10.1007/s11069-021-04698-6', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s11069-021-04698-6\"\n     onclick=\"log_download('saharia-jain-baishya-haobam-sreejith-pai-rafieeinasab-indiafloodinventorycreationofamultisourcenationalgeospatialdatabasetofacilitatecomprehensivefloodresearch-2021', 'http://doi.org/10.1007/s11069-021-04698-6', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/saharia-jain-baishya-haobam-sreejith-pai-rafieeinasab-indiafloodinventorycreationofamultisourcenationalgeospatialdatabasetofacilitatecomprehensivefloodresearch-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('saharia-jain-baishya-haobam-sreejith-pai-rafieeinasab-indiafloodinventorycreationofamultisourcenationalgeospatialdatabasetofacilitatecomprehensivefloodresearch-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{saharia_india_2021,\n\ttitle = {India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research},\n\tissn = {1573-0840},\n\tshorttitle = {India flood inventory},\n\turl = {https://doi.org/10.1007/s11069-021-04698-6},\n\tdoi = {10.1007/s11069-021-04698-6},\n\tabstract = {Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the existence of a historical inventory of floods is imperative to promote such research endeavors. Though, a few global inventories exist, they lack the spatio-temporal fidelity necessary to make them useful for computational research due to reasons such as concentrating exclusively on large floods, limited temporal scope, non-standard data formats. Therefore, there is an urgent need for developing a new database that combines data from global and hitherto-underutilized local datasets using an extensible and common schema. This paper describes the ongoing effort of building the India Flood Inventory (IFI), which is the first freely available, analysis-ready geospatial dataset over the region with detailed qualitative and quantitative information regarding floods, including spatial extents. The paper outlines the methodology that has been adopted as well as some preliminary findings using the data contained in this inventory. This dataset is expected to advance the understanding of flood processes in the worst affected region of the world.},\n\tlanguage = {en},\n\turldate = {2021-03-31},\n\tjournal = {Natural Hazards},\n\tauthor = {Saharia, Manabendra and Jain, Avish and Baishya, Ronit Raj and Haobam, Saagar and Sreejith, O. P. and Pai, D. S. and Rafieeinasab, Arezoo},\n\tmonth = mar,\n\tyear = {2021},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the existence of a historical inventory of floods is imperative to promote such research endeavors. Though, a few global inventories exist, they lack the spatio-temporal fidelity necessary to make them useful for computational research due to reasons such as concentrating exclusively on large floods, limited temporal scope, non-standard data formats. Therefore, there is an urgent need for developing a new database that combines data from global and hitherto-underutilized local datasets using an extensible and common schema. This paper describes the ongoing effort of building the India Flood Inventory (IFI), which is the first freely available, analysis-ready geospatial dataset over the region with detailed qualitative and quantitative information regarding floods, including spatial extents. The paper outlines the methodology that has been adopted as well as some preliminary findings using the data contained in this inventory. This dataset is expected to advance the understanding of flood processes in the worst affected region of the world.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"kim-sadeghi-limon-saharia-seo-philpott-bell-brown-etal-assessingtheskillofmediumrangeensembleprecipitationandstreamflowforecastsfromthehydrologicensembleforecastservicehefsfortheuppertrinityriverbasininnorthtexas-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://journals.ametsoc.org/view/journals/hydr/19/9/jhm-d-18-0027_1.xml\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kim-sadeghi-limon-saharia-seo-philpott-bell-brown-etal-assessingtheskillofmediumrangeensembleprecipitationandstreamflowforecastsfromthehydrologicensembleforecastservicehefsfortheuppertrinityriverbasininnorthtexas-2018', 'https://journals.ametsoc.org/view/journals/hydr/19/9/jhm-d-18-0027_1.xml', event);\">\n    Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kim, S.; Sadeghi, H.; Limon, R. A.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Seo, D.; Philpott, A.; Bell, F.; Brown, J.;  and He, M.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrometeorology</i>, 19(9): 1467–1483. September 2018.\n  <span class=\"note\">Publisher: American Meteorological Society Section: Journal of Hydrometeorology</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://journals.ametsoc.org/view/journals/hydr/19/9/jhm-d-18-0027_1.xml\"\n     onclick=\"log_download('kim-sadeghi-limon-saharia-seo-philpott-bell-brown-etal-assessingtheskillofmediumrangeensembleprecipitationandstreamflowforecastsfromthehydrologicensembleforecastservicehefsfortheuppertrinityriverbasininnorthtexas-2018', 'https://journals.ametsoc.org/view/journals/hydr/19/9/jhm-d-18-0027_1.xml', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1175/JHM-D-18-0027.1\"\n     onclick=\"log_download('kim-sadeghi-limon-saharia-seo-philpott-bell-brown-etal-assessingtheskillofmediumrangeensembleprecipitationandstreamflowforecastsfromthehydrologicensembleforecastservicehefsfortheuppertrinityriverbasininnorthtexas-2018', 'http://doi.org/10.1175/JHM-D-18-0027.1', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kim-sadeghi-limon-saharia-seo-philpott-bell-brown-etal-assessingtheskillofmediumrangeensembleprecipitationandstreamflowforecastsfromthehydrologicensembleforecastservicehefsfortheuppertrinityriverbasininnorthtexas-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kim-sadeghi-limon-saharia-seo-philpott-bell-brown-etal-assessingtheskillofmediumrangeensembleprecipitationandstreamflowforecastsfromthehydrologicensembleforecastservicehefsfortheuppertrinityriverbasininnorthtexas-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{kim_assessing_2018,\n\ttitle = {Assessing the {Skill} of {Medium}-{Range} {Ensemble} {Precipitation} and {Streamflow} {Forecasts} from the {Hydrologic} {Ensemble} {Forecast} {Service} ({HEFS}) for the {Upper} {Trinity} {River} {Basin} in {North} {Texas}},\n\tvolume = {19},\n\tissn = {1525-7541, 1525-755X},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/19/9/jhm-d-18-0027_1.xml},\n\tdoi = {10.1175/JHM-D-18-0027.1},\n\tabstract = {Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20\\% over climatological forecast for the largest 1\\% of the observed biweekly flow.},\n\tlanguage = {EN},\n\tnumber = {9},\n\turldate = {2022-09-27},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Kim, Sunghee and Sadeghi, Hossein and Limon, Reza Ahmad and Saharia, Manabendra and Seo, Dong-Jun and Philpott, Andrew and Bell, Frank and Brown, James and He, Minxue},\n\tmonth = sep,\n\tyear = {2018},\n\tnote = {Publisher: American Meteorological Society\nSection: Journal of Hydrometeorology},\n\tpages = {1467--1483},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"saharia-kirstetter-vergara-gourley-hong-giroud-mappingflashfloodseverityintheunitedstates-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0082_1.xml\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'saharia-kirstetter-vergara-gourley-hong-giroud-mappingflashfloodseverityintheunitedstates-2017', 'https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0082_1.xml', event);\">\n    Mapping Flash Flood Severity in the United States.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Kirstetter, P.; Vergara, H.; Gourley, J. J.; Hong, Y.;  and Giroud, M.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrometeorology</i>, 18(2): 397–411. February 2017.\n  <span class=\"note\">Publisher: American Meteorological Society Section: Journal of Hydrometeorology</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0082_1.xml\"\n     onclick=\"log_download('saharia-kirstetter-vergara-gourley-hong-giroud-mappingflashfloodseverityintheunitedstates-2017', 'https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0082_1.xml', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Mapping Flash Flood Severity in the United States [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1175/JHM-D-16-0082.1\"\n     onclick=\"log_download('saharia-kirstetter-vergara-gourley-hong-giroud-mappingflashfloodseverityintheunitedstates-2017', 'http://doi.org/10.1175/JHM-D-16-0082.1', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/saharia-kirstetter-vergara-gourley-hong-giroud-mappingflashfloodseverityintheunitedstates-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('saharia-kirstetter-vergara-gourley-hong-giroud-mappingflashfloodseverityintheunitedstates-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{saharia_mapping_2017,\n\ttitle = {Mapping {Flash} {Flood} {Severity} in the {United} {States}},\n\tvolume = {18},\n\tissn = {1525-7541, 1525-755X},\n\turl = {https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0082_1.xml},\n\tdoi = {10.1175/JHM-D-16-0082.1},\n\tabstract = {{\\textless}section class=&quot;abstract&quot;{\\textgreater}{\\textless}h2 class=&quot;abstractTitle text-title my-1&quot; id=&quot;d3028e2&quot;{\\textgreater}Abstract{\\textless}/h2{\\textgreater}{\\textless}p{\\textgreater}Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.{\\textless}/p{\\textgreater}{\\textless}/section{\\textgreater}},\n\tlanguage = {EN},\n\tnumber = {2},\n\turldate = {2021-04-09},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Saharia, Manabendra and Kirstetter, Pierre-Emmanuel and Vergara, Humberto and Gourley, Jonathan J. and Hong, Yang and Giroud, Marine},\n\tmonth = feb,\n\tyear = {2017},\n\tnote = {Publisher: American Meteorological Society\nSection: Journal of Hydrometeorology},\n\tpages = {397--411},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  \\textlesssection class=\"abstract\"\\textgreater\\textlessh2 class=\"abstractTitle text-title my-1\" id=\"d3028e2\"\\textgreaterAbstract\\textless/h2\\textgreater\\textlessp\\textgreaterFlash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.\\textless/p\\textgreater\\textless/section\\textgreater\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"saharia-kirstetter-vergara-gourley-hong-characterizationoffloodsintheunitedstates-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0022169417301476\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'saharia-kirstetter-vergara-gourley-hong-characterizationoffloodsintheunitedstates-2017', 'http://www.sciencedirect.com/science/article/pii/S0022169417301476', event);\">\n    Characterization of floods in the United States.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Kirstetter, P.; Vergara, H.; Gourley, J. J.;  and Hong, Y.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrology</i>, 548: 524–535. May 2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0022169417301476\"\n     onclick=\"log_download('saharia-kirstetter-vergara-gourley-hong-characterizationoffloodsintheunitedstates-2017', 'http://www.sciencedirect.com/science/article/pii/S0022169417301476', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Characterization of floods in the United States [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jhydrol.2017.03.010\"\n     onclick=\"log_download('saharia-kirstetter-vergara-gourley-hong-characterizationoffloodsintheunitedstates-2017', 'http://doi.org/10.1016/j.jhydrol.2017.03.010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/saharia-kirstetter-vergara-gourley-hong-characterizationoffloodsintheunitedstates-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('saharia-kirstetter-vergara-gourley-hong-characterizationoffloodsintheunitedstates-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{saharia_characterization_2017,\n\ttitle = {Characterization of floods in the {United} {States}},\n\tvolume = {548},\n\tcopyright = {CC0 1.0 Universal Public Domain Dedication},\n\tissn = {0022-1694},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0022169417301476},\n\tdoi = {10.1016/j.jhydrol.2017.03.010},\n\tabstract = {Floods have gained increasing global significance in the recent past due to their devastating nature and potential for causing significant economic and human losses. Until now, flood characterization studies in the United States have been limited due to the lack of a comprehensive database matching flood characteristics such as peak discharges and flood duration with geospatial and geomorphologic information. The availability of a representative and long archive of flooding events spanning 78 years over a variety of hydroclimatic regions results in a spatially and temporally comprehensive flood characterization over the continental U.S. This study, for the first time, employs a large-event database that is based on actual National Weather Service (NWS) definitions of floods instead of the frequently-adopted case study or frequentist approach, allowing us to base our findings on real definitions of floods. It examines flooding characteristics to identify how space and time scales of floods vary with climatic regimes and geomorphology. Flood events were characterized by linking flood response variables in gauged basins to spatially distributed variables describing climatology, geomorphology, and topography. The primary findings of this study are that the magnitude of flooding is highest is regions such as West Coast and southeastern U.S. which experience the most extraordinary precipitation. The seasonality of flooding varies greatly from maxima during the cool season on the West Coast, warm season in the desert Southwest, and early spring in the Southeast. The fastest responding events tend to be in steep basins of the arid Southwest caused by intense monsoon thunderstorms and steep terrain. The envelope curves of unit peak discharge are consistent with those reported for Europe and worldwide. But significant seasonal variability was observed in floods of the U.S. compared to Europe that is attributed to the diversity of causative rainfall ranging from synoptic scales with orographic enhancements in the West Coast, monsoon thunderstorms in the desert Southwest, to land-falling tropical storms and localized, intense thunderstorms in the Southeast.},\n\turldate = {2017-03-29},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Saharia, Manabendra and Kirstetter, Pierre-Emmanuel and Vergara, Humberto and Gourley, Jonathan J. and Hong, Yang},\n\tmonth = may,\n\tyear = {2017},\n\tpages = {524--535},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Floods have gained increasing global significance in the recent past due to their devastating nature and potential for causing significant economic and human losses. Until now, flood characterization studies in the United States have been limited due to the lack of a comprehensive database matching flood characteristics such as peak discharges and flood duration with geospatial and geomorphologic information. The availability of a representative and long archive of flooding events spanning 78 years over a variety of hydroclimatic regions results in a spatially and temporally comprehensive flood characterization over the continental U.S. This study, for the first time, employs a large-event database that is based on actual National Weather Service (NWS) definitions of floods instead of the frequently-adopted case study or frequentist approach, allowing us to base our findings on real definitions of floods. It examines flooding characteristics to identify how space and time scales of floods vary with climatic regimes and geomorphology. Flood events were characterized by linking flood response variables in gauged basins to spatially distributed variables describing climatology, geomorphology, and topography. The primary findings of this study are that the magnitude of flooding is highest is regions such as West Coast and southeastern U.S. which experience the most extraordinary precipitation. The seasonality of flooding varies greatly from maxima during the cool season on the West Coast, warm season in the desert Southwest, and early spring in the Southeast. The fastest responding events tend to be in steep basins of the arid Southwest caused by intense monsoon thunderstorms and steep terrain. The envelope curves of unit peak discharge are consistent with those reported for Europe and worldwide. But significant seasonal variability was observed in floods of the U.S. compared to Europe that is attributed to the diversity of causative rainfall ranging from synoptic scales with orographic enhancements in the West Coast, monsoon thunderstorms in the desert Southwest, to land-falling tropical storms and localized, intense thunderstorms in the Southeast.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"li-liu-hong-zhang-wan-saharia-sun-yao-etal-apubliccloudbasedchinaslandslideinventorydatabasecsliddevelopmentzoneandspatiotemporalanalysisforsignificanthistoricalevents19492011-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1007/s11629-015-3659-7\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'li-liu-hong-zhang-wan-saharia-sun-yao-etal-apubliccloudbasedchinaslandslideinventorydatabasecsliddevelopmentzoneandspatiotemporalanalysisforsignificanthistoricalevents19492011-2016', 'https://doi.org/10.1007/s11629-015-3659-7', event);\">\n    A public Cloud-based China’s Landslide Inventory Database (CsLID): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Li, W.; Liu, C.; Hong, Y.; Zhang, X.; Wan, Z.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Sun, W.; Yao, D.; Chen, W.; Chen, S.; Yang, X.;  and Yue, Y.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Mountain Science</i>, 13(7): 1275–1285. July 2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1007/s11629-015-3659-7\"\n     onclick=\"log_download('li-liu-hong-zhang-wan-saharia-sun-yao-etal-apubliccloudbasedchinaslandslideinventorydatabasecsliddevelopmentzoneandspatiotemporalanalysisforsignificanthistoricalevents19492011-2016', 'https://doi.org/10.1007/s11629-015-3659-7', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A public Cloud-based China’s Landslide Inventory Database (CsLID): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011 [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s11629-015-3659-7\"\n     onclick=\"log_download('li-liu-hong-zhang-wan-saharia-sun-yao-etal-apubliccloudbasedchinaslandslideinventorydatabasecsliddevelopmentzoneandspatiotemporalanalysisforsignificanthistoricalevents19492011-2016', 'http://doi.org/10.1007/s11629-015-3659-7', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/li-liu-hong-zhang-wan-saharia-sun-yao-etal-apubliccloudbasedchinaslandslideinventorydatabasecsliddevelopmentzoneandspatiotemporalanalysisforsignificanthistoricalevents19492011-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('li-liu-hong-zhang-wan-saharia-sun-yao-etal-apubliccloudbasedchinaslandslideinventorydatabasecsliddevelopmentzoneandspatiotemporalanalysisforsignificanthistoricalevents19492011-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{li_public_2016,\n\ttitle = {A public {Cloud}-based {China}’s {Landslide} {Inventory} {Database} ({CsLID}): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011},\n\tvolume = {13},\n\tissn = {1993-0321},\n\tshorttitle = {A public {Cloud}-based {China}’s {Landslide} {Inventory} {Database} ({CsLID})},\n\turl = {https://doi.org/10.1007/s11629-015-3659-7},\n\tdoi = {10.1007/s11629-015-3659-7},\n\tabstract = {Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China&#x27;s Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the CsLID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the CsLID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2021-03-31},\n\tjournal = {Journal of Mountain Science},\n\tauthor = {Li, Wei-yue and Liu, Chun and Hong, Yang and Zhang, Xin-hua and Wan, Zhan-ming and Saharia, Manabendra and Sun, Wei-wei and Yao, Dong-jing and Chen, Wen and Chen, Sheng and Yang, Xiu-qin and Yue, Yue},\n\tmonth = jul,\n\tyear = {2016},\n\tpages = {1275--1285},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the CsLID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the CsLID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"li-liu-hong-saharia-sun-yao-chen-rainstorminducedshallowlandslidesprocessandevaluationacasestudyfromthreehotspotschina-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1080/19475705.2016.1179685\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'li-liu-hong-saharia-sun-yao-chen-rainstorminducedshallowlandslidesprocessandevaluationacasestudyfromthreehotspotschina-2016', 'https://doi.org/10.1080/19475705.2016.1179685', event);\">\n    Rainstorm-induced shallow landslides process and evaluation – a case study from three hot spots, China.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Li, W.; Liu, C.; Hong, Y.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Sun, W.; Yao, D.;  and Chen, W.\n</span>\n\n  \n\n</span>\n\n  <i>Geomatics, Natural Hazards and Risk</i>, 7(6): 1908–1918. November 2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1080/19475705.2016.1179685\"\n     onclick=\"log_download('li-liu-hong-saharia-sun-yao-chen-rainstorminducedshallowlandslidesprocessandevaluationacasestudyfromthreehotspotschina-2016', 'https://doi.org/10.1080/19475705.2016.1179685', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Rainstorm-induced shallow landslides process and evaluation – a case study from three hot spots, China [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1080/19475705.2016.1179685\"\n     onclick=\"log_download('li-liu-hong-saharia-sun-yao-chen-rainstorminducedshallowlandslidesprocessandevaluationacasestudyfromthreehotspotschina-2016', 'http://doi.org/10.1080/19475705.2016.1179685', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/li-liu-hong-saharia-sun-yao-chen-rainstorminducedshallowlandslidesprocessandevaluationacasestudyfromthreehotspotschina-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('li-liu-hong-saharia-sun-yao-chen-rainstorminducedshallowlandslidesprocessandevaluationacasestudyfromthreehotspotschina-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{li_rainstorm-induced_2016,\n\ttitle = {Rainstorm-induced shallow landslides process and evaluation – a case study from three hot spots, {China}},\n\tvolume = {7},\n\tissn = {1947-5705},\n\turl = {https://doi.org/10.1080/19475705.2016.1179685},\n\tdoi = {10.1080/19475705.2016.1179685},\n\tabstract = {The critical stage in the evaluation of rainfall-induced landslide failure is in formulating reasonable models to better simulate spatiotemporal changes of slopes in the hilly terrains. A physically based model can take into account the contribution of rainfall infiltration and shear strength of saturated soil layer, and therefore help revealing the landslide formation mechanisms. This paper presents a physically based approach to simulate the landslide process triggered by rainstorm. On the basis of previous solutions, we select the simplified infiltration model Slope-Infiltration-Distributed Equilibrium (SLIDE) to illustrate the dynamical relations between factor of safety (FS) and accumulation of rainfall over time. This model is tested with three representative landslide events in the southwest, southeast, and south central of China during rainstorm. Results show that the time of landslide failure predicted from the SLIDE model is consistent with the reality. Meanwhile, this paper illustrates the differences of FS among the different slope gradients in the vicinity of same soil texture and relationship between FS and rainfall accumulation. This work formulates a methodology of rainstorm-induced landslide evaluation and improves upon the existing landslide prediction methods.},\n\tnumber = {6},\n\turldate = {2018-07-27},\n\tjournal = {Geomatics, Natural Hazards and Risk},\n\tauthor = {Li, Weiyue and Liu, Chun and Hong, Yang and Saharia, Manabendra and Sun, Weiwei and Yao, Dongjing and Chen, Wen},\n\tmonth = nov,\n\tyear = {2016},\n\tpages = {1908--1918},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The critical stage in the evaluation of rainfall-induced landslide failure is in formulating reasonable models to better simulate spatiotemporal changes of slopes in the hilly terrains. A physically based model can take into account the contribution of rainfall infiltration and shear strength of saturated soil layer, and therefore help revealing the landslide formation mechanisms. This paper presents a physically based approach to simulate the landslide process triggered by rainstorm. On the basis of previous solutions, we select the simplified infiltration model Slope-Infiltration-Distributed Equilibrium (SLIDE) to illustrate the dynamical relations between factor of safety (FS) and accumulation of rainfall over time. This model is tested with three representative landslide events in the southwest, southeast, and south central of China during rainstorm. Results show that the time of landslide failure predicted from the SLIDE model is consistent with the reality. Meanwhile, this paper illustrates the differences of FS among the different slope gradients in the vicinity of same soil texture and relationship between FS and rainfall accumulation. This work formulates a methodology of rainstorm-induced landslide evaluation and improves upon the existing landslide prediction methods.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"shen-xiong-hong-yu-pan-chen-saharia-uncertaintyanalysisoffivesatellitebasedprecipitationproductsandevaluationofthreeoptimallymergedmultialgorithmproductsoverthetibetanplateau-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1080/01431161.2014.960612\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shen-xiong-hong-yu-pan-chen-saharia-uncertaintyanalysisoffivesatellitebasedprecipitationproductsandevaluationofthreeoptimallymergedmultialgorithmproductsoverthetibetanplateau-2014', 'https://doi.org/10.1080/01431161.2014.960612', event);\">\n    Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Shen, Y.; Xiong, A.; Hong, Y.; Yu, J.; Pan, Y.; Chen, Z.;  and <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Remote Sensing</i>, 35(19): 6843–6858. October 2014.\n  <span class=\"note\">Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/01431161.2014.960612</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1080/01431161.2014.960612\"\n     onclick=\"log_download('shen-xiong-hong-yu-pan-chen-saharia-uncertaintyanalysisoffivesatellitebasedprecipitationproductsandevaluationofthreeoptimallymergedmultialgorithmproductsoverthetibetanplateau-2014', 'https://doi.org/10.1080/01431161.2014.960612', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1080/01431161.2014.960612\"\n     onclick=\"log_download('shen-xiong-hong-yu-pan-chen-saharia-uncertaintyanalysisoffivesatellitebasedprecipitationproductsandevaluationofthreeoptimallymergedmultialgorithmproductsoverthetibetanplateau-2014', 'http://doi.org/10.1080/01431161.2014.960612', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shen-xiong-hong-yu-pan-chen-saharia-uncertaintyanalysisoffivesatellitebasedprecipitationproductsandevaluationofthreeoptimallymergedmultialgorithmproductsoverthetibetanplateau-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shen-xiong-hong-yu-pan-chen-saharia-uncertaintyanalysisoffivesatellitebasedprecipitationproductsandevaluationofthreeoptimallymergedmultialgorithmproductsoverthetibetanplateau-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{shen_uncertainty_2014,\n\ttitle = {Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the {Tibetan} {Plateau}},\n\tvolume = {35},\n\tissn = {0143-1161},\n\turl = {https://doi.org/10.1080/01431161.2014.960612},\n\tdoi = {10.1080/01431161.2014.960612},\n\tabstract = {This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.},\n\tnumber = {19},\n\turldate = {2021-10-30},\n\tjournal = {International Journal of Remote Sensing},\n\tauthor = {Shen, Yan and Xiong, Anyuan and Hong, Yang and Yu, Jingjing and Pan, Yang and Chen, Zhuoqi and Saharia, Manabendra},\n\tmonth = oct,\n\tyear = {2014},\n\tnote = {Publisher: Taylor \\&amp; Francis\n\\_eprint: https://doi.org/10.1080/01431161.2014.960612},\n\tpages = {6843--6858},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"zhang-hong-wang-gourley-xue-saharia-ni-wang-etal-hydrometeorologicalanalysisandremotesensingofextremeswasthejuly2012beijingfloodeventdetectableandpredictablebyglobalsatelliteobservingandglobalweathermodelingsystems-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-14-0048.1\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'zhang-hong-wang-gourley-xue-saharia-ni-wang-etal-hydrometeorologicalanalysisandremotesensingofextremeswasthejuly2012beijingfloodeventdetectableandpredictablebyglobalsatelliteobservingandglobalweathermodelingsystems-2014', 'https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-14-0048.1', event);\">\n    Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems?.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Zhang, Y.; Hong, Y.; Wang, X.; Gourley, J. J.; Xue, X.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>; Ni, G.; Wang, G.; Huang, Y.; Chen, S.;  and Tang, G.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrometeorology</i>, 16(1): 381–395. October 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-14-0048.1\"\n     onclick=\"log_download('zhang-hong-wang-gourley-xue-saharia-ni-wang-etal-hydrometeorologicalanalysisandremotesensingofextremeswasthejuly2012beijingfloodeventdetectableandpredictablebyglobalsatelliteobservingandglobalweathermodelingsystems-2014', 'https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-14-0048.1', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems? [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1175/JHM-D-14-0048.1\"\n     onclick=\"log_download('zhang-hong-wang-gourley-xue-saharia-ni-wang-etal-hydrometeorologicalanalysisandremotesensingofextremeswasthejuly2012beijingfloodeventdetectableandpredictablebyglobalsatelliteobservingandglobalweathermodelingsystems-2014', 'http://doi.org/10.1175/JHM-D-14-0048.1', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/zhang-hong-wang-gourley-xue-saharia-ni-wang-etal-hydrometeorologicalanalysisandremotesensingofextremeswasthejuly2012beijingfloodeventdetectableandpredictablebyglobalsatelliteobservingandglobalweathermodelingsystems-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('zhang-hong-wang-gourley-xue-saharia-ni-wang-etal-hydrometeorologicalanalysisandremotesensingofextremeswasthejuly2012beijingfloodeventdetectableandpredictablebyglobalsatelliteobservingandglobalweathermodelingsystems-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{zhang_hydrometeorological_2014,\n\ttitle = {Hydrometeorological {Analysis} and {Remote} {Sensing} of {Extremes}: {Was} the {July} 2012 {Beijing} {Flood} {Event} {Detectable} and {Predictable} by {Global} {Satellite} {Observing} and {Global} {Weather} {Modeling} {Systems}?},\n\tvolume = {16},\n\tcopyright = {CC0 1.0 Universal Public Domain Dedication},\n\tissn = {1525-755X},\n\tshorttitle = {Hydrometeorological {Analysis} and {Remote} {Sensing} of {Extremes}},\n\turl = {https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-14-0048.1},\n\tdoi = {10.1175/JHM-D-14-0048.1},\n\tabstract = {Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of \\$1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.},\n\tnumber = {1},\n\turldate = {2018-07-27},\n\tjournal = {Journal of Hydrometeorology},\n\tauthor = {Zhang, Yu and Hong, Yang and Wang, Xuguang and Gourley, Jonathan J. and Xue, Xianwu and Saharia, Manabendra and Ni, Guangheng and Wang, Gaili and Huang, Yong and Chen, Sheng and Tang, Guoqiang},\n\tmonth = oct,\n\tyear = {2014},\n\tpages = {381--395},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"roy-saharia-choudhury-riverreachesfloodflowpredictionusingprnnmodels-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://journals.indexcopernicus.com/abstract.php?icid=1144099\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'roy-saharia-choudhury-riverreachesfloodflowpredictionusingprnnmodels-2014', 'http://journals.indexcopernicus.com/abstract.php?icid=1144099', event);\">\n    River Reaches Flood Flow Prediction using PRNN Models.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Roy, P.; <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>;  and Choudhury, P.\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development (IJCSEIERD)</i>, 1(4): 119–126.  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://journals.indexcopernicus.com/abstract.php?icid=1144099\"\n     onclick=\"log_download('roy-saharia-choudhury-riverreachesfloodflowpredictionusingprnnmodels-2014', 'http://journals.indexcopernicus.com/abstract.php?icid=1144099', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"River Reaches Flood Flow Prediction using PRNN Models [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/roy-saharia-choudhury-riverreachesfloodflowpredictionusingprnnmodels-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('roy-saharia-choudhury-riverreachesfloodflowpredictionusingprnnmodels-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{roy_river_2014,\n\ttitle = {River {Reaches} {Flood} {Flow} {Prediction} using {PRNN} {Models}},\n\tvolume = {1},\n\tcopyright = {CC0 1.0 Universal Public Domain Dedication},\n\tissn = {2249-6866},\n\turl = {http://journals.indexcopernicus.com/abstract.php?icid=1144099},\n\tnumber = {4},\n\turldate = {2016-10-05},\n\tjournal = {International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development (IJCSEIERD)},\n\tauthor = {Roy, Parthajit and Saharia, Manabendra and Choudhury, P.},\n\tyear = {2014},\n\tpages = {119--126},\n}\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2013\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2013')\"\n      onkeypress=\"toggleGroup('group_2013')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2013</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"jain-kumar-saharia-analysisofrainfallandtemperaturetrendsinnortheastindia-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3483\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'jain-kumar-saharia-analysisofrainfallandtemperaturetrendsinnortheastindia-2013', 'https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3483', event);\">\n    Analysis of rainfall and temperature trends in northeast India.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Jain, S. K.; Kumar, V.;  and <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Climatology</i>, 33(4): 968–978.  2013.\n  <span class=\"note\">_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.3483</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3483\"\n     onclick=\"log_download('jain-kumar-saharia-analysisofrainfallandtemperaturetrendsinnortheastindia-2013', 'https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3483', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Analysis of rainfall and temperature trends in northeast India [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/https://doi.org/10.1002/joc.3483\"\n     onclick=\"log_download('jain-kumar-saharia-analysisofrainfallandtemperaturetrendsinnortheastindia-2013', 'http://doi.org/https://doi.org/10.1002/joc.3483', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/jain-kumar-saharia-analysisofrainfallandtemperaturetrendsinnortheastindia-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('jain-kumar-saharia-analysisofrainfallandtemperaturetrendsinnortheastindia-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{jain_analysis_2013,\n\ttitle = {Analysis of rainfall and temperature trends in northeast {India}},\n\tvolume = {33},\n\tcopyright = {Copyright © 2012 Royal Meteorological Society},\n\tissn = {1097-0088},\n\turl = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3483},\n\tdoi = {https://doi.org/10.1002/joc.3483},\n\tabstract = {The northeast region (NER) of India covers an area of 0.26 million km2. This region is one of the highest rainfall-receiving regions on the planet. Consequently, it has huge water and hydropower potential and analysis of rainfall and temperature trends would be of interest to water and energy planners. Trends in monthly, seasonal, and annual rainfall and temperature on the subdivision and regional scale for the NER were examined in this study. Trend analysis of rainfall data series for 1871–2008 did not show any clear trend for the region as a whole, although there are seasonal trends for some seasons and for some hydro-meteorological subdivisions. Similar analysis for temperature data showed that all the four temperature variables (maximum, minimum, and mean temperatures and temperature range) had rising trend. Notably for the post-monsoon season, the Sen&#x27;s estimator of slope ( °C/year) was 0.019, 0.011, and 0.015 for the maximum, minimum, and mean temperature, respectively. Copyright © 2012 Royal Meteorological Society},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2021-03-31},\n\tjournal = {International Journal of Climatology},\n\tauthor = {Jain, S. K. and Kumar, Vijay and Saharia, M.},\n\tyear = {2013},\n\tnote = {\\_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.3483},\n\tkeywords = {northeast India, rainfall, temperature, trend analysis},\n\tpages = {968--978},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The northeast region (NER) of India covers an area of 0.26 million km2. This region is one of the highest rainfall-receiving regions on the planet. Consequently, it has huge water and hydropower potential and analysis of rainfall and temperature trends would be of interest to water and energy planners. Trends in monthly, seasonal, and annual rainfall and temperature on the subdivision and regional scale for the NER were examined in this study. Trend analysis of rainfall data series for 1871–2008 did not show any clear trend for the region as a whole, although there are seasonal trends for some seasons and for some hydro-meteorological subdivisions. Similar analysis for temperature data showed that all the four temperature variables (maximum, minimum, and mean temperatures and temperature range) had rising trend. Notably for the post-monsoon season, the Sen's estimator of slope ( °C/year) was 0.019, 0.011, and 0.015 for the maximum, minimum, and mean temperature, respectively. Copyright © 2012 Royal Meteorological Society\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2012\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2012')\"\n      onkeypress=\"toggleGroup('group_2012')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2012</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"saharia-bhattacharjya-geomorphologybasedtimelaggedrecurrentneuralnetworksforrunoffforecasting-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1007/s12205-012-1463-2\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'saharia-bhattacharjya-geomorphologybasedtimelaggedrecurrentneuralnetworksforrunoffforecasting-2012', 'https://doi.org/10.1007/s12205-012-1463-2', event);\">\n    Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://hydrosense.iitd.ac.in/publications/\">Saharia, M.</a>;  and Bhattacharjya, R. K.\n</span>\n\n  \n\n</span>\n\n  <i>KSCE Journal of Civil Engineering</i>, 16(5): 862–869. July 2012.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1007/s12205-012-1463-2\"\n     onclick=\"log_download('saharia-bhattacharjya-geomorphologybasedtimelaggedrecurrentneuralnetworksforrunoffforecasting-2012', 'https://doi.org/10.1007/s12205-012-1463-2', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s12205-012-1463-2\"\n     onclick=\"log_download('saharia-bhattacharjya-geomorphologybasedtimelaggedrecurrentneuralnetworksforrunoffforecasting-2012', 'http://doi.org/10.1007/s12205-012-1463-2', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/saharia-bhattacharjya-geomorphologybasedtimelaggedrecurrentneuralnetworksforrunoffforecasting-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('saharia-bhattacharjya-geomorphologybasedtimelaggedrecurrentneuralnetworksforrunoffforecasting-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{saharia_geomorphology-based_2012,\n\ttitle = {Geomorphology-based {Time}-{Lagged} {Recurrent} {Neural} {Networks} for runoff forecasting},\n\tvolume = {16},\n\tissn = {1976-3808},\n\turl = {https://doi.org/10.1007/s12205-012-1463-2},\n\tdoi = {10.1007/s12205-012-1463-2},\n\tabstract = {Artificial Neural Networks have been widely used to develop effective runoff-forecasting models. An overwhelming majority of networks are static in nature and also developed without incorporating geomorphologic information of the watershed. The objective of this study is to develop an efficient dynamic neural network model which also accounts for morphometric characteristics of the catchment. The model developed using Time-Lagged Recurrent Neural Networks (TLRNs) is used to estimate runoff for river Dikrong, a tributary of river Brahmaputra in India. Comparisons with traditional static models, with and without integration of geomorphologic data, reveal the proposed model to be a promising tool in operational hydrology.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2021-03-31},\n\tjournal = {KSCE Journal of Civil Engineering},\n\tauthor = {Saharia, Manabendra and Bhattacharjya, Rajib Kumar},\n\tmonth = jul,\n\tyear = {2012},\n\tpages = {862--869},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Artificial Neural Networks have been widely used to develop effective runoff-forecasting models. An overwhelming majority of networks are static in nature and also developed without incorporating geomorphologic information of the watershed. The objective of this study is to develop an efficient dynamic neural network model which also accounts for morphometric characteristics of the catchment. The model developed using Time-Lagged Recurrent Neural Networks (TLRNs) is used to estimate runoff for river Dikrong, a tributary of river Brahmaputra in India. Comparisons with traditional static models, with and without integration of geomorphologic data, reveal the proposed model to be a promising tool in operational hydrology.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);