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://bibbase.org/zotero/kasicamper\",\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Root hydraulic properties: An exploration of their variability across scales\",\"volume\":\"8\",\"issn\":\"2475-4455, 2475-4455\",\"shorttitle\":\"Root hydraulic properties\",\"url\":\"https://onlinelibrary.wiley.com/doi/10.1002/pld3.582\",\"doi\":\"10.1002/pld3.582\",\"abstract\":\"Abstract Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment‐, individual root‐, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation. We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition, p  \\\\textless .001) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients, p  \\\\textless .001). Furthermore, whole root system conductance increased significantly with root system age across several crop species ( p  \\\\textless .01), causing very large variation in the data (\\\\textgreater2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state‐of‐the‐art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species. These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database ( https://roothydraulic-properties.shinyapps.io/database/ ), fostering scientific exchange.\",\"language\":\"en\",\"number\":\"4\",\"urldate\":\"2024-04-25\",\"journal\":\"Plant Direct\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Baca\",\"Cabrera\"],\"firstnames\":[\"Juan\",\"C.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vanderborght\"],\"firstnames\":[\"Jan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Couvreur\"],\"firstnames\":[\"Valentin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Behrend\"],\"firstnames\":[\"Dominik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gaiser\"],\"firstnames\":[\"Thomas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nguyen\"],\"firstnames\":[\"Thuy\",\"Huu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lobet\"],\"firstnames\":[\"Guillaume\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2024\",\"pages\":\"e582\",\"bibtex\":\"@article{baca_cabrera_root_2024,\\n\\ttitle = {Root hydraulic properties: {An} exploration of their variability across scales},\\n\\tvolume = {8},\\n\\tissn = {2475-4455, 2475-4455},\\n\\tshorttitle = {Root hydraulic properties},\\n\\turl = {https://onlinelibrary.wiley.com/doi/10.1002/pld3.582},\\n\\tdoi = {10.1002/pld3.582},\\n\\tabstract = {Abstract\\n            Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment‐, individual root‐, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation.\\n            \\n              We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition,\\n              p\\n               {\\\\textless} .001) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients,\\n              p\\n               {\\\\textless} .001). Furthermore, whole root system conductance increased significantly with root system age across several crop species (\\n              p\\n               {\\\\textless} .01), causing very large variation in the data ({\\\\textgreater}2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state‐of‐the‐art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species.\\n            \\n            \\n              These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database (\\n              https://roothydraulic-properties.shinyapps.io/database/\\n              ), fostering scientific exchange.},\\n\\tlanguage = {en},\\n\\tnumber = {4},\\n\\turldate = {2024-04-25},\\n\\tjournal = {Plant Direct},\\n\\tauthor = {Baca Cabrera, Juan C. and Vanderborght, Jan and Couvreur, Valentin and Behrend, Dominik and Gaiser, Thomas and Nguyen, Thuy Huu and Lobet, Guillaume},\\n\\tmonth = apr,\\n\\tyear = {2024},\\n\\tpages = {e582},\\n}\\n\\n\",\"author_short\":[\"Baca Cabrera, J. C.\",\"Vanderborght, J.\",\"Couvreur, V.\",\"Behrend, D.\",\"Gaiser, T.\",\"Nguyen, T. H.\",\"Lobet, G.\"],\"key\":\"baca_cabrera_root_2024\",\"id\":\"baca_cabrera_root_2024\",\"bibbaseid\":\"bacacabrera-vanderborght-couvreur-behrend-gaiser-nguyen-lobet-roothydraulicpropertiesanexplorationoftheirvariabilityacrossscales-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://onlinelibrary.wiley.com/doi/10.1002/pld3.582\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation\",\"volume\":\"17\",\"copyright\":\"https://creativecommons.org/licenses/by/4.0/\",\"issn\":\"1991-9603\",\"shorttitle\":\"Focal-TSMP\",\"url\":\"https://gmd.copernicus.org/articles/17/2987/2024/\",\"doi\":\"10.5194/gmd-17-2987-2024\",\"abstract\":\"Abstract. Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model’s applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP, last access: 4 April 2024).\",\"language\":\"en\",\"number\":\"7\",\"urldate\":\"2024-04-25\",\"journal\":\"Geoscientific Model Development\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shams\",\"Eddin\"],\"firstnames\":[\"Mohamad\",\"Hakam\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gall\"],\"firstnames\":[\"Juergen\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2024\",\"pages\":\"2987–3023\",\"bibtex\":\"@article{shams_eddin_focal-tsmp_2024,\\n\\ttitle = {Focal-{TSMP}: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation},\\n\\tvolume = {17},\\n\\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\\n\\tissn = {1991-9603},\\n\\tshorttitle = {Focal-{TSMP}},\\n\\turl = {https://gmd.copernicus.org/articles/17/2987/2024/},\\n\\tdoi = {10.5194/gmd-17-2987-2024},\\n\\tabstract = {Abstract. Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model’s applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP, last access: 4 April 2024).},\\n\\tlanguage = {en},\\n\\tnumber = {7},\\n\\turldate = {2024-04-25},\\n\\tjournal = {Geoscientific Model Development},\\n\\tauthor = {Shams Eddin, Mohamad Hakam and Gall, Juergen},\\n\\tmonth = apr,\\n\\tyear = {2024},\\n\\tpages = {2987--3023},\\n}\\n\\n\",\"author_short\":[\"Shams Eddin, M. H.\",\"Gall, J.\"],\"key\":\"shams_eddin_focal-tsmp_2024\",\"id\":\"shams_eddin_focal-tsmp_2024\",\"bibbaseid\":\"shamseddin-gall-focaltsmpdeeplearningforvegetationhealthpredictionandagriculturaldroughtassessmentfromaregionalclimatesimulation-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://gmd.copernicus.org/articles/17/2987/2024/\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"The asymmetric impacts of international agricultural trade on water use scarcity, inequality and inequity\",\"issn\":\"2731-6084\",\"url\":\"https://www.nature.com/articles/s44221-024-00224-7\",\"doi\":\"10.1038/s44221-024-00224-7\",\"language\":\"en\",\"urldate\":\"2024-04-25\",\"journal\":\"Nature Water\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Gu\"],\"firstnames\":[\"Weiyi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Fang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Siebert\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kummu\"],\"firstnames\":[\"Matti\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Xuhui\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hong\"],\"firstnames\":[\"Chaopeng\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhou\"],\"firstnames\":[\"Feng\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhu\"],\"firstnames\":[\"Qing\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Liu\"],\"firstnames\":[\"Yong\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Qin\"],\"firstnames\":[\"Yue\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2024\",\"bibtex\":\"@article{gu_asymmetric_2024,\\n\\ttitle = {The asymmetric impacts of international agricultural trade on water use scarcity, inequality and inequity},\\n\\tissn = {2731-6084},\\n\\turl = {https://www.nature.com/articles/s44221-024-00224-7},\\n\\tdoi = {10.1038/s44221-024-00224-7},\\n\\tlanguage = {en},\\n\\turldate = {2024-04-25},\\n\\tjournal = {Nature Water},\\n\\tauthor = {Gu, Weiyi and Wang, Fang and Siebert, Stefan and Kummu, Matti and Wang, Xuhui and Hong, Chaopeng and Zhou, Feng and Zhu, Qing and Liu, Yong and Qin, Yue},\\n\\tmonth = apr,\\n\\tyear = {2024},\\n}\\n\\n\",\"author_short\":[\"Gu, W.\",\"Wang, F.\",\"Siebert, S.\",\"Kummu, M.\",\"Wang, X.\",\"Hong, C.\",\"Zhou, F.\",\"Zhu, Q.\",\"Liu, Y.\",\"Qin, Y.\"],\"key\":\"gu_asymmetric_2024\",\"id\":\"gu_asymmetric_2024\",\"bibbaseid\":\"gu-wang-siebert-kummu-wang-hong-zhou-zhu-etal-theasymmetricimpactsofinternationalagriculturaltradeonwaterusescarcityinequalityandinequity-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.nature.com/articles/s44221-024-00224-7\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Location-Aware Adaptive Normalization: A Deep Learning Approach for Wildfire Danger Forecasting\",\"volume\":\"61\",\"copyright\":\"https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html\",\"issn\":\"0196-2892, 1558-0644\",\"shorttitle\":\"Location-Aware Adaptive Normalization\",\"url\":\"https://ieeexplore.ieee.org/document/10149031/\",\"doi\":\"10.1109/TGRS.2023.3285401\",\"urldate\":\"2024-04-25\",\"journal\":\"IEEE Transactions on Geoscience and Remote Sensing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shams\",\"Eddin\"],\"firstnames\":[\"Mohamad\",\"Hakam\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Roscher\"],\"firstnames\":[\"Ribana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gall\"],\"firstnames\":[\"Juergen\"],\"suffixes\":[]}],\"year\":\"2023\",\"pages\":\"1–18\",\"bibtex\":\"@article{shams_eddin_location-aware_2023,\\n\\ttitle = {Location-{Aware} {Adaptive} {Normalization}: {A} {Deep} {Learning} {Approach} for {Wildfire} {Danger} {Forecasting}},\\n\\tvolume = {61},\\n\\tcopyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},\\n\\tissn = {0196-2892, 1558-0644},\\n\\tshorttitle = {Location-{Aware} {Adaptive} {Normalization}},\\n\\turl = {https://ieeexplore.ieee.org/document/10149031/},\\n\\tdoi = {10.1109/TGRS.2023.3285401},\\n\\turldate = {2024-04-25},\\n\\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\\n\\tauthor = {Shams Eddin, Mohamad Hakam and Roscher, Ribana and Gall, Juergen},\\n\\tyear = {2023},\\n\\tpages = {1--18},\\n}\\n\\n\",\"author_short\":[\"Shams Eddin, M. H.\",\"Roscher, R.\",\"Gall, J.\"],\"key\":\"shams_eddin_location-aware_2023\",\"id\":\"shams_eddin_location-aware_2023\",\"bibbaseid\":\"shamseddin-roscher-gall-locationawareadaptivenormalizationadeeplearningapproachforwildfiredangerforecasting-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ieeexplore.ieee.org/document/10149031/\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Half of twenty-first century global irrigation expansion has been in water-stressed regions\",\"issn\":\"2731-6084\",\"url\":\"https://www.nature.com/articles/s44221-024-00206-9\",\"doi\":\"10.1038/s44221-024-00206-9\",\"abstract\":\"Abstract The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress on freshwater resources. Ensuring that increases in irrigated production occur only in places where water is relatively abundant is a key objective of sustainable agriculture and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet, a spatially detailed understanding of the evolution of the global area equipped for irrigation (AEI) is missing. In this study, we used the latest subnational irrigation statistics (covering 17,298 administrative units) from various official sources to develop a gridded (5 arcmin resolution) global product of AEI for the years 2000, 2005, 2010 and 2015. We found that AEI increased by 11% from 2000 (297 Mha) to 2015 (330 Mha), with areas of both substantial expansion, such as northwest India and northeast China, and decline, such as Russia. Combining these outputs with information on green (that is, rainfall) and blue (that is, surface and ground) water stress, we also examined to what extent irrigation has expanded unsustainably in places already experiencing water stress. We found that more than half (52%) of the irrigation expansion has taken place in areas that were already water-stressed in the year 2000, with India alone accounting for 36% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security.\",\"language\":\"en\",\"urldate\":\"2024-03-11\",\"journal\":\"Nature Water\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Mehta\"],\"firstnames\":[\"Piyush\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Siebert\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kummu\"],\"firstnames\":[\"Matti\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Deng\"],\"firstnames\":[\"Qinyu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ali\"],\"firstnames\":[\"Tariq\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marston\"],\"firstnames\":[\"Landon\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Xie\"],\"firstnames\":[\"Wei\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Davis\"],\"firstnames\":[\"Kyle\",\"Frankel\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2024\",\"bibtex\":\"@article{mehta_half_2024,\\n\\ttitle = {Half of twenty-first century global irrigation expansion has been in water-stressed regions},\\n\\tissn = {2731-6084},\\n\\turl = {https://www.nature.com/articles/s44221-024-00206-9},\\n\\tdoi = {10.1038/s44221-024-00206-9},\\n\\tabstract = {Abstract\\n            The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress on freshwater resources. Ensuring that increases in irrigated production occur only in places where water is relatively abundant is a key objective of sustainable agriculture and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet, a spatially detailed understanding of the evolution of the global area equipped for irrigation (AEI) is missing. In this study, we used the latest subnational irrigation statistics (covering 17,298 administrative units) from various official sources to develop a gridded (5 arcmin resolution) global product of AEI for the years 2000, 2005, 2010 and 2015. We found that AEI increased by 11\\\\% from 2000 (297 Mha) to 2015 (330 Mha), with areas of both substantial expansion, such as northwest India and northeast China, and decline, such as Russia. Combining these outputs with information on green (that is, rainfall) and blue (that is, surface and ground) water stress, we also examined to what extent irrigation has expanded unsustainably in places already experiencing water stress. We found that more than half (52\\\\%) of the irrigation expansion has taken place in areas that were already water-stressed in the year 2000, with India alone accounting for 36\\\\% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security.},\\n\\tlanguage = {en},\\n\\turldate = {2024-03-11},\\n\\tjournal = {Nature Water},\\n\\tauthor = {Mehta, Piyush and Siebert, Stefan and Kummu, Matti and Deng, Qinyu and Ali, Tariq and Marston, Landon and Xie, Wei and Davis, Kyle Frankel},\\n\\tmonth = mar,\\n\\tyear = {2024},\\n}\\n\\n\",\"author_short\":[\"Mehta, P.\",\"Siebert, S.\",\"Kummu, M.\",\"Deng, Q.\",\"Ali, T.\",\"Marston, L.\",\"Xie, W.\",\"Davis, K. F.\"],\"key\":\"mehta_half_2024\",\"id\":\"mehta_half_2024\",\"bibbaseid\":\"mehta-siebert-kummu-deng-ali-marston-xie-davis-halfoftwentyfirstcenturyglobalirrigationexpansionhasbeeninwaterstressedregions-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.nature.com/articles/s44221-024-00206-9\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data\",\"volume\":\"28\",\"issn\":\"1607-7938\",\"url\":\"https://hess.copernicus.org/articles/28/1001/2024/\",\"doi\":\"10.5194/hess-28-1001-2024\",\"abstract\":\"Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.\",\"language\":\"en\",\"number\":\"4\",\"urldate\":\"2024-03-05\",\"journal\":\"Hydrology and Earth System Sciences\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Strebel\"],\"firstnames\":[\"Lukas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bogena\"],\"firstnames\":[\"Heye\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vereecken\"],\"firstnames\":[\"Harry\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Andreasen\"],\"firstnames\":[\"Mie\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Aranda-Barranco\"],\"firstnames\":[\"Sergio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hendricks\",\"Franssen\"],\"firstnames\":[\"Harrie-Jan\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2024\",\"pages\":\"1001–1026\",\"bibtex\":\"@article{strebel_evapotranspiration_2024,\\n\\ttitle = {Evapotranspiration prediction for {European} forest sites does not improve with assimilation of in situ soil water content data},\\n\\tvolume = {28},\\n\\tissn = {1607-7938},\\n\\turl = {https://hess.copernicus.org/articles/28/1001/2024/},\\n\\tdoi = {10.5194/hess-28-1001-2024},\\n\\tabstract = {Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 \\\\% to 64 \\\\%, the root-mean-square error for the evapotranspiration estimation is increased by 4 \\\\%. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.},\\n\\tlanguage = {en},\\n\\tnumber = {4},\\n\\turldate = {2024-03-05},\\n\\tjournal = {Hydrology and Earth System Sciences},\\n\\tauthor = {Strebel, Lukas and Bogena, Heye and Vereecken, Harry and Andreasen, Mie and Aranda-Barranco, Sergio and Hendricks Franssen, Harrie-Jan},\\n\\tmonth = feb,\\n\\tyear = {2024},\\n\\tpages = {1001--1026},\\n}\\n\\n\",\"author_short\":[\"Strebel, L.\",\"Bogena, H.\",\"Vereecken, H.\",\"Andreasen, M.\",\"Aranda-Barranco, S.\",\"Hendricks Franssen, H.\"],\"key\":\"strebel_evapotranspiration_2024\",\"id\":\"strebel_evapotranspiration_2024\",\"bibbaseid\":\"strebel-bogena-vereecken-andreasen-arandabarranco-hendricksfranssen-evapotranspirationpredictionforeuropeanforestsitesdoesnotimprovewithassimilationofinsitusoilwatercontentdata-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://hess.copernicus.org/articles/28/1001/2024/\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"techreport\",\"type\":\"preprint\",\"title\":\"Generating Views Using Atmospheric Correction for Contrastive Self-supervised Learning of Multi-spectral Images\",\"url\":\"https://www.techrxiv.org/doi/full/10.36227/techrxiv.21493218.v3\",\"abstract\":\"\\\\textlessp\\\\textgreaterIn remote sensing, plenty of multi-spectral images are publicly available from various landcover satellite missions. Contrastive self-supervised learning is commonly applied to unlabeled data but relies on domain-specific transformations used for learning. When focusing on vegetation, standard transformations from image processing cannot be applied to the NIR channel, which carries valuable information about the vegetation state. Therefore, we use contrastive learning, relying on different views of unlabelled, multi-spectral images to obtain a pre-trained model to improve the accuracy scores on small-sized remote sensing datasets. This study presents the generation of additional views tailored to remote sensing images using atmospheric correction as an alternative transformation to color jittering. The purpose of the atmospheric transformation is to provide a physically consistent transformation. The proposed transformation can be easily integrated with multiple channels to exploit spectral signatures of objects. Our approach can be applied to other remote sensing tasks. Using this transformation leads to improved classification accuracy of up to 6%.\\\\textless/p\\\\textgreater\",\"urldate\":\"2024-03-05\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Patnala\"],\"firstnames\":[\"Ankit\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stadtler\"],\"firstnames\":[\"Scarlet\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schultz\"],\"firstnames\":[\"Martin\",\"G\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gall\"],\"firstnames\":[\"Juergen\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2023\",\"doi\":\"10.36227/techrxiv.21493218.v3\",\"bibtex\":\"@techreport{patnala_generating_2023,\\n\\ttype = {preprint},\\n\\ttitle = {Generating {Views} {Using} {Atmospheric} {Correction} for {Contrastive} {Self}-supervised {Learning} of {Multi}-spectral {Images}},\\n\\turl = {https://www.techrxiv.org/doi/full/10.36227/techrxiv.21493218.v3},\\n\\tabstract = {{\\\\textless}p{\\\\textgreater}In remote sensing, plenty of multi-spectral images are publicly available from various landcover satellite missions. Contrastive self-supervised learning is commonly applied to unlabeled data but relies on domain-specific transformations used for learning. When focusing on vegetation, standard transformations from image processing cannot be applied to the NIR channel, which carries valuable information about the vegetation state. Therefore, we use contrastive learning, relying on different views of unlabelled, multi-spectral images to obtain a pre-trained model to improve the accuracy scores on small-sized remote sensing datasets. This study presents the generation of additional views tailored to remote sensing images using atmospheric correction as an alternative transformation to color jittering. The purpose of the atmospheric transformation is to provide a physically consistent transformation. The proposed transformation can be easily integrated with multiple channels to exploit spectral signatures of objects. Our approach can be applied to other remote sensing tasks. Using this transformation leads to improved classification accuracy of up to 6\\\\%.{\\\\textless}/p{\\\\textgreater}},\\n\\turldate = {2024-03-05},\\n\\tauthor = {Patnala, Ankit and Stadtler, Scarlet and Schultz, Martin G and Gall, Juergen},\\n\\tmonth = nov,\\n\\tyear = {2023},\\n\\tdoi = {10.36227/techrxiv.21493218.v3},\\n}\\n\\n\",\"author_short\":[\"Patnala, A.\",\"Stadtler, S.\",\"Schultz, M. G\",\"Gall, J.\"],\"key\":\"patnala_generating_2023\",\"id\":\"patnala_generating_2023\",\"bibbaseid\":\"patnala-stadtler-schultz-gall-generatingviewsusingatmosphericcorrectionforcontrastiveselfsupervisedlearningofmultispectralimages-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.techrxiv.org/doi/full/10.36227/techrxiv.21493218.v3\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"On study of the Earth topography correction for the GRACE surface mass estimation\",\"volume\":\"96\",\"issn\":\"0949-7714, 1432-1394\",\"url\":\"https://link.springer.com/10.1007/s00190-022-01683-0\",\"doi\":\"10.1007/s00190-022-01683-0\",\"language\":\"en\",\"number\":\"12\",\"urldate\":\"2024-03-05\",\"journal\":\"Journal of Geodesy\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yang\"],\"firstnames\":[\"Fan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Luo\"],\"firstnames\":[\"ZhiCai\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhou\"],\"firstnames\":[\"Hao\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kusche\"],\"firstnames\":[\"Jürgen\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2022\",\"pages\":\"95\",\"bibtex\":\"@article{yang_study_2022,\\n\\ttitle = {On study of the {Earth} topography correction for the {GRACE} surface mass estimation},\\n\\tvolume = {96},\\n\\tissn = {0949-7714, 1432-1394},\\n\\turl = {https://link.springer.com/10.1007/s00190-022-01683-0},\\n\\tdoi = {10.1007/s00190-022-01683-0},\\n\\tlanguage = {en},\\n\\tnumber = {12},\\n\\turldate = {2024-03-05},\\n\\tjournal = {Journal of Geodesy},\\n\\tauthor = {Yang, Fan and Luo, ZhiCai and Zhou, Hao and Kusche, Jürgen},\\n\\tmonth = dec,\\n\\tyear = {2022},\\n\\tpages = {95},\\n}\\n\\n\",\"author_short\":[\"Yang, F.\",\"Luo, Z.\",\"Zhou, H.\",\"Kusche, J.\"],\"key\":\"yang_study_2022\",\"id\":\"yang_study_2022\",\"bibbaseid\":\"yang-luo-zhou-kusche-onstudyoftheearthtopographycorrectionforthegracesurfacemassestimation-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://link.springer.com/10.1007/s00190-022-01683-0\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Snowmelt risk telecouplings for irrigated agriculture\",\"volume\":\"12\",\"issn\":\"1758-678X, 1758-6798\",\"url\":\"https://www.nature.com/articles/s41558-022-01509-z\",\"doi\":\"10.1038/s41558-022-01509-z\",\"language\":\"en\",\"number\":\"11\",\"urldate\":\"2024-03-05\",\"journal\":\"Nature Climate Change\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Qin\"],\"firstnames\":[\"Yue\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hong\"],\"firstnames\":[\"Chaopeng\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhao\"],\"firstnames\":[\"Hongyan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Siebert\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Abatzoglou\"],\"firstnames\":[\"John\",\"T.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Huning\"],\"firstnames\":[\"Laurie\",\"S.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sloat\"],\"firstnames\":[\"Lindsey\",\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Park\"],\"firstnames\":[\"Sohyun\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Li\"],\"firstnames\":[\"Shiyu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Munroe\"],\"firstnames\":[\"Darla\",\"K.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhu\"],\"firstnames\":[\"Tong\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Davis\"],\"firstnames\":[\"Steven\",\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mueller\"],\"firstnames\":[\"Nathaniel\",\"D.\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2022\",\"pages\":\"1007–1015\",\"bibtex\":\"@article{qin_snowmelt_2022,\\n\\ttitle = {Snowmelt risk telecouplings for irrigated agriculture},\\n\\tvolume = {12},\\n\\tissn = {1758-678X, 1758-6798},\\n\\turl = {https://www.nature.com/articles/s41558-022-01509-z},\\n\\tdoi = {10.1038/s41558-022-01509-z},\\n\\tlanguage = {en},\\n\\tnumber = {11},\\n\\turldate = {2024-03-05},\\n\\tjournal = {Nature Climate Change},\\n\\tauthor = {Qin, Yue and Hong, Chaopeng and Zhao, Hongyan and Siebert, Stefan and Abatzoglou, John T. and Huning, Laurie S. and Sloat, Lindsey L. and Park, Sohyun and Li, Shiyu and Munroe, Darla K. and Zhu, Tong and Davis, Steven J. and Mueller, Nathaniel D.},\\n\\tmonth = nov,\\n\\tyear = {2022},\\n\\tpages = {1007--1015},\\n}\\n\\n\",\"author_short\":[\"Qin, Y.\",\"Hong, C.\",\"Zhao, H.\",\"Siebert, S.\",\"Abatzoglou, J. T.\",\"Huning, L. S.\",\"Sloat, L. L.\",\"Park, S.\",\"Li, S.\",\"Munroe, D. K.\",\"Zhu, T.\",\"Davis, S. J.\",\"Mueller, N. D.\"],\"key\":\"qin_snowmelt_2022\",\"id\":\"qin_snowmelt_2022\",\"bibbaseid\":\"qin-hong-zhao-siebert-abatzoglou-huning-sloat-park-etal-snowmeltrisktelecouplingsforirrigatedagriculture-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.nature.com/articles/s41558-022-01509-z\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Mapping land degradation risk due to land susceptibility to dust emission and water erosion\",\"volume\":\"9\",\"issn\":\"2199-398X\",\"url\":\"https://soil.copernicus.org/articles/9/411/2023/\",\"doi\":\"10.5194/soil-9-411-2023\",\"abstract\":\"Abstract. Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation due to wind and water erosion in some areas of the world, the combined study of both types of erosion in the same area receives relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran, encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing the artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the area under the curve (AUC) of the receiver operating characteristic (ROC) metric. The water and aeolian soil erosion maps are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for in an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere.\",\"language\":\"en\",\"number\":\"2\",\"urldate\":\"2024-02-26\",\"journal\":\"SOIL\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Boroughani\"],\"firstnames\":[\"Mahdi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mirchooli\"],\"firstnames\":[\"Fahimeh\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hadavifar\"],\"firstnames\":[\"Mojtaba\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fiedler\"],\"firstnames\":[\"Stephanie\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2023\",\"pages\":\"411–423\",\"bibtex\":\"@article{boroughani_mapping_2023,\\n\\ttitle = {Mapping land degradation risk due to land susceptibility to dust emission and water erosion},\\n\\tvolume = {9},\\n\\tissn = {2199-398X},\\n\\turl = {https://soil.copernicus.org/articles/9/411/2023/},\\n\\tdoi = {10.5194/soil-9-411-2023},\\n\\tabstract = {Abstract. Land degradation is a cause of many social, economic, and environmental\\nproblems. Therefore identification and monitoring of high-risk areas for\\nland degradation are necessary. Despite the importance of land degradation\\ndue to wind and water erosion in some areas of the world, the combined study\\nof both types of erosion in the same area receives relatively little\\nattention. The present study aims to create a land degradation map in terms\\nof soil erosion caused by wind and water erosion of semi-dry land. We focus\\non the Lut watershed in Iran, encompassing the Lut Desert that is influenced\\nby both monsoon rainfalls and dust storms. Dust sources are identified using\\nMODIS satellite images with the help of four different indices to quantify\\nuncertainty. The dust source maps are assessed with three machine learning\\nalgorithms encompassing the artificial neural network (ANN), random forest (RF),\\nand flexible discriminant analysis (FDA) to map dust sources paired with\\nsoil erosion susceptibility due to water. We assess the accuracy of the maps\\nfrom the machine learning results with the area under the curve (AUC)\\nof the receiver operating characteristic (ROC) metric. The water and aeolian soil\\nerosion maps are used to identify different classes of land degradation\\nrisks. The results show that 43 \\\\% of the watershed is prone to land\\ndegradation in terms of both aeolian and water erosion. Most regions\\n(45 \\\\%) have a risk of water erosion and some regions (7 \\\\%) a risk of\\naeolian erosion. Only a small fraction (4 \\\\%) of the total area of the\\nregion had a low to very low susceptibility for land degradation. The\\nresults of this study underline the risk of land degradation for in an\\ninhabited region in Iran. Future work should focus on land degradation\\nassociated with soil erosion from water and storms in larger regions to\\nevaluate the risks also elsewhere.},\\n\\tlanguage = {en},\\n\\tnumber = {2},\\n\\turldate = {2024-02-26},\\n\\tjournal = {SOIL},\\n\\tauthor = {Boroughani, Mahdi and Mirchooli, Fahimeh and Hadavifar, Mojtaba and Fiedler, Stephanie},\\n\\tmonth = jul,\\n\\tyear = {2023},\\n\\tpages = {411--423},\\n}\\n\\n\",\"author_short\":[\"Boroughani, M.\",\"Mirchooli, F.\",\"Hadavifar, M.\",\"Fiedler, S.\"],\"key\":\"boroughani_mapping_2023\",\"id\":\"boroughani_mapping_2023\",\"bibbaseid\":\"boroughani-mirchooli-hadavifar-fiedler-mappinglanddegradationriskduetolandsusceptibilitytodustemissionandwatererosion-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://soil.copernicus.org/articles/9/411/2023/\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale?\",\"volume\":\"60\",\"issn\":\"0043-1397, 1944-7973\",\"url\":\"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056\",\"doi\":\"10.1029/2023WR035056\",\"abstract\":\"Abstract Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40% in 2016 and 16% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15% RMSE reduction of monthly ET in the wet year and 9% in the dry year. , Key Points Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale Soil moisture characterization improved more in a wet year than in a very dry year Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture\",\"language\":\"en\",\"number\":\"1\",\"urldate\":\"2024-02-26\",\"journal\":\"Water Resources Research\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Li\"],\"firstnames\":[\"Fang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bogena\"],\"firstnames\":[\"Heye\",\"Reemt\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bayat\"],\"firstnames\":[\"Bagher\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kurtz\"],\"firstnames\":[\"Wolfgang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hendricks\",\"Franssen\"],\"firstnames\":[\"Harrie‐Jan\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2024\",\"pages\":\"e2023WR035056\",\"bibtex\":\"@article{li_can_2024,\\n\\ttitle = {Can a {Sparse} {Network} of {Cosmic} {Ray} {Neutron} {Sensors} {Improve} {Soil} {Moisture} and {Evapotranspiration} {Estimation} at the {Larger} {Catchment} {Scale}?},\\n\\tvolume = {60},\\n\\tissn = {0043-1397, 1944-7973},\\n\\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056},\\n\\tdoi = {10.1029/2023WR035056},\\n\\tabstract = {Abstract\\n            Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60\\\\% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15\\\\% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40\\\\% in 2016 and 16\\\\% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15\\\\% RMSE reduction of monthly ET in the wet year and 9\\\\% in the dry year.\\n          , \\n            Key Points\\n            \\n              \\n                \\n                  Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale\\n                \\n                \\n                  Soil moisture characterization improved more in a wet year than in a very dry year\\n                \\n                \\n                  Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture},\\n\\tlanguage = {en},\\n\\tnumber = {1},\\n\\turldate = {2024-02-26},\\n\\tjournal = {Water Resources Research},\\n\\tauthor = {Li, Fang and Bogena, Heye Reemt and Bayat, Bagher and Kurtz, Wolfgang and Hendricks Franssen, Harrie‐Jan},\\n\\tmonth = jan,\\n\\tyear = {2024},\\n\\tpages = {e2023WR035056},\\n}\\n\\n\",\"author_short\":[\"Li, F.\",\"Bogena, H. R.\",\"Bayat, B.\",\"Kurtz, W.\",\"Hendricks Franssen, H.\"],\"key\":\"li_can_2024\",\"id\":\"li_can_2024\",\"bibbaseid\":\"li-bogena-bayat-kurtz-hendricksfranssen-canasparsenetworkofcosmicrayneutronsensorsimprovesoilmoistureandevapotranspirationestimationatthelargercatchmentscale-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"A detailed analysis of stochastic models applied to temporal gravity field recovery with GRACE observations\",\"volume\":\"236\",\"issn\":\"0956-540X, 1365-246X\",\"url\":\"https://academic.oup.com/gji/article/236/1/516/7382201\",\"doi\":\"10.1093/gji/ggad441\",\"abstract\":\"SUMMARY In this study, we analysed the impacts of errors in background force models and observed non-gravitational forces on the pseudo-observations (pre-fits) during gravity field recovery based on the Gravity Recovery and Climate Experiment (GRACE) satellite gravity mission. To reduce these effects, we introduced the stochastic parameters into the functional model of the variational equation integration approach to absorb this type of noise contribution. Simultaneously, the prior variances of observed orbits and K-band range rates used in traditional method are re-estimated with least-squares variance component estimation (LS-VCE) after considering these stochastic parameters. To improve the computing efficiency, a modified method of the calculation of sensitivity matrices related to the introduced stochastic parameters is proposed. Compared to the method of variation of constants widely used in the precise orbit determination and gravity field recovery, the modified method decreases the computational time of these matrices by about four times. Furthermore, an efficient LS-VCE algorithm is derived in a more generalized case. The efficient algorithm only costs 1 per cent of the time of the unoptimized method. With the GRACE data, we analysed the benefits of these refinements in gravity field recovery, and the results show that these improvements can mitigate the impacts of errors in background force models and accelerometer data on recovered gravity field models, especially in the high-degree signals. Furthermore, the quality of results has less dependence on parametrization.\",\"language\":\"en\",\"number\":\"1\",\"urldate\":\"2024-02-26\",\"journal\":\"Geophysical Journal International\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yu\"],\"firstnames\":[\"Biao\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"You\"],\"firstnames\":[\"Wei\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kusche\"],\"firstnames\":[\"Jürgen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fan\"],\"firstnames\":[\"Dongming\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Su\"],\"firstnames\":[\"Yong\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"Jiahui\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2023\",\"pages\":\"516–536\",\"bibtex\":\"@article{yu_detailed_2023,\\n\\ttitle = {A detailed analysis of stochastic models applied to temporal gravity field recovery with {GRACE} observations},\\n\\tvolume = {236},\\n\\tissn = {0956-540X, 1365-246X},\\n\\turl = {https://academic.oup.com/gji/article/236/1/516/7382201},\\n\\tdoi = {10.1093/gji/ggad441},\\n\\tabstract = {SUMMARY\\n            In this study, we analysed the impacts of errors in background force models and observed non-gravitational forces on the pseudo-observations (pre-fits) during gravity field recovery based on the Gravity Recovery and Climate Experiment (GRACE) satellite gravity mission. To reduce these effects, we introduced the stochastic parameters into the functional model of the variational equation integration approach to absorb this type of noise contribution. Simultaneously, the prior variances of observed orbits and K-band range rates used in traditional method are re-estimated with least-squares variance component estimation (LS-VCE) after considering these stochastic parameters. To improve the computing efficiency, a modified method of the calculation of sensitivity matrices related to the introduced stochastic parameters is proposed. Compared to the method of variation of constants widely used in the precise orbit determination and gravity field recovery, the modified method decreases the computational time of these matrices by about four times. Furthermore, an efficient LS-VCE algorithm is derived in a more generalized case. The efficient algorithm only costs 1 per cent of the time of the unoptimized method. With the GRACE data, we analysed the benefits of these refinements in gravity field recovery, and the results show that these improvements can mitigate the impacts of errors in background force models and accelerometer data on recovered gravity field models, especially in the high-degree signals. Furthermore, the quality of results has less dependence on parametrization.},\\n\\tlanguage = {en},\\n\\tnumber = {1},\\n\\turldate = {2024-02-26},\\n\\tjournal = {Geophysical Journal International},\\n\\tauthor = {Yu, Biao and You, Wei and Kusche, Jürgen and Fan, Dongming and Su, Yong and Zhang, Jiahui},\\n\\tmonth = nov,\\n\\tyear = {2023},\\n\\tpages = {516--536},\\n}\\n\\n\",\"author_short\":[\"Yu, B.\",\"You, W.\",\"Kusche, J.\",\"Fan, D.\",\"Su, Y.\",\"Zhang, J.\"],\"key\":\"yu_detailed_2023\",\"id\":\"yu_detailed_2023\",\"bibbaseid\":\"yu-you-kusche-fan-su-zhang-adetailedanalysisofstochasticmodelsappliedtotemporalgravityfieldrecoverywithgraceobservations-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://academic.oup.com/gji/article/236/1/516/7382201\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Combining root and soil hydraulics in macroscopic representations of root water uptake\",\"issn\":\"1539-1663, 1539-1663\",\"url\":\"https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20273\",\"doi\":\"10.1002/vzj2.20273\",\"abstract\":\"Abstract Plant water uptake and plant and soil water status are important for the soil water balance and plant growth. They depend on atmospheric water demand and the accessibility of soil water to plant roots, which is in turn related to the hydraulic properties of the root system and the soil around root segments. We present a simulation model that describes water flow in the soil–plant system mechanistically considering both root and soil hydraulic properties. We developed an approach to upscale three‐dimensional (3D) flow in the soil toward root segments of a 3D root architecture to a model that considers one‐dimensional flow between horizontal soil layers and radial flow to root segments in that layer. The upscaled model couples upscaled linear flow equations in the root system with an analytical solution of the nonlinear radial flow equation between the soil and roots. The upscaled model avoids simplifying assumptions about root hydraulic properties and water potential drops near roots made in, respectively, soil‐ and root‐centered models. Xylem water potentials and soil–root interface potentials are explicitly simulated and show, respectively, large variations with depth and large deviations from bulk soil water potentials under dry soil conditions. Accounting for hydraulic gradients in the soil around root segments led to an earlier but slower reduction of transpiration during a drought period and a better plant water status with higher nighttime plant water potentials. , Core Ideas Root water uptake depends on root and soil hydraulic properties. Water uptake at root element scale was upscaled to the root system scale. The upscaled model can be implemented in one‐dimensional soil water flow models. Low conductance of dry soil prevents low nighttime plant water potentials.\",\"language\":\"en\",\"urldate\":\"2024-01-29\",\"journal\":\"Vadose Zone Journal\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Vanderborght\"],\"firstnames\":[\"Jan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Leitner\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schnepf\"],\"firstnames\":[\"Andrea\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Couvreur\"],\"firstnames\":[\"Valentin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vereecken\"],\"firstnames\":[\"Harry\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Javaux\"],\"firstnames\":[\"Mathieu\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2023\",\"pages\":\"e20273\",\"bibtex\":\"@article{vanderborght_combining_2023,\\n\\ttitle = {Combining root and soil hydraulics in macroscopic representations of root water uptake},\\n\\tissn = {1539-1663, 1539-1663},\\n\\turl = {https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20273},\\n\\tdoi = {10.1002/vzj2.20273},\\n\\tabstract = {Abstract\\n            Plant water uptake and plant and soil water status are important for the soil water balance and plant growth. They depend on atmospheric water demand and the accessibility of soil water to plant roots, which is in turn related to the hydraulic properties of the root system and the soil around root segments. We present a simulation model that describes water flow in the soil–plant system mechanistically considering both root and soil hydraulic properties. We developed an approach to upscale three‐dimensional (3D) flow in the soil toward root segments of a 3D root architecture to a model that considers one‐dimensional flow between horizontal soil layers and radial flow to root segments in that layer. The upscaled model couples upscaled linear flow equations in the root system with an analytical solution of the nonlinear radial flow equation between the soil and roots. The upscaled model avoids simplifying assumptions about root hydraulic properties and water potential drops near roots made in, respectively, soil‐ and root‐centered models. Xylem water potentials and soil–root interface potentials are explicitly simulated and show, respectively, large variations with depth and large deviations from bulk soil water potentials under dry soil conditions. Accounting for hydraulic gradients in the soil around root segments led to an earlier but slower reduction of transpiration during a drought period and a better plant water status with higher nighttime plant water potentials.\\n          , \\n            Core Ideas\\n            \\n              \\n                \\n                  Root water uptake depends on root and soil hydraulic properties.\\n                \\n                \\n                  Water uptake at root element scale was upscaled to the root system scale.\\n                \\n                \\n                  The upscaled model can be implemented in one‐dimensional soil water flow models.\\n                \\n                \\n                  Low conductance of dry soil prevents low nighttime plant water potentials.},\\n\\tlanguage = {en},\\n\\turldate = {2024-01-29},\\n\\tjournal = {Vadose Zone Journal},\\n\\tauthor = {Vanderborght, Jan and Leitner, Daniel and Schnepf, Andrea and Couvreur, Valentin and Vereecken, Harry and Javaux, Mathieu},\\n\\tmonth = jul,\\n\\tyear = {2023},\\n\\tpages = {e20273},\\n}\\n\\n\",\"author_short\":[\"Vanderborght, J.\",\"Leitner, D.\",\"Schnepf, A.\",\"Couvreur, V.\",\"Vereecken, H.\",\"Javaux, M.\"],\"key\":\"vanderborght_combining_2023\",\"id\":\"vanderborght_combining_2023\",\"bibbaseid\":\"vanderborght-leitner-schnepf-couvreur-vereecken-javaux-combiningrootandsoilhydraulicsinmacroscopicrepresentationsofrootwateruptake-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20273\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"address\":\"Cham\",\"title\":\"Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks\",\"volume\":\"13485\",\"isbn\":\"978-3-031-16787-4 978-3-031-16788-1\",\"url\":\"https://link.springer.com/10.1007/978-3-031-16788-1_29\",\"language\":\"en\",\"urldate\":\"2023-11-27\",\"booktitle\":\"Pattern Recognition\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Leonhardt\"],\"firstnames\":[\"Johannes\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Drees\"],\"firstnames\":[\"Lukas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jung\"],\"firstnames\":[\"Peter\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Roscher\"],\"firstnames\":[\"Ribana\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Andres\"],\"firstnames\":[\"Björn\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bernard\"],\"firstnames\":[\"Florian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cremers\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Frintrop\"],\"firstnames\":[\"Simone\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Goldlücke\"],\"firstnames\":[\"Bastian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ihrke\"],\"firstnames\":[\"Ivo\"],\"suffixes\":[]}],\"year\":\"2022\",\"doi\":\"10.1007/978-3-031-16788-1_29\",\"note\":\"Series Title: Lecture Notes in Computer Science\",\"pages\":\"479–494\",\"bibtex\":\"@incollection{andres_probabilistic_2022,\\n\\taddress = {Cham},\\n\\ttitle = {Probabilistic {Biomass} {Estimation} with {Conditional} {Generative} {Adversarial} {Networks}},\\n\\tvolume = {13485},\\n\\tisbn = {978-3-031-16787-4 978-3-031-16788-1},\\n\\turl = {https://link.springer.com/10.1007/978-3-031-16788-1_29},\\n\\tlanguage = {en},\\n\\turldate = {2023-11-27},\\n\\tbooktitle = {Pattern {Recognition}},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {Leonhardt, Johannes and Drees, Lukas and Jung, Peter and Roscher, Ribana},\\n\\teditor = {Andres, Björn and Bernard, Florian and Cremers, Daniel and Frintrop, Simone and Goldlücke, Bastian and Ihrke, Ivo},\\n\\tyear = {2022},\\n\\tdoi = {10.1007/978-3-031-16788-1_29},\\n\\tnote = {Series Title: Lecture Notes in Computer Science},\\n\\tpages = {479--494},\\n}\\n\\n\",\"author_short\":[\"Leonhardt, J.\",\"Drees, L.\",\"Jung, P.\",\"Roscher, R.\"],\"editor_short\":[\"Andres, B.\",\"Bernard, F.\",\"Cremers, D.\",\"Frintrop, S.\",\"Goldlücke, B.\",\"Ihrke, I.\"],\"key\":\"andres_probabilistic_2022\",\"id\":\"andres_probabilistic_2022\",\"bibbaseid\":\"leonhardt-drees-jung-roscher-probabilisticbiomassestimationwithconditionalgenerativeadversarialnetworks-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://link.springer.com/10.1007/978-3-031-16788-1_29\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Organic soils\",\"isbn\":\"978-0-323-95133-3\",\"url\":\"https://linkinghub.elsevier.com/retrieve/pii/B9780128229743000732\",\"language\":\"en\",\"urldate\":\"2023-11-27\",\"booktitle\":\"Encyclopedia of Soils in the Environment\",\"publisher\":\"Elsevier\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Schimmel\"],\"firstnames\":[\"Heike\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Amelung\"],\"firstnames\":[\"Wulf\"],\"suffixes\":[]}],\"year\":\"2023\",\"doi\":\"10.1016/B978-0-12-822974-3.00073-2\",\"pages\":\"383–397\",\"bibtex\":\"@incollection{schimmel_organic_2023,\\n\\ttitle = {Organic soils},\\n\\tisbn = {978-0-323-95133-3},\\n\\turl = {https://linkinghub.elsevier.com/retrieve/pii/B9780128229743000732},\\n\\tlanguage = {en},\\n\\turldate = {2023-11-27},\\n\\tbooktitle = {Encyclopedia of {Soils} in the {Environment}},\\n\\tpublisher = {Elsevier},\\n\\tauthor = {Schimmel, Heike and Amelung, Wulf},\\n\\tyear = {2023},\\n\\tdoi = {10.1016/B978-0-12-822974-3.00073-2},\\n\\tpages = {383--397},\\n}\\n\\n\",\"author_short\":[\"Schimmel, H.\",\"Amelung, W.\"],\"key\":\"schimmel_organic_2023\",\"id\":\"schimmel_organic_2023\",\"bibbaseid\":\"schimmel-amelung-organicsoils-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://linkinghub.elsevier.com/retrieve/pii/B9780128229743000732\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Evaluation of Sentinel-3A altimetry over Songhua river Basin\",\"volume\":\"618\",\"issn\":\"00221694\",\"url\":\"https://linkinghub.elsevier.com/retrieve/pii/S0022169423001397\",\"doi\":\"10.1016/j.jhydrol.2023.129197\",\"language\":\"en\",\"urldate\":\"2023-10-31\",\"journal\":\"Journal of Hydrology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Chen\"],\"firstnames\":[\"Jiaming\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fenoglio\"],\"firstnames\":[\"Luciana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kusche\"],\"firstnames\":[\"Jürgen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Liao\"],\"firstnames\":[\"Jingjuan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Uyanik\"],\"firstnames\":[\"Hakan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nadzir\"],\"firstnames\":[\"Zulfikar\",\"Adlan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lou\"],\"firstnames\":[\"Yanhan\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2023\",\"pages\":\"129197\",\"bibtex\":\"@article{chen_evaluation_2023,\\n\\ttitle = {Evaluation of {Sentinel}-{3A} altimetry over {Songhua} river {Basin}},\\n\\tvolume = {618},\\n\\tissn = {00221694},\\n\\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423001397},\\n\\tdoi = {10.1016/j.jhydrol.2023.129197},\\n\\tlanguage = {en},\\n\\turldate = {2023-10-31},\\n\\tjournal = {Journal of Hydrology},\\n\\tauthor = {Chen, Jiaming and Fenoglio, Luciana and Kusche, Jürgen and Liao, Jingjuan and Uyanik, Hakan and Nadzir, Zulfikar Adlan and Lou, Yanhan},\\n\\tmonth = mar,\\n\\tyear = {2023},\\n\\tpages = {129197},\\n}\\n\\n\",\"author_short\":[\"Chen, J.\",\"Fenoglio, L.\",\"Kusche, J.\",\"Liao, J.\",\"Uyanik, H.\",\"Nadzir, Z. A.\",\"Lou, Y.\"],\"key\":\"chen_evaluation_2023\",\"id\":\"chen_evaluation_2023\",\"bibbaseid\":\"chen-fenoglio-kusche-liao-uyanik-nadzir-lou-evaluationofsentinel3aaltimetryoversonghuariverbasin-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://linkinghub.elsevier.com/retrieve/pii/S0022169423001397\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Veränderungen der Wasserspeicherung in Deutschland seit 2002 aus Beobachtungen der Satellitengravimetrie – Water storage changes in Germany since 2002 from satellite gravity observations\",\"issn\":\"1439-1783\",\"url\":\"https://doi.bafg.de/HyWa/2023/HyWa_2023.2_1.pdf\",\"doi\":\"10.5675/HYWA_2023.2_1\",\"urldate\":\"2023-10-30\",\"journal\":\"Hydrologie und Wasserbewirtschaftung\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Güntner\"],\"firstnames\":[\"Andreas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gerdener\"],\"firstnames\":[\"Helena\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Boergens\"],\"firstnames\":[\"Eva\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kusche\"],\"firstnames\":[\"Jürgen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kollet\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dobslaw\"],\"firstnames\":[\"Henryk\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hartick\"],\"firstnames\":[\"Carl\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sharifi\"],\"firstnames\":[\"Ehsan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Flechtner\"],\"firstnames\":[\"Frank\"],\"suffixes\":[]}],\"year\":\"2023\",\"note\":\"Publisher: Bundesanstalt für Gewässerkunde, Koblenz\",\"bibtex\":\"@article{guntner_veranderungen_2023,\\n\\ttitle = {Veränderungen der {Wasserspeicherung} in {Deutschland} seit 2002 aus {Beobachtungen} der {Satellitengravimetrie} – {Water} storage changes in {Germany} since 2002 from satellite gravity observations},\\n\\tissn = {1439-1783},\\n\\turl = {https://doi.bafg.de/HyWa/2023/HyWa_2023.2_1.pdf},\\n\\tdoi = {10.5675/HYWA_2023.2_1},\\n\\turldate = {2023-10-30},\\n\\tjournal = {Hydrologie und Wasserbewirtschaftung},\\n\\tauthor = {Güntner, Andreas and Gerdener, Helena and Boergens, Eva and Kusche, Jürgen and Kollet, Stefan and Dobslaw, Henryk and Hartick, Carl and Sharifi, Ehsan and Flechtner, Frank},\\n\\tyear = {2023},\\n\\tnote = {Publisher: Bundesanstalt für Gewässerkunde, Koblenz},\\n}\\n\\n\",\"author_short\":[\"Güntner, A.\",\"Gerdener, H.\",\"Boergens, E.\",\"Kusche, J.\",\"Kollet, S.\",\"Dobslaw, H.\",\"Hartick, C.\",\"Sharifi, E.\",\"Flechtner, F.\"],\"key\":\"guntner_veranderungen_2023\",\"id\":\"guntner_veranderungen_2023\",\"bibbaseid\":\"gntner-gerdener-boergens-kusche-kollet-dobslaw-hartick-sharifi-etal-vernderungenderwasserspeicherungindeutschlandseit2002ausbeobachtungendersatellitengravimetriewaterstoragechangesingermanysince2002fromsatellitegravityobservations-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.bafg.de/HyWa/2023/HyWa_2023.2_1.pdf\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"SIMPLACE—a versatile modelling and simulation framework for sustainable crops and agroecosystems\",\"volume\":\"5\",\"issn\":\"2517-5025\",\"url\":\"https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad006/7176223\",\"doi\":\"10.1093/insilicoplants/diad006\",\"abstract\":\"Abstract Agricultural system analysis has considerably evolved over the last years, allowing scientists to quantify complex interactions in crops and agroecosystems. Computer-based models have become a central tool for such analysis, using formulated mathematical representations (algorithms) of different biophysical processes to simulate complex system’s behaviour. Nevertheless, the current large variety of algorithms in combination with nonstandardization in their use limits rapid and rigorous model improvement and testing. This is particularly important because contextualization is a key aspect used to formulate the appropriate model structure for a specific research question, framing a clear demand for ‘next generation’ models being modular and flexible. This paper aims to describe the Scientific Impact assessment and Modelling PLatform for Advanced Crop and Ecosystem management (SIMPLACE), which has been developed over the last decade to address the various aforementioned issues and support appropriate model formulations and interoperability. We describe its main technical implementation and features to develop customized model solutions that can be applied to a number of cropping systems with high flexibility, performance and transparency. A brief review of exemplary applications of SIMPLACE is provided covering the different topics, crops and cropping systems, spatial scales and geographies. We stress that standardized documentation of modules, variables ontology and data archives are key requirements to maintain and assist model development and reproducibility. The increasing demand for more complex, diversified and integrated production systems (e.g. intercropping, livestock-grazing, agroforestry) and the associated impacts on sustainable food systems also require the strong collaboration of a multidisciplinary community of modellers and stakeholders.\",\"language\":\"en\",\"number\":\"1\",\"urldate\":\"2023-10-30\",\"journal\":\"in silico Plants\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Enders\"],\"firstnames\":[\"Andreas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vianna\"],\"firstnames\":[\"Murilo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gaiser\"],\"firstnames\":[\"Thomas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Krauss\"],\"firstnames\":[\"Gunther\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Webber\"],\"firstnames\":[\"Heidi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Srivastava\"],\"firstnames\":[\"Amit\",\"Kumar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Seidel\"],\"firstnames\":[\"Sabine\",\"Julia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tewes\"],\"firstnames\":[\"Andreas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rezaei\"],\"firstnames\":[\"Ehsan\",\"Eyshi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ewert\"],\"firstnames\":[\"Frank\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Marshall-Colon\"],\"firstnames\":[\"Amy\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2023\",\"pages\":\"diad006\",\"bibtex\":\"@article{enders_simplaceversatile_2023,\\n\\ttitle = {{SIMPLACE}—a versatile modelling and simulation framework for sustainable crops and agroecosystems},\\n\\tvolume = {5},\\n\\tissn = {2517-5025},\\n\\turl = {https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad006/7176223},\\n\\tdoi = {10.1093/insilicoplants/diad006},\\n\\tabstract = {Abstract\\n            Agricultural system analysis has considerably evolved over the last years, allowing scientists to quantify complex interactions in crops and agroecosystems. Computer-based models have become a central tool for such analysis, using formulated mathematical representations (algorithms) of different biophysical processes to simulate complex system’s behaviour. Nevertheless, the current large variety of algorithms in combination with nonstandardization in their use limits rapid and rigorous model improvement and testing. This is particularly important because contextualization is a key aspect used to formulate the appropriate model structure for a specific research question, framing a clear demand for ‘next generation’ models being modular and flexible. This paper aims to describe the Scientific Impact assessment and Modelling PLatform for Advanced Crop and Ecosystem management (SIMPLACE), which has been developed over the last decade to address the various aforementioned issues and support appropriate model formulations and interoperability. We describe its main technical implementation and features to develop customized model solutions that can be applied to a number of cropping systems with high flexibility, performance and transparency. A brief review of exemplary applications of SIMPLACE is provided covering the different topics, crops and cropping systems, spatial scales and geographies. We stress that standardized documentation of modules, variables ontology and data archives are key requirements to maintain and assist model development and reproducibility. The increasing demand for more complex, diversified and integrated production systems (e.g. intercropping, livestock-grazing, agroforestry) and the associated impacts on sustainable food systems also require the strong collaboration of a multidisciplinary community of modellers and stakeholders.},\\n\\tlanguage = {en},\\n\\tnumber = {1},\\n\\turldate = {2023-10-30},\\n\\tjournal = {in silico Plants},\\n\\tauthor = {Enders, Andreas and Vianna, Murilo and Gaiser, Thomas and Krauss, Gunther and Webber, Heidi and Srivastava, Amit Kumar and Seidel, Sabine Julia and Tewes, Andreas and Rezaei, Ehsan Eyshi and Ewert, Frank},\\n\\teditor = {Marshall-Colon, Amy},\\n\\tmonth = jan,\\n\\tyear = {2023},\\n\\tpages = {diad006},\\n}\\n\\n\",\"author_short\":[\"Enders, A.\",\"Vianna, M.\",\"Gaiser, T.\",\"Krauss, G.\",\"Webber, H.\",\"Srivastava, A. K.\",\"Seidel, S. J.\",\"Tewes, A.\",\"Rezaei, E. E.\",\"Ewert, F.\"],\"editor_short\":[\"Marshall-Colon, A.\"],\"key\":\"enders_simplaceversatile_2023\",\"id\":\"enders_simplaceversatile_2023\",\"bibbaseid\":\"enders-vianna-gaiser-krauss-webber-srivastava-seidel-tewes-etal-simplaceaversatilemodellingandsimulationframeworkforsustainablecropsandagroecosystems-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad006/7176223\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects\",\"volume\":\"4\",\"issn\":\"2662-4435\",\"url\":\"https://www.nature.com/articles/s43247-023-00958-4\",\"doi\":\"10.1038/s43247-023-00958-4\",\"abstract\":\"Abstract Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.\",\"language\":\"en\",\"number\":\"1\",\"urldate\":\"2023-10-30\",\"journal\":\"Communications Earth & Environment\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Graf\"],\"firstnames\":[\"Alexander\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wohlfahrt\"],\"firstnames\":[\"Georg\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Aranda-Barranco\"],\"firstnames\":[\"Sergio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Arriga\"],\"firstnames\":[\"Nicola\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brümmer\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ceschia\"],\"firstnames\":[\"Eric\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ciais\"],\"firstnames\":[\"Philippe\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Desai\"],\"firstnames\":[\"Ankur\",\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Di\",\"Lonardo\"],\"firstnames\":[\"Sara\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gharun\"],\"firstnames\":[\"Mana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Grünwald\"],\"firstnames\":[\"Thomas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hörtnagl\"],\"firstnames\":[\"Lukas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kasak\"],\"firstnames\":[\"Kuno\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Klosterhalfen\"],\"firstnames\":[\"Anne\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Knohl\"],\"firstnames\":[\"Alexander\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kowalska\"],\"firstnames\":[\"Natalia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Leuchner\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lindroth\"],\"firstnames\":[\"Anders\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mauder\"],\"firstnames\":[\"Matthias\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Migliavacca\"],\"firstnames\":[\"Mirco\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Morel\"],\"firstnames\":[\"Alexandra\",\"C.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pfennig\"],\"firstnames\":[\"Andreas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Poorter\"],\"firstnames\":[\"Hendrik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Terán\"],\"firstnames\":[\"Christian\",\"Poppe\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Reitz\"],\"firstnames\":[\"Oliver\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rebmann\"],\"firstnames\":[\"Corinna\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sanchez-Azofeifa\"],\"firstnames\":[\"Arturo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schmidt\"],\"firstnames\":[\"Marius\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Šigut\"],\"firstnames\":[\"Ladislav\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tomelleri\"],\"firstnames\":[\"Enrico\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yu\"],\"firstnames\":[\"Ke\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Varlagin\"],\"firstnames\":[\"Andrej\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vereecken\"],\"firstnames\":[\"Harry\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2023\",\"pages\":\"298\",\"bibtex\":\"@article{graf_joint_2023,\\n\\ttitle = {Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects},\\n\\tvolume = {4},\\n\\tissn = {2662-4435},\\n\\turl = {https://www.nature.com/articles/s43247-023-00958-4},\\n\\tdoi = {10.1038/s43247-023-00958-4},\\n\\tabstract = {Abstract\\n            Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.},\\n\\tlanguage = {en},\\n\\tnumber = {1},\\n\\turldate = {2023-10-30},\\n\\tjournal = {Communications Earth \\\\& Environment},\\n\\tauthor = {Graf, Alexander and Wohlfahrt, Georg and Aranda-Barranco, Sergio and Arriga, Nicola and Brümmer, Christian and Ceschia, Eric and Ciais, Philippe and Desai, Ankur R. and Di Lonardo, Sara and Gharun, Mana and Grünwald, Thomas and Hörtnagl, Lukas and Kasak, Kuno and Klosterhalfen, Anne and Knohl, Alexander and Kowalska, Natalia and Leuchner, Michael and Lindroth, Anders and Mauder, Matthias and Migliavacca, Mirco and Morel, Alexandra C. and Pfennig, Andreas and Poorter, Hendrik and Terán, Christian Poppe and Reitz, Oliver and Rebmann, Corinna and Sanchez-Azofeifa, Arturo and Schmidt, Marius and Šigut, Ladislav and Tomelleri, Enrico and Yu, Ke and Varlagin, Andrej and Vereecken, Harry},\\n\\tmonth = aug,\\n\\tyear = {2023},\\n\\tpages = {298},\\n}\\n\\n\",\"author_short\":[\"Graf, A.\",\"Wohlfahrt, G.\",\"Aranda-Barranco, S.\",\"Arriga, N.\",\"Brümmer, C.\",\"Ceschia, E.\",\"Ciais, P.\",\"Desai, A. R.\",\"Di Lonardo, S.\",\"Gharun, M.\",\"Grünwald, T.\",\"Hörtnagl, L.\",\"Kasak, K.\",\"Klosterhalfen, A.\",\"Knohl, A.\",\"Kowalska, N.\",\"Leuchner, M.\",\"Lindroth, A.\",\"Mauder, M.\",\"Migliavacca, M.\",\"Morel, A. C.\",\"Pfennig, A.\",\"Poorter, H.\",\"Terán, C. P.\",\"Reitz, O.\",\"Rebmann, C.\",\"Sanchez-Azofeifa, A.\",\"Schmidt, M.\",\"Šigut, L.\",\"Tomelleri, E.\",\"Yu, K.\",\"Varlagin, A.\",\"Vereecken, H.\"],\"key\":\"graf_joint_2023\",\"id\":\"graf_joint_2023\",\"bibbaseid\":\"graf-wohlfahrt-arandabarranco-arriga-brmmer-ceschia-ciais-desai-etal-jointoptimizationoflandcarbonuptakeandalbedocanhelpachievemoderateinstantaneousandlongtermcoolingeffects-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.nature.com/articles/s43247-023-00958-4\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Water table depth assimilation in integrated terrestrial system models at the larger catchment scale\",\"volume\":\"5\",\"issn\":\"2624-9375\",\"url\":\"https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full\",\"doi\":\"10.3389/frwa.2023.1150999\",\"abstract\":\"As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites &lt;2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).\",\"urldate\":\"2023-10-30\",\"journal\":\"Frontiers in Water\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Li\"],\"firstnames\":[\"Fang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kurtz\"],\"firstnames\":[\"Wolfgang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hung\"],\"firstnames\":[\"Ching\",\"Pui\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vereecken\"],\"firstnames\":[\"Harry\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hendricks\",\"Franssen\"],\"firstnames\":[\"Harrie-Jan\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2023\",\"pages\":\"1150999\",\"bibtex\":\"@article{li_water_2023,\\n\\ttitle = {Water table depth assimilation in integrated terrestrial system models at the larger catchment scale},\\n\\tvolume = {5},\\n\\tissn = {2624-9375},\\n\\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full},\\n\\tdoi = {10.3389/frwa.2023.1150999},\\n\\tabstract = {As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81\\\\% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites \\\\&lt;2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30\\\\% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).},\\n\\turldate = {2023-10-30},\\n\\tjournal = {Frontiers in Water},\\n\\tauthor = {Li, Fang and Kurtz, Wolfgang and Hung, Ching Pui and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},\\n\\tmonth = mar,\\n\\tyear = {2023},\\n\\tpages = {1150999},\\n}\\n\\n\",\"author_short\":[\"Li, F.\",\"Kurtz, W.\",\"Hung, C. P.\",\"Vereecken, H.\",\"Hendricks Franssen, H.\"],\"key\":\"li_water_2023\",\"id\":\"li_water_2023\",\"bibbaseid\":\"li-kurtz-hung-vereecken-hendricksfranssen-watertabledepthassimilationinintegratedterrestrialsystemmodelsatthelargercatchmentscale-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Wildfire precursors show complementary predictability in different timescales\",\"volume\":\"14\",\"issn\":\"2041-1723\",\"url\":\"https://www.nature.com/articles/s41467-023-42597-5\",\"doi\":\"10.1038/s41467-023-42597-5\",\"abstract\":\"Abstract In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3% and 26.7% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.\",\"language\":\"en\",\"number\":\"1\",\"urldate\":\"2023-10-30\",\"journal\":\"Nature Communications\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Qu\"],\"firstnames\":[\"Yuquan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Miralles\"],\"firstnames\":[\"Diego\",\"G.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Veraverbeke\"],\"firstnames\":[\"Sander\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vereecken\"],\"firstnames\":[\"Harry\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Montzka\"],\"firstnames\":[\"Carsten\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2023\",\"pages\":\"6829\",\"bibtex\":\"@article{qu_wildfire_2023,\\n\\ttitle = {Wildfire precursors show complementary predictability in different timescales},\\n\\tvolume = {14},\\n\\tissn = {2041-1723},\\n\\turl = {https://www.nature.com/articles/s41467-023-42597-5},\\n\\tdoi = {10.1038/s41467-023-42597-5},\\n\\tabstract = {Abstract\\n            In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3\\\\% and 26.7\\\\% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.},\\n\\tlanguage = {en},\\n\\tnumber = {1},\\n\\turldate = {2023-10-30},\\n\\tjournal = {Nature Communications},\\n\\tauthor = {Qu, Yuquan and Miralles, Diego G. and Veraverbeke, Sander and Vereecken, Harry and Montzka, Carsten},\\n\\tmonth = oct,\\n\\tyear = {2023},\\n\\tpages = {6829},\\n}\\n\\n\",\"author_short\":[\"Qu, Y.\",\"Miralles, D. G.\",\"Veraverbeke, S.\",\"Vereecken, H.\",\"Montzka, C.\"],\"key\":\"qu_wildfire_2023\",\"id\":\"qu_wildfire_2023\",\"bibbaseid\":\"qu-miralles-veraverbeke-vereecken-montzka-wildfireprecursorsshowcomplementarypredictabilityindifferenttimescales-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.nature.com/articles/s41467-023-42597-5\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine\",\"issn\":\"2096-4471, 2574-5417\",\"url\":\"https://www.tandfonline.com/doi/full/10.1080/20964471.2023.2257905\",\"doi\":\"10.1080/20964471.2023.2257905\",\"language\":\"en\",\"urldate\":\"2023-10-30\",\"journal\":\"Big Earth Data\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Mohseni\"],\"firstnames\":[\"Farzane\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ahrari\"],\"firstnames\":[\"Amirhossein\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Haunert\"],\"firstnames\":[\"Jan-Henrik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Montzka\"],\"firstnames\":[\"Carsten\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2023\",\"pages\":\"1–25\",\"bibtex\":\"@article{mohseni_synergies_2023,\\n\\ttitle = {The synergies of {SMAP} enhanced and {MODIS} products in a random forest regression for estimating 1 km soil moisture over {Africa} using {Google} {Earth} {Engine}},\\n\\tissn = {2096-4471, 2574-5417},\\n\\turl = {https://www.tandfonline.com/doi/full/10.1080/20964471.2023.2257905},\\n\\tdoi = {10.1080/20964471.2023.2257905},\\n\\tlanguage = {en},\\n\\turldate = {2023-10-30},\\n\\tjournal = {Big Earth Data},\\n\\tauthor = {Mohseni, Farzane and Ahrari, Amirhossein and Haunert, Jan-Henrik and Montzka, Carsten},\\n\\tmonth = sep,\\n\\tyear = {2023},\\n\\tpages = {1--25},\\n}\\n\\n\",\"author_short\":[\"Mohseni, F.\",\"Ahrari, A.\",\"Haunert, J.\",\"Montzka, C.\"],\"key\":\"mohseni_synergies_2023\",\"id\":\"mohseni_synergies_2023\",\"bibbaseid\":\"mohseni-ahrari-haunert-montzka-thesynergiesofsmapenhancedandmodisproductsinarandomforestregressionforestimating1kmsoilmoistureoverafricausinggoogleearthengine-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.tandfonline.com/doi/full/10.1080/20964471.2023.2257905\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Introducing the Idea of Classifying Sets of Permanent GNSS Stations as Benchmarks for Hydrogeodesy\",\"volume\":\"128\",\"issn\":\"2169-9313, 2169-9356\",\"url\":\"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026988\",\"doi\":\"10.1029/2023JB026988\",\"abstract\":\"Abstract We propose a novel approach to classify sets of Global Navigation Satellite System (GNSS) permanent stations as benchmarks for hydrogeodesy. Benchmarks are trusted sets of GNSS stations whose displacements are classified as significantly and positively correlated with hydrospheric changes and identified in a three temporal‐scales: short‐term, seasonal and long‐term. We use 63 vertical displacement time series processed at the Nevada Geodetic Laboratory for the period 1998–2021 from stations located within Amazon basin and show that estimates of trends and annual signals, including the annual phase maximum, are very coherent with water surface levels provided by altimetry missions. We compute vertical displacements from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gravity missions and predict those also from Global Land Water Storage (GLWS) v2.0 data set which values are produced by assimilation of GRACE into WaterGAP Global Hydrological Model (WGHM). We divide vertical displacements from the three data sets into the pre‐defined temporal‐scales of short‐term, seasonal and long‐term, using non‐parametric wavelet analysis. For each temporal‐scale, correlation coefficients are computed between GNSS‐measured and GRACE‐derived/GLWS‐predicted displacements. We present the benefits of applying high‐resolution GRACE‐assimilating hydrology model to benchmark GNSS stations, which are particularly evident when using spherical harmonic coefficients higher than 120. Their increase causes the number of stations included in the benchmarks to rise by up to 15% for short‐term. Benchmarking allows hydrogeodesy to take advantage of a broader set of GNSS stations that were previously omitted, such as earthquake‐affected sites and those where a possible poroelastic response is observed. , Plain Language Summary Displacements of the Earth's crust measured by permanent Global Navigation Satellite System (GNSS) ground stations are used for many geophysical interpretations. However, it is common to omit the evaluation of the sensitivity of the system to the measurement of displacements from different sources, assuming in advance 100% sensitivity of the system to a given effect. Consequently, the fact that at a given station several effects can be recorded simultaneously is overlooked. This is particularly evident in earthquake‐affected areas, where GNSS stations are excluded from most analyses of non‐tectonic effects. We solve this problem and propose to divide GNSS stations into trusted sets, which we call benchmarks. Benchmarking is performed by indicating the stations that are certain to register a given effect in three pre‐defined temporal‐scales: short‐term, seasonal and long‐term. We present the analysis for the Amazon area, known for its large hydrosphere‐related signal, and demonstrate that the benchmarking allows for the inclusion of GNSS stations that were previously omitted in analyses of this type. , Key Points Displacements measured by Global Positioning System (GPS) correlate well with surface water levels derived from radar altimetry missions Trusted sets of GPS stations are classified at the three pre‐defined temporal‐scales: short‐term, seasonal and long‐term Benchmarking allows to include more GPS stations in hydrogeodetic analyses\",\"language\":\"en\",\"number\":\"9\",\"urldate\":\"2023-10-30\",\"journal\":\"Journal of Geophysical Research: Solid Earth\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Klos\"],\"firstnames\":[\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kusche\"],\"firstnames\":[\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Leszczuk\"],\"firstnames\":[\"G.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gerdener\"],\"firstnames\":[\"H.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schulze\"],\"firstnames\":[\"K.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lenczuk\"],\"firstnames\":[\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bogusz\"],\"firstnames\":[\"J.\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2023\",\"pages\":\"e2023JB026988\",\"bibtex\":\"@article{klos_introducing_2023,\\n\\ttitle = {Introducing the {Idea} of {Classifying} {Sets} of {Permanent} {GNSS} {Stations} as {Benchmarks} for {Hydrogeodesy}},\\n\\tvolume = {128},\\n\\tissn = {2169-9313, 2169-9356},\\n\\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026988},\\n\\tdoi = {10.1029/2023JB026988},\\n\\tabstract = {Abstract\\n            We propose a novel approach to classify sets of Global Navigation Satellite System (GNSS) permanent stations as benchmarks for hydrogeodesy. Benchmarks are trusted sets of GNSS stations whose displacements are classified as significantly and positively correlated with hydrospheric changes and identified in a three temporal‐scales: short‐term, seasonal and long‐term. We use 63 vertical displacement time series processed at the Nevada Geodetic Laboratory for the period 1998–2021 from stations located within Amazon basin and show that estimates of trends and annual signals, including the annual phase maximum, are very coherent with water surface levels provided by altimetry missions. We compute vertical displacements from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gravity missions and predict those also from Global Land Water Storage (GLWS) v2.0 data set which values are produced by assimilation of GRACE into WaterGAP Global Hydrological Model (WGHM). We divide vertical displacements from the three data sets into the pre‐defined temporal‐scales of short‐term, seasonal and long‐term, using non‐parametric wavelet analysis. For each temporal‐scale, correlation coefficients are computed between GNSS‐measured and GRACE‐derived/GLWS‐predicted displacements. We present the benefits of applying high‐resolution GRACE‐assimilating hydrology model to benchmark GNSS stations, which are particularly evident when using spherical harmonic coefficients higher than 120. Their increase causes the number of stations included in the benchmarks to rise by up to 15\\\\% for short‐term. Benchmarking allows hydrogeodesy to take advantage of a broader set of GNSS stations that were previously omitted, such as earthquake‐affected sites and those where a possible poroelastic response is observed.\\n          , \\n            Plain Language Summary\\n            Displacements of the Earth's crust measured by permanent Global Navigation Satellite System (GNSS) ground stations are used for many geophysical interpretations. However, it is common to omit the evaluation of the sensitivity of the system to the measurement of displacements from different sources, assuming in advance 100\\\\% sensitivity of the system to a given effect. Consequently, the fact that at a given station several effects can be recorded simultaneously is overlooked. This is particularly evident in earthquake‐affected areas, where GNSS stations are excluded from most analyses of non‐tectonic effects. We solve this problem and propose to divide GNSS stations into trusted sets, which we call benchmarks. Benchmarking is performed by indicating the stations that are certain to register a given effect in three pre‐defined temporal‐scales: short‐term, seasonal and long‐term. We present the analysis for the Amazon area, known for its large hydrosphere‐related signal, and demonstrate that the benchmarking allows for the inclusion of GNSS stations that were previously omitted in analyses of this type.\\n          , \\n            Key Points\\n            \\n              \\n                \\n                  Displacements measured by Global Positioning System (GPS) correlate well with surface water levels derived from radar altimetry missions\\n                \\n                \\n                  Trusted sets of GPS stations are classified at the three pre‐defined temporal‐scales: short‐term, seasonal and long‐term\\n                \\n                \\n                  Benchmarking allows to include more GPS stations in hydrogeodetic analyses},\\n\\tlanguage = {en},\\n\\tnumber = {9},\\n\\turldate = {2023-10-30},\\n\\tjournal = {Journal of Geophysical Research: Solid Earth},\\n\\tauthor = {Klos, A. and Kusche, J. and Leszczuk, G. and Gerdener, H. and Schulze, K. and Lenczuk, A. and Bogusz, J.},\\n\\tmonth = sep,\\n\\tyear = {2023},\\n\\tpages = {e2023JB026988},\\n}\\n\\n\",\"author_short\":[\"Klos, A.\",\"Kusche, J.\",\"Leszczuk, G.\",\"Gerdener, H.\",\"Schulze, K.\",\"Lenczuk, A.\",\"Bogusz, J.\"],\"key\":\"klos_introducing_2023\",\"id\":\"klos_introducing_2023\",\"bibbaseid\":\"klos-kusche-leszczuk-gerdener-schulze-lenczuk-bogusz-introducingtheideaofclassifyingsetsofpermanentgnssstationsasbenchmarksforhydrogeodesy-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026988\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into a global hydrological model\",\"volume\":\"97\",\"issn\":\"0949-7714, 1432-1394\",\"url\":\"https://link.springer.com/10.1007/s00190-023-01763-9\",\"doi\":\"10.1007/s00190-023-01763-9\",\"abstract\":\"Abstract We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5 $$\\\\textasciicircum\\\\circ $$ ∘ , covering the time frame 2003 to 2019 without gaps, and including monthly uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with a version of the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the average over more than 1000 stations globally, GLWS2.0 correlates better with GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands than GRACE/-FO. While some differences exist, overall GLWS2.0 agrees reasonably well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases. Highlights We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies over the global land with a spatial resolution of 0.5 $$\\\\textasciicircum\\\\circ $$ ∘ , covering the period 2003 to 2019 without gaps, and including uncertainty quantification. GLWS2.0 synthesizes monthly GRACE/-FO mass change maps with daily precipitation and radiation data via the WaterGAP model framework, taking data and model uncertainty into account. Here we describe the methods and data sets that went into GLWS2.0 and its validation from a geodetic applications perspective. We find that, in the global average, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading.\",\"language\":\"en\",\"number\":\"7\",\"urldate\":\"2023-10-30\",\"journal\":\"Journal of Geodesy\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Gerdener\"],\"firstnames\":[\"Helena\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kusche\"],\"firstnames\":[\"Jürgen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schulze\"],\"firstnames\":[\"Kerstin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Döll\"],\"firstnames\":[\"Petra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Klos\"],\"firstnames\":[\"Anna\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2023\",\"pages\":\"73\",\"bibtex\":\"@article{gerdener_global_2023,\\n\\ttitle = {The global land water storage data set release 2 ({GLWS2}.0) derived via assimilating {GRACE} and {GRACE}-{FO} data into a global hydrological model},\\n\\tvolume = {97},\\n\\tissn = {0949-7714, 1432-1394},\\n\\turl = {https://link.springer.com/10.1007/s00190-023-01763-9},\\n\\tdoi = {10.1007/s00190-023-01763-9},\\n\\tabstract = {Abstract\\n            \\n              We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5\\n              \\n                \\n                  \\\\$\\\\${\\\\textasciicircum}{\\\\textbackslash}circ \\\\$\\\\$\\n                  \\n                    \\n                      \\n                      ∘\\n                    \\n                  \\n                \\n              \\n              , covering the time frame 2003 to 2019 without gaps, and including monthly uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with a version of the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the average over more than 1000 stations globally, GLWS2.0 correlates better with GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands than GRACE/-FO. While some differences exist, overall GLWS2.0 agrees reasonably well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases.\\n              Highlights\\n              \\n                \\n                  \\n                    We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies over the global land with a spatial resolution of 0.5\\n                    \\n                      \\n                        \\\\$\\\\${\\\\textasciicircum}{\\\\textbackslash}circ \\\\$\\\\$\\n                        \\n                          \\n                            \\n                            ∘\\n                          \\n                        \\n                      \\n                    \\n                    , covering the period 2003 to 2019 without gaps, and including uncertainty quantification.\\n                  \\n                \\n                \\n                  GLWS2.0 synthesizes monthly GRACE/-FO mass change maps with daily precipitation and radiation data via the WaterGAP model framework, taking data and model uncertainty into account.\\n                \\n                \\n                  Here we describe the methods and data sets that went into GLWS2.0 and its validation from a geodetic applications perspective. We find that, in the global average, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading.},\\n\\tlanguage = {en},\\n\\tnumber = {7},\\n\\turldate = {2023-10-30},\\n\\tjournal = {Journal of Geodesy},\\n\\tauthor = {Gerdener, Helena and Kusche, Jürgen and Schulze, Kerstin and Döll, Petra and Klos, Anna},\\n\\tmonth = jul,\\n\\tyear = {2023},\\n\\tpages = {73},\\n}\\n\\n\",\"author_short\":[\"Gerdener, H.\",\"Kusche, J.\",\"Schulze, K.\",\"Döll, P.\",\"Klos, A.\"],\"key\":\"gerdener_global_2023\",\"id\":\"gerdener_global_2023\",\"bibbaseid\":\"gerdener-kusche-schulze-dll-klos-thegloballandwaterstoragedatasetrelease2glws20derivedviaassimilatinggraceandgracefodataintoaglobalhydrologicalmodel-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://link.springer.com/10.1007/s00190-023-01763-9\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Soil water status shapes nutrient cycling in agroecosystems from micrometer to landscape scales\",\"volume\":\"185\",\"issn\":\"1436-8730, 1522-2624\",\"url\":\"https://onlinelibrary.wiley.com/doi/10.1002/jpln.202200357\",\"doi\":\"10.1002/jpln.202200357\",\"language\":\"en\",\"number\":\"6\",\"urldate\":\"2023-06-02\",\"journal\":\"Journal of Plant Nutrition and Soil Science\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Bauke\"],\"firstnames\":[\"Sara\",\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Amelung\"],\"firstnames\":[\"Wulf\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bol\"],\"firstnames\":[\"Roland\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brandt\"],\"firstnames\":[\"Luise\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brüggemann\"],\"firstnames\":[\"Nicolas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kandeler\"],\"firstnames\":[\"Ellen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Meyer\"],\"firstnames\":[\"Nele\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Or\"],\"firstnames\":[\"Dani\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schnepf\"],\"firstnames\":[\"Andrea\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schloter\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schulz\"],\"firstnames\":[\"Stefanie\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Siebers\"],\"firstnames\":[\"Nina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Von\",\"Sperber\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vereecken\"],\"firstnames\":[\"Harry\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2022\",\"pages\":\"773–792\",\"bibtex\":\"@article{bauke_soil_2022,\\n\\ttitle = {Soil water status shapes nutrient cycling in agroecosystems from micrometer to landscape scales},\\n\\tvolume = {185},\\n\\tissn = {1436-8730, 1522-2624},\\n\\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202200357},\\n\\tdoi = {10.1002/jpln.202200357},\\n\\tlanguage = {en},\\n\\tnumber = {6},\\n\\turldate = {2023-06-02},\\n\\tjournal = {Journal of Plant Nutrition and Soil Science},\\n\\tauthor = {Bauke, Sara L. and Amelung, Wulf and Bol, Roland and Brandt, Luise and Brüggemann, Nicolas and Kandeler, Ellen and Meyer, Nele and Or, Dani and Schnepf, Andrea and Schloter, Michael and Schulz, Stefanie and Siebers, Nina and Von Sperber, Christian and Vereecken, Harry},\\n\\tmonth = dec,\\n\\tyear = {2022},\\n\\tpages = {773--792},\\n}\\n\\n\",\"author_short\":[\"Bauke, S. L.\",\"Amelung, W.\",\"Bol, R.\",\"Brandt, L.\",\"Brüggemann, N.\",\"Kandeler, E.\",\"Meyer, N.\",\"Or, D.\",\"Schnepf, A.\",\"Schloter, M.\",\"Schulz, S.\",\"Siebers, N.\",\"Von Sperber, C.\",\"Vereecken, H.\"],\"key\":\"bauke_soil_2022\",\"id\":\"bauke_soil_2022\",\"bibbaseid\":\"bauke-amelung-bol-brandt-brggemann-kandeler-meyer-or-etal-soilwaterstatusshapesnutrientcyclinginagroecosystemsfrommicrometertolandscapescales-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://onlinelibrary.wiley.com/doi/10.1002/jpln.202200357\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe\",\"volume\":\"16\",\"issn\":\"1991-9603\",\"url\":\"https://gmd.copernicus.org/articles/16/1617/2023/\",\"doi\":\"10.5194/gmd-16-1617-2023\",\"abstract\":\"Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R\\\\textgreater0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.\",\"language\":\"en\",\"number\":\"6\",\"urldate\":\"2023-03-31\",\"journal\":\"Geoscientific Model Development\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Naz\"],\"firstnames\":[\"Bibi\",\"S.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sharples\"],\"firstnames\":[\"Wendy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ma\"],\"firstnames\":[\"Yueling\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Goergen\"],\"firstnames\":[\"Klaus\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kollet\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2023\",\"pages\":\"1617–1639\",\"bibtex\":\"@article{naz_continental-scale_2023,\\n\\ttitle = {Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, {ParFlow}-{CLM} (v3.6.0), over {Europe}},\\n\\tvolume = {16},\\n\\tissn = {1991-9603},\\n\\turl = {https://gmd.copernicus.org/articles/16/1617/2023/},\\n\\tdoi = {10.5194/gmd-16-1617-2023},\\n\\tabstract = {Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R{\\\\textgreater}0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.},\\n\\tlanguage = {en},\\n\\tnumber = {6},\\n\\turldate = {2023-03-31},\\n\\tjournal = {Geoscientific Model Development},\\n\\tauthor = {Naz, Bibi S. and Sharples, Wendy and Ma, Yueling and Goergen, Klaus and Kollet, Stefan},\\n\\tmonth = mar,\\n\\tyear = {2023},\\n\\tpages = {1617--1639},\\n}\\n\\n\",\"author_short\":[\"Naz, B. S.\",\"Sharples, W.\",\"Ma, Y.\",\"Goergen, K.\",\"Kollet, S.\"],\"key\":\"naz_continental-scale_2023\",\"id\":\"naz_continental-scale_2023\",\"bibbaseid\":\"naz-sharples-ma-goergen-kollet-continentalscaleevaluationofafullydistributedcoupledlandsurfaceandgroundwatermodelparflowclmv360overeurope-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://gmd.copernicus.org/articles/16/1617/2023/\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"A review on irrigation parameterizations in Earth system models\",\"volume\":\"4\",\"issn\":\"2624-9375\",\"url\":\"https://www.frontiersin.org/articles/10.3389/frwa.2022.906664/full\",\"doi\":\"10.3389/frwa.2022.906664\",\"abstract\":\"Irrigation is the process of artificially providing water to agricultural lands in order to provide crops with the necessary water supply to ensure or foster the growth of the plants. However, its implications reach beyond the agro-economic aspect as irrigation affects the soil-land-atmosphere interactions and thus influences the water and energy cycles in the Earth system. Past studies have shown how through these interactions, an increase in soil moisture due to irrigation also affects the atmospheric state and its dynamics. Thus, the lack of representation of irrigation in numerical Earth system models—be it for reanalysis, weather forecasting or climate prediction—can lead to significant errors and biases in various parameters of the system including but not limited to surface temperature and precipitation. In this study, we aim to summarize and discuss currently available irrigation parameterizations across different numerical models. This provides a reference framework to understand the impact of irrigation on the various components of Earth system models. Specifically, we discuss the impact of these parameterizations in the context of their spatio-temporal scale representation and point out the benefits and limitations of the various approaches. In fact, most of the parameterizations use irrigation as a direct modification of soil moisture with just a few implementations add irrigation as a form of surface water. While the former method might be suitable for coarse spatio-temporal scales, the latter better resembles the range of employed irrigation techniques. From the analysis, we find that not only the method or the spatio-temporal scales but the actual amount of water used is of great importance to the response of the Earth system model.\",\"urldate\":\"2023-01-30\",\"journal\":\"Frontiers in Water\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Valmassoi\"],\"firstnames\":[\"Arianna\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Keller\"],\"firstnames\":[\"Jan\",\"D.\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2022\",\"pages\":\"906664\",\"bibtex\":\"@article{valmassoi_review_2022,\\n\\ttitle = {A review on irrigation parameterizations in {Earth} system models},\\n\\tvolume = {4},\\n\\tissn = {2624-9375},\\n\\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2022.906664/full},\\n\\tdoi = {10.3389/frwa.2022.906664},\\n\\tabstract = {Irrigation is the process of artificially providing water to agricultural lands in order to provide crops with the necessary water supply to ensure or foster the growth of the plants. However, its implications reach beyond the agro-economic aspect as irrigation affects the soil-land-atmosphere interactions and thus influences the water and energy cycles in the Earth system. Past studies have shown how through these interactions, an increase in soil moisture due to irrigation also affects the atmospheric state and its dynamics. Thus, the lack of representation of irrigation in numerical Earth system models—be it for reanalysis, weather forecasting or climate prediction—can lead to significant errors and biases in various parameters of the system including but not limited to surface temperature and precipitation. In this study, we aim to summarize and discuss currently available irrigation parameterizations across different numerical models. This provides a reference framework to understand the impact of irrigation on the various components of Earth system models. Specifically, we discuss the impact of these parameterizations in the context of their spatio-temporal scale representation and point out the benefits and limitations of the various approaches. In fact, most of the parameterizations use irrigation as a direct modification of soil moisture with just a few implementations add irrigation as a form of surface water. While the former method might be suitable for coarse spatio-temporal scales, the latter better resembles the range of employed irrigation techniques. From the analysis, we find that not only the method or the spatio-temporal scales but the actual amount of water used is of great importance to the response of the Earth system model.},\\n\\turldate = {2023-01-30},\\n\\tjournal = {Frontiers in Water},\\n\\tauthor = {Valmassoi, Arianna and Keller, Jan D.},\\n\\tmonth = nov,\\n\\tyear = {2022},\\n\\tpages = {906664},\\n}\\n\\n\",\"author_short\":[\"Valmassoi, A.\",\"Keller, J. D.\"],\"key\":\"valmassoi_review_2022\",\"id\":\"valmassoi_review_2022\",\"bibbaseid\":\"valmassoi-keller-areviewonirrigationparameterizationsinearthsystemmodels-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.frontiersin.org/articles/10.3389/frwa.2022.906664/full\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"The Role of Space-Based Observations for Groundwater Resource Monitoring over Africa\",\"issn\":\"1573-0956\",\"url\":\"https://doi.org/10.1007/s10712-022-09759-4\",\"doi\":\"10.1007/s10712-022-09759-4\",\"abstract\":\"Africa is particularly vulnerable to climate change impacts, which threatens food security, ecosystem protection and restoration initiatives, and fresh water resources availability and quality. Groundwater largely contributes to the mitigation of climate change effects by offering short- to long-term transient water storage. However, groundwater storage remains extremely difficult to monitor. In this paper, we review the strengths and weaknesses of satellite remote sensing techniques for addressing groundwater quantity issues with a focus on GRACE space gravimetry, as well as concepts to combine satellite observations with numerical models and ground observations. One particular focus is the quantification of changes in groundwater resources in the different climatic regions of Africa and the discussion of possible climatic and anthropogenic drivers. We include a thorough literature review on studies that use satellite observations for groundwater research in Africa. Finally, we identify gaps in research and possible future directions for employing satellite remote sensing to groundwater monitoring and management on the African continent.\",\"journal\":\"Surveys in Geophysics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Springer\"],\"firstnames\":[\"Anne\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lopez\"],\"firstnames\":[\"Teodolina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Owor\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Frappart\"],\"firstnames\":[\"Frédéric\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stieglitz\"],\"firstnames\":[\"Thomas\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2023\",\"bibtex\":\"@article{springer_role_2023,\\n\\ttitle = {The {Role} of {Space}-{Based} {Observations} for {Groundwater} {Resource} {Monitoring} over {Africa}},\\n\\tissn = {1573-0956},\\n\\turl = {https://doi.org/10.1007/s10712-022-09759-4},\\n\\tdoi = {10.1007/s10712-022-09759-4},\\n\\tabstract = {Africa is particularly vulnerable to climate change impacts, which threatens food security, ecosystem protection and restoration initiatives, and fresh water resources availability and quality. Groundwater largely contributes to the mitigation of climate change effects by offering short- to long-term transient water storage. However, groundwater storage remains extremely difficult to monitor. In this paper, we review the strengths and weaknesses of satellite remote sensing techniques for addressing groundwater quantity issues with a focus on GRACE space gravimetry, as well as concepts to combine satellite observations with numerical models and ground observations. One particular focus is the quantification of changes in groundwater resources in the different climatic regions of Africa and the discussion of possible climatic and anthropogenic drivers. We include a thorough literature review on studies that use satellite observations for groundwater research in Africa. Finally, we identify gaps in research and possible future directions for employing satellite remote sensing to groundwater monitoring and management on the African continent.},\\n\\tjournal = {Surveys in Geophysics},\\n\\tauthor = {Springer, Anne and Lopez, Teodolina and Owor, Michael and Frappart, Frédéric and Stieglitz, Thomas},\\n\\tmonth = jan,\\n\\tyear = {2023},\\n}\\n\\n\",\"author_short\":[\"Springer, A.\",\"Lopez, T.\",\"Owor, M.\",\"Frappart, F.\",\"Stieglitz, T.\"],\"key\":\"springer_role_2023\",\"id\":\"springer_role_2023\",\"bibbaseid\":\"springer-lopez-owor-frappart-stieglitz-theroleofspacebasedobservationsforgroundwaterresourcemonitoringoverafrica-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/s10712-022-09759-4\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Raspberry Pi Reflector (RPR): A Low‐cost Water‐level Monitoring System based on GNSS Interferometric Reflectometry\",\"issn\":\"0043-1397, 1944-7973\",\"shorttitle\":\"Raspberry Pi Reflector (RPR)\",\"url\":\"https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR031713\",\"doi\":\"10.1029/2021WR031713\",\"language\":\"en\",\"urldate\":\"2022-11-24\",\"journal\":\"Water Resources Research\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Karegar\"],\"firstnames\":[\"Makan\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kusche\"],\"firstnames\":[\"Jürgen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Geremia‐Nievinski\"],\"firstnames\":[\"Felipe\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Larson\"],\"firstnames\":[\"Kristine\",\"M.\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2022\",\"bibtex\":\"@article{karegar_raspberry_2022,\\n\\ttitle = {Raspberry {Pi} {Reflector} ({RPR}): {A} {Low}‐cost {Water}‐level {Monitoring} {System} based on {GNSS} {Interferometric} {Reflectometry}},\\n\\tissn = {0043-1397, 1944-7973},\\n\\tshorttitle = {Raspberry {Pi} {Reflector} ({RPR})},\\n\\turl = {https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR031713},\\n\\tdoi = {10.1029/2021WR031713},\\n\\tlanguage = {en},\\n\\turldate = {2022-11-24},\\n\\tjournal = {Water Resources Research},\\n\\tauthor = {Karegar, Makan A. and Kusche, Jürgen and Geremia‐Nievinski, Felipe and Larson, Kristine M.},\\n\\tmonth = nov,\\n\\tyear = {2022},\\n}\\n\\n\",\"author_short\":[\"Karegar, M. A.\",\"Kusche, J.\",\"Geremia‐Nievinski, F.\",\"Larson, K. M.\"],\"key\":\"karegar_raspberry_2022\",\"id\":\"karegar_raspberry_2022\",\"bibbaseid\":\"karegar-kusche-geremianievinski-larson-raspberrypireflectorrpralowcostwaterlevelmonitoringsystembasedongnssinterferometricreflectometry-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR031713\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Soil hydrology in the Earth system\",\"volume\":\"3\",\"issn\":\"2662-138X\",\"url\":\"https://www.nature.com/articles/s43017-022-00324-6\",\"doi\":\"10.1038/s43017-022-00324-6\",\"language\":\"en\",\"number\":\"9\",\"urldate\":\"2023-01-26\",\"journal\":\"Nature Reviews Earth & Environment\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Vereecken\"],\"firstnames\":[\"Harry\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Amelung\"],\"firstnames\":[\"Wulf\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bauke\"],\"firstnames\":[\"Sara\",\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bogena\"],\"firstnames\":[\"Heye\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brüggemann\"],\"firstnames\":[\"Nicolas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Montzka\"],\"firstnames\":[\"Carsten\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vanderborght\"],\"firstnames\":[\"Jan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bechtold\"],\"firstnames\":[\"Michel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Blöschl\"],\"firstnames\":[\"Günter\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Carminati\"],\"firstnames\":[\"Andrea\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Javaux\"],\"firstnames\":[\"Mathieu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Konings\"],\"firstnames\":[\"Alexandra\",\"G.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kusche\"],\"firstnames\":[\"Jürgen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Neuweiler\"],\"firstnames\":[\"Insa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Or\"],\"firstnames\":[\"Dani\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Steele-Dunne\"],\"firstnames\":[\"Susan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Verhoef\"],\"firstnames\":[\"Anne\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Young\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"Yonggen\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2022\",\"pages\":\"573–587\",\"bibtex\":\"@article{vereecken_soil_2022,\\n\\ttitle = {Soil hydrology in the {Earth} system},\\n\\tvolume = {3},\\n\\tissn = {2662-138X},\\n\\turl = {https://www.nature.com/articles/s43017-022-00324-6},\\n\\tdoi = {10.1038/s43017-022-00324-6},\\n\\tlanguage = {en},\\n\\tnumber = {9},\\n\\turldate = {2023-01-26},\\n\\tjournal = {Nature Reviews Earth \\\\& Environment},\\n\\tauthor = {Vereecken, Harry and Amelung, Wulf and Bauke, Sara L. and Bogena, Heye and Brüggemann, Nicolas and Montzka, Carsten and Vanderborght, Jan and Bechtold, Michel and Blöschl, Günter and Carminati, Andrea and Javaux, Mathieu and Konings, Alexandra G. and Kusche, Jürgen and Neuweiler, Insa and Or, Dani and Steele-Dunne, Susan and Verhoef, Anne and Young, Michael and Zhang, Yonggen},\\n\\tmonth = aug,\\n\\tyear = {2022},\\n\\tpages = {573--587},\\n}\\n\",\"author_short\":[\"Vereecken, H.\",\"Amelung, W.\",\"Bauke, S. L.\",\"Bogena, H.\",\"Brüggemann, N.\",\"Montzka, C.\",\"Vanderborght, J.\",\"Bechtold, M.\",\"Blöschl, G.\",\"Carminati, A.\",\"Javaux, M.\",\"Konings, A. G.\",\"Kusche, J.\",\"Neuweiler, I.\",\"Or, D.\",\"Steele-Dunne, S.\",\"Verhoef, A.\",\"Young, M.\",\"Zhang, Y.\"],\"key\":\"vereecken_soil_2022\",\"id\":\"vereecken_soil_2022\",\"bibbaseid\":\"vereecken-amelung-bauke-bogena-brggemann-montzka-vanderborght-bechtold-etal-soilhydrologyintheearthsystem-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.nature.com/articles/s43017-022-00324-6\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":7,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\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=\"kasicamper\" href=\"data:text/bibtex;base64,@article{baca_cabrera_root_2024,
	title = {Root hydraulic properties: {An} exploration of their variability across scales},
	volume = {8},
	issn = {2475-4455, 2475-4455},
	shorttitle = {Root hydraulic properties},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/pld3.582},
	doi = {10.1002/pld3.582},
	abstract = {Abstract
            Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment‐, individual root‐, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation.
            
              We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition,
              p
               {\textless} .001) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients,
              p
               {\textless} .001). Furthermore, whole root system conductance increased significantly with root system age across several crop species (
              p
               {\textless} .01), causing very large variation in the data ({\textgreater}2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state‐of‐the‐art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species.
            
            
              These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database (
              https://roothydraulic-properties.shinyapps.io/database/
              ), fostering scientific exchange.},
	language = {en},
	number = {4},
	urldate = {2024-04-25},
	journal = {Plant Direct},
	author = {Baca Cabrera, Juan C. and Vanderborght, Jan and Couvreur, Valentin and Behrend, Dominik and Gaiser, Thomas and Nguyen, Thuy Huu and Lobet, Guillaume},
	month = apr,
	year = {2024},
	pages = {e582},
}



@article{shams_eddin_focal-tsmp_2024,
	title = {Focal-{TSMP}: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation},
	volume = {17},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1991-9603},
	shorttitle = {Focal-{TSMP}},
	url = {https://gmd.copernicus.org/articles/17/2987/2024/},
	doi = {10.5194/gmd-17-2987-2024},
	abstract = {Abstract. Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model’s applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP, last access: 4 April 2024).},
	language = {en},
	number = {7},
	urldate = {2024-04-25},
	journal = {Geoscientific Model Development},
	author = {Shams Eddin, Mohamad Hakam and Gall, Juergen},
	month = apr,
	year = {2024},
	pages = {2987--3023},
}



@article{gu_asymmetric_2024,
	title = {The asymmetric impacts of international agricultural trade on water use scarcity, inequality and inequity},
	issn = {2731-6084},
	url = {https://www.nature.com/articles/s44221-024-00224-7},
	doi = {10.1038/s44221-024-00224-7},
	language = {en},
	urldate = {2024-04-25},
	journal = {Nature Water},
	author = {Gu, Weiyi and Wang, Fang and Siebert, Stefan and Kummu, Matti and Wang, Xuhui and Hong, Chaopeng and Zhou, Feng and Zhu, Qing and Liu, Yong and Qin, Yue},
	month = apr,
	year = {2024},
}



@article{shams_eddin_location-aware_2023,
	title = {Location-{Aware} {Adaptive} {Normalization}: {A} {Deep} {Learning} {Approach} for {Wildfire} {Danger} {Forecasting}},
	volume = {61},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {0196-2892, 1558-0644},
	shorttitle = {Location-{Aware} {Adaptive} {Normalization}},
	url = {https://ieeexplore.ieee.org/document/10149031/},
	doi = {10.1109/TGRS.2023.3285401},
	urldate = {2024-04-25},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Shams Eddin, Mohamad Hakam and Roscher, Ribana and Gall, Juergen},
	year = {2023},
	pages = {1--18},
}



@article{mehta_half_2024,
	title = {Half of twenty-first century global irrigation expansion has been in water-stressed regions},
	issn = {2731-6084},
	url = {https://www.nature.com/articles/s44221-024-00206-9},
	doi = {10.1038/s44221-024-00206-9},
	abstract = {Abstract
            The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress on freshwater resources. Ensuring that increases in irrigated production occur only in places where water is relatively abundant is a key objective of sustainable agriculture and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet, a spatially detailed understanding of the evolution of the global area equipped for irrigation (AEI) is missing. In this study, we used the latest subnational irrigation statistics (covering 17,298 administrative units) from various official sources to develop a gridded (5 arcmin resolution) global product of AEI for the years 2000, 2005, 2010 and 2015. We found that AEI increased by 11\% from 2000 (297 Mha) to 2015 (330 Mha), with areas of both substantial expansion, such as northwest India and northeast China, and decline, such as Russia. Combining these outputs with information on green (that is, rainfall) and blue (that is, surface and ground) water stress, we also examined to what extent irrigation has expanded unsustainably in places already experiencing water stress. We found that more than half (52\%) of the irrigation expansion has taken place in areas that were already water-stressed in the year 2000, with India alone accounting for 36\% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security.},
	language = {en},
	urldate = {2024-03-11},
	journal = {Nature Water},
	author = {Mehta, Piyush and Siebert, Stefan and Kummu, Matti and Deng, Qinyu and Ali, Tariq and Marston, Landon and Xie, Wei and Davis, Kyle Frankel},
	month = mar,
	year = {2024},
}



@article{strebel_evapotranspiration_2024,
	title = {Evapotranspiration prediction for {European} forest sites does not improve with assimilation of in situ soil water content data},
	volume = {28},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/28/1001/2024/},
	doi = {10.5194/hess-28-1001-2024},
	abstract = {Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 \% to 64 \%, the root-mean-square error for the evapotranspiration estimation is increased by 4 \%. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.},
	language = {en},
	number = {4},
	urldate = {2024-03-05},
	journal = {Hydrology and Earth System Sciences},
	author = {Strebel, Lukas and Bogena, Heye and Vereecken, Harry and Andreasen, Mie and Aranda-Barranco, Sergio and Hendricks Franssen, Harrie-Jan},
	month = feb,
	year = {2024},
	pages = {1001--1026},
}



@techreport{patnala_generating_2023,
	type = {preprint},
	title = {Generating {Views} {Using} {Atmospheric} {Correction} for {Contrastive} {Self}-supervised {Learning} of {Multi}-spectral {Images}},
	url = {https://www.techrxiv.org/doi/full/10.36227/techrxiv.21493218.v3},
	abstract = {{\textless}p{\textgreater}In remote sensing, plenty of multi-spectral images are publicly available from various landcover satellite missions. Contrastive self-supervised learning is commonly applied to unlabeled data but relies on domain-specific transformations used for learning. When focusing on vegetation, standard transformations from image processing cannot be applied to the NIR channel, which carries valuable information about the vegetation state. Therefore, we use contrastive learning, relying on different views of unlabelled, multi-spectral images to obtain a pre-trained model to improve the accuracy scores on small-sized remote sensing datasets. This study presents the generation of additional views tailored to remote sensing images using atmospheric correction as an alternative transformation to color jittering. The purpose of the atmospheric transformation is to provide a physically consistent transformation. The proposed transformation can be easily integrated with multiple channels to exploit spectral signatures of objects. Our approach can be applied to other remote sensing tasks. Using this transformation leads to improved classification accuracy of up to 6\%.{\textless}/p{\textgreater}},
	urldate = {2024-03-05},
	author = {Patnala, Ankit and Stadtler, Scarlet and Schultz, Martin G and Gall, Juergen},
	month = nov,
	year = {2023},
	doi = {10.36227/techrxiv.21493218.v3},
}



@article{yang_study_2022,
	title = {On study of the {Earth} topography correction for the {GRACE} surface mass estimation},
	volume = {96},
	issn = {0949-7714, 1432-1394},
	url = {https://link.springer.com/10.1007/s00190-022-01683-0},
	doi = {10.1007/s00190-022-01683-0},
	language = {en},
	number = {12},
	urldate = {2024-03-05},
	journal = {Journal of Geodesy},
	author = {Yang, Fan and Luo, ZhiCai and Zhou, Hao and Kusche, Jürgen},
	month = dec,
	year = {2022},
	pages = {95},
}



@article{qin_snowmelt_2022,
	title = {Snowmelt risk telecouplings for irrigated agriculture},
	volume = {12},
	issn = {1758-678X, 1758-6798},
	url = {https://www.nature.com/articles/s41558-022-01509-z},
	doi = {10.1038/s41558-022-01509-z},
	language = {en},
	number = {11},
	urldate = {2024-03-05},
	journal = {Nature Climate Change},
	author = {Qin, Yue and Hong, Chaopeng and Zhao, Hongyan and Siebert, Stefan and Abatzoglou, John T. and Huning, Laurie S. and Sloat, Lindsey L. and Park, Sohyun and Li, Shiyu and Munroe, Darla K. and Zhu, Tong and Davis, Steven J. and Mueller, Nathaniel D.},
	month = nov,
	year = {2022},
	pages = {1007--1015},
}



@article{boroughani_mapping_2023,
	title = {Mapping land degradation risk due to land susceptibility to dust emission and water erosion},
	volume = {9},
	issn = {2199-398X},
	url = {https://soil.copernicus.org/articles/9/411/2023/},
	doi = {10.5194/soil-9-411-2023},
	abstract = {Abstract. Land degradation is a cause of many social, economic, and environmental
problems. Therefore identification and monitoring of high-risk areas for
land degradation are necessary. Despite the importance of land degradation
due to wind and water erosion in some areas of the world, the combined study
of both types of erosion in the same area receives relatively little
attention. The present study aims to create a land degradation map in terms
of soil erosion caused by wind and water erosion of semi-dry land. We focus
on the Lut watershed in Iran, encompassing the Lut Desert that is influenced
by both monsoon rainfalls and dust storms. Dust sources are identified using
MODIS satellite images with the help of four different indices to quantify
uncertainty. The dust source maps are assessed with three machine learning
algorithms encompassing the artificial neural network (ANN), random forest (RF),
and flexible discriminant analysis (FDA) to map dust sources paired with
soil erosion susceptibility due to water. We assess the accuracy of the maps
from the machine learning results with the area under the curve (AUC)
of the receiver operating characteristic (ROC) metric. The water and aeolian soil
erosion maps are used to identify different classes of land degradation
risks. The results show that 43 \% of the watershed is prone to land
degradation in terms of both aeolian and water erosion. Most regions
(45 \%) have a risk of water erosion and some regions (7 \%) a risk of
aeolian erosion. Only a small fraction (4 \%) of the total area of the
region had a low to very low susceptibility for land degradation. The
results of this study underline the risk of land degradation for in an
inhabited region in Iran. Future work should focus on land degradation
associated with soil erosion from water and storms in larger regions to
evaluate the risks also elsewhere.},
	language = {en},
	number = {2},
	urldate = {2024-02-26},
	journal = {SOIL},
	author = {Boroughani, Mahdi and Mirchooli, Fahimeh and Hadavifar, Mojtaba and Fiedler, Stephanie},
	month = jul,
	year = {2023},
	pages = {411--423},
}



@article{li_can_2024,
	title = {Can a {Sparse} {Network} of {Cosmic} {Ray} {Neutron} {Sensors} {Improve} {Soil} {Moisture} and {Evapotranspiration} {Estimation} at the {Larger} {Catchment} {Scale}?},
	volume = {60},
	issn = {0043-1397, 1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056},
	doi = {10.1029/2023WR035056},
	abstract = {Abstract
            Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60\% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15\% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40\% in 2016 and 16\% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15\% RMSE reduction of monthly ET in the wet year and 9\% in the dry year.
          , 
            Key Points
            
              
                
                  Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale
                
                
                  Soil moisture characterization improved more in a wet year than in a very dry year
                
                
                  Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture},
	language = {en},
	number = {1},
	urldate = {2024-02-26},
	journal = {Water Resources Research},
	author = {Li, Fang and Bogena, Heye Reemt and Bayat, Bagher and Kurtz, Wolfgang and Hendricks Franssen, Harrie‐Jan},
	month = jan,
	year = {2024},
	pages = {e2023WR035056},
}



@article{yu_detailed_2023,
	title = {A detailed analysis of stochastic models applied to temporal gravity field recovery with {GRACE} observations},
	volume = {236},
	issn = {0956-540X, 1365-246X},
	url = {https://academic.oup.com/gji/article/236/1/516/7382201},
	doi = {10.1093/gji/ggad441},
	abstract = {SUMMARY
            In this study, we analysed the impacts of errors in background force models and observed non-gravitational forces on the pseudo-observations (pre-fits) during gravity field recovery based on the Gravity Recovery and Climate Experiment (GRACE) satellite gravity mission. To reduce these effects, we introduced the stochastic parameters into the functional model of the variational equation integration approach to absorb this type of noise contribution. Simultaneously, the prior variances of observed orbits and K-band range rates used in traditional method are re-estimated with least-squares variance component estimation (LS-VCE) after considering these stochastic parameters. To improve the computing efficiency, a modified method of the calculation of sensitivity matrices related to the introduced stochastic parameters is proposed. Compared to the method of variation of constants widely used in the precise orbit determination and gravity field recovery, the modified method decreases the computational time of these matrices by about four times. Furthermore, an efficient LS-VCE algorithm is derived in a more generalized case. The efficient algorithm only costs 1 per cent of the time of the unoptimized method. With the GRACE data, we analysed the benefits of these refinements in gravity field recovery, and the results show that these improvements can mitigate the impacts of errors in background force models and accelerometer data on recovered gravity field models, especially in the high-degree signals. Furthermore, the quality of results has less dependence on parametrization.},
	language = {en},
	number = {1},
	urldate = {2024-02-26},
	journal = {Geophysical Journal International},
	author = {Yu, Biao and You, Wei and Kusche, Jürgen and Fan, Dongming and Su, Yong and Zhang, Jiahui},
	month = nov,
	year = {2023},
	pages = {516--536},
}



@article{vanderborght_combining_2023,
	title = {Combining root and soil hydraulics in macroscopic representations of root water uptake},
	issn = {1539-1663, 1539-1663},
	url = {https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20273},
	doi = {10.1002/vzj2.20273},
	abstract = {Abstract
            Plant water uptake and plant and soil water status are important for the soil water balance and plant growth. They depend on atmospheric water demand and the accessibility of soil water to plant roots, which is in turn related to the hydraulic properties of the root system and the soil around root segments. We present a simulation model that describes water flow in the soil–plant system mechanistically considering both root and soil hydraulic properties. We developed an approach to upscale three‐dimensional (3D) flow in the soil toward root segments of a 3D root architecture to a model that considers one‐dimensional flow between horizontal soil layers and radial flow to root segments in that layer. The upscaled model couples upscaled linear flow equations in the root system with an analytical solution of the nonlinear radial flow equation between the soil and roots. The upscaled model avoids simplifying assumptions about root hydraulic properties and water potential drops near roots made in, respectively, soil‐ and root‐centered models. Xylem water potentials and soil–root interface potentials are explicitly simulated and show, respectively, large variations with depth and large deviations from bulk soil water potentials under dry soil conditions. Accounting for hydraulic gradients in the soil around root segments led to an earlier but slower reduction of transpiration during a drought period and a better plant water status with higher nighttime plant water potentials.
          , 
            Core Ideas
            
              
                
                  Root water uptake depends on root and soil hydraulic properties.
                
                
                  Water uptake at root element scale was upscaled to the root system scale.
                
                
                  The upscaled model can be implemented in one‐dimensional soil water flow models.
                
                
                  Low conductance of dry soil prevents low nighttime plant water potentials.},
	language = {en},
	urldate = {2024-01-29},
	journal = {Vadose Zone Journal},
	author = {Vanderborght, Jan and Leitner, Daniel and Schnepf, Andrea and Couvreur, Valentin and Vereecken, Harry and Javaux, Mathieu},
	month = jul,
	year = {2023},
	pages = {e20273},
}



@incollection{andres_probabilistic_2022,
	address = {Cham},
	title = {Probabilistic {Biomass} {Estimation} with {Conditional} {Generative} {Adversarial} {Networks}},
	volume = {13485},
	isbn = {978-3-031-16787-4 978-3-031-16788-1},
	url = {https://link.springer.com/10.1007/978-3-031-16788-1_29},
	language = {en},
	urldate = {2023-11-27},
	booktitle = {Pattern {Recognition}},
	publisher = {Springer International Publishing},
	author = {Leonhardt, Johannes and Drees, Lukas and Jung, Peter and Roscher, Ribana},
	editor = {Andres, Björn and Bernard, Florian and Cremers, Daniel and Frintrop, Simone and Goldlücke, Bastian and Ihrke, Ivo},
	year = {2022},
	doi = {10.1007/978-3-031-16788-1_29},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {479--494},
}



@incollection{schimmel_organic_2023,
	title = {Organic soils},
	isbn = {978-0-323-95133-3},
	url = {https://linkinghub.elsevier.com/retrieve/pii/B9780128229743000732},
	language = {en},
	urldate = {2023-11-27},
	booktitle = {Encyclopedia of {Soils} in the {Environment}},
	publisher = {Elsevier},
	author = {Schimmel, Heike and Amelung, Wulf},
	year = {2023},
	doi = {10.1016/B978-0-12-822974-3.00073-2},
	pages = {383--397},
}



@article{chen_evaluation_2023,
	title = {Evaluation of {Sentinel}-{3A} altimetry over {Songhua} river {Basin}},
	volume = {618},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423001397},
	doi = {10.1016/j.jhydrol.2023.129197},
	language = {en},
	urldate = {2023-10-31},
	journal = {Journal of Hydrology},
	author = {Chen, Jiaming and Fenoglio, Luciana and Kusche, Jürgen and Liao, Jingjuan and Uyanik, Hakan and Nadzir, Zulfikar Adlan and Lou, Yanhan},
	month = mar,
	year = {2023},
	pages = {129197},
}



@article{guntner_veranderungen_2023,
	title = {Veränderungen der {Wasserspeicherung} in {Deutschland} seit 2002 aus {Beobachtungen} der {Satellitengravimetrie} – {Water} storage changes in {Germany} since 2002 from satellite gravity observations},
	issn = {1439-1783},
	url = {https://doi.bafg.de/HyWa/2023/HyWa_2023.2_1.pdf},
	doi = {10.5675/HYWA_2023.2_1},
	urldate = {2023-10-30},
	journal = {Hydrologie und Wasserbewirtschaftung},
	author = {Güntner, Andreas and Gerdener, Helena and Boergens, Eva and Kusche, Jürgen and Kollet, Stefan and Dobslaw, Henryk and Hartick, Carl and Sharifi, Ehsan and Flechtner, Frank},
	year = {2023},
	note = {Publisher: Bundesanstalt für Gewässerkunde, Koblenz},
}



@article{enders_simplaceversatile_2023,
	title = {{SIMPLACE}—a versatile modelling and simulation framework for sustainable crops and agroecosystems},
	volume = {5},
	issn = {2517-5025},
	url = {https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad006/7176223},
	doi = {10.1093/insilicoplants/diad006},
	abstract = {Abstract
            Agricultural system analysis has considerably evolved over the last years, allowing scientists to quantify complex interactions in crops and agroecosystems. Computer-based models have become a central tool for such analysis, using formulated mathematical representations (algorithms) of different biophysical processes to simulate complex system’s behaviour. Nevertheless, the current large variety of algorithms in combination with nonstandardization in their use limits rapid and rigorous model improvement and testing. This is particularly important because contextualization is a key aspect used to formulate the appropriate model structure for a specific research question, framing a clear demand for ‘next generation’ models being modular and flexible. This paper aims to describe the Scientific Impact assessment and Modelling PLatform for Advanced Crop and Ecosystem management (SIMPLACE), which has been developed over the last decade to address the various aforementioned issues and support appropriate model formulations and interoperability. We describe its main technical implementation and features to develop customized model solutions that can be applied to a number of cropping systems with high flexibility, performance and transparency. A brief review of exemplary applications of SIMPLACE is provided covering the different topics, crops and cropping systems, spatial scales and geographies. We stress that standardized documentation of modules, variables ontology and data archives are key requirements to maintain and assist model development and reproducibility. The increasing demand for more complex, diversified and integrated production systems (e.g. intercropping, livestock-grazing, agroforestry) and the associated impacts on sustainable food systems also require the strong collaboration of a multidisciplinary community of modellers and stakeholders.},
	language = {en},
	number = {1},
	urldate = {2023-10-30},
	journal = {in silico Plants},
	author = {Enders, Andreas and Vianna, Murilo and Gaiser, Thomas and Krauss, Gunther and Webber, Heidi and Srivastava, Amit Kumar and Seidel, Sabine Julia and Tewes, Andreas and Rezaei, Ehsan Eyshi and Ewert, Frank},
	editor = {Marshall-Colon, Amy},
	month = jan,
	year = {2023},
	pages = {diad006},
}



@article{graf_joint_2023,
	title = {Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects},
	volume = {4},
	issn = {2662-4435},
	url = {https://www.nature.com/articles/s43247-023-00958-4},
	doi = {10.1038/s43247-023-00958-4},
	abstract = {Abstract
            Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.},
	language = {en},
	number = {1},
	urldate = {2023-10-30},
	journal = {Communications Earth \& Environment},
	author = {Graf, Alexander and Wohlfahrt, Georg and Aranda-Barranco, Sergio and Arriga, Nicola and Brümmer, Christian and Ceschia, Eric and Ciais, Philippe and Desai, Ankur R. and Di Lonardo, Sara and Gharun, Mana and Grünwald, Thomas and Hörtnagl, Lukas and Kasak, Kuno and Klosterhalfen, Anne and Knohl, Alexander and Kowalska, Natalia and Leuchner, Michael and Lindroth, Anders and Mauder, Matthias and Migliavacca, Mirco and Morel, Alexandra C. and Pfennig, Andreas and Poorter, Hendrik and Terán, Christian Poppe and Reitz, Oliver and Rebmann, Corinna and Sanchez-Azofeifa, Arturo and Schmidt, Marius and Šigut, Ladislav and Tomelleri, Enrico and Yu, Ke and Varlagin, Andrej and Vereecken, Harry},
	month = aug,
	year = {2023},
	pages = {298},
}



@article{li_water_2023,
	title = {Water table depth assimilation in integrated terrestrial system models at the larger catchment scale},
	volume = {5},
	issn = {2624-9375},
	url = {https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full},
	doi = {10.3389/frwa.2023.1150999},
	abstract = {As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81\% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites \&lt;2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30\% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).},
	urldate = {2023-10-30},
	journal = {Frontiers in Water},
	author = {Li, Fang and Kurtz, Wolfgang and Hung, Ching Pui and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},
	month = mar,
	year = {2023},
	pages = {1150999},
}



@article{qu_wildfire_2023,
	title = {Wildfire precursors show complementary predictability in different timescales},
	volume = {14},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-023-42597-5},
	doi = {10.1038/s41467-023-42597-5},
	abstract = {Abstract
            In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3\% and 26.7\% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.},
	language = {en},
	number = {1},
	urldate = {2023-10-30},
	journal = {Nature Communications},
	author = {Qu, Yuquan and Miralles, Diego G. and Veraverbeke, Sander and Vereecken, Harry and Montzka, Carsten},
	month = oct,
	year = {2023},
	pages = {6829},
}



@article{mohseni_synergies_2023,
	title = {The synergies of {SMAP} enhanced and {MODIS} products in a random forest regression for estimating 1 km soil moisture over {Africa} using {Google} {Earth} {Engine}},
	issn = {2096-4471, 2574-5417},
	url = {https://www.tandfonline.com/doi/full/10.1080/20964471.2023.2257905},
	doi = {10.1080/20964471.2023.2257905},
	language = {en},
	urldate = {2023-10-30},
	journal = {Big Earth Data},
	author = {Mohseni, Farzane and Ahrari, Amirhossein and Haunert, Jan-Henrik and Montzka, Carsten},
	month = sep,
	year = {2023},
	pages = {1--25},
}



@article{klos_introducing_2023,
	title = {Introducing the {Idea} of {Classifying} {Sets} of {Permanent} {GNSS} {Stations} as {Benchmarks} for {Hydrogeodesy}},
	volume = {128},
	issn = {2169-9313, 2169-9356},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026988},
	doi = {10.1029/2023JB026988},
	abstract = {Abstract
            We propose a novel approach to classify sets of Global Navigation Satellite System (GNSS) permanent stations as benchmarks for hydrogeodesy. Benchmarks are trusted sets of GNSS stations whose displacements are classified as significantly and positively correlated with hydrospheric changes and identified in a three temporal‐scales: short‐term, seasonal and long‐term. We use 63 vertical displacement time series processed at the Nevada Geodetic Laboratory for the period 1998–2021 from stations located within Amazon basin and show that estimates of trends and annual signals, including the annual phase maximum, are very coherent with water surface levels provided by altimetry missions. We compute vertical displacements from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gravity missions and predict those also from Global Land Water Storage (GLWS) v2.0 data set which values are produced by assimilation of GRACE into WaterGAP Global Hydrological Model (WGHM). We divide vertical displacements from the three data sets into the pre‐defined temporal‐scales of short‐term, seasonal and long‐term, using non‐parametric wavelet analysis. For each temporal‐scale, correlation coefficients are computed between GNSS‐measured and GRACE‐derived/GLWS‐predicted displacements. We present the benefits of applying high‐resolution GRACE‐assimilating hydrology model to benchmark GNSS stations, which are particularly evident when using spherical harmonic coefficients higher than 120. Their increase causes the number of stations included in the benchmarks to rise by up to 15\% for short‐term. Benchmarking allows hydrogeodesy to take advantage of a broader set of GNSS stations that were previously omitted, such as earthquake‐affected sites and those where a possible poroelastic response is observed.
          , 
            Plain Language Summary
            Displacements of the Earth's crust measured by permanent Global Navigation Satellite System (GNSS) ground stations are used for many geophysical interpretations. However, it is common to omit the evaluation of the sensitivity of the system to the measurement of displacements from different sources, assuming in advance 100\% sensitivity of the system to a given effect. Consequently, the fact that at a given station several effects can be recorded simultaneously is overlooked. This is particularly evident in earthquake‐affected areas, where GNSS stations are excluded from most analyses of non‐tectonic effects. We solve this problem and propose to divide GNSS stations into trusted sets, which we call benchmarks. Benchmarking is performed by indicating the stations that are certain to register a given effect in three pre‐defined temporal‐scales: short‐term, seasonal and long‐term. We present the analysis for the Amazon area, known for its large hydrosphere‐related signal, and demonstrate that the benchmarking allows for the inclusion of GNSS stations that were previously omitted in analyses of this type.
          , 
            Key Points
            
              
                
                  Displacements measured by Global Positioning System (GPS) correlate well with surface water levels derived from radar altimetry missions
                
                
                  Trusted sets of GPS stations are classified at the three pre‐defined temporal‐scales: short‐term, seasonal and long‐term
                
                
                  Benchmarking allows to include more GPS stations in hydrogeodetic analyses},
	language = {en},
	number = {9},
	urldate = {2023-10-30},
	journal = {Journal of Geophysical Research: Solid Earth},
	author = {Klos, A. and Kusche, J. and Leszczuk, G. and Gerdener, H. and Schulze, K. and Lenczuk, A. and Bogusz, J.},
	month = sep,
	year = {2023},
	pages = {e2023JB026988},
}



@article{gerdener_global_2023,
	title = {The global land water storage data set release 2 ({GLWS2}.0) derived via assimilating {GRACE} and {GRACE}-{FO} data into a global hydrological model},
	volume = {97},
	issn = {0949-7714, 1432-1394},
	url = {https://link.springer.com/10.1007/s00190-023-01763-9},
	doi = {10.1007/s00190-023-01763-9},
	abstract = {Abstract
            
              We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5
              
                
                  \$\${\textasciicircum}{\textbackslash}circ \$\$
                  
                    
                      
                      ∘
                    
                  
                
              
              , covering the time frame 2003 to 2019 without gaps, and including monthly uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with a version of the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the average over more than 1000 stations globally, GLWS2.0 correlates better with GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands than GRACE/-FO. While some differences exist, overall GLWS2.0 agrees reasonably well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases.
              Highlights
              
                
                  
                    We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies over the global land with a spatial resolution of 0.5
                    
                      
                        \$\${\textasciicircum}{\textbackslash}circ \$\$
                        
                          
                            
                            ∘
                          
                        
                      
                    
                    , covering the period 2003 to 2019 without gaps, and including uncertainty quantification.
                  
                
                
                  GLWS2.0 synthesizes monthly GRACE/-FO mass change maps with daily precipitation and radiation data via the WaterGAP model framework, taking data and model uncertainty into account.
                
                
                  Here we describe the methods and data sets that went into GLWS2.0 and its validation from a geodetic applications perspective. We find that, in the global average, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading.},
	language = {en},
	number = {7},
	urldate = {2023-10-30},
	journal = {Journal of Geodesy},
	author = {Gerdener, Helena and Kusche, Jürgen and Schulze, Kerstin and Döll, Petra and Klos, Anna},
	month = jul,
	year = {2023},
	pages = {73},
}



@article{bauke_soil_2022,
	title = {Soil water status shapes nutrient cycling in agroecosystems from micrometer to landscape scales},
	volume = {185},
	issn = {1436-8730, 1522-2624},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202200357},
	doi = {10.1002/jpln.202200357},
	language = {en},
	number = {6},
	urldate = {2023-06-02},
	journal = {Journal of Plant Nutrition and Soil Science},
	author = {Bauke, Sara L. and Amelung, Wulf and Bol, Roland and Brandt, Luise and Brüggemann, Nicolas and Kandeler, Ellen and Meyer, Nele and Or, Dani and Schnepf, Andrea and Schloter, Michael and Schulz, Stefanie and Siebers, Nina and Von Sperber, Christian and Vereecken, Harry},
	month = dec,
	year = {2022},
	pages = {773--792},
}



@article{naz_continental-scale_2023,
	title = {Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, {ParFlow}-{CLM} (v3.6.0), over {Europe}},
	volume = {16},
	issn = {1991-9603},
	url = {https://gmd.copernicus.org/articles/16/1617/2023/},
	doi = {10.5194/gmd-16-1617-2023},
	abstract = {Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R{\textgreater}0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.},
	language = {en},
	number = {6},
	urldate = {2023-03-31},
	journal = {Geoscientific Model Development},
	author = {Naz, Bibi S. and Sharples, Wendy and Ma, Yueling and Goergen, Klaus and Kollet, Stefan},
	month = mar,
	year = {2023},
	pages = {1617--1639},
}



@article{valmassoi_review_2022,
	title = {A review on irrigation parameterizations in {Earth} system models},
	volume = {4},
	issn = {2624-9375},
	url = {https://www.frontiersin.org/articles/10.3389/frwa.2022.906664/full},
	doi = {10.3389/frwa.2022.906664},
	abstract = {Irrigation is the process of artificially providing water to agricultural lands in order to provide crops with the necessary water supply to ensure or foster the growth of the plants. However, its implications reach beyond the agro-economic aspect as irrigation affects the soil-land-atmosphere interactions and thus influences the water and energy cycles in the Earth system. Past studies have shown how through these interactions, an increase in soil moisture due to irrigation also affects the atmospheric state and its dynamics. Thus, the lack of representation of irrigation in numerical Earth system models—be it for reanalysis, weather forecasting or climate prediction—can lead to significant errors and biases in various parameters of the system including but not limited to surface temperature and precipitation. In this study, we aim to summarize and discuss currently available irrigation parameterizations across different numerical models. This provides a reference framework to understand the impact of irrigation on the various components of Earth system models. Specifically, we discuss the impact of these parameterizations in the context of their spatio-temporal scale representation and point out the benefits and limitations of the various approaches. In fact, most of the parameterizations use irrigation as a direct modification of soil moisture with just a few implementations add irrigation as a form of surface water. While the former method might be suitable for coarse spatio-temporal scales, the latter better resembles the range of employed irrigation techniques. From the analysis, we find that not only the method or the spatio-temporal scales but the actual amount of water used is of great importance to the response of the Earth system model.},
	urldate = {2023-01-30},
	journal = {Frontiers in Water},
	author = {Valmassoi, Arianna and Keller, Jan D.},
	month = nov,
	year = {2022},
	pages = {906664},
}



@article{springer_role_2023,
	title = {The {Role} of {Space}-{Based} {Observations} for {Groundwater} {Resource} {Monitoring} over {Africa}},
	issn = {1573-0956},
	url = {https://doi.org/10.1007/s10712-022-09759-4},
	doi = {10.1007/s10712-022-09759-4},
	abstract = {Africa is particularly vulnerable to climate change impacts, which threatens food security, ecosystem protection and restoration initiatives, and fresh water resources availability and quality. Groundwater largely contributes to the mitigation of climate change effects by offering short- to long-term transient water storage. However, groundwater storage remains extremely difficult to monitor. In this paper, we review the strengths and weaknesses of satellite remote sensing techniques for addressing groundwater quantity issues with a focus on GRACE space gravimetry, as well as concepts to combine satellite observations with numerical models and ground observations. One particular focus is the quantification of changes in groundwater resources in the different climatic regions of Africa and the discussion of possible climatic and anthropogenic drivers. We include a thorough literature review on studies that use satellite observations for groundwater research in Africa. Finally, we identify gaps in research and possible future directions for employing satellite remote sensing to groundwater monitoring and management on the African continent.},
	journal = {Surveys in Geophysics},
	author = {Springer, Anne and Lopez, Teodolina and Owor, Michael and Frappart, Frédéric and Stieglitz, Thomas},
	month = jan,
	year = {2023},
}



@article{karegar_raspberry_2022,
	title = {Raspberry {Pi} {Reflector} ({RPR}): {A} {Low}‐cost {Water}‐level {Monitoring} {System} based on {GNSS} {Interferometric} {Reflectometry}},
	issn = {0043-1397, 1944-7973},
	shorttitle = {Raspberry {Pi} {Reflector} ({RPR})},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR031713},
	doi = {10.1029/2021WR031713},
	language = {en},
	urldate = {2022-11-24},
	journal = {Water Resources Research},
	author = {Karegar, Makan A. and Kusche, Jürgen and Geremia‐Nievinski, Felipe and Larson, Kristine M.},
	month = nov,
	year = {2022},
}



@article{vereecken_soil_2022,
	title = {Soil hydrology in the {Earth} system},
	volume = {3},
	issn = {2662-138X},
	url = {https://www.nature.com/articles/s43017-022-00324-6},
	doi = {10.1038/s43017-022-00324-6},
	language = {en},
	number = {9},
	urldate = {2023-01-26},
	journal = {Nature Reviews Earth \& Environment},
	author = {Vereecken, Harry and Amelung, Wulf and Bauke, Sara L. and Bogena, Heye and Brüggemann, Nicolas and Montzka, Carsten and Vanderborght, Jan and Bechtold, Michel and Blöschl, Günter and Carminati, Andrea and Javaux, Mathieu and Konings, Alexandra G. and Kusche, Jürgen and Neuweiler, Insa and Or, Dani and Steele-Dunne, Susan and Verhoef, Anne and Young, Michael and Zhang, Yonggen},
	month = aug,
	year = {2022},
	pages = {573--587},
}
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C.; Vanderborght, J.; Couvreur, V.; Behrend, D.; Gaiser, T.; Nguyen, T. H.;  and Lobet, G.\n</span>\n\n  \n\n</span>\n\n  <i>Plant Direct</i>, 8(4): e582. 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://onlinelibrary.wiley.com/doi/10.1002/pld3.582\"\n     onclick=\"log_download('bacacabrera-vanderborght-couvreur-behrend-gaiser-nguyen-lobet-roothydraulicpropertiesanexplorationoftheirvariabilityacrossscales-2024', 'https://onlinelibrary.wiley.com/doi/10.1002/pld3.582', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Root hydraulic properties: An exploration of their variability across scales [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/10.1002/pld3.582\"\n     onclick=\"log_download('bacacabrera-vanderborght-couvreur-behrend-gaiser-nguyen-lobet-roothydraulicpropertiesanexplorationoftheirvariabilityacrossscales-2024', 'http://doi.org/10.1002/pld3.582', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bacacabrera-vanderborght-couvreur-behrend-gaiser-nguyen-lobet-roothydraulicpropertiesanexplorationoftheirvariabilityacrossscales-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('bacacabrera-vanderborght-couvreur-behrend-gaiser-nguyen-lobet-roothydraulicpropertiesanexplorationoftheirvariabilityacrossscales-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{baca_cabrera_root_2024,\n\ttitle = {Root hydraulic properties: {An} exploration of their variability across scales},\n\tvolume = {8},\n\tissn = {2475-4455, 2475-4455},\n\tshorttitle = {Root hydraulic properties},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/pld3.582},\n\tdoi = {10.1002/pld3.582},\n\tabstract = {Abstract\n            Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment‐, individual root‐, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation.\n            \n              We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition,\n              p\n               {\\textless} .001) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients,\n              p\n               {\\textless} .001). Furthermore, whole root system conductance increased significantly with root system age across several crop species (\n              p\n               {\\textless} .01), causing very large variation in the data ({\\textgreater}2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state‐of‐the‐art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species.\n            \n            \n              These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database (\n              https://roothydraulic-properties.shinyapps.io/database/\n              ), fostering scientific exchange.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2024-04-25},\n\tjournal = {Plant Direct},\n\tauthor = {Baca Cabrera, Juan C. and Vanderborght, Jan and Couvreur, Valentin and Behrend, Dominik and Gaiser, Thomas and Nguyen, Thuy Huu and Lobet, Guillaume},\n\tmonth = apr,\n\tyear = {2024},\n\tpages = {e582},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment‐, individual root‐, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation. We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition, p  \\textless .001) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients, p  \\textless .001). Furthermore, whole root system conductance increased significantly with root system age across several crop species ( p  \\textless .01), causing very large variation in the data (\\textgreater2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state‐of‐the‐art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species. These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database ( https://roothydraulic-properties.shinyapps.io/database/ ), fostering scientific exchange.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"shamseddin-gall-focaltsmpdeeplearningforvegetationhealthpredictionandagriculturaldroughtassessmentfromaregionalclimatesimulation-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://gmd.copernicus.org/articles/17/2987/2024/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shamseddin-gall-focaltsmpdeeplearningforvegetationhealthpredictionandagriculturaldroughtassessmentfromaregionalclimatesimulation-2024', 'https://gmd.copernicus.org/articles/17/2987/2024/', event);\">\n    Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Shams Eddin, M. H.;  and Gall, J.\n</span>\n\n  \n\n</span>\n\n  <i>Geoscientific Model Development</i>, 17(7): 2987–3023. 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://gmd.copernicus.org/articles/17/2987/2024/\"\n     onclick=\"log_download('shamseddin-gall-focaltsmpdeeplearningforvegetationhealthpredictionandagriculturaldroughtassessmentfromaregionalclimatesimulation-2024', 'https://gmd.copernicus.org/articles/17/2987/2024/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation [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/gmd-17-2987-2024\"\n     onclick=\"log_download('shamseddin-gall-focaltsmpdeeplearningforvegetationhealthpredictionandagriculturaldroughtassessmentfromaregionalclimatesimulation-2024', 'http://doi.org/10.5194/gmd-17-2987-2024', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shamseddin-gall-focaltsmpdeeplearningforvegetationhealthpredictionandagriculturaldroughtassessmentfromaregionalclimatesimulation-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('shamseddin-gall-focaltsmpdeeplearningforvegetationhealthpredictionandagriculturaldroughtassessmentfromaregionalclimatesimulation-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{shams_eddin_focal-tsmp_2024,\n\ttitle = {Focal-{TSMP}: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation},\n\tvolume = {17},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1991-9603},\n\tshorttitle = {Focal-{TSMP}},\n\turl = {https://gmd.copernicus.org/articles/17/2987/2024/},\n\tdoi = {10.5194/gmd-17-2987-2024},\n\tabstract = {Abstract. Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model’s applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP, last access: 4 April 2024).},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2024-04-25},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Shams Eddin, Mohamad Hakam and Gall, Juergen},\n\tmonth = apr,\n\tyear = {2024},\n\tpages = {2987--3023},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract. Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model’s applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP, last access: 4 April 2024).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"gu-wang-siebert-kummu-wang-hong-zhou-zhu-etal-theasymmetricimpactsofinternationalagriculturaltradeonwaterusescarcityinequalityandinequity-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/s44221-024-00224-7\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gu-wang-siebert-kummu-wang-hong-zhou-zhu-etal-theasymmetricimpactsofinternationalagriculturaltradeonwaterusescarcityinequalityandinequity-2024', 'https://www.nature.com/articles/s44221-024-00224-7', event);\">\n    The asymmetric impacts of international agricultural trade on water use scarcity, inequality and inequity.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gu, W.; Wang, F.; Siebert, S.; Kummu, M.; Wang, X.; Hong, C.; Zhou, F.; Zhu, Q.; Liu, Y.;  and Qin, Y.\n</span>\n\n  \n\n</span>\n\n  <i>Nature Water</i>. 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/s44221-024-00224-7\"\n     onclick=\"log_download('gu-wang-siebert-kummu-wang-hong-zhou-zhu-etal-theasymmetricimpactsofinternationalagriculturaltradeonwaterusescarcityinequalityandinequity-2024', 'https://www.nature.com/articles/s44221-024-00224-7', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"The asymmetric impacts of international agricultural trade on water use scarcity, inequality and inequity [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/s44221-024-00224-7\"\n     onclick=\"log_download('gu-wang-siebert-kummu-wang-hong-zhou-zhu-etal-theasymmetricimpactsofinternationalagriculturaltradeonwaterusescarcityinequalityandinequity-2024', 'http://doi.org/10.1038/s44221-024-00224-7', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gu-wang-siebert-kummu-wang-hong-zhou-zhu-etal-theasymmetricimpactsofinternationalagriculturaltradeonwaterusescarcityinequalityandinequity-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gu-wang-siebert-kummu-wang-hong-zhou-zhu-etal-theasymmetricimpactsofinternationalagriculturaltradeonwaterusescarcityinequalityandinequity-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{gu_asymmetric_2024,\n\ttitle = {The asymmetric impacts of international agricultural trade on water use scarcity, inequality and inequity},\n\tissn = {2731-6084},\n\turl = {https://www.nature.com/articles/s44221-024-00224-7},\n\tdoi = {10.1038/s44221-024-00224-7},\n\tlanguage = {en},\n\turldate = {2024-04-25},\n\tjournal = {Nature Water},\n\tauthor = {Gu, Weiyi and Wang, Fang and Siebert, Stefan and Kummu, Matti and Wang, Xuhui and Hong, Chaopeng and Zhou, Feng and Zhu, Qing and Liu, Yong and Qin, Yue},\n\tmonth = apr,\n\tyear = {2024},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mehta-siebert-kummu-deng-ali-marston-xie-davis-halfoftwentyfirstcenturyglobalirrigationexpansionhasbeeninwaterstressedregions-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/s44221-024-00206-9\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mehta-siebert-kummu-deng-ali-marston-xie-davis-halfoftwentyfirstcenturyglobalirrigationexpansionhasbeeninwaterstressedregions-2024', 'https://www.nature.com/articles/s44221-024-00206-9', event);\">\n    Half of twenty-first century global irrigation expansion has been in water-stressed regions.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Mehta, P.; Siebert, S.; Kummu, M.; Deng, Q.; Ali, T.; Marston, L.; Xie, W.;  and Davis, K. F.\n</span>\n\n  \n\n</span>\n\n  <i>Nature Water</i>. March 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/s44221-024-00206-9\"\n     onclick=\"log_download('mehta-siebert-kummu-deng-ali-marston-xie-davis-halfoftwentyfirstcenturyglobalirrigationexpansionhasbeeninwaterstressedregions-2024', 'https://www.nature.com/articles/s44221-024-00206-9', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Half of twenty-first century global irrigation expansion has been in water-stressed regions [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/s44221-024-00206-9\"\n     onclick=\"log_download('mehta-siebert-kummu-deng-ali-marston-xie-davis-halfoftwentyfirstcenturyglobalirrigationexpansionhasbeeninwaterstressedregions-2024', 'http://doi.org/10.1038/s44221-024-00206-9', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mehta-siebert-kummu-deng-ali-marston-xie-davis-halfoftwentyfirstcenturyglobalirrigationexpansionhasbeeninwaterstressedregions-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('mehta-siebert-kummu-deng-ali-marston-xie-davis-halfoftwentyfirstcenturyglobalirrigationexpansionhasbeeninwaterstressedregions-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{mehta_half_2024,\n\ttitle = {Half of twenty-first century global irrigation expansion has been in water-stressed regions},\n\tissn = {2731-6084},\n\turl = {https://www.nature.com/articles/s44221-024-00206-9},\n\tdoi = {10.1038/s44221-024-00206-9},\n\tabstract = {Abstract\n            The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress on freshwater resources. Ensuring that increases in irrigated production occur only in places where water is relatively abundant is a key objective of sustainable agriculture and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet, a spatially detailed understanding of the evolution of the global area equipped for irrigation (AEI) is missing. In this study, we used the latest subnational irrigation statistics (covering 17,298 administrative units) from various official sources to develop a gridded (5 arcmin resolution) global product of AEI for the years 2000, 2005, 2010 and 2015. We found that AEI increased by 11\\% from 2000 (297 Mha) to 2015 (330 Mha), with areas of both substantial expansion, such as northwest India and northeast China, and decline, such as Russia. Combining these outputs with information on green (that is, rainfall) and blue (that is, surface and ground) water stress, we also examined to what extent irrigation has expanded unsustainably in places already experiencing water stress. We found that more than half (52\\%) of the irrigation expansion has taken place in areas that were already water-stressed in the year 2000, with India alone accounting for 36\\% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security.},\n\tlanguage = {en},\n\turldate = {2024-03-11},\n\tjournal = {Nature Water},\n\tauthor = {Mehta, Piyush and Siebert, Stefan and Kummu, Matti and Deng, Qinyu and Ali, Tariq and Marston, Landon and Xie, Wei and Davis, Kyle Frankel},\n\tmonth = mar,\n\tyear = {2024},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress on freshwater resources. Ensuring that increases in irrigated production occur only in places where water is relatively abundant is a key objective of sustainable agriculture and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet, a spatially detailed understanding of the evolution of the global area equipped for irrigation (AEI) is missing. In this study, we used the latest subnational irrigation statistics (covering 17,298 administrative units) from various official sources to develop a gridded (5 arcmin resolution) global product of AEI for the years 2000, 2005, 2010 and 2015. We found that AEI increased by 11% from 2000 (297 Mha) to 2015 (330 Mha), with areas of both substantial expansion, such as northwest India and northeast China, and decline, such as Russia. Combining these outputs with information on green (that is, rainfall) and blue (that is, surface and ground) water stress, we also examined to what extent irrigation has expanded unsustainably in places already experiencing water stress. We found that more than half (52%) of the irrigation expansion has taken place in areas that were already water-stressed in the year 2000, with India alone accounting for 36% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"strebel-bogena-vereecken-andreasen-arandabarranco-hendricksfranssen-evapotranspirationpredictionforeuropeanforestsitesdoesnotimprovewithassimilationofinsitusoilwatercontentdata-2024\">\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/28/1001/2024/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'strebel-bogena-vereecken-andreasen-arandabarranco-hendricksfranssen-evapotranspirationpredictionforeuropeanforestsitesdoesnotimprovewithassimilationofinsitusoilwatercontentdata-2024', 'https://hess.copernicus.org/articles/28/1001/2024/', event);\">\n    Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Strebel, L.; Bogena, H.; Vereecken, H.; Andreasen, M.; Aranda-Barranco, S.;  and Hendricks Franssen, H.\n</span>\n\n  \n\n</span>\n\n  <i>Hydrology and Earth System Sciences</i>, 28(4): 1001–1026. 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://hess.copernicus.org/articles/28/1001/2024/\"\n     onclick=\"log_download('strebel-bogena-vereecken-andreasen-arandabarranco-hendricksfranssen-evapotranspirationpredictionforeuropeanforestsitesdoesnotimprovewithassimilationofinsitusoilwatercontentdata-2024', 'https://hess.copernicus.org/articles/28/1001/2024/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content 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/10.5194/hess-28-1001-2024\"\n     onclick=\"log_download('strebel-bogena-vereecken-andreasen-arandabarranco-hendricksfranssen-evapotranspirationpredictionforeuropeanforestsitesdoesnotimprovewithassimilationofinsitusoilwatercontentdata-2024', 'http://doi.org/10.5194/hess-28-1001-2024', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/strebel-bogena-vereecken-andreasen-arandabarranco-hendricksfranssen-evapotranspirationpredictionforeuropeanforestsitesdoesnotimprovewithassimilationofinsitusoilwatercontentdata-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('strebel-bogena-vereecken-andreasen-arandabarranco-hendricksfranssen-evapotranspirationpredictionforeuropeanforestsitesdoesnotimprovewithassimilationofinsitusoilwatercontentdata-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{strebel_evapotranspiration_2024,\n\ttitle = {Evapotranspiration prediction for {European} forest sites does not improve with assimilation of in situ soil water content data},\n\tvolume = {28},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/28/1001/2024/},\n\tdoi = {10.5194/hess-28-1001-2024},\n\tabstract = {Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 \\% to 64 \\%, the root-mean-square error for the evapotranspiration estimation is increased by 4 \\%. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2024-03-05},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Strebel, Lukas and Bogena, Heye and Vereecken, Harry and Andreasen, Mie and Aranda-Barranco, Sergio and Hendricks Franssen, Harrie-Jan},\n\tmonth = feb,\n\tyear = {2024},\n\tpages = {1001--1026},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"li-bogena-bayat-kurtz-hendricksfranssen-canasparsenetworkofcosmicrayneutronsensorsimprovesoilmoistureandevapotranspirationestimationatthelargercatchmentscale-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'li-bogena-bayat-kurtz-hendricksfranssen-canasparsenetworkofcosmicrayneutronsensorsimprovesoilmoistureandevapotranspirationestimationatthelargercatchmentscale-2024', 'https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056', event);\">\n    Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale?.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Li, F.; Bogena, H. R.; Bayat, B.; Kurtz, W.;  and Hendricks Franssen, H.\n</span>\n\n  \n\n</span>\n\n  <i>Water Resources Research</i>, 60(1): e2023WR035056. January 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://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056\"\n     onclick=\"log_download('li-bogena-bayat-kurtz-hendricksfranssen-canasparsenetworkofcosmicrayneutronsensorsimprovesoilmoistureandevapotranspirationestimationatthelargercatchmentscale-2024', 'https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale? [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/10.1029/2023WR035056\"\n     onclick=\"log_download('li-bogena-bayat-kurtz-hendricksfranssen-canasparsenetworkofcosmicrayneutronsensorsimprovesoilmoistureandevapotranspirationestimationatthelargercatchmentscale-2024', 'http://doi.org/10.1029/2023WR035056', 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-bogena-bayat-kurtz-hendricksfranssen-canasparsenetworkofcosmicrayneutronsensorsimprovesoilmoistureandevapotranspirationestimationatthelargercatchmentscale-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('li-bogena-bayat-kurtz-hendricksfranssen-canasparsenetworkofcosmicrayneutronsensorsimprovesoilmoistureandevapotranspirationestimationatthelargercatchmentscale-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{li_can_2024,\n\ttitle = {Can a {Sparse} {Network} of {Cosmic} {Ray} {Neutron} {Sensors} {Improve} {Soil} {Moisture} and {Evapotranspiration} {Estimation} at the {Larger} {Catchment} {Scale}?},\n\tvolume = {60},\n\tissn = {0043-1397, 1944-7973},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056},\n\tdoi = {10.1029/2023WR035056},\n\tabstract = {Abstract\n            Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60\\% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15\\% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40\\% in 2016 and 16\\% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15\\% RMSE reduction of monthly ET in the wet year and 9\\% in the dry year.\n          , \n            Key Points\n            \n              \n                \n                  Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale\n                \n                \n                  Soil moisture characterization improved more in a wet year than in a very dry year\n                \n                \n                  Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-02-26},\n\tjournal = {Water Resources Research},\n\tauthor = {Li, Fang and Bogena, Heye Reemt and Bayat, Bagher and Kurtz, Wolfgang and Hendricks Franssen, Harrie‐Jan},\n\tmonth = jan,\n\tyear = {2024},\n\tpages = {e2023WR035056},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40% in 2016 and 16% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15% RMSE reduction of monthly ET in the wet year and 9% in the dry year. , Key Points Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale Soil moisture characterization improved more in a wet year than in a very dry year Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture\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        (17)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"shamseddin-roscher-gall-locationawareadaptivenormalizationadeeplearningapproachforwildfiredangerforecasting-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/10149031/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shamseddin-roscher-gall-locationawareadaptivenormalizationadeeplearningapproachforwildfiredangerforecasting-2023', 'https://ieeexplore.ieee.org/document/10149031/', event);\">\n    Location-Aware Adaptive Normalization: A Deep Learning Approach for Wildfire Danger Forecasting.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Shams Eddin, M. H.; Roscher, R.;  and Gall, J.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Geoscience and Remote Sensing</i>, 61: 1–18.  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://ieeexplore.ieee.org/document/10149031/\"\n     onclick=\"log_download('shamseddin-roscher-gall-locationawareadaptivenormalizationadeeplearningapproachforwildfiredangerforecasting-2023', 'https://ieeexplore.ieee.org/document/10149031/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Location-Aware Adaptive Normalization: A Deep Learning Approach for Wildfire Danger 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/10.1109/TGRS.2023.3285401\"\n     onclick=\"log_download('shamseddin-roscher-gall-locationawareadaptivenormalizationadeeplearningapproachforwildfiredangerforecasting-2023', 'http://doi.org/10.1109/TGRS.2023.3285401', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shamseddin-roscher-gall-locationawareadaptivenormalizationadeeplearningapproachforwildfiredangerforecasting-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shamseddin-roscher-gall-locationawareadaptivenormalizationadeeplearningapproachforwildfiredangerforecasting-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{shams_eddin_location-aware_2023,\n\ttitle = {Location-{Aware} {Adaptive} {Normalization}: {A} {Deep} {Learning} {Approach} for {Wildfire} {Danger} {Forecasting}},\n\tvolume = {61},\n\tcopyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},\n\tissn = {0196-2892, 1558-0644},\n\tshorttitle = {Location-{Aware} {Adaptive} {Normalization}},\n\turl = {https://ieeexplore.ieee.org/document/10149031/},\n\tdoi = {10.1109/TGRS.2023.3285401},\n\turldate = {2024-04-25},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing},\n\tauthor = {Shams Eddin, Mohamad Hakam and Roscher, Ribana and Gall, Juergen},\n\tyear = {2023},\n\tpages = {1--18},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"patnala-stadtler-schultz-gall-generatingviewsusingatmosphericcorrectionforcontrastiveselfsupervisedlearningofmultispectralimages-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.techrxiv.org/doi/full/10.36227/techrxiv.21493218.v3\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'patnala-stadtler-schultz-gall-generatingviewsusingatmosphericcorrectionforcontrastiveselfsupervisedlearningofmultispectralimages-2023', 'https://www.techrxiv.org/doi/full/10.36227/techrxiv.21493218.v3', event);\">\n    Generating Views Using Atmospheric Correction for Contrastive Self-supervised Learning of Multi-spectral Images.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Patnala, A.; Stadtler, S.; Schultz, M. G;  and Gall, J.\n</span>\n\n  \n\n</span>\n\n  Technical Report November 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.techrxiv.org/doi/full/10.36227/techrxiv.21493218.v3\"\n     onclick=\"log_download('patnala-stadtler-schultz-gall-generatingviewsusingatmosphericcorrectionforcontrastiveselfsupervisedlearningofmultispectralimages-2023', 'https://www.techrxiv.org/doi/full/10.36227/techrxiv.21493218.v3', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Generating Views Using Atmospheric Correction for Contrastive Self-supervised Learning of Multi-spectral Images [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/10.36227/techrxiv.21493218.v3\"\n     onclick=\"log_download('patnala-stadtler-schultz-gall-generatingviewsusingatmosphericcorrectionforcontrastiveselfsupervisedlearningofmultispectralimages-2023', 'http://doi.org/10.36227/techrxiv.21493218.v3', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/patnala-stadtler-schultz-gall-generatingviewsusingatmosphericcorrectionforcontrastiveselfsupervisedlearningofmultispectralimages-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('patnala-stadtler-schultz-gall-generatingviewsusingatmosphericcorrectionforcontrastiveselfsupervisedlearningofmultispectralimages-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@techreport{patnala_generating_2023,\n\ttype = {preprint},\n\ttitle = {Generating {Views} {Using} {Atmospheric} {Correction} for {Contrastive} {Self}-supervised {Learning} of {Multi}-spectral {Images}},\n\turl = {https://www.techrxiv.org/doi/full/10.36227/techrxiv.21493218.v3},\n\tabstract = {{\\textless}p{\\textgreater}In remote sensing, plenty of multi-spectral images are publicly available from various landcover satellite missions. Contrastive self-supervised learning is commonly applied to unlabeled data but relies on domain-specific transformations used for learning. When focusing on vegetation, standard transformations from image processing cannot be applied to the NIR channel, which carries valuable information about the vegetation state. Therefore, we use contrastive learning, relying on different views of unlabelled, multi-spectral images to obtain a pre-trained model to improve the accuracy scores on small-sized remote sensing datasets. This study presents the generation of additional views tailored to remote sensing images using atmospheric correction as an alternative transformation to color jittering. The purpose of the atmospheric transformation is to provide a physically consistent transformation. The proposed transformation can be easily integrated with multiple channels to exploit spectral signatures of objects. Our approach can be applied to other remote sensing tasks. Using this transformation leads to improved classification accuracy of up to 6\\%.{\\textless}/p{\\textgreater}},\n\turldate = {2024-03-05},\n\tauthor = {Patnala, Ankit and Stadtler, Scarlet and Schultz, Martin G and Gall, Juergen},\n\tmonth = nov,\n\tyear = {2023},\n\tdoi = {10.36227/techrxiv.21493218.v3},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  \\textlessp\\textgreaterIn remote sensing, plenty of multi-spectral images are publicly available from various landcover satellite missions. Contrastive self-supervised learning is commonly applied to unlabeled data but relies on domain-specific transformations used for learning. When focusing on vegetation, standard transformations from image processing cannot be applied to the NIR channel, which carries valuable information about the vegetation state. Therefore, we use contrastive learning, relying on different views of unlabelled, multi-spectral images to obtain a pre-trained model to improve the accuracy scores on small-sized remote sensing datasets. This study presents the generation of additional views tailored to remote sensing images using atmospheric correction as an alternative transformation to color jittering. The purpose of the atmospheric transformation is to provide a physically consistent transformation. The proposed transformation can be easily integrated with multiple channels to exploit spectral signatures of objects. Our approach can be applied to other remote sensing tasks. Using this transformation leads to improved classification accuracy of up to 6%.\\textless/p\\textgreater\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"boroughani-mirchooli-hadavifar-fiedler-mappinglanddegradationriskduetolandsusceptibilitytodustemissionandwatererosion-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://soil.copernicus.org/articles/9/411/2023/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'boroughani-mirchooli-hadavifar-fiedler-mappinglanddegradationriskduetolandsusceptibilitytodustemissionandwatererosion-2023', 'https://soil.copernicus.org/articles/9/411/2023/', event);\">\n    Mapping land degradation risk due to land susceptibility to dust emission and water erosion.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Boroughani, M.; Mirchooli, F.; Hadavifar, M.;  and Fiedler, S.\n</span>\n\n  \n\n</span>\n\n  <i>SOIL</i>, 9(2): 411–423. July 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://soil.copernicus.org/articles/9/411/2023/\"\n     onclick=\"log_download('boroughani-mirchooli-hadavifar-fiedler-mappinglanddegradationriskduetolandsusceptibilitytodustemissionandwatererosion-2023', 'https://soil.copernicus.org/articles/9/411/2023/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Mapping land degradation risk due to land susceptibility to dust emission and water erosion [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/soil-9-411-2023\"\n     onclick=\"log_download('boroughani-mirchooli-hadavifar-fiedler-mappinglanddegradationriskduetolandsusceptibilitytodustemissionandwatererosion-2023', 'http://doi.org/10.5194/soil-9-411-2023', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/boroughani-mirchooli-hadavifar-fiedler-mappinglanddegradationriskduetolandsusceptibilitytodustemissionandwatererosion-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('boroughani-mirchooli-hadavifar-fiedler-mappinglanddegradationriskduetolandsusceptibilitytodustemissionandwatererosion-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{boroughani_mapping_2023,\n\ttitle = {Mapping land degradation risk due to land susceptibility to dust emission and water erosion},\n\tvolume = {9},\n\tissn = {2199-398X},\n\turl = {https://soil.copernicus.org/articles/9/411/2023/},\n\tdoi = {10.5194/soil-9-411-2023},\n\tabstract = {Abstract. Land degradation is a cause of many social, economic, and environmental\nproblems. Therefore identification and monitoring of high-risk areas for\nland degradation are necessary. Despite the importance of land degradation\ndue to wind and water erosion in some areas of the world, the combined study\nof both types of erosion in the same area receives relatively little\nattention. The present study aims to create a land degradation map in terms\nof soil erosion caused by wind and water erosion of semi-dry land. We focus\non the Lut watershed in Iran, encompassing the Lut Desert that is influenced\nby both monsoon rainfalls and dust storms. Dust sources are identified using\nMODIS satellite images with the help of four different indices to quantify\nuncertainty. The dust source maps are assessed with three machine learning\nalgorithms encompassing the artificial neural network (ANN), random forest (RF),\nand flexible discriminant analysis (FDA) to map dust sources paired with\nsoil erosion susceptibility due to water. We assess the accuracy of the maps\nfrom the machine learning results with the area under the curve (AUC)\nof the receiver operating characteristic (ROC) metric. The water and aeolian soil\nerosion maps are used to identify different classes of land degradation\nrisks. The results show that 43 \\% of the watershed is prone to land\ndegradation in terms of both aeolian and water erosion. Most regions\n(45 \\%) have a risk of water erosion and some regions (7 \\%) a risk of\naeolian erosion. Only a small fraction (4 \\%) of the total area of the\nregion had a low to very low susceptibility for land degradation. The\nresults of this study underline the risk of land degradation for in an\ninhabited region in Iran. Future work should focus on land degradation\nassociated with soil erosion from water and storms in larger regions to\nevaluate the risks also elsewhere.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-02-26},\n\tjournal = {SOIL},\n\tauthor = {Boroughani, Mahdi and Mirchooli, Fahimeh and Hadavifar, Mojtaba and Fiedler, Stephanie},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {411--423},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract. Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation due to wind and water erosion in some areas of the world, the combined study of both types of erosion in the same area receives relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran, encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing the artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the area under the curve (AUC) of the receiver operating characteristic (ROC) metric. The water and aeolian soil erosion maps are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for in an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"yu-you-kusche-fan-su-zhang-adetailedanalysisofstochasticmodelsappliedtotemporalgravityfieldrecoverywithgraceobservations-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://academic.oup.com/gji/article/236/1/516/7382201\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'yu-you-kusche-fan-su-zhang-adetailedanalysisofstochasticmodelsappliedtotemporalgravityfieldrecoverywithgraceobservations-2023', 'https://academic.oup.com/gji/article/236/1/516/7382201', event);\">\n    A detailed analysis of stochastic models applied to temporal gravity field recovery with GRACE observations.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yu, B.; You, W.; Kusche, J.; Fan, D.; Su, Y.;  and Zhang, J.\n</span>\n\n  \n\n</span>\n\n  <i>Geophysical Journal International</i>, 236(1): 516–536. November 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://academic.oup.com/gji/article/236/1/516/7382201\"\n     onclick=\"log_download('yu-you-kusche-fan-su-zhang-adetailedanalysisofstochasticmodelsappliedtotemporalgravityfieldrecoverywithgraceobservations-2023', 'https://academic.oup.com/gji/article/236/1/516/7382201', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A detailed analysis of stochastic models applied to temporal gravity field recovery with GRACE observations [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/10.1093/gji/ggad441\"\n     onclick=\"log_download('yu-you-kusche-fan-su-zhang-adetailedanalysisofstochasticmodelsappliedtotemporalgravityfieldrecoverywithgraceobservations-2023', 'http://doi.org/10.1093/gji/ggad441', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yu-you-kusche-fan-su-zhang-adetailedanalysisofstochasticmodelsappliedtotemporalgravityfieldrecoverywithgraceobservations-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('yu-you-kusche-fan-su-zhang-adetailedanalysisofstochasticmodelsappliedtotemporalgravityfieldrecoverywithgraceobservations-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{yu_detailed_2023,\n\ttitle = {A detailed analysis of stochastic models applied to temporal gravity field recovery with {GRACE} observations},\n\tvolume = {236},\n\tissn = {0956-540X, 1365-246X},\n\turl = {https://academic.oup.com/gji/article/236/1/516/7382201},\n\tdoi = {10.1093/gji/ggad441},\n\tabstract = {SUMMARY\n            In this study, we analysed the impacts of errors in background force models and observed non-gravitational forces on the pseudo-observations (pre-fits) during gravity field recovery based on the Gravity Recovery and Climate Experiment (GRACE) satellite gravity mission. To reduce these effects, we introduced the stochastic parameters into the functional model of the variational equation integration approach to absorb this type of noise contribution. Simultaneously, the prior variances of observed orbits and K-band range rates used in traditional method are re-estimated with least-squares variance component estimation (LS-VCE) after considering these stochastic parameters. To improve the computing efficiency, a modified method of the calculation of sensitivity matrices related to the introduced stochastic parameters is proposed. Compared to the method of variation of constants widely used in the precise orbit determination and gravity field recovery, the modified method decreases the computational time of these matrices by about four times. Furthermore, an efficient LS-VCE algorithm is derived in a more generalized case. The efficient algorithm only costs 1 per cent of the time of the unoptimized method. With the GRACE data, we analysed the benefits of these refinements in gravity field recovery, and the results show that these improvements can mitigate the impacts of errors in background force models and accelerometer data on recovered gravity field models, especially in the high-degree signals. Furthermore, the quality of results has less dependence on parametrization.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-02-26},\n\tjournal = {Geophysical Journal International},\n\tauthor = {Yu, Biao and You, Wei and Kusche, Jürgen and Fan, Dongming and Su, Yong and Zhang, Jiahui},\n\tmonth = nov,\n\tyear = {2023},\n\tpages = {516--536},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  SUMMARY In this study, we analysed the impacts of errors in background force models and observed non-gravitational forces on the pseudo-observations (pre-fits) during gravity field recovery based on the Gravity Recovery and Climate Experiment (GRACE) satellite gravity mission. To reduce these effects, we introduced the stochastic parameters into the functional model of the variational equation integration approach to absorb this type of noise contribution. Simultaneously, the prior variances of observed orbits and K-band range rates used in traditional method are re-estimated with least-squares variance component estimation (LS-VCE) after considering these stochastic parameters. To improve the computing efficiency, a modified method of the calculation of sensitivity matrices related to the introduced stochastic parameters is proposed. Compared to the method of variation of constants widely used in the precise orbit determination and gravity field recovery, the modified method decreases the computational time of these matrices by about four times. Furthermore, an efficient LS-VCE algorithm is derived in a more generalized case. The efficient algorithm only costs 1 per cent of the time of the unoptimized method. With the GRACE data, we analysed the benefits of these refinements in gravity field recovery, and the results show that these improvements can mitigate the impacts of errors in background force models and accelerometer data on recovered gravity field models, especially in the high-degree signals. Furthermore, the quality of results has less dependence on parametrization.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"vanderborght-leitner-schnepf-couvreur-vereecken-javaux-combiningrootandsoilhydraulicsinmacroscopicrepresentationsofrootwateruptake-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20273\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'vanderborght-leitner-schnepf-couvreur-vereecken-javaux-combiningrootandsoilhydraulicsinmacroscopicrepresentationsofrootwateruptake-2023', 'https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20273', event);\">\n    Combining root and soil hydraulics in macroscopic representations of root water uptake.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Vanderborght, J.; Leitner, D.; Schnepf, A.; Couvreur, V.; Vereecken, H.;  and Javaux, M.\n</span>\n\n  \n\n</span>\n\n  <i>Vadose Zone Journal</i>,e20273. July 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://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20273\"\n     onclick=\"log_download('vanderborght-leitner-schnepf-couvreur-vereecken-javaux-combiningrootandsoilhydraulicsinmacroscopicrepresentationsofrootwateruptake-2023', 'https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20273', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Combining root and soil hydraulics in macroscopic representations of root water uptake [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/10.1002/vzj2.20273\"\n     onclick=\"log_download('vanderborght-leitner-schnepf-couvreur-vereecken-javaux-combiningrootandsoilhydraulicsinmacroscopicrepresentationsofrootwateruptake-2023', 'http://doi.org/10.1002/vzj2.20273', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vanderborght-leitner-schnepf-couvreur-vereecken-javaux-combiningrootandsoilhydraulicsinmacroscopicrepresentationsofrootwateruptake-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('vanderborght-leitner-schnepf-couvreur-vereecken-javaux-combiningrootandsoilhydraulicsinmacroscopicrepresentationsofrootwateruptake-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{vanderborght_combining_2023,\n\ttitle = {Combining root and soil hydraulics in macroscopic representations of root water uptake},\n\tissn = {1539-1663, 1539-1663},\n\turl = {https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20273},\n\tdoi = {10.1002/vzj2.20273},\n\tabstract = {Abstract\n            Plant water uptake and plant and soil water status are important for the soil water balance and plant growth. They depend on atmospheric water demand and the accessibility of soil water to plant roots, which is in turn related to the hydraulic properties of the root system and the soil around root segments. We present a simulation model that describes water flow in the soil–plant system mechanistically considering both root and soil hydraulic properties. We developed an approach to upscale three‐dimensional (3D) flow in the soil toward root segments of a 3D root architecture to a model that considers one‐dimensional flow between horizontal soil layers and radial flow to root segments in that layer. The upscaled model couples upscaled linear flow equations in the root system with an analytical solution of the nonlinear radial flow equation between the soil and roots. The upscaled model avoids simplifying assumptions about root hydraulic properties and water potential drops near roots made in, respectively, soil‐ and root‐centered models. Xylem water potentials and soil–root interface potentials are explicitly simulated and show, respectively, large variations with depth and large deviations from bulk soil water potentials under dry soil conditions. Accounting for hydraulic gradients in the soil around root segments led to an earlier but slower reduction of transpiration during a drought period and a better plant water status with higher nighttime plant water potentials.\n          , \n            Core Ideas\n            \n              \n                \n                  Root water uptake depends on root and soil hydraulic properties.\n                \n                \n                  Water uptake at root element scale was upscaled to the root system scale.\n                \n                \n                  The upscaled model can be implemented in one‐dimensional soil water flow models.\n                \n                \n                  Low conductance of dry soil prevents low nighttime plant water potentials.},\n\tlanguage = {en},\n\turldate = {2024-01-29},\n\tjournal = {Vadose Zone Journal},\n\tauthor = {Vanderborght, Jan and Leitner, Daniel and Schnepf, Andrea and Couvreur, Valentin and Vereecken, Harry and Javaux, Mathieu},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {e20273},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract Plant water uptake and plant and soil water status are important for the soil water balance and plant growth. They depend on atmospheric water demand and the accessibility of soil water to plant roots, which is in turn related to the hydraulic properties of the root system and the soil around root segments. We present a simulation model that describes water flow in the soil–plant system mechanistically considering both root and soil hydraulic properties. We developed an approach to upscale three‐dimensional (3D) flow in the soil toward root segments of a 3D root architecture to a model that considers one‐dimensional flow between horizontal soil layers and radial flow to root segments in that layer. The upscaled model couples upscaled linear flow equations in the root system with an analytical solution of the nonlinear radial flow equation between the soil and roots. The upscaled model avoids simplifying assumptions about root hydraulic properties and water potential drops near roots made in, respectively, soil‐ and root‐centered models. Xylem water potentials and soil–root interface potentials are explicitly simulated and show, respectively, large variations with depth and large deviations from bulk soil water potentials under dry soil conditions. Accounting for hydraulic gradients in the soil around root segments led to an earlier but slower reduction of transpiration during a drought period and a better plant water status with higher nighttime plant water potentials. , Core Ideas Root water uptake depends on root and soil hydraulic properties. Water uptake at root element scale was upscaled to the root system scale. The upscaled model can be implemented in one‐dimensional soil water flow models. Low conductance of dry soil prevents low nighttime plant water potentials.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"schimmel-amelung-organicsoils-2023\">\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/B9780128229743000732\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'schimmel-amelung-organicsoils-2023', 'https://linkinghub.elsevier.com/retrieve/pii/B9780128229743000732', event);\">\n    Organic soils.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Schimmel, H.;  and Amelung, W.\n</span>\n\n  \n\n</span>\n\n  In <i>Encyclopedia of Soils in the Environment</i>, pages 383–397. Elsevier,  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://linkinghub.elsevier.com/retrieve/pii/B9780128229743000732\"\n     onclick=\"log_download('schimmel-amelung-organicsoils-2023', 'https://linkinghub.elsevier.com/retrieve/pii/B9780128229743000732', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Organic soils [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/B978-0-12-822974-3.00073-2\"\n     onclick=\"log_download('schimmel-amelung-organicsoils-2023', 'http://doi.org/10.1016/B978-0-12-822974-3.00073-2', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/schimmel-amelung-organicsoils-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('schimmel-amelung-organicsoils-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{schimmel_organic_2023,\n\ttitle = {Organic soils},\n\tisbn = {978-0-323-95133-3},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/B9780128229743000732},\n\tlanguage = {en},\n\turldate = {2023-11-27},\n\tbooktitle = {Encyclopedia of {Soils} in the {Environment}},\n\tpublisher = {Elsevier},\n\tauthor = {Schimmel, Heike and Amelung, Wulf},\n\tyear = {2023},\n\tdoi = {10.1016/B978-0-12-822974-3.00073-2},\n\tpages = {383--397},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chen-fenoglio-kusche-liao-uyanik-nadzir-lou-evaluationofsentinel3aaltimetryoversonghuariverbasin-2023\">\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/S0022169423001397\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chen-fenoglio-kusche-liao-uyanik-nadzir-lou-evaluationofsentinel3aaltimetryoversonghuariverbasin-2023', 'https://linkinghub.elsevier.com/retrieve/pii/S0022169423001397', event);\">\n    Evaluation of Sentinel-3A altimetry over Songhua river Basin.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chen, J.; Fenoglio, L.; Kusche, J.; Liao, J.; Uyanik, H.; Nadzir, Z. A.;  and Lou, Y.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Hydrology</i>, 618: 129197. March 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://linkinghub.elsevier.com/retrieve/pii/S0022169423001397\"\n     onclick=\"log_download('chen-fenoglio-kusche-liao-uyanik-nadzir-lou-evaluationofsentinel3aaltimetryoversonghuariverbasin-2023', 'https://linkinghub.elsevier.com/retrieve/pii/S0022169423001397', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Evaluation of Sentinel-3A altimetry over Songhua river Basin [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.2023.129197\"\n     onclick=\"log_download('chen-fenoglio-kusche-liao-uyanik-nadzir-lou-evaluationofsentinel3aaltimetryoversonghuariverbasin-2023', 'http://doi.org/10.1016/j.jhydrol.2023.129197', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chen-fenoglio-kusche-liao-uyanik-nadzir-lou-evaluationofsentinel3aaltimetryoversonghuariverbasin-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chen-fenoglio-kusche-liao-uyanik-nadzir-lou-evaluationofsentinel3aaltimetryoversonghuariverbasin-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{chen_evaluation_2023,\n\ttitle = {Evaluation of {Sentinel}-{3A} altimetry over {Songhua} river {Basin}},\n\tvolume = {618},\n\tissn = {00221694},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423001397},\n\tdoi = {10.1016/j.jhydrol.2023.129197},\n\tlanguage = {en},\n\turldate = {2023-10-31},\n\tjournal = {Journal of Hydrology},\n\tauthor = {Chen, Jiaming and Fenoglio, Luciana and Kusche, Jürgen and Liao, Jingjuan and Uyanik, Hakan and Nadzir, Zulfikar Adlan and Lou, Yanhan},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {129197},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"gntner-gerdener-boergens-kusche-kollet-dobslaw-hartick-sharifi-etal-vernderungenderwasserspeicherungindeutschlandseit2002ausbeobachtungendersatellitengravimetriewaterstoragechangesingermanysince2002fromsatellitegravityobservations-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.bafg.de/HyWa/2023/HyWa_2023.2_1.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gntner-gerdener-boergens-kusche-kollet-dobslaw-hartick-sharifi-etal-vernderungenderwasserspeicherungindeutschlandseit2002ausbeobachtungendersatellitengravimetriewaterstoragechangesingermanysince2002fromsatellitegravityobservations-2023', 'https://doi.bafg.de/HyWa/2023/HyWa_2023.2_1.pdf', event);\">\n    Veränderungen der Wasserspeicherung in Deutschland seit 2002 aus Beobachtungen der Satellitengravimetrie – Water storage changes in Germany since 2002 from satellite gravity observations.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Güntner, A.; Gerdener, H.; Boergens, E.; Kusche, J.; Kollet, S.; Dobslaw, H.; Hartick, C.; Sharifi, E.;  and Flechtner, F.\n</span>\n\n  \n\n</span>\n\n  <i>Hydrologie und Wasserbewirtschaftung</i>.  2023.\n  <span class=\"note\">Publisher: Bundesanstalt für Gewässerkunde, Koblenz</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.bafg.de/HyWa/2023/HyWa_2023.2_1.pdf\"\n     onclick=\"log_download('gntner-gerdener-boergens-kusche-kollet-dobslaw-hartick-sharifi-etal-vernderungenderwasserspeicherungindeutschlandseit2002ausbeobachtungendersatellitengravimetriewaterstoragechangesingermanysince2002fromsatellitegravityobservations-2023', 'https://doi.bafg.de/HyWa/2023/HyWa_2023.2_1.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Veränderungen der Wasserspeicherung in Deutschland seit 2002 aus Beobachtungen der Satellitengravimetrie – Water storage changes in Germany since 2002 from satellite gravity observations [pdf]\"\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/10.5675/HYWA_2023.2_1\"\n     onclick=\"log_download('gntner-gerdener-boergens-kusche-kollet-dobslaw-hartick-sharifi-etal-vernderungenderwasserspeicherungindeutschlandseit2002ausbeobachtungendersatellitengravimetriewaterstoragechangesingermanysince2002fromsatellitegravityobservations-2023', 'http://doi.org/10.5675/HYWA_2023.2_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/gntner-gerdener-boergens-kusche-kollet-dobslaw-hartick-sharifi-etal-vernderungenderwasserspeicherungindeutschlandseit2002ausbeobachtungendersatellitengravimetriewaterstoragechangesingermanysince2002fromsatellitegravityobservations-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gntner-gerdener-boergens-kusche-kollet-dobslaw-hartick-sharifi-etal-vernderungenderwasserspeicherungindeutschlandseit2002ausbeobachtungendersatellitengravimetriewaterstoragechangesingermanysince2002fromsatellitegravityobservations-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{guntner_veranderungen_2023,\n\ttitle = {Veränderungen der {Wasserspeicherung} in {Deutschland} seit 2002 aus {Beobachtungen} der {Satellitengravimetrie} – {Water} storage changes in {Germany} since 2002 from satellite gravity observations},\n\tissn = {1439-1783},\n\turl = {https://doi.bafg.de/HyWa/2023/HyWa_2023.2_1.pdf},\n\tdoi = {10.5675/HYWA_2023.2_1},\n\turldate = {2023-10-30},\n\tjournal = {Hydrologie und Wasserbewirtschaftung},\n\tauthor = {Güntner, Andreas and Gerdener, Helena and Boergens, Eva and Kusche, Jürgen and Kollet, Stefan and Dobslaw, Henryk and Hartick, Carl and Sharifi, Ehsan and Flechtner, Frank},\n\tyear = {2023},\n\tnote = {Publisher: Bundesanstalt für Gewässerkunde, Koblenz},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"enders-vianna-gaiser-krauss-webber-srivastava-seidel-tewes-etal-simplaceaversatilemodellingandsimulationframeworkforsustainablecropsandagroecosystems-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad006/7176223\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'enders-vianna-gaiser-krauss-webber-srivastava-seidel-tewes-etal-simplaceaversatilemodellingandsimulationframeworkforsustainablecropsandagroecosystems-2023', 'https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad006/7176223', event);\">\n    SIMPLACE—a versatile modelling and simulation framework for sustainable crops and agroecosystems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Enders, A.; Vianna, M.; Gaiser, T.; Krauss, G.; Webber, H.; Srivastava, A. K.; Seidel, S. J.; Tewes, A.; Rezaei, E. E.;  and Ewert, F.\n</span>\n\n  \n\n</span>\n\n  <i>in silico Plants</i>, 5(1): diad006. 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  <a href=\"https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad006/7176223\"\n     onclick=\"log_download('enders-vianna-gaiser-krauss-webber-srivastava-seidel-tewes-etal-simplaceaversatilemodellingandsimulationframeworkforsustainablecropsandagroecosystems-2023', 'https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad006/7176223', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"SIMPLACE—a versatile modelling and simulation framework for sustainable crops and agroecosystems [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/10.1093/insilicoplants/diad006\"\n     onclick=\"log_download('enders-vianna-gaiser-krauss-webber-srivastava-seidel-tewes-etal-simplaceaversatilemodellingandsimulationframeworkforsustainablecropsandagroecosystems-2023', 'http://doi.org/10.1093/insilicoplants/diad006', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/enders-vianna-gaiser-krauss-webber-srivastava-seidel-tewes-etal-simplaceaversatilemodellingandsimulationframeworkforsustainablecropsandagroecosystems-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('enders-vianna-gaiser-krauss-webber-srivastava-seidel-tewes-etal-simplaceaversatilemodellingandsimulationframeworkforsustainablecropsandagroecosystems-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{enders_simplaceversatile_2023,\n\ttitle = {{SIMPLACE}—a versatile modelling and simulation framework for sustainable crops and agroecosystems},\n\tvolume = {5},\n\tissn = {2517-5025},\n\turl = {https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad006/7176223},\n\tdoi = {10.1093/insilicoplants/diad006},\n\tabstract = {Abstract\n            Agricultural system analysis has considerably evolved over the last years, allowing scientists to quantify complex interactions in crops and agroecosystems. Computer-based models have become a central tool for such analysis, using formulated mathematical representations (algorithms) of different biophysical processes to simulate complex system’s behaviour. Nevertheless, the current large variety of algorithms in combination with nonstandardization in their use limits rapid and rigorous model improvement and testing. This is particularly important because contextualization is a key aspect used to formulate the appropriate model structure for a specific research question, framing a clear demand for ‘next generation’ models being modular and flexible. This paper aims to describe the Scientific Impact assessment and Modelling PLatform for Advanced Crop and Ecosystem management (SIMPLACE), which has been developed over the last decade to address the various aforementioned issues and support appropriate model formulations and interoperability. We describe its main technical implementation and features to develop customized model solutions that can be applied to a number of cropping systems with high flexibility, performance and transparency. A brief review of exemplary applications of SIMPLACE is provided covering the different topics, crops and cropping systems, spatial scales and geographies. We stress that standardized documentation of modules, variables ontology and data archives are key requirements to maintain and assist model development and reproducibility. The increasing demand for more complex, diversified and integrated production systems (e.g. intercropping, livestock-grazing, agroforestry) and the associated impacts on sustainable food systems also require the strong collaboration of a multidisciplinary community of modellers and stakeholders.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-10-30},\n\tjournal = {in silico Plants},\n\tauthor = {Enders, Andreas and Vianna, Murilo and Gaiser, Thomas and Krauss, Gunther and Webber, Heidi and Srivastava, Amit Kumar and Seidel, Sabine Julia and Tewes, Andreas and Rezaei, Ehsan Eyshi and Ewert, Frank},\n\teditor = {Marshall-Colon, Amy},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {diad006},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract Agricultural system analysis has considerably evolved over the last years, allowing scientists to quantify complex interactions in crops and agroecosystems. Computer-based models have become a central tool for such analysis, using formulated mathematical representations (algorithms) of different biophysical processes to simulate complex system’s behaviour. Nevertheless, the current large variety of algorithms in combination with nonstandardization in their use limits rapid and rigorous model improvement and testing. This is particularly important because contextualization is a key aspect used to formulate the appropriate model structure for a specific research question, framing a clear demand for ‘next generation’ models being modular and flexible. This paper aims to describe the Scientific Impact assessment and Modelling PLatform for Advanced Crop and Ecosystem management (SIMPLACE), which has been developed over the last decade to address the various aforementioned issues and support appropriate model formulations and interoperability. We describe its main technical implementation and features to develop customized model solutions that can be applied to a number of cropping systems with high flexibility, performance and transparency. A brief review of exemplary applications of SIMPLACE is provided covering the different topics, crops and cropping systems, spatial scales and geographies. We stress that standardized documentation of modules, variables ontology and data archives are key requirements to maintain and assist model development and reproducibility. The increasing demand for more complex, diversified and integrated production systems (e.g. intercropping, livestock-grazing, agroforestry) and the associated impacts on sustainable food systems also require the strong collaboration of a multidisciplinary community of modellers and stakeholders.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"graf-wohlfahrt-arandabarranco-arriga-brmmer-ceschia-ciais-desai-etal-jointoptimizationoflandcarbonuptakeandalbedocanhelpachievemoderateinstantaneousandlongtermcoolingeffects-2023\">\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/s43247-023-00958-4\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'graf-wohlfahrt-arandabarranco-arriga-brmmer-ceschia-ciais-desai-etal-jointoptimizationoflandcarbonuptakeandalbedocanhelpachievemoderateinstantaneousandlongtermcoolingeffects-2023', 'https://www.nature.com/articles/s43247-023-00958-4', event);\">\n    Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Graf, A.; Wohlfahrt, G.; Aranda-Barranco, S.; Arriga, N.; Brümmer, C.; Ceschia, E.; Ciais, P.; Desai, A. R.; Di Lonardo, S.; Gharun, M.; Grünwald, T.; Hörtnagl, L.; Kasak, K.; Klosterhalfen, A.; Knohl, A.; Kowalska, N.; Leuchner, M.; Lindroth, A.; Mauder, M.; Migliavacca, M.; Morel, A. C.; Pfennig, A.; Poorter, H.; Terán, C. P.; Reitz, O.; Rebmann, C.; Sanchez-Azofeifa, A.; Schmidt, M.; Šigut, L.; Tomelleri, E.; Yu, K.; Varlagin, A.;  and Vereecken, H.\n</span>\n\n  \n\n</span>\n\n  <i>Communications Earth & Environment</i>, 4(1): 298. August 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.nature.com/articles/s43247-023-00958-4\"\n     onclick=\"log_download('graf-wohlfahrt-arandabarranco-arriga-brmmer-ceschia-ciais-desai-etal-jointoptimizationoflandcarbonuptakeandalbedocanhelpachievemoderateinstantaneousandlongtermcoolingeffects-2023', 'https://www.nature.com/articles/s43247-023-00958-4', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects [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/s43247-023-00958-4\"\n     onclick=\"log_download('graf-wohlfahrt-arandabarranco-arriga-brmmer-ceschia-ciais-desai-etal-jointoptimizationoflandcarbonuptakeandalbedocanhelpachievemoderateinstantaneousandlongtermcoolingeffects-2023', 'http://doi.org/10.1038/s43247-023-00958-4', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/graf-wohlfahrt-arandabarranco-arriga-brmmer-ceschia-ciais-desai-etal-jointoptimizationoflandcarbonuptakeandalbedocanhelpachievemoderateinstantaneousandlongtermcoolingeffects-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('graf-wohlfahrt-arandabarranco-arriga-brmmer-ceschia-ciais-desai-etal-jointoptimizationoflandcarbonuptakeandalbedocanhelpachievemoderateinstantaneousandlongtermcoolingeffects-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{graf_joint_2023,\n\ttitle = {Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects},\n\tvolume = {4},\n\tissn = {2662-4435},\n\turl = {https://www.nature.com/articles/s43247-023-00958-4},\n\tdoi = {10.1038/s43247-023-00958-4},\n\tabstract = {Abstract\n            Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-10-30},\n\tjournal = {Communications Earth \\&amp; Environment},\n\tauthor = {Graf, Alexander and Wohlfahrt, Georg and Aranda-Barranco, Sergio and Arriga, Nicola and Brümmer, Christian and Ceschia, Eric and Ciais, Philippe and Desai, Ankur R. and Di Lonardo, Sara and Gharun, Mana and Grünwald, Thomas and Hörtnagl, Lukas and Kasak, Kuno and Klosterhalfen, Anne and Knohl, Alexander and Kowalska, Natalia and Leuchner, Michael and Lindroth, Anders and Mauder, Matthias and Migliavacca, Mirco and Morel, Alexandra C. and Pfennig, Andreas and Poorter, Hendrik and Terán, Christian Poppe and Reitz, Oliver and Rebmann, Corinna and Sanchez-Azofeifa, Arturo and Schmidt, Marius and Šigut, Ladislav and Tomelleri, Enrico and Yu, Ke and Varlagin, Andrej and Vereecken, Harry},\n\tmonth = aug,\n\tyear = {2023},\n\tpages = {298},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"li-kurtz-hung-vereecken-hendricksfranssen-watertabledepthassimilationinintegratedterrestrialsystemmodelsatthelargercatchmentscale-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'li-kurtz-hung-vereecken-hendricksfranssen-watertabledepthassimilationinintegratedterrestrialsystemmodelsatthelargercatchmentscale-2023', 'https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full', event);\">\n    Water table depth assimilation in integrated terrestrial system models at the larger catchment scale.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Li, F.; Kurtz, W.; Hung, C. P.; Vereecken, H.;  and Hendricks Franssen, H.\n</span>\n\n  \n\n</span>\n\n  <i>Frontiers in Water</i>, 5: 1150999. March 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.frontiersin.org/articles/10.3389/frwa.2023.1150999/full\"\n     onclick=\"log_download('li-kurtz-hung-vereecken-hendricksfranssen-watertabledepthassimilationinintegratedterrestrialsystemmodelsatthelargercatchmentscale-2023', 'https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Water table depth assimilation in integrated terrestrial system models at the larger catchment scale [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/10.3389/frwa.2023.1150999\"\n     onclick=\"log_download('li-kurtz-hung-vereecken-hendricksfranssen-watertabledepthassimilationinintegratedterrestrialsystemmodelsatthelargercatchmentscale-2023', 'http://doi.org/10.3389/frwa.2023.1150999', 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-kurtz-hung-vereecken-hendricksfranssen-watertabledepthassimilationinintegratedterrestrialsystemmodelsatthelargercatchmentscale-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('li-kurtz-hung-vereecken-hendricksfranssen-watertabledepthassimilationinintegratedterrestrialsystemmodelsatthelargercatchmentscale-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</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_water_2023,\n\ttitle = {Water table depth assimilation in integrated terrestrial system models at the larger catchment scale},\n\tvolume = {5},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full},\n\tdoi = {10.3389/frwa.2023.1150999},\n\tabstract = {As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81\\% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites \\&amp;lt;2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30\\% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).},\n\turldate = {2023-10-30},\n\tjournal = {Frontiers in Water},\n\tauthor = {Li, Fang and Kurtz, Wolfgang and Hung, Ching Pui and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {1150999},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites &lt;2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"qu-miralles-veraverbeke-vereecken-montzka-wildfireprecursorsshowcomplementarypredictabilityindifferenttimescales-2023\">\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/s41467-023-42597-5\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'qu-miralles-veraverbeke-vereecken-montzka-wildfireprecursorsshowcomplementarypredictabilityindifferenttimescales-2023', 'https://www.nature.com/articles/s41467-023-42597-5', event);\">\n    Wildfire precursors show complementary predictability in different timescales.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Qu, Y.; Miralles, D. G.; Veraverbeke, S.; Vereecken, H.;  and Montzka, C.\n</span>\n\n  \n\n</span>\n\n  <i>Nature Communications</i>, 14(1): 6829. October 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.nature.com/articles/s41467-023-42597-5\"\n     onclick=\"log_download('qu-miralles-veraverbeke-vereecken-montzka-wildfireprecursorsshowcomplementarypredictabilityindifferenttimescales-2023', 'https://www.nature.com/articles/s41467-023-42597-5', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Wildfire precursors show complementary predictability in different timescales [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/s41467-023-42597-5\"\n     onclick=\"log_download('qu-miralles-veraverbeke-vereecken-montzka-wildfireprecursorsshowcomplementarypredictabilityindifferenttimescales-2023', 'http://doi.org/10.1038/s41467-023-42597-5', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/qu-miralles-veraverbeke-vereecken-montzka-wildfireprecursorsshowcomplementarypredictabilityindifferenttimescales-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('qu-miralles-veraverbeke-vereecken-montzka-wildfireprecursorsshowcomplementarypredictabilityindifferenttimescales-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{qu_wildfire_2023,\n\ttitle = {Wildfire precursors show complementary predictability in different timescales},\n\tvolume = {14},\n\tissn = {2041-1723},\n\turl = {https://www.nature.com/articles/s41467-023-42597-5},\n\tdoi = {10.1038/s41467-023-42597-5},\n\tabstract = {Abstract\n            In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3\\% and 26.7\\% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-10-30},\n\tjournal = {Nature Communications},\n\tauthor = {Qu, Yuquan and Miralles, Diego G. and Veraverbeke, Sander and Vereecken, Harry and Montzka, Carsten},\n\tmonth = oct,\n\tyear = {2023},\n\tpages = {6829},\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 most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3% and 26.7% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mohseni-ahrari-haunert-montzka-thesynergiesofsmapenhancedandmodisproductsinarandomforestregressionforestimating1kmsoilmoistureoverafricausinggoogleearthengine-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.tandfonline.com/doi/full/10.1080/20964471.2023.2257905\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mohseni-ahrari-haunert-montzka-thesynergiesofsmapenhancedandmodisproductsinarandomforestregressionforestimating1kmsoilmoistureoverafricausinggoogleearthengine-2023', 'https://www.tandfonline.com/doi/full/10.1080/20964471.2023.2257905', event);\">\n    The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Mohseni, F.; Ahrari, A.; Haunert, J.;  and Montzka, C.\n</span>\n\n  \n\n</span>\n\n  <i>Big Earth Data</i>,1–25. 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.tandfonline.com/doi/full/10.1080/20964471.2023.2257905\"\n     onclick=\"log_download('mohseni-ahrari-haunert-montzka-thesynergiesofsmapenhancedandmodisproductsinarandomforestregressionforestimating1kmsoilmoistureoverafricausinggoogleearthengine-2023', 'https://www.tandfonline.com/doi/full/10.1080/20964471.2023.2257905', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine [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/10.1080/20964471.2023.2257905\"\n     onclick=\"log_download('mohseni-ahrari-haunert-montzka-thesynergiesofsmapenhancedandmodisproductsinarandomforestregressionforestimating1kmsoilmoistureoverafricausinggoogleearthengine-2023', 'http://doi.org/10.1080/20964471.2023.2257905', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mohseni-ahrari-haunert-montzka-thesynergiesofsmapenhancedandmodisproductsinarandomforestregressionforestimating1kmsoilmoistureoverafricausinggoogleearthengine-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mohseni-ahrari-haunert-montzka-thesynergiesofsmapenhancedandmodisproductsinarandomforestregressionforestimating1kmsoilmoistureoverafricausinggoogleearthengine-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{mohseni_synergies_2023,\n\ttitle = {The synergies of {SMAP} enhanced and {MODIS} products in a random forest regression for estimating 1 km soil moisture over {Africa} using {Google} {Earth} {Engine}},\n\tissn = {2096-4471, 2574-5417},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/20964471.2023.2257905},\n\tdoi = {10.1080/20964471.2023.2257905},\n\tlanguage = {en},\n\turldate = {2023-10-30},\n\tjournal = {Big Earth Data},\n\tauthor = {Mohseni, Farzane and Ahrari, Amirhossein and Haunert, Jan-Henrik and Montzka, Carsten},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {1--25},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"klos-kusche-leszczuk-gerdener-schulze-lenczuk-bogusz-introducingtheideaofclassifyingsetsofpermanentgnssstationsasbenchmarksforhydrogeodesy-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026988\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'klos-kusche-leszczuk-gerdener-schulze-lenczuk-bogusz-introducingtheideaofclassifyingsetsofpermanentgnssstationsasbenchmarksforhydrogeodesy-2023', 'https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026988', event);\">\n    Introducing the Idea of Classifying Sets of Permanent GNSS Stations as Benchmarks for Hydrogeodesy.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Klos, A.; Kusche, J.; Leszczuk, G.; Gerdener, H.; Schulze, K.; Lenczuk, A.;  and Bogusz, J.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Geophysical Research: Solid Earth</i>, 128(9): e2023JB026988. 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://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026988\"\n     onclick=\"log_download('klos-kusche-leszczuk-gerdener-schulze-lenczuk-bogusz-introducingtheideaofclassifyingsetsofpermanentgnssstationsasbenchmarksforhydrogeodesy-2023', 'https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026988', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Introducing the Idea of Classifying Sets of Permanent GNSS Stations as Benchmarks for Hydrogeodesy [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/10.1029/2023JB026988\"\n     onclick=\"log_download('klos-kusche-leszczuk-gerdener-schulze-lenczuk-bogusz-introducingtheideaofclassifyingsetsofpermanentgnssstationsasbenchmarksforhydrogeodesy-2023', 'http://doi.org/10.1029/2023JB026988', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/klos-kusche-leszczuk-gerdener-schulze-lenczuk-bogusz-introducingtheideaofclassifyingsetsofpermanentgnssstationsasbenchmarksforhydrogeodesy-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('klos-kusche-leszczuk-gerdener-schulze-lenczuk-bogusz-introducingtheideaofclassifyingsetsofpermanentgnssstationsasbenchmarksforhydrogeodesy-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{klos_introducing_2023,\n\ttitle = {Introducing the {Idea} of {Classifying} {Sets} of {Permanent} {GNSS} {Stations} as {Benchmarks} for {Hydrogeodesy}},\n\tvolume = {128},\n\tissn = {2169-9313, 2169-9356},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026988},\n\tdoi = {10.1029/2023JB026988},\n\tabstract = {Abstract\n            We propose a novel approach to classify sets of Global Navigation Satellite System (GNSS) permanent stations as benchmarks for hydrogeodesy. Benchmarks are trusted sets of GNSS stations whose displacements are classified as significantly and positively correlated with hydrospheric changes and identified in a three temporal‐scales: short‐term, seasonal and long‐term. We use 63 vertical displacement time series processed at the Nevada Geodetic Laboratory for the period 1998–2021 from stations located within Amazon basin and show that estimates of trends and annual signals, including the annual phase maximum, are very coherent with water surface levels provided by altimetry missions. We compute vertical displacements from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gravity missions and predict those also from Global Land Water Storage (GLWS) v2.0 data set which values are produced by assimilation of GRACE into WaterGAP Global Hydrological Model (WGHM). We divide vertical displacements from the three data sets into the pre‐defined temporal‐scales of short‐term, seasonal and long‐term, using non‐parametric wavelet analysis. For each temporal‐scale, correlation coefficients are computed between GNSS‐measured and GRACE‐derived/GLWS‐predicted displacements. We present the benefits of applying high‐resolution GRACE‐assimilating hydrology model to benchmark GNSS stations, which are particularly evident when using spherical harmonic coefficients higher than 120. Their increase causes the number of stations included in the benchmarks to rise by up to 15\\% for short‐term. Benchmarking allows hydrogeodesy to take advantage of a broader set of GNSS stations that were previously omitted, such as earthquake‐affected sites and those where a possible poroelastic response is observed.\n          , \n            Plain Language Summary\n            Displacements of the Earth&#x27;s crust measured by permanent Global Navigation Satellite System (GNSS) ground stations are used for many geophysical interpretations. However, it is common to omit the evaluation of the sensitivity of the system to the measurement of displacements from different sources, assuming in advance 100\\% sensitivity of the system to a given effect. Consequently, the fact that at a given station several effects can be recorded simultaneously is overlooked. This is particularly evident in earthquake‐affected areas, where GNSS stations are excluded from most analyses of non‐tectonic effects. We solve this problem and propose to divide GNSS stations into trusted sets, which we call benchmarks. Benchmarking is performed by indicating the stations that are certain to register a given effect in three pre‐defined temporal‐scales: short‐term, seasonal and long‐term. We present the analysis for the Amazon area, known for its large hydrosphere‐related signal, and demonstrate that the benchmarking allows for the inclusion of GNSS stations that were previously omitted in analyses of this type.\n          , \n            Key Points\n            \n              \n                \n                  Displacements measured by Global Positioning System (GPS) correlate well with surface water levels derived from radar altimetry missions\n                \n                \n                  Trusted sets of GPS stations are classified at the three pre‐defined temporal‐scales: short‐term, seasonal and long‐term\n                \n                \n                  Benchmarking allows to include more GPS stations in hydrogeodetic analyses},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-10-30},\n\tjournal = {Journal of Geophysical Research: Solid Earth},\n\tauthor = {Klos, A. and Kusche, J. and Leszczuk, G. and Gerdener, H. and Schulze, K. and Lenczuk, A. and Bogusz, J.},\n\tmonth = sep,\n\tyear = {2023},\n\tpages = {e2023JB026988},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract We propose a novel approach to classify sets of Global Navigation Satellite System (GNSS) permanent stations as benchmarks for hydrogeodesy. Benchmarks are trusted sets of GNSS stations whose displacements are classified as significantly and positively correlated with hydrospheric changes and identified in a three temporal‐scales: short‐term, seasonal and long‐term. We use 63 vertical displacement time series processed at the Nevada Geodetic Laboratory for the period 1998–2021 from stations located within Amazon basin and show that estimates of trends and annual signals, including the annual phase maximum, are very coherent with water surface levels provided by altimetry missions. We compute vertical displacements from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gravity missions and predict those also from Global Land Water Storage (GLWS) v2.0 data set which values are produced by assimilation of GRACE into WaterGAP Global Hydrological Model (WGHM). We divide vertical displacements from the three data sets into the pre‐defined temporal‐scales of short‐term, seasonal and long‐term, using non‐parametric wavelet analysis. For each temporal‐scale, correlation coefficients are computed between GNSS‐measured and GRACE‐derived/GLWS‐predicted displacements. We present the benefits of applying high‐resolution GRACE‐assimilating hydrology model to benchmark GNSS stations, which are particularly evident when using spherical harmonic coefficients higher than 120. Their increase causes the number of stations included in the benchmarks to rise by up to 15% for short‐term. Benchmarking allows hydrogeodesy to take advantage of a broader set of GNSS stations that were previously omitted, such as earthquake‐affected sites and those where a possible poroelastic response is observed. , Plain Language Summary Displacements of the Earth's crust measured by permanent Global Navigation Satellite System (GNSS) ground stations are used for many geophysical interpretations. However, it is common to omit the evaluation of the sensitivity of the system to the measurement of displacements from different sources, assuming in advance 100% sensitivity of the system to a given effect. Consequently, the fact that at a given station several effects can be recorded simultaneously is overlooked. This is particularly evident in earthquake‐affected areas, where GNSS stations are excluded from most analyses of non‐tectonic effects. We solve this problem and propose to divide GNSS stations into trusted sets, which we call benchmarks. Benchmarking is performed by indicating the stations that are certain to register a given effect in three pre‐defined temporal‐scales: short‐term, seasonal and long‐term. We present the analysis for the Amazon area, known for its large hydrosphere‐related signal, and demonstrate that the benchmarking allows for the inclusion of GNSS stations that were previously omitted in analyses of this type. , Key Points Displacements measured by Global Positioning System (GPS) correlate well with surface water levels derived from radar altimetry missions Trusted sets of GPS stations are classified at the three pre‐defined temporal‐scales: short‐term, seasonal and long‐term Benchmarking allows to include more GPS stations in hydrogeodetic analyses\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"gerdener-kusche-schulze-dll-klos-thegloballandwaterstoragedatasetrelease2glws20derivedviaassimilatinggraceandgracefodataintoaglobalhydrologicalmodel-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/10.1007/s00190-023-01763-9\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gerdener-kusche-schulze-dll-klos-thegloballandwaterstoragedatasetrelease2glws20derivedviaassimilatinggraceandgracefodataintoaglobalhydrologicalmodel-2023', 'https://link.springer.com/10.1007/s00190-023-01763-9', event);\">\n    The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into a global hydrological model.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gerdener, H.; Kusche, J.; Schulze, K.; Döll, P.;  and Klos, A.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Geodesy</i>, 97(7): 73. July 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://link.springer.com/10.1007/s00190-023-01763-9\"\n     onclick=\"log_download('gerdener-kusche-schulze-dll-klos-thegloballandwaterstoragedatasetrelease2glws20derivedviaassimilatinggraceandgracefodataintoaglobalhydrologicalmodel-2023', 'https://link.springer.com/10.1007/s00190-023-01763-9', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into a global hydrological model [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/s00190-023-01763-9\"\n     onclick=\"log_download('gerdener-kusche-schulze-dll-klos-thegloballandwaterstoragedatasetrelease2glws20derivedviaassimilatinggraceandgracefodataintoaglobalhydrologicalmodel-2023', 'http://doi.org/10.1007/s00190-023-01763-9', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gerdener-kusche-schulze-dll-klos-thegloballandwaterstoragedatasetrelease2glws20derivedviaassimilatinggraceandgracefodataintoaglobalhydrologicalmodel-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('gerdener-kusche-schulze-dll-klos-thegloballandwaterstoragedatasetrelease2glws20derivedviaassimilatinggraceandgracefodataintoaglobalhydrologicalmodel-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{gerdener_global_2023,\n\ttitle = {The global land water storage data set release 2 ({GLWS2}.0) derived via assimilating {GRACE} and {GRACE}-{FO} data into a global hydrological model},\n\tvolume = {97},\n\tissn = {0949-7714, 1432-1394},\n\turl = {https://link.springer.com/10.1007/s00190-023-01763-9},\n\tdoi = {10.1007/s00190-023-01763-9},\n\tabstract = {Abstract\n            \n              We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5\n              \n                \n                  \\$\\${\\textasciicircum}{\\textbackslash}circ \\$\\$\n                  \n                    \n                      \n                      ∘\n                    \n                  \n                \n              \n              , covering the time frame 2003 to 2019 without gaps, and including monthly uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with a version of the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the average over more than 1000 stations globally, GLWS2.0 correlates better with GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands than GRACE/-FO. While some differences exist, overall GLWS2.0 agrees reasonably well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases.\n              Highlights\n              \n                \n                  \n                    We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies over the global land with a spatial resolution of 0.5\n                    \n                      \n                        \\$\\${\\textasciicircum}{\\textbackslash}circ \\$\\$\n                        \n                          \n                            \n                            ∘\n                          \n                        \n                      \n                    \n                    , covering the period 2003 to 2019 without gaps, and including uncertainty quantification.\n                  \n                \n                \n                  GLWS2.0 synthesizes monthly GRACE/-FO mass change maps with daily precipitation and radiation data via the WaterGAP model framework, taking data and model uncertainty into account.\n                \n                \n                  Here we describe the methods and data sets that went into GLWS2.0 and its validation from a geodetic applications perspective. We find that, in the global average, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2023-10-30},\n\tjournal = {Journal of Geodesy},\n\tauthor = {Gerdener, Helena and Kusche, Jürgen and Schulze, Kerstin and Döll, Petra and Klos, Anna},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {73},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5 $$\\textasciicircum\\circ $$ ∘ , covering the time frame 2003 to 2019 without gaps, and including monthly uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with a version of the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the average over more than 1000 stations globally, GLWS2.0 correlates better with GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands than GRACE/-FO. While some differences exist, overall GLWS2.0 agrees reasonably well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases. Highlights We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies over the global land with a spatial resolution of 0.5 $$\\textasciicircum\\circ $$ ∘ , covering the period 2003 to 2019 without gaps, and including uncertainty quantification. GLWS2.0 synthesizes monthly GRACE/-FO mass change maps with daily precipitation and radiation data via the WaterGAP model framework, taking data and model uncertainty into account. Here we describe the methods and data sets that went into GLWS2.0 and its validation from a geodetic applications perspective. We find that, in the global average, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"naz-sharples-ma-goergen-kollet-continentalscaleevaluationofafullydistributedcoupledlandsurfaceandgroundwatermodelparflowclmv360overeurope-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://gmd.copernicus.org/articles/16/1617/2023/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'naz-sharples-ma-goergen-kollet-continentalscaleevaluationofafullydistributedcoupledlandsurfaceandgroundwatermodelparflowclmv360overeurope-2023', 'https://gmd.copernicus.org/articles/16/1617/2023/', event);\">\n    Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Naz, B. S.; Sharples, W.; Ma, Y.; Goergen, K.;  and Kollet, S.\n</span>\n\n  \n\n</span>\n\n  <i>Geoscientific Model Development</i>, 16(6): 1617–1639. March 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://gmd.copernicus.org/articles/16/1617/2023/\"\n     onclick=\"log_download('naz-sharples-ma-goergen-kollet-continentalscaleevaluationofafullydistributedcoupledlandsurfaceandgroundwatermodelparflowclmv360overeurope-2023', 'https://gmd.copernicus.org/articles/16/1617/2023/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe [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/gmd-16-1617-2023\"\n     onclick=\"log_download('naz-sharples-ma-goergen-kollet-continentalscaleevaluationofafullydistributedcoupledlandsurfaceandgroundwatermodelparflowclmv360overeurope-2023', 'http://doi.org/10.5194/gmd-16-1617-2023', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/naz-sharples-ma-goergen-kollet-continentalscaleevaluationofafullydistributedcoupledlandsurfaceandgroundwatermodelparflowclmv360overeurope-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('naz-sharples-ma-goergen-kollet-continentalscaleevaluationofafullydistributedcoupledlandsurfaceandgroundwatermodelparflowclmv360overeurope-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{naz_continental-scale_2023,\n\ttitle = {Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, {ParFlow}-{CLM} (v3.6.0), over {Europe}},\n\tvolume = {16},\n\tissn = {1991-9603},\n\turl = {https://gmd.copernicus.org/articles/16/1617/2023/},\n\tdoi = {10.5194/gmd-16-1617-2023},\n\tabstract = {Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman&#x27;s rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R{\\textgreater}0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2023-03-31},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Naz, Bibi S. and Sharples, Wendy and Ma, Yueling and Goergen, Klaus and Kollet, Stefan},\n\tmonth = mar,\n\tyear = {2023},\n\tpages = {1617--1639},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R\\textgreater0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"springer-lopez-owor-frappart-stieglitz-theroleofspacebasedobservationsforgroundwaterresourcemonitoringoverafrica-2023\">\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/s10712-022-09759-4\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'springer-lopez-owor-frappart-stieglitz-theroleofspacebasedobservationsforgroundwaterresourcemonitoringoverafrica-2023', 'https://doi.org/10.1007/s10712-022-09759-4', event);\">\n    The Role of Space-Based Observations for Groundwater Resource Monitoring over Africa.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Springer, A.; Lopez, T.; Owor, M.; Frappart, F.;  and Stieglitz, T.\n</span>\n\n  \n\n</span>\n\n  <i>Surveys in Geophysics</i>. 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  <a href=\"https://doi.org/10.1007/s10712-022-09759-4\"\n     onclick=\"log_download('springer-lopez-owor-frappart-stieglitz-theroleofspacebasedobservationsforgroundwaterresourcemonitoringoverafrica-2023', 'https://doi.org/10.1007/s10712-022-09759-4', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"The Role of Space-Based Observations for Groundwater Resource Monitoring over Africa [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/s10712-022-09759-4\"\n     onclick=\"log_download('springer-lopez-owor-frappart-stieglitz-theroleofspacebasedobservationsforgroundwaterresourcemonitoringoverafrica-2023', 'http://doi.org/10.1007/s10712-022-09759-4', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/springer-lopez-owor-frappart-stieglitz-theroleofspacebasedobservationsforgroundwaterresourcemonitoringoverafrica-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('springer-lopez-owor-frappart-stieglitz-theroleofspacebasedobservationsforgroundwaterresourcemonitoringoverafrica-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</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{springer_role_2023,\n\ttitle = {The {Role} of {Space}-{Based} {Observations} for {Groundwater} {Resource} {Monitoring} over {Africa}},\n\tissn = {1573-0956},\n\turl = {https://doi.org/10.1007/s10712-022-09759-4},\n\tdoi = {10.1007/s10712-022-09759-4},\n\tabstract = {Africa is particularly vulnerable to climate change impacts, which threatens food security, ecosystem protection and restoration initiatives, and fresh water resources availability and quality. Groundwater largely contributes to the mitigation of climate change effects by offering short- to long-term transient water storage. However, groundwater storage remains extremely difficult to monitor. In this paper, we review the strengths and weaknesses of satellite remote sensing techniques for addressing groundwater quantity issues with a focus on GRACE space gravimetry, as well as concepts to combine satellite observations with numerical models and ground observations. One particular focus is the quantification of changes in groundwater resources in the different climatic regions of Africa and the discussion of possible climatic and anthropogenic drivers. We include a thorough literature review on studies that use satellite observations for groundwater research in Africa. Finally, we identify gaps in research and possible future directions for employing satellite remote sensing to groundwater monitoring and management on the African continent.},\n\tjournal = {Surveys in Geophysics},\n\tauthor = {Springer, Anne and Lopez, Teodolina and Owor, Michael and Frappart, Frédéric and Stieglitz, Thomas},\n\tmonth = jan,\n\tyear = {2023},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Africa is particularly vulnerable to climate change impacts, which threatens food security, ecosystem protection and restoration initiatives, and fresh water resources availability and quality. Groundwater largely contributes to the mitigation of climate change effects by offering short- to long-term transient water storage. However, groundwater storage remains extremely difficult to monitor. In this paper, we review the strengths and weaknesses of satellite remote sensing techniques for addressing groundwater quantity issues with a focus on GRACE space gravimetry, as well as concepts to combine satellite observations with numerical models and ground observations. One particular focus is the quantification of changes in groundwater resources in the different climatic regions of Africa and the discussion of possible climatic and anthropogenic drivers. We include a thorough literature review on studies that use satellite observations for groundwater research in Africa. Finally, we identify gaps in research and possible future directions for employing satellite remote sensing to groundwater monitoring and management on the African continent.\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        (7)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"yang-luo-zhou-kusche-onstudyoftheearthtopographycorrectionforthegracesurfacemassestimation-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/10.1007/s00190-022-01683-0\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'yang-luo-zhou-kusche-onstudyoftheearthtopographycorrectionforthegracesurfacemassestimation-2022', 'https://link.springer.com/10.1007/s00190-022-01683-0', event);\">\n    On study of the Earth topography correction for the GRACE surface mass estimation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yang, F.; Luo, Z.; Zhou, H.;  and Kusche, J.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Geodesy</i>, 96(12): 95. 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://link.springer.com/10.1007/s00190-022-01683-0\"\n     onclick=\"log_download('yang-luo-zhou-kusche-onstudyoftheearthtopographycorrectionforthegracesurfacemassestimation-2022', 'https://link.springer.com/10.1007/s00190-022-01683-0', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"On study of the Earth topography correction for the GRACE surface mass estimation [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/s00190-022-01683-0\"\n     onclick=\"log_download('yang-luo-zhou-kusche-onstudyoftheearthtopographycorrectionforthegracesurfacemassestimation-2022', 'http://doi.org/10.1007/s00190-022-01683-0', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yang-luo-zhou-kusche-onstudyoftheearthtopographycorrectionforthegracesurfacemassestimation-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yang-luo-zhou-kusche-onstudyoftheearthtopographycorrectionforthegracesurfacemassestimation-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{yang_study_2022,\n\ttitle = {On study of the {Earth} topography correction for the {GRACE} surface mass estimation},\n\tvolume = {96},\n\tissn = {0949-7714, 1432-1394},\n\turl = {https://link.springer.com/10.1007/s00190-022-01683-0},\n\tdoi = {10.1007/s00190-022-01683-0},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2024-03-05},\n\tjournal = {Journal of Geodesy},\n\tauthor = {Yang, Fan and Luo, ZhiCai and Zhou, Hao and Kusche, Jürgen},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {95},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"qin-hong-zhao-siebert-abatzoglou-huning-sloat-park-etal-snowmeltrisktelecouplingsforirrigatedagriculture-2022\">\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/s41558-022-01509-z\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'qin-hong-zhao-siebert-abatzoglou-huning-sloat-park-etal-snowmeltrisktelecouplingsforirrigatedagriculture-2022', 'https://www.nature.com/articles/s41558-022-01509-z', event);\">\n    Snowmelt risk telecouplings for irrigated agriculture.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Qin, Y.; Hong, C.; Zhao, H.; Siebert, S.; Abatzoglou, J. T.; Huning, L. S.; Sloat, L. L.; Park, S.; Li, S.; Munroe, D. K.; Zhu, T.; Davis, S. J.;  and Mueller, N. D.\n</span>\n\n  \n\n</span>\n\n  <i>Nature Climate Change</i>, 12(11): 1007–1015. 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.nature.com/articles/s41558-022-01509-z\"\n     onclick=\"log_download('qin-hong-zhao-siebert-abatzoglou-huning-sloat-park-etal-snowmeltrisktelecouplingsforirrigatedagriculture-2022', 'https://www.nature.com/articles/s41558-022-01509-z', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Snowmelt risk telecouplings for irrigated agriculture [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/s41558-022-01509-z\"\n     onclick=\"log_download('qin-hong-zhao-siebert-abatzoglou-huning-sloat-park-etal-snowmeltrisktelecouplingsforirrigatedagriculture-2022', 'http://doi.org/10.1038/s41558-022-01509-z', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/qin-hong-zhao-siebert-abatzoglou-huning-sloat-park-etal-snowmeltrisktelecouplingsforirrigatedagriculture-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('qin-hong-zhao-siebert-abatzoglou-huning-sloat-park-etal-snowmeltrisktelecouplingsforirrigatedagriculture-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{qin_snowmelt_2022,\n\ttitle = {Snowmelt risk telecouplings for irrigated agriculture},\n\tvolume = {12},\n\tissn = {1758-678X, 1758-6798},\n\turl = {https://www.nature.com/articles/s41558-022-01509-z},\n\tdoi = {10.1038/s41558-022-01509-z},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2024-03-05},\n\tjournal = {Nature Climate Change},\n\tauthor = {Qin, Yue and Hong, Chaopeng and Zhao, Hongyan and Siebert, Stefan and Abatzoglou, John T. and Huning, Laurie S. and Sloat, Lindsey L. and Park, Sohyun and Li, Shiyu and Munroe, Darla K. and Zhu, Tong and Davis, Steven J. and Mueller, Nathaniel D.},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {1007--1015},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"leonhardt-drees-jung-roscher-probabilisticbiomassestimationwithconditionalgenerativeadversarialnetworks-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/10.1007/978-3-031-16788-1_29\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'leonhardt-drees-jung-roscher-probabilisticbiomassestimationwithconditionalgenerativeadversarialnetworks-2022', 'https://link.springer.com/10.1007/978-3-031-16788-1_29', event);\">\n    Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Leonhardt, J.; Drees, L.; Jung, P.;  and Roscher, R.\n</span>\n\n  \n\n</span>\n\n  In Andres, B.; Bernard, F.; Cremers, D.; Frintrop, S.; Goldlücke, B.;  and Ihrke, I., editor(s), <i>Pattern Recognition</i>, volume 13485, pages 479–494. Springer International Publishing, Cham,  2022.\n  <span class=\"note\">Series Title: Lecture Notes in Computer Science</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://link.springer.com/10.1007/978-3-031-16788-1_29\"\n     onclick=\"log_download('leonhardt-drees-jung-roscher-probabilisticbiomassestimationwithconditionalgenerativeadversarialnetworks-2022', 'https://link.springer.com/10.1007/978-3-031-16788-1_29', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks [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/10.1007/978-3-031-16788-1_29\"\n     onclick=\"log_download('leonhardt-drees-jung-roscher-probabilisticbiomassestimationwithconditionalgenerativeadversarialnetworks-2022', 'http://doi.org/10.1007/978-3-031-16788-1_29', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/leonhardt-drees-jung-roscher-probabilisticbiomassestimationwithconditionalgenerativeadversarialnetworks-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('leonhardt-drees-jung-roscher-probabilisticbiomassestimationwithconditionalgenerativeadversarialnetworks-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;\">@incollection{andres_probabilistic_2022,\n\taddress = {Cham},\n\ttitle = {Probabilistic {Biomass} {Estimation} with {Conditional} {Generative} {Adversarial} {Networks}},\n\tvolume = {13485},\n\tisbn = {978-3-031-16787-4 978-3-031-16788-1},\n\turl = {https://link.springer.com/10.1007/978-3-031-16788-1_29},\n\tlanguage = {en},\n\turldate = {2023-11-27},\n\tbooktitle = {Pattern {Recognition}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Leonhardt, Johannes and Drees, Lukas and Jung, Peter and Roscher, Ribana},\n\teditor = {Andres, Björn and Bernard, Florian and Cremers, Daniel and Frintrop, Simone and Goldlücke, Bastian and Ihrke, Ivo},\n\tyear = {2022},\n\tdoi = {10.1007/978-3-031-16788-1_29},\n\tnote = {Series Title: Lecture Notes in Computer Science},\n\tpages = {479--494},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"bauke-amelung-bol-brandt-brggemann-kandeler-meyer-or-etal-soilwaterstatusshapesnutrientcyclinginagroecosystemsfrommicrometertolandscapescales-2022\">\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/10.1002/jpln.202200357\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'bauke-amelung-bol-brandt-brggemann-kandeler-meyer-or-etal-soilwaterstatusshapesnutrientcyclinginagroecosystemsfrommicrometertolandscapescales-2022', 'https://onlinelibrary.wiley.com/doi/10.1002/jpln.202200357', event);\">\n    Soil water status shapes nutrient cycling in agroecosystems from micrometer to landscape scales.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Bauke, S. L.; Amelung, W.; Bol, R.; Brandt, L.; Brüggemann, N.; Kandeler, E.; Meyer, N.; Or, D.; Schnepf, A.; Schloter, M.; Schulz, S.; Siebers, N.; Von Sperber, C.;  and Vereecken, H.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Plant Nutrition and Soil Science</i>, 185(6): 773–792. 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://onlinelibrary.wiley.com/doi/10.1002/jpln.202200357\"\n     onclick=\"log_download('bauke-amelung-bol-brandt-brggemann-kandeler-meyer-or-etal-soilwaterstatusshapesnutrientcyclinginagroecosystemsfrommicrometertolandscapescales-2022', 'https://onlinelibrary.wiley.com/doi/10.1002/jpln.202200357', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Soil water status shapes nutrient cycling in agroecosystems from micrometer to landscape scales [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/10.1002/jpln.202200357\"\n     onclick=\"log_download('bauke-amelung-bol-brandt-brggemann-kandeler-meyer-or-etal-soilwaterstatusshapesnutrientcyclinginagroecosystemsfrommicrometertolandscapescales-2022', 'http://doi.org/10.1002/jpln.202200357', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bauke-amelung-bol-brandt-brggemann-kandeler-meyer-or-etal-soilwaterstatusshapesnutrientcyclinginagroecosystemsfrommicrometertolandscapescales-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('bauke-amelung-bol-brandt-brggemann-kandeler-meyer-or-etal-soilwaterstatusshapesnutrientcyclinginagroecosystemsfrommicrometertolandscapescales-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{bauke_soil_2022,\n\ttitle = {Soil water status shapes nutrient cycling in agroecosystems from micrometer to landscape scales},\n\tvolume = {185},\n\tissn = {1436-8730, 1522-2624},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202200357},\n\tdoi = {10.1002/jpln.202200357},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2023-06-02},\n\tjournal = {Journal of Plant Nutrition and Soil Science},\n\tauthor = {Bauke, Sara L. and Amelung, Wulf and Bol, Roland and Brandt, Luise and Brüggemann, Nicolas and Kandeler, Ellen and Meyer, Nele and Or, Dani and Schnepf, Andrea and Schloter, Michael and Schulz, Stefanie and Siebers, Nina and Von Sperber, Christian and Vereecken, Harry},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {773--792},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"valmassoi-keller-areviewonirrigationparameterizationsinearthsystemmodels-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.frontiersin.org/articles/10.3389/frwa.2022.906664/full\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'valmassoi-keller-areviewonirrigationparameterizationsinearthsystemmodels-2022', 'https://www.frontiersin.org/articles/10.3389/frwa.2022.906664/full', event);\">\n    A review on irrigation parameterizations in Earth system models.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Valmassoi, A.;  and Keller, J. D.\n</span>\n\n  \n\n</span>\n\n  <i>Frontiers in Water</i>, 4: 906664. 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.frontiersin.org/articles/10.3389/frwa.2022.906664/full\"\n     onclick=\"log_download('valmassoi-keller-areviewonirrigationparameterizationsinearthsystemmodels-2022', 'https://www.frontiersin.org/articles/10.3389/frwa.2022.906664/full', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A review on irrigation parameterizations in Earth system 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  <a href=\"http://doi.org/10.3389/frwa.2022.906664\"\n     onclick=\"log_download('valmassoi-keller-areviewonirrigationparameterizationsinearthsystemmodels-2022', 'http://doi.org/10.3389/frwa.2022.906664', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/valmassoi-keller-areviewonirrigationparameterizationsinearthsystemmodels-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\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('valmassoi-keller-areviewonirrigationparameterizationsinearthsystemmodels-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{valmassoi_review_2022,\n\ttitle = {A review on irrigation parameterizations in {Earth} system models},\n\tvolume = {4},\n\tissn = {2624-9375},\n\turl = {https://www.frontiersin.org/articles/10.3389/frwa.2022.906664/full},\n\tdoi = {10.3389/frwa.2022.906664},\n\tabstract = {Irrigation is the process of artificially providing water to agricultural lands in order to provide crops with the necessary water supply to ensure or foster the growth of the plants. However, its implications reach beyond the agro-economic aspect as irrigation affects the soil-land-atmosphere interactions and thus influences the water and energy cycles in the Earth system. Past studies have shown how through these interactions, an increase in soil moisture due to irrigation also affects the atmospheric state and its dynamics. Thus, the lack of representation of irrigation in numerical Earth system models—be it for reanalysis, weather forecasting or climate prediction—can lead to significant errors and biases in various parameters of the system including but not limited to surface temperature and precipitation. In this study, we aim to summarize and discuss currently available irrigation parameterizations across different numerical models. This provides a reference framework to understand the impact of irrigation on the various components of Earth system models. Specifically, we discuss the impact of these parameterizations in the context of their spatio-temporal scale representation and point out the benefits and limitations of the various approaches. In fact, most of the parameterizations use irrigation as a direct modification of soil moisture with just a few implementations add irrigation as a form of surface water. While the former method might be suitable for coarse spatio-temporal scales, the latter better resembles the range of employed irrigation techniques. From the analysis, we find that not only the method or the spatio-temporal scales but the actual amount of water used is of great importance to the response of the Earth system model.},\n\turldate = {2023-01-30},\n\tjournal = {Frontiers in Water},\n\tauthor = {Valmassoi, Arianna and Keller, Jan D.},\n\tmonth = nov,\n\tyear = {2022},\n\tpages = {906664},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Irrigation is the process of artificially providing water to agricultural lands in order to provide crops with the necessary water supply to ensure or foster the growth of the plants. However, its implications reach beyond the agro-economic aspect as irrigation affects the soil-land-atmosphere interactions and thus influences the water and energy cycles in the Earth system. Past studies have shown how through these interactions, an increase in soil moisture due to irrigation also affects the atmospheric state and its dynamics. Thus, the lack of representation of irrigation in numerical Earth system models—be it for reanalysis, weather forecasting or climate prediction—can lead to significant errors and biases in various parameters of the system including but not limited to surface temperature and precipitation. In this study, we aim to summarize and discuss currently available irrigation parameterizations across different numerical models. This provides a reference framework to understand the impact of irrigation on the various components of Earth system models. Specifically, we discuss the impact of these parameterizations in the context of their spatio-temporal scale representation and point out the benefits and limitations of the various approaches. In fact, most of the parameterizations use irrigation as a direct modification of soil moisture with just a few implementations add irrigation as a form of surface water. While the former method might be suitable for coarse spatio-temporal scales, the latter better resembles the range of employed irrigation techniques. From the analysis, we find that not only the method or the spatio-temporal scales but the actual amount of water used is of great importance to the response of the Earth system model.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"karegar-kusche-geremianievinski-larson-raspberrypireflectorrpralowcostwaterlevelmonitoringsystembasedongnssinterferometricreflectometry-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR031713\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'karegar-kusche-geremianievinski-larson-raspberrypireflectorrpralowcostwaterlevelmonitoringsystembasedongnssinterferometricreflectometry-2022', 'https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR031713', event);\">\n    Raspberry Pi Reflector (RPR): A Low‐cost Water‐level Monitoring System based on GNSS Interferometric Reflectometry.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Karegar, M. A.; Kusche, J.; Geremia‐Nievinski, F.;  and Larson, K. M.\n</span>\n\n  \n\n</span>\n\n  <i>Water Resources Research</i>. 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://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR031713\"\n     onclick=\"log_download('karegar-kusche-geremianievinski-larson-raspberrypireflectorrpralowcostwaterlevelmonitoringsystembasedongnssinterferometricreflectometry-2022', 'https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR031713', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Raspberry Pi Reflector (RPR): A Low‐cost Water‐level Monitoring System based on GNSS Interferometric Reflectometry [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/10.1029/2021WR031713\"\n     onclick=\"log_download('karegar-kusche-geremianievinski-larson-raspberrypireflectorrpralowcostwaterlevelmonitoringsystembasedongnssinterferometricreflectometry-2022', 'http://doi.org/10.1029/2021WR031713', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/karegar-kusche-geremianievinski-larson-raspberrypireflectorrpralowcostwaterlevelmonitoringsystembasedongnssinterferometricreflectometry-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\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('karegar-kusche-geremianievinski-larson-raspberrypireflectorrpralowcostwaterlevelmonitoringsystembasedongnssinterferometricreflectometry-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{karegar_raspberry_2022,\n\ttitle = {Raspberry {Pi} {Reflector} ({RPR}): {A} {Low}‐cost {Water}‐level {Monitoring} {System} based on {GNSS} {Interferometric} {Reflectometry}},\n\tissn = {0043-1397, 1944-7973},\n\tshorttitle = {Raspberry {Pi} {Reflector} ({RPR})},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR031713},\n\tdoi = {10.1029/2021WR031713},\n\tlanguage = {en},\n\turldate = {2022-11-24},\n\tjournal = {Water Resources Research},\n\tauthor = {Karegar, Makan A. and Kusche, Jürgen and Geremia‐Nievinski, Felipe and Larson, Kristine M.},\n\tmonth = nov,\n\tyear = {2022},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"vereecken-amelung-bauke-bogena-brggemann-montzka-vanderborght-bechtold-etal-soilhydrologyintheearthsystem-2022\">\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/s43017-022-00324-6\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'vereecken-amelung-bauke-bogena-brggemann-montzka-vanderborght-bechtold-etal-soilhydrologyintheearthsystem-2022', 'https://www.nature.com/articles/s43017-022-00324-6', event);\">\n    Soil hydrology in the Earth system.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Vereecken, H.; Amelung, W.; Bauke, S. L.; Bogena, H.; Brüggemann, N.; Montzka, C.; Vanderborght, J.; Bechtold, M.; Blöschl, G.; Carminati, A.; Javaux, M.; Konings, A. G.; Kusche, J.; Neuweiler, I.; Or, D.; Steele-Dunne, S.; Verhoef, A.; Young, M.;  and Zhang, Y.\n</span>\n\n  \n\n</span>\n\n  <i>Nature Reviews Earth & Environment</i>, 3(9): 573–587. August 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.nature.com/articles/s43017-022-00324-6\"\n     onclick=\"log_download('vereecken-amelung-bauke-bogena-brggemann-montzka-vanderborght-bechtold-etal-soilhydrologyintheearthsystem-2022', 'https://www.nature.com/articles/s43017-022-00324-6', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Soil hydrology in the Earth 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/10.1038/s43017-022-00324-6\"\n     onclick=\"log_download('vereecken-amelung-bauke-bogena-brggemann-montzka-vanderborght-bechtold-etal-soilhydrologyintheearthsystem-2022', 'http://doi.org/10.1038/s43017-022-00324-6', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vereecken-amelung-bauke-bogena-brggemann-montzka-vanderborght-bechtold-etal-soilhydrologyintheearthsystem-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\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>7 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vereecken-amelung-bauke-bogena-brggemann-montzka-vanderborght-bechtold-etal-soilhydrologyintheearthsystem-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{vereecken_soil_2022,\n\ttitle = {Soil hydrology in the {Earth} system},\n\tvolume = {3},\n\tissn = {2662-138X},\n\turl = {https://www.nature.com/articles/s43017-022-00324-6},\n\tdoi = {10.1038/s43017-022-00324-6},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2023-01-26},\n\tjournal = {Nature Reviews Earth \\&amp; Environment},\n\tauthor = {Vereecken, Harry and Amelung, Wulf and Bauke, Sara L. and Bogena, Heye and Brüggemann, Nicolas and Montzka, Carsten and Vanderborght, Jan and Bechtold, Michel and Blöschl, Günter and Carminati, Andrea and Javaux, Mathieu and Konings, Alexandra G. and Kusche, Jürgen and Neuweiler, Insa and Or, Dani and Steele-Dunne, Susan and Verhoef, Anne and Young, Michael and Zhang, Yonggen},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {573--587},\n}\n</pre>\n</div>\n\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);