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\n@article{nguyen_multi-year_2024,\n\ttitle = {Multi-year aboveground data of minirhizotron facilities in {Selhausen}},\n\tvolume = {11},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-024-03535-2},\n\tdoi = {10.1038/s41597-024-03535-2},\n\tabstract = {Abstract\n \n Improved understanding of crops’ response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO\n 2\n and H\n 2\n O gas fluxes, and crop growth processes are rare. Here, we presented a comprehensive data set collected from leaf to canopy using sophisticated and comprehensive sensing techniques (leaf chlorophyll, stomatal conductance and photosynthesis, canopy CO\n 2\n exchange, sap flow, and canopy temperature) including detailed crop growth characteristics based on destructive methods (crop height, leaf area index, aboveground biomass, and yield). Data were acquired under field conditions with contrasting soil types, water treatments, and different cultivars of wheat and maize. The data from 2016 up to now will be made available for studying soil/water-plant relations and improving soil-plant-atmospheric continuum models.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-08-20},\n\tjournal = {Scientific Data},\n\tauthor = {Nguyen, Thuy Huu and Lopez, Gina and Seidel, Sabine J. and Lärm, Lena and Bauer, Felix Maximilian and Klotzsche, Anja and Schnepf, Andrea and Gaiser, Thomas and Hüging, Hubert and Ewert, Frank},\n\tmonth = jun,\n\tyear = {2024},\n\tpages = {674},\n}\n\n\n\n\n
@article{larson_gnssrefl_2024,\n\ttitle = {Gnssrefl: an open source software package in python for {GNSS} interferometric reflectometry applications},\n\tvolume = {28},\n\tissn = {1080-5370, 1521-1886},\n\tshorttitle = {Gnssrefl},\n\turl = {https://link.springer.com/10.1007/s10291-024-01694-8},\n\tdoi = {10.1007/s10291-024-01694-8},\n\tabstract = {Abstract\n An open source software package has been developed for Global Navigation Satellite Systems (GNSS) interferometric reflectometry. The gnssrefl package is written in python; it can be installed from the source code, the python packaging index website, or via a docker. It includes modules that download GNSS data and orbit data from global archives. A periodogram is used to retrieve the height of the GNSS antenna over the reflecting surface using signal to noise ratio data. Signals from the Global Positioning System, Glonass, Galileo, and Beidou constellations are supported. Modules are provided to estimate volumetric water content of soil, snow depth/accumulation, and water level. Utilities for mapping and assessing reflection zones and determining the maximum resolvable height are available.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2024-08-20},\n\tjournal = {GPS Solutions},\n\tauthor = {Larson, Kristine M.},\n\tmonth = oct,\n\tyear = {2024},\n\tpages = {165},\n}\n\n\n\n\n\n\n\n\n
@article{springer_regionally_2024,\n\ttitle = {A {Regionally} {Refined} and {Mass}‐{Consistent} {Atmospheric} and {Hydrological} {De}‐{Aliasing} {Product} for {GRACE}, {GRACE}‐{FO} and {Future} {Gravity} {Missions}},\n\tvolume = {129},\n\tissn = {2169-9313, 2169-9356},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB027883},\n\tdoi = {10.1029/2023JB027883},\n\tabstract = {Abstract\n De‐aliasing products are used in the estimation process of satellite‐based gravity field computation to reduce errors from high‐frequency mass variations that alias into monthly gravity fields. The latest official product is AOD1B RL07 and describes non‐tidal atmosphere and oceanic mass variations at 3‐hourly resolution. However, the model‐based de‐aliasing products are inevitably incomplete and prone to temporally and spatially correlated errors that substantially contribute to errors in the estimated gravity fields. Here, we investigate possible enhancement of current de‐aliasing products by nesting a regional high‐resolution atmospheric reanalysis over Europe into a global reanalysis. As further novelty we include almost mass consistent terrestrial water storage variability from a regional hydrological model nested into a global model as additional component of the de‐aliasing product. While we find in agreement with earlier studies only minor contributions from increasing the temporal resolution beyond 3‐hourly data, our investigations suggest that contributions from continental hydrology and from regional non‐hydrostatic atmospheric modeling to sub‐monthly mass variations could be relevant already for gravity fields estimated from current gravity missions. Moreover, in the context of extreme events, we find regionally contributions from additional moisture fields, such as cloud liquid water, in the order of a few mm over Europe. We suggest this needs to be taken into account when preparing data analysis schemes for future space gravimetric missions.\n , \n Plain Language Summary\n Observing temporal variations in the Earth's gravity field with satellite gravimetry plays an essential role for monitoring mass transports on and underneath the Earth's surface. This is crucial for understanding the evolution of floods and droughts, the role of groundwater pumping, and to quantify the melting of ice sheets and glaciers and the resulting sea level rise. In order to isolate the target variable (e.g., terrestrial water storage changes) unwanted signals (e.g., fast mass variations in the atmosphere) need to be removed in the gravity field estimation process using background models, so‐called de‐aliasing models. This paper focuses on improving background models by incorporating regional high‐resolution models, which more specifically resolve certain processes in the atmosphere. Our hypothesis is that this will lead to better gravity fields with increased spatial resolution and less noise. Moreover, we find that considering fast hydrological variations as additional background model could improve gravity fields from the current satellite mission GRACE‐FO. For the first time, we quantify contributions from so far neglected atmospheric moisture fields, such as cloud liquid water, to enhance background models in the context of extreme events—which, however, will likely be in particular relevant for more sensitive gravity missions in the future.\n , \n Key Points\n \n \n \n We provide an atmosphere‐hydrology de‐aliasing product with regional mass‐consistent refinement over Europe\n \n \n Using non‐hydrostatic as opposed to hydrostatic numerical weather prediction model output significantly impacts the de‐aliasing product\n \n \n We found that for extreme events additional moisture fields unaccounted in present Atmosphere and Ocean De‐aliasing (AOD) models can reach magnitudes relevant for de‐aliasing},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2024-07-23},\n\tjournal = {Journal of Geophysical Research: Solid Earth},\n\tauthor = {Springer, Anne and Mielke, Christian A. and Liu, Ziyu and Dixit, Shashi and Friederichs, Petra and Kusche, Jürgen},\n\tmonth = may,\n\tyear = {2024},\n\tpages = {e2023JB027883},\n}\n\n\n\n\n
@article{martre_global_2024,\n\ttitle = {Global needs for nitrogen fertilizer to improve wheat yield under climate change},\n\tissn = {2055-0278},\n\turl = {https://www.nature.com/articles/s41477-024-01739-3},\n\tdoi = {10.1038/s41477-024-01739-3},\n\tlanguage = {en},\n\turldate = {2024-07-22},\n\tjournal = {Nature Plants},\n\tauthor = {Martre, Pierre and Dueri, Sibylle and Guarin, Jose Rafael and Ewert, Frank and Webber, Heidi and Calderini, Daniel and Molero, Gemma and Reynolds, Matthew and Miralles, Daniel and Garcia, Guillermo and Brown, Hamish and George, Mike and Craigie, Rob and Cohan, Jean-Pierre and Deswarte, Jean-Charles and Slafer, Gustavo and Giunta, Francesco and Cammarano, Davide and Ferrise, Roberto and Gaiser, Thomas and Gao, Yujing and Hochman, Zvi and Hoogenboom, Gerrit and Hunt, Leslie A. and Kersebaum, Kurt C. and Nendel, Claas and Padovan, Gloria and Ruane, Alex C. and Srivastava, Amit Kumar and Stella, Tommaso and Supit, Iwan and Thorburn, Peter and Wang, Enli and Wolf, Joost and Zhao, Chuang and Zhao, Zhigan and Asseng, Senthold},\n\tmonth = jul,\n\tyear = {2024},\n}\n\n\n\n\n\n\n\n\n
@article{chen_convectionpermitting_2024,\n\ttitle = {Convection‐{Permitting} {ICON}‐{LAM} {Simulations} for {Renewable} {Energy} {Potential} {Estimates} {Over} {Southern} {Africa}},\n\tvolume = {129},\n\tissn = {2169-897X, 2169-8996},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JD039569},\n\tdoi = {10.1029/2023JD039569},\n\tabstract = {Abstract\n \n Renewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub‐Saharan Africa, where three‐quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON‐NWP) model in its Limited Area Mode (ICON‐LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection‐permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON‐LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s\n −1\n over land. Likewise the simulated rsds with an ME of 50 W m\n −2\n well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets.\n \n , \n Plain Language Summary\n There are still approximately 580 million people in Africa without reliable electricity supply. Renewable energy is broadly accepted as an important solution for Africa to fill the power supply gap and to mitigate climate change. Renewable energy potential (REP) information is thereby imperative for the expansion of renewable energy power planning. With conventional REP estimates, challenges are often linked to the meteorological input data, due to either the relatively coarse spatial resolution, data gaps in space and time, or data quality in general. In this study, we implemented and ran the atmospheric model ICOsahedral Nonhydrostatic (ICON) from the German Weather Service and partners at 3 km high‐resolution. The renewable energy variables wind speed and solar irradiance from these simulations are evaluated against an extensive in situ observations data set, as well as satellite, and other composite data products. In a comparison with more than 200 stations from three different in situ observation networks, it can be shown that ICON can reproduce REP‐related variables with a level of sophistication that the data is likely to offer added value over conventional inputs to REP assessments. The study is an example on how numerical models can fill in gaps in data‐scarce regions to produce useable information.\n , \n Key Points\n \n \n \n A new convection‐permitting regional ICOsahedral Nonhydrostatic (ICON) model setup over southern Africa is presented and evaluated\n \n \n The spatially and temporally highly resolved ICON wind and solar fields are inputs for improved renewable energy potential estimates\n \n \n ICON outputs agree well with hourly in situ observations and satellite data; wind speeds are slightly overestimated},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2024-07-10},\n\tjournal = {Journal of Geophysical Research: Atmospheres},\n\tauthor = {Chen, Shuying and Poll, Stefan and Hendricks Franssen, Harrie‐Jan and Heinrichs, Heidi and Vereecken, Harry and Goergen, Klaus},\n\tmonth = mar,\n\tyear = {2024},\n\tpages = {e2023JD039569},\n}\n\n\n\n\n
@article{storm_probabilistic_2024,\n\ttitle = {Probabilistic programming for embedding theory and quantifying uncertainty in econometric analysis},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {0165-1587, 1464-3618},\n\turl = {https://academic.oup.com/erae/advance-article/doi/10.1093/erae/jbae016/7704545},\n\tdoi = {10.1093/erae/jbae016},\n\tabstract = {Abstract\n The replication crisis in empirical research calls for a more mindful approach to how we apply and report statistical models. For empirical research to have a lasting (policy) impact, these concerns are crucial. In this paper, we present Probabilistic Programming (PP) as a way forward. The PP workflow with an explicit data-generating process enhances the communication of model assumptions, code testing and consistency between theory and estimation. By simplifying Bayesian analysis, it also offers advantages for the interpretation, communication and modelling of uncertainty. We outline the advantages of PP to encourage its adoption in our community.},\n\tlanguage = {en},\n\turldate = {2024-07-10},\n\tjournal = {European Review of Agricultural Economics},\n\tauthor = {Storm, Hugo and Heckelei, Thomas and Baylis, Kathy},\n\tmonth = jul,\n\tyear = {2024},\n\tpages = {jbae016},\n}\n\n\n\n\n\n\n\n\n
@article{fiedler_interactions_2024,\n\ttitle = {Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from {AerChemMIP}, {PDRMIP}, and {RFMIP}},\n\tvolume = {17},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/},\n\tissn = {1991-9603},\n\turl = {https://gmd.copernicus.org/articles/17/2387/2024/},\n\tdoi = {10.5194/gmd-17-2387-2024},\n\tabstract = {Abstract. The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2024-06-26},\n\tjournal = {Geoscientific Model Development},\n\tauthor = {Fiedler, Stephanie and Naik, Vaishali and O'Connor, Fiona M. and Smith, Christopher J. and Griffiths, Paul and Kramer, Ryan J. and Takemura, Toshihiko and Allen, Robert J. and Im, Ulas and Kasoar, Matthew and Modak, Angshuman and Turnock, Steven and Voulgarakis, Apostolos and Watson-Parris, Duncan and Westervelt, Daniel M. and Wilcox, Laura J. and Zhao, Alcide and Collins, William J. and Schulz, Michael and Myhre, Gunnar and Forster, Piers M.},\n\tmonth = mar,\n\tyear = {2024},\n\tpages = {2387--2417},\n}\n\n\n\n\n\n\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n
@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\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n
@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\n\n\n
@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\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n\n\n\n\n
@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 \\<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\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n
@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\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n\n\n\n\n
@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\n\n\n
@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\n\n\n
@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\n\n\n
@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\n\n\n
@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\n