Articles | Volume 16, issue 5
https://doi.org/10.5194/gmd-16-1553-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1553-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Mar 2023
Model description paper |
| 17 Mar 2023
| 17 Mar 2023
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
Jiangtao Liu
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
David Hughes
Department of Entomology, The Pennsylvania State University,
University Park, PA, USA
Department of Biology, The Pennsylvania State University, University
Park, PA, USA
The Current and Emerging Threat to Crop Innovation Lab, The
Pennsylvania State University, University Park, PA, USA
Farshid Rahmani
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
Kathryn Lawson
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
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Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen
Biogeosciences, 20, 2671–2692, https://doi.org/10.5194/bg-20-2671-2023, https://doi.org/10.5194/bg-20-2671-2023, 2023
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Photosynthesis is critical for life and has been affected by the changing climate. Many parameters come into play while modeling, but traditional calibration approaches face many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279, https://doi.org/10.5194/hess-2024-279, 2024
Preprint under review for HESS
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Hydrologic models are needed to provide simulations of water availability, floods and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models, and thus cannot easily be used to select an appropriate model for a specific place.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen
Biogeosciences, 20, 2671–2692, https://doi.org/10.5194/bg-20-2671-2023, https://doi.org/10.5194/bg-20-2671-2023, 2023
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Photosynthesis is critical for life and has been affected by the changing climate. Many parameters come into play while modeling, but traditional calibration approaches face many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 27, 2357–2373, https://doi.org/10.5194/hess-27-2357-2023, https://doi.org/10.5194/hess-27-2357-2023, 2023
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Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, Kuo-Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li, and Wen-Ping Tsai
Hydrol. Earth Syst. Sci., 22, 5639–5656, https://doi.org/10.5194/hess-22-5639-2018, https://doi.org/10.5194/hess-22-5639-2018, 2018
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Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.
W. J. Riley and C. Shen
Hydrol. Earth Syst. Sci., 18, 2463–2483, https://doi.org/10.5194/hess-18-2463-2014, https://doi.org/10.5194/hess-18-2463-2014, 2014
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The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of the dynamic SWP.
Jenny Kupzig, Nina Kupzig, and Martina Flörke
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Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying climate change impacts. Adapting GHMs to ungauged basins requires regionalization, enabling valid simulations. In this study, we highlight the impact of regionalization of GHMs on runoff simulations using an ensemble of regionalization methods for WaterGAP3. We have found that regionalization leads to temporally and spatially varying uncertainty, potentially reaching up to inter-model differences.
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
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STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
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River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
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The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
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This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
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This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
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The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
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Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
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We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
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The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
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Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
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We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
João Careto, Rita Cardoso, Ana Russo, Daniela Lima, and Pedro Soares
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-9, https://doi.org/10.5194/gmd-2024-9, 2024
Revised manuscript accepted for GMD
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In this study, a new drought index is proposed, which not only is able to identify the same events but also can improve the results obtained from other established drought indices. The index is empirically based and is extremely straightforward to compute. It is as well, a daily drought index with the ability to not only assess flash droughts but also events at longer aggregation scales, such as the traditional monthly indices.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-151, https://doi.org/10.5194/gmd-2023-151, 2023
Revised manuscript accepted for GMD
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We develop an operational forecast system, COATLINES-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model requires a relatively small computational demand and results compare well with near real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and waves predictions can improve in accuracy.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
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A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Cited articles
Aboelyazeed, D., Xu, C., Hoffman, F. M., Jones, A. W., Rackauckas, C., Lawson, K. E., and Shen, C.: A differentiable ecosystem modeling framework for large-scale inverse problems: demonstration with photosynthesis simulations, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2022-211, in review, 2022.
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017.
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018.
Al-Yaari, A., Wigneron, J.-P., Kerr, Y., Rodriguez-Fernandez, N., O'Neill,
P. E., Jackson, T. J., De Lannoy, G. J. M., Al Bitar, A., Mialon, A.,
Richaume, P., Walker, J. P., Mahmoodi, A., and Yueh, S.: Evaluating soil
moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature
datasets, Remote Sens. Environ., 193, 257–273,
https://doi.org/10.1016/j.rse.2017.03.010, 2017.
Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A.,
Malczyk, J., and Jetz, W.: A suite of global, cross-scale topographic
variables for environmental and biodiversity modeling, Sci. Data, 5, 180040,
https://doi.org/10.1038/sdata.2018.40, 2018.
Armesto, J. J. and Martnez, J. A.: Relations between vegetation structure
and slope aspect in the Mediterranean region of Chile, J. Ecol., 66,
881–889, https://doi.org/10.2307/2259301, 1978.
Baraniuk, C.: Locust Swarms Are Getting So Big That We Need Radar to Track
Them, Medium, https://onezero.medium.com/locust-swarms-are-getting-so-big-that-we-need-radar-to-track-them-dc79c06496a0 (last access: 1 August 2022), 2020.
Beaudoing, H. and Rodell, M.: GLDAS Noah Land Surface Model
L4 3 hourly 0.25 x 0.25 degree V2.0 (GLDAS_NOAH025_3H 2.0), Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/342OHQM9AK6Q, 2019.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Dijk, A.
I. J. M. van, McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly
0.1∘ Precipitation: Methodology and Quantitative Assessment, B.
Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1,
2019.
Beck, H. E., Pan, M., Miralles, D. G., Reichle, R. H., Dorigo, W. A., Hahn, S., Sheffield, J., Karthikeyan, L., Balsamo, G., Parinussa, R. M., van Dijk, A. I. J. M., Du, J., Kimball, J. S., Vergopolan, N., and Wood, E. F.: Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors, Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, 2021.
Bennie, J., Hill, M. O., Baxter, R., and Huntley, B.: Influence of slope and
aspect on long-term vegetation change in British chalk grasslands, J. Ecol.,
94, 355–368, https://doi.org/10.1111/j.1365-2745.2006.01104.x, 2006.
Bentley, A. R., Donovan, J., Sonder, K., Baudron, F., Lewis, J. M., Voss,
R., Rutsaert, P., Poole, N., Kamoun, S., Saunders, D. G. O., Hodson, D.,
Hughes, D. P., Negra, C., Ibba, M. I., Snapp, S., Sida, T. S., Jaleta, M.,
Tesfaye, K., Becker-Reshef, I., and Govaerts, B.: Near- to long-term
measures to stabilize global wheat supplies and food security, Nat. Food, 3,
483–486, https://doi.org/10.1038/s43016-022-00559-y, 2022.
Bindas, T., Tsai, W.-P., Liu, J., Rahmani, F., Feng, D., Bian, Y., Lawson,
K., and Shen, C.: Improving large-basin streamflow simulation using a
modular, differentiable, learnable graph model for routing, ESS Open Archive [preprint],
https://doi.org/10.1002/essoar.10512512.1, 2022.
de Jeu, R. and Owe, M.: AMSR2/GCOM-W1 surface soil moisture (LPRM)
L3 1 day 10 km x 10 km ascending V001, Goddard Earth Sciences Data and Information Services Center (GES DISC) (Bill Teng), Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/B0GHODHJLDA8,
2013.
Didan, K.: MOD13C2: MODIS/Terra Vegetation Indices Monthly L3 Global 0.05Deg
CMG version 6, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD13C2.006, 2015.
Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T.: The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, https://doi.org/10.5194/hess-15-1675-2011, 2011.
Dorigo, W. A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A.,
Sanchis-Dufau, A. D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global automated quality control of in situ soil moisture data from the
international soil moisture network, Vadose Zone J., 12, vzj2012.0097,
https://doi.org/10.2136/vzj2012.0097, 2013.
Ellenburg, W. L., Mishra, V., Roberts, J. B., Limaye, A. S., Case, J. L.,
Blankenship, C. B., and Cressman, K.: Detecting desert locust breeding
grounds: A satellite-assisted modeling approach, Remote Sens., 13, 1276,
https://doi.org/10.3390/rs13071276, 2021.
Entekhabi, D.: The Soil Moisture Active Passive (SMAP) mission, P. IEEE,
98, 704–716, https://doi.org/10/bz3xhb, 2010.
ESA: Land Cover CCI Product User Guide Version 2, http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 1 August 2022), 2017.
Fang, K. and Shen, C.: Near-real-time forecast of satellite-based soil
moisture using long short-term memory with an adaptive data integration
kernel, J. Hydrometeorol., 21, 399–413,
https://doi.org/10.1175/jhm-d-19-0169.1, 2020.
Fang, K., Shen, C., Kifer, D., and Yang, X.: Prolongation of SMAP to
spatiotemporally seamless coverage of continental U.S. using a deep learning
neural network, Geophys. Res. Lett., 44, 11030–11039,
https://doi.org/10.1002/2017gl075619, 2017.
Fang, K., Pan, M., and Shen, C.: The value of SMAP for long-term soil
moisture estimation with the help of deep learning, IEEE T. Geosci.
Remote, 57, 2221–2233, https://doi.org/10/gghp3v, 2019.
Fang, K., Kifer, D., Lawson, K., Feng, D., and Shen, C.: The data synergy
effects of time-series deep learning models in hydrology, Water Resour.
Res., 58, e2021WR029583, https://doi.org/10.1029/2021WR029583, 2022.
FAO, IIASA, ISRIC, ISSCAS, and JRC: Harmonized World Soil Database (version
1.2), FAO IIASA, ISRIC, ISSCAS, and JRC [data set], http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last access: 1 August 2022), 2012.
Feng, D., Fang, K., and Shen, C.: Enhancing streamflow forecast and
extracting insights using long-short term memory networks with data
integration at continental scales, Water Resour. Res., 56, e2019WR026793,
https://doi.org/10.1029/2019WR026793, 2020.
Feng, D., Lawson, K., and Shen, C.: Mitigating prediction error of deep
learning streamflow models in large data-sparse regions with ensemble
modeling and soft data, Geophys. Res. Lett., 48, e2021GL092999,
https://doi.org/10.1029/2021GL092999, 2021.
Feng, D., Liu, J., Lawson, K., and Shen, C.: Differentiable, learnable,
regionalized process-based models with multiphysical outputs can approach
state-of-the-art hydrologic prediction accuracy, Water Resour. Res., 58,
e2022WR032404, https://doi.org/10.1029/2022WR032404, 2022.
Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H. T., Verelst,
L., and Wiberg, D.: Global Agro-Ecological Zones Assessment for Agriculture
(GAEZ 2008), IIASA Laxenburg, Austria and FAO, Rome, Italy, 2008.
Friedman, J. H.: Greedy Function Approximation: A Gradient Boosting Machine,
Ann. Stat., 29, 1189–1232, 2001.
Gauch, M., Mai, J., and Lin, J.: The Proper Care and Feeding of CAMELS: How
Limited Training Data Affects Streamflow Prediction, Environ. Modell. Softw., 135, 104926, https://doi.org/10.1016/j.envsoft.2020.104926, 2021.
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural. Comput.,
9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J.,
Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret,
U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut,
R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB)–a review, Hydrol. Sci. J.,
58, 1198–1255, https://doi.org/10/gfsq5q, 2013.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.:
GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06
(GPM_3IMERGDF 06), edited by: Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set],
https://doi.org/10.5067/GPM/IMERGDF/DAY/06, 2019.
Hunter-Jones, P.: Egg development in the Desert Locust (Schistocerca
gregaria Forsk.) in relation to the availability of water, Proc. R. Entomol.
Soc. A, 39, 25–33,
https://doi.org/10.1111/j.1365-3032.1964.tb00781.x, 1964.
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F.,
Boutin, J., Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S.
E., Drinkwater, M. R., Hahne, A., Martín-Neira, M., and Mecklenburg,
S.: The SMOS mission: New tool for monitoring key elements of the global
water cycle, P. IEEE, 98, 666–687, https://doi.org/10/b9szx6, 2010.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019.
Liu, J., Rahmani, F., Lawson, K., and Shen, C.: A multiscale deep learning
model for soil moisture integrating satellite and in situ data, Geophys.
Res. Lett., 49, e2021GL096847, https://doi.org/10.1029/2021GL096847, 2022a.
Liu, J., Hughes, D., Rahmani, F., Lawson, K., and Shen, C.: Global Soil Moisture Dataset From a Multitask Model (GSM3), Zenodo [data set], https://doi.org/10.5281/zenodo.7344484, 2022b.
Meyal, A. Y., Versteeg, R., Alper, E., Johnson, D., Rodzianko, A., Franklin,
M., and Wainwright, H.: Automated cloud based long short-term memory neural
network based SWE prediction, Front. Water, 2, 1–12,
https://doi.org/10.3389/frwa.2020.574917, 2020.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Narasimhan, B. and Srinivasan, R.: Development and evaluation of Soil
Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI)
for agricultural drought monitoring, Agr. Forest Meteorol., 133, 69–88,
https://doi.org/10/fq9wdv, 2005.
Natural Earth: Free vector and raster map data, Natural Earth [data set], https://www.naturalearthdata.com/, last access: 1 August 2022.
Norbiato, D., Borga, M., Degli Esposti, S., Gaume, E., and Anquetin, S.:
Flash flood warning based on rainfall thresholds and soil moisture
conditions: An assessment for gauged and ungauged basins, J. Hydrol., 362,
274–290, https://doi.org/10/dtw4hm, 2008.
Nuwer, R.: As Locusts Swarmed East Africa, This Tech Helped Squash Them, N.
Y. Times, 8th April, https://www.nytimes.com/2021/04/08/science/locust-swarms-africa.html (last access: 1 August 2022), 2021.
O, S. and Orth, R.: Global soil moisture data derived through machine
learning trained with in-situ measurements, Sci. Data, 8, 170,
https://doi.org/10.1038/s41597-021-00964-1, 2021.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., Chaubell,
J., and Colliander, A.: SMAP Enhanced L3 Radiometer Global and Polar Grid
Daily 9 km EASE-Grid Soil Moisture, Version 5 (SPL3SMP_E), Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/4DQ54OUIJ9DL, 2021.
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of
satellite-derived global land surface moisture, J. Geophys. Res., 113,
1–17, https://doi.org/10.1029/2007JF000769, 2008.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., and Dubourg, V.: Scikit-learn:
Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Rahmani, F., Shen, C., Oliver, S., Lawson, K., and Appling, A.: Deep
learning approaches for improving prediction of daily stream temperature in
data-scarce, unmonitored, and dammed basins, Hydrol. Process., 35, e14400,
https://doi.org/10.1002/hyp.14400, 2021a.
Rahmani, F., Lawson, K., Ouyang, W., Appling, A., Oliver, S., and Shen, C.:
Exploring the exceptional performance of a deep learning stream temperature
model and the value of streamflow data, Environ. Res. Lett., 16, 024025,
https://doi.org/10.1088/1748-9326/abd501, 2021b.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394,
https://doi.org/10.1175/BAMS-85-3-381, 2004.
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Daily L3
Global – 500m V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A3.061, 2021.
Sheffield, J. and Wood, E. F.: Global trends and variability in soil
moisture and drought characteristics, 1950–2000, from observation-driven
simulations of the terrestrial hydrologic cycle, J. Climate, 21, 432–458,
https://doi.org/10.1175/2007JCLI1822.1, 2008.
Shen, C.: A transdisciplinary review of deep learning research and its
relevance for water resources scientists, Water Resour. Res., 54,
8558–8593, https://doi.org/10.1029/2018wr022643, 2018.
Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky,
A., Baity-Jesi, M., Fenicia, F., Kifer, D., Li, L., Liu, X., Ren, W., Zheng,
Y., Harman, C. J., Clark, M., Farthing, M., Feng, D., Kumar, P.,
Aboelyazeed, D., Rahmani, F., Beck, H. E., Bindas, T., Dwivedi, D., Fang,
K., Höge, M., Rackauckas, C., Roy, T., Xu, C., and Lawson, K.:
Differentiable modeling to unify machine learning and physical models and
advance Geosciences, arXiv [preprint], https://doi.org/10.48550/arXiv.2301.04027, 10 January
2023.
Support CATDS: CATDS-PDC L3SM Simple UDP – 1 day soil moisture Simple User
Data Product from SMOS satellite, CATDS (CNES, IFREMER, CESBIO) [data set]
https://doi.org/10.12770/8db7102b-1b22-4db3-949d-e51269417aae, 2022.
Tsai, W.-P., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J., and
Shen, C.: From calibration to parameter learning: Harnessing the scaling
effects of big data in geoscientific modeling, Nat. Commun., 12, 5988,
https://doi.org/10.1038/s41467-021-26107-z, 2021.
UN WFP: Stop locusts in East Africa now or pay much more to help people
later, U. N. UN World Food Programme WFP, 14th February, https://www.wfp.org/news/stop-locusts-east-africa-now-or-pay-much-more-help-people-later-wfp (last access: 1 August 2022), 2020.
Wan, Z., Hook, S., and Hulley, G.: MODIS/Aqua Land Surface
Temperature/Emissivity Daily L3 Global 1km SIN Grid V061, NASA EOSDIS Land Processes DAAC [data set],
https://doi.org/10.5067/MODIS/MYD11A1.061, 2021.
Xue, R., Yang, Q., Miao, F., Wang, X., Shen, Y., Xue, R., Yang, Q., Miao,
F., Wang, X., and Shen, Y.: Slope aspect influences plant biomass, soil
properties and microbial composition in alpine meadow on the Qinghai-Tibetan
plateau, J. Soil Sci. Plant Nut., 18, 1–12,
https://doi.org/10.4067/S0718-95162018005000101, 2018.
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res.-Atmos., 116, D12110, https://doi.org/10.1029/2010JD015140, 2011.
Zhi, W., Feng, D., Tsai, W.-P., Sterle, G., Harpold, A., Shen, C., and Li,
L.: From hydrometeorology to river water quality: Can a deep learning model
predict dissolved oxygen at the continental scale?, Environ. Sci. Technol.,
55, 2357–2368, https://doi.org/10.1021/acs.est.0c06783, 2021.
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Under-monitored regions like Africa need high-quality soil moisture predictions to help with...
