Articles | Volume 28, issue 14
https://doi.org/10.5194/hess-28-3305-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-3305-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jul 2024
Research article |
| 25 Jul 2024
| 25 Jul 2024
Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China
Rutong Liu
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei, 430072, PR China
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei, 430072, PR China
Louise Slater
School of Geography and the Environment, University of Oxford, Oxford, UK
Shengyu Kang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei, 430072, PR China
Yuanhang Yang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei, 430072, PR China
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei, 430072, PR China
Jiali Guo
Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang 443002, Hubei Province, China
College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, Hubei 443002, PR China
School of Environmental Studies, China University of Geosciences, Wuhan 430074, PR China
Xiang Zhang
National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, PR China
Aliaksandr Volchak
Engineering Systems and Ecology Faculty, Brest State Technical University, Moskovskaya 267, 224017 Brest, Belarus
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Xueying Li, Yan Sun, Xihui Gu, Niko Wanders, Bridget R. Scanlon, and Louise J. Slater
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-282, https://doi.org/10.5194/essd-2026-282, 2026
Preprint under review for ESSD
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Existing datasets of terrestrial water storage anomalies are too coarse to capture sub-regional variations, limiting understanding of fine-scale water processes. Here we use physically guided deep learning to produce a higher-resolution dataset across China for 2002–2023, increasing spatial detail from 3° to 0.1° resolution. The dataset preserves large-scale satellite observations and shows good consistency in process-based evaluations, supporting sub-regional hydrologic analysis.
Shengyu Kang, Jiabo Yin, and Dai Yamazaki
Geosci. Model Dev., 19, 5623–5640, https://doi.org/10.5194/gmd-19-5623-2026, https://doi.org/10.5194/gmd-19-5623-2026, 2026
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Global floods pose serious risks, but existing models are too slow for large-scale prediction. We redesigned the Catchment-based Macro-scale Floodplain (CaMa-Flood) model for graphics processing units (GPUs), reformulating irregular river networks, flux updates, and floodplain dynamics into highly parallel algorithms. CaMa-Flood-GPU runs global simulations in hours instead of days with the same accuracy, enabling larger ensembles, better flood-risk analysis, and improved preparedness worldwide.
Boen Zhang, Michel Wortmann, Yinxue Liu, Frederik Kratzert, Simon Moulds, and Louise Slater
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-279, https://doi.org/10.5194/essd-2026-279, 2026
Preprint under review for ESSD
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We created a new global hydro-environmental database to better describe how rivers are connected and how surrounding environments vary across space and time. Most existing databases simplify river networks, miss complex flow paths, and are largely based on older global datasets. Our database provides more than 300 attributes for 19.6 million km of rivers and streams. It supports more accurate study of water movement, environmental change, and climate impacts worldwide.
Haomei Lin, Peirong Lin, Fenghe Zhang, Louise Slater, Yuan Yang, Ming Pan, Qiming Qin, and Aizhong Hou
EGUsphere, https://doi.org/10.5194/egusphere-2026-1834, https://doi.org/10.5194/egusphere-2026-1834, 2026
Preprint archived
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Accurate streamflow reconstruction is crucial for water resources assessment. Traditional models struggle with land-surface changes and human impacts. This study proposes a residual learning framework that leverages Google’s high-resolution satellite embeddings—compact Earth observation data—to improve hydrologic simulations. Our results show that this AI-driven spatial context enhances river modeling accuracy, offering a scalable solution for data-scarce regions.
Emma Ford, Manuela I. Brunner, Hannah Christensen, and Louise Slater
Hydrol. Earth Syst. Sci., 30, 2135–2160, https://doi.org/10.5194/hess-30-2135-2026, https://doi.org/10.5194/hess-30-2135-2026, 2026
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This study aims to improve prediction and understanding of extreme flood events in near-natural catchments across the United Kingdom. We develop a machine learning framework to assess the contribution of different features to flood magnitude estimation. We find weather patterns are weak predictors and stress the importance of evaluating model performance across and within catchments.
Chao Ma, Weifeng Hao, Qing Cheng, Fan Ye, Ying Qu, Jiabo Yin, Fang Xu, Haojian Wu, and Fei Li
Earth Syst. Sci. Data, 17, 4757–4778, https://doi.org/10.5194/essd-17-4757-2025, https://doi.org/10.5194/essd-17-4757-2025, 2025
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Antarctic sea ice albedo is a key factor influencing the global energy balance. Here we present a daily 1 km shortwave albedo product for Antarctic sea ice from 2012 to 2021, based on Visible Infrared Imaging Radiometer Suite (VIIRS) reflectance data. The albedo for missing pixels due to cloud cover was also reconstructed. This dataset can be used to assess changes in Antarctic sea ice, radiation budget, and the strength of sea ice albedo feedback, as well as their potential interconnections.
Jiaoyang Wang, Dedi Liu, Shenglian Guo, Lihua Xiong, Pan Liu, Hua Chen, Jie Chen, Jiabo Yin, and Yuling Zhang
Hydrol. Earth Syst. Sci., 29, 3315–3339, https://doi.org/10.5194/hess-29-3315-2025, https://doi.org/10.5194/hess-29-3315-2025, 2025
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The unclear feedback loops of water supply–hydropower generation–environmental conservation (SHE) nexuses with inter-basin water diversion projects (IWDPs) increase the uncertainty in the rational scheduling of water resources for water receiving and water donation areas. To address the different impacts of IWDPs on dynamic SHE nexuses and explore synergies, a framework is proposed to identify these effects across the different temporal and spatial scales in a reservoir group.
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025, https://doi.org/10.5194/hess-29-2393-2025, 2025
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Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to 4 months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.
Ruikang Zhang, Dedi Liu, Lihua Xiong, Jie Chen, Hua Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci., 28, 5229–5247, https://doi.org/10.5194/hess-28-5229-2024, https://doi.org/10.5194/hess-28-5229-2024, 2024
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Flash flood warnings cannot be effective without people’s responses to them. We propose a method to determine the threshold of issuing warnings based on a people’s response process simulation. The results show that adjusting the warning threshold according to people’s tolerance levels to the failed warnings can improve warning effectiveness, but the prerequisite is to increase forecasting accuracy and decrease forecasting variance.
Jiahui Sheng, Yuqi He, Tao Lu, Fang Wang, Yunjing Huang, Bingrong Leng, Xiang Zhang, and Yiqun Chen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-2024, 303–310, https://doi.org/10.5194/isprs-annals-X-4-2024-303-2024, https://doi.org/10.5194/isprs-annals-X-4-2024-303-2024, 2024
Yuqi He, Jiahui Sheng, Xiang Li, Guang Chen, Xiang Zhang, and Yiqun Chen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-2024, 155–161, https://doi.org/10.5194/isprs-annals-X-4-2024-155-2024, https://doi.org/10.5194/isprs-annals-X-4-2024-155-2024, 2024
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
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This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Jinghua Xiong, Shenglian Guo, Abhishek, Jiabo Yin, Chongyu Xu, Jun Wang, and Jing Guo
Hydrol. Earth Syst. Sci., 28, 1873–1895, https://doi.org/10.5194/hess-28-1873-2024, https://doi.org/10.5194/hess-28-1873-2024, 2024
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Temporal variability and spatial heterogeneity of climate systems challenge accurate estimation of probable maximum precipitation (PMP) in China. We use high-resolution precipitation data and climate models to explore the variability, trends, and shifts of PMP under climate change. Validated with multi-source estimations, our observations and simulations show significant spatiotemporal divergence of PMP over the country, which is projected to amplify in future due to land–atmosphere coupling.
Bailey J. Anderson, Manuela I. Brunner, Louise J. Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 28, 1567–1583, https://doi.org/10.5194/hess-28-1567-2024, https://doi.org/10.5194/hess-28-1567-2024, 2024
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Elasticityrefers to how much the amount of water in a river changes with precipitation. We usually calculate this using average streamflow values; however, the amount of water within rivers is also dependent on stored water sources. Here, we look at how elasticity varies across the streamflow distribution and show that not only do low and high streamflows respond differently to precipitation change, but also these differences vary with water storage availability.
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
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This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). Our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets.
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.
Jinghua Xiong, Shenglian Guo, Abhishek, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci., 26, 6457–6476, https://doi.org/10.5194/hess-26-6457-2022, https://doi.org/10.5194/hess-26-6457-2022, 2022
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Although the "dry gets drier, and wet gets wetter (DDWW)" paradigm is prevalent in summarizing wetting and drying trends, we show that only 11.01 %–40.84 % of the global land confirms and 10.21 %–35.43 % contradicts the paradigm during 1985–2014 from a terrestrial water storage change perspective. Similar proportions that intensify with the increasing emission scenarios persist until the end of the 21st century. Findings benefit understanding of global hydrological responses to climate change.
Youjiang Shen, Dedi Liu, Liguang Jiang, Karina Nielsen, Jiabo Yin, Jun Liu, and Peter Bauer-Gottwein
Earth Syst. Sci. Data, 14, 5671–5694, https://doi.org/10.5194/essd-14-5671-2022, https://doi.org/10.5194/essd-14-5671-2022, 2022
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A data gap of 338 Chinese reservoirs with their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC) during 2010–2021. Validation against the in situ observations of 93 reservoirs indicates the relatively high accuracy and reliability of the datasets. The unique and novel remotely sensed dataset would benefit studies involving many aspects (e.g., hydrological models, water resources related studies, and more).
Yunfan Zhang, Lei Cheng, Lu Zhang, Shujing Qin, Liu Liu, Pan Liu, and Yanghe Liu
Hydrol. Earth Syst. Sci., 26, 6379–6397, https://doi.org/10.5194/hess-26-6379-2022, https://doi.org/10.5194/hess-26-6379-2022, 2022
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Multiyear drought has been demonstrated to cause non-stationary rainfall–runoff relationship. But whether changes can invalidate the most fundamental method (i.e., paired-catchment method (PCM)) for separating vegetation change impacts is still unknown. Using paired-catchment data with 10-year drought, PCM is shown to still be reliable even in catchments with non-stationarity. A new framework is further proposed to separate impacts of two non-stationary drivers, using paired-catchment data.
Jing Tian, Zhengke Pan, Shenglian Guo, Jiabo Yin, Yanlai Zhou, and Jun Wang
Hydrol. Earth Syst. Sci., 26, 4853–4874, https://doi.org/10.5194/hess-26-4853-2022, https://doi.org/10.5194/hess-26-4853-2022, 2022
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Most of the literature has focused on the runoff response to climate change, while neglecting the impacts of the potential variation in the active catchment water storage capacity (ACWSC) that plays an essential role in the transfer of climate inputs to the catchment runoff. This study aims to systematically identify the response of the ACWSC to a long-term meteorological drought and asymptotic climate change.
Louise J. Slater, Chris Huntingford, Richard F. Pywell, John W. Redhead, and Elizabeth J. Kendon
Earth Syst. Dynam., 13, 1377–1396, https://doi.org/10.5194/esd-13-1377-2022, https://doi.org/10.5194/esd-13-1377-2022, 2022
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This work considers how wheat yields are affected by weather conditions during the three main wheat growth stages in the UK. Impacts are strongest in years with compound weather extremes across multiple growth stages. Future climate projections are beneficial for wheat yields, on average, but indicate a high risk of unseen weather conditions which farmers may struggle to adapt to and mitigate against.
Kang Xie, Pan Liu, Qian Xia, Xiao Li, Weibo Liu, Xiaojing Zhang, Lei Cheng, Guoqing Wang, and Jianyun Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-217, https://doi.org/10.5194/essd-2022-217, 2022
Revised manuscript not accepted
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There are currently no available common datasets of the Soil moisture storage capacity (SMSC) on a global scale, especially for hydrological models. Here, we produce a dataset of the SMSC parameter for global hydrological models. The global SMSC is constructed based on the deep residual network at 0.5° resolution. SMSC products are validated on global grids and typical catchments from different climatic regions.
Yujie Zeng, Dedi Liu, Shenglian Guo, Lihua Xiong, Pan Liu, Jiabo Yin, and Zhenhui Wu
Hydrol. Earth Syst. Sci., 26, 3965–3988, https://doi.org/10.5194/hess-26-3965-2022, https://doi.org/10.5194/hess-26-3965-2022, 2022
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The sustainability of the water–energy–food (WEF) nexus remains challenge, as interactions between WEF and human sensitivity and water resource allocation in water systems are often neglected. We incorporated human sensitivity and water resource allocation into a WEF nexus and assessed their impacts on the integrated system. This study can contribute to understanding the interactions across the water–energy–food–society nexus and improving the efficiency of resource management.
Thomas Lees, Steven Reece, Frederik Kratzert, Daniel Klotz, Martin Gauch, Jens De Bruijn, Reetik Kumar Sahu, Peter Greve, Louise Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022, https://doi.org/10.5194/hess-26-3079-2022, 2022
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Despite the accuracy of deep learning rainfall-runoff models, we are currently uncertain of what these models have learned. In this study we explore the internals of one deep learning architecture and demonstrate that the model learns about intermediate hydrological stores of soil moisture and snow water, despite never having seen data about these processes during training. Therefore, we find evidence that the deep learning approach learns a physically realistic mapping from inputs to outputs.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
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Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Jinghua Xiong, Shenglian Guo, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-645, https://doi.org/10.5194/hess-2021-645, 2022
Manuscript not accepted for further review
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Although the “dry gets drier and wet gets wetter” (DDWW) paradigm is widely used to describe the trends in wetting and drying globally, we show that 27.1 % of global land agrees with the paradigm, while 22.4 % shows the opposite pattern during the period 1985–2014 from the perspective of terrestrial water storage change. Similar percentages are discovered under different scenarios during the future period. Our findings will benefit the understanding of hydrological responses under climate change.
Thomas Lees, Marcus Buechel, Bailey Anderson, Louise Slater, Steven Reece, Gemma Coxon, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, https://doi.org/10.5194/hess-25-5517-2021, 2021
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We used deep learning (DL) models to simulate the amount of water moving through a river channel (discharge) based on the rainfall, temperature and potential evaporation in the previous days. We tested the DL models on catchments across Great Britain finding that the model can accurately simulate hydrological systems across a variety of catchment conditions. Ultimately, the model struggled most in areas where there is chalky bedrock and where human influence on the catchment is large.
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Short summary
Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Climate change accelerates the water cycle and alters the spatiotemporal distribution of...
