Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2575-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2575-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
13 Jun 2025
Data description article |
| 13 Jun 2025
| 13 Jun 2025
Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data
Nehar Mandal
Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, JH 826004, India
Prabal Das
Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
Kironmala Chanda
CORRESPONDING AUTHOR
Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, JH 826004, India
Centre for Water Resource Management, Indian Institute of Technology (Indian School of Mines), Dhanbad, JH 826004, India
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Cited
18 citations as recorded by crossref.
- Multidecadal reconstruction of terrestrial water storage changes by combining pre-GRACE satellite observations and climate data C. Hacker et al. https://doi.org/10.5194/essd-18-1747-2026
- A combination of time-variable gravity field solutions from multi-satellite datasets (1993–2024) via constrained collocation model L. Zhang et al. https://doi.org/10.5194/essd-18-5167-2026
- Large-scale climate drivers of extreme compound events in the Amazon from interannual to long-term timescales (1982–2024) G. Botetano et al. https://doi.org/10.1007/s00382-026-08382-y
- Improving the Quality of the Surface Heat Flux Data over the Tibetan Plateau by Using an Optuna–CatBoost–Shapley Additive exPlanation Method L. Li et al. https://doi.org/10.3390/rs18183242
- Enhancing water science in Earth’s second lung: AI-generated centenary hydrological insights from two decades of satellite data in the Congo Basin J. Awange & J. Wang https://doi.org/10.1016/j.srs.2026.100402
- Groundwater Storage Variations in the Huadian Photovoltaic Base of the Tengger Desert Based on Machine Learning–Downscaled GRACE Data R. Chen et al. https://doi.org/10.3390/w18070781
- Nonlinear trend decomposition for reconstructing terrestrial water storage anomalies across the GRACE/-FO data gap in Mainland China S. Nie et al. https://doi.org/10.1016/j.ejrh.2026.103666
- Improved grid-based semi-centennial reconstruction and diagnostic assessment of global water storage leveraging ensemble machine learning R. Anjaneyulu et al. https://doi.org/10.1007/s00190-026-02098-x
- Vision transformer–based reconstruction of terrestrial water storage anomalies from GRACE/GRACE-FO over the conterminous United States J. Chen et al. https://doi.org/10.1016/j.bdes.2026.100075
- A spatiotemporal reconstruction framework for GRACE/GRACE-FO-derived groundwater storage anomalies based on historical phase-constrained gap filling and physics-guided global–local downscaling L. Zhang et al. https://doi.org/10.1016/j.jag.2026.105603
- Reconstruction and evolution analysis of long-term terrestrial water storage anomalies in Xinjiang based on time series decomposition and multi-model coupling Z. Li et al. https://doi.org/10.1016/j.jhydrol.2026.136007
- A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX) P. Saemian et al. https://doi.org/10.1038/s41597-026-06604-w
- Attributing terrestrial water storage changes on the Tibetan Plateau to climate and human drivers using a hybrid deep learning approach Z. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135599
- Spatial-temporal dynamics of meteorological and groundwater drought in Northwest China: Propagation, threshold, recovery time, drivers J. Shan et al. https://doi.org/10.1016/j.ejrh.2025.103090
- Decadal prediction of terrestrial water storage and sea-level change using physics-informed neural networks M. Kiani Shahvandi https://doi.org/10.1016/j.geog.2026.07.002
- Retrievals and simulations of terrestrial water storage changes and runoff over the Tibetan Plateau: Challenges and opportunities X. Li et al. https://doi.org/10.1016/j.fmre.2025.11.012
- Identifying global key regions where vapor pressure deficit dominates drought intensification Y. Miao et al. https://doi.org/10.1016/j.gloplacha.2026.105593
- Future NDVI projections and ensemble strategy comparison in Inner Mongolia under CMIP6 scenarios H. Li et al. https://doi.org/10.3389/fevo.2026.1921058
18 citations as recorded by crossref.
- Multidecadal reconstruction of terrestrial water storage changes by combining pre-GRACE satellite observations and climate data C. Hacker et al. https://doi.org/10.5194/essd-18-1747-2026
- A combination of time-variable gravity field solutions from multi-satellite datasets (1993–2024) via constrained collocation model L. Zhang et al. https://doi.org/10.5194/essd-18-5167-2026
- Large-scale climate drivers of extreme compound events in the Amazon from interannual to long-term timescales (1982–2024) G. Botetano et al. https://doi.org/10.1007/s00382-026-08382-y
- Improving the Quality of the Surface Heat Flux Data over the Tibetan Plateau by Using an Optuna–CatBoost–Shapley Additive exPlanation Method L. Li et al. https://doi.org/10.3390/rs18183242
- Enhancing water science in Earth’s second lung: AI-generated centenary hydrological insights from two decades of satellite data in the Congo Basin J. Awange & J. Wang https://doi.org/10.1016/j.srs.2026.100402
- Groundwater Storage Variations in the Huadian Photovoltaic Base of the Tengger Desert Based on Machine Learning–Downscaled GRACE Data R. Chen et al. https://doi.org/10.3390/w18070781
- Nonlinear trend decomposition for reconstructing terrestrial water storage anomalies across the GRACE/-FO data gap in Mainland China S. Nie et al. https://doi.org/10.1016/j.ejrh.2026.103666
- Improved grid-based semi-centennial reconstruction and diagnostic assessment of global water storage leveraging ensemble machine learning R. Anjaneyulu et al. https://doi.org/10.1007/s00190-026-02098-x
- Vision transformer–based reconstruction of terrestrial water storage anomalies from GRACE/GRACE-FO over the conterminous United States J. Chen et al. https://doi.org/10.1016/j.bdes.2026.100075
- A spatiotemporal reconstruction framework for GRACE/GRACE-FO-derived groundwater storage anomalies based on historical phase-constrained gap filling and physics-guided global–local downscaling L. Zhang et al. https://doi.org/10.1016/j.jag.2026.105603
- Reconstruction and evolution analysis of long-term terrestrial water storage anomalies in Xinjiang based on time series decomposition and multi-model coupling Z. Li et al. https://doi.org/10.1016/j.jhydrol.2026.136007
- A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX) P. Saemian et al. https://doi.org/10.1038/s41597-026-06604-w
- Attributing terrestrial water storage changes on the Tibetan Plateau to climate and human drivers using a hybrid deep learning approach Z. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135599
- Spatial-temporal dynamics of meteorological and groundwater drought in Northwest China: Propagation, threshold, recovery time, drivers J. Shan et al. https://doi.org/10.1016/j.ejrh.2025.103090
- Decadal prediction of terrestrial water storage and sea-level change using physics-informed neural networks M. Kiani Shahvandi https://doi.org/10.1016/j.geog.2026.07.002
- Retrievals and simulations of terrestrial water storage changes and runoff over the Tibetan Plateau: Challenges and opportunities X. Li et al. https://doi.org/10.1016/j.fmre.2025.11.012
- Identifying global key regions where vapor pressure deficit dominates drought intensification Y. Miao et al. https://doi.org/10.1016/j.gloplacha.2026.105593
- Future NDVI projections and ensemble strategy comparison in Inner Mongolia under CMIP6 scenarios H. Li et al. https://doi.org/10.3389/fevo.2026.1921058
Saved (final revised paper)
Latest update: 28 Sep 2026
Short summary
Optimal features among hydroclimatic variables and land surface model (LSM) outputs are selected using a novel Bayesian network (BN) approach for simulating terrestrial water storage anomalies (TWSAs). TWSAs are reconstructed (BNML_TWSA) with grid-specific leader models (among four machine learning models) from January 1960 to December 2022 to generate a continuous global gridded dataset. The uncertainty in the reconstructed BNML_TWSA product is also assessed in terms of standard error.
Optimal features among hydroclimatic variables and land surface model (LSM) outputs are selected...
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