Articles | Volume 15, issue 12
https://doi.org/10.5194/essd-15-5597-2023
© 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/essd-15-5597-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Dec 2023
Data description paper |
| 08 Dec 2023
| 08 Dec 2023
GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei, PR China
Louise J. Slater
School of Geography and the Environment, University of Oxford, Oxford, UK
Abdou Khouakhi
School of Water, Energy and Environment, Cranfield Environment Centre, Cranfield University, Cranfield, UK
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China
Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing, China
Department of Earth System Science, Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Tsinghua University, Beijing, China
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei, PR China
Fupeng Li
Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany
Yadu Pokhrel
Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI, USA
Pierre Gentine
Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
Climate School, Columbia University, New York, NY, USA
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Cited
16 citations as recorded by crossref.
- Unlocking the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage trends across the China-Pakistan Economic Corridor T. Javed et al. 10.1016/j.scitotenv.2024.176977
- 中国陆域干旱的大气环流机制及旱情传播规律 子. 顾 et al. 10.1360/SSTe-2023-0245
- An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management C. Deng et al. 10.3390/rs16244666
- Revealing temporal variation of baseflow and its underlying causes in the source region of the Yangtze River (China) G. Wu et al. 10.2166/nh.2024.185
- A novel XGBoost-based approach for reconstruction terrestrial water storage variations with GNSS in the Northeastern Tibetan Plateau T. Zhang et al. 10.1016/j.jhydrol.2025.133255
- Reconstructed centennial precipitation-driven water storage anomalies in the Nile River Basin using RecNet and their suitability for studying ENSO and IOD impacts J. Wang et al. 10.1016/j.jhydrol.2024.132272
- Annual memory in the terrestrial water cycle W. Berghuijs et al. 10.5194/hess-29-1319-2025
- Integrating Hidden Markov and Multinomial models for hydrological drought prediction under nonstationarity M. Santos & L. Slater 10.1016/j.advwatres.2025.104974
- Advances in GRACE satellite studies on terrestrial water storage: a comprehensive review J. Karki et al. 10.1080/10106049.2025.2482706
- Impact of atmospheric circulations on droughts and drought propagation over China Z. Gu et al. 10.1007/s11430-023-1329-x
- Global terrestrial drought and its projected socioeconomic implications under different warming targets N. He et al. 10.1016/j.scitotenv.2024.174292
- Improving understanding of drought using extended and downscaled GRACE data in the Pearl River Basin X. Wan et al. 10.1016/j.ejrh.2025.102277
- A 6-hourly 0.1° resolution freezing rain dataset of China during 2000–2019 based on deep kernel learning J. Liu et al. 10.1038/s41597-025-04582-z
- GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks I. Palazzoli et al. 10.1038/s41597-025-04403-3
- Annual memory in the terrestrial water cycle W. Berghuijs et al. 10.5194/hess-29-1319-2025
- GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present J. Yin et al. 10.5194/essd-15-5597-2023
14 citations as recorded by crossref.
- Unlocking the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage trends across the China-Pakistan Economic Corridor T. Javed et al. 10.1016/j.scitotenv.2024.176977
- 中国陆域干旱的大气环流机制及旱情传播规律 子. 顾 et al. 10.1360/SSTe-2023-0245
- An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management C. Deng et al. 10.3390/rs16244666
- Revealing temporal variation of baseflow and its underlying causes in the source region of the Yangtze River (China) G. Wu et al. 10.2166/nh.2024.185
- A novel XGBoost-based approach for reconstruction terrestrial water storage variations with GNSS in the Northeastern Tibetan Plateau T. Zhang et al. 10.1016/j.jhydrol.2025.133255
- Reconstructed centennial precipitation-driven water storage anomalies in the Nile River Basin using RecNet and their suitability for studying ENSO and IOD impacts J. Wang et al. 10.1016/j.jhydrol.2024.132272
- Annual memory in the terrestrial water cycle W. Berghuijs et al. 10.5194/hess-29-1319-2025
- Integrating Hidden Markov and Multinomial models for hydrological drought prediction under nonstationarity M. Santos & L. Slater 10.1016/j.advwatres.2025.104974
- Advances in GRACE satellite studies on terrestrial water storage: a comprehensive review J. Karki et al. 10.1080/10106049.2025.2482706
- Impact of atmospheric circulations on droughts and drought propagation over China Z. Gu et al. 10.1007/s11430-023-1329-x
- Global terrestrial drought and its projected socioeconomic implications under different warming targets N. He et al. 10.1016/j.scitotenv.2024.174292
- Improving understanding of drought using extended and downscaled GRACE data in the Pearl River Basin X. Wan et al. 10.1016/j.ejrh.2025.102277
- A 6-hourly 0.1° resolution freezing rain dataset of China during 2000–2019 based on deep kernel learning J. Liu et al. 10.1038/s41597-025-04582-z
- GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks I. Palazzoli et al. 10.1038/s41597-025-04403-3
Latest update: 23 Apr 2025
Short summary
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.
This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time...
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