[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124780-en":3,"doc-seo-124780-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124780,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","GTWS-MLrec - global terrestrial water storage reconstruction by machine learning from 1940 to present","Terrestrial water storage (TWS) reflects water stored on and below land and underpins global water and energy budgets, yet satellite GRACE observations only cover 2002 onward. This study reconstructs monthly TWS anomalies from 1940–2022 with 0.25° resolution using machine-learning models driven by climatic and hydrological predictors, land use/land cover, and vegetation indicators. The GTWS-MLrec estimates match GRACE/GRACE-FO in the GRACE era and are validated against independent assessments, including basin budgets and streamflow at 10,168 gauges, reproducing impacts of climate variability such as strong El Niño events.","Earth Syst. Sci. Data, 15, 5597–5615, 2023 [https://doi.org/10.5194/essd-15-5597-2023](https://doi.org/10.5194/essd-15-5597-2023)[ ](https://doi.org/10.5194/essd-15-5597-2023)© Author(s) 2023 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nGTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present  \nJiabo Yin 1 , Louise J. Slater2 , Abdou Khouakhi3 , Le Yu4,5,6 , Pan Liu 1 , Fupeng Li7 , Yadu Pokhrel8 , and  \nPierre Gentine9, 10  \n1 State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei, PR China  \n2 School of Geography and the Environment, University of Oxford, Oxford, UK  \n3 School of Water, Energy and Environment, Cranfield Environment Centre, Cranfield University, Cranfield, UK  \n4Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China  \n5Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing, China  \n6Department of Earth System Science, Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Tsinghua University, Beijing, China  \n7Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany  \n8Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI, USA  \n9Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA  \n10 Climate School, Columbia University, New York, NY, USA Correspondence: Jiabo Yin ([jboyn@whu.edu.cn](jboyn@whu.edu.cn))  \nReceived: 3 August 2023 – Discussion started: 31 August 2023  \nRevised: 16 October 2023 – Accepted: 22 October 2023 – Published: 8 December 2023  \nAbstract. Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940–2022) and relatively 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). The outcome, machine-learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land–ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10 168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability such as strong El Niño events. The GTWS-MLrec dataset consists of three reconstructions based on (a) mascons of the Jet Propulsion Laboratory of the California Institute of Technology, the Center for Space Research at the University of Texas at Austin, and the Goddard Space Flight Center of NASA; (b) three detrended and de-seasonalized reconstructions; and (c) six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management. GTWS-MLrec is availa","cbCaisScE3NGl1mJ","https://ap.wps.com/l/cbCaisScE3NGl1mJ","pdf",16211902,1,19,"English","en",105,"# Introduction\n## Terrestrial water storage and climate sensitivity\n## Limits of GRACE-era observations\n## Motivation for long-term ML reconstruction\n# Reconstruction approach\n## Machine-learning models and predictors\n## Output datasets and variants\n# Validation and applications\n## Comparisons with GRACE/GRACE-FO\n## Evaluation with independent products and gauges\n## Geoscience applications","[{\"question\":\"为什么需要从1940年重建陆地水储量（TWS）？\",\"answer\":\"GRACE卫星对TWS的直接观测仅从2002年开始，限制了对陆地水循环长期趋势与气候变率的认识，因此需要更长时间尺度的重建数据。\"},{\"question\":\"GTWS-MLrec的重建是如何实现的？\",\"answer\":\"研究使用多种机器学习模型，并引入大量预测因子，包括气候与水文变量、土地利用/覆被数据以及植被指标（如叶面积指数）。\"},{\"question\":\"GTWS-MLrec如何被验证其可靠性？\",\"answer\":\"结果在GRACE/GRACE-FO时期与观测吻合，表现为高相关与低偏差；同时还通过陆海质量预算、气象与陆地水量收支，以及341个大流域的评估和10,168个站点的径流观测进行检验。\"}]","GTWS-MLrec - global terrestrial water storage reconstruction by machine learning from 1940 to present | PDF",1785894602,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"gtws-mlrec-global-terrestrial-water-storage-reconstruction-by-machine-learning-from-1940-to-present","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/gtws-mlrec-global-terrestrial-water-storage-reconstruction-by-machine-learning-from-1940-to-present/124780/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么需要从1940年重建陆地水储量（TWS）？","Question",{"text":75,"@type":76},"GRACE卫星对TWS的直接观测仅从2002年开始，限制了对陆地水循环长期趋势与气候变率的认识，因此需要更长时间尺度的重建数据。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"GTWS-MLrec的重建是如何实现的？",{"text":80,"@type":76},"研究使用多种机器学习模型，并引入大量预测因子，包括气候与水文变量、土地利用/覆被数据以及植被指标（如叶面积指数）。",{"name":82,"@type":73,"acceptedAnswer":83},"GTWS-MLrec如何被验证其可靠性？",{"text":84,"@type":76},"结果在GRACE/GRACE-FO时期与观测吻合，表现为高相关与低偏差；同时还通过陆海质量预算、气象与陆地水量收支，以及341个大流域的评估和10,168个站点的径流观测进行检验。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]