{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:52:46Z","timestamp":1778824366426,"version":"3.51.4"},"reference-count":112,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2023YFC3209102"],"award-info":[{"award-number":["2023YFC3209102"]}]},{"name":"National Key R&amp;D Program of China","award":["SKS-2022008"],"award-info":[{"award-number":["SKS-2022008"]}]},{"name":"National Key R&amp;D Program of China","award":["42101034"],"award-info":[{"award-number":["42101034"]}]},{"name":"Ministry of Water Resources, China","award":["2023YFC3209102"],"award-info":[{"award-number":["2023YFC3209102"]}]},{"name":"Ministry of Water Resources, China","award":["SKS-2022008"],"award-info":[{"award-number":["SKS-2022008"]}]},{"name":"Ministry of Water Resources, China","award":["42101034"],"award-info":[{"award-number":["42101034"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023YFC3209102"],"award-info":[{"award-number":["2023YFC3209102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["SKS-2022008"],"award-info":[{"award-number":["SKS-2022008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101034"],"award-info":[{"award-number":["42101034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Utah State University","award":["2023YFC3209102"],"award-info":[{"award-number":["2023YFC3209102"]}]},{"name":"Utah State University","award":["SKS-2022008"],"award-info":[{"award-number":["SKS-2022008"]}]},{"name":"Utah State University","award":["42101034"],"award-info":[{"award-number":["42101034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Gravity Recovery and Climate Experiment (GRACE) enables large-scale monitoring of terrestrial water storage changes, significantly contributing to hydrology and related fields. However, the coarse resolution of groundwater storage anomaly (GWSA) data limits local-scale research utilizing GRACE and GRACE-FO missions. In this study, we develop a regional downscaling model based on the linear regression relationship between GWSA and environmental variables, reducing the grid resolution of GWSA obtained from GRACE from approximately 25 km to 1 km. First, we estimate the missing values of monthly continuous terrestrial water storage anomaly (TWSA) for the period from 2003 to 2020 using interpolated multi-channel singular spectrum analysis (IMSSA). Next, we apply the water balance equation to separate GWSA from TWSA, which is provided jointly by the Global Land Data Assimilation System (GLDAS) and the distributed ecohydrological model ESSI-3. We then employ a partial least squares regression (PLSR) model to identify the most significant environmental variables related to GWSA. Precipitation (Prec), normalized difference vegetation index (NDVI), and actual evapotranspiration (AET), with variable importance in projection (VIP) values greater than 1.0, are recognized as effective variables for reconstructing long-term, high-resolution groundwater storage changes. Finally, we downscale and reconstruct the long-term (2003\u20132020), high-resolution (1 km \u00d7 1 km) monthly GWSA in the Songhua River Basin using fused and supplemented GRACE\/GRACE-FO data, employing either geographically weighted regression (GWR) or random forest (RF) models. The results demonstrate superior performance of the GWR model (CC = 0.995, NSE = 0.989, RMSE = 2.505 mm) compared to the RF model in downscaling. The downscaled GWSA in the Songhua River Basin not only achieves high spatial resolution but also exhibits improved accuracy when compared to in situ groundwater observation records. This research enhances understanding of spatiotemporal variations in regional groundwater due to local agricultural and industrial water use, providing a scientific basis for regional water resource management.<\/jats:p>","DOI":"10.3390\/rs16234566","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T08:33:51Z","timestamp":1733387631000},"page":"4566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Reconstructing Long-Term, High-Resolution Groundwater Storage Changes in the Songhua River Basin Using Supplemented GRACE and GRACE-FO Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Chuanqi","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Environment and Society, Utah State University, Logan, UT 84322, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5480-6878","authenticated-orcid":false,"given":"Chi","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Environment Criteria and Risk Assessment, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, State Environmental Protection Key Laboratory for Lake Pollution Control, Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2607-4628","authenticated-orcid":false,"given":"Wanchang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1016\/j.gsf.2019.12.013","article-title":"Mapping Favorable Groundwater Potential Recharge Zones Using a GIS-Based Analytical Hierarchical Process and Probability Frequency Ratio Model: A Case Study from an Agro-Urban Region of Pakistan","volume":"11","author":"Arshad","year":"2020","journal-title":"Geosci. 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