{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T17:30:14Z","timestamp":1764351014711,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and technology project of China Southern Power Grid Yunnan Power Grid Co., Ltd","award":["YNKJXM20222329","B240203007","524015222"],"award-info":[{"award-number":["YNKJXM20222329","B240203007","524015222"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["YNKJXM20222329","B240203007","524015222"],"award-info":[{"award-number":["YNKJXM20222329","B240203007","524015222"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fund of National Key Laboratory of Water Disaster Prevention","award":["YNKJXM20222329","B240203007","524015222"],"award-info":[{"award-number":["YNKJXM20222329","B240203007","524015222"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Small- and medium-sized reservoirs significantly alter natural flood processes, making it essential to understand their impact on runoff for effective water resource management. However, the lack of measured data for most small reservoirs poses challenges for accurately simulating their behavior. This study proposes a novel method that utilizes readily available satellite observation data, integrating hydraulic, hydrological, and mathematical formulas to derive outflow coefficients. Based on the Grid-XinAnJiang (GXAJ) model, the enhanced GXAJ-R model accounts for the storage and release effects of ungauged reservoirs and is applied to the Tunxi watershed. Results show that the original GXAJ model achieved a stable performance with an average NSE of 0.88 during calibration, while the NSE values of the GXAJ and GXAJ-R models during validation ranged from 0.78 to 0.97 and 0.85 to 0.99, respectively, with an average improvement of 0.03 in the GXAJ-R model. This enhanced model significantly improves peak flow simulation accuracy, reduces relative flood peak error by approximately 10%, and replicates the flood flow process with higher fidelity. Additionally, the area\u2013volume model derived from classified small-scale data demonstrates high accuracy and reliability, with correlation coefficients above 0.8, making it applicable to other ungauged reservoirs. The OTSU-NDWI method, which improves the NDWI, effectively enhances the accuracy of water body extraction from remote sensing, achieving overall accuracy and kappa coefficient values exceeding 0.8 and 0.6, respectively. This study highlights the potential of integrating satellite data with hydrological models to enhance the understanding of reservoir behavior in data-scarce regions. It also suggests the possibility of broader applications in similarly ungauged basins, providing valuable tools for flood management and risk assessment.<\/jats:p>","DOI":"10.3390\/rs16234399","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T08:38:24Z","timestamp":1732523904000},"page":"4399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Flood Streamflow Estimation of Ungauged Small Reservoir Basins Using Remote Sensing and Hydrological Modeling"],"prefix":"10.3390","volume":"16","author":[{"given":"Fangrong","family":"Zhou","sequence":"first","affiliation":[{"name":"Joint Laboratory of Power Remote Sensing Technology, Electric Power Research Institute, Yunnan Power Grid Company Ltd., China Southern Power Grid, Kunming 650217, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5536-7198","authenticated-orcid":false,"given":"Nan","family":"Wu","sequence":"additional","affiliation":[{"name":"The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210024, China"},{"name":"Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210024, China"},{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"}]},{"given":"Yuning","family":"Luo","sequence":"additional","affiliation":[{"name":"The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210024, China"},{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"}]},{"given":"Yuhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210024, China"},{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"Joint Laboratory of Power Remote Sensing Technology, Electric Power Research Institute, Yunnan Power Grid Company Ltd., China Southern Power Grid, Kunming 650217, China"}]},{"given":"Yifan","family":"Wang","sequence":"additional","affiliation":[{"name":"Joint Laboratory of Power Remote Sensing Technology, Electric Power Research Institute, Yunnan Power Grid Company Ltd., China Southern Power Grid, Kunming 650217, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5288-9372","authenticated-orcid":false,"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[{"name":"The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210024, China"},{"name":"Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210024, China"},{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China"},{"name":"China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing 210024, China"},{"name":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 210024, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2020WR027590","DOI":"10.1029\/2020WR027590","article-title":"Improving Reservoir Outflow Estimation for Ungauged Basins Using Satellite Observations and a Hydrological Model","volume":"56","author":"Han","year":"2020","journal-title":"Water Resour. 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