{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:08:04Z","timestamp":1768676884292,"version":"3.49.0"},"reference-count":81,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["U2240216"],"award-info":[{"award-number":["U2240216"]}]},{"name":"Natural Science Foundation of China","award":["51809074"],"award-info":[{"award-number":["51809074"]}]},{"name":"Natural Science Foundation of China","award":["212300410202"],"award-info":[{"award-number":["212300410202"]}]},{"name":"Natural Science Foundation of China","award":["2019YFC0409000"],"award-info":[{"award-number":["2019YFC0409000"]}]},{"name":"Henan Province Science Foundation for Youths","award":["U2240216"],"award-info":[{"award-number":["U2240216"]}]},{"name":"Henan Province Science Foundation for Youths","award":["51809074"],"award-info":[{"award-number":["51809074"]}]},{"name":"Henan Province Science Foundation for Youths","award":["212300410202"],"award-info":[{"award-number":["212300410202"]}]},{"name":"Henan Province Science Foundation for Youths","award":["2019YFC0409000"],"award-info":[{"award-number":["2019YFC0409000"]}]},{"name":"National Key R&amp;D Program of China","award":["U2240216"],"award-info":[{"award-number":["U2240216"]}]},{"name":"National Key R&amp;D Program of China","award":["51809074"],"award-info":[{"award-number":["51809074"]}]},{"name":"National Key R&amp;D Program of China","award":["212300410202"],"award-info":[{"award-number":["212300410202"]}]},{"name":"National Key R&amp;D Program of China","award":["2019YFC0409000"],"award-info":[{"award-number":["2019YFC0409000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin\u2019anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models.<\/jats:p>","DOI":"10.3390\/rs14184609","type":"journal-article","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T01:35:10Z","timestamp":1663292110000},"page":"4609","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Estimating the Routing Parameter of the Xin\u2019anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6510-2302","authenticated-orcid":false,"given":"Yuanhao","family":"Fang","sequence":"first","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}]},{"given":"Yizhi","family":"Huang","sequence":"additional","affiliation":[{"name":"Pearl River Hydrology and Water Resources Survey Center, Guangzhou 510370, China"}]},{"given":"Bo","family":"Qu","sequence":"additional","affiliation":[{"name":"Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China"}]},{"given":"Xingnan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Changjiang Water Resources Commission, Wuhan 430010, China"}]},{"given":"Dazhong","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7090","DOI":"10.1002\/2015WR017780","article-title":"Physically based modeling in catchment hydrology at 50: Survey and outlook","volume":"51","author":"Paniconi","year":"2015","journal-title":"Water Resour. 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