{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T03:00:23Z","timestamp":1770519623963,"version":"3.49.0"},"reference-count":85,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T00:00:00Z","timestamp":1580256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41775106, 545 41861144014, U1811464 and 41905101"],"award-info":[{"award-number":["41775106, 545 41861144014, U1811464 and 41905101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2017YFA0604300"],"award-info":[{"award-number":["2017YFA0604300"]}]},{"name":"the Program for Guangdong Introducing Innovative and Enterpreneurial Teams","award":["2017ZT07X355"],"award-info":[{"award-number":["2017ZT07X355"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hydrological models are usually calibrated against observed streamflow (Qobs), which is not applicable for ungauged river basins. A few studies have exploited remotely sensed evapotranspiration (ETRS) for model calibration but their effectiveness on streamflow simulation remains uncertain. This paper investigates the use of ETRS in the hydrological calibration of a widely used land surface model coupled with a source\u2013sink routing scheme and global optimization algorithm for 28 natural river basins. A baseline simulation is a setup based on the latest model developments and inputs. Sensitive parameters are determined for Qobs and ETRS-based model calibrations, respectively, through comprehensive sensitivity tests. The ETRS-based model calibration results in a mean Kling\u2013Gupta Efficiency (KGE) value of 0.54 for streamflow simulation; 61% of the river basins have KGE &gt; 0.5 in the validation period, which is consistent with the calibration period and provides a significant improvement over the baseline. Compared to Qobs, the ETRS calibration produces better or similar streamflow simulations in 29% of the basins, while further significant improvements are achieved when either better ET or precipitation observations are used. Furthermore, the model results show better or similar performance in 68% of the basins and outperform the baseline simulations in 90% of the river basins using model parameters from the best ETRS calibration runs. This study confirms that with reasonable precipitation input, the ETRS-based spatially distributed calibration can efficiently tune parameters for better ET and streamflow simulations. The application of ETRS for global scale hydrological model calibration promises even better streamflow accuracy as the satellite-based ETRS observations continue to improve.<\/jats:p>","DOI":"10.3390\/rs12030428","type":"journal-article","created":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T10:51:07Z","timestamp":1580295067000},"page":"428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Satellite-Based Evapotranspiration in Hydrological Model Calibration"],"prefix":"10.3390","volume":"12","author":[{"given":"Lulu","family":"Jiang","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China"},{"name":"Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2920-8860","authenticated-orcid":false,"given":"Huan","family":"Wu","sequence":"additional","affiliation":[{"name":"Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Southern Marine Science and Engineering Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA"}]},{"given":"Jing","family":"Tao","sequence":"additional","affiliation":[{"name":"Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"},{"name":"Civil &amp; Environmental Engineering University of Washington, Seattle, WA  98195, USA"}]},{"given":"John S.","family":"Kimball","sequence":"additional","affiliation":[{"name":"Numerical Terradynamic Simulation Group, W.A. Franke College of Forestry &amp; Conservation, The University of Montana, Missoula, MT 59812, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3616-386X","authenticated-orcid":false,"given":"Lorenzo","family":"Alfieri","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre, 21027 Ispra, Italy"}]},{"given":"Xiuwan","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Troy, T.J., Wood, E.F., and Sheffield, J. (2008). An efficient calibration method for continental-scale land surface modeling. Water Resour. 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