{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:06:00Z","timestamp":1760241960140,"version":"build-2065373602"},"reference-count":122,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,23]],"date-time":"2018-11-23T00:00:00Z","timestamp":1542931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFA0602302 and 2016YFB0502502"],"award-info":[{"award-number":["2016YFA0602302 and 2016YFB0502502"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41175088"],"award-info":[{"award-number":["No. 41175088"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To compare the effectivenesses of different precipitation datasets on hydrological modelling, five precipitation datasets derived from various approaches were used to simulate a two-week runoff process after a heavy rainfall event in the Wangjiaba (WJB) watershed, which covers an area of 30,000 km2 in eastern China. The five precipitation datasets contained one traditional in situ observation, two satellite products, and two predictions obtained from the Numerical Weather Prediction (NWP) models. They were the station observations collected from the China Meteorological Administration (CMA), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG), the merged data of the Climate Prediction Center Morphing (merged CMORPH), and the outputs of the Weather Research and Forecasting (WRF) model and the WRF four-dimensional variational (4D-Var) data assimilation system, respectively. Apart from the outlet discharge, the simulated soil moisture was also assessed via the Soil Moisture Active Passive (SMAP) product. These investigations suggested that (1) all the five precipitation datasets could yield reasonable simulations of the studied rainfall-runoff process. The Nash-Sutcliffe coefficients reached the highest value (0.658) with the in situ CMA precipitation and the lowest value (0.464) with the WRF-predicted precipitation. (2) The traditional in situ observation were still the most reliable precipitation data to simulate the study case, whereas the two NWP-predicted precipitation datasets performed the worst. Nevertheless, the NWP-predicted precipitation is irreplaceable in hydrological modelling because of its fine spatiotemporal resolutions and ability to forecast precipitation in the future. (3) Gauge correction and 4D-Var data assimilation had positive impacts on improving the accuracies of the merged CMORPH and the WRF 4D-Var prediction, respectively, but the effectiveness of the latter on the rainfall-runoff simulation was mainly weakened by the poor quality of the GPM IMERG used in the study case. This study provides a reference for the applications of different precipitation datasets, including in situ observations, remote sensing estimations and NWP simulations, in hydrological modelling.<\/jats:p>","DOI":"10.3390\/rs10121872","type":"journal-article","created":{"date-parts":[[2018,11,23]],"date-time":"2018-11-23T12:20:28Z","timestamp":1542975628000},"page":"1872","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Lu","family":"Yi","sequence":"first","affiliation":[{"name":"State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China"},{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Wanchang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Xiangyang","family":"Li","sequence":"additional","affiliation":[{"name":"Yellow River Conservancy Commission of the Ministry of Water Resources, Zhengzhou 210046, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1175\/JHM-D-15-0138.1","article-title":"Can precipitation and temperature from meteorological reanalyses be used for hydrological modeling?","volume":"17","author":"Essou","year":"2016","journal-title":"J. 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