{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T13:00:28Z","timestamp":1770987628732,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T00:00:00Z","timestamp":1579737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2016YFC0501603"],"award-info":[{"award-number":["2016YFC0501603"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gridded precipitation products are the potential alternatives in hydrological studies, and the evaluation of their accuracy and potential use is very important for reliable simulations. The objective of this study was to investigate the applicability of gridded precipitation products in the Yellow River Basin of China. Five gridded precipitation products, i.e., Multi-Source Weighted-Ensemble Precipitation (MSWEP), CPC Morphing Technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), were evaluated against observations made during 2001\u22122014 at daily, monthly, and annual scales. The results showed that MSWEP had a higher correlation and lower percent bias and root mean square error, while CMORPH and GSMaP made overestimations compared to the observations. All the datasets underestimated the frequency of dry days, and overestimated the frequency and the intensity of wet days (0\u20135 mm\/day). MSWEP and TRMM showed consistent interannual variations and spatial patterns while CMORPH and GSMaP had larger discrepancies with the observations. At the sub-basin scale, all the datasets performed poorly in the Beiluo River and Qingjian River, whereas they were applicable in other sub-basins. Based on its superior performance, MSWEP was identified as more suitable for hydrological applications.<\/jats:p>","DOI":"10.3390\/rs12030374","type":"journal-article","created":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T10:36:02Z","timestamp":1579775762000},"page":"374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Reliability of Gridded Precipitation Products in the Yellow River Basin, China"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0080-2834","authenticated-orcid":false,"given":"Yanfen","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&amp;F University, Yangling 712100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5604-6329","authenticated-orcid":false,"given":"Jing","family":"Wu","sequence":"additional","affiliation":[{"name":"Lanzhou Central Meteorological Observatory, Lanzhou 730020, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2354-7880","authenticated-orcid":false,"given":"Lei","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Navigation, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Bing","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&amp;F University, Yangling 712100, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/0022-1694(93)90023-3","article-title":"The effect of gauge sampling density on the accuracy of streamflow prediction for rural catchments","volume":"142","author":"Duncan","year":"1993","journal-title":"J. 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