Article(id=1223210584905208743, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221280, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1655654400000, receivedDateStr=2022-06-20, revisedDate=1660406400000, revisedDateStr=2022-08-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1769565729594, onlineDateStr=2026-01-28, pubDate=1687622400000, pubDateStr=2023-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769565729594, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769565729594, creator=13701087609, updateTime=1769565729594, updator=13701087609, issue=Issue{id=1223210584024400210, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='6', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769565729385, creator=13701087609, updateTime=1769593153259, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223325608164348105, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223325608164348106, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=9, endPage=12, ext={EN=ArticleExt(id=1223210585228170154, articleId=1223210584905208743, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Application of Multi-source Precipitation Data in Short-term Hydrological Forecast of Small and Medium-sized Watersheds in Chongqing City, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

Taking four small and medium-sized watersheds in the mountainous city (Chongqing) as the research objects, the application prospects of multi-source rainfall data in short-term hydrological forecasting were explored. The GPM-IMERG remote sensing precipitation data was introduced as the source of the satellite precipitation data, and the Mean Bias Correction (MBC) method, Linear Regression Model (LRM) and Geographically Weighted Regression (GWR) method were used to realize the fusion with the ground station measured data. A leave-one-out validation method was used to evaluate the accuracy of the fused precipitation data, and the SWAT model was applied to simulate the runoff of the selected basins using the precipitation data corrected by MBC, LRM, GWR, the original IMERG-early, and the measured data. The results show that the GWR was the best fused effect. The topography had great influence on the accuracy of the IMERG-early data and the fusion method. The accuracy of the fusion method was relatively high in low elevation regions, while in mountainous areas, the accuracy became low comparatively. The results showed a better agreement between the SWAT model and the measured runoff process using all kinds of precipitation data, in which the model using precipitation data corrected by GWR method was more accurate. Results can be referenced for hydrological forecast in similar small basins in the future.

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以山地城市（重庆市）境内四条中小流域为例，引入GPM-IMERG遥感降水数据作为卫星降水数据来源，分别采用平均偏差校正法、线性回归模型和改进地理加权回归法实现其与地面站点实测数据融合，采用留一验证法进行融合降水数据的精度评估，并将各类降水数据输入SWAT模型模拟实际流域径流。结果表明，改进地理加权回归法对降雨数据的融合效果最佳，流域地形对IMERG-early数据及融合方法精度影响较大，在地势较低且地形起伏变化小的区域，融合方法精度较高，而在山丘区精度相对较低；不同降水数据驱动SWAT模型得到的径流计算值均与实测值吻合较好，且经改进地理加权法融合后的降水数据模拟径流最准确。结果可为IMERG-early卫星降水数据在类似中小流域水文预报的应用提供借鉴。

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孙健（1983-），男，硕士、正高级工程师，研究方向为水处理及水环境，E-mail：sjian686@163.com

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孙健（1983-），男，硕士、正高级工程师，研究方向为水处理及水环境，E-mail：sjian686@163.com

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