Article(id=1223196814048875468, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222455, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1668960000000, receivedDateStr=2022-11-21, revisedDate=1677513600000, revisedDateStr=2023-02-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1769562446366, onlineDateStr=2026-01-28, pubDate=1692892800000, pubDateStr=2023-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769562446366, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769562446366, creator=13701087609, updateTime=1769562446366, updator=13701087609, issue=Issue{id=1223196803663778281, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='8', pageStart='1', pageEnd='222', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769562443888, creator=13701087609, updateTime=1769563793740, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223202465391166440, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223202465391166441, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1, endPage=4, ext={EN=ArticleExt(id=1223196815726597116, articleId=1223196814048875468, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Application of Stochastic Forest Precipitation Fusion Algorithm With Covariates in Yangtze River Basin, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

The precipitation data with high quality and high spatial and temporal resolution is of great significance to the research of hydrology, meteorology and other fields. At present, remote sensing and reanalysis of precipitation data are widely used, but there are problems such as low resolution, high uncertainty of accuracy, etc. In this paper, a random forest precipitation fusion algorithm considering covariates is proposed to fuse seven sets of precipitation products, namely CMA, CN05, ERA5, GLDAS, TRMM, IMERG and PERSIANN. Three typical sub-basins of the Yangtze River basin (Jinsha River, Sanxiaqujian and Poyang Lake) are selected to test the effect of random forest fusion data (RFF). The results show that for the accuracy of precipitation products, the accuracy of random forest fusion data is improved compared with the original precipitation products. For the accuracy assessment of different precipitation events, with the increase of rainfall intensity, the T_TS score of each precipitation product shows a decreasing trend, and the T_TS score of RFF is better than the original precipitation product. Data fusion of precipitation products through random forest model considering covariates can improve the accuracy of precipitation data and the reliability of different precipitation events, which provides support for hydrological simulation.

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高质量、高时空分辨率的降水数据对水文、气象等领域的研究具有重要意义。目前遥感、再分析降水数据应用普遍，但存在分辨率低、精度不确定性大等问题。对此，提出了考虑协变量的随机森林降水融合算法，对CMA、CN05、ERA5、GLDAS、TRMM、IMERG、PERSIANN七套降水产品进行融合，并选取长江流域的三个典型子流域（金沙江、三峡区间、鄱阳湖）进行随机森林融合数据（RFF）的效果检验。结果显示，对于降水产品准确性，随机森林融合数据精度相较于原始降水产品有所提升。对于不同降水事件的准确性评估，随着降雨强度增加，各降水产品风险评分（T_TS）评分均呈减小趋势，RFF的T_TS评分优于原始降水产品。通过考虑协变量的随机森林模型对降水产品进行数据融合，可以提升降水数据的精度及不同降水事件发生的可靠性，为水文模拟等提供支持。

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徐志（1990-），男，博士、工程师，研究方向为水文学及水资源，E-mail：xu_zhi1@ctg.com.cn

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徐志（1990-），男，博士、工程师，研究方向为水文学及水资源，E-mail：xu_zhi1@ctg.com.cn

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