Article(id=1223278139023954508, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222456, 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=1672848000000, revisedDateStr=2023-01-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1769581835751, onlineDateStr=2026-01-28, pubDate=1698163200000, pubDateStr=2023-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769581835751, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769581835751, creator=13701087609, updateTime=1769581835751, updator=13701087609, issue=Issue{id=1223278131956551924, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='10', pageStart='1', pageEnd='228', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769581834067, creator=13701087609, updateTime=1769584342407, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223288652869030422, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223288652869030423, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=27, endPage=31, ext={EN=ArticleExt(id=1223278140676510401, articleId=1223278139023954508, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Attribution Analysis of Runoff Variation Based on WA-BP Coupling Model, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

To study the trend of runoff evolution under the influence of climate change and human activities, the Qinhuai River Basin was taken as the research area based on the hydrometeorological data of representative stations. Firstly, the Mann-Kendall method was used to study the variation trend of hydrometeorological elements in the basin. Secondly, the abrupt change points of the runoff series were discussed by orderly cluster method and double cumulative curve method. Thirdly, the key factors affecting runoff were screened from numerous hydrometeorological factors, and the Wavelet Analysis-BP neural network coupling model was established to predict the runoff. Finally, attribution analysis method was used to identify contribution of climate change and human activities to the runoff variation. The results show that the rainfall of the Xinhe river sluice presents a downward trend, while the Wudingmen sluice and the whole basin show an upward trend, but none of the trends were significant. The runoff of the basin shows an upward trend, and the increase trend of Xinhe river sluice is not significant, both the Wudingmen sluice and the whole basin increase trend pass the test of 0.01 confidence level, and the annual evaporation tends to decrease. There is a great consistency between the Wudingmen sluice and the whole basin in the variation trend of rainfall, runoff, and the time of abrupt change point. The contribution rates of human activities and climate change to runoff increase were 73.92% and 26.08%, respectively, the human activities were an important factor that led to the runoff variation. The conclusion provides a scientific basis for flood and drought prevention and water resources planning in this basin.

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为了研究气候变化和人类活动双重影响下径流演变趋势，以秦淮河流域为例，基于代表站点的水文气象资料，首先，采用Mann-Kendall趋势检验分析流域水文气象要素的变化趋势；其次，利用有序聚类法和双累计曲线法判断径流序列的可疑突变点；而后，从众多水文气象因子中筛选影响径流的关键因子，构建小波分析-BP神经网络（WA-BP）耦合模型预测突变点后的天然径流；最后，结合归因分析法定量识别气候变化和人类活动对径流变化的贡献率。结果表明，降雨方面，新河闸呈下降趋势，武定门闸和流域整体呈上升趋势，但变化趋势均不显著；径流方面，流域总体呈上升趋势，其中新河闸增加趋势不显著，武定门闸和流域整体增加趋势均通过了置信度为0.01的检验；流域蒸发总体呈下降趋势。武定门闸与流域整体在降雨径流变化趋势、突变点发生时间等方面具有较大的一致性。人类活动和气候变化对径流增加的贡献率分别为73.92%、26.08%，可见人类活动是秦淮河流域径流变化的重要原因。结果对于该流域水旱灾害防治及水资源规划提供了科学依据。

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刘雨（1998-），男，硕士研究生，研究方向为气候变化与水循环，E-mail：2811306430@qq.com

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刘雨（1998-），男，硕士研究生，研究方向为气候变化与水循环，E-mail：2811306430@qq.com

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