中文版 | English
Title

Investigating the spatiotemporal variations of extreme rainfall and its potential driving factors with improved partial wavelet coherence

Author
Corresponding AuthorLiu,Suning; Shi,Haiyun
Publication Years
2022-09-01
DOI
Source Title
EISSN
2296-665X
Volume10
Abstract
Extreme rainfall can be affected by various climatic factors such as the large-scale climate patterns (LCPs). Understanding the changing LCPs can improve the accuracy of extreme rainfall prediction. This study explores the variation trend of extreme rainfall in the middle and lower reaches of the Yangtze River Basin (MLRYRB) and the telecorrelation with four LCPs, namely WPSHI (Western Pacific Subtropical High Index), EAMI (East Asia Monsoon Index), ENSO (El Niño-Southern Oscillation) and PDO (Pacific Decadal Oscillation), through modified Mann-Kendall (MMK) analysis, Pearson correlation coefficient, wavelet coherence analysis (WTC) and improved partial wavelet analysis (PWC). Previous studies have ignored the interdependence between these climate indices when analyzing their effects on precipitation. This study introduces the improved PWC, which can remove the correlations between them and reveal the influence of a single LCP. The results show that: 1) extreme rainfall in the MLRYRB has an obvious increasing trend and has a significant correlation with the LCPs; 2) the LCPs have a significant cyclical relationship with extreme rainfall, which can be significantly affected by the intergenerational variation of the LCPs; and 3) the improved PWC can accurately reveal the influence of a single LCP. EAMI is the main influencing factor in the 1-year cycle, while WPSHI is the main influencing factor in the 5-year cycle. ENSO and PDO can always influence extreme rainfall by coupling WPSHI or EAMI.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
[51909117] ; [JCYJ20210324105014039] ; [2017B030301012]
WOS Research Area
Environmental Sciences & Ecology
WOS Subject
Environmental Sciences
WOS Accession No
WOS:000860789800001
Publisher
Scopus EID
2-s2.0-85138231578
Data Source
Scopus
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/402727
DepartmentSchool of Environmental Science and Engineering
Affiliation
1.State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,China
2.Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,China
3.Center for Climate Physics,Institute for Basic Science,Busan,South Korea
4.Department of Civil Engineering,The University of Hong Kong,Hong Kong
First Author AffilicationSchool of Environmental Science and Engineering
Corresponding Author AffilicationSchool of Environmental Science and Engineering
First Author's First AffilicationSchool of Environmental Science and Engineering
Recommended Citation
GB/T 7714
Wang,Yao,Liu,Suning,Chen,Ji,et al. Investigating the spatiotemporal variations of extreme rainfall and its potential driving factors with improved partial wavelet coherence[J]. Frontiers in Environmental Science,2022,10.
APA
Wang,Yao,Liu,Suning,Chen,Ji,Zhou,Zhaoqiang,&Shi,Haiyun.(2022).Investigating the spatiotemporal variations of extreme rainfall and its potential driving factors with improved partial wavelet coherence.Frontiers in Environmental Science,10.
MLA
Wang,Yao,et al."Investigating the spatiotemporal variations of extreme rainfall and its potential driving factors with improved partial wavelet coherence".Frontiers in Environmental Science 10(2022).
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