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

Wang，Yao1,2; Liu，Suning3; Chen，Ji4; Zhou，Zhaoqiang1,2; Shi，Haiyun1,2

2022-09-01

10.3389/fenvs.2022.951468

Frontiers in Environmental Science   Impact Factor & Quartile

2296-665X

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.

driving factor extreme rainfall large-scale climate patterns partial wavelet analysis yangtze river

SCI

English

First ; Corresponding

[51909117] ; [JCYJ20210324105014039] ; [2017B030301012]

Environmental Sciences & Ecology

Environmental Sciences

WOS:000860789800001

FRONTIERS MEDIA SA

2-s2.0-85138231578

Scopus

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

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.

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.

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).