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Investigating the Spatiotemporal Complexity of Rainfall from a Chaotic Perspective: Case Study in the Jinsha River Basin, China

AbstractInvestigating the complexity of rainfall systems is an important way to understand the impacts of climate change. Based on daily rainfall recorded over the past 50 years from 24 meteorological stations in the Jinsha River Basin (JRB), the phase space analysis, power spectrum, correlation dim...

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Published in:	Journal of hydrologic engineering 2024-08, Vol.29 (4)
Main Authors:	Yu, Siyi, Wang, Wensheng, Liang, Hanxu, Zhang, Dan
Format:	Article
Language:	English
Subjects:	Algorithms Case Studies Case Study Climate change Complexity Correlation analysis Daily Daily rainfall Environmental impact Growth rate Hydrometeorological research Hydrometeorology Interpolation Latitude Monthly Monthly rainfall Precipitation Rainfall Rainfall-climatic change relationships River basins Rivers Seasonal rainfall Weather stations
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creator	Yu, Siyi Wang, Wensheng Liang, Hanxu Zhang, Dan
description	AbstractInvestigating the complexity of rainfall systems is an important way to understand the impacts of climate change. Based on daily rainfall recorded over the past 50 years from 24 meteorological stations in the Jinsha River Basin (JRB), the phase space analysis, power spectrum, correlation dimension method, and 0–1 test algorithm were used to develop a comprehensive analysis of the spatiotemporal complexity of rainfall at various time scales (daily, monthly, and seasonal). According to spatial patterns of rainfall complexity obtained from the spatial interpolation of asymptotic growth rates (K), the JRB was divided into two subregions by the 29°N line, and the influence of topography and climate on the complexity of regional rainfall was further investigated by correlation analysis. The results show that the rainfall dynamics of the JRB are chaotic, with a relatively strong state at daily and monthly scales, and a weak state at seasonal scale. Spatially, the daily rainfall complexity displays relatively small differences among 24 meteorological stations, while the monthly and seasonal rainfall complexity is ranked as lower reaches > source area > middle reaches. Additionally, monthly rainfall complexity has a significant positive and negative correlation (p
doi_str_mv	10.1061/JHYEFF.HEENG-6038
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Spatially, the daily rainfall complexity displays relatively small differences among 24 meteorological stations, while the monthly and seasonal rainfall complexity is ranked as lower reaches > source area > middle reaches. Additionally, monthly rainfall complexity has a significant positive and negative correlation (p<0.05) with elevation at lower and higher latitudes, respectively. Seasonal rainfall complexity has a significant positive correlation (p<0.05) with the dryness index (DI) at higher latitudes. The results of this study improve the understanding of the rainfall complexity in the JRB and can be further applied to the research on hydrometeorological zoning.</description><identifier>ISSN: 1084-0699</identifier><identifier>EISSN: 1943-5584</identifier><identifier>DOI: 10.1061/JHYEFF.HEENG-6038</identifier><language>eng</language><publisher>New York: American Society of Civil Engineers</publisher><subject>Algorithms ; Case Studies ; Case Study ; Climate change ; Complexity ; Correlation analysis ; Daily ; Daily rainfall ; Environmental impact ; Growth rate ; Hydrometeorological research ; Hydrometeorology ; Interpolation ; Latitude ; Monthly ; Monthly rainfall ; Precipitation ; Rainfall ; Rainfall-climatic change relationships ; River basins ; Rivers ; Seasonal rainfall ; Weather stations</subject><ispartof>Journal of hydrologic engineering, 2024-08, Vol.29 (4)</ispartof><rights>2024 American Society of Civil Engineers</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-a318t-65343c543cef4adfbff99d8d6130f1e4906e37ea2e5656d081cc2a1026097fb53</cites><orcidid>0000-0002-1045-6146 ; 0000-0002-5871-7990</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttp://ascelibrary.org/doi/pdf/10.1061/JHYEFF.HEENG-6038$$EPDF$$P50$$Gasce$$H</linktopdf><linktohtml>$$Uhttp://ascelibrary.org/doi/abs/10.1061/JHYEFF.HEENG-6038$$EHTML$$P50$$Gasce$$H</linktohtml><link.rule.ids>314,780,784,3252,10068,27924,27925,76191,76199</link.rule.ids></links><search><creatorcontrib>Yu, Siyi</creatorcontrib><creatorcontrib>Wang, Wensheng</creatorcontrib><creatorcontrib>Liang, Hanxu</creatorcontrib><creatorcontrib>Zhang, Dan</creatorcontrib><title>Investigating the Spatiotemporal Complexity of Rainfall from a Chaotic Perspective: Case Study in the Jinsha River Basin, China</title><title>Journal of hydrologic engineering</title><description>AbstractInvestigating the complexity of rainfall systems is an important way to understand the impacts of climate change. Based on daily rainfall recorded over the past 50 years from 24 meteorological stations in the Jinsha River Basin (JRB), the phase space analysis, power spectrum, correlation dimension method, and 0–1 test algorithm were used to develop a comprehensive analysis of the spatiotemporal complexity of rainfall at various time scales (daily, monthly, and seasonal). According to spatial patterns of rainfall complexity obtained from the spatial interpolation of asymptotic growth rates (K), the JRB was divided into two subregions by the 29°N line, and the influence of topography and climate on the complexity of regional rainfall was further investigated by correlation analysis. The results show that the rainfall dynamics of the JRB are chaotic, with a relatively strong state at daily and monthly scales, and a weak state at seasonal scale. Spatially, the daily rainfall complexity displays relatively small differences among 24 meteorological stations, while the monthly and seasonal rainfall complexity is ranked as lower reaches > source area > middle reaches. Additionally, monthly rainfall complexity has a significant positive and negative correlation (p<0.05) with elevation at lower and higher latitudes, respectively. Seasonal rainfall complexity has a significant positive correlation (p<0.05) with the dryness index (DI) at higher latitudes. 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Based on daily rainfall recorded over the past 50 years from 24 meteorological stations in the Jinsha River Basin (JRB), the phase space analysis, power spectrum, correlation dimension method, and 0–1 test algorithm were used to develop a comprehensive analysis of the spatiotemporal complexity of rainfall at various time scales (daily, monthly, and seasonal). According to spatial patterns of rainfall complexity obtained from the spatial interpolation of asymptotic growth rates (K), the JRB was divided into two subregions by the 29°N line, and the influence of topography and climate on the complexity of regional rainfall was further investigated by correlation analysis. The results show that the rainfall dynamics of the JRB are chaotic, with a relatively strong state at daily and monthly scales, and a weak state at seasonal scale. 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subjects	Algorithms Case Studies Case Study Climate change Complexity Correlation analysis Daily Daily rainfall Environmental impact Growth rate Hydrometeorological research Hydrometeorology Interpolation Latitude Monthly Monthly rainfall Precipitation Rainfall Rainfall-climatic change relationships River basins Rivers Seasonal rainfall Weather stations
title	Investigating the Spatiotemporal Complexity of Rainfall from a Chaotic Perspective: Case Study in the Jinsha River Basin, China
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