{"title":"Integrating multi-model frameworks to unravel the spatiotemporal dynamics of flash floods in the Tianshan Mountain, China","authors":"Biao Zhang , Haiyan Fang , Guotao Zhang , Songqing Li , Shufang Wu , Kadambot H.M. Siddique","doi":"10.1016/j.ecolind.2025.113259","DOIUrl":null,"url":null,"abstract":"<div><div>The Tianshan Mountain in China (CTM), a critical water resource and climate change hotspot in Central Asia, faces escalating flash flood events due to global climate change and intensified human activities. This study applied the Geodetector (GD) to select driving factors with spatiotemporal variability of flash floods in the CTM from 1975 to 2015. The filtered drivers were integrated with historical flash flood data using the Geographically and Temporally Weighted Regression model (GTWR) to explore the spatiotemporal heterogeneity of the driving effects. Subsequently, the Partial Least Squares Structural Equation (PLS-SEM) was used to explore the direct and indirect influence pathways among driving factors. The analysis revealed a fluctuating upward trend in flash floods, accelerating after 1995 and showing different trends in various subregions after 2010. By the early 21st century, a symmetrical north–south distribution pattern emerged, with extreme precipitation events as the key driver. Terrain rainstorms were the main trigger in the eastern Tianshan Mountains (ETM), while landscape diversity, reduced snowmelt, and artificial flood control mitigated floods in the northern Tianshan Mountains (NSTM). The southern Tianshan Mountains (SSTM) experienced significant flood changes due to abundant precipitation. This study constructs a comprehensive analytical framework for investigating flash flood changes in the CTM by integrating GD, GTWR, and PLS-SEM models and proposes flash flood management strategies based on the identified mechanisms in different subzones.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113259"},"PeriodicalIF":7.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X25001888","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The Tianshan Mountain in China (CTM), a critical water resource and climate change hotspot in Central Asia, faces escalating flash flood events due to global climate change and intensified human activities. This study applied the Geodetector (GD) to select driving factors with spatiotemporal variability of flash floods in the CTM from 1975 to 2015. The filtered drivers were integrated with historical flash flood data using the Geographically and Temporally Weighted Regression model (GTWR) to explore the spatiotemporal heterogeneity of the driving effects. Subsequently, the Partial Least Squares Structural Equation (PLS-SEM) was used to explore the direct and indirect influence pathways among driving factors. The analysis revealed a fluctuating upward trend in flash floods, accelerating after 1995 and showing different trends in various subregions after 2010. By the early 21st century, a symmetrical north–south distribution pattern emerged, with extreme precipitation events as the key driver. Terrain rainstorms were the main trigger in the eastern Tianshan Mountains (ETM), while landscape diversity, reduced snowmelt, and artificial flood control mitigated floods in the northern Tianshan Mountains (NSTM). The southern Tianshan Mountains (SSTM) experienced significant flood changes due to abundant precipitation. This study constructs a comprehensive analytical framework for investigating flash flood changes in the CTM by integrating GD, GTWR, and PLS-SEM models and proposes flash flood management strategies based on the identified mechanisms in different subzones.
期刊介绍:
The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published.
• All aspects of ecological and environmental indicators and indices.
• New indicators, and new approaches and methods for indicator development, testing and use.
• Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources.
• Analysis and research of resource, system- and scale-specific indicators.
• Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs.
• How research indicators can be transformed into direct application for management purposes.
• Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators.
• Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.