水科学进展2025，Vol.36Issue(2)：307-319,13.DOI:10.14042/j.cnki.32.1309.2025.02.012
基于时空聚类的上海内涝积水时空分布规律
Spatiotemporal distribution patterns of urban waterlogging in Shanghai based on spatiotemporal clustering analysis
摘要
Abstract
Under the combined influence of frequent rainstorm events and rapid urbanization,urban waterlogging has emerged as a critical challenge in contemporary urban development.Based on Shanghai waterlogging data from 2013 to 2023,this study investigates the seasonal variations,spatial distribution,and characteristics of typical rainstorm-induced waterlogging utilizing K-means clustering and probability density analysis.Results indicate significant seasonality,with peak occurrence in summer strongly synchronized with typhoon-related rainstorms.Road inundation constitutes the primary waterlogging type,exhibiting prominent spatial heterogeneity;notably,the central urban areas experience frequent inundation primarily due to limited drainage capacities and insufficient water retention spaces.Among the three waterlogging categories identified via K-means clustering,high-risk zones with deeper water depths are concentrated at the interface between Jiading Distract and central urban areas.Typical storm-related inundation events are characterized by rapid accumulation and recession("fast in and fast out"),with durations typically not exceeding 10 hours.It is recommended that the risk early-warning lead time be controlled within 40 minutes.These findings provide critical insights to support the refinement of urban waterlogging management and enhance emergency response capabilities.关键词
城市内涝/K-means聚类/概率密度分析/时空分布/上海Key words
urban waterlogging/K-means clustering/probability density analysis/spatiotemporal distribution/Shanghai分类
水利科学引用本文复制引用
周正正,徐嘉言,刘曙光,万晖,孙丽,刘炎..基于时空聚类的上海内涝积水时空分布规律[J].水科学进展,2025,36(2):307-319,13.基金项目
国家自然科学基金项目(42371030 （）
42271031) The study is financially supported by the National Natural Science Foundation of China(No.42371030 （）
No.42271031). （）
