Spatiotemporal differentiation characteristics of flood risk based on spatial statistical analysis: a study of Jing–Jin–Ji region in China

Lei Gao (), Xiaoxue Liu and Hao Liu
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Lei Gao: Institute of Disaster Prevention
Xiaoxue Liu: Institute of Disaster Prevention
Hao Liu: Institute of Disaster Prevention

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 2, No 22, 1736 pages

Abstract: Abstract Torrential rains frequently lead to severe flood damage, a prevalent disaster in China. Significantly, the enduring flood risk stems from the decoupling of rainfall's spatial and temporal variability and the existing flood prevention capabilities. This study delves into the spatial characteristics of flood risk, focusing on risk identification, spatial autocorrelation, and clustering to enhance flood control planning and management strategies. Through the development of a flood risk assessment indicator system, utilizing multiple data sources, the study identifies risk zones within the target area. An integrated framework combining spatial autocorrelation with risk clustering is then introduced to examine the spatial clustering tendencies of flood disaster risk more closely. Applying county-wide data from the Jing–Jin–Ji region, the study evaluates flood risk indicators and validates the research methodology through visualization techniques. Analysis of the spatial characteristics of flood risk culminates in actionable planning and policy recommendations. Offering insights into flood risk management, urban infrastructure development, and adaptive strategies from diverse viewpoints, this study serves as a resourceful guide for mitigating flood risk and safeguarding human lives. Moreover, the research indicators and methods proposed herein extend valuable references for both domestic and international scholarly endeavors in related fields.

Keywords: Flood hazard; Spatial autocorrelation; Risk clustering; Multisource data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-024-06876-8

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