Spatiotemporal assessment of snowstorm resilience: a case study in Northeast China

Peijun Lu and Yicheng Wang ()
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Peijun Lu: Tsinghua University
Yicheng Wang: Politecnico di Torino

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 17, No 26, 20147-20170

Abstract: Abstract With global warming, the frequency and intensity of snowstorms have increased, posing significant risks for high-latitude regions in Northeast China, where underdeveloped infrastructure and high vulnerability amplify these threats. This study introduces an indicator-based framework for quantitatively assessing snow resilience, grounded in the Hazard-Exposure-Vulnerability-Adaptation (HEVA) model. Applied across Heilongjiang, Jilin, and Liaoning provinces, the framework captures resilience patterns from 2005 to 2020 using geostatistical analysis and machine learning techniques. Results reveal a strong correlation between snow resilience and snow hazard losses, validating the framework’s effectiveness in snow disaster management for high-risk areas. Spatial distribution patterns indicate hot and cold resilience spots, reflecting variations in vulnerability, exposure, and adaptive capacity across regions over time. The framework’s machine learning clustering approach further classifies resilience characteristics, while a Gradient Boosting Machine (GBM) analysis identifies infrastructure density, grassland extent, and transportation networks as key resilience-enhancing factors. Findings underscore the critical role of well-developed infrastructure in mitigating snow hazard impacts and enhancing adaptive capacity, offering targeted insights for resilience-building interventions. This study addresses a crucial research gap by providing a systematic, spatiotemporal approach to understanding resilience in snowstorm-prone regions. Its results support policymakers in disaster reduction planning and highlight the interconnected roles of social, environmental, and infrastructural elements in resilience. The framework contributes a robust model for global application, facilitating enhanced preparedness and adaptation strategies in regions increasingly vulnerable to intensified snow hazards due to climate change.

Keywords: Resilience assessment; Snow disaster; Spatiotemporal analysis; Machine learning; Influence factors (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07591-8

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