文献类型：期刊文献

英文题名：Land-Use and Flood Risk Assessment Under Uncertainty: A Monte Carlo Approach in Hunan Province, China

作者：Li, Qiong[1];Huang, Xinying[1];Pan, Fei[1];Hu, Qiang[1];Xu, Xinran[2]

机构：[1]East China Univ Sci & Technol, Sch Social & Publ Adm, Mei Long Rd 130, Shanghai 200237, Peoples R China;[2]Guangzhou Univ, Sch Publ Adm, Wai Huan Xi Rd 230, Guangzhou 510006, Peoples R China

年份：2026

卷号：15

期号：4

外文期刊名：LAND

收录：;WOS:【SSCI(收录号:WOS:001750865900001)】；

基金：This research was funded by the Major Project of the National Social Science Fund of China (grant number 23&ZD142), the Guangdong Provincial Philosophy and Social Science Planning 2026 Project-Youth Project (grant number GD26YSH05).

语种：英文

外文关键词：Monte Carlo simulation; flood risk assessment; land use planning; spatial risk zoning; disaster resilience

摘要：Climate change and rapid urbanization are intensifying flood risks in China, particularly in regions with complex terrain and dense populations. Traditional risk assessment methods often lack the flexibility to handle uncertainties in multi-dimensional risk systems. This study proposes a probabilistic flood risk assessment framework integrating Monte Carlo simulation with a composite indicator system from the perspective of disaster system theory. Taking Hunan Province as a case study, we constructed a hierarchical indicator system encompassing environmental susceptibility, hazard intensity, exposure vulnerability, and mitigation capacity. The analytic hierarchy process (AHP) and coefficient of variation (CV) methods were combined for indicator weighting, and Monte Carlo simulation was employed to quantify uncertainties and classify risk levels. Results reveal significant spatial heterogeneity in flood risk across the province, with high-risk areas concentrated in regions exhibiting intense rainfall, dense river networks, and insufficient mitigation infrastructure. The study provides a transferable, data-driven approach for spatially explicit flood risk zoning, offering evidence-based insights for land-use planning, resilient infrastructure development, and sustainable flood governance. This research contributes to the integration of probabilistic modeling into land system science, supporting disaster risk reduction and climate adaptation strategies aligned with SDG 11. This study also provides policy-relevant insights for regional flood governance by supporting risk-informed land-use planning, targeted infrastructure investment, and adaptive flood management strategies, thereby contributing to more resilient and sustainable land system development under increasing climate uncertainty.