摘要
本研究旨在开展南京市玄武区栅格尺度极端降水风险评估,以弥补现有指标体系不足,提升风险精细化刻画水平。通过整合物理、社会、环境维度的指标,构建风险评估框架,以全面表征极端降水风险特征。研究采用熵权法计算指标权重,结合ArcGIS技术及K-means聚类算法,分析百年一遇极端降水情景下的风险空间分布特征,并挖掘不同风险等级区域的关键影响指标。结果表明:玄武区极端降水风险等级呈现显著空间异质性,整体呈现中部低风险,四周高风险的分布格局。关键指标影响机制呈现分级响应特征:低风险区主要受城乡、工矿、居民用地淹没面积,水域面积,土壤侵蚀程度,归一化植被指数控制;中风险区受城乡、工矿、居民用地淹没面积,林地淹没面积,紧急服务到达受灾区域的速度,土壤侵蚀程度,归一化植被指数影响;高风险区由城乡、工矿、居民用地淹没面积,林地淹没面积,归一化植被指数共同主导;极高风险区则受林地淹没面积,紧急服务到达受灾区域的速度,最大拼块占景观面积比例三重因素驱动。完善了极端降水风险评估指标体系,明确了风险驱动指标的分级响应规律,为玄武区制定差异化防洪策略提供科学依据,对提升区域洪涝灾害防御能力具有重要理论支撑。
This study aims to conduct a grid-scale extreme precipitation risk assessment in Xuanwu District,Nanjing,so as to fill the gaps in existing indicator systems and improve the precision of risk characterization.By integrating physical,social,and environmental indicators,a risk assessment framework was constructed to comprehensively represent the characteristics of extreme precipitation risk.This study applied the entropy weight method to calculate indicator weights,combined with Arc‑GIS technology and the K-means clustering algorithm,to analyze the spatial distribution characteristics of risk under a 100-year extreme precipitation scenario and to identify key influencing indicators across different risk levels.The results showed that extreme precipitation risk levels in Xuanwu District exhibited significant spatial heterogeneity,with an overall distribution pattern of low risk in the central area and high risk in the surrounding areas.The influence mechanisms of key indicators showed tiered response characteristics:the low-risk areas were mainly controlled by the submerged areas of urban and rural,industrial and mining,and residential lands,water body area,soil erosion level,and normalized difference vegetation index(NDVI).The medium-risk areas were influenced by the submerged areas of urban and rural,industrial and mining,residential lands,the submerged areas of forest land,emergency service response time to disaster-affected areas,soil erosion level,and NDVI.The high-risk areas were jointly dominated by the submerged areas of urban and rural,industrial and mining,residential lands,the submerged areas of forest land,and NDVI.The extremely high-risk areas were driven by three factors—the submerged areas of forest land,emergency service response time to disaster-affected areas,and the proportion of the largest patch to the landscape area.This study improves the indicator system for extreme precipitation risk assessment and clarifies the tiered response patterns of risk-driving indicators,providing a scientific basis for developing differentiated flood control strategies in Xuanwu District while offering important theoretical support for improving regional flood disaster resilience.
作者
张健磊
褚宸坤
王鹏
ZHANG Jianlei;CHU Chenkun;WANG Peng(Faculty of Civil Engineering and Mechanics,Jiangsu University,Zhenjiang 212013,China)
出处
《防灾减灾工程学报》
北大核心
2025年第4期736-744,共9页
Journal of Disaster Prevention and Mitigation Engineering
基金
国家自然科学基金项目(51908249)
国家重点研发计划项目(2023YFC3205703)资助。
关键词
极端降水
栅格尺度
风险
熵权法
关键指标识别
extreme precipitation
grid scale
risk
entropy weight method
key indicator identification
