一种快速的短临暴雨灾害风险评估方法

A rapid risk assessment method for short-term rainstorm disaster

摘要

【目的】针对短临暴雨灾害风险评估中存在的暴雨突变性强、预警时效短、灾害风险评估精度不足等问题, 建立一种融合实时气象数据与动态风险源的精细化风险评估与预警方法。【方法】基于南京地区3 km×3 km 网格的逐小时降水实况与预报数据, 构建致灾因子危险性量化模型(包含暴雨预警等级、范围、过程降水量等5 类指标), 结合承灾体脆弱性( 江河水位、动态人口密度、灾情数据)与降水概率指数, 通过GIS 空间叠加与求积法计算综合风险指数, 划分特别重大(R≥9)、重大(7<R<9)、较大(5<R<7)、一般(3<R<5)4个风险等级。模型中各因子权重经由历史事件反演与敏感性分析初步验证。【结果】2024年9月南京暴雨事件检验表明, 模型可较好识别高风险区域(空间分辨率0.03°×0.03°), 溧水区白马镇南部风险指数达9.2、高淳区桠溪镇北部风险指数达9.1(特别重大等级), 与实地灾情核查结果(树干、树枝倒伏1652例、广告牌脱落79处) 吻合; 在数据同步更新的前提下, 动态评估响应时间小于1h, 预警准确率达到87.3%。【结论】该方法通过多源数据融合与网格化动态计算, 显著提升短临暴雨风险评估的时效性与精确性, 可为城市防灾减灾提供街道级精细化预警支持。

Abstract

[Objective]To address the challenges in short-term rainstorm disaster risk assessment, including strong suddenness of rainstorms, limited warning time, and insufficient assessment accuracy, a refined risk assessment and early-warning method is proposed, which integrates real-time meteorological data with dynamic risk factors. [Methods] Based on hourly observed and forecast precipitation data on a 3km×3km grid in Nanjing, a risk quantification model of disaster-causing factors was developed (including five indicators such as rainstorm warning level, affected area, and process precipitation). Combined with vulnerability indicators of exposed elements (including river water level, dynamic population density, and disaster data) and the precipitation probability index, the comprehensive risk index (R) was calculated by GIS spatial overlay and a multiplicative integration approach to classify four risk levels: extremely severe (R≥9), severe (7<R<9), moderate (5<R<7), and general (3<R<5). The weights of the factors in the model were preliminarily validated through historical event inversion and sensitivity analysis. [Results] The model validation during the September 2024 Nanjing rainstorm event demonstrated that high-risk areas could be accurately identified (at 0.03°×0.03° spatial resolution). The risk indices calculated reached 9. 2 in southern Baima Town of Lishui District and 9. 1 in northern Yaxi Town of Gaochun District, both classified as extremely severe level, which was consistent with the field disaster investigation result (1 652 cases of fallen trunks/ branches and 79 cases of fallen billboards). With realtime data updates, the dynamic assessment response time was maintained within one hour, with an early warning accuracy rate of 87.3%. [Conclusion] Through multi-source data integration and grid-based dynamic calculation, the proposed method significantly improves the timeliness and accuracy of short-term rainstorm risk assessment and provides street-level refined early warning support for urban disaster prevention and mitigation.

关键词

短临暴雨 / 致灾因子 / 灾害风险 / 量化评估预警 / 降水 / 气候变化 / 城市防灾减灾 / 多元数据融合

Key words

short-term rainstorm / disaster-causing factors / disaster risk / quantitative assessment and early warning / precipitation / climate change / urban disaster prevention and mitigation / multi-source data integration

基金资助

国家自然科学基金项目(41905026)

江苏省自然科学基金项目(BK20170945)

南京信息工程大学人才启动基金项目(2016r028)

中国博士后科学基金第63批面上课题(2018M631554)

江苏省333 高层次人才培养工程((2022)3-16-375)

江苏省高等教育教改立项研究课题(2023JSJG330)