A rapid risk assessment method for short-term rainstorm disaster

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.

Keywords

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