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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 ArcGIS 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. © 2025 Editorial Office of Journal of Disaster Prevention and Mitigation Engineering. All rights reserved.
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Conducting an importance analysis of rainfall disaster-causing factors and hazard assessment is crucial for preventing rainstorm disasters. Based on precipitation observation data from 66 national meteorological stations from 1981 to 2020 and 1,038 provincial meteorological stations from 2010 to 2020 in Fujian Province, the correlation coefficient weighting method was used to calculate and compare the weight coefficients of the rainfall disaster-causing factors of non-typhoon rainstorms in 5 seasons (early spring, rainy season, summer, autumn and winter) and typhoon rainstorms in 3 periods (early, common and late). Then, the comprehensive index method was employed to conduct the hazard assessment and validation of non-typhoon rainstorms. The results are as follows: (1) The spatial extent of heavy precipitation is the primary factor in causing disasters for non-typhoon rainstorms in all seasons and for late typhoon rainstorms. Short-duration intense precipitation is the major factor causing disasters in summer non-typhoon rainstorms, early typhoon rainstorms, and common typhoon rainstorms. (2) The results of the disaster risk assessment of non-typhoon rainstorms show that the hazard of the non-typhoon rainstorm event from June 14 to 26, 2010 was the highest. There is an overall spatial distribution indicating a pattern of being high in the east and west while low in the central area, with two contiguous high and very high risk areas in the northwest inland and southeast coastal areas, respectively. (3) The spatial distributions of disaster risk for three historical non-typhoon rainstorm events from 2010 to 2020 obtained from the non-typhoon rainstorm disaster assessment model are basically consistent with the disaster situation. © Editorial Office of Torrential Rain and Disasters. OA under CC BY−NC−ND 4.0.
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In recent years,rapid urbanization and global warming have led to frequent and severe rainstorm and flood disasters in the Sichuan-Chongqing region. This change will not only have a serious impact on the ecological environment and socio-economic development of the area,but also significantly increase the pressure on urban infrastructure and threaten the safety of people's lives and property. Therefore,it is particularly important to scientifically and accurately analyze the disaster risk of rainstorm and flood in Sichuan-Chongqing region in the past and future. This paper utilized daily precipitation data from 50 selected meteorological stations in the Sichuan-Chongqing region,precipitation data from 5 CMIP6 models,gridded population and economic data under Shared Socioeconomic Pathways(SSPs),as well as DEM and land use remote sensing data. Firstly,using Taylor diagrams,quantitative indices(S),and standardized anomaly sequences,the study evaluated the simulation performance of 5 individual CMIP6 models,an equal-weighted aggregation of 5 models(EWA-5),and unequally-weighted aggregations of 5 models(UEWA-5)for five selected extreme precipitation indices. Then,by building a comprehensive risk assessment model of rainstorm and flood disaster based on disaster risk and vulnerability of disaster bearing body,the study conducted risk assessments,future projections,and comparative analyses of rainstorm and flood disasters during baseline(1995-2014)and future near-term(2025-2044)and long-term (2045-2064)periods under three different climate change scenarios(SSP1-2. 6,SSP2-4. 5,SSP5-8. 5). Results indicated:(1)The EC-Earth3 model performed best in simulating the five extreme precipitation indices,with correlation coefficients between simulated and observed values of 0. 78 for R95p,0. 90 for RX1day,and 0. 77 for RX5day. Overall,the simulation performance of UEWA-5 exceeded that of EWA-5.(2)During the baseline period,central Sichuan exhibited high values for the five extreme precipitation indices,followed by eastern Sichuan and Chongqing,while western Sichuan showed lower values. The year 1998 recorded peak values for all five indices,with a maximum single-day precipitation of 86 mm for RX1day and an intensity(SDII)value of 11. 3 mm·d-1.(3)In future periods,the five extreme precipitation indices display a spatial distribution characterized by higher values in central regions and lower values around the periphery. Higher levels of social vulnerability and radiative forcing correlate with larger values of extreme precipitation indices. Comparing the two future periods,values of the indices are larger in the long term,notably with R95p averaging 846. 8 mm,an increase of 169. 2 mm compared to the near term.(4)During historical periods,areas with higher comprehensive risk of rainstorm and flood disasters were concentrated in central Sichuan and downtown Chongqing. In the two future periods,the high and moderately high-risk areas in central Sichuan are expected to expand,while the moderate-risk areas will shrink. The range of low-risk areas in the western Sichuan Plateau will also decrease,and the risk levels in southern Sichuan and eastern Sichuan-Chongqing border areas will respectively decrease to moderate-low and low-risk zones. Comparing the two future periods,the range of moderately high and moderate-risk areas in central Sichuan is expected to expand,while southwestern Chongqing will transition to a moderate-risk area in the long term. Other regions will generally maintain their original risk levels. Changes in disaster risk levels in the Sichuan-Chongqing region are less pronounced with increasing social vulnerability and radiative forcing,especially in the western Sichuan Plateau and northeastern Sichuan,where changes in disaster risk levels are minimal. The study results can provide important references for reducing disaster risks,enhancing emergency response capabilities,and making scientifically informed decisions for disaster prevention in the Sichuan-Chongqing region. © Editorial Department of Plateau Meteorology.