江西省地市暴雨灾害特征及其灾损评估模型研究

Characteristics of rainstorm disasters and disaster loss assessment models at prefectural-level city in Jiangxi Province

摘要

为精细化评估江西省 11 个地市的暴雨灾害造成的损失，本文利用 1961—2020 年暴雨过程数据、1984—2020 年暴雨灾情数据和 GDP 资料，对江西省 11 个地市开展暴雨特征及其灾损评估模型研究。首先，分析 11 个地市暴雨过程及其所造成灾害的时空分布特征；然后，通过显著相关系数法筛选区域差异化因子，构建地市暴雨综合强度指数 (I) 及灾损评估模型；最后，运用指数回归法和极端梯度提升法 (XGBoost) 构建灾损等级评估模型，并通过训练样本与独立样本检验模型效果。结果表明：(1) 江西省 11 个地市暴雨过程及其灾害特征存在区域差异，上饶、景德镇、鹰潭市为暴雨过程多发、重发区；地市平均暴雨过程频次、单站平均累积雨量及灾害频次均随年份增加呈显著上升趋势，灾损率则呈微弱下降趋势。(2) 11 个地市与暴雨灾害损失显著相关的因子数量和种类有所不同，其中单站过程累积雨量、区域过程累积雨量和持续天数是最核心的三个因子。各地市I 和灾损率均呈显著相关，相关系数介于 0.58~0.75 之间，建立的灾损评估模型在大多数地市有良好的拟合效果，其中抚州市和南昌市的拟合效果最优，决定系数 (R²) 分别为 0.82 和 0.80。(3) 对于灾损等级评估模型，两种方法在训练样本测试中误差均控制在 2 个等级以内，XGBoost 法在大多数地市中表现更优；在独立样本测试中，模型误差基本上在 1 个等级以内，指数回归法稳定性更佳。将两种方法结合能够有效确定暴雨灾害损失的等级区间，在实际业务中有较好的指导意义。

Abstract

To conduct a more refined assessment of rainstorm disasters at prefectural-level city scale in Jiangxi Province, this study utilizes precipitation data from 1961 to 2020, rainstorm disaster data from 1984 to 2020, and GDP data to investigate the characteristics of rainstorm events and their disaster loss assessment models for 11 prefectural-level cities in Jiangxi. Firstly, it systematically analyzes the temporal and spatial distribution characteristics of rainstorm events and their associated disasters across the 11 prefectural-level cities. Secondly, regionally differentiated factors are screened using the significant correlation coefficient method to construct a comprehensive rainstorm intensity index (I) and disaster loss assessment models for 11 prefectural-level cities. Finally, the exponential regression method and the extreme gradient boosting method (XGBoost) are employed to build disaster loss grade assessment models, with model performance validated using training samples and independent samples. The results are as follows. (1) Obvious differences exist in the characteristics of rainstorm events and disasters among 11 prefectural-level cities in Jiangxi.Shangrao, Jingdezhen, and Yingtan are areas where rainstorm events occur frequently and severely. The frequency, cumulative rainfall and disaster frequency in Jiangxi have a significant upward trend with the increase of years, but the disaster loss rate shows a weak downward trend. (2) The number and types of significant correlation factors in different prefectural-level cities have obvious differences, among which the maximum process rainfall of a station, cumulative rainfall and duration are the most common three significant correlation factors. The I is significantly correlated to the loss rate in 11 prefectural-level cities. And the loss assessment models exhibit good fitting results for most prefectural-level cities, particularly for Fuzhou and Nanchang, with determination coefficients (R²) of 0.82 and 0.80, respectively. (3) For the disaster loss grade assessment models, testing on training samples shows that errors of both methods are within 2 levels, with XGBoost performing better in most prefectural-level cities.Testing on independent samples indicates that the model errors were basically within the one level, and the exponential regression method demonstrates better stability. Moreover, combining these two methods could accurately provide the level range intervals of rainstorm disaster losses.

关键词

暴雨灾害 / 暴雨综合强度指数 / 损失评估 / GDP / 极端梯度提升法

Key words

rainstorm disaster / rainstorm comprehensive intensity index / loss assessment / GDP / extreme gradient boosting method

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基金资助

中国气象局创新发展专项(CXFZ2024Q007)

江西省科技厅“科技+应急”联合计划项目(S2023KJSLG0003)

江西省气象局灾害风险普查项目(JX2022ZHFXPC06)

江西省气象局青年项目(JX2023Q07)

南昌国家气候观象台专项(JX2023Z09)

珠江流域气象科研开放基金项目(ZJLY202312)