基于CMIP6模式降水的河北省滑坡灾害风险变化预测研究  

Prediction of Landslide Disaster Risk Changes in Hebei Province Based on CMIP6 Model Precipitation

作　　者：林诚杰 王洁 梁峰铭 季静静 谈松林 刘宇 LIN Cheng-jie;WANG Jie;LIANG Feing-ming;JI Jing-jing;TANG Song-lin;LIU Yu(School of Hydrology and Water Resources Engineering,Nanjing University of Information Science and Technology,Nanjing210044,Jiangsu Province,China;Key Laboratory of Hydrological and Meteorological Disaster Mechanisms and Early Warning of the Ministry of Water Resources,Nanjing210044,Jiangsu Province,China)

机构地区：[1]南京信息工程大学水文与水资源工程学院,江苏南京210044 [2]水利部水文气象灾害机理与预警重点实验室,江苏南京210044

出　　处：《中国农村水利水电》2025年第1期201-208,214,共9页China Rural Water and Hydropower

基　　金：河北省省级科技计划资助项目(19275408D);江苏省水利科技项目(2024007);国家自然科学基金面上项目(41671022,41877158);江苏省研究生科研与实践创新计划项目(KYCX23_1375)。

摘　　要：为探究未来气候变化下河北省滑坡灾害发生的变化规律,从而对防灾减灾的统筹规划提供科学的依据,使得人们更能规避滑坡灾害带来的危害风险,运用ENMeval算法(Ecological Niche Modeling evaluation Algorithm,ENMeval)对最大熵模型(Maximum Entropy Model,MaxEnt)进行优化,基于调查、收集到的860个滑坡灾害点和影响灾害的11个因子,在探讨主要影响因子的基础上进行滑坡灾害风险评估,并结合未来(近期:2041-2060年、中期:2061-2080年、远期:2081-2100年)三期气候数据的3种辐射强迫情景模式(低等强迫情景:SSP126、中高等强迫情景:SSP370、高等强迫情景:SSP585),预测滑坡灾害潜在风险区的空间分布格局和变化规律。结果表明:(1)经过优化的MaxEnt模型的AUC值(Area Under the Curve,AUC)在0.9以上,说明该模型在预测滑坡灾害潜在风险区方面表现出色。(2)通过优化Maxent模型计算的综合贡献率分析,确定影响滑坡灾害的主要因子依次为:最湿季度降水量、最湿月降水量、高程和降雨量季节变异系数,这表明降水相关的因子是影响滑坡发生的最主要因素。(3)对14个CMIP6气候模式(Coupled Model Intercomparison Project Phase 6,CMIP6)数据进行综合评估,得出BCC-CSM2-MR在降水方面相比其他气候模式,具有最优的模拟能力,其次是CMCC-ESM2和ACCESS-CM2。(4)在未来气候强迫情景下,滑坡灾害低风险区面积均有所下降,中、中高、高风险区的面积占比均有所增加,这和未来降水量的变化预测趋势相同。To explore the changing patterns of landslide disasters in Hebei Province under future climate change,and provide scientific basis for the overall planning of disaster prevention and reduction,so that people can better avoid the hazards and risks brought by landslide disasters.This article uses the Ecological Niche Modeling Evaluation Algorithm(ENMeval)to optimize the Maximum Entropy Model(MaxEnt).Based on 860 landslide disaster points collected from surveys and 11 factors affecting disasters,the article carries out hazard risk assessment on the basis of the main influencing factors,and combines three radiative forcing scenario models of future(recent:2041-2060,mid-term:2061-2080,long-term:2081-2100)climate data(low level forcing scenario:SSP126,medium and high level forcing scenario:SSP370,high level forcing scenario:SSP585),predict the spatial distribution pattern and change pattern of potential risk areas for landslide disasters.The results show that:①The Area Under the Curve(AUC)of the optimized MaxEnt model is above 0.9,indicating that the model performs well in predicting potential risk areas of landslide disasters.②By optimizing the comprehensive contribution rate analysis calculated by the Maxent model,the main factors affecting landslide disasters are determined to be:precipitation in the wettest quarter,precipitation in the wettest month,and seasonal variation coefficients of elevation and rainfall.This indicates that precipitation related factors are the most important factors affecting landslide occurrence.③A comprehensive evaluation of 14 Coupled Model Intercomparison Project Phase 6(CMIP6)data revealed that BCC-CSM2-MR has the best simulation ability in precipitation compared to other climate models,followed by CMCC-ESM2 and ACCESS-CM2.④In the future climate forcing scenario,the area of low-risk areas for landslide disasters has decreased,while the proportion of areas with medium,medium high,and high risk has increased,which is consistent with the predicted trend of future precipitation chan

关 键 词：滑坡灾害 模型优化 气候模式 风险预测 

分 类 号：P642.22[天文地球—工程地质学] X43[天文地球—地质矿产勘探]