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Future Projection of Rainstorm and Flood Disaster Risk in Southwest China Based on CMIP6 Models
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摘要: 为预估全球变暖背景下中国西南地区未来暴雨洪涝灾害风险的变化特征，研究挑选5个CMIP6模式和5个极端降水指数，结合地形因子、社会经济数据和耕地面积百分比，构建暴雨洪涝灾害风险评估模型，对西南暴雨洪涝灾害风险进行基准期(1995—2014年)评估、未来两个时期(2021—2040年，2041—2060年)3种情景(SSP1-2.6，SSP2-4.5，SSP5-8.5)下的预估和对比分析。结果表明：EC-Earth3, EC-Earth3-Veg两个单模式对5个极端降水指数的模拟效果较好，不等权重集合(UEWA-5)的效果整体优于等权重集合(EWA-5)。西南地区5个极端降水指数的高值区位于云南西部、广西东北部以及四川盆地西缘，社会脆弱性和辐射强迫越高，极端降水指数平均值和最大值越大; 从基准期到未来两个时期，5个极端降水指数均呈增大趋势。未来暴雨洪涝灾害的中高风险区和高风险区主要分布在四川成都市、重庆中心和四川盆地西部、云南昆明市、广西中南部和桂林市等局部较发达地区; 未来两个时期SSP2-4.5情景下的中高风险区和高风险区面积最大; 从基准期到未来远期，中高风险区和高风险区面积将随着时间增长而增加。Abstract: Under the background of global warming and frequent extreme weather and climate events, the occurrence of rainstorm and flood disasters in Southwest China continues to increase, causing great losses to social economy and people's lives and property. In order to project the characteristics of future rainstorm and flood disaster risk in Southwest China, 5 CMIP6 models and 5 extreme precipitation indices are selected to construct a risk assessment model, combined with topographic factors, socio-economic data and percentage of cultivated land area, by comprehensively considering disastrous factors and vulnerability. The rainstorm and flood disaster risks are mainly assessed for the baseline period (1995-2014), projected under three scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) for two future periods (2021-2040, 2041-2060), and comparatively analyzed. The results show that the simulation performance of EC-Earth3 and EC-Earth3-Veg on the selected 5 extreme precipitation indices is excellent, and the performance of un-equal weighted aggregation (UEWA-5) is better than equal weighted aggregation (EWA-5). According to the prediction results, 5 extreme precipitation indices are high in western Yunnan, northeastern Guangxi, and the western margin of Sichuan Basin. Higher social vulnerability and radiation forcing lead to greater extreme precipitation index. From the baseline period to the next two periods, the extreme precipitation indices and risk of disastrous factors show an increasing trend. The high vulnerability areas are distributed in the economically and agriculturally developed regional central cities and the change of vulnerability is not obvious under different scenarios. The medium-high risk areas and high risk areas of rainstorm and flood disasters are mainly distributed in Chengdu City of Sichuan, the center of Chongqing and western Sichuan Basin, Kunming City of Yunnan, Guilin City and south-central parts of Guangxi. The medium-high risk areas and high risk areas in Southwest China increase with time from the base period to the far future, especially under the SSP2-4.5 scenario.
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图 2 1995—2014年西南地区5个CMIP6模式(a)和多模式集合(b)各极端降水指数泰勒图
Fig. 2 Taylor diagrams of extreme precipitation indices for 5 CMIP6 models(a) and model ensembles(b) in Southwest China during 1995-2014
图 3 1995—2014年西南地区标准化的5个极端降水指数
Fig. 3 Standardized series of 5 extreme precipitation indices in Southwest China during 1995-2014
图 4 未来两个时期3种情景下西南地区5个极端降水指数时间序列
Fig. 4 Time series of 5 extreme precipitation indices in Southwest China under 3 scenarios during 2 future periods
图 5 基准期和未来两个时期3种情景下西南地区R50空间分布
Fig. 5 Spatial distribution of R50 in Southwest China under 3 scenarios during the base period and 2 future periods
图 6 未来两个时期3种情景下西南地区暴雨洪涝致灾危险性空间分布
Fig. 6 Spatial distribution of disaster risk caused by rainstorm and flood in Southwest China under 3 scenarios during 2 future periods
图 7 未来两个时期3种情景下西南地区承灾体易损度空间分布
Fig. 7 Spatial distribution of vulnerability of disaster-bearing body in Southwest China under 3 scenarios during 2 future periods
图 8 基准期和未来两个时期3种情景下西南地区暴雨洪涝灾害综合风险空间分布
Fig. 8 Spatial distribution of integrated risk of rainstorm and flood disaster in Southwest China under 3 scenarios during the base period and 2 future periods
表 1 CMIP6中5个模式基本信息
Table 1 Information of 5 models in CMIP6
模式名称 国家和地区 机构 格点数 BCC-CSM2-MR 中国 BCC 160×320 EC-Earth3 欧洲 EC 256×512 EC-Earth3-Veg 欧洲 EC 256×512 GFDL-ESM4 美国 NOAA-GFDL 180×288 MPI-ESM1-2-HR 德国 MPI-M 192×384 
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表 2 5个极端降水指数的定义
Table 2 Definitions of 5 extreme precipitation indices
指数 英文缩写 定义 单位 大雨日数 R20 日降水量不小于20 mm的日数 d 暴雨日数 R50 日降水量不小于50 mm的日数 d 5 d最大降水量 RX5day 最大连续5 d降水量 mm 年降水量 PRCPTOT 年降水量 mm 降水强度 SDII 湿日总降水量/湿日日数 mm·d-1
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表 3 5个CMIP6模式的S值排名
Table 3 Ranking of S-value for 5 models in CMIP6
模式 5个极端降水指数S值排名 综合排名 R20 R50 RX5day PRCPTOT SDII BCC-CSM2-MR 3 3 4 3 4 3 EC-Earth3 2 2 2 2 2 2 EC-Earth3-Veg 1 1 1 1 1 1 GFDL-ESM4 4 4 5 5 3 4 MPI-ESM1-2-HR 5 5 3 4 5 5
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表 4 西南地区暴雨洪涝灾害综合风险区面积占比(单位：%)
Table 4 Area proportion of comprehensive risk zone of rainstorm and flood disaster in Southwest China (unit: %)
情景 时期 暴雨洪涝灾害风险等级 Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ 基准期 25.85 53.68 13.59 5.85 1.02 SSP1-2.6 近期 22.70 54.26 14.97 6.99 1.08 远期 22.50 54.28 14.46 7.46 1.29 SSP2-4.5 近期 21.61 55.12 14.02 8.02 1.24 远期 21.46 53.16 14.58 8.92 1.88 SSP5-8.5 近期 22.04 54.60 14.38 7.81 1.17 远期 21.36 53.42 14.75 8.73 1.73
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