地球科学进展 ›› 2022, Vol. 37 ›› Issue (5): 519 -534. doi: 10.11867/j.issn.1001-8166.2022.019

研究论文 上一篇    下一篇

基于 CMIP6的中国未来暴雨危险性变化评估
唐明秀 1 , 2( ), 孙劭 3, 朱秀芳 1 , 2( ), 张世喆 1, 徐昆 1, 郭春华 1   
  1. 1.北京师范大学环境演变与自然灾害教育部重点实验室,北京 100875
    2.北京师范大学地理科学 学部遥感科学与工程研究院,北京 100875
    3.中国气象局国家气候中心,北京 100081
  • 收稿日期:2021-08-11 修回日期:2022-01-22 出版日期:2022-05-10
  • 通讯作者: 朱秀芳 E-mail:202131051050@mail.bnu.edu.cn;zhuxiufang@bnu.edu.cn
  • 基金资助:
    国家重点研发计划项目“不同温升情景下区域气象灾害综合风险预估”(2019YFA0606900);国家自然科学基金面上项目“高温和干旱交互胁迫下中国区玉米成害机理及其风险评价”(42077436)

CMIP6 Assessment of Changes in Hazard of Future Rainstorms in China

Mingxiu TANG 1 , 2( ), Shao SUN 3, Xiufang ZHU 1 , 2( ), Shizhe ZHANG 1, Kun XU 1, Chunhua GUO 1   

  1. 1.Key Laboratory of Environmental Change and Natural Disasters,Ministry of Education,Beijing Normal University,Beijing 100875,China
    2.Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China
    3.National Climate Center,China Meteorological Administration,Beijing 100081,China
  • Received:2021-08-11 Revised:2022-01-22 Online:2022-05-10 Published:2022-05-31
  • Contact: Xiufang ZHU E-mail:202131051050@mail.bnu.edu.cn;zhuxiufang@bnu.edu.cn
  • About author:TANG Mingxiu (1999-), female, Dezhou City, Shandong Province, Ph.D student. Research areas include remote sensing applications and natural disasters. E-mail: 202131051050@mail.bnu.edu.cn
  • Supported by:
    the National Key Resaearch & Development Program of China “Comprehensive risk prediction of regional meteorological disasters under different temperature rise scenarios”(2019YFA0606900);The National Natural Science Foundation of China “Formation mechanism and risk assessment of corn damage in China under the interactive stress of high temperature and drought”(42077436)

使用1961—2019年全国2 510个气象站点日值降水数据和2030—2100年第六次耦合模式比较计划的12个耦合模式的SSP2-4.5未来情景的降水模拟数据,基于核密度函数分别计算4个重现期(五年一遇、十年一遇、二十年一遇和五十年一遇)历史和未来情景中3个年暴雨要素(年暴雨日数、年暴雨量和年暴雨强度)的数值,在此基础上评估了中国未来暴雨危险性的变化。得到的主要结论如下: 从全国来看,在未来年暴雨日数和雨量呈增加趋势,而年暴雨强度在不同重现期下表现不一样。4个重现期下全国的年暴雨日数变化均值分别为0.36、0.57、0.73和0.92 d;年暴雨量变化均值分别为22.30、36.24、46.92和60.12 mm;年暴雨强度变化均值分别为2.43、0.27、-1.95和 -4.86 mm/d。 从不同气候区来看,年暴雨量和雨日在青藏高原、东部干旱区、东北地区、华北地区和西南地区呈增加趋势,在西部干旱(半干旱)区、华中地区和华南地区呈减少趋势。4个重现期下年暴雨强度在青藏高原均呈增加趋势,而其他地区以减少为主要趋势。 热点分析结果显示青藏高原东南部和南部是暴雨危险性增加最显著的区域;年暴雨日数和雨量减少的区域集中分布在西南地区的中部、华中和华南地区的中部和南部,年暴雨强度减少的区域集中在东部干旱区的中部、西南地区西部及华北地区北部和南部。

Using the daily precipitation data of 2 510 meteorological stations in China from 1961 to 2019 and the precipitation simulation data of 12 coupling models of CMIP6 from 2030 to 2100 under the SSP2-4.5 future scenario, the values of three annual rainstorm elements (annual rainstorm days, annual rainstorm rainfall, and annual rainstorm intensity) in the historical and future scenarios were calculated based on the kernel density function under four return periods (5,10,20,50 years). Based on this, the future hazard change of rainstorms in China was assessed. The main conclusions are as follows: From a national perspective, the number of rainstorm days and rainfall amount are estimated to increase in the future, and the annual rainstorm intensity is estimated to change differently in different return periods. Under the four return periods, the mean change of annual rainstorm days in China is expected to be 0.36, 0.57, 0.73 and 0.92 days; the mean change of annual rainstorm rainfall is expected to be 22.30, 36.24, 46.92 and 60.12 mm; and the mean change of annual rainstorm intensity is expected to be 2.43, 0.27, -1.95 and -4.86 mm/d. From the perspective of different climatic zones, the annual rainstorm rainfall and rainstorm days showed an increasing trend in the Qinghai-Tibet Plateau, Eastern arid zone, Northeast China, North China, and Southwest China, and a decreasing trend in the western arid (semi-arid) zone, Central China, and South China. The annual rainstorm intensity is expected to increase in the Qinghai-Tibet Plateau and decrease in most other regions under the four return periods. The hot spot analysis shows that the southeastern and southern Qinghai-Tibet Plateau are expected to be the areas with the most significant increases in rainstorm hazards. The areas with reduced annual rainstorm days and annual rainstorm rainfall are expected to be concentrated in the middle of Southwest China, the middle and south of Central China and South China. Finally, the areas with reduced annual rainstorm intensities are expected to be concentrated in the middle of the eastern arid zone, the west of Southwest China, and the north and south of North China.

中图分类号: 

1 SHI Peijun. Theory and practice on disaster system research in a fifth time[J]. Journal of Natural Disasters, 2009, 18(5): 1-9.
史培军. 五论灾害系统研究的理论与实践[J]. 自然灾害学报, 2009, 18(5): 1-9.
2 LI Dawei, SHI Shuzhong, YANG Fuping, et al. Summary of natural disaster risk assessment[J]. Value Engineering, 2014, 33(26): 322-325.
李大卫, 石树中, 杨福平, 等. 自然灾害风险评估综述[J]. 价值工程, 2014, 33(26): 322-325.
3 YANG Yang, KE Liping, NIE Xiang, et al. Analysis on spatial and temporal distribution characteristics of Bijie heavy rain[J]. Meteorological, Hydrological and Marine Instruments, 2020, 37(4): 16-18.
杨洋, 柯莉萍, 聂祥, 等. 毕节市暴雨时空分布特征分析[J]. 气象水文海洋仪器, 2020, 37(4): 16-18.
4 ZHANG Zhiru. Risk assessment of rainstorm disasters under different return periods: a case study of Bohai rim[D]. Dalian: Liaoning Normal University, 2020.
张志茹. 不同重现期下环渤海地区暴雨灾害风险评估[D]. 大连: 辽宁师范大学, 2020.
5 CHENG Zhengquan, CHEN Lianshou, XU Xiangde, et al. Research progress on typhoon heavy rainfall in China for last ten years[J]. Meteorological Monthly, 2005, 31(12): 3-9.
程正泉, 陈联寿, 徐祥德, 等. 近10年中国台风暴雨研究进展[J]. 气象, 2005, 31(12): 3-9.
6 SHUSTER W D, BONTA J, THURSTON H, et al. Impacts of impervious surface on watershed hydrology: a review[J]. Urban Water Journal, 2005, 2(4): 263-275.
7 DIEM J E, MOTE T L. Interepochal changes in summer precipitation in the southeastern United States: evidence of possible urban effects near Atlanta, Georgia[J]. Journal of Applied Meteorology and Climatology, 2005. DOI:10.1175/JAM2221.1 .
8 YIN J, YU D P, WILBY R. Modelling the impact of land subsidence on urban pluvial flooding: a case study of downtown Shanghai, China[J]. The Science of the Total Environment, 2016, 544: 744-753.
9 SHI Peijun, KONG Feng. Research on related factors to decadal accumulated heavy rainfall spatio-temporal patterns change in China during 1951-2010[J]. Scientia Geographica Sinica, 2016, 36(10): 1 457-1 465.
史培军, 孔锋. 1951—2010年中国年代际累积暴雨时空格局变化的相关因素研究[J]. 地理科学, 2016, 36(10): 1 457-1 465.
10 LI Rouke, LI Yaohui, XU Ying. Projection of rainstorm and flooding disaster risk in China in the 21st century[J]. Journal of Arid Meteorology, 2018, 36(3): 341-352.
李柔珂, 李耀辉, 徐影. 未来中国地区的暴雨洪涝灾害风险预估[J]. 干旱气象, 2018, 36(3): 341-352.
11 WANG G, ZHANG Q, YU H Q, et al. Double increase in precipitation extremes across China in a 1.5 ℃/2.0 ℃ warmer climate[J]. The Science of the Total Environment, 2020, 746: 140807.
12 XIE Wenhuan, ZHANG Youzhi, LIU Shubin. Research progress on agricultural flood disasters[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2020, 41(1): 204-211.
解文欢, 张有智, 刘述彬. 农业洪涝灾害研究进展[J]. 中国农业资源与区划, 2020, 41(1): 204-211.
13 FENG Yu. Temporal and spatial distribution of rainstorm and probability analysis of recurrence period in Liaoning Province[D]. Dalian: Liaoning Normal University, 2018.
冯玉. 辽宁省暴雨时空分布及重现期发生概率分析[D]. 大连: 辽宁师范大学, 2018.
14 SHEN Xinkai, Yiqing LÜ, ZHANG Jing, et al. Risk assessment of rainstorm disaster in Shanxi Province based on ArcGIS[J]. Scientific and Technological Management of Land and Resources, 2020, 37(1): 61-73.
申欣凯, 吕义清, 张静, 等. 基于ArcGIS的山西省暴雨灾害风险评估[J]. 国土资源科技管理, 2020, 37(1): 61-73.
15 KONG Feng. Multi-attribute spatio-temporal evolution characteristics and regional differences of heavy rainfall in different months in China from 1961 to 2016[J]. Water Resources and Hydropower Engineering, 2020, 51(2): 26-37.
孔锋. 中国不同月份暴雨的多属性时空演变特征及区域差异(1961—2016年)[J]. 水利水电技术, 2020, 51(2): 26-37.
16 SHI Peijun, KONG Feng, FANG Jiayi. Spatio-temporal patterns of China decadal storm rainfall[J]. Scientia Geographica Sinica, 2014, 34(11): 1 281-1 290.
史培军, 孔锋, 方佳毅. 中国年代际暴雨时空变化格局[J]. 地理科学, 2014, 34(11): 1 281-1 290.
17 KONG Feng. SSPs scenarios-based evolution comparison and mutation characteristics pre-estimation of global sea-land rainstorm time series[J]. Water Resources and Hydropower Engineering, 2020, 51(10): 1-9.
孔锋. 基于SSPs情景的全球海陆暴雨时序演变对比和突变特征预估[J]. 水利水电技术, 2020, 51(10): 1-9.
18 GAO Li, CHEN Jing, ZHENG Jiawen, et al. Progress in researches on ensemble forecasting of extreme weather based on numerical models[J]. Advances in Earth Science, 2019, 34(7): 706-716.
高丽, 陈静, 郑嘉雯, 等. 极端天气的数值模式集合预报研究进展[J]. 地球科学进展, 2019, 34(7): 706-716.
19 LI Donghuan, ZOU Liwei, ZHOU Tianjun. Changes of extreme indices over China in response to 1.5 ℃ global warming projected by a regional climate model[J]. Advances in Earth Science, 2017, 32(4): 446-457.
李东欢, 邹立维, 周天军. 全球1.5℃温升背景下中国极端事件变化的区域模式预估[J]. 地球科学进展, 2017, 32(4): 446-457.
20 HUANG Ping, ZHOU Shijie. Advances and challenges in the study on the tropical rainfall changes under global warming[J]. Advances in Earth Science, 2018, 33(11): 1 181-1 192.
黄平, 周士杰. 全球变暖下热带降水变化研究回顾与挑战[J]. 地球科学进展, 2018, 33(11): 1 181-1 192.
21 XU Y, GAO X J, GIORGI F, et al. Projected changes in temperature and precipitation extremes over China as measured by 50-yr return values and periods based on a CMIP5 ensemble[J]. Advances in Atmospheric Sciences, 2018, 35(4): 376-388.
22 ZHOU Botao, WEN Qiuzihan, XU Ying, et al. Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles[J]. Journal of Climate, 2014, 27(17): 6 591-6 611.
[1] 朱欢欢, 姜胜, 江志红. 基于可靠性集合平均方法的全球 1.5/2.0 °C变暖下中国极端气候的未来预估[J]. 地球科学进展, 2022, 37(6): 612-626.
[2] 贾明瑞, 张晋韬, 王芳. 《巴黎协定》未来气候情景下“一带一路”沿线区域气候舒适度预估[J]. 地球科学进展, 2022, 37(5): 505-518.
[3] 李稚, 李玉朋, 李鸿威, 刘永昌, 王川. 中亚地区干旱变化及其影响分析[J]. 地球科学进展, 2022, 37(1): 37-50.
阅读次数
全文


摘要