全球升温1.5℃和2.0℃背景下北京市暴雨洪涝淹没风险研究

doi:10.12006/j.issn.1673-1719.2019.073

摘要/Abstract

摘要：

基于第五次耦合模式比较计划(CMIP5)的5个气候模式模拟结果,结合FloodArea模型,对RCP8.5情景下全球升温1.5℃和2.0℃时,北京市极端降水和淹没风险进行分析。结果表明：北京市极端降水量呈从西南向东北逐渐减少的分布趋势。在升温2.0℃时,极端降水和淹没风险增加较升温1.5℃时明显,郊区极端降水增加最明显的地区是房山和门头沟,城区极端降水量增加最明显的地区是海淀、石景山和丰台区。海淀区出现一级和二级淹没风险的面积最大,其次是丰台和石景山区。郊区的延庆和怀柔是发生一级淹没风险面积最大的地区。

关键词: 暴雨洪涝, 淹没模拟, 灾害风险, FloodArea模型, 北京

Abstract:

Based on five climate model simulation results of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the FloodArea model, this paper analyzes the extreme precipitation and inundation risks of Beijing with global warming of 1.5℃ and 2.0℃ under the scenario of RCP8.5. The extreme precipitation in Beijing shows a decreasing distribution trend from southwest to northeast. When the temperature rises to 2.0℃, the risk of extreme precipitation and inundation increases more obviously than when the temperature rises to 1.5℃. The districts with the most obvious increase in extreme precipitation in suburbs are Fangshan and Mentougou, and Haidian, Shijingshan and Fengtai are the districts with the most obvious increase in extreme precipitation in urban areas. Haidian district has the largest area of flood risk, followed by Fengtai and Shijingshan district. The suburbs of Yanqing and Huairou are the areas with the largest flood area.

Key words: Rainstorm and flood, Flooding simulation, Disaster risk, FloodArea model, Beijing

张君枝,袁冯,王冀,孙赫敏,刘洪,马文林. 全球升温1.5℃和2.0℃背景下北京市暴雨洪涝淹没风险研究[J]. 气候变化研究进展, 2020, 16(1): 78-87.

Jun-Zhi ZHANG,Feng YUAN,Ji WANG,He-Min SUN,Hong LIU,Wen-Lin MA. The rainstorm and flooding disaster risk in Beijing under the global warming of 1.5℃ and 2.0℃[J]. Climate Change Research, 2020, 16(1): 78-87.

图/表 9

表1 CMIP5 5个气候模式概况

Table 1 Information of 5 climate models

表2 内涝灾害等级划分标准（淹没深度/m）

Table 2 The criteria for classification of waterlogging disasters

图1 2012年7月20日10时至21日01时北京市城区降水实况(a)与淹没模拟(b)情况

Fig. 1 The actual precipitation and simulated submerged depth from 10:00 BT on 20 July to 01:00 BT on 21 July 2012 in urban area in Beijing

表3 实际淹没深度与模拟淹没值对比

Table 3 The comparison between actual submerged depth and simulated submerged depth

图2 基准期（1986—2005年）和全球升温1.5℃（2017—2036年）与2.0℃时（2030—2049年）北京市极端降水分布

Fig. 2 The extreme precipitation in Beijing during the baseline period (a) and two future periods with global warming of 1.5℃ (b) and 2.0℃ (c)

图3 基准期和全球升温1.5℃与2.0℃时北京市城区和郊区淹没分布

Fig. 3 The submerged depth and range in the city and urban area in Beijing during the baseline period and two future periods with global warming of 1.5℃ and 2.0℃ (a) baseline period, (b) global warming of 1.5℃, (c) global warming of 2.0℃

表4 升温1.5℃和2.0℃与基准期的淹没风险面积和比例

Table 4 The submerged area and proportion during 2017-2036, 2030-2049 and the baseline period

图4 基准期和全球升温1.5℃与2.0℃时北京市城区淹没面积

Fig. 4 The submerged area of the city in Beijing during the baseline period and two future periods with global warming of 1.5℃ and 2.0℃

图5 基准期和全球升温1.5℃与2.0℃时北京市郊区淹没面积

Fig. 5 The submerged area of the suburbs in Beijing during the baseline period and two future periods

参考文献

[1]	姜克隽 . IPCC 1.5℃特别报告发布, 温室气体减排新时代的标志[J]. 气候变化研究进展, 2018,14(6):640-642.
[1]	Jiang K J . IPCC special report on 1.5℃ warming: a starting of new era of global mitigation[J]. Climate Change Research, 2018,14(6):640-642 (in Chinese)
[2]	景丞, 陶辉, 王艳君 , 等. 基于区域气候模式CCLM的中国极端降水事件预估[J]. 自然资源学报, 2017 ( 2):266-277.
[2]	Jing C, Tao H, Wang Y J , et al. Projection of extreme precipitation events in China based on regional climate model CCLM[J]. Journal of Natural Resources, 2017 ( 2):266-277 (in Chinese)
[3]	徐影, 张冰, 周波涛 , 等. 基于CMIP5模式的中国地区未来洪涝灾害风险变化预估[J]. 气候变化研究进展, 2014,10(4):268-275.
[3]	Xu Y, Zhang B, Zhou B T , et al. Projected risk of flooding disaster in China based on CMIP5 models[J]. Progressus Inquisitiones de Mutatione Climatis, 2014,10(4):268-275 (in Chinese)
[4]	任正果, 张明军, 王圣杰 , 等. 1961—2011年中国南方地区极端降水事件变化[J]. 地理学报, 2014,69(5):640-649.
[4]	Ren Z G, Zhang M J, Wang S J , et al. Changes in precipitation extremes in South China during 1961-2011[J]. Acta Geographica Sinica, 2014,69(5):640-649 (in Chinese)
[5]	钱维宏, 符娇兰, 张玮玮 , 等. 近40年中国平均气候与极值气候变化的概述[J]. 地球科学进展, 2007,22(7):673-684.
[5]	Qian W H, Fu J L, Zhang W W , et al. Changes in mean climate and extreme climate in China during the last 40 years[J]. Advances in Earth Science, 2007,22(7):673-684 (in Chinese)
[6]	黄泽钧 . 关于城市内涝灾害问题与对策的思考[J]. 水科学与工程技术, 2012 ( 1):7-10.
[6]	Huang Z J . Problems and countermeasures of city waterlogging disaster[J]. Water Sciences and Engineering Technology, 2012 ( 1):7-10 (in Chinese)
[7]	张冬冬, 严登华, 王义成 , 等. 城市内涝灾害风险评估及综合应对研究进展[J]. 灾害学, 2014,29(1):144-149.
[7]	Zhang D D, Yan D H, Wang Y C , et al. Research progress on risk assessment and integrated strategies for urban pluvial flooding[J]. Journal of Catastrophology, 2014,29(1):144-149 (in Chinese)
[8]	郭雪梅, 任国玉, 郭玉喜 , 等. 我国城市内涝灾害的影响因子及气象服务对策[J]. 灾害学, 2008,23(2):46-49.
[8]	Guo X M, Ren G Y, Guo Y X , et al. Influential factors of city water logging and the meteorological service[J]. Journal of Catastrophology, 2008,23(2):46-49 (in Chinese)
[9]	景学义, 刘宇飞, 王永波 , 等. 哈尔滨市城市内涝监测预警系统建设[J]. 灾害学, 2009,24(1):54-57.
[9]	Jing X Y, Liu Y F, Wang Y B , et al. Construction of the urban water logging monitoring and forecasting system of Harbin city[J]. Journal of Catastrophology, 2009,24(1):54-57 (in Chinese)
[10]	初祁, 彭定志, 徐宗学 , 等. 基于MIKE 11和MIKE 21的城市暴雨洪涝灾害风险分析[J]. 北京师范大学学报: 自然科学版, 2014 ( 5):446-451.
[10]	Chu Q, Peng D Z, Xu Z X , et al. Risk analysis of urban flooding by using MIKE 11 and MIKE 21[J]. Journal of Beijing Normal University: Natural Science, 2014 ( 5):446-451 (in Chinese)
[11]	麻蓉, 白涛, 黄强 , 等. MIKE 21模型及其在城市内涝模拟中的应用[J]. 自然灾害学报, 2017 ( 4):172-179.
[11]	Ma R, Bai T, Huang Q , et al. MIKE 21 model and its application on urban waterlogging simulation[J]. Journal of Natural Disasters, 2017 ( 4):172-179 (in Chinese)
[12]	喻海军, 黄国如, 武传号 . 基于SWMM模型的济南城市雨洪模拟研究[J]. 水资源研究, 2012,1(6):433-439.
[12]	Yu H J, Huang G R, Wu C H . Study on urban storm water modeling at Jinan city using SWMM model[J]. Journal of Water Resources Research, 2012,1(6):433-439 (in Chinese)
[13]	闫磊, 熊立华, 王景芸 . 基于SWMM的武汉市典型城区降雨径流模拟分析[J]. 水资源研究, 2014,3(3):216-228.
[13]	Yan L, Xiong L H, Wang J Y . Analysis of storm runoff simulation in typical urban region of Wuhan based on SWMM[J]. Journal of Water Resources Research, 2014,3(3):216-228 (in Chinese)
[14]	王慧亮, 吴泽宁, 胡彩虹 . 基于GIS与SWMM耦合的城市暴雨洪水淹没分析[J]. 人民黄河, 2017,39(8):31-35.
[14]	Wang H L, Wu Z N, Hu C H . Rainstorm waterlogging and submergence model and its application in urban areas based on GIS and SWMM[J]. Yellow River, 2017,39(8):31-35 (in Chinese)
[15]	阳宇恒, 蔡展航, 周勤 . 基于SWMM与ArcGIS的城市内涝灾害风险评估研究: 以南浦岛为例[J]. 环境科学与管理, 2016,41(12):178-182.
[15]	Yang Y H, Cai Z H, Zhou Q . Risk assessment of urban waterlogging based on SWMM and ArcGIS: case study on Nanpu Island[J]. Environmental Science and Management, 2016,41(12):178-182 (in Chinese)
[16]	曹罗丹, 李加林 . 基于遥感与GIS的浙江省洪涝灾害综合风险评估研究[J]. 自然灾害学报, 2015 ( 4):111-119.
[16]	Cao L D, Li J L . Integrated risk assessment of flood disaster in Zhejiang province based on RS and GIS[J]. Journal of Natural Disasters, 2015 ( 4):111-119 (in Chinese)
[17]	殷杰, 尹占娥, 王军 , 等. 基于GIS的城市社区暴雨内涝灾害风险评估[J]. 地理与地理信息科学, 2009,25(6):92-95.
[17]	Yin J, Yin Z E, Wang J , et al. GIS-based disaster risk assessment of the urban community rainstorm waterlogging[J]. Geography and Geo-Information Science, 2009,25(6):92-95 (in Chinese)
[18]	叶丽梅, 周月华, 向华 , 等. 基于GIS淹没模型的城市道路内涝灾害风险区划研究[J]. 长江流域资源与环境, 2016,25(6):1002-1008.
[18]	Ye L M, Zhou Y H, Xiang H , et al. Risk regionalization of urban roads waterlogging disasters based on GIS rainstorm flood inundation model[J]. Resources and Environment in the Yangtze Basin, 2016,25(6):1002-1008 (in Chinese)
[19]	戴雨菡, 黄路婷, 李明志 , 等. 基于FloodArea模型的龙须河流域暴雨洪涝淹没模拟研究[J]. 气象研究与应用, 2016,37(4):46-49.
[19]	Dai Y H, Huang L T, Li M Z , et al. Simulation study of flood in Longxu River basin based on the FloodArea model[J]. Journal of Meteorological Research and Application, 2016,37(4):46-49 (in Chinese)
[20]	张磊, 王文, 文明章 , 等. 基于“FloodArea”模型的山洪灾害精细化预警方法研究[J]. 复旦学报:自然科学版, 2015,54(3):282-287.
[20]	Zhang L, Wang W, Wen M Z , et al. Research on refined early-warning method of mountain flood disaster based on FloodArea[J]. Journal of Fudan University: Natural Science, 2015,54(3):282-287 (in Chinese)
[21]	姬兴杰, 李凤秀, 朱业玉 , 等. 河南省洛河上游暴雨山洪致灾临界面雨量的确定[J]. 气象与环境学报, 2015,31(6):43-50.
[21]	Ji X J, Li F X, Zhu Y Y , et al. Determination of area rainfall threshold of flash flood in the upper reaches of Luohe River of Henan[J]. Journal of Meteorology and Environment, 2015,31(6):43-50 (in Chinese)
[22]	Lambert S J, Boer G J . CMIP1 evaluation and intercomparison of coupled climate models[J]. Climate Dynamics, 2001,17(2):83-106. DOI: 10.1007/PL0001373
[23]	Jiang D B, Sui Y, Lang X M . Timing and associated climate change of a 2℃ global warming[J]. International Journal of Climatology, 2016,36(14):4512-4522
[24]	苏布达, 姜彤, 郭业友 , 等. 基于GIS栅格数据的洪水风险动态模拟模型及其应用[J]. 河海大学学报:自然科学版, 2005,33(4):370-374.
[24]	Su B D, Jiang T, Guo Y Y , et al. GIS raster data-based dynamic flood risk simulation model and its application[J]. Journal of Hohai University: Natural Sciences, 2005,33(4):370-374 (in Chinese)
[25]	宋晓猛, 张建云, 孔凡哲 . 基于极值理论的北京市极端降水概率分布研究[J]. 中国科学: 技术科学, 2018 ( 6):639-650.
[25]	Song X M, Zhang J Y, Kong F Z . Probability distribution of extreme precipitation in Beijing based on extreme value theory[J]. Scientia Sinica: Technologica, 2018 ( 6):639-650 (in Chinese)
[26]	张天宇, 范莉, 程炳岩 , 等. 1961—2007年三峡库区极端降水指数R5d的变化规律及其未来情景预估[J]. 中国农业气象, 2010,31(2):176-182.
[26]	Zhang T Y, Fan L, Cheng B Y , et al. Variation of extreme precipitation index (maximum consecutive 5-day precipitation totals) in the Three Gorges Reservoir[J]. Chinese Journal of Agrometeorology, 2010,31(2):176-182 (in Chinese)
[27]	吴灿, 赵景波, 王格慧 , 等. 黄河流域极端气温指数的气候演变特征分析[J]. 中国农业气象, 2015,36(5):525-535.
[27]	Wu C, Zhao J B, Wang G H , et al. Characteristic analysis of the climatic revolution in the Yellow River extreme temperature index[J]. Chinese Journal of Agrometeorology, 2015,36(5):525-535
[28]	王冀, 蒋大凯, 张英娟 . 华北地区极端气候事件的时空变化规律分析[J]. 中国农业气象, 2012,33(2):166-173.
[28]	Wang J, Jiang D K, Zhang Y J . Analysis on spatial and temporal variation of extreme climate events in North China[J]. Chinese Journal of Agrometeorology, 2012,33(2):166-173 (in Chinese)
[29]	汪宝龙, 张明军, 魏军林 , 等. 1960—2009年青海省极端气温事件的变化特征[J]. 中国农业气象, 2012,33(1):41-47.
[29]	Wang B L, Zhang M J, Wei J L . et al. Characteristics of extreme temperature in Qinghai province during 1960-2009[J]. Chinese Journal of Agrometeorology, 2012,33(1):41-47 (in Chinese)
[30]	Li H, Sheffield J, Wood E F . Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching[J]. Journal of Geophysical Research: Atmospheres, 2010,115:D10101. DOI: 10.1029/2009JD012882
[31]	黄金龙, 苏布达, 朱娴韵 , 等. CMIP5多模式集合对南亚印度河流域气候变化的模拟与预估[J]. 冰川冻土, 2015,37(2):297-307.
[31]	Huang J L, Su B D, Zhu X Y , et al. Simulating and estimating the climate change in the Indus River basin, South Asia, based on CMIP5 multi-model ensemble[J]. Journal of Glaciology and Geocryology, 2015,37(2):297-307 (in Chinese)
[32]	陈活泼 . CMIP5模式对21世纪末中国极端降水事件变化的预估[J]. 科学通报, 2013,58(8):743-752.
[32]	Chen H P . Projected change in extreme rainfall events in China by the end of the 21st century using CMIP5 models[J]. Chinese Science Bulletin, 2013,58(8):743-752 (in Chinese)
[33]	查力 . 北京城区内涝的空间分布特征研究及风险分析[D]. 北京:首都师范大学, 2014: 48-55.
[33]	Zha L . Study on spatial distribution characteristics of waterlogging in Beijing urban area and risk analysis[D]. Beijing: Capital Normal University, 2014: 48-55 (in Chinese)