气候变化背景下辽宁省未来气象干旱危险性风险评估
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摘要
利用第5次耦合模式国际比较计划CMIP5中的27个模式历史1 990 s数据、RCP2.6,RCP4.5和RCP8.5情景下的未来气象数据以及观测数据评估模型模拟气象要素性能。结果表明,CanESM2,CNRM-CM5,MIROC5以及MRI-CGCM3模式模拟降水性能最好。用上述4个气候模式的数据计算描述干旱危险性强度的指标——标准化降水蒸散指数(SPEI),基于信息扩散理论得到未来不同干旱等级的超越概率。在此基础上着重分析了多模式在不同典型浓度路径(RCPs)下对未来气候变化特征的预估。结果表明:未来时段较1 990 s的轻旱危险性风险变化最显著(P<0.05)。各个干旱等级的超越概率增加率最大的地区位于西部以及南部环渤海地区。辽河流域西部是中度及重度危险性高值区,次高值区位于辽宁省南部地区包括环渤海地区;北部危险性变异程度较大,在不同未来情景下随时间表现的规律不同;东部及中部地区在各个情景下均较稳定,分别被超低和低以及低和中风险覆盖。
Using the 27 model current data in 1 990 s and the future meteorological data under the scenarios of RCP2.6, RCP4.5 and RCP8.5 in the Fifth Coupled Mode International Comparison Program CMIP5 and the observed data to evaluate the model performance for simulating the meteorological elements. The results show that CanESM2,CNRM-CM5,MIROC5 and MRI-CGCM3 models have the best simulation performance for precipitation. Using the data of the above four climate models, the standardized precipitation evapotranspiration index(SPEI), which is an index describing the intensity of drought risk, was calculated. Based on the information diffusion theory, the surpassing probability in different drought levels was estimated. On this basis, the analysis of the future climate change characteristics of multi-models under different typical concentration paths(RCPs) was analyzed. The results show that the change of light drought risk in the future is most significant compared to the 1 990 s(P<0.05).The areas with the highest increase rates of droughts surpassing probability are located in the western and southern around Bohai Sea regions. The west of Liaohe River Basin is a moderately and severely high-risk region. The sub-high value regions are located in the southern part of Liaoning Province, including the Bohai Sea region; The degree of in the north is great, and the law of change over time under different future scenarios are different; risk variation in the eastern and central regions are relatively stable under each scenario,which perform ultra-low and low and low and medium risks respectively.
Using the 27 model current data in 1 990 s and the future meteorological data under the scenarios of RCP2.6, RCP4.5 and RCP8.5 in the Fifth Coupled Mode International Comparison Program CMIP5 and the observed data to evaluate the model performance for simulating the meteorological elements. The results show that CanESM2,CNRM-CM5,MIROC5 and MRI-CGCM3 models have the best simulation performance for precipitation. Using the data of the above four climate models, the standardized precipitation evapotranspiration index(SPEI), which is an index describing the intensity of drought risk, was calculated. Based on the information diffusion theory, the surpassing probability in different drought levels was estimated. On this basis, the analysis of the future climate change characteristics of multi-models under different typical concentration paths(RCPs) was analyzed. The results show that the change of light drought risk in the future is most significant compared to the 1 990 s(P<0.05).The areas with the highest increase rates of droughts surpassing probability are located in the western and southern around Bohai Sea regions. The west of Liaohe River Basin is a moderately and severely high-risk region. The sub-high value regions are located in the southern part of Liaoning Province, including the Bohai Sea region; The degree of in the north is great, and the law of change over time under different future scenarios are different; risk variation in the eastern and central regions are relatively stable under each scenario,which perform ultra-low and low and low and medium risks respectively.
引文
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[9] 马建勇, 许吟隆, 潘婕, 等. 东北地区农业气象灾害的趋势变化及其对粮食产量的影响[J]. 中国农业气象, 2012, 33(2): 283-288MA Jianyong, XU Yinlong, PAN Jie, et al. Trends of agricultural meteorological disasters in Northeast China and their effects on grain yield[J]. China Agricultural Meteorology, 2012, 33(2): 283-288. (in Chinese)
[10] 赵春雨, 曲晓波, 王颖, 等. 辽宁气候变化及若干气象灾害的事实分析[J]. 灾害学, 2007, 22(4): 77-80.ZHAO Chunyu, QU Xiaobo, WANG Ying, et al. Analysis on climate change and some meteorological disasters in Liaoning[J]. Journal of Disasters, 2007, 22(4): 77-80.
[11] 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, 24(4): 111-119. (in Chinese)CAO Luodan, LI Jialin. Research on comprehensive risk assessment of flood and waterlogging disasters in zhejiang province based on remote sensing and GIS[J]. Journal of Natural Disasters, 2015, 24(4): 111-119. (in Chinese)
[12] 陶纯苇, 姜超, 孙建新, 等. CMIP5模式对中国东北气候模拟能力的评估[J]. 气候与环境研究, 2016, 21(3): 357-366.TAO Chunwei, JIANG Chao, SUN Jianxin, et al. Evaluation of climate modeling capability in northeast China using CMIP5 model[J]. Climate and Environment Research, 2016, 21(3): 357-366. (in Chinese)
[13] 吴昊旻, 黄安宁, 何清, 等. 10个CMIP5模式预估中亚地区未来50 a降水时空变化特征[J]. 干旱区地理, 2013, 36(4): 670-678.WU Haomin, HUANG Anning, HE Qing, et al. Temporal and spatial variations of precipitation over the next 50 years in Central Asia based on 10 CMIP5 models[J]. Arid Land Geography, 2013, 36(4): 670-678. (in Chinese)
[14] Vicente-Serrano S M, BEGUERíA, S, LóPEZ-MORENO J I.A multi-scalar drought index sensitive to global warning: the standardized precipitation evapotranspiration index[J].Journal of Climate,2010,23(7):1696-1718.
[15] 李伟光, 侯美亭, 陈汇林, 等. 基于标准化降水蒸散指数的华南干旱趋势研究[J]. 自然灾害学报, 2012, 21(4): 84-90.LI Weiguang, HOU Meiting, CHEN Huilin, et al. Study on drought trends in South China based on standardized precipitation evapotranspiration index[J]. Journal of Natural Disasters, 2012, 21(4): 84-90. (in Chinese)
[16] WANG Zhaoli, ZHONG Reida, LAI Chengguang, et al. Climate change enhances the severity and variability of drought in the pearl river basin in south China in the 21st century[J]. Agricultural and Forest Meteorology, 2018, 249(12): 149-162.
[17] 单琨, 刘布春, 刘园. 基于自然灾害系统理论的辽宁省玉米干旱风险分析[J]. 农业工程学报, 2012, 28(8): 186-194.SHAN Kun, LIU Buchun, LIU Yuan. Analysis of maize drought risk in liaoning province based on natural disaster system theory[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(8): 186-194. (in Chinese)
[18] 董朝阳, 刘志娟, 杨晓光. 北方地区不同干旱等级对春玉米产量影响[J]. 农业工程学报, 2015, 31(11): 157-164.DONG Chaoyang, LIU Zhijuan,YANG Xiangguang. Effects of different drought grades on spring maize yield in north China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(11): 157-164. (in Chinese)
[19] 沈洪政, 马青青, 金建华. 以减产损失为指标的农业干旱评价方法研究[R]. 天津: 科技创新导报, 2015.SHEN Hongzheng, MA Qingqing, JIN Jianhua. Agricultural drought assessment method based on reduced production loss[R]. Tianjin: Science & Technology Innovation Review, 2015. (in Chinese)
[20] 赵一磊. 中国区域性气象干旱事件的模拟和预估[D]. 南京: 南京信息工程大学, 2013: 1-62.ZHAO Yilei. Simulation and Prediction of Regional Meteorological Drought Events in China[D]. Nanjing: Nanjing Information Engineering University, 2013: 1-62. (in Chinese)
[21] 沈国强, 郑海峰, 雷振锋. 基于SPEI指数的1961—2014年东北地区气象干旱时空特征研究[J]. 生态学报, 2017, 37(17): 5882-5893.SHEN Guoqiang, ZHENG Haifeng, LEI Zhenfeng. Spatiotemporal analysis of meteorological drought (1961—2014) in Northeast China using a standardized precipitation evapotranspiration index[J]. Acta Ecologica Sinica, 2017, 37(17): 5882-5893. (in Chinese)
[22] 柴荣繁. 中国干旱对气候变化响应的区域性差异及其原因分析[D]. 南京: 南京信息工程大学, 2017: 1-80.CHAI Rongfan. Regional Differences in Response to Climate Change in China and its Causes[D]. Nanjing: Nanjing University of Information Science & Technology, 2017: 1-80. (in Chinese)
[23] 陶纯苇, 姜超, 孙建新. CMIP5多模式集合对东北三省未来气候变化的预估研究[J]. 地球物理学报, 2016, 59(10): 3580-3591.TAO Chunwei, JIANG Chao, SUN Jianxin. Study on the future climate change prediction of the three provinces in northeast China with CMIP5 multi-model set[J]. Chinese Journal of Geophysics, 2016, 59(10): 3580-3591. (in Chinese)
[24] ZHAO Tiaobao, CHEN Liang, MA Zhuguo. Simulation of historical and projected climate change in arid and semi-arid areas by CMIP5 models[J]. Chin. Sci. Bull, 2014, 59(4): 412-429.
[2] 李世奎, 霍治国, 王素艳, 等. 农业气象灾害风险评估体系及模型研究[J]. 自然灾害学报, 2004, 13(1): 77-87.LI Shikui, HUO Zhiguo, WANG Suyan, et al. Research on risk assessment system and model of agricultural meteorological disasters[J]. Journal of Natural Disasters, 2004, 13(1): 77-87. (in Chinese)
[3] HAO Lu, ZHANG Xiaoyu, LIU Shoudong. Risk assessment to China’s agricultural drought disaster in county unit[J]. Nat Hazards, 2012, 61(2): 785-801.
[4] 张竟竟. 基于信息扩散理论的河南省农业旱灾风险评估[J]. 资源科学, 2012, 34(2): 280-286.ZHANG Jingjing. Analysis of agricultural drought risk in henan province based on information diffusion theory[J]. Resources Science, 2012, 34(2): 280-286. (in Chinese)
[5] 秦越, 徐翔宇, 许凯, 等. 农业干旱灾害风险模糊评价体系及其应用[J]. 农业工程学报, 2013, 29(10): 83-91.QIN Yu, XU Xiangyu, XU Kai, et al. Fuzzy evaluation system for agricultural drought risk and its application[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(10): 83-91. (in Chinese)
[6] WU Di, YAN Denghua, YAN Guiyu, et al. Assessment on agricultural drought vulnerability in the yellow river basin based on a fuzzy clustering iterative model[J]. Nat Hazards, 2013, 67(2): 919-936.
[7] 丁青云, 艾萍, 吴军斓, 等. 基于信息扩散理论的干旱灾害风险评估[J]. 中国农村水利水电, 2015(3): 99-102.DING Qingyun, AI Ping, WU Junlan, et al. Risk assessment of drought disaster based on information diffusion theory[J]. China Rural Water and Hydropower, 2015(3): 99-102. (in Chinese)
[8] 郭庆法, 王庆成, 汪黎明. 中国玉米栽培学[M]. 上海: 上海科技出版社, 2004: 32-33.GUO Qingfa, WANG Qingcheng, WANG Liming. Corn cultivation in China[M]. Shanghai: Shanghai Science and Technology Press, 2004: 32-33. (in Chinese)
[9] 马建勇, 许吟隆, 潘婕, 等. 东北地区农业气象灾害的趋势变化及其对粮食产量的影响[J]. 中国农业气象, 2012, 33(2): 283-288MA Jianyong, XU Yinlong, PAN Jie, et al. Trends of agricultural meteorological disasters in Northeast China and their effects on grain yield[J]. China Agricultural Meteorology, 2012, 33(2): 283-288. (in Chinese)
[10] 赵春雨, 曲晓波, 王颖, 等. 辽宁气候变化及若干气象灾害的事实分析[J]. 灾害学, 2007, 22(4): 77-80.ZHAO Chunyu, QU Xiaobo, WANG Ying, et al. Analysis on climate change and some meteorological disasters in Liaoning[J]. Journal of Disasters, 2007, 22(4): 77-80.
[11] 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, 24(4): 111-119. (in Chinese)CAO Luodan, LI Jialin. Research on comprehensive risk assessment of flood and waterlogging disasters in zhejiang province based on remote sensing and GIS[J]. Journal of Natural Disasters, 2015, 24(4): 111-119. (in Chinese)
[12] 陶纯苇, 姜超, 孙建新, 等. CMIP5模式对中国东北气候模拟能力的评估[J]. 气候与环境研究, 2016, 21(3): 357-366.TAO Chunwei, JIANG Chao, SUN Jianxin, et al. Evaluation of climate modeling capability in northeast China using CMIP5 model[J]. Climate and Environment Research, 2016, 21(3): 357-366. (in Chinese)
[13] 吴昊旻, 黄安宁, 何清, 等. 10个CMIP5模式预估中亚地区未来50 a降水时空变化特征[J]. 干旱区地理, 2013, 36(4): 670-678.WU Haomin, HUANG Anning, HE Qing, et al. Temporal and spatial variations of precipitation over the next 50 years in Central Asia based on 10 CMIP5 models[J]. Arid Land Geography, 2013, 36(4): 670-678. (in Chinese)
[14] Vicente-Serrano S M, BEGUERíA, S, LóPEZ-MORENO J I.A multi-scalar drought index sensitive to global warning: the standardized precipitation evapotranspiration index[J].Journal of Climate,2010,23(7):1696-1718.
[15] 李伟光, 侯美亭, 陈汇林, 等. 基于标准化降水蒸散指数的华南干旱趋势研究[J]. 自然灾害学报, 2012, 21(4): 84-90.LI Weiguang, HOU Meiting, CHEN Huilin, et al. Study on drought trends in South China based on standardized precipitation evapotranspiration index[J]. Journal of Natural Disasters, 2012, 21(4): 84-90. (in Chinese)
[16] WANG Zhaoli, ZHONG Reida, LAI Chengguang, et al. Climate change enhances the severity and variability of drought in the pearl river basin in south China in the 21st century[J]. Agricultural and Forest Meteorology, 2018, 249(12): 149-162.
[17] 单琨, 刘布春, 刘园. 基于自然灾害系统理论的辽宁省玉米干旱风险分析[J]. 农业工程学报, 2012, 28(8): 186-194.SHAN Kun, LIU Buchun, LIU Yuan. Analysis of maize drought risk in liaoning province based on natural disaster system theory[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(8): 186-194. (in Chinese)
[18] 董朝阳, 刘志娟, 杨晓光. 北方地区不同干旱等级对春玉米产量影响[J]. 农业工程学报, 2015, 31(11): 157-164.DONG Chaoyang, LIU Zhijuan,YANG Xiangguang. Effects of different drought grades on spring maize yield in north China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(11): 157-164. (in Chinese)
[19] 沈洪政, 马青青, 金建华. 以减产损失为指标的农业干旱评价方法研究[R]. 天津: 科技创新导报, 2015.SHEN Hongzheng, MA Qingqing, JIN Jianhua. Agricultural drought assessment method based on reduced production loss[R]. Tianjin: Science & Technology Innovation Review, 2015. (in Chinese)
[20] 赵一磊. 中国区域性气象干旱事件的模拟和预估[D]. 南京: 南京信息工程大学, 2013: 1-62.ZHAO Yilei. Simulation and Prediction of Regional Meteorological Drought Events in China[D]. Nanjing: Nanjing Information Engineering University, 2013: 1-62. (in Chinese)
[21] 沈国强, 郑海峰, 雷振锋. 基于SPEI指数的1961—2014年东北地区气象干旱时空特征研究[J]. 生态学报, 2017, 37(17): 5882-5893.SHEN Guoqiang, ZHENG Haifeng, LEI Zhenfeng. Spatiotemporal analysis of meteorological drought (1961—2014) in Northeast China using a standardized precipitation evapotranspiration index[J]. Acta Ecologica Sinica, 2017, 37(17): 5882-5893. (in Chinese)
[22] 柴荣繁. 中国干旱对气候变化响应的区域性差异及其原因分析[D]. 南京: 南京信息工程大学, 2017: 1-80.CHAI Rongfan. Regional Differences in Response to Climate Change in China and its Causes[D]. Nanjing: Nanjing University of Information Science & Technology, 2017: 1-80. (in Chinese)
[23] 陶纯苇, 姜超, 孙建新. CMIP5多模式集合对东北三省未来气候变化的预估研究[J]. 地球物理学报, 2016, 59(10): 3580-3591.TAO Chunwei, JIANG Chao, SUN Jianxin. Study on the future climate change prediction of the three provinces in northeast China with CMIP5 multi-model set[J]. Chinese Journal of Geophysics, 2016, 59(10): 3580-3591. (in Chinese)
[24] ZHAO Tiaobao, CHEN Liang, MA Zhuguo. Simulation of historical and projected climate change in arid and semi-arid areas by CMIP5 models[J]. Chin. Sci. Bull, 2014, 59(4): 412-429.
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