基于NEX-GDDP数据集的青藏高原牧区雪灾风险预估  

Quantitative Assessment of Snow Risk about Livestock in the Qinghai-Tibet Plateau

作　　者：陈虹举 杨建平 丁永建 贺青山 冀钦 王彦霞 唐凡 葛秋伶 CHEN Hongju;YANG Jianping;DING Yongjian;HE Qingshan;JI Qin;WANG Yanxia;TANG Fan;GE Qiulin(State Key Laboratory of Cryospheric Science,Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;Key Laboratory of Ecohydrology of Inland River Basin,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China;University of Chinese Academy of Sciences,Beijing 100049,China;College of Resource and Environment, University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院西北生态环境资源研究院冰冻圈科学国家重点实验室,甘肃兰州730000 [2]中国科学院西北生态环境资源研究院内陆河流域生态水文重点实验室,甘肃兰州730000 [3]中国科学院大学,北京100049 [4]中国科学院大学资源与环境学院,北京100049

出　　处：《灾害学》2022年第2期102-110,共9页Journal of Catastrophology

基　　金：国家重点研发计划项目“全球变化人口与经济系统风险全球定量评估研究”(2016YFA0602404);“美丽中国生态文明建设科技工程专项”(XDA23060704)。

摘　　要：依托中国逐日雪深模拟预估数据集、草地生产力数据、气象站点数据、灾害统计资料以及统计年鉴,选取了历史基准时段(1986—2005年)、未来近期(2016—2035年)和未来远期(2046—2065年)三个时间段,以及RCP4.5和RCP8.5两种情景,分析了青藏高原牧区雪灾危险性、牧区牲畜暴露量以及脆弱性,在此基础上,定量预估了青藏高原畜牧业雪灾风险。结果表明:①青藏高原区域内,中国逐日雪深模拟预估数据中,CESM1-BGC模式模拟的积雪深度数据更接近于站点雪深观测值,模拟精度最高,此次研究选用该模式下雪深数据识别雪灾危险性。雪灾危险性从时序看,相比于历史时期,RCP4.5情景下未来近期、未来远期和RCP8.5情景下未来近期、未来远期发生雪灾危险性的范围减少6%、11%、6%和14%;但是雪灾危险性强度减弱并不明显,RCP4.5情景下,未来远期,甚至增强;空间分布来看,危险性指数较高的区域主要分布在藏北高原、冈底斯山脉沿线、昆仑山脉西段沿线、祁连山脉沿线、三江源区域和横断山脉山脉区域。②与2000年青藏高原牧区草地载畜量相比,2017年载畜量增加11%,未来载畜量将可能进一步增加。③相比较于历史时期,RCP4.5情景下未来近期、未来远期和RCP8.5情景下未来近期、未来远期牲畜受雪灾损失量分别减少了10.2%、10.3%、28.3%和45.5%。未来RCP8.5情景下雪灾风险最小,空间分布来看,牲畜损失较大区域与雪灾危险指数较高区域相一致。牧区雪灾造成牲畜死亡量变化主要受到雪灾发生范围变化所影响。Relying on the future daily snow depth data inversion from NEX-GDDP,grassland productivity data,meteorological station data,disaster statistics,and statistical yearbooks,three time periods of historical base time(1986-2005),near future(2016-2035)and far future(2046-2065),and two RCP4.5 and RCP8.5 scenarios,the snow hazard,livestock exposure and vulnerability of pastoral areas on the Qinghai-Tibet Plateau were analyzed,and on this basis,the snow risk of the Qinghai-Tibet Plateau livestock industry was quantitatively assessed.The results show that(1)among all the models snow depth data inversion from NEX-GDDP in the Qinghai-Tibet Plateau region,CESM1-BGC model is closer to the site snow depth observation and has the highest inversion accuracy.Using snow depth data from CESM1-BGC model identification of snow hazard index.compared with the historical period,the range of snow hazard occurring in the near future,far future under RCP4.5 scenario and in the near future,far future under RCP8.5 scenario is 6%,11%,6%and14%;less;however,the weakening of snow hazard intensity is not obvious,and the RCP4.5 scenario,far future,may even enhance;spatial distribution,the regions with higher hazard index are mainly distributed in the northern Tibetan plateau,along the Gangdis Range,along the western section of the Kunlun Mountains,along the Qilian Mountains,the Sanjiangyuan region and the Hengduan Mountains Range region.(2)Compared with the livestock carrying capacity of pasture areas on the Tibetan Plateau in 2000,the livestock carrying capacity increased by 11%in 2017,and the livestock carrying capacity will likely increase further in the future.(3)Compared with the historical period,the future near-term,future far-term and future near-term and future far-term livestock losses to snowstorm under RCP4.5 scenario and RCP8.5 scenario decreased by 10.2%,10.3%,28.3%and 45.5%,respectively.The future RCP8.5 scenario has the least risk of snowstorm,and the spatial distribution shows that the areas with higher livestock losses are consist

关 键 词：青藏高原 雪灾 畜牧业 定量预估 

分 类 号：X43[环境科学与工程—灾害防治] X915.5