摘要
基于第6次国际耦合模式比较计划(the coupled model intercomparison program in phase 6,CMIP6)中的12个气候模式,以莱茵河流域为研究对象,开展了气候模式对极端气温和极端降水模拟能力的评估优选。在此基础上,应用多模式集合平均方法预估了不同情景下未来时期(2021—2050年)极端气温和降水的变化情况,并通过构建SWAT(soil and water assessment tool)模型预估了未来极端洪水变化趋势。结果表明:未来时期莱茵河流域极端降水量增加且基本呈上升趋势,流域内极端高温事件增加;流域内罗克瑙站和特里尔站未来时期年径流量相对历史时期有所增加,洛比特站和法兰克福站则减少;不同情景下洛比特站、法兰克福站和罗克瑙站未来极端洪水强度和频率相对历史时期均呈减小趋势,特里尔站变化的不确定性较大。
12 climate models in the coupled model intercomparison program in phase 6(CMIP6)were evaluated and optimized depending on their simulation performances on extreme temperature and precipitation over the Rhine River Basin.On this basis,the changes of extreme temperature and precipitation in the future periods(2021—2050)were estimated with multi-model aggregate averaging method,and the trend of future extreme runoff changes was estimated by constructing a soil and water assessment tool(SWAT)model.The main conclusions are as follows.The extreme precipitation over the Rhine River Basin will increase in the future,and basically show an upward trend.The extreme high temperature events will increase.The annual runoff at Rockenau Station and Trier Station will increase in the future,while it will decrease at Lobith Station and Frankfurt Station on the contrary.The intensity and frequency of future extreme floods at Lobith Station,Frankfurt Station and Rockenau Station all show a decreasing trend relative to the historical period under different scenarios,and there is great uncertainty in the changes at Trier Station.
作者
周芷菱
张利平
王惠筠
佘敦先
宁理科
夏军
ZHOU Zhiling;ZHANG Liping;WANG Huiyun;SHE Dunxian;NING Like;XIA Jun(State Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University,Wuhan 430072,China;Institute of Water Carbon Cycle and Carbon Neutralization,School of Water Resources and Hydropower Engineering,Wuhan University,Wuhan 430072,China;Institutes of Geographic Sciences and Natural Resources Research,CAS,Beijing 100101,China)
出处
《武汉大学学报(工学版)》
CAS
CSCD
北大核心
2022年第4期328-338,共11页
Engineering Journal of Wuhan University
基金
国家重点研发计划项目(编号:2017YFA0603704)。