为研究气候变化下汉江上游流域的径流响应,基于RBF神经网络降尺度模型,利用2020—2099年CanESM2模式下RCP8.5(高温室气体排放)和RCP2.6(低温室气体排放)两种气候情景,生成未来气温与降水数据;耦合SWAT水文模型,模拟分析流域2020—2099年径流变化对不同气候情景的响应特征。结果表明,汉江上游年径流量均呈不明显增加趋势,RCP8.5情景下的径流增加趋势比RCP2.6情景稍小,径流量年内分配与基准期大致相同,两种情景下汛期径流量稍有减小,可能是降尺度模型生成的降水量极大值偏小导致的。研究结果可为汉江流域水文气象综合管理提供一定的科学依据。
Abstract
To study the runoff response of the upper Hanjiang River basin under future climate change,a downscaling model based on the RBF (Radial Basis Function) neural network and the SWAT (Soil and Water Assessment Tool) hydrological model are coupled. The RCP8.5 and RCP2.6 scenario data under the CanESM2 model from 2020 to 2099 are downscaled to each station in the basin to generate future climate elements(temperature and precipitation) and simulate the runoff response of the basin under future climate change. Results unveil that the runoff in the upper Hanjiang River will increase slightly in the future,with a slightly smaller increase in runoff in the RCP8.5 scenario than that in the RCP2.6 scenario. The annual runoff distribution in both scenarios is roughly the same with that in the base period. In both scenarios,runoff in flood season reduces slightly possibly because of a smaller maximum precipitation generated by the downscaling model. The research results provide a scientific basis for the comprehensive management in the Hanjiang River Basin.
关键词
气候变化 /
径流变化 /
SWAT /
RBF /
降尺度模型 /
汉江上游流域
Key words
climatic change /
runoff change /
SWAT(Soil and Water Assessment Tool) /
RBF(Radial Basis Function) /
statistical downscaling model /
upper Hanjiang River basin
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1]潘家华. 减缓气候变化的经济分析. 北京:气象出版社, 2003.
张秀琴, 王亚华. 中国水资源管理适应气候变化的研究综述.长江流域资源与环境, 2015, 24(12): 2061-2068.
长江水利委员会. 2020年长江流域及西南诸河水资源公报. 武汉:长江水利委员会,2021.
陈 华, 郭生练, 郭海晋,等. 汉江流域1951—2003年降水气温时空变化趋势分析. 长江流域资源与环境, 2006,15(3): 340-345.
李峰平,章光新,董李勤. 气候变化对水循环与水资源的影响研究综述. 地理科学,2013,33(4):457-464.
SHARMA A, TIWARI K N. Predicting Non-Point Source of Pollution in Maithon Reservoir Using a Semi-Distributed Hydrological Model. Environmental Monitoring and Assessment, 2019, 191(8): 522.
叶加俊, 卢健涛, 银星黎,等. 基于SDSM-SWAT模型的汉江上游径流变化模拟. 水电能源科学, 2019, 37(4): 1-4,12.
马新萍, 武 涛, 余玉洋. 基于SWAT模型的汉江上游流域径流情景预测研究. 国土资源遥感, 2021, 33(1): 174-182.
张利平, 胡志芳, 秦琳琳,等. 2050年前南水北调中线工程水源区地表径流的变化趋势. 气候变化研究进展, 2010, 6(6): 391-397.
杨 珍, 张利平 ,秦琳琳,等. 丹江口水库入库洪水变化特征及对未来气候情景的响应分析.长江流域资源与环境, 2013,22(5): 588-594.
范丽军, 符淙斌, 陈德亮. 统计降尺度法对华北地区未来区域气温变化情景的预估. 大气科学, 2007,31(5): 887-897.
刘昌明, 刘文彬, 傅国斌,等.气候影响评价中统计降尺度若干问题的探讨. 水科学进展, 2012, 23(3): 427-437.
郭 靖, 郭生练, 陈 华, 等. ANN统计降尺度法对汉江流域降水变化预测. 武汉大学学报(工学版), 2010, 43(2):148-152.
COULIBALY P, DIBIKE Y B, ANCTIL F. Downscaling Precipitation and Temperature with Temporal Neural Networks. Journal of Hydrometeorology, 2005, 6(4): 483-496.
TOLIKA K, MAHERAS P, VAFIADIS M, et al. Simulation of Seasonal Precipitation and Raindays over Greece: a Statistical Downscaling Technique Based on Artificial Neural Networks (ANNs). International Journal of Climatology, 2007, 27(7): 861-881.
陈燕飞, 张 翔. 汉江流域降水、蒸发及径流长期变化趋势及持续性分析. 水电能源科学, 2012, 30(6): 6-8,215.
冯 婧. 多全球模式对中国区域气候的模拟评估和预估. 南京:南京信息工程大学, 2012.
WILBY R L, DAWSON C W, BARROW E M. SDSM—A Decision Support Tool for the Assessment of Regional Climate Change Impacts. Environmental Modelling & Software, 2002, 17(2): 145-157.
叶加俊. 汉江上游流域径流模拟及未来气候变化响应. 武汉:华中科技大学, 2018.
PDF(5002 KB)
