摘要
以2008—2016年格尔木河流域实测径流量数据为基础,构建SWAT分布式水文模型,采用SUFI-2算法进行参数率定、验证及不确定性分析,并设置不同的气候情景(RCP2.6、RCP4.5和RCP6.0),预测流域2022—2050年的径流变化趋势,分析了研究区未来降水、气温的变化趋势并探究了这些气候要素对格尔木河流域径流的影响。结果表明:(1)SWAT模型在格尔木河流域径流过程的模拟中具有较好的适用性,率定期R2和ENS分别为0.84和0.73,验证期R2和ENS分别为0.74和0.70;(2)径流预测不确定性较小;(3)未来流域降水呈现增加趋势而气温降低;(4)未来时段流域径流增加显著,且降水是控制流域径流的主要因素。
Based on the measured runoff data of the Golmud River basin from 2008 to 2016,this paper constructed a SWAT dis⁃tributed hydrological model with the use of the SUFI-2 algorithm for parameter calibration,verification and uncertainty analysis,and set up different climate scenarios(RCP2.6,RCP4.5 and RCP6.0)to predict the runoff change trend of the basin from 2022 to 2050,analyzed the future precipitation and temperature change trends in the study area,and explored the impacts of these cli⁃matic factors on the runoff of the basin.The results show that:(1)the SWAT model has good applicability in the simulation of the runoff process of the basin.For the calibration period R2 and ENS are 0.84 and 0.73,respectively,and for the verification pe⁃riod R2 and ENS are 0.74 and 0.70,respectively;(2)the runoff prediction uncertainty interval is small;(3)In the future,the precipitation in the basin will increase while the temperature will decrease;(4)The runoff in the basin will increase significant⁃ly in the future time period,and precipitation is the main factor controlling the runoff in the basin.
作者
邰理想
饶文波
檀涛
谭红兵
姜三元
张西营
TAI Lixiang;RAO Wenbo;TAN Tao;TAN Hongbing;JIANG Sanyuan;ZHANG Xiying(College of Earth Sciences and Engineering,Hohai University,Nanjing 211100,China;Key Laboratory of Watershed Geographic Sciences,Jiangsu Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences,Nanjing 210008,China;Qinghai Institute of Salt Lakes,CAS,Xining 810008,China)
出处
《水文》
CSCD
北大核心
2023年第2期46-51,共6页
Journal of China Hydrology
基金
国家重点研发计划资助项目(2018YFC0406601)
江苏省自然科学基金面上项目(BK20191304)。
关键词
SWAT模型
高寒干旱区
格尔木河流域
气候变化
径流模拟预测
SWAT model
alpine and arid region
Golmud River basin
climate change
runoff simulation and prediction