摘要
Abstract
With the intensification of climate change and human activities,the stability of runoffsequences has been disrupted.To address the challenge of accurately estimating runoff,this study collected monthly meteorological data from ten meteorological stations in the upper reaches of the Yellow River,monthly runoffdata from the Tangnaihai hydrological station,and GCM outputs from seven CMIP6 models.Statistical downscaling methods were applied to process the GCM data,and three machine learning models including support vector machine,multiple linear regression and random forest were used to simulate historical runoff.After comparative analysis,the random forest model was selected to estimate runoffchanges in the upper reaches of the Yellow River under different future climate scenarios.The results showed that all three machine learning algorithms effectively simulate monthly runoffbut perform poorly in capturing several runoff peaks.In the long-term future(after 2070),runoff under the SSP5-8.5 scenario will be significantly higher than under the SSP2-4.5 climate scenario.Annual runoffexhibits a clear increasing trend,which is more pronounced under the SSP5-8.5 scenario,with an increase rate of approximately 1.92 m3/s per year,surpassing the 0.94 m3/s per year observed under the SSP2-4.5 scenario.Rising temperatures lead to earlier melting of ice and snow,while precipitation in the Yellow River Basin is projected to increase significantly.These findings provide a valuable reference for runoffprediction under different climate scenarios.关键词
径流预估/机器学习/气候变化/降水量/黄河上游Key words
runoffprediction/machine learning/climate change/precipitation/upper reaches of Yellow River分类
建筑与水利
