摘要
预报因子选取和预报模型构建是长期径流预报的两大难点。本研究采用随机森林模型从当年1月份至10月份长江干流的实测径流和国家气候中心74项水文—气象特征因子共750个变量中选取预报因子集合,对长江上游屏山站、寸滩站枯水期(当年11月～次年5月)径流预报进行了研究。结果显示,随预见期增加,径流自相关关系逐渐减弱,水文—气象遥相关关系逐渐强于径流自相关关系。在屏山站和寸滩站的径流预报中,预报结果与实测结果呈显著线性关系,平均相对误差在20%以内。月径流预报误差还较大,枯水期总径流预报精度优于单月径流预报。不确定性分析结果表明随机森林模型除了预报径流变化趋势,还可以用于预报径流丰枯概率。
Predictor selection and model construction are two key issues in long-term streamflow forecasting.This study introduces a random forests model for selecting predictor set from measured streamflows and 74 hydro-climatic indices of the period from January to October provided by China National Climate Center,and predicts seasonal low flow in the upper Yangtze for the period from November to next May.The results show that：1） as predicting lead-time increases,streamflow auto-correlation becomes weaker and hydro-climatic teleconnection gets stronger than the auto-correlation;2） the predicted streamflows at the Pingshan and Cuntan gauges show a strong linear relationship with the observations and their relative errors are less than 20%;3） analysis of the prediction uncertainty indicates that the random forests model can also be applied to probabilistic prediction of streamflow.
出处
《水力发电学报》
EI
CSCD
北大核心
2012年第3期18-24,38,共8页
Journal of Hydroelectric Engineering
基金
十一五国家科技支持计划项目课题九(2008BAB29B09)
水利部公益项目"南水北调中线水源区中长期径流预报技术研究"(201001004)
国家自然科学基金项目(50928901)
关键词
水文学
长期径流预报
径流自相关关系
水文-气象遥相关关系
随机森林模型
hydrology
long-term streamflow prediction
streamflow auto-correlation
hydro-climatic teleconnection
random forests model
