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丹江口水库入库水量与气象因子的响应及其预测
引用本文:秦鹏程,刘敏,肖莺,王苗,方思达,任永建.丹江口水库入库水量与气象因子的响应及其预测[J].长江流域资源与环境,2018,27(3):638-647.
作者姓名:秦鹏程  刘敏  肖莺  王苗  方思达  任永建
作者单位:(武汉区域气候中心,湖北 武汉 430074)
基金项目:中国气象局气候变化专项(CCSF201620),国家重点研发计划(2016YFE0102400)
摘    要:利用丹江口水库1959~2016年逐月入库流量及水源区41个气象站同期气象观测资料,通过相关分析确定了影响入库水量的关键气象因子及其影响时效,利用回归分析建立了逐月入库流量预报模型并对误差来源进行了系统分析。结果表明:各月入库水量与前期不同月份降水量呈显著正相关,最大相关系数0.40~0.85,与平均气温呈显著负相关,最大相关系数-0.26~-0.54,其中汛期月份相关性高,影响时效在前1~3个月,冬春季相关性较弱,影响时效在2~6个月。以前期不同月份降水量、气温为预报因子,采用对数变换后的逐步回归模型预报效果最好,各月解释方差在45%~88%之间,汛期月份在75%以上,十折交叉验证月平均绝对误差率20.5%~40.7%,年平均预测误差9.6%。降水的时空分布特征对预测误差具有明显影响,上游汉中、石泉、安康及白河子流域降水偏多,流域边缘降水偏少,预报结果偏低,反之预报结果偏高,同时降水集中或强度大预报结果易偏低,而降水时间分布均匀预报结果易偏高。 关键词: 丹江口水库;入库水量;回归分析;预报误差

关 键 词:丹江口水库  入库水量  回归分析  预报误差  Danjiangkou  Reservoir  inflow  regression  analysis  forecasting  error

Response of Danjiangkou Reservoir Inflow to Meteorological Factors and Forecasting
QIN Peng-cheng,LIU Min,XIAO Ying,WANG Miao,FANG Si-da,REN Yong-jian.Response of Danjiangkou Reservoir Inflow to Meteorological Factors and Forecasting[J].Resources and Environment in the Yangtza Basin,2018,27(3):638-647.
Authors:QIN Peng-cheng  LIU Min  XIAO Ying  WANG Miao  FANG Si-da  REN Yong-jian
Institution:(Wuhan Regional Climate Center, Wuhan 430074, China)
Abstract:Based on the monthly inflow data of Danjiangkou reservoir as well as the meteorological data of 41 weather stations in the upper basin during 1959-2016,the key meteorological factors and timing that affecting inflow were identified by correlation analysis,and then forecasting models for each month were established and systematic errors were also analyzed.The results show that the reservoir inflow is significantly and positively correlated with precipitation,with the highest correlation ranging from 0.45 to 0.85 for each month,and negatively correlated with temperature,with the highest correlation ranging from-0.26 to-0.54.generally,the rainy season has a higher correlation,and the influencing timing is in the latest 1-3 months,while the nonrainy season has a lower correlation,with a more longer influencing timing of 2-6 months.Using mean temperature and precipitation in the last several months as predictor variables,stepwise regression model with logarithmic transformations was chosen as the best fitted model,the models could explain 45%-88% of the total variance for each month,and more than 75% for rainy season months.10-fold cross validation give a mean absolute percentage error within 20.5%-40.7% for each month,and 9.6% for whole year.The spatial and temporal distribution of precipitation can have a significant impact on forecast accuracy,with precipitation of the upper subbasin of hanzhong,shiquan,ankang and baihe above normal level,or precipitation in the edge of the basin below normal level,there may be a lower forecast discrepancy,while higher forecast discrepancy on the contrary,and a more concentrated or heavy precipitation results in a lower forecast discrepancy,a more even precipitation distribution leads to higher discrepancy.
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