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Research on the Monthly Precipitation and Runoff Forecastingfor the Heihe River Basin

机译:黑河流域每月降水量和径流预测研究

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One step ahead monthly forecasts of precipitation and streamflow in the Heihe River Basin in North — west China have been greatly improved by considering meteorological teleconnections. Enhanced values of explained variance for stream flows were obtained by incorporating antecedent values of the sea surface temperatures along the Pacific coast of China and the phase of the Southern Oscillation in addition to antecedent monthly precipitation air temperature and runoff into the forecast procedure. Although correlation analysis was employed to make the initial choice of independent variables , the forecast model was developed in the form of an Artificial Neural Network. The forecast model obtained was found to be capable of reproducing the behavior of low and medium sized flows to a high degree of accuracy. Nevertheless, the levels of explained variance achieved in this study by considering teleconnections were found to be high. This enhanced performance can be partly attributed to the regularity of the flow regime in the Heihe River Basin, for which the snowmelt in spring and the frequent interchanges between surface water and groundwater are particular features.
机译:通过考虑气象电信连接,大力提高了海河河流盆地海河流域的降水和流出的每月预测。通过纳入中国太平洋海岸的海面温度和南方振荡的阶段的沿着先行的月度降水空气温度和径流进入预测程序,通过加入流动流量的解释方差的增强值。尽管采用相关性分析来进行独立变量的初始选择,但预测模型是以人工神经网络的形式开发的。发现所获得的预测模型能够将低和中等大小流的行为再现为高精度。然而,发现通过考虑电信连接在本研究中实现的解释方差水平很高。这种增强的性能可以部分地归因于黑河河流域的流动制度的规律性,其中春天的散户和地表水和地下水之间的频繁交换是特别的特征。

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