基于数值降雨预报信息的中期径流预报方法研究

随着数值气象预报水平的不断发展,考虑数值降雨预报信息有利于提高流域径流预报的精度,能够为水库未来的兴利调度决策提供可靠的信息支撑。为此,本文以浑江桓仁水库流域为研究实例,分别采用新安江模型和多元线性回归模型建立流域汛期和非汛期的中期旬径流预报模型,其中模型参数分别采用遗传算法和最小二乘法进行优化率定。在以上模型的基础上,采用美国全球预报系统发布的未来10 d数值降雨预报信息作为降雨输入,预报桓仁水库的中期旬径流。研究结果表明中期径流预报受降雨预报信息的不确定性影响,预报精度随预见期延长而降低,但仍高于传统不考虑降雨预报信息的中期径流预报。

Medium-term river runoff forecast model using data of quantitative precipitation forecasts

With incessant improvement in weather forecast technology, medium-term quantitative precipitation forecasts (MT-QPFs) become increasingly beneficial to medium-term forecasting of river runoff to achieve better accuracy. In this study, we have examined the application of MR-QPFs to the case of the Huanren reservoir on the Hun River. First, a Xinanjiang model and a multiple linear regression model were developed for medium-term runoff forecasting in flood and non-flood seasons, respectively. Then, real time precipitation forecasts with ten days leading time issued by the Global Forecast System (GFS) were applied to forecast the medium-term runoff of the Huanren reservoir. The results show that accuracy in these runoff forecasts decreases with leading time, and the accuracy of this model is higher than that of the traditional method.