RF-SVR降尺度模型在滦河流域的适用性分析

探讨了RF-SVR统计降尺度模型用于汛期极端降雨模拟的可能性。该统计降尺度模型由降雨状态分类和降雨量预测回归两部分构成，降雨状态分类过程中采用了随机森林（RF）方法，降雨量预测回归过程采用了支持向量机回归（SVR）法。选用1961-2000年的NCEP/NCAR再分析资料及滦河流域10个雨量站点的降雨观测数据进行模型率定，并用2001-2012年数据进行了验证。将RF-SVR统计降尺度模型与SVR模型降尺度效果做了对比。结果表明：RF-SVR模型模拟的滦河流域日降雨量偏差显著减小，并可以改善流域极端降雨的模拟预测效果。

Applicability of RF-SVR downscaling model to Luanhe River Basin

The applicability of RF-SVR statistical downscaling model to the simulation of extreme rainfall in flood season is discussed. The structure of the proposed downscaling model is composed of two parts, which is the rainfall state classification and the regression of rainfall amount prediction. The random forest (RF) method is used for the rainfall state classification, and the support vector regression (SVR) is used for the rainfall amount prediction. The NCEP / NCAR reanalysis data from 1961 to 2000 and the rainfall observation data of 10 stations in the Luanhe River Basin are selected for model calibration, and the data from 2001 to 2012 are used for validation. The downscaling effect of the RF-SVR statistical downscaling model is compared with that of the SVR model. The results show that the daily rainfall deviation of the Luanhe River Basin simulated by the RF-SVR model is significantly reduced, and this model can improve the simulation of extreme rainfall prediction of the basin.