Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin
Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin
复制标题
使用平滑支持向量机方法降尺度 GCM 来预测汉江流域的日降水量
DOI：
10.1007/s00376-009-8071-1
复制
发表时间：
2010-03-01
影响因子：
5.8
通讯作者：
Xu, Chong-Yu
中科院分区：
文献类型：
--
作者：
Chen Hua;Guo Jing;Xu, Chong-Yu
大气环流模式（GCM）经常用于评估全球和大陆尺度气候变化的影响。然而，在相对较小的尺度上，如单个流域，GCM模拟的气候因子不一致。基于平滑支持向量机（SSVM）方法，构建了一种统计降尺度方法，用于预测汉江流域气候变化背景下的日降水量。利用NCEP/NCAR再分析资料建立了较大尺度气候预报因子与观测降水量之间的统计关系。获得的关系被用来预测未来的降水量从两个大气环流模型（CGCM 2和HadCM 3）的A2排放情景。使用SSVM得到的结果进行了比较，从人工神经网络（ANN）。比较表明，SSVM是适合进行气候影响研究，在这一地区作为一个统计降尺度工具。基于A2排放情景的SSVM预测结果表明，2011-2040年汉江上游流域降水量减少，2071年后整个流域降水量增加。
General circulation models (GCMs) are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine (SSVM) method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the statistical relationship between the larger scale climate predictors and observed precipitation. The relationship obtained was used to project future precipitation from two GCMs (CGCM2 and HadCM3) for the A2 emission scenario. The results obtained using SSVM were compared with those from an artificial neural network (ANN). The comparisons showed that SSVM is suitable for conducting climate impact studies as a statistical downscaling tool in this region. The temporal trends projected by SSVM based on the A2 emission scenario for CGCM2 and HadCM3 were for rainfall to decrease during the period 2011–2040 in the upper basin and to increase after 2071 in the whole of Hanjiang Basin.