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| Content Provider | Springer Nature Link |
|---|---|
| Author | Chen, Hua Guo, Jing Xiong, Wei Guo, Shenglian Xu, Chong Yu |
| Copyright Year | 2010 |
| Abstract | 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. |
| Starting Page | 274 |
| Ending Page | 284 |
| Page Count | 11 |
| File Format | |
| ISSN | 02561530 |
| Journal | Advances in Atmospheric Sciences |
| Volume Number | 27 |
| Issue Number | 2 |
| e-ISSN | 18619533 |
| Language | English |
| Publisher | SP Science Press |
| Publisher Date | 2010-02-16 |
| Publisher Place | Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | SSVM GCM statistical downscaling precipitation Hanjiang Basin Geophysics/Geodesy Meteorology/Climatology |
| Content Type | Text |
| Resource Type | Article |
| Subject | Atmospheric Science |
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