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- Guo, Jing, et al.
(författare)
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Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling
- 2012
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Ingår i: Stochastic environmental research and risk assessment (Print). - : Springer Science and Business Media LLC. - 1436-3240 .- 1436-3259. ; 26:2, s. 157-176
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Tidskriftsartikel (refereegranskat)abstract
- Many impact studies require climate change information at a finer resolution than that provided by general circulation models (GCMs). Therefore the outputs from GCMs have to be downscaled to obtain the finer resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach is proposed for predicting the daily precipitation of 138 main meteorological stations in the Yangtze River basin for 2010-2099 by statistical downscaling of the outputs of general circulation model (HadCM3) under A2 and B2 scenarios. After that, the spatial-temporal changes of the amount and the extremes of predicted precipitation in the Yangtze River basin are investigated by Mann-Kendall trend test and spatial interpolation. The results showed that: (1) the amount and the change pattern of precipitation could be reasonably simulated by ASD; (2) the predicted annual precipitation will decrease in all sub-catchments during 2020s, while increase in all sub-catchments of the Yangtze River Basin during 2050s and during 2080s, respectively, under A2 scenario. However, they have mix-trend in each sub-catchment of Yangtze River basin during 2020s, but increase in all sub-catchments during 2050s and 2080s, except for Hanjiang River region during 2080s, as far as B2 scenario is concerned; and (3) the significant increasing trend of the precipitation intensity and maximum precipitation are mainly occurred in the northwest upper part and the middle part of the Yangtze River basin for the whole year and summer under both climate change scenarios and the middle of 2040-2060 can be regarded as the starting point for pattern change of precipitation maxima.
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| 2. |
- Hua, Chen, et al.
(författare)
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Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin
- 2010
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Ingår i: Advances in Atmospheric Sciences. - : Springer Science and Business Media LLC. - 0256-1530 .- 1861-9533. ; 27:2, s. 274-284
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Tidskriftsartikel (refereegranskat)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.
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