Downscaling model for rainfall based on the influence of typhoon under climate change. Issue 6 (18th May 2022) - Lin, Shiu-Shin; Hu, Yan-Li; Zhu, Kai-Yang
Abstract: The effects of typhoon rainfall cannot be easily investigated to downscale the GCMs (general circulation models) data, because typhoons are short-term highly nonlinear processes. In this study, to explore the impact on the typhoon rainfall for downscaling models, the monthly rainfalls are divided into typhoon rainfall and non-typhoon rainfall. The GA-RBFN downscaling models, integrating genetic algorithm to optimize a radial basis function neural network, were established to enquire into the future rainfall under the effect of typhoons in Taichung and Hualien, Taiwan. The GCM data in this study included MRI-CGCM3 and CSIRO-Mk3.0 from the 5th Assessment Report of the Intergovernmental Panel on Climate Change. The historical monthly rainfall of Taichung and Hualien stations are collected from the Central Weather Bureau, Taiwan, and the best track of typhoons from the Joint Typhoon Warning Center. Principal component analysis (PCA) and the stepwise regression procedure (SRP) were adopted, respectively, to select input variables from the GCM data. The future rainfall trends and uncertainties are evaluated by the best GA-RBFN model, which is selected with the highest performance by applying holistic information criteria. The simulated results show that the model with variables transformed by PCA performs well in forecasting non-typhoon rainfalls, while the model with variables transformed by SRP performs well in forecasting monthly total rainfalls. According to the three classifications of future rainfalls in wet and dry seasons, the mid- and long-term rainfall amount are mainly low to normal for Taichung and normal to high for Hualien. For the long-term rainfall assessment and the probability analysis on exceeding historical rainfalls, the long-term rainfall variability of the two stations is higher than the mid-term rainfall variability. It demonstrates that the future rainfall estimation has high uncertainty under the force of typhoons. Finally, the rainfall variability in the dry period is higher than that in the wet season, and the average probability of the future dry season rainfalls exceeding historical rainfall is high. HIGHLIGHTS: The study estimated the influence of rainfall of typhoons for developing the statistical downscaling model using AR5 GCMs. Both at same northern latitudes point rainfall were compared. The methodology of integration of machine learning approach and the influence of typhoon to investigate the future trend of rainfall is new in this area. Graphical Abstract