Application of Multi-site Weather Generators for Investigating Wet and Dry Spell Lengths under Climate Change: A Case Study in Southern Taiwan

The study compared the performances of three weather generators (WGs), including a parametric model and two non-parametric models, in producing synthetic daily rainfall time series for multiple sites. The observed daily rainfalls of six raingauges during 1979~2008 in the catchment of Tseng-Wen Reservoir in Southern Taiwan were used as the data set. The generated results reveal that the k-nearest neighbor WG with a fixed window (i.e., a non-parametric model) is the best for daily rainfall generation at each site and performs well in preserving spatial correlation of rainfall among sites. The best WG was further applied to assess the impact of climate change on rainfall temporal characteristics (i.e., annual number of wet day, annual maximum number of continuous wet days and annual maximum number of continuous dry days) by using the downscaling results of 24 GCMs under the A1B emission scenario during 2020~2039. It is found that the rainfall temporal characteristics will change in the future which may make Southern Taiwan tend to face a longer period with no rain.