Evaluation of a multi-site weather generator in simulating precipitation in the Qiantang River Basin, East China

Recent years have seen a surge in assessment of potential impacts of climate change. As one of the most important tools for generating synthetic hydrological model inputs, weather generators have played an important role in climate change impact analysis of water management. However, most weather generators like statistical downscaling model (SDSM) and long Ashton research station weather generator (LARS-WG) are designed for single site data generation. Considering the significance of spatial correlations of hydro-meteorological data, multi-site weather data generation becomes a necessity. In this study we aim to evaluate the performance of a new multi-site stochastic model, geo-spatial temporal weather generator (GiST), in simulating precipitation in the Qiantang River Basin, East China. The correlation matrix, precipitation amount and occurrence of observed and GiST-generated data are first compared for the evaluation process. Then we use the GiST model combined with the change factor method (CFM) to investigate future changes of precipitation (2071–2100) in the study area using one global climate model, Hadgem2_ES, and an extreme emission scenario RCP 8.5. The final results show that the simulated precipitation amount and occurrence by GiST matched their historical counterparts reasonably. The correlation coefficients between simulated and historical precipitations show good consistence as well. Compared with the baseline period (1961–1990), precipitation in the future time period (2071–2100) at high elevation stations will probably increase while at other stations decreases will occur. This study implies potential application of the GiST stochastic model in investigating the impact of climate change on hydrology and water resources.