The Effects of Climate Change on Extreme Precipitation Events in the Upper Thames River Basin: A Comparison of Downscaling Approaches

Abstract

Future changes in climatic conditions from increasing greenhouse gas concentrations will have a major impact on the hydrologic cycle. It is important to understand and predict future changes in temperature and precipitation in order to effectively manage water resources. Atmosphere-Ocean coupled Global Climate Models (AOGCMs) are used widely to predict the effects of greenhouse-gas forcing on global climate conditions. However, their spatial and temporal resolutions are quite large so their outputs must be modified to represent local climate conditions. This process is called downscaling, and there are a variety of tools available to achieve this goal. This study compares three downscaling approaches, namely the Statistical DownScaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG), and the K-NN Weather Generator with Principal Component Analysis (WG-PCA). Each weather generator is used to simulate the historical climate for the Upper Thames River Basin in Ontario, Canada for use in a comparison of downscaling tools. Future climate conditions are simulated by LARS-WG and WG-PCA from six different AOGCMs, each with two to three emissions scenarios, for a total of 15 different models. In simulation of historical climate variability, the models generally perform better in terms of mean daily precipitation and total monthly precipitation. LARS-WG simulates precipitation events well but cannot reproduce means and variances in the daily temperature series. SDSM adequately simulates both temperatures and precipitation events. WG-PCA reproduces daily temperatures very well but overestimates the occurrence of some extreme precipitation events. Results are variable for the downscaling of AOGCMs; however, the downscaling tools generally predict a rise in winter, spring and fall precipitation totals, as well as an overall increase in mean annual precipitation in future decades.