Generation of Daily and Hourly Weather Variables for use in Climate Change Vulnerability Assessment

Abstract

This paper evaluates the impact of climate change, as projected by two Global Climate Models (GCMs) on the occurrence of extreme precipitation events in the Upper Thames River Basin in the Canadian province of Ontario. The modelling approach presented herein involves a two-stage process of generating daily weather data followed by disaggregation to an hourly time step of select variables for some events. Monthly change fields for three weather variables (maximum temperature, minimum temperature, and precipitation) were obtained from the output of two GCMs. The historical data set is modified by applying change fields to the weather variables simultaneously and then using this as the driving data set for an improved K-nearest neighbour weather-generating model. Weather sequences representative of climatic conditions in 2050 were simulated. Disaggregation of precipitation data is carried out using a new method that is a hybrid key site approach. A distinct practical advantage of the approach presented here is that extreme wet and dry spells are simulated, which is crucial for evaluation of effective flood and drought management policies for the basin.