DOWNSCALING GLOBAL CLIMATE MODEL OUTPUTS TO STUDY THE HYDROLOGIC IMPACT OF CLIMATE CHANGE PART I: CALIBRATION AND VALIDATION OF DOWNSCALING MODELS

Climate-change impact studies on hydrologic regime have been relatively rare until recently, mainly because Global Circulation Models, which are widely used to simulate future climate scenarios, do not provide hourly or daily rainfall reliable enough for hydrological modeling. Nevertheless, more reliable rainfall series corresponding to future climate scenarios can be derived from GCM outputs using the so called 'downscaling techniques'. Though these conversion methods do not correct the GCM model inaccuracies, they can provide future daily rainfall scenarios relevant to impact studies on flood regime. This paper presents the results from the investigation of some promising statistical downscaling techniques and compared the results with that of artificial neural network based downscaling using precipitation and temperature data of Chute-des-Passes station located in the Saguenay watershed, in northern Québec, Canada.