CR 51:185-200 (2012)  -  DOI: https://doi.org/10.3354/cr01062

ABSTRACT: A major obstacle in quantifying the hydrological impacts of climate change is the mismatch between the coarse resolution of climate model outputs (general circulation models and regional climate models) and the fine resolution requirements of hydrological models. This research presents a statistical downscaling approach combining the attributes of both the stochastic weather generator (WG) and the change factor (CF) method to overcome this problem. It is further compared against the commonly used CF method in terms of quantifying the hydrological impacts of climate change over the next century for a Canadian watershed (Quebec Province). Both downscaling methods suggested increases in winter (November−April) discharge and decreases in summer (June−October), especially for those downscaled by the WG-based method. The WG-based method predicted higher peak discharges than the CF method. The 2 downscaling methods suggested significantly different increases in annual and seasonal discharges, particularly for low flows. Hydrology results show that precipitation and temperature variability play a very important role in the runoff generating process, and that neglecting to address this variability can lead to biased results. In particular, the WG-based method has a significant advantage in simulating low flows because it takes into account the change of precipitation occurrence. The results also outline the uncertainty linked to the choice of a downscaling method.

KEY WORDS: Statistical downscaling · Stochastic weather generator · Change factor method · Hydrology · Climate change