Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed
Regional climate models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value.
Institut National de la Recherche Scientifique, Centre eau, terre et environnement, Université du Québec, 490 rue de la Couronne, Québec city, QC G1K 9A9, Canada; Agriculture and Agri-Food Canada, 2560 Hochelaga Blvd., Québec city, QC G1V 2J3, Canada
Recommended Citation:
Gagnon P,, Rousseau A,N. Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed[J]. Hydrology and Earth System Sciences,2014-01-01,18(5)