Linking Climate Change to Impact and Adaptation Studies: Modeling of Extreme Rainfall Processes over a Wide Range of Space and Time Scales

Climate change has been recognized as having a profound impact on the design, planning, and management of various urban infrastructures (urban drainage systems, water supply systems, transportation systems, etc.), and Global Climate Models (GCMs) have been extensively used in many studies for assessing this impact. However, outputs from these models are usually at resolutions that are too coarse (generally more than 200km) and not suitable for the impact assessment studies for small urban catchment areas (e.g., less than 500 km²) or for a given location. Hence, different downscaling methods have been proposed for linking GCM climate change projections to urban hydrologic processes at the relevant space and time scales that are suitable for these impact studies. Of particular importance for water engineering applications are those procedures dealing with the linkage of the large-scale climate variability to the historical observations of the rainfall process at a local site or at many different locations over a given urban area. If this linkage could be established, then the projected change of climate conditions given by a GCM could be used to predict the resulting changes of the rainfall (and resulting runoff) characteristics at a location of interest. Therefore, the overall objective of the present paper is to provide an overview of some recent progress in the modeling of extreme rainfall processes in a changing climate from both theoretical and practical viewpoints. In particular, the main focus of this lecture is on recently developed statistical downscaling (SD) methods for linking GCM climate predictors to the observed daily and sub-daily rainfall extremes at a single site as well as at many sites concurrently. Examples of various applications using data from different climatic conditions in Canada will be presented to illustrate the feasibility and accuracy of the proposed SD methods.