Abstract:
In climate change studies, the temporal scales could vary from a very short time interval of 5 minutes (for urban water cycle) to a yearly time scale (for annual water balance computation). The spatial resolutions could be from a few square kilometers (for urban and rural watersheds) to several thousand square kilometers (for large river basins). General Circulation Models (GCMs) have been recognized to be able to represent reasonably well the main features of the global distribution of basic climate parameters, but these models so far could not reproduce well details of regional climate conditions at temporal and spatial scales of relevance to hydrological studies. In other words, outputs from GCMs are usually at resolution that is too coarse (generally greater than 2.00 for both latitude and longitude, and greater than 200km for middle latitudes) for many climate change impact studies. Hence, there is a great need to develop tools for downscaling GCM predictions of climate change to regional and local or station scales. In recent years, different downscaling methods have been proposed in a number of studies around the world. Of particular importance for the management of water resources systems are those procedures dealing with the linkage of the large-scale climate variability to the historical observations of the surface parameters of interest (e.g., precipitation and temperature). If this linkage could be established, then the projected change of climate conditions given by a GCM could be used to predict the resulting change of the selected surface parameters for hydrological impact studies. The required linkage can be developed using a wide range of downscaling methods. These notes briefly explain the fundamentals of downscaling methods. Detailed treatment of the statistical method is available in Wilby et al. (2004).
