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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. The required linkage could be
developed using downscaling methods.
In general, two broad categories of downscaling procedures currently
exist: dynamical downscaling (DD) and statistical downscaling (SD). 1 DD
procedures are mainly based on regional climate models (RCMs) that
describe the climate processes using fundamental conservation laws for
mass, energy and momentum. DD methods contain thus more complete
physics than SD techniques. However, the more complete physics signifi-
cantly increases computational cost, which limits the simulation of a cli-
mate by RCMs to typically a single realization. On the other hand, SD
approaches are relatively fast and much less expensive. These advantages
of the SD allow the users to develop a large number of different climate
realizations and thus to be able to quantify the confidence interval of sim-
ulated climate variables. In addition, SD methods can directly account for
the observed weather data available at the study site. The results are hence
more consistent with the local climate conditions.
Some recent comparisons of DD and SD techniques for climate impact
studies 1 - 3 have indicated that neither technique was consistently better
than the other. In particular, based on the assessment of the climate change
impacts on the hydrologic regimes of a number of selected basins in the
United States, Gutowski et al . 3 have found that these two methods could
reproduce some general features of the basin climatology, but both displayed
systematic biases with respect to observations as well. Furthermore, a main
finding from this study was that the assessment results were dependent
on the specific climatology of the basin under consideration. Hence, it is
necessary to test different downscaling methods in order to find the most
suitable approach for a particular region of interest. However, it has been
widely recognized that SD methods offer several practical advantages over
DD procedures, especially in terms of flexible adaptation to specific study
purposes, and inexpensive computing resource requirements. 4 , 5
In view of the above-mentioned issues, the main objective of the present
study is to perform a critical assessment of the adequacy of various existing
SD techniques to find the most suitable procedure for hydrological impact
studies. Of particular interest is the ability of SD techniques to simulate
accurately the characteristics of precipitation and temperature extremes
since these two parameters are the main components of the hydrologic cycle.
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