Abstract:
Global climate models predict changes in precipitation patterns in many areas of the world. Extreme precipitation in particular is poorly represented in climate models and there are significant difficulties involved in assessing the frequency and severity of future extreme precipitation events. In this study, several methods have been reviewed and compared for estimating projected changes in Intensity-Duration-Frequency (IDF) curves, commonly used in urban hydrology. A theoretical approach based on geostatistical considerations is employed to derive reasonable areal-reduction factors that make it possible to compare gridded model data with observations.
The mean value method and QQ-mapping have been used to remove biases from modeled data. A simple scaling model has been developed to construct IDF curves using the bias-corrected modeled data for the control and future climate. To investigate uncertainties in predicted changes, different simulations from the North American Regional Climate Change Assessment Program (NARCCAP) have been analyzed.
