This study investigates the utilization of hydrological information in regional flood frequency analysis (RFFA) to enforce desired properties for a group of gauged stations. Neighbourhoods are particular types of regions that are centred on target locations. A challenge for using neighbourhoods in RFFA is that hydrological information is not available at target locations and cannot be completely replaced by the available physiographical information. Instead of using the available physiographic characteristics to define the centre of a target location, this study proposes to introduce estimates of reference hydrological variables to ensure a better homogeneity. These reference variables represent nonlinear relations with the site characteristics obtained by projection pursuit regression, a nonparametric regression method. The resulting neighbourhoods are investigated in combination with commonly used regional models: the index-flood model and regression-based models. The complete approach is illustrated in a real-world case study with gauged sites from the southern part of the province of Qu�bec, Canada, and is compared with the traditional approaches such as region of influence and canonical correlation analysis. The evaluation focuses on the neighbourhood properties as well as prediction performances, with special attention devoted to problematic stations. Results show clear improvements in neighbourhood definitions and quantile estimates. � Author(s) 2016. CC Attribution 3.0 License.
Universit� du Qu�bec � Trois-Rivi�res, University of Qu�bec, 3351, Blvd. des Forges, C.P. 500, Trois-Rivi�res, Canada; Institut National de Recherche Scientifique (INRS-ETE), University of Qu�bec, 490 de la Couronne, Qu�bec, Canada; Institute Center for Water Advanced Technology and Environmental Research (IWater), Masdar Institue of Science and Technology, P.O. Box 54224, Abu Dhabi, United Arab Emirates
Recommended Citation:
Durocher M,, Chebana F,, Ouarda T,et al. Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression[J]. Hydrology and Earth System Sciences,2016-01-01,20(12)