Articles | Volume 20, issue 12
https://doi.org/10.5194/hess-20-4717-2016
https://doi.org/10.5194/hess-20-4717-2016
Research article
 | 
29 Nov 2016
Research article |  | 29 Nov 2016

Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression

Martin Durocher, Fateh Chebana, and Taha B. M. J. Ouarda

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Cited articles

Bishop, C. M.: Neural networks for pattern recognition, Oxford University Press, Oxford, UK, 1995.
Burn, D. H.: An appraisal of the "region of influence" approach to flood frequency analysis, Hydrol. Sci. J. 35, 149–166. https://doi.org/10.1080/02626669009492415, 1990.
Castiglioni, S., Castellarin, A., and Montanari, A.: Prediction of low-flow indices in ungauged basins through physiographical space-based interpolation, J. Hydrol., 378, 272–280, https://doi.org/10.1016/j.jhydrol.2009.09.032, 2009.
Chebana, F. and Ouarda, T. B. M. J.: Multivariate L-moment homogeneity test, Water Resour. Res., 43, W08406, https://doi.org/10.1029/2006WR005639, 2007.
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Short summary
For regional flood frequency, it is challenging to identify regions with similar hydrological properties. Therefore, previous works have mainly proposed to use regions with similar physiographical properties. This research proposes instead to nonlinearly predict the desired hydrological properties before using them for delineation. The presented method is applied to a case study in Québec, Canada, and leads to hydrologically relevant regions, while enhancing predictions made inside them.