Regional flood frequency analysis using residual kriging in physiographical space

• Published in: Hydrological processes Vol. 24; no. 15; pp. 2045 - 2055
• Main Authors: Nezhad, M. Kamali, Chokmani, K, Ouarda, T.B.M.J, Barbet, M, Bruneau, P
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 15.07.2010; Wiley
• Subjects: canonical correlation analysis; canonical kriging; Earth sciences; Earth, ocean, space; Estimates; Exact sciences and technology; flood frequency analysis; Floods; Freshwater; Hydrology; Hydrology. Hydrogeology; Kriging; physiographical space-based kriging; Quantiles; Regional; regional estimation; Regression; residual kriging; Trends; residual kriging; floods; kriging; canonical kriging; flood frequency analysis: canonical correlation analysis; lead; frequency; correlation; regression; performances; physiographical space―based kriging; regional estimation; gauging
• ISSN: 0885-6087; 1099-1085; 1099-1085
• DOI: 10.1002/hyp.7631

In this article, an approach using residual kriging (RK) in physiographical space is proposed for regional flood frequency analysis. The physiographical space is constructed using physiographical/climatic characteristics of gauging basins by means of canonical correlation analysis (CCA). This approach is a modified version of the original method, based on ordinary kriging (OK). It is intended to handle effectively any possible spatial trends within the hydrological variables over the physiographical space. In this approach, the trend is first quantified and removed from the hydrological variable by a quadratic spatial regression. OK is therefore applied to the regression residual values. The final estimated value of a specific quantile at an ungauged station is the sum of the spatial regression estimate and the kriged residual. To evaluate the performance of the proposed method, a cross-validation procedure is applied. Results of the proposed method indicate that RK in CCA physiographical space leads to more efficient estimates of regional flood quantiles when compared to the original approach and to a straightforward regression-based estimator. Copyright © 2010 John Wiley & Sons, Ltd.