Data-based comparison of seasonality-based regional flood frequency methods

• Published in: Journal of hydrology (Amsterdam) Vol. 330; no. 1; pp. 329 - 339
• Main Authors: Ouarda, T.B.M.J., Cunderlik, J.M., St-Hilaire, A., Barbet, M., Bruneau, P., Bobée, B.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 30.10.2006; Elsevier Science
• Subjects: Earth sciences; Earth, ocean, space; Exact sciences and technology; Flood seasonality; floods; Freshwater; Hydrology. Hydrogeology; jack-knife procedure; Jackknife; mathematics and statistics; Peaks-over-threshold; Quantile; regional flood frequency; Regional flood frequency analysis; Regionalization; Regression; regression analysis; seasonal variation; spring; Spring flood; watershed hydrology; watersheds; Quebec; Canada; Eastern Canada; Canada, Quebec; Spring flood; Flood seasonality; Regional flood frequency analysis; Jackknife; Regression; Regionalization; Peaks-over-threshold; Quantile; floods; springs; accuracy; North America; lead; frequency; drainage basins; regression; Flood seasonally
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2006.03.023

During the last 10 years a growing number of regional flood frequency estimation studies have used flood seasonality descriptors for delineating hydrologically homogeneous regions. Seasonality of floods reflects a complex catchment’s hydrologic response to flood producing processes. Due to the high accuracy and robustness of flood date data, the use of flood seasonality indices became an attractive alternative to traditional regionalization approaches for the delineation of homogeneous regions, a key step in any regionalization study. This paper presents a data-based comparison of three flood seasonality regionalization methods. The methods are applied to a set of catchments in the province of Québec (Canada). A jack-knife procedure is used to assess the performance of the methods in regional quantile estimation. The results are compared to those obtained from a traditional regionalization approach based on catchment physiographic similarities, and to the results from a scenario where information available in all catchments is used in the analysis. The seasonality method based on the peaks-over-threshold approach was concluded to lead to best results.