Change point detection of flood events using a functional data framework. (March 2020)

• Record Type: Journal Article
• Authors: Alaya, Mohamed Ali Ben; Ternynck, Camille; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B.M.J.
• Is Part Of: Advances in water resources. Volume 137(2020)
• Publication Date: 2020-03
• Subjects: Functional data analysis -- Change point detection -- Hydrology -- Flood; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2020.103522
• Languages: English
• ISSNs: 0309-1708

Abstract

Abstract: Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteristics that may not include the entire information available in a discharge time series. The aim of the current work is to present a new approach to detect changes in flood events based on a functional data analysis framework. The use of the functional approach allows taking into account the whole information contained in the discharge time series of flood events. The presented methodology is illustrated on a flood analysis case study, from the province of Quebec, Canada. Obtained results using the proposed approach are consistent with those obtained using a traditional change point method, and demonstrate the capability of the functional framework to simultaneously consider several flood features and, therefore, presenting a comprehensive way for a better exploitation of the information contained in a discharge time series.