A functional framework for flow-duration-curve and daily streamflow estimation at ungauged sites. (March 2018)

• Record Type: Journal Article
• Authors: Requena, Ana I.; Chebana, Fateh; Ouarda, Taha B.M.J.
• Is Part Of: Advances in water resources. Volume 113(2018)
• Page Start: 328
• Page End: 340
• Publication Date: 2018-03
• Subjects: Ungauged site -- Functional data analysis -- Functional multiple regression -- Flow duration curve -- Daily streamflow estimation -- Quantile; FDA functional data analysis -- FDC flow duration curve -- MR multiple regression -- FMR functional multiple regression -- FANOVA functional analysis of variance -- RSBFA regional streamflow estimation based frequency analysis; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2018.01.019
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: Functional regression is proposed for flow duration curve estimation. Framework and benefits of functional approach are provided. The approach is recommended to estimate a number of flow duration curve quantiles. Insight into descriptor influence on flow duration curve quantiles is supplied. Improved daily streamflow estimates are obtained for most sites. Abstract: Flow duration curves (FDC) are used to obtain daily streamflow series at ungauged sites. In this study, functional multiple regression (FMR) is proposed for FDC estimation. Its natural framework for dealing with curves allows obtaining the FDC as a whole instead of a limited number of single points. FMR assessment is performed through a case study in Quebec, Canada. FMR provides a better mean FDC estimation when obtained over sites by considering simultaneously all FDC quantiles in the assessment of each given site. However, traditional regression provides a better mean FDC estimation when obtained over given FDC quantiles by considering all sites in the assessment of each quantile separately. Mean daily streamflow estimation is similar; yet FMR provides an improved estimation for most sites. Furthermore, FMR represents a more suitable framework and provides a number of practical advantages, such as insight into descriptor influence on FDC quantiles. Hence, traditional regression may be preferred if only few FDC quantiles are of interest; whereas FMR would be more suitable if a large number of FDC quantiles is of interest, and therefore to estimate daily streamflows.