Introduction of the GAM model for regional low-flow frequency analysis at ungauged basins and comparison with commonly used approaches. (November 2018)

• Record Type: Journal Article
• Authors: Ouarda, T.B.M.J.; Charron, C.; Hundecha, Y.; St-Hilaire, A.; Chebana, F.
• Is Part Of: Environmental modelling & software. Volume 109(2018)
• Page Start: 256
• Page End: 271
• Publication Date: 2018-11
• Subjects: Low flows -- Regional estimation -- Canonical correlation analysis -- Region of influence -- Hierarchical cluster analysis -- Generalized additive models; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.08.031
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Generalized Additive Models (GAMs) are introduced in this study for the regional estimation of low-flow characteristics at ungauged basins and compared to other approaches commonly used for this purpose. GAMs provide more flexibility in the shape of the relationships between the response and explanatory variables in comparison to classical models such as multiple linear regression (MLR). Homogeneous regions are defined here using the methods of hierarchical cluster analysis, canonical correlation analysis and region of influence. GAMs and MLR are then used within the delineated regions and also for the whole study area. In addition, a spatial interpolation method is also tested. The different models are applied for the regional estimation of summer and winter low-flow quantiles at stations in Quebec, Canada. Results show that for a given regional delineation method, GAMs provide improved performances compared to MLR. Highlights: GAMs are introduced for the regional estimation of low-flows at ungauged basins. GAMs are compared to commonly used approaches for low-flow regionalization. GAMs provide more flexibility than classical models. They model non-linear links between meteorological/physiographic & hydrological variables. GAMs are shown to provide improved performances compared to other approaches.