Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices |
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| Authors: | Deepti Joshi Andre St-Hilaire Taha B. M. J. Ouarda Anik Daigle Nathalie Thiemonge |
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| Affiliation: | 1. INRS-ETE, University of Quebec, Quebec City, Quebec, Canadajoshi.deepti@ete.inrs.ca;3. INRS-ETE, University of Quebec, Quebec City, Quebec, Canada;4. Water, Environmental Engineering, Masdar Institute, Abu Dhabi, United Arab Emirates;5. Cégep Garneau, Quebec City, Québec, Canada;6. Unité Conception des aménagements de production Hydrauliques et géotechniques, Hydro-Quebec, Montreal, Quebec, Canada |
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| Abstract: | ABSTRACTThis work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability. Editor D. Koutsoyiannis; Associate editor K. Hamed |
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| Keywords: | statistical downscaling relevance vector machine multiple linear regression canonical correlation analysis low flows hydrological modelling |
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