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Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices.

Joshi, Deepti; St-Hilaire, André; Ouarda, Taha B. M. J.; Daigle, Anik; Thiemonge, Nathalie (2016). Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices. Hydrological Sciences Journal , vol. 61 , nº 11. p. 1996-2010. DOI: 10.1080/02626667.2014.966719.

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Résumé

This work explores the ability of two methodologies in downscaling hydrological indices (HIs) characterising 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 HIs 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.

Type de document:
Mots-clés libres: statistical downscaling; relevance vector machine; multiple linear regression; canonical correlation analysis; low flows; hydrological modeling
Centre: Centre Eau Terre Environnement
Date de dépôt: 21 sept. 2016 21:14
Dernière modification: 11 janv. 2017 16:00
URI: http://espace.inrs.ca/id/eprint/3601

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