Dépôt numérique

Diagnostic study and modeling of the annual positive water temperature onset.

Daigle, Anik, St-Hilaire, André, Ouellet, Valérie, Corriveau, Julie, Ouarda, Taha B. M. J. et Bilodeau, Laurent (2009). Diagnostic study and modeling of the annual positive water temperature onset. Journal of Hydrology , vol. 370 , nº 1-4. p. 29-38. DOI: 10.1016/j.jhydrol.2009.02.042.

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A data-driven model is designed using artificial neural networks (ANN) to predict the average onset for the annual water temperature cycle of North-American streams. The data base is composed of daily water temperature time series recorded at 48 hydrometric stations in Québec (Canada) and northern US, as well as the geographic and physiographic variables extracted from the 48 associated drainage basins. The impact of individual and combined drainage area characteristics on the stream annual temperature cycle starting date is investigated by testing different combinations of input variables. The best model allows to predict the average temperature onset for a site, given its geographical coordinates and vegetation and lake coverage characteristics, with a root mean square error (RMSE) of 5.6 days. The best ANN model was compared favourably with parametric approaches.

Type de document: Article
Mots-clés libres: river water temperature; prediction; model; neural networks; regression; multivariate statistics
Centre: Centre Eau Terre Environnement
Date de dépôt: 11 janv. 2021 15:03
Dernière modification: 11 janv. 2021 15:03
URI: https://espace.inrs.ca/id/eprint/10803

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