Diagnostic study and modeling of the annual positive water temperature onset |
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Authors: | Anik Daigle Andr St-Hilaire Valrie Ouellet Julie Corriveau Taha BMJ Ouarda Laurent Bilodeau |
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Institution: | aChair in Statistical Hydrology, INRS-ETE, 490 De la Couronne St. Québec City, Québec, Canada G1K 9A9;bCanadian Rivers Institute, University of New Brunswick, Fredericton, NB, Canada E3B 6E1;cGraduate Student, INRS-ETE, 490 De la Couronne St. Québec City, Québec, Canada G1K 9A9;dHydro-Québec, 885 Ste-Catherine Est, Montréal, Québec, Canada |
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Abstract: | 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. |
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Keywords: | River water temperature Prediction Model Neural networks Regression Multivariate statistics |
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