HESS Hydrology and Earth System Sciences HESS Hydrol. Earth Syst. Sci. 1607-7938 Copernicus Publications Göttingen, Germany 10.5194/hess-13-2221-2009 An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting Velázquez J. A. 1 Petit T. 1 Lavoie A. 1 Boucher M.-A. 1 Turcotte R. 2 Fortin V. 3 Anctil F. 1 Chaire de recherche EDS en prévisions et actions hydrologiques, Université Laval, Québec, Canada Centre d'expertise hydrique du Québec, Québec, Canada Recherche en prévision numérique environnementale, Environnement Canada, Montréal, Canada 25 11 2009 13 11 2221 2231 Copyright: © 2009 J. A. Velázquez et al. 2009 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://hess.copernicus.org/articles/13/2221/2009/hess-13-2221-2009.html The full text article is available as a PDF file from https://hess.copernicus.org/articles/13/2221/2009/hess-13-2221-2009.pdf

Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. <br><br> In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

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