Statistical Ensemble Seasonal Streamflow Forecasting in the South Saskatchewan River Basin by a Modified Nearest Neighbors Resampling
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 6
Abstract
An ensemble seasonal streamflow forecasting model is developed for two watersheds in the South Saskatchewan River Basin of southern Alberta, Canada. The ensembles are generated from a mean forecast by using a modified -nearest neighbor algorithm. The mean forecasts are produced by a robust M-regression model that uses snow water equivalent and large-scale climate information as predictors where the best combination of predictors is automatically selected by the generalized cross-validation criterion. It is shown that skillful forecasts of the April–September flow can be obtained as early as the beginning of December preceding the runoff year, thus extending the current forecast lead time by up to two months. An assessment of the potential economic value of the forecasts shows that with the same set of predictors, ensemble forecasts offer superior economic value for a wide range of end-users as compared to conditional median forecasts.
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Acknowledgments
Partial support for this work was provided by the Natural Science and Engineering Research Council (NSERC) of Canada and the Canadian Water Networks (CWN). The first author was also supported by a graduate teaching assistantship of the University of Alberta. The Nino3 index was obtained from the online Climate Data Library of the International Research Institute (IRI) at Columbia University (http://ingrid.ldeo.columbia.edu/SOURCES/.Indices/.nino/.EXTENDED/). The PDO and PNA indices were obtained from the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) at the University of Washington, http://www.jisao.washington.edu/data_sets/
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Information
Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 6 • June 2009
Pages: 628 - 639
Copyright
© 2009 ASCE.
History
Received: Jul 18, 2007
Accepted: Sep 5, 2008
Published online: Feb 11, 2009
Published in print: Jun 2009
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