Abstract
Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders. Because no single hydrologic model is able to perfectly simulate the observed flow, multimodel combination techniques are developed to combine forecasts obtained from different models and to quantify the uncertainties with the goal of improving upon single-model performance. In this study, a comprehensive set of multimodel ensemble averaging techniques with varying complexities are investigated for operational forecasting over four river basins in the Western United States. Ensemble merging models are divided into three categories of simple, intermediate, and complex, and comparison is made between each class by using a bootstrap approach. Analysis suggests that model combination effectively improves most of the individual seasonal forecasts and can outperform the best forecast model. Simple average, median, Bates-Granger, constrained linear regression, and Bayesian model averaging optimized by expectation maximization showed better results compared with other methods over three basins. For the Rogue River basin, the intermediate and complex models outperformed most of the individual forecasts and the simple methods. Multimodeling techniques based on information criteria showed similar performances.
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Acknowledgments
Partial financial support for this work was provided by NOAA-CSTAR Grant No. NA11NWS4680002. The authors would also like to thank Andrew Wood from the NWRFC, David Garen from USDA-NRCS, and Randal Wortman from USACE for providing the data sets and individual model results used in this study.
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Journal of Hydrologic Engineering
Volume 21 • Issue 1 • January 2016
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© 2015 American Society of Civil Engineers.
History
Received: Jul 29, 2014
Accepted: Apr 10, 2015
Published online: Jun 5, 2015
Discussion open until: Nov 5, 2015
Published in print: Jan 1, 2016
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