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Water 2018, 10(11), 1604; https://doi.org/10.3390/w10111604

Multi-Model Approaches for Improving Seasonal Ensemble Streamflow Prediction Scheme with Various Statistical Post-Processing Techniques in the Canadian Prairie Region

1
Department of Civil Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
2
Hydrologic Forecasting & Coordination, Manitoba Infrastructure, Winnipeg, MB R3C 0R8, Canada
3
Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
*
Author to whom correspondence should be addressed.
Received: 22 September 2018 / Revised: 5 November 2018 / Accepted: 5 November 2018 / Published: 8 November 2018
(This article belongs to the Section Hydrology)
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Abstract

Hydrologic models are an approximation of reality, and thus, are not able to perfectly simulate observed streamflow because of various sources of uncertainty. On the other hand, skillful operational hydrologic forecasts are vital in water resources engineering and management for preparedness against flooding and extreme events. Multi-model techniques can be used to help represent and quantify various uncertainties in forecasting. In this paper, we assess the performance of a Multi-model Seasonal Ensemble Streamflow Prediction (MSESP) scheme coupled with statistical post-processing techniques to issue operational uncertainty for the Manitoba Hydrologic Forecasting Centre (HFC). The Ensemble Streamflow Predictions (ESPs) from WATFLOOD and SWAT hydrologic models were used along with four statistical post-processing techniques: Linear Regression (LR), Quantile Mapping (QM), Quantile Model Averaging (QMA), and Bayesian Model Averaging (BMA)]. The quality of MSESP was investigated from April to July with a lead time of three months for the Upper Assiniboine River Basin (UARB) at Kamsack, Canada. While multi-model ESPs coupled with post-processing techniques improve predictability (in general), results suggest that additional avenues for improving the skill and value of seasonal streamflow prediction. Next steps towards an operational ESP system include adding more operationally used models, improving models calibration methods to reduce model bias, increasing ESP sample size, and testing ESP schemes at multiple lead times, which, once developed, will not only help HFCs in Canada but would also help Centers South of the Border. View Full-Text
Keywords: ensemble streamflow forecast; multi-model combination; post-processing; uncertainty estimation; WATFLOOD; SWAT ensemble streamflow forecast; multi-model combination; post-processing; uncertainty estimation; WATFLOOD; SWAT
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Muhammad, A.; Stadnyk, T.A.; Unduche, F.; Coulibaly, P. Multi-Model Approaches for Improving Seasonal Ensemble Streamflow Prediction Scheme with Various Statistical Post-Processing Techniques in the Canadian Prairie Region. Water 2018, 10, 1604.

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