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.
Highlights
The science of hydrological forecasting has greatly improved with the introduction of numerous distributed hydrologic models, numerical weather models (NWMs), and data assimilation techniques [1]
In Scheme-1, we presented results when post-processing techniques (LR and Quantile Mapping (QM) only) were applied on individual model Ensemble Streamflow Predictions (ESPs), as well as an MM ESP when ensembles are combined in a linear fashion
ESP is a key component of operational, long-lead streamflow prediction, which is currently utilized by Hydrologic Forecasting Centre (HFC) in the US, UK, Australia, and other countries
Summary
The science of hydrological forecasting has greatly improved with the introduction of numerous distributed hydrologic models, numerical weather models (NWMs), and data assimilation techniques [1]. A large number of hydrological forecasting models are in operation around the globe, and several of these are used in Canada [2]. Distributed and physically based hydrologic models are data-intensive but are considered to be reliable in providing improved streamflow forecasting, mainly because they are capable of leveraging a variety of spatially distributed data [4,5]. In operational forecasting, there are trade-offs between the complexity of the model, the inclusion and accuracy of catchment-scale processes affecting runoff generation, and the model speed. The goal is always, and always must be, the highest accuracy forecasts for reliable, operational flood management and warning [6,7]
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