EVALUATION OF SNOWMELT ESTIMATION TECHNIQUES FOR ENHANCED SPRING PEAK FLOW PREDICTION

Abstract

In cold and snowy countries, water resources management and planning require accurate and reliable spring peak flow forecasts which call for adequate snowmelt estimation techniques. Thus, exploring the potential of snowmelt models to improve the spring peak flow prediction has been an active research area. Snow models vary in degree of complexity from simple empirical models to complex physically based models. Whereas majority of studies on snowmelt modeling have focused on comparing the performance of empirical snowmelt estimation techniques with physically based methods, very few studies have investigated empirical methods and conceptual models for hydrological applications. This study investigates the potential of a simple Degree-Day Method (DDM) to effectively and accurately predict peak flows compared to sophisticated SNOW-17 model at La-Grande River Basin (LGRB), Quebec and Upper Assiniboine river at Shellmouth Reservoir (UASR), Manitoba. Moreover, since hydrologic models highly rely on estimated parameter vectors to produce accurate streamflow simulations, accurate and efficient parameter optimization techniques are essential. The study also investigates the benefits of seasonal model calibration versus annual model calibration approach. The study is performed using two hydrological models, namely MAC-HBV (McMaster University Hydrologiska Byrans Vattenbalansavdelning) and SAC-SMA (Sacramento Soil Moisture Accounting) and their model combinations thereof. Results indicate that the simple DDM performed consistently better at both study sites and showed significant improvement in prediction accuracy at UASR. Moreover, seasonal model calibration appears to be an effective and efficient alternative to annually calibrated model especially when extreme events are of particular interest. Furthermore, results suggest that SAC-SMA model outperformed MAC-HBV model, no matter what snowmelt computation method, calibration approach or study basin is used. Conclusively, DDM and seasonal model optimization approach coupled with SAC-SMA hydrologic model appears to be a robust model combination for enhanced spring peak flow prediction. A significant advantage of aforementioned modeling approach for operational hydrology is that it demonstrates computational efficiency, ease of implementation and is less time-consuming.