Evaluation of Snowmelt Estimation Techniques for Enhanced Spring Peak Flow Prediction

Jetal Agnihotri,Paulin Coulibaly

doi:10.3390/w12051290

Abstract

Water resources management and planning requires accurate and reliable spring flood forecasts. In cold and snowy countries, particularly in snow-dominated watersheds, enhanced flood prediction requires adequate snowmelt estimation techniques. Whereas the majority of the studies on snow modeling have focused on comparing the performance of empirical techniques and physically based methods, very few studies have investigated empirical models and conceptual models for improving spring peak flow prediction. The objective of this study is to investigate the potential of empirical degree-day method (DDM) to effectively and accurately predict peak flows compared to sophisticated and conceptual SNOW-17 model at two watersheds in Canada: the La-Grande River Basin (LGRB) and the Upper Assiniboine river at Shellmouth Reservoir (UASR). Additional insightful contributions include the evaluation of a seasonal model calibration approach, an annual model calibration method, and two hydrological models: McMaster University Hydrologiska Byrans Vattenbalansavdelning (MAC-HBV) and Sacramento Soil Moisture Accounting model (SAC-SMA). A total of eight model scenarios were considered for each watershed. Results indicate that DDM was very competitive with SNOW-17 at both the study sites, whereas it showed significant improvement in prediction accuracy at UASR. Moreover, the seasonally calibrated model appears to be an effective alternative to an annual model calibration approach, while the SAC-SMA model outperformed the MAC-HBV model, no matter which snowmelt computation method, calibration approach, or study basin is used. Conclusively, the DDM and seasonal model calibration approach coupled with the SAC-SMA hydrologic model appears to be a robust model combination for spring peak flow estimation.

Highlights

Accurate time- and site- specific spring flood forecasting is essential for water resources management and planning [1]
Annual model optimization improves the median of Nash-Sutcliffe efficiency (NSE) for MAC-HBV-degree-day method (DDM), Sacramento Soil Moisture Accounting model (SAC-SMA)-DDM, and SAC-SMA-SNOW17 models marginally when evaluated for the calibration period
The median of NSEs drastically increase by 56%, 17%, and 28% when the seasonal MAC-HBV-SNOW17, SAC-SMA-DDM, and SAC-SMA-SNOW17 models are used as compared to annual models for the validation period, respectively

Summary

Introduction

Accurate time- and site- specific spring flood forecasting is essential for water resources management and planning [1]. Reliable spring flow predictions are needed by operators of hydropower reservoirs as well as by water resources managers. They hold considerable economic value through enhanced operational decision making, efficient hydroelectric power generation, reduced downstream flood occurrences, and better managed hydraulic structures. Despite these advantages, reliable spring flow prediction remains challenging. A few techniques that can be adopted for improving the spring flow prediction accuracy include: 1) enhancing meteorological forecast and hydrometric input data (i.e., different input forcing [1,2]); 2) improving optimization techniques Water 2020, 12, 1290 optimal model parameters [3,4]); 3) using multi-models (i.e., use different models and inter-compare their performances [5,6,7]) and 4) Modifying complexity of physical processes governing the hydrologic model structure (e.g., snowmelt routing or other representative water balance component [4,5,7,8,9]).

Objectives

Methods

Results

Discussion

Conclusion