Abstract
Accurate and reliable flow forecasting in complex Canadian prairie watersheds has been one of the major challenges faced by hydrologists. In an attempt to improve the accuracy and reliability of a reservoir inflow forecast, this study investigates structurally different hydrological models along with ensemble precipitation forecasts to identify the most skillful and reliable model. The key goal is to assess whether short- and medium-range ensemble flood forecasting in large complex basins can be accurately achieved by simple conceptual lumped models (e.g., SACSMA with SNOW17 and MACHBV with SNOW17) or it requires a medium level distributed model (e.g., WATFLOOD) or an advanced macroscale land-surface based model (VIC coupled with routing module (RVIC)). Eleven (11)-member precipitation forecasts from second-generation Global Ensemble Forecast System reforecast (GEFSv2) were used as inputs. Each of the ensemble members was bias-corrected by Empirical Quantile Mapping method using the Canadian Precipitation Analysis (CaPA) as a training/verification dataset. Forecast evaluation is performed for 1-day up to 8-days forecast lead times in a 6-month hindcast period. Results indicate that bias-correcting precipitation forecasts using verifying datasets (such as CaPA) for a training period of at least two years before the forecast time, produces skillful ensemble hydrological forecasts. A comparison of models in forecast mode shows that the two lumped models (SACSMA and MACHBV) can provide better overall forecast performance than the benchmark WATFLOOD and the macroscale Variable Infiltration Capacity (VIC) model. However, for shorter lead-times, particularly up to day 3, the benchmark distributed model provides competitive reliability, as compared to the lumped models. In general, the SACSMA model provided better forecast quality, reliability and differentiation skill than other considered models at all lead times.
Highlights
Prairie watersheds are characterized by several small depressions, potholes, and wetlands, and poorly connected drainage systems that may or may not contribute to the main river system [1].They are often featured by their long winter periods, high spring snowmelt contribution to annual runoff, deep-frozen soils and rapid infiltration, intense rainfall in spring and early summer, lowerWater 2019, 11, 2201; doi:10.3390/w11112201 www.mdpi.com/journal/waterWater 2019, 11, 2201 soil moisture, and evaporation from summer to fall [1]
It is highly likely that this trend will continue past 2016 and the near future due to anticipated climate change impact in the region and other similar factors that caused the recent high consecutive flood years
Since the challenge of achieving the accurate reservoir inflow forecasting arises during flood periods, and the objective of the paper focuses on improving the accuracy of flood forecasting in large complex watersheds, the hydrological models were trained/calibrated with the recent flood years
Summary
Prairie watersheds are characterized by several small depressions, potholes, and wetlands, and poorly connected drainage systems that may or may not contribute to the main river system [1].They are often featured by their long winter periods, high spring snowmelt contribution to annual runoff, deep-frozen soils and rapid infiltration, intense rainfall in spring and early summer, lowerWater 2019, 11, 2201; doi:10.3390/w11112201 www.mdpi.com/journal/waterWater 2019, 11, 2201 soil moisture, and evaporation from summer to fall [1]. Prairie watersheds are characterized by several small depressions, potholes, and wetlands, and poorly connected drainage systems that may or may not contribute to the main river system [1]. They are often featured by their long winter periods, high spring snowmelt contribution to annual runoff, deep-frozen soils and rapid infiltration, intense rainfall in spring and early summer, lower. Velázquez et al [18], for example, analyzed 16 lumped hydrological models with 50-member ensemble weather inputs. Pietroniro et al [27], assessed the benefit of using Environment Canada’s MESH (Modelisation Environmentale Communautaire-MEC Surface and Hydrology) model in the Great
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