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Open AccessArticle

Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed

1
Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S4L7, Canada
2
Jointly in School of Geography and Earth Sciences and Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S4K1, Canada
3
School of Environmental Engineering, Technical University of Crete, 73100 Chania, Greece
4
Hydrologic Forecasting & Coordination, Manitoba Infrastructure, 280 Broadway, Winnipeg, MB R3C 0R8, Canada
*
Author to whom correspondence should be addressed.
Water 2019, 11(11), 2201; https://doi.org/10.3390/w11112201
Received: 11 September 2019 / Revised: 8 October 2019 / Accepted: 18 October 2019 / Published: 23 October 2019
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. View Full-Text
Keywords: hydrological models; ensemble hydrological forecasting; bias-correction; SACSMA; complex watersheds; reservoir inflow hydrological models; ensemble hydrological forecasting; bias-correction; SACSMA; complex watersheds; reservoir inflow
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Awol, F.S.; Coulibaly, P.; Tsanis, I.; Unduche, F. Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed. Water 2019, 11, 2201.

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