The complexities of the Prairie watersheds, including potholes, drainage interconnectivities, changing land-use patterns, dynamic watershed boundaries and hydro-meteorological factors, have made hydrological modelling on Prairie watersheds one of the most complex task for hydrologists and operational hydrological forecasters. In this study, four hydrological models (WATFLOOD, HBV-EC, HSPF and HEC-HMS) were developed, calibrated and tested for their efficiency and accuracy to be used as operational flood forecasting tools. The Upper Assiniboine River, which flows into the Shellmouth Reservoir, Canada, was selected for the analysis. The performance of the models was evaluated by the standard statistical methods: the Nash-Sutcliffe efficiency coefficient, correlation coefficient, root mean squared error, mean absolute relative error and deviation of runoff volumes. The models were evaluated on their accuracy in simulating the observed runoff for calibration and verification periods (2005-2015 and 1994-2004, respectively) and also their use in operational forecasting of the 2016 and 2017 runoff.

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.

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The Prairie Pothole Region (PPR) is known for its hydrologically complex landscape with a large number of pothole wetlands. However, most watershed-scale hydrologic models that are applied in this region are incapable of representing the dynamic nature of contributing area and fill-spill processes affected by pothole wetlands. The inability to simulate these processes represents a critical limitation for operators and flood forecasters and may hinder the management of large reservoirs. We used a modified version of the soil water assessment tool (SWAT) model capable of simulating the dynamics of variable contributing areas and fill-spill processes to assess the impact of climate change on upstream inflows into the Shellmouth reservoir (also called Lake of the Prairie), which is an important reservoir built to provide multiple purposes, including flood and drought mitigation. We calibrated our modified SWAT model at a daily time step using SUFI-2 algorithm within SWAT-CUP for the period 1991–2000 and validated for 2005–2014, which gave acceptable performance statistics for both the calibration (KGE = 0.70, PBIAS = −13.5) and validation (KGE = 0.70, PBIAS = 21.5) periods. We then forced the calibrated model with future climate projections using representative concentration pathways (RCPs; 4.5, 8.5) for the near (2011–2040) and middle futures (2041–2070) of multiple regional climate models (RCMs). Our modeling results suggest that climate change will lead to a two-fold increase in winter streamflow, a slight increase in summer flow, and decrease spring peak flows into the Shellmouth reservoir. Investigating the impact of climate change on the operation of the Shellmouth reservoir is critically important because climate change could present significant challenges to the operation and management of the reservoir.

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.2 of 27 soil moisture, and evaporation from summer to fall [1]. Relevant methodologies were proposed to assess several aspects of the hydrological cycle such as snowpack, spring melt, soil moisture, rainfall frequency, and evaporation, in the Canadian Prairie regions [2][3][4][5]. The effect of climate, land use, and ecosystem change on the hydrological processes of cold and wetland regions were also studied [6][7][8]. Even though some efforts were made to formulate the realistic representation of wetland processes in hydrological models [9][10][11][12][13], challenges of hydrological forecasting and flood predictions in such complex watersheds remain at large.Several important works have already been performed for enhancing flood prediction in several watersheds: for example, using single or multiple hydrological models [14-22], or feeding ensemble numerical weather products to models [23][24][25][26][27][28][29]. Velázquez et al. [18], for example, analyzed 16 lumped hydrological models with 50-member ensemble weather inputs. They detected that the multi-model approach of a grand member ensemble provided more...