 A proposed event-based calibration process integrating multi-site, and single and multiobjective optimizations was used to select representative SWMM5 model parameter sets in a semi-urban watershed. Four calibration approaches (Multi-site simultaneous (MS-S), Multi-site average objective function (MS-S), Multi-event multi-site (ME-MS) and a benchmark At-catchment outlet (OU)) were compared for their performances at different gauging stations. Using the single objective DDS algorithm in MS-A approach to find the best average performance of five gauging stations in the catchment area is found to be more efficient than using the multi-objective PA-DDS algorithm in MS-S to find non-dominated Pareto-front of five individual performances. The study discovered that combination of efficient optimization tools with a series of calibration approaches and steps is important in finding candidate parameters sets and representing distributed catchments by event-based hydrological models.

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...

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