Identification of Hydrologic Models, Inputs, and Calibration Approaches for Enhanced Flood Forecasting

Abstract

The primary goal of this research is to evaluate and identify proper calibration approaches, skillful hydrological models, and suitable weather forecast inputs to improve the accuracy and reliability of hydrological forecasting in different types of watersheds. The research started by formulating an approach that examined single- and multi-site, and single- and multi-objective optimization methods for calibrating an event-based hydrological model to improve flood prediction in a semi-urban catchment. Then it assessed whether reservoir inflow in a large complex watershed could be accurately and reliably forecasted by simple lumped, medium-level distributed, or advanced land-surface based hydrological models. Then it is followed by a comparison of multiple combinations of hydrological models and weather forecast inputs to identify the best possible model-input integration for an enhanced short-range flood forecasting in a semi-urban catchment. In the end, Numerical Weather Predictions (NWPs) with different spatial and temporal resolutions were evaluated across Canada’s varied geographical environments to find candidate precipitation input products for improved flood forecasting. Results indicated that aggregating the objective functions across multiple sites into a single objective function provided better representative parameter sets of a semi-distributed hydrological model for an enhanced peak flow simulation. Proficient lumped hydrological models with proper forecast inputs appeared to show better hydrological forecast performance than distributed and land-surface models in two distinct watersheds. For example, forcing the simple lumped model (SACSMA) with bias-corrected ensemble inputs offered a reliable reservoir inflow forecast in a sizeable complex Prairie watershed; and a combination of the lumped model (MACHBV) with the high-resolution weather forecast input (HRDPS) provided skillful and economically viable short-term flood forecasts in a small semi-urban catchment. The comprehensive verification has identified low-resolution NWPs (GEFSv2 and GFS) over Western and Central parts of Canada and high-resolution NWPs (HRRR and HRDPS) in Southern Ontario regions that have a promising potential for forecasting the timing, intensity, and volume of floods.