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. / Thesis / Doctor of Philosophy (PhD) / Accurate hydrological models and inputs play essential roles in creating a successful flood forecasting and early warning system. The main objective of this research is to identify adequately calibrated hydrological models and skillful weather forecast inputs to improve the accuracy of hydrological forecasting in various watershed landscapes. The key contributions include: (1) A finding that a combination of efficient optimization tools with a series of calibration steps is essential in obtaining representative parameters sets of hydrological models; (2) Simple lumped hydrological models, if used appropriately, can provide accurate and reliable hydrological forecasts in different watershed types, besides being computationally efficient; and (3) Candidate weather forecast products identified in Canada’s diverse geographical regions can be used as inputs to hydrological models for improved flood forecasting. The findings from this thesis are expected to benefit hydrological forecasting centers and researchers working on model and input improvements.