Tucker, Madeline Gabriela2025-09-222025-09-222025-09-222025-09-16https://hdl.handle.net/10012/22515Wetlands are abundant natural systems that serve as important ecosystems, mechanisms for nutrient filtering and storage, and providers of flood mitigation services. Wetlands strongly influence the hydrologic response and water balance on a landscape. The practice of water resources management often relies on numerical computer models that represent hydrologic features within a watershed like wetlands, lakes, and rivers, to accurately simulate the movement of water. However, representation of wetlands in hydrologic models is challenged by their small-scale nature, numerous classification schemes that are not readily associated with a water balance conceptual model, and sometimes complex hydrology. A shortcoming of existing wetland modelling studies includes the lack of multiple wetland types being represented, often due to the complexity that accompanies wetland classification schemes. In this study, we address three research objectives: 1) to inventory existing wetland modelling methods and develop a catalogue of conceptual-numerical wetland modelling methods in hydrology based on wetland classifications and numerical water balance equations, 2) to implement conceptual-numerical wetland modelling methods in a regional hydrologic model case study and evaluate model performance to determine the impact of wetlands on simulation results, and 3) to examine how available wetland mapping products can inform wetland modelling. A hydrologic model of the Nipissing watershed in Ontario was built using the Raven Hydrologic Framework and calibrated in a multi-objective calibration to both high and low flow objective functions in three modelling scenarios. The first modelling scenario (Scenario 1) contained no wetland representation; the second modelling scenario (Scenario 2) contained explicit wetland representation of one wetland conceptual-numerical model type; and the third modelling scenario contained explicit wetland representation of three wetland conceptual-numerical model types based on connectivity of wetlands to modelled streams and lakes. Calibration results indicated good model performance for all model scenarios, as an adequate performance threshold of 0.50 for the Kling Gupta Efficiency (KGE) and log transformed Nash Sutcliffe Efficiency (logNSE) was achieved for both performance metrics. In calibration, Scenario 2 most often outperformed Scenario 1 (no wetland scenario) at individual calibration gauges and Scenario 3 (most complex wetland scenario) due to pareto solution uncertainty and site-specific properties. Validation results indicated that Scenario 3 most often outperformed the other two scenarios across multiple performance metrics at individual flow gauges and handled low flows especially well when analyzing low flow performance metrics and hydrographs. This is attributed to Scenario 3 storing the most water in wetland depressions out of all modelling scenarios from abstraction, lateral diversion of water accounting for wetland contributing areas, and groundwater process parameters set up for each simulated wetland type. Percent bias median and spread across all flow gauges significantly decreased by 15% from Scenario 2 to Scenario 3, highlighting the importance of low flow accuracy to hydrologic model performance. Flow duration curves and hydrographs plotted by flow gauge demonstrated that site-specific properties of the entire study area and individual gauge drainage areas can impact simulation results. There was no relationship found between gauge drainage area, wetland coverage percent by area, and model performance at individual gauges in this study. Four wetland mapping datasets in Ontario were compared to select a wetland data input to the Nipissing model. By comparing each wetland dataset, a formalized checklist is provided for modellers to use as a reference when making similar comparisons between their own wetland mapping products. It is recommended that wetland mapping product comparisons for project suitability be performed by first comparing wetland coverage between datasets using the wetland polygon coverage by area, then comparing spatial variability between datasets by inspecting areas of overlap and non-overlap, and finally comparing data attributes, particularly wetland classifications and any discrepancies between dataset attributes. While the results of this study demonstrate the importance of low flow accuracy to model performance through the representation of wetlands, improvements could be made to aid future studies. It is recommended that future studies select a watershed with high quality flow and meteorological data, basins with varying wetland coverage, and little to no water regulation influence (e.g., hydroelectric dams). It is also recommended that the wetland conceptual-numerical models presented in this thesis be further tested on watersheds of different sizes, different combinations of wetland types, and varying degrees of complexity.enhydrologic modellingwetlandswetland modellinghydrologyMaster Thesis