The Prairie Pothole Region (PPR) of Canada contains millions of small isolated wetlands and is unique to North America. The goods and services of these isolated wetlands are highly sensitive to variations in precipitation and temperature. We evaluated the flood proofing of isolated wetlands (pothole wetlands) under various climate change scenarios in the Upper Assiniboine River Basin (UARB) at Kamsack, a headwater catchment of the Lake of the Prairies in the Canadian portion of the PPR. A modified version of the Soil Water Assessment Tool (SWAT) model was utilized to simulate projected streamflow under the potential impacts of climate change, along with changes to the distribution of pothole wetlands. Significant increases in winter streamflow (~200%) and decreases (~11%) in summer flow, driven by changes in future climates, were simulated. Simulated changes in streamflow resulting from pothole removal were between 55% for winter and 15% for summer, suggesting that climate will be the primary driver in the future hydrologic regime of the study region. This research serves as an important guide to the various stakeholder organizations involved in quantifying the aggregate impacts of pothole wetlands in the hydrology of the Canadian Prairie Region.
Spatial accuracy of hydrologic modeling inputs influences the output from hydrologic models. A pertinent question is to know the optimal level of soil sampling or how many soil samples are needed for model input, in order to improve model predictions. In this study, measured soil properties were clustered into five different configurations as inputs to the Soil and Water Assessment Tool (SWAT) simulation of the Castor River watershed (11-km2 area) in southern Quebec, Canada. SWAT is a process-based model that predicts the impacts of climate and land use management on water yield, sediment, and nutrient fluxes. SWAT requires geographical information system inputs such as the digital elevation model as well as soil and land use maps. Mean values of soil properties are used in soil polygons (soil series); thus, the spatial variability of these properties is neglected. The primary objective of this study was to quantify the impacts of spatial variability of soil properties on the prediction of runoff, sediment, and total phosphorus using SWAT. The spatial clustering of the measured soil properties was undertaken using the regionalized with dynamically constrained agglomerative clustering and partitioning method. Measured soil data were clustered into 5, 10, 15, 20, and 24 heterogeneous regions. Soil data from the Castor watershed which have been used in previous studies was also set up and termed “Reference”. Overall, there was no significant difference in runoff simulation across the five configurations including the reference. This may be attributable to SWAT's use of the soil conservation service curve number method in flow simulation. Therefore having high spatial resolution inputs for soil data may not necessarily improve predictions when they are used in hydrologic modeling.
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