Abstract A better understanding of cold regions hydrological processes and regimes in transitional environments is critical for predicting future Arctic freshwater fluxes under climate and vegetation change. A physically based hydrological model using the Cold Regions Hydrological Model platform was created for a small Arctic basin in the tundra-taiga transition region. The model represents snow redistribution and sublimation by wind and vegetation, snowmelt energy budget, evapotranspiration, subsurface flow through organic terrain, infiltration to frozen soils, freezing and thawing of soils, permafrost and streamflow routing. The model was used to reconstruct the basin water cycle over 28 years to understand and quantify the mass fluxes controlling its hydrological regime. Model structure and parameters were set from the current understanding of Arctic hydrology, remote sensing, field research in the basin and region, and calibration against streamflow observations. Calibration was restricted to subsurface hydraulic and storage parameters. Multi-objective evaluation of the model using observed streamflow, snow accumulation and ground freeze/thaw state showed adequate simulation. Significant spatial variability in the winter mass fluxes was found between tundra, shrubs and forested sites, particularly due to the substantial blowing snow redistribution and sublimation from the wind-swept upper basin, as well as sublimation of canopy intercepted snow from the forest (about 17% of snowfall). At the basin scale, the model showed that evapotranspiration is the largest loss of water (47%), followed by streamflow (39%) and sublimation (14%). The models streamflow performance sensitivity to a set of parameter was analysed, as well as the mean annual mass balance uncertainty associated with these parameters.

Abstract The relatively low water flow velocities in reservoirs cause them to have high capacities for retaining sediments and pollutants, which can lead to a reduction in downstream nutrient loading. Hence, nutrients can progressively accumulate in reservoirs, resulting in the deterioration of aquatic ecosystems and water quality. Lake Diefenbaker (LD) is a large multipurpose reservoir, located on the South Saskatchewan River (SSR), that serves as a major source of freshwater in Saskatchewan, Canada. Over the past several years, changes in land use (e.g. expansion of urban areas and industrial developments) in the reservoir’s catchment have heightened concerns about future water quality in the catchment and in the reservoir. Intensification of agricultural activities has led to an increase in augmented the application of manure and fertilizer for crops and pasture. Although previous research has attempted to quantify nutrient retention in LD, there is a knowledge gap related to the identification of major nutrient sources and quantification of nutrient export from the catchment at different spatial scales. Using the SPAtially Referenced Regression On Watershed (SPARROW) model, this gap has been addressed by assessing water quality regionally, and identifying spatial patterns of factors and processes that affect water quality in the LD catchment. Model results indicate that LD retains about 70% of the inflowing total nitrogen (TN) and 90% of the inflowing total phosphorus (TP) loads, of which fertilizer and manure applied to agricultural fields contribute the greatest proportion. The SPARROW model will be useful as a tool to guide the optimal implementation of nutrient management plans to reduce nutrient inputs to LD.