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Cold climate regions provide a multitude of ecosystem services. However, cold regions under a changing climate could be more vulnerable than others because their glaciers, freezing soils and peatlands are sensitive to the slightest of changes in climate. This has posed serious threats to the water resources, sustainable goods production and ecosystem services that depend on regional water quality. Therefore, proper watershed management is imperative. In this paper, we investigate this issue in a cold climate watershed in central Alberta, Canada with the main objective of quantifying the impacts of climate change on water quality status. We modified specific water quality related processes of a process-based model-Soil and Water Assessment Tool (SWAT) with a view of better representing the reality of cold climate regions. A SWAT model is then built-up, followed up by a multi-site and multi-objective (streamflow, sediment and water quality) calibration, validation and uncertainty analysis in a baseline period (1983-2013). The calibrated and validated model is then fed with a high spatial resolution (25 km) daily future climate datathe CanRCM4. Improvements on stream water temperature (Ts) and dissolved oxygen (DO) simulations justified the modifications. This model is able to simulate the dynamics of other water quality variables (carbonaceous biochemical oxygen demand-cBOD, total nitrogen-TN and phosphorus-TP) with a wide range of accuracy (very good to satisfactory) in the base period. Agriculture areas account for the highest amount of annual TN (11.16 kgN/ha) and TP (2.88 kgP/ha) yield rate in the base period leading to poor water quality status in the immediate downstream reaches. The situation would be further exacerbated (16.52 kgN/ha and 4.89 kgP/ha) in future. Finally, we tested different alternative management options to compare the water quality status of the Athabasca River Basin (ARB) under a changing climate. Significant reduction in future nutrient concentrations (~ 20% on TN and 60% on TP) can be achieved using a certain combination of management practices and the ecological status of the basin can be improved. This demonstrates that the modified SWAT model can be applied to other cold climate regions, and that the results can be translated to help in managing the ARB in a more holistic way.

Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

This paper focuses on understanding the effects of projected climate change on streamflow dynamics of the Grand and Thames rivers of the Northern Lake Erie (NLE) basin. A soil water assessment tool (SWAT) model is developed, calibrated, and validated in a base-period. The model is able to simulate the monthly streamflow dynamics with ‘Good’ to ‘Very Good’ accuracy. The calibrated and validated model is then subjected with daily bias-corrected future climatic data from the Canadian Regional Climate Model (CanRCM4). Five bias-correction methods and their 12 combinations were evaluated using the Climate Model data for hydrologic modeling (CMhyd). Distribution mapping (DM) performed the best and was used for further analysis. Two future time-periods and two IPCC AR5 representative concentration pathways (RCPs) are considered. Results showed marked temporal and spatial variability in precipitation (−37% to +63%) and temperature (−3 °C to +14 °C) changes, which are reflected in evapotranspiration (−52% to +412%) and soil water storage (−60% to +12%) changes, resulting in heterogeneity in streamflow (−77% to +170%) changes. On average, increases in winter (+11%), and decreases in spring (–33%), summer (−23%), and autumn (−15%) streamflow are expected in future. This is the first work of this kind in the NLE and such marked variability in water resources availability poses considerable challenges to water resources planners and managers.

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