The Arctic region is the most sensitive region to climate change. Hydrological models are fundamental tools for climate change impact assessment. However, due to the extreme weather conditions, specific hydrological process, and data acquisition challenges in the Arctic, it is crucial to select suitable hydrological model(s) for this region. In this paper, a comprehensive review and comparison of different models is conducted based on recently available studies. The functionality, limitations, and suitability of the potential hydrological models for the Arctic hydrological process are analyzed, including: (1) The surface hydrological models Topoflow, DMHS (deterministic modeling hydrological system), HBV (Hydrologiska Byråns Vattenbalansavdelning), SWAT (soil and water assessment tool), WaSiM (water balance simulation model), ECOMAG (ecological model for applied geophysics), and CRHM (cold regions hydrological model); and (2) the cryo-hydrogeological models ATS (arctic terrestrial simulator), CryoGrid 3, GEOtop, SUTRA-ICE (ice variant of the existing saturated/unsaturated transport model), and PFLOTRAN-ICE (ice variant of the existing massively parallel subsurface flow and reactive transport model). The review finds that Topoflow, HBV, SWAT, ECOMAG, and CRHM are suitable for studying surface hydrology rather than other processes in permafrost environments, whereas DMHS, WaSiM, and the cryo-hydrogeological models have higher capacities for subsurface hydrology, since they take into account the three phase changes of water in the near-surface soil. Of the cryo-hydrogeological models reviewed here, GEOtop, SUTRA-ICE, and PFLOTRAN-ICE are found to be suitable for small-scale catchments, whereas ATS and CryoGrid 3 are potentially suitable for large-scale catchments. Especially, ATS and GEOtop are the first tools that couple surface/subsurface permafrost thermal hydrology. If the accuracy of simulating the active layer dynamics is targeted, DMHS, ATS, GEOtop, and PFLOTRAN-ICE are potential tools compared to the other models. Further, data acquisition is a challenging task for cryo-hydrogeological models due to the complex boundary conditions when compared to the surface hydrological models HBV, SWAT, and CRHM, and the cryo-hydrogeological models are more difficult for non-expert users and more expensive to run compared to other models.

The high-resolution Climate Forecast System Reanalysis (CFSR) data have recently become an alternative input for hydrological models in data-sparse regions. However, the quality of CFSR data for running hydrological models in the Arctic is not well studied yet. This paper aims to compare the quality of CFSR data with ground-based data for hydrological modeling in an Arctic watershed, Målselv. The QSWAT model, a coupling of the hydrological model SWAT (soil and water assessment tool) and the QGIS, was applied in this study. The model ran from 1995 to 2012 with a 3-year warm-up period (1995–1997). Calibration (1998–2007), validation (2008–2012), and uncertainty analyses were performed by the Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm in the SWAT Calibration Uncertainties Program for each dataset at five hydro-gauging stations within the watershed. The objective function Nash–Sutcliffe coefficient of efficiency for calibration is 0.65–0.82 with CFSR data and 0.55–0.74 with ground-based data, which indicate higher performance of the high-resolution CFSR data than the existing scattered ground-based data. The CFSR weather grid points showed higher variation in precipitation than the ground-based weather stations across the whole watershed. The calculated average annual rainfall by CFSR data for the whole watershed is approximately 24% higher than that by ground-based data, which results in some higher water balance components. The CFSR data also demonstrates its high capacities to replicate the streamflow hydrograph, in terms of timing and magnitude of peak and low flow. Through examination of the uncertainty coefficients P-factors (≥0.7) and R-factors (≤1.5), this study concludes that CFSR data is a reliable source for running hydrological models in the Arctic watershed Målselv.

The effects of watershed subdivisions on hydrological simulations have not been evaluated in Arctic conditions yet. This study applied the Soil and Water Assessment Tool and the threshold drainage area (TDA) technique to evaluate the impacts of watershed subdivision on hydrological simulations at a 5,913-km2 Arctic watershed, Målselv. The watershed was discretized according to four TDA scheme scales including 200, 2,000, 5,000, and 10,000 ha. The impacts of different TDA schemes on hydrological simulations in water balance components, snowmelt runoff, and streamflow were investigated. The study revealed that the complexity of terrain and topographic attributes altered significantly in the coarse discretizations: (1) total stream length (−47.2 to −74.6%); (2) average stream slope (−68 to −83%); and (3) drainage density (−24.2 to −51.5%). The spatial density of weather grid integration reduced from −5 to −33.33% in the coarse schemes. The annual mean potential evapotranspiration, evapotranspiration, and lateral flow slightly decreased, while areal rainfall, surface runoff, and water yield slightly increased with the increases of TDAs. It was concluded that the fine TDAs produced finer and higher ranges of snowmelt runoff volume across the watershed. All TDAs had similar capacities to replicate the observed tendency of monthly mean streamflow hydrograph, except overestimated/underestimated peak flows. Spatial variation of streamflow was well analyzed in the fine schemes with high density of stream networks, while the coarse schemes simplified this. Watershed subdivisions affected model performances, in the way of decreasing the accuracy of monthly streamflow simulation, at 60% of investigated hydro-gauging stations (3/5 stations) and in the upstream. Furthermore, watershed subdivisions strongly affected the calibration process regarding the changes in sensitivity ranking of 18 calibrated model parameters and time it took to calibrate.

The hydrological model SWAT is a state-of-the-art tool for environmental and water resources management. Like other semi-distributed models, the whole river basin in the SWAT model is delineated into smaller sub-basins prior to conducting the simulation. Watershed delineation is an importance step since it could potentially influence the modelling results. The present study aimed to conducte an investigation of the effects of watershed delineation schemes on hydrological simulation in an Arctic watershed Målselv, north of Norway. Four delineation schemes were set up with different threshold drainage area (TDA) from fine to coarse including 100 ha, 2,000 ha, 5,000 ha and 10,000 ha. The model was run on monthly time step from 1979-2012. The results showed higher variation of average annual precipitation and runoff especially in the upstream sections of the watershed by the fine TDA schemes compared to the coarse ones. The average monthly precipitation and runoff slightly increased from the finest TDA scheme to the coarsest scheme. All TDA schemes reproduced the observed tendency of the average monthly and annual streamflow although the peak flow was over and under estimated at different hydro-gauging stations. The higher value of estimated streamflow was found at the coarsest scheme.

This study approached a physically based, semi-distributed SWAT model to test the model sensitivity to the spatial distribution of precipitation. Ten scenarios of precipitation from five scattered rain gauges in an Arctic watershed Målselv in northern Norway were used as inputs to run the SWAT model. Streamflows were simulated. The model runs at monthly time interval based on the historical data of precipitation from 1979-2012. The study used statistical parameters, values of long-term average monthly streamflow and streamflow hydrograph between simulated and observed data for sensitivity analysis. The study found that the result of streamflow simulation is highly sensitive with spatial distribution of rain gauges input. For instance, the scenarios integrating rain gauge number 3, locating inside the watershed with lower precipitation amount than average of selected rain gauges, provided model unsatisfactory (statistical coefficient NSE < 0.5) in streamflow simulation. However, streamflow simulation is satisfactory (NSE: 0.5-0.6) at hydro-gauging station Lundberg far away from rain gauge 3. The hydrograph showed underestimated streamflow simulation in scenario 3,5,6-10 that integrated rain gauge 3, while scenario 1,2,4 that excluded rain gauge 3 showed reasonable agreement between simulated and observed flow. Underestimated streamflow was only found in scenario 3 and 5 at Lundberg. Moreover, the curves of average monthly streamflow showed that the simulated peak discharge in scenario 1,2,4 was performed better than the remaining scenarios.

Investments in wind power are booming in Norway. Several wind power plants have been constructed to exploit the wind energy. However, construction of wind farms has potential impacts on the surrounding environment. Particularly, waste from construction site or activities of workers, leakage of oil and chemical would spread into the nearby waterbodies by runoff or infiltrating into ground. Hence, quality of surface water and ground water would be degraded. Raudfjell and Kvitfjell is the biggest on-shore wind power project in the north of Norway and is underconstruction. However, water quality around these two wind farms has been reported to be seriously polluted. The pollutants in the water are expected to disperse to surrounding area by runoff and accumulate at certain accumulation areas in the watershed. Therefore, it is of great importance to identify the directions of pollutants transportation and the potential vulnerable areas that may be affected by the pollution. This paper built up a D8 flow model to investigate pollutants dispersion at the small-scale watershed Bogdalen located between Raudfjell and Kvitfjell wind farm area. The model identified in detail possible single-flow directions on each cell of ground surface and detected the potential pathways where pollutants are expected to transport to and accumulate. The results stated that the model has provided promising results for pollution management.