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The frost table depth is a critical state variable for hydrological modelling in cold regions as frozen ground controls runoff generation, subsurface water storage and the permafrost regime. Calculation of the frost table depth is typically performed using a modified version of the Stefan equation, which is driven with the ground surface temperature. Ground surface temperatures have usually been estimated as linear functions of air temperature, referred to as ‘n-factors’ in permafrost studies. However, these linear functions perform poorly early in the thaw season and vary widely with slope, aspect and vegetation cover, requiring site-specific calibration. In order to improve estimation of the ground surface temperature and avoid site-specific calibration, an empirical radiative–conductive–convective (RCC) approach is proposed that uses air temperature, net radiation and antecedent frost table position as driving variables. The RCC algorithm was developed from forested and open sites on the eastern slope of the Coastal Mountains in southern Yukon, Canada, and tested at a high-altitude site in the Canadian Rockies, and a peatland in the southern Northwest Territories. The RCC approach performed well in a variety of land types without any local calibration and particularly improved estimation of ground temperature compared with linear functions during the first month of the thaw season, with mean absolute errors

The hydrological sensitivity of a northern Canadian mountain basin to change in temperature and precipitation was examined. A physically based hydrological model was created and included important snow and frozen soil infiltration processes. The model was discretized into hydrological response units in order to simulate snow accumulation and melt regimes and basin discharge. Model parameters were drawn from scientific studies in the basin except for calibration of routing and drainage. The model was able to simulate snow surveys and discharge measurements with very good accuracy. The forcing inputs of the hourly air temperatures and daily precipitation were scaled linearly to examine the model sensitivity to conditions included in a range of climate change scenarios: warming of up to 5 °C and change in precipitation of +/− 20%. The results show that peak seasonal snow accumulation, snow season length, evapotranspiration, runoff, peak runoff, and the timing of peak runoff have a pronounced sensitivity to both warming and precipitation change, where the impact of warming is partly compensated for by increased precipitation and dramatically enhanced by decreased precipitation. The snow regime, including peak snow accumulation, snow-free period, intercepted snow sublimation, and blowing snow transport, was most sensitive to temperature, and the impact of a warming of 5 °C could not be compensated for by a precipitation increase of 20%. However, basin discharge was more sensitive to precipitation, and the impact of warming could be compensated for by a slight increase in precipitation. The impacts of 5 °C warming with a +/−20% change in precipitation resulted in snow accumulation, runoff, and peak streamflow decreasing by from one half to one fifth and the snow-free period lengthening by from 46 to 60 days; in both cases, the smaller change is associated with increased precipitation and the larger change with decreased precipitation. These results show that mountain hydrology in Northern Canada is extremely sensitive to warming, that snow regime is more sensitive to warming than streamflow and that changes in precipitation can partly modulate this response. Copyright © 2014 John Wiley & Sons, Ltd.