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Continuous data on spatial and temporal patterns of snowmelt rates are essential for hydrological studies, but are commonly not available, especially in the subarctic, mainly due to high monitoring costs. In this study, temperature loggers were used to measure local and microscale variations in snowpack temperature, in order to understand snowmelt processes and rates in subarctic northern Finland. The loggers were deployed on six test plots along a hillslope with varying topography (elevation and aspect) and vegetation (forest, transitional zone and mires, i.e. treeless peatlands) during two consecutive winters (2014 and 2015). At each test plot, the sensors were installed in five locations, at two heights in a snow profile. Algorithms were developed to analyse the snowmelt rates from high-resolution snowpack temperature data. The validity of the results was evaluated using snow depth and soil moisture data from adjacent reference sensors and the results were tested using an empirical degree-day snow model calibrated for each test plot. Snowmelt rates were relatively similar in mires (median 2.3 mm d−1 °C−1) and forests (median 2.6 mm d−1 °C−1) with apparent inter-annual variation. The observed melt rates were highest in the highest elevation plots, in transition zone in 2014 (median 4.6 mm d−1 °C−1) and southwest-facing forest line in 2015 (median 3.2 mm d−1 °C−1). The timing of the modelled meltwater outflow and snowpack ablation showed good agreement with the snowpack temperature-derived estimates and the soil moisture and snow depth measurements. The simple approach used represents a novel and cost-effective method to improve the spatial accuracy of in situ snow cover ablation measurements and melt rates and the precision of snowmelt models in the subarctic. An open-access R-based model is provided with this paper for analysis of high-frequency snow temperature data.

Summary The time-stability concept suggests that the spatial pattern of the soil water content does not change with time when the observations are ranked according to the magnitude of the soil water. The time-stability holds for soils in agricultural fields and forest compartments, yet broader scale effects of boreal climate on time-stability are poorly understood. We studied the spatial persistence of soil water through the boreal climatic gradient (60–68°N) on the Baltic Shield using multi-year (2002–2010) automatic monitoring at sites (up to 18 in number) with diverse soil texture and land use. We found that the soils exhibited significant time-stability, Spearman’s rank correlation coefficients rs = 0.94–0.95, p

Summary Methodology was developed to estimate surface water–groundwater interactions in a changing climate in cold, snow-dominated regions and tested on an unconfined esker aquifer in northern Finland. The Watershed Simulation and Forecasting System (WSFS) model was used to simulate surface water levels. A coupled heat and mass transfer model for soil–plant–atmosphere systems (CoupModel) was used to simulate recharge. The CoupModel is able to simulate water flow through frozen soil. The simulated surface water level and recharge data were linked to the Groundwater Modelling System (GMS) version 6.5, and the three-dimensional groundwater flow model MODFLOW was used to study the impact of climate variability on groundwater levels and groundwater–surface water interactions. In addition, under A1B climate change scenario changes in aquifer storage and groundwater–surface water interactions were studied through the end of the 21st century. Recharge was shown to be sensitive to changes in soil frost and hence modelling approaches should include a soil frost component to account for the impact of frost on hydraulic conductivity. Under A1B climate change scenario study, groundwater recharge is projected to increase in winter months due to increase in snowmelt and decrease in soil frost depth. The spring snowmelt peak in late spring will decrease. This will reduce aquifer storage in early spring, increasing the vulnerability to summer droughts.