Icings are widespread yet poorly understood winter hydrological phenomena that develop over the winter by freezing successive overflows of groundwater to the surface. Groundwater hydrology in arctic regions is constrained by geological setting and permafrost extent, and overflows are possibly driven by cold winters, winter warming intervals, high antecedent autumn rainfall, and low early winter snowfall. Consequently, icings are spatially recurrent but not necessarily annually nor to the same extent. We test the significance of identified meteorological forcing variables against a long‐term data set of icing dynamics and distribution we developed for the Great Slave region around Yellowknife, Northwest Territories. Climate is regionally consistent, but variable geology and permafrost create hydrological conditions representative of much of the subarctic. We mapped 5500 icings in the study area (21,887 km²) with a semiautomated approach utilizing late spring Landsat archival images (1985 to 2014). Individual icing size, ranging 3 orders of magnitude (1.8 × 10⁻³ km² to 4.1 km²), is related to return frequency. Infrequent ice (25% return frequency) accounts for 94% of the total icing area (86 km²). Winter warming intervals (≥5°C; typically over 1–3 days) and autumn rainfall (September and October) explain 28% of icing density interannual variation overall. Interannual icing variation and significant meteorological forcing variables differ among ecoregions where varied geological settings and permafrost conditions influence the hydrological regime. Future icings may develop less frequently due to decreasing winter warming intervals, but increasing autumn rainfall may increase icing density where Canadian Shield leads to strong threshold‐mediated runoff generation processes.

Area-based measurements of snow water equivalent (SWE) are important for understanding earth system processes such as glacier mass balance, winter hydrological storage in drainage basins, and ground thermal regimes. Remote sensing techniques are ideally suited for wide-scale area-based mapping with the most commonly used technique to measure SWE being passive microwave, which is limited to coarse spatial resolutions of 25 km or greater and to areas without significant topographic variation. Passive microwave also has a negative bias for large SWE. Another method is repeat-pass synthetic aperture radar interferometry (InSAR) that allows measurement of SWE change at much higher spatial resolution. However, it has not been widely adopted because (1) the phase unwrapping problem has not been robustly addressed, especially for interferograms with poor coherence, and (2) SWE change maps scaled directly from repeat-pass interferograms are not an absolute measurement but contain unknown offsets for each contiguous coherent area. We develop and test a novel method for repeat-pass InSAR-based dry-snow SWE estimation that exploits the sensitivity of the dry-snow refraction-induced InSAR phase to topographic variations. The method robustly estimates absolute SWE change at spatial resolutions of < 1 km without the need for phase unwrapping. We derive a quantitative signal model for this new SWE change estimator and identify the relevant sources of bias. The method is demonstrated using both simulated SWE distributions and a 9-year RADARSAT-2 (C-band, 5.405 GHz) spotlight-mode dataset near Inuvik, Northwest Territories (NWT), Canada. SWE results are compared to in situ snow survey measurements and estimates from ERA5 reanalysis. Our method performs well in high-relief areas, thus providing complementary coverage to passive-microwave-based SWE estimation. Further, our method has the advantage of requiring only a single wavelength band and thus can utilize existing spaceborne synthetic aperture radar systems.