In the semiarid Andes of Chile, farmers and industry in the cordillera lowlands depend on water from snowmelt, as annual rainfall is insufficient to meet their needs. Despite the importance of snow cover for water resources in this region, understanding of snow depth distribution and snow mass balance is limited. Whilst the effect of wind on snow cover pattern distribution has been assessed, the relative importance of melt versus sublimation has only been studied at the point scale over one catchment. Analyzing relative ablation rates and evaluating uncertainties are critical for understanding snow depth sensitivity to variations in climate and simulating the evolution of the snowpack over a larger area and over time. Using a distributed snowpack model (SnowModel), this study aims to simulate melt and sublimation rates over the instrumented watershed of La Laguna (513 km², 3150-5630 a.s.l., 30° S, 70° W), during two hydrologically contrasting years (i.e., dry vs. wet). The model is calibrated and forced with meteorological data from nine Automatic Weather Stations (AWSs) located in the watershed and atmospheric simulation outputs from the Weather Research and Forecasting (WRF) model. Results of simulations indicate first a large uncertainty in sublimation-to-melt ratios depending on the forcing as the WRF data have a cold bias and overestimate precipitation in this region. These input differences cause a doubling of the sublimation-to-melt ratio using WRF forcing inputs compared to AWS. Therefore, the use of WRF model output in such environments must be carefully adjusted so as to reduce errors caused by inherent bias in the model data. For both input datasets, the simulations indicate a similar sublimation fraction for both study years, but ratios of sublimation to melt vary with elevation as melt rates decrease with elevation due to decreasing temperatures. Finally results indicate that snow persistence during the spring period decreases the ratio of sublimation due to higher melt rates.

The Canadian Arctic Archipelago comprises multiple small glaciers and ice caps, mostly concentrated on Ellesmere and Baffin Islands in the northern (NCAA, Northern Canadian Arctic Archipelago) and southern parts (SCAA, Southern Canadian Arctic Archipelago) of the archipelago, respectively. Because
these glaciers are small and show complex geometries, current regional climate models, using 5- to 20-km horizontal resolution, do not properly resolve surface mass balance patterns. Here we present a 58-year (1958–2015) reconstruction of daily surface mass balance of the Canadian Arctic Archipelago, statistically
downscaled to 1 km from the output of the regional climate model RACMO2.3 at 11 km. By correcting for biases in elevation and ice albedo, the downscaling method significantly improves runoff estimates over narrow outlet glaciers and isolated ice fields. Since the last two decades, NCAA and SCAA glaciers
have experienced warmer conditions (+1.1 ∘ C) resulting in continued mass loss of 28.2±11.5 and 22.0±4.5 Gt/year, respectively, more than doubling (11.9 Gt/year) and doubling (11.9 Gt/year) the pre-1996 average. While the interior of NCAA ice caps can still buffer most of the additional melt, the lack
of a perennial firn area over low-lying SCAA glaciers has caused uninterrupted mass loss since the 1980s. In the absence of significant refreezing capacity, this indicates inevitable disappearance of these highly sensitive glaciers.