Imagery acquired by the Moderate-resolution Imaging Spectroradiometer (MODIS) provides a global archive of dailyNormalized Difference Snow Index (NDSI) at 500 m nominal resolution since the year 2000. While Sentinel-2 (S2) NDSI provides an increased spatial resolution of 20 m since the year 2015, the temporal resolution amounts to only 5 days and thus lacks the high temporal resolution of MODIS. Efforts to combine NDSI datasets for an increased temporal and spatial resolution have so far focused on the deriving binary snow cover maps or combining data from other sensors. In contrast, we produce fine scale (20 m) fractional snow cover (FSC) by downscaling MODIS NDSI to S2 resolution. Random forest regression predicts S2 NDSI based on dynamic features (MODIS NDSI, day-of-year) and static, topographic features for an alpine study site. Subsequently, FSC is derived from S2 NDSI. Cross-validation results in R2 of 0.795 and RMSE of 0.155 for FSC and outperforms common resampling methods. Multi-annual S2 NDSI metrics are able to slightly improve model accuracy. Our results suggest that combining topographical data and low-resolution NDSI allows to produce daily, high-resolution S2 NDSI and FSC and improve fine scale characterization of snow cover dynamics in mountain landscapes.@en

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.@en

This paper explores the reliability of a hydrological modeling framework in a mesoscale (1515 km2) catchment of the dry Andes (30° S) where irrigation water use and snow sublimation represent a significant part of the annual water balance. To this end, a 20-year simulation period encompassing a wide range of climate and water-use conditions was selected to evaluate three types of integrated models referred to as A, B and C. These models share the same runoff generation and routing module but differ in their approach to snowmelt modeling and irrigation water use. Model A relies on a simple degree-day approach to estimate snowmelt rates and assumes that irrigation impacts can be neglected at the catchment scale. Model B ignores irrigation impacts just as Model A but uses an enhanced degree-day approach to account for the effects of net radiation and sublimation on melt rates. Model C relies on the same snowmelt routine as Model B but incorporates irrigation impacts on natural streamflow using a conceptual irrigation module. Overall, the reliability of probabilistic streamflow predictions was greatly improved with Model C, resulting in narrow uncertainty bands and reduced structural errors, notably during dry years. This model-based analysis also stressed the importance of considering sublimation in empirical snowmelt models used in the subtropics, and provided evidence that water abstractions from the unregulated river are impacting on the hydrological response of the system. This work also highlighted areas requiring additional research, including the need for a better conceptualization of runoff generation processes in the dry Andes. @en