Surface reflectance simulations of fresh and aged snow with light absorbing impurities

Monitoring the spatial and temporal changes of seasonal snow cover helps to predict and mitigate floods, avalanches, frost damage, among other hazards. It can also help estimating the supply for human consumption of fresh water, irrigation and hydropower stations, and may contribute to the improvement of weather forecasts and the understanding of the climate system. However, global warming and the effect of Light Absorbing Impurities (LAIs) are affecting the spatial and temporal variability of snowpacks and its complex processes and environmental interactions. This study is a first approach for assessing the impact of LAIs in the Argentinean snowpacks albedo through radiative transfer models. The results suggest that determining the state of the snowpack before estimating the presence, concentration and type of impurity is essential due to the fact that the albedo simulations vary significantly according to the snow grain size. Additionally, change percentage from unpolluted fresh and aged snow was calculated for snowpacks with high concentrations of black carbon, dust and ash considering the impurity values found in South America. Finally, the albedo results obtained with the SNICAR model were compared with the Landsat-8 relative spectral response in order to assess the capabilities of the sensor to estimate the presence of LAIs, which showed similar results than the ones obtained with SNICAR.