Abstract

The Andes Mountains in South America span a length of 7,000 km. Rivers in snow-dominant watersheds support water supplies for ecological and human activities. Andes Mountains have sparse and unevenly distributed ground station climate data. The optical remote sensing approach has been developed as a great tool to avoid this limitation. We ran consecutive steps of temporal and spatial algorithms to reduce cloud presence over daily MODIS snow cover products (Aqua and Terra). Then, we calculate the snow persistence (SP) as the fraction of time with snow cover for each year between 2000-2021 in annual and seasonal time steps. We calculate Mann-Kendall trend analyses to identify areas of decrease, increase, or no change in SP. A large area (~60,000 km2) between 29-36°S is affected by a significant loss of snow cover (2-5 days less day of snow per year) and elevation of 15-30 m per year of the snowline. The Spring season showed the most significant SP reduction. The use of spatial and temporal algorithms over Aqua and Terra MODIS snow significantly reduced cloud presence and extended previous areas available to use the optical remote sensing data.