Hydroclimate and vegetation variability of high Andean ecosystems

Mountain ecosystems are sensitive to climate fluctuations; however, the scarcity of instrumental data makes necessary the use of complementary information to study the effect of climate change on these systems. Remote sensing permits studying the dynamics of vegetation productivity and wetlands in response to climate variability at different scales. In this study we identified the main climate variables that control vegetation dynamics and water balance in Cumbres Calchaquíes, NW Argentina. For this, we built annual time series from 1986 to 2019 of Soil Adjusted Vegetation Index (SAVI, to quantify spare vegetation productivity), lake area, and snow-ice cover of peatlands, as indicators of mountain productivity and hydrology. We used a decompose function to explore trend, seasonality and random signal of the three-time series, and explored for significant changes in the mean value of consecutive periods. We used correlational analysis to explore their associations with climate records at local, regional, and global scales. The results showed that, SAVI and hydrological indicators presented different fluctuation patterns more pronounced since 2012, when they showed divergent trends with increasing SAVI and decreasing lake area and snow-ice cover. The three indicators responded differently to climate; SAVI increased in warmer years and lake area reflected the water balance of previous years. Snow-ice cover of peatlands was highly correlated with lake area. La Niña had a positive effect on lake area and snow-ice cover and a negative on SAVI, while El Niño had a negative effect on SAVI. Fluctuations of lake areas were synchronized with lake area in the nearby Argentinian puna, suggesting that climate signals have regional extent. The information provided by the three hydroclimate indicators is complementary and reflects different climate components and processes; biological processes (SAVI), physical processes (snow ice cover) and their combination (lake area). This study provides a systematic accessible replicable tool for mountain eco-hydrology long-term monitoring.