Spatiotemporal analysis of snow cover dynamics in small and medium-sized mountainous basins of Armenia using satellite imagery

In the mountainous regions of Armenia, the formation of maximum river flow is heavily influenced by seasonal snowmelt processes. For small and medium-sized river basins, which often lack ground-based gauging stations, understanding the timing and duration of snow cover is critical for flood risk assessment and water resource management. This study focuses on monitoring snow cover patterns over the last decade using Sentinel-2 satellite data.

The research utilizes the Normalized Difference Snow Index (NDSI) to accurately identify snow-covered areas across diverse topographic gradients. The primary objective is to establish a 10-year baseline for snow phenology, specifically identifying the "First Snow Day" (onset) and the "Last Snow Day" (melt-off) for several pilot basins in Armenia. By processing multi-temporal image stacks through Google Earth Engine (GEE), the study analyzes the rate of snow depletion during the spring season.

The research aims to quantify the inter-annual variability in snow duration as a function of shifting temperature patterns and elevation gradients. By establishing this 10-year baseline, the study expects to demonstrate that the timing of the final snowmelt can serve as a primary proxy indicator for predicting maximum flows in ungauged catchments. This remote sensing approach intends to provide a robust, cost-effective alternative to traditional monitoring, offering a scalable tool for modeling peak flows in data-scarce environments. Ultimately, the integration of these satellite-derived snow dynamics into hydrological frameworks will enhance the accuracy of flood risk mapping.