Climate warming poses a significant risk to seasonal snow water storage, which impacts over a billion people worldwide. Snowpack shrinkage results in a drastic reduction in meltwater supply, a temporal shift in runoff, and a decrease in its seasonal predictability. Therefore, understanding seasonal snow dynamics, the potential changes, and their implications is crucial for effective water resource management.
Remote sensing of seasonal snow is a key tool in this regard, as it provides a wide range of techniques and data across various spatial and temporal scales. This session focuses on studies linking remote sensing of seasonal snow to hydrological applications. The works presented will aim to (i) monitor, model and understand snow-related processes and dynamics (snowfall, melting, evaporation, wind redistribution and sublimation), (ii) quantify the snowpack characteristics (e.g., snow water equivalent, snow grain size, albedo, light-absorbing particles, snow cover area, snow depth and snow density), (iii) assess hydrological impacts of the snowpack dynamics.
We welcome contributions that integrate methods and data from traditional or from emerging technologies, including time-lapse imagery, laser scanning, radar, passive microwave, optical photography, photogrammetry, thermal and hyperspectral sensing. All types of platform and all spatial and temporal scales of studies are welcome, from UAV plot-level studies to global multi-year time series of satellite observations.