PICO
Seasonal snow constitutes a freshwater resource for over a billion people worldwide. Climate warming poses a significant risk to snow water storage, potentially leading to a drastic reduction in water supply and causing adverse effects on the ecosystems. Therefore, understanding seasonal snow dynamics, possible changes, and their implications has become crucial for effective water resources 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 technology is essential for monitoring snow properties and their hydrological impacts, enabling a better understanding of the interaction between snow and its environment at a small scale, rapid snow changes, rain-on-snow events, and snow-vegetation interactions.
This session focuses on studies linking remote sensing of seasonal snow to hydrological applications to: (i) quantify snow characteristics (e.g., SWE, snow grain size, albedo, pollution load, snow cover area, snow depth and snow density), (ii) understand and model snow-related processes and dynamics (snowfall, melting, evaporation, wind redistribution and sublimation), (iii) assess the snow hydrological impacts and snow environmental effects.
We welcome contributions that integrate methods and data from diverse technologies, including time-lapse imagery, laser scanning, radar, optical photography, thermal and hyperspectral sensing, as well as emerging applications, across a range of spatial scales (from plot-level to global) and temporal scales (from instantaneous observations to multi-year time series).