Description:

Millions of people worldwide rely on snow accumulation and melt for water resources. Particularly in very remote regions, in-situ snow data are rarely available due to financial constraints and safety concerns. Thus, assessing the volume of water contained in the snowpack can be difficult but is extremely important for water resources management. Without information about the snowpack, these regions are particularly susceptible to flooding or drought, which may have broad reaching political and economic implications on the security of the region. Accurate estimates of snow water equivalent (SWE), snow covered area (SCA), melt timing, and other properties of snow are critical in accurately predicting runoff response for water resource management and thus aspects of water, agriculture, and societal stability. Remote sensing and modeling techniques provide methods for observing and detecting snow evolution, onset of snowmelt, and spatial extent. Existing and novel remote sensing techniques have been employed to observe snow characteristics. Local and regional snow models have shown the ability to estimate snow properties, such as snow volume, liquid water content, and melt. In-situ datasets that drive these models with meteorological inputs and modify the model through data assimilation techniques are critical in accurately portraying snow evolution. A single sensor, field measurement, or model likely cannot accurately represent all types of snow globally; instead, integrative approaches are needed for capturing a complete spatiotemporal understanding of snow conditions and relevant hydrological processes.  Following with the theme of the 103rd Annual Meeting, improved understanding of snow conditions and properties is highly important for “Driving Science, Informing Decisions, and Enriching Humanity

This session invites research on existing and novel methods of field measurements; unpiloted aerial system (UAS), airborne, and satellite remote sensing; modeling; and data assimilation/analytics along with machine and deep learning (ML/DL) for snow hydrology and relevant extreme events (e.g., floods, drought, and wildfire). Particularly, we encourage submissions that aim to overcome gaps in the current knowledge of snow observation and modeling and/or consider data merging environments for integration of in situ, remote sensing, and model data.