Millions of people worldwide rely on snow accumulation and melt for water resources. However, assessing the volume of water contained in the snowpack and its spatial and temporal change can be difficult in a changing climate because snowpack is one of the fastest changing variables in hydrologic cycle. Without accurate and timely information about the snowpack at scales relevant to hydrologic processes, snow-dominant regions are particularly susceptible to flooding or drought, with broad-reaching societal and economic implications on the security of the region. Accurate estimates of snow water equivalent (SWE), snow covered area (SCA), melt timing, and other snow properties are critical in accurately predicting runoff response for water resource management and thus aspects of water, agriculture, energy, 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 and hydrologic models have shown the ability to estimate snow properties and snowmelt-driven streamflow. 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 may not 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.
This session welcomes research on existing and novel methods of field measurements, remote sensing, physics-based models, and data assimilation, along with machine and deep learning for snow hydrology and relevant extreme events (e.g., floods, drought, and wildfire). 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.
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