In snow-dominated basins across the globe, efficient water resource management requires accurate, timely estimates of both snow water equivalent (SWE) and snow melt onset. Melting snow provides a reliable water supply and can also produce wide-scale flooding hazards, particularly when combined with rainfall. An accurate estimate of snow volume, melt timing and the spatial distribution of both parameters is important for predicting runoff response for water resource and hydropower management as well as providing insight into important ecological and biogeochemical processes.  Remote sensing and modeling techniques provide methods for observing and detecting snow evolution, onset of snowmelt, spatial extent of melt processes, and vulnerability to extreme flood hazards that may result.  Both existing and novel remote sensing techniques have been developed to estimate snow evolution timing including the detection of liquid water in the snowpack.  Snow reconstruction and energy balance snow models have shown the ability to estimate snow properties, such as snow volume, liquid water content and melt. Observational, in-situ datasets that drive these models with meteorological inputs and modify the model through data assimilation techniques are critical in accurately portraying the natural phenomena of snow evolution. Reanalysis datasets have also proven valuable to forensically investigate large flooding events caused by snow melt. This session invites interdisciplinary research on existing and novel methods for remote sensing, modeling, and data assimilation of snow evolution, particularly snow melt timing and efforts linked to increased volume of discharge for water resource and hydropower management as well as resiliency and vulnerability to extreme flood events.