Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
CR6.1 | Sensing the Seasonal Snowpack to Inform Complex Cryosphere Processes
EDI
Sensing the Seasonal Snowpack to Inform Complex Cryosphere Processes
Convener: Elias J. Deeb | Co-conveners: Juha Lemmetyinen, Robyn Barbato, HP Marshall, Jorge Jorge RuizECSECS
The global seasonal snowpack represents the largest component of the Cryosphere in terms of areal extent and, in turn, has a large control on the surface energy balance and provides more than one-sixth of the world’s population with their annual water supply. From snow microstructure and the microbes that may live within (or below), to bulk snow properties that impact the amount and timing of seasonal runoff, properties of the snowpack vary dramatically across space through a variety of snow climates and through time from sub-daily to seasonal scales for a given year. In-situ and field measurements of the seasonal snowpack provide snapshots of snow properties across small geographic areas at point and transect scales; however, the processes governing some of these relationships and optimum sampling strategies remain poorly understood. Sensing the seasonal snowpack through remote methods (e.g., satellite, airborne, UAS, and other non-destructive field instrumentation) provide valuable insights at spatially and temporally relevant scales to better inform our understanding of complex cryosphere processes. These measurements are critical to assess changes in snowpack that are accelerated by climate change.

This session seeks a broad set of contributions that represent state-of-the-art and novel methods used to remotely sense properties of the seasonal snowpack in addition to field-based methods to measure snow phenomena. Moreover, abstracts are encouraged across a variety of temporal and spatial scales, remote sensing modalities and field observations, and interdisciplinary application areas. Contributions that integrate/assimilate remotely sensed data sets of snowpack properties into modeling infrastructure as well as those that address methods of processing large data sets or historical archives are welcomed. Other submissions that address opportunities for snow remote sensing from planned or proposed satellite missions are also encouraged.