Final Reminder: Snow session at virtual AMS 2021. Abstracts due Aug 31

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Final Reminder: Snow session at virtual AMS 2021. Abstracts due Aug 31

Wrzesien, Melissa

Hello Cryolist,

 

Final announcement for abstract submissions to the snow processes session at the 2021 AMS meeting -- Linking Snow Hydrology and Society through Remote Sensing, Modeling, and Data Assimilation. The abstract deadline is now Monday, August 31st.

 

To submit: https://ams.confex.com/ams/101ANNUAL/webprogrampreliminary/Session56734.html

 

The 2021 annual meeting will now be virtual. This is a great opportunity to share your work, especially if you cannot usually attend AMS due to travel/the start of the academic semester.

 

AMS has announced lower registration rates this year, due to the all virtual format.

 

Feel free to reach out if you have any questions.

 

Best,

Melissa Wrzesien ([hidden email])

Eli Deeb ([hidden email])

Carrie Vuyovich ([hidden email])

 

Linking Snow Hydrology and Society through Remote Sensing, Modeling, and Data Assimilation

In snow-dominated basins across the globe, efficient water resource management requires accurate, timely estimates of snow water equivalent (SWE), snow melt onset, and other key snow and hydrologic variables. Melting snow provides a reliable water supply and can also produce wide-scale flooding hazards, particularly when combined with rainfall. Accurate estimates of snow volume, melt timing, and other heterogeneous properties of the snow and landscape are critical in accurately predicting runoff response for water resource and hydropower management. 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. Local and regional snow models have shown the ability to estimate snow properties, such as snow mass, density, and albedo. 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. This session invites research on existing and novel methods for remote sensing, modeling, and data assimilation of snow hydrology, particularly efforts that identify and overcome gaps in the current knowledge of snow observation and modeling. We encourage submissions that connect snow accumulation and melt with human impacts, such as data assimilation to improve streamflow forecasting or operational modeling of snowmelt-derived flooding.

 

 


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