J10.4 Sensitivity Analysis of passive microwave brightness temperatures to distributed snowmelt

Tuesday, 12 January 2016: 9:15 AM
Room 240/241 ( New Orleans Ernest N. Morial Convention Center)
Carrie Vuyovich, U.S. Army Corps of Engineers, Hanover, NH; and J. Jacobs, C. A. Hiemstra, E. Deeb, S. F. Daly, and J. B. Eylander

Melting snow provides an essential source of water in many regions of the world and can also produce devastating, wide-scale flooding, particularly when combined with rainfall precipitation. However, measurement of snowmelt distribution and evolution can be challenging given the heterogeneous and dynamic nature of snow. Remote sensing offers a potentially viable way of detecting liquid water content (LWC) in the snow across a distributed landscape. Global datasets of recorded passive microwave emissions provide non-destructive, daily information on snow processes, and the microwave signal is highly responsive to snow wetness due to the sensitivity of the radiance to changes in the dielectric constant. A key challenge to using the microwave melt signal is that its spatial resolution is quite coarse and not able to explicitly characterize sub-grid scale variations needed for most water resource applications. The objective of this research is to test the sensitivity of brightness temperatures within a microwave pixel as it relates to spatially distributed liquid water content of the snowpack. Daily snow states are simulated for a 14-year period using a high-resolution (50 m) energy balance snow model over a 25x25 km pixel. These data are fed into a microwave emission model to simulate brightness temperatures and compared to AMSR-E passive microwave satellite data at the pixel scale for validation. A sensitivity analysis is conducted by adjusting the percent area and spatial distribution of wet snow, as well as other snowpack characteristics. The final result is a relationship between the change in microwave brightness temperature and the percent area affected by liquid water content in the snowpack, which can then be used to understand the hydrological impact of large-scale snowmelt events as detected by passive microwave data.
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