Poster Session 8 Snow Hydrology Applications Through Remote Sensing, Modeling, and Data Assimilation, Posters

Wednesday, 10 January 2018: 3:45 PM-5:30 PM
Exhibit Hall 3 (ACC) (Austin, Texas)
Host: 32nd Conference on Hydrology
John B. Eylander, U.S. Army Corps of Engineers, Hanover, NH; Elias J. Deeb, Cold Regions Research and Engineering Lab, Remote Sensing and GIS, Hanover, NH; Carrie Vuyovich, Cold Regions Research and Engineering Lab, Remote Sensing and GIS, Hanover, NH and Jennifer M. Jacobs, Univ. of New Hampshire, Department of Civil Engineering, Durham, NH

Snow is a valuable freshwater reservoir throughout the globe. In regions where summer precipitation is limited and infrequent, the amount of water in the snowpack is crucial as these regions rely on annual spring melt for their water supply (both potable and agricultural). Moreover, large seasonal snow packs as well as other hydrologic characteristics (including SWE, soil moisture, ground water, frozen ground, etc.) often cause extraordinary flood hazards or may impact recreation and tourism during years with limited winter precipitation. In situ observations provide point-scale estimates of snow; however, these observations are sparse and are often not suitable for global-, regional-, or even watershed-scale investigations. Terrain, vegetation, and micrometeorological conditions often interact to cause large spatial variations in snow pack properties at sub-km scales, due to differential accumulation, redistribution, and melt, complicating the estimate of snow in mountainous regions. Therefore, satellite remote sensing and land surface modeling offer a means to address both the spatial and temporal scales of the sampling problem. Existing and novel remote sensing techniques have been developed to directly estimate snow properties. Land surface and hydrologic models have shown the use of snow properties, such as snow water equivalent, as important prognostic and diagnostic variables through modeling efforts. This session invites research on existing and novel methods for remote sensing of snow properties, modeling efforts to estimate snow properties, data assimilation techniques of using remote sensing observations within a modeling construct, and combinations of these frameworks to improve snow estimation capabilities.

Evaluation of the NASA MEaSUREs Enhanced-Resolution Brightness Temperature Data in the Red River of the North Basin
Marina Reilly-Collette, U.S. Army Corps of Engineers, Hanover, NH; and C. Vuyovich, R. Schroeder, M. J. Brodzik, and J. M. Jacobs

Snow Contributions to Seasonal Streamflow Prediction Skill in the NOAA National Water Model and Pathways to Improvement
Aubrey Dugger, NCAR, Boulder, CO; and K. Rittger, K. A. Watson, L. Karsten, J. McCreight, D. J. Gochis, and B. Cosgrove

Impact of Snow Grain Shape and Internal Mixing with Black Carbon Aerosol on Snow Albedo and Radiative Effect Analysis
Cenlin He, Univ. of California, Los Angeles, CA; and K. N. Liou, Y. Takano, P. Yang, and F. Chen

High-Resolution Flood Hazard Simulation of the Red River of the North Using a Single-Pass InSAR DEM
Guy J.-P. Schumann, Univ. of Bristol, Bristol, United Kingdom; and R. Schroeder, D. Faherty, D. Moller, and J. M. Jacobs

Evaluating the Effects of Inundated Land Area on Downstream Flow Using the LIS-VIC Model
Stuart D. Smith, Purdue Univ., West Lafayette, IN; and S. Wang, A. W. Wood, M. M. DeWeese, K. A. Cherkauer, and L. C. Bowling

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