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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #329255

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Remote sensing of drivers of spring snowmelt flooding in the North Central US

Author
item TUTTLE, SAMUEL - University Of New Hampshire
item CHO, EUNSANG - University Of New Hampshire
item RESTREPO, PEDRO - National Oceanic & Atmospheric Administration (NOAA)
item JIA, XINHUA - North Dakota State University
item VUYOVICH, CARRIE - Collaborator
item Cosh, Michael
item JACOBS, J. - University Of New Hampshire

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 11/4/2016
Publication Date: 11/4/2016
Citation: Tuttle, S., Cho, E., Restrepo, P., Jia, X., Vuyovich, C., Cosh, M.H., Jacobs, J. 2016. Remote sensing of drivers of spring snowmelt flooding in the North Central US. In: Lakshmi, V., editor. Remote Sensing of Hydrological Extremes. Switzerland: Springer International Publishing. p. 21-45.

Interpretive Summary:

Technical Abstract: Spring snowmelt poses an annual flood risk in non-mountainous regions, such as the northern Great Plains of North America. However, ground observations are often not sufficient to characterize the spatiotemporal variation of drivers of snowmelt floods for operational flood forecasting purposes. Remote sensing platforms are well suited to non-mountainous, low vegetation areas, and can add value by providing estimates of hydrological states important for flood prediction. In this chapter, we review the use of remote sensing observations, primarily from passive microwave instruments, to constrain drivers of spring snowmelt floods, with a special focus on the Red River of the North basin in the north central United States. While many factors affect snowmelt flooding, snow water equivalent (SWE) and fall soil moisture play a significant role in determining flood severity in the region. Methods to estimate SWE and soil moisture are summarized, and past remote sensing research conducted in the region is reviewed. Considerations for incorporation of remote sensing estimates into the operational flood forecasting workflow and models are also discussed, using the NOAA National Weather Service (NWS) North Central River Forecast Center (NCRFC) as an example.