Location: Hydrology and Remote Sensing LaboratoryTitle: Remote sensing of drivers of spring snowmelt flooding in the North Central US
|TUTTLE, SAMUEL - University Of New Hampshire|
|CHO, EUNSANG - University Of New Hampshire|
|RESTREPO, PEDRO - National Oceanic & Atmospheric Administration (NOAA)|
|JIA, XINHUA - North Dakota State University|
|VUYOVICH, CARRIE - Collaborator|
|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.
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.