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NASA and the French space agency, CNES, with contributions from the Canadian Space Agency (CSA) and United Kingdom Space Agency (UKSA) are developing new wide swath altimetry technology that will cover most of the world's ocean and surface freshwater bodies. The proposed Surface Water and Ocean Topography (SWOT) mission will have the capability to make observations of surface water (lakes, rivers, wetland) heights and measurements of ocean surface topography with unprecedented spatial coverage, temporal sampling, and spatial resolution compared to existing technologies. These data will be useful for monitoring the hydrologic cycle, flooding, and characterizing human impacts on a changing environment. The applied science community is a key element in the success of the SWOT mission, demonstrating the high value of the science and data products in addressing societal issues and needs. The SWOT applications framework includes a working group made up of applications specialists, SWOT science team members, academics and SWOT Project members to promote applications research and engage a broad community of potential SWOT data users. A defined plan and a guide describing a program to engage early adopters in using proxies for SWOT data, including sophisticated ocean and hydrology simulators, an airborne analogue for SWOT (AirSWOT), and existing satellite datasets, are cornerstones for the program. A user survey is in development and the first user workshop was held in 2015, with annual workshops planned. The anticipated science and engineering advances that SWOT will provide can be transformed into valuable services to decision makers and civic organizations focused on addressing global disaster risk reduction initiatives and potential science-based mitigation activities for water resources challenges of the future. With the surface water measurements anticipated from SWOT, a broad range of applications can inform inland and coastal managers and marine operators of terrestrial and oceanic phenomena relevant to their work. [ABSTRACT FROM AUTHOR]

Geographic variations in river form are often estimated using the framework of downstream hydraulic geometry (DHG), which links spatial changes in discharge to channel width, depth, and velocity through power-law models. These empirical relationships are developed from limited in situ data and do not capture the full variability in channel form. Here, we present a data set of 1:2 x 106 river widths in the Mississippi Basin measured from the Landsat-derived National Land Cover Dataset that characterizes width variability observationally. We construct DHG for the Mississippi drainage by linking digital elevation model (DEM)- estimated discharge values to each width measurement. Well-developed DHG exists over the entire Mississippi Basin, though individual sub-basins vary substantially from existing width-discharge scaling. Comparison of depth predictions from traditional depth-discharge relationships with a new model incorporating width into the DHG framework shows that including width improves depth estimates by, on average, 24 %. Results suggest that channel geometry derived from remotely sensed imagery better characterizes variability in river form than do estimates based on DHG. [ABSTRACT FROM AUTHOR]

Geographic variations in river form are often estimated using the framework of downstream hydraulic geometry (DHG), which links spatial changes in discharge to channel width, depth, and velocity through power-law models. These empirical relationships are derived from limited in situ data and do not capture the full variability in channel form. Here, we present a dataset of 1.2 x 106 river widths in the Mississippi Basin measured from the Landsat-derived National Land Cover Dataset that characterizes width variability observationally. We construct DHG for the Mississippi drainage by linking DEM-estimated discharge values to each width measurement. Well-developed DHG exists over the entire Mississippi Basin, though individual sub-basins vary substantially from existing width-discharge scaling. Comparison of depth predictions from traditional depth--discharge relationships with a new model incorporating width into the DHG framework shows that including width improves depth estimates by, on average, 24 %. Results suggest that channel geometry derived from remotely sensed imagery better characterizes variability in river form than do the assumptions of DHG. [ABSTRACT FROM AUTHOR]