Abstract
Existing publicly available digital elevation models (DEMs) provide global-scale data but are often not precise enough for studying processes that depend on small-scale topographic features in rivers. For example, slope breaks and knickpoints in rivers can be important in understanding tectonic processes, and riffle-pool structures are important drivers of riverine ecology. More precise data (e.g. lidar) are available in some areas, but their spatial extent limits large-scale research. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission is planned to launch in 2021 and will provide measurements of elevation and inundation extent of surface waters between 78° north and south latitude on average twice every 21 days. We present a novel noise reduction method for multitemporal river water surface elevation profiles from SWOT that combines a truncated singular value decomposition and a slope-constrained least-squares estimator. We use simulated SWOT data of 85-145 km sections of the Po, Sacramento, and Tanana Rivers to show that 3-12 months of simulated SWOT data can produce elevation profiles with mean absolute errors of 5.38-12.55 cm at 100-200 m along-stream resolution. Mean absolute errors can be reduced further to 4-11 cm by averaging all observations. The average profiles have errors much lower than existing DEMs, allowing new advances in riverine research globally. We consider two case studies in geomorphology and ecology that highlight the scientific value of the more accurate in-river DEMs expected from SWOT. Simulated SWOT elevation profiles for the Po reveal convexities in the river longitudinal profile that are spatially coincident with the upward projection of blind thrust faults that are buried beneath the Po Plain at the northern termination of the Apennine Mountains. Meanwhile, simulated SWOT data for the Sacramento River reveals locally steep sections of the river profile that represent important habitat for benthic invertebrates at a spatial scale previously unrecognizable in large-scale digital elevation models presently available for this river.
Highlights
Accurate measurements of river water surface elevation (WSE) and slope at fine spatial scales are useful for monitoring river discharge (LeFavour and Alsdorf, 2005), calculating stream power in erosional models (Whipple and Tucker, 1999), interpreting underlying geology (Schumm, 1986), identifying knickpoints (Hayakawa and Oguchi, 2006), and characterizing habitat fragmentation for freshwater fish (Dias et al, 2013)
The application of the Constrained LRA (CLRA) method to our simulated Surface Water and Ocean Topography (SWOT) data sets decreased the mean absolute error (MAE) of every simulated profile when compared to the raw SWOT node elevations (Figure 5)
The CLRA method presented here greatly improves the node-level errors of river elevation profiles from SWOT by taking advantage of repeated measurements
Summary
Accurate measurements of river water surface elevation (WSE) and slope at fine spatial scales are useful for monitoring river discharge (LeFavour and Alsdorf, 2005), calculating stream power in erosional models (Whipple and Tucker, 1999), interpreting underlying geology (Schumm, 1986), identifying knickpoints (Hayakawa and Oguchi, 2006), and characterizing habitat fragmentation for freshwater fish (Dias et al, 2013). Even in the most heavily montiored parts of the world, the gauge network is sparse and becoming sparser (Hannah et al, 2011). This problem is worse in less developed areas like the Arctic (Shiklomanov et al, 2002). Existing global DEMs are insufficient over open water due to missing data, large vertical errors, coarse spatial resolution, or limited temporal resolution (Alsdorf et al, 2007)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
