AbstractAccurate river bathymetry characterization is important to understanding all aspects of the riparian environment and provides crucial information for ensuring the safe passage of vessels and guiding channel maintenance operations. Verified models based on readily collected physical data facilitate accurate predictions of changes to a riverbed caused by traffic, weather, and other influences. This paper presents a methodology for estimating river bathymetry from surface velocity data by applying variational inverse modeling to the shallow-water equations. The paper describes the mathematical framework for the methodology and the algorithm, and the numerical tools developed to test the methodology. The hydrodynamic modeling uses 2D depth-averaged solvers (under the hydrostatic assumption) and applies a standard empirical correlation that relates depth-averaged velocity to surface velocity. The application of the bathymetry estimation algorithm to water-surface velocity data was tested on a 95-km reach of the Columbia River in Washington State. The root-mean-square error (RMSE) of the estimated bathymetry field relative to the ground truth data is approximately 2 m over the entire reach. The results of the test case indicate that this approach can be used to estimate river bathymetry to a close approximation based on the bank-to-bank surface velocity data on the reach of interest.
Journal of Atmospheric and Oceanic Technology – American Meteorological Society
Published: Jan 17, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera