A bedform tracking tool coupled with Fast Fourier Transform decomposition

Loading...

Persistent link to this item

https://doi.org/10.13020/e1fe-sb56
https://hdl.handle.net/11299/218358

Statistics

View Statistics

Keywords

sediment transport

river morphodynamics

bedform

Collection period

Date completed

Date updated

Time period coverage

Geographic coverage

Source information

Journal Title

Journal ISSN

Volume Title

Title

A bedform tracking tool coupled with Fast Fourier Transform decomposition

Published Date

2021-02-12

Authors

Lee, Jiyong

Musa, Mirko

Guala, Michele

Group

Turbulent Boundary Layer plus research team

Author Contact

Lee, Jiyong
lee02291@umn.edu

Type

Dataset
Statistical Computing Software Code

Abstract

Quantifying bedform characteristics is crucial because bedforms are omnipresent and play an important role in fluvial environments. Bedforms induce form drag against flows and can significantly alter water depth, flow velocity, and sediment transport rate (i.e. the hydraulic roughness of channels can be parameterized with bedforms). In addition, ship navigation can be constrained by the presence and distributions of bedform crests; and localized scour within bedform troughs can deteriorate performance of fluvial infrastructures (e.g. containment walls, embedded pipes, or groynes). Despite of its importance, characterizing bedforms has been challenges due to inherent multi-scale features observed in channel bathymetries in both natural rivers and laboratory flumes. To tackle such challenges, we developed a bedform tracking tool coupled with Fast Fourier Transform (FFT) decomposition. A key advantage of the presented bedform tracking method is that bedform characteristics (morphology and kinematics) can be quantified in a wider range of scales.

Description

This bedform tracking tool identifies bedforms based on the sign change of the gradient of streambed profiles, dz/dx. Before fed to the bedform tracking algorithm, bed elevation profiles (BEPs) are filtered using Fast Fourier Transform (FFT) decomposition to extract large bedforms buried underneath superimposed secondary bedform features. Thereafter, characteristics (morphology and kinematics) of individual bedforms are quantified. Please refer README for more details.

Referenced by

When using this dataset, please also cite the paper:
Lee, J., Musa, M., & Guala, M. (2021). Scale‐dependent bedform migration and deformation in the physical and spectral domains. Journal of Geophysical Research: Earth Surface, e2020JF005811.
https://doi.org/10.1029/2020JF005811

Related to

Replaces

License

CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/

Publisher

Collections

Data Repository for U of M (DRUM)

Funding information

National Science Foundation (NSF) Career Grant Geophysical Flow Control (Grant No. 1351303)

item.page.sponsorshipfunderid

item.page.sponsorshipfundingagency

item.page.sponsorshipgrant

Previously Published Citation

Suggested citation

Lee, Jiyong; Musa, Mirko; Guala, Michele. (2021). A bedform tracking tool coupled with Fast Fourier Transform decomposition. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/e1fe-sb56.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.