Thesis
Estimating river sediment discharge in the upper Mississippi river using landsat imagery
Washington State University
Master of Science (MS), Washington State University
05/2020
DOI:
https://doi.org/10.7273/000000068
Handle:
https://hdl.handle.net/2376/119068
Abstract
Excessive sediment transport adversely affects the hydrologic regime and water quality
of a river and its drainage basin. With the decline of operational gauges monitoring sediments,
establishing viable means of quantifying sediment transport is pressingly needed. In this study, I
developed an alternative approach to address this issue where I applied the relationships between
hydraulic geometry of river channels, river discharge, water-leaving surface reflectance (SR), and
suspended sediment concentration (SSC) to quantify sediment discharge with the aid of spacebased observations. I investigated the approach with nine USGS gauging stations along the
Upper Mississippi River — an important river system comprising almost half of the entire
Mississippi River. I took advantage of the use of recent advances in remote sensing such as
RivWidthCloud, Bayesian discharge inference coupled with at-many-stations hydraulic
geometry (AMHG), and SSC-SR retrieval models.
I examined 5,490 Landsat scenes to estimate the water discharge, SSC levels, and
sediment discharge at nine locations along the Upper Mississippi River. Results showed that
RivWidthCloud can be effectively used for Bayesian-AMHG discharge inference while the relationships between the SSC and Landsat SR are statistically significant at significance level α
= 0.01 for all the study sites except in Clinton, IA. Acceptable gauge-specific SSC-SR model was
obtained in the downstream having coefficients of determination R
2
> 0.50. Similarly, acceptable
regional-scale model was developed with R
2
= 0.50. Further, results suggest that the three study
sites at St. Louis, MO, Chester, IL, and Thebes, IL, in the downstream portion of the river, were
the best locations for Q and SSC estimation with Landsat imagery. Estimations of Q were
sensitive to the center of Q prior, which induces bias when inadequately estimated. Relatively,
estimations of SSC were likely influenced by low reflectance of sediments during low flows due
to chlorophyll and algae mixing in the water column. Lastly, I established in this study that
combining the discharge and SSC retrieval from Landsat imagery can yield reasonable sediment
discharge estimates having an average relative bias of 0.23, RRMSE of 0.95, and NSE of 0.40 for
certain river segments in the Upper Mississippi River.
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Details
- Title
- Estimating river sediment discharge in the upper Mississippi river using landsat imagery
- Creators
- Jonathan Acero Flores
- Contributors
- JOAN QIONG WU (Degree Supervisor) - Washington State University, Biological Systems Engineering, Department ofCLAUDIO OSVALDO STOCKLE (Degree Supervisor) - Washington State University, Biological Systems Engineering, Department ofR Troy Peters (Committee Member) - Washington State University, WSU Prosser IAREC
- Awarding Institution
- Washington State University
- Academic Unit
- Biological Systems Engineering, Department of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Format
- pdf
- Number of pages
- 68
- Identifiers
- 99900590962401842
- Language
- English
- Resource Type
- Thesis