Modeling and Analysis of Sediment Trapping Efficiency of Large Dams Using Remote Sensing

Author:

Moragoda Nishani1ORCID,Cohen Sagy1ORCID,Gardner John2ORCID,Muñoz David345ORCID,Narayanan Anuska16ORCID,Moftakhari Hamed34ORCID,Pavelsky Tamlin M.7ORCID

Affiliation:

1. Department of Geography University of Alabama AL Tuscaloosa USA

2. Department of Geology and Environmental Science University of Pittsburgh PA Pittsburgh USA

3. Department of Civil, Construction and Environmental Engineering University of Alabama AL Tuscaloosa USA

4. Center for Complex Hydrosystems Research, The University of Alabama AL Tuscaloosa USA

5. Now at Department of Civil and Environmental Engineering Virginia Tech VA Blacksburg USA

6. Now at Department of Geography University of Florida FL Gainesville USA

7. Department of Earth, Marine and Environmental Sciences University of North Carolina NC Chapel Hill USA

Abstract

AbstractQuantifying the role of sediment trapping by dams is important due to its control on fluvial and coastal geomorphology, aquatic ecology, water quality, and human water uses. Sediment trapping behind dams is a major source of bias in large‐scale hydrogeomorphic models, hindering robust analyses of anthropogenic influences on sediment fluxes in freshwater and coastal systems. This study focuses on developing a new reservoir trapping efficiency (Te) parameter to account for the impacts of dams in hydrological models. This goal was achieved by harnessing a novel remote sensing data product which offers high‐resolution and spatially continuous maps of suspended sediment concentration across the Contiguous United States (CONUS). Validation of remote sensing‐derived surface sediment fluxes against USGS depth‐averaged sediment fluxes showed that this remote sensing data set can be used to calculate Te with high accuracy (R2 = 0.98). Te calculated for 222 dams across the CONUS, using incoming and outgoing sediment fluxes from their reservoirs, range from 0.13% to 98.3% with a mean of 45.8%. Contrary to the previous understanding that large reservoirs have larger Te, remote sensing data show that large reservoirs can have a wide range of Te values. A suite of 22 explanatory variables were used to develop an empirical Te model. The strongest model predicts Te using four variables: incoming sediment flux, outgoing water discharge, reservoir length, and reservoir storage. A global model was also developed using explanatory variables obtained from a global dam database.

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

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