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Modeling and Analysis of Sediment Trapping Efficiency of Large Dams using Remote Sensing
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  • Nishani Moragoda,
  • Cohen Sagy,
  • John R Gardner,
  • David Muñoz,
  • Anuska Narayanan,
  • Hamed R Moftakhari,
  • Tamlin M Pavelsky
Nishani Moragoda
The University of Alabama

Corresponding Author:[email protected]

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Cohen Sagy
University of Alabama, Tuscaloosa
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John R Gardner
University of Pittsburgh
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David Muñoz
University of Alabama
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Anuska Narayanan
The University of Alabama
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Hamed R Moftakhari
The University of Alabama
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Tamlin M Pavelsky
University of North Carolina at Chapel Hill
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Sediment trapping behind dams is currently a major source of bias in large-scale hydro-geomorphic 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 dataset can be used to calculate Te with high accuracy (R2 = 0.98). Te calculated for 116 dams across the CONUS, using upstream and downstream sediment fluxes from their reservoirs, range from 0.3% to 98% with a mean of 43%. Contrary to the previous understanding that large reservoirs have larger Te and vice versa, these data reveal that large reservoirs can have a wide range of Te values. A suite of 21 explanatory variables were used to develop an empirical Te model using multiple regression. The strongest model predicts Te using five variables: dam height, incoming sediment flux, outgoing water discharge, reservoir length, and Aridity Index. A global model was also developed using explanatory variables obtained from a global dam database to conduct a global-scale analysis of Te. These CONUS- and global-scale Te models can be integrated into hydro-geomorphic models to more accurately predict river sediment transport by representing sediment trapping in reservoirs.