Sensitivity of Sediment Transport Analyses in Dam Removal Applications

Abstract

Dam removal has become a widespread river management practice in the US for a variety of goals including ecosystem restoration, removing aging infrastructure, flood risk management, and recreation. The ability to forecast the sediment impacts of dam removal is critical to evaluating different management alternatives that can minimize adverse consequences for ecosystems and human communities. Tullos et al. (2016) identified seven Common Management Concerns (CMCs) associated with dam removal. Four of these CMCs; degree and rate of reservoir sediment erosion, excessive channel incision upstream of reservoirs, downstream sediment aggradation, and elevated downstream turbidity are associated with stored sediment release and changing fluvial hydraulics. There are a range of existing qualitative and quantitative tools developed to infer or quantify geomorphic implications of disturbances like these in river environments (McKay et al. 2019). This study investigated how a one-dimensional (1D) sediment transport model can inform these four CMCs, develop an approach for assessing sediment transport model sensitivity in the context of the Simkins Dam removal, and use sensitivity analyses to identify key uncertainties, which can inform data collection and model building for other dam removal projects. For the selected case study, model outputs including the mean effective invert change (MEIC) and eroded sediment volume from reservoir were highly sensitive to the variation of the reservoir sediment gradation and sorting method selection. These model outputs also showed some sensitivity to the selected transport functions. Erosion method sensitivity using the channel evolution method will vary depending on side slope and channel parameter selection.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2023
Accession Number
AD1210482

Entities

People

  • S. Kyle McKay
  • Susan E. Bailey
  • Waleska Echevarria-doyle

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Case Studies
  • Civil Engineering
  • Coordinate Systems
  • Dams
  • Drainage Basins
  • Ecology
  • Ecosystems
  • Engineering
  • Engineers
  • Environment
  • Flood Control
  • Flood Hazards
  • Floods
  • Geometry
  • Hydraulics
  • Information Systems
  • Monitoring
  • Natural Resources
  • Risk Management
  • Sedimentation
  • Sediments
  • Sensitivity
  • Water Resources

Fields of Study

  • Environmental science

Readers

  • Coastal Oceanography
  • Computational Modeling and Simulation
  • Hydraulic Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy