Model Library

Environmental Fluid Dynamics Code (EFDC)

Model name: Environmental Fluid Dynamics Code (EFDC)

Developed by: Dr. John M. Hamrick at the Virginia Institute of Marine Science (VIMS) and School of Marine Science of The College of William and Mary, later adopted by U.S. Environmental Protection Agency (EPA) and the National Oceanic and Atmospheric Administration's Sea Grant Program. Tetra Tech, Inc. (Last update: 2007)

Model type: 1D/2D/3D, distributed, deterministic, hydrodynamic waterbody model

Computational requirements: Windows 95/98/NT/2K/XP.

Link to download model

Capabilities and Limitations:

Capabilities

  • Its ability to simulate wetting and drying cycles (U.S. EPA, n.d.);
  • Including near field mixing zone model that is fully coupled with a far field transport of salinity, temperature, sediment, contaminant, and eutrophication variables (U.S. EPA, n.d.);
  • Containing hydraulic structure representation, vegetative resistance, and Lagrangian particle tracking (U.S. EPA, n.d.);
  • Allowing the simulation of longshore currents and wave-induced sediment transport (U.S. EPA, n.d.).

Limitations

  • The model has a large spatial resolution (approximately 1,000 m long). It is not suitable for appropriate simulation of any occurrence with a spatial scale smaller than the cell width and length. Such modeling practices necessitate smaller scales (Environment, 2009);
  • The model does not include Zooplankton and detritus compartments (Zouiten et al., 2013; Jiang et al., 2018).
  • EFDC does not directly import DEM files (like .tif or .asc). Instead, DEM data is processed using GIS tools (like ArcGIS, QGIS, or MATLAB) to create a bathymetric grid. This processed elevation data is then converted into EFDC-compatible files (like CELL.INP and GEFDC.INP) to define the depth and geometry of the model's computational domain.

Model Inputs and Outputs: 

Inputs

Grid parameters, Bathymetry, Initial conditions, Boundary conditions, Physical parameters, Meteorological data, Hydrological data, Water quality data

Outputs

  • Time-series hydrodynamic simulation.
  • Time-series sediment transport, salinity, and water quality simulation (N, P, Organic carbon, DO, COD, TAM, FCB, Nutrient sediment fluxes, Algae, Silica).
  • 2D and 3D Visualization.
  • Mass Balance Reports.
  • Model Diagnostics.
  • Analysis of scenario simulations.

Examples:

References

Shin, S., Her, Y., Muñoz-Carpena, R., & Yu, X. (2023). Quantifying the contribution of external loadings and internal hydrodynamic processes to the water quality of Lake Okeechobee. Science of The Total Environment, 883, 163713. https://doi.org/10.1016/j.scitotenv.2023.163713

Ji, N., Zou, R., Jiang, Q., Liang, Z., Hu, M., Liu, Y., Yu, Y., Wang, Z., & Wang, H. (2021). Internal positive feedback promotes water quality improvement for a recovering hyper-eutrophic lake: A three-dimensional nutrient flux tracking model. Science of The Total Environment, 772, 145505. https://doi.org/10.1016/j.scitotenv.2021.145505

Objectives

This study examined the main drivers of algal blooms in a large and shallow lake, Lake Okeechobee, and quantified the contribution of external and internal factors to the blooms.

The objectives of the study were to explore the driving mechanisms of internal cycling and to quantify the contributions of external and internal multi-process fluxes to unravel the forces driving nutrient changes in Lake Dianchi.