Coupled Barrier-Bay-Marsh-Forest Transect Model
BarrierBMFT is an coupled model framework for exploring morphodynamic interactions across components of the entire coastal barrier system, from the ocean shoreface to the mainland forest. The model framework couples Barrier3D (Reeves et al., 2021), a spatially explicit model of barrier evolution, with the Python version of the Coastal Landscape Transect model (CoLT; Valentine et al., 2023), known as PyBMFT-C (Bay-Marsh-Forest Transect Model with Carbon). In the BarrierBMFT coupled model framework, two PyBMFT-C simulations drive evolution of back-barrier marsh, bay, mainland marsh, and forest ecosystems, and a Barrier3D simulation drives evolution of barrier and back-barrier marsh ecosystems. As these model components simultaneously advance, they dynamically evolve together by sharing information annually to capture the effects of key cross-landscape couplings. BarrierBMFT contains no new governing equations or parameterizations itself, but rather is a framework for trading information between Barrier3D and PyBMFT-C. Detailed descriptions of BarrierBMFT and the coupled models involved can be found in publications listed below under References.
Copyright (C) 2021 Ian R.B. Reeves (principal developer) licensed under the GNU General Public License v3.0
BarrierBMFT requires Python 3, and the libraries listed in the project's requirements.txt file.
First, download the source code for BarrierBMFT, PyBMFT-C, and Barrier3D into separate subdirectories within the same project directory. The correct versions of PyBMFT-C and Barrier3D must be used to ensure BarrierBMFT works properly. To get the source code, download the zip files for:
BarrierBMFT
https://github.com/UNC-CECL/BarrierBMFT/archive/refs/heads/main.zip
Barrier3D v2.0 release
https://github.com/UNC-CECL/Barrier3D/archive/refs/tags/v2.0.zip
and PyBMFT-C v1.0 release
https://github.com/UNC-CECL/PyBMFT-C/archive/refs/tags/v1.0.zip
You should now have directories organized as:
Your_Project_Directory __________ BarrierBMFT
|
|__ Barrier3D
|
|__ PyBMFT-C
Lastly, for each of the of BarrierBMFT, PyBMFT-C, and Barrier3D directories, run the following from their top-level folders
(the ones that contain setup.py) to install each of them into the current environment:
pip install -e .
A main set of commonly-manipulated parameters can be adjusted in the initializarion of the BarrierBMFT class in the run script (see example below).
Input files are located in the BarrierBMFT/Input/Barrier3D directory. Barrier3D parameter values can be
adjusted in the barrier3d-parameters.yaml file; a decription of each variable is available in
Barrier3D/barrier3d/configuration.py. In the initialization of BarrierBMFT, the simulation duration,
relative sea-level rise (RSLR) rate, and bay depth are set according the values of the same parameters in PyBMFT-C,
therefore the value of these parameters within the barrier3d-parameters.yaml file has no effect on the simulation.
Input files are located in the BarrierBMFT/Input/PyBMFT-C directory. PyBMFT-C parameter values can be set
in the initialization of the PyBMFT-C classes within barrierbmft.py. Note that because BarrierBMFT runs two separate
instances ofthe PyBMFT-C model during a simulation, both instances can be set with different parameters, though a warning
will be printed to screen if specific parameter values do not match between both instances in the initialization.
The following describes the approach for running a basic BarrierBMFT simulation. For a complete example run
script, see BarrierBMFT/scripts/run_BarrierBMFT.py.
To run BarrierBMFT, first set the working direcory to the main BarrierBMFT directory.
Then, import BarrierBMFT and dependencies:
from barrierbmft.barrierbmft import BarrierBMFT
import numpy as np
import matplotlib.pyplot as plt
Next, initialize an instance of the BarrierBMFT class. Here, you can set certain parameter values such as the RSLR rate (mm/yr) or the external suspended sediment supply (i.e., reference concentration; mg/L):
# Create an instance of the class
barrierbmft = BarrierBMFT(
time_step_count=20,
relative_sea_level_rise=12,
reference_concentration=60,
)
print(barrierbmft.name)
Next, run the simulation by progressing through time:
# Loop through time
for time_step in range(int(barrierbmft.bmftc.dur)):
# Run time step
barrierbmft.update(time_step)
# Check for breaks (e.g., barrier drowning)
if barrierbmft.BMFTC_Break or barrierbmft.Barrier3D_Break:
break
Once the simulation finishes, we can plot some results. For example, the full barrier-to-mainland-forest transect every at simulation end:
# Time index at simulatione end
t = barrierbmft.bmftc_BB.dur - 1
# Back-barrier transect
BB_transect = barrierbmft.bmftc_BB.elevation[barrierbmft.bmftc_BB.startyear + t - 1, int(barrierbmft.bmftc_BB.Marsh_edge[barrierbmft.bmftc_BB.startyear + t]):] # Trim off bay of ML transect (use identical bay in BB transect)
BB_transect = np.flip(BB_transect)
# Mainland transect
if barrierbmft.x_b_TS_ML[t] < 0: # If far end of bay extends beyond end of transect, append additional bay cells for visualization
ML_transect = np.append(np.ones([abs(int(barrierbmft.x_b_TS_ML[t]))]) * barrierbmft.bmftc_ML.elevation[barrierbmft.bmftc_ML.startyear + t - 1, 1], barrierbmft.bmftc_ML.elevation[barrierbmft.bmftc_ML.startyear + t - 1, :])
elif barrierbmft.x_b_TS_ML[t] > 0: # Far end of bay to end of mainland forest
ML_transect = barrierbmft.bmftc_ML.elevation[barrierbmft.bmftc_ML.startyear + t - 1, int(barrierbmft.x_b_TS_ML[t]):]
# Combine transects and plot
whole_transect = np.append(BB_transect, ML_transect)
plt.figure()
plt.plot(whole_transect)
plt.xlabel("Distance")
plt.ylabel("Elevation [m MSL]")
Or, plot the extent over time for each ecosystem:
# Landscape extent
widths = barrierbmft.LandscapeTypeWidth_TS
barrier = widths[:, 0]
BBmarsh = widths[:, 1]
bay = widths[:, 2]
MLmarsh = widths[:, 3]
forest = widths[:, 4]
BBpond = widths[:, 5]
MLpond = widths[:, 6]
barrier_marsh = barrier + BBmarsh
plt.figure()
# Barrier
plt.subplot(2, 3, 1)
plt.plot(barrier, c="red")
plt.ylabel("Barrier [m]")
# Back-Barrier Marsh
plt.subplot(2, 3, 2)
plt.plot(BBmarsh, c="red")
plt.plot(BBpond, c="red", linestyle='dashed')
plt.ylabel("Back-Barrier Marsh [m]")
# Barrier + Marsh
plt.subplot(2, 3, 3)
plt.plot(barrier_marsh, c="red")
plt.ylabel("Barrier + Marsh [m]")
# Bay
plt.subplot(2, 3, 4)
plt.plot(bay, c="red")
plt.ylabel("Bay [m]")
# Mainland marsh
plt.subplot(2, 3, 5)
plt.plot(MLmarsh, c="red")
plt.plot(MLpond, c="red", linestyle='dashed')
plt.ylabel("Mainland Marsh [m]")
plt.xlabel("Time [yr]")
# Forest
plt.subplot(2, 3, 6)
plt.plot(forest, c="red")
plt.ylabel("Forest [m]")
plt.tight_layout()
plt.show()
BarrierBMFT Paper: Reeves, I.R.B., Moore, L.J., Valentine, K., Fagherazzi, S., & Kirwan, M.L. (in review). Sediment exchange across coastal
barrier landscapes alters ecosystem extents.
CSDMS Wiki: https://csdms.colorado.edu/wiki/Model:Barrier3D
Repository: https://github.com/UNC-CECL/Barrier3D
Barrier3D Paper: Reeves, I.R.B., Moore, L.J., Murray, A.B., Anarde, K.A., & Goldstein, E.B. (2021). Dune dynamics drive discontinuous
barrier retreat. Geophysical Research Letters, 48(13), e2021GL092958. https://doi.org/10.1029/2021GL092958.
Repository: https://github.com/UNC-CECL/PyBMFT-C
CoLT Paper: Valentine, K., Herbert, E. R., Walters, D. C., Chen, Y., Smith, A. J., & Kirwan, M. L. (2023). Climate-driven tradeoffs between
landscape connectivity and the maintenance of the coastal carbon sink. Nature Communications, 14, 1137.
https://doi.org/10.1038/s41467-023-36803-7.