Earthworks: Modeling Coastal Infrastructure

earthworks

terrain

coast

raster

beginner

Model levees and alluvial ridges with r.earthworks.

Author

Brendan Harmon

Published

July 6, 2025

Modified

July 18, 2025

Learn the how to model levees and ridges with r.earthworks. Download the Bayou L’Ours Dataset. The coordinate reference system for this project is Louisiana State Plane South in meters. We will model two of the coastal infrastructure projects in the Louisiana Coastal Protection and Restoration Authority’s 2023 Coastal Masterplan. Improvements are planned to the Larose to Golden Meadow Levee System which will raise existing earthen levees to an elevation between 3.6-6.4 meters NAVD88. The Bayou L’Ours Ridge Restoration project will restore the natural levee by closing gaps, rebuilding landforms, and reforesting uplands. While the improved levee system will better protect the towns, the restored ridge will attenuate storm surge. This tutorial covers:

Visualizing existing conditions
Modeling ridge restoration
Modeling levee improvement
Modeling gap closure
Simulating flooding

Computational notebook

This tutorial can be run as a computational notebook. Learn how to work with notebooks in the tutorial Get started with GRASS & Python in Jupyter Notebooks.

Setup

Project

Download and unarchive the Bayou L’Ours Dataset. Then start a GRASS session in the Bayou L’Ours project. Since the dataset is approximately 120MB, it may take a couple minutes to download.

Command line
Python

grass ~/grassdata/bayou_lours/PERMANENT

# Import libraries
import os
import sys
import subprocess
from pathlib import Path
import urllib.request
from zipfile import ZipFile

# Find GRASS Python packages
sys.path.append(
  subprocess.check_output(
    ["grass", "--config", "python_path"],
    text=True
    ).strip()
  )

# Import GRASS packages
import grass.script as gs
import grass.jupyter as gj

# Set GRASS database
gisdbase = os.path.join(Path.home(), "grassdata")

# Download dataset
url = "https://zenodo.org/records/15870442/files/bayou_lours.zip?download=1"
filepath = os.path.join(gisdbase, "bayou_lours.zip")
try:
    urllib.request.urlretrieve(url, filepath)
except Exception as e:
    print(f"Error downloading file: {e}")

# Unarchive dataset
with ZipFile(filepath, 'r') as archive:
    archive.extractall(gisdbase)

# Delete archive
os.remove(filepath)

# Start GRASS in Bayou L'Ours project
session = gj.init(Path(gisdbase, "bayou_lours"))

Installation

Install r.earthworks with g.extension.

Command line
Python

g.extension extension=r.earthworks

# Install extension
gs.run_command("g.extension", extension="r.earthworks")

Region

Use g.region to set the extent and resolution of the computational region.

Command line
Python

g.region n=105100 s=102600 w=1105000 e=1115000 res=5

# Set region
gs.run_command("g.region", n=105100, s=102600, w=1105000, e=1115000, res=5)

Existing Conditions

Use hillshading, flood simulations, and data from the Louisiana Coastal Protection and Restoration Authority to visualize the existing conditions and the proposed plans for coastal infrastructure. The existing ring levee system - in the west of our study area - protects the communities of Larose, Cutoff, Galliano, and Golden Meadow along Bayou Lafourche. This section of the levee is between 4.5-5 meters high. It may be raised as high as 6.4 meters to provide protection from 100-storms. The alluvial ridge along Bayou L’Ours - which runs through the middle of our study area - has eroded, subsided, and been cut apart by oil and gas exploration canals. The gaps should be closed with sheet pile structures, the ridge rebuilt to an elevation of 1.5 meters NAVD88, and its uplands reforested. Once restored this natural levee would attenuate storm surge and provide upland coastal habitat.

Imagery

Use r.relief for hillshading and d.shade to drape the imagery over the hillshade. Adjust the brighten parameter to better illuminate the map.

Command line
Python

r.relief input=elevation output=relief zscale=10
d.shade shade=relief color=imagery brighten=36

# Model relief
gs.run_command("r.relief", input="elevation", output="relief", zscale=10)

# Display imagery
m = gj.Map(width=800)
m.d_shade(shade="relief", color="imagery", brighten=36)
m.show()
m.save("images/levees_01.webp")

Terrain

Use d.shade to drape the elevation over the hillshade.

Command line
Python

d.shade shade=relief color=elevation brighten=36
d.legend raster=elevation digits=0 color=white at=5,95,1,3

# Display elevation
m = gj.Map(width=800)
m.d_shade(shade="relief", color="elevation", brighten=36)
m.d_legend(raster="elevation", digits=0, color="white", at=(5, 95, 1, 3))
m.show()
m.save("images/levees_02.webp")

Flood Simulation

Use r.lake to model inundation from storm surge. Set the water level to one meter and the coordinates to 1109000, 102750. The simulation will show that the degraded ridge would be overtopped by a meter of surge.

Command line
Python

r.lake elevation=elevation water_level=1 lake=flooding coordinates=1109000,102750
d.shade shade=relief color=elevation brighten=36
d.rast map=flooding
d.legend raster=flooding digits=1 color=white at=5,95,1,3

# Simulate flooding
gs.run_command(
    "r.lake",
    elevation="elevation",
    water_level=1,
    lake="flooding",
    coordinates=[1109000, 102750]
    )

# Display flooding
m = gj.Map(width=800)
m.d_shade(shade="relief", color="elevation", brighten=36)
m.d_rast(map="flooding")
m.d_legend(raster="flooding", digits=1, color="white", at=(5, 95, 1, 3))
m.show()
m.save("images/levees_03.webp")

Coastal Restoration

Map the proposed coastal infrastructure projects. Layer the vector maps of proposed ridge restoration, levee improvement, and gap closure projects over the shaded relief.

Command line
Python

d.shade shade=relief color=elevation brighten=36
d.vect map=levees color=white width=4
d.vect map=ridges color=yellow width=4
d.vect map=gaps icon=basic/pin_dot size=25 color=white fill_color=white
d.text text="Levee improvement" color=white size=7 at=22,10
d.text text="Ridge restoration" color=yellow size=7 at=34,65
d.text text="Gap closure" color=white size=7 at=49,10
d.legend raster=flooding digits=1 color=white at=5,95,1,3

# Display projects
m = gj.Map(width=800)
m.d_shade(shade="relief", color="elevation", brighten=36)
m.d_vect(map="levees", color="white", width=4)
m.d_vect(map="ridges", color="yellow", width=4)
m.d_vect(map="gaps", icon="basic/pin_dot", size=25, color="white", fill_color="white")
m.d_text(text="Levee improvement", color="white", size=7, at=(22,10))
m.d_text(text="Ridge restoration", color="yellow", size=7, at=(34,65))
m.d_text(text="Gap closure", color="white", size=7, at=(49,10))
m.show()
m.save("images/levees_04.webp")

Coastal restoration and protection projects

Ridge Restoration

Use r.earthworks to model the restored ridge and the borrow needed to build it. The restored ridge should have a crest elevation of 1.5 meters, a crest width of 15.2 meters, and 20% slopes with a ratio of 5H:1V, i.e. a horizontal run of 5 meters for a vertical rise of 1 meter. The soil to rebuild the ridge should be taken onsite from an adjacent borrow area that would be offset by 7.6 meters from the foot of the ridge.

Borrow Area

Start by modeling the borrow area - an excavation for sourcing material for construction - needed to restore the ridge. To model the borrow area, offset the proposed ridge and then perform a cut operation. First use v.transform to shift the vector map of the proposed ridgeline 36 meters to the north. Then use a cut operation with r.earthworks to model the borrow area. Set elevation to the elevation raster, lines to the borrow vector, z to -1.5, rate to 0.2, and flat to 7.6.

Command line
Python

v.transform input=ridges output=borrow yshift=36
r.earthworks elevation=elevation earthworks=earthworks operation=cut lines=borrow z=-1.5 rate=0.2 flat=7.6
d.rast map=earthworks
d.legend raster=earthworks color=white at=5,95,1,3

# Shift ridgeline
gs.run_command("v.transform", input="ridges", output="borrow", yshift=36)

# Model borrow
gs.run_command(
    "r.earthworks",
    elevation="elevation",
    earthworks="earthworks",
    operation="cut",
    lines="borrow",
    z=[-1.5],
    rate=0.2,
    flat=7.6
    )

# Visualize
m = gj.Map(width=800)
m.d_rast(map="earthworks")
m.d_legend(raster="earthworks", color="white", at=(5, 95, 1, 3))
m.show()
m.save("images/levees_05.webp")

Ridge

Use a fill operation with r.earthworks to model the restored ridge. Set elevation to the earthworks raster, lines to the ridge vector, z to 1.5, rate to 0.2, and flat to 7.6.

Command line
Python

r.earthworks elevation=earthworks earthworks=earthworks operation=fill lines=ridges z=1.5 rate=0.2 flat=7.6 --overwrite
d.rast map=earthworks
d.legend raster=earthworks color=white at=5,95,1,3

# Model ridges
gs.run_command(
    "r.earthworks",
    elevation="earthworks",
    earthworks="earthworks",
    operation="fill",
    lines="ridges",
    z=1.5,
    rate=0.2,
    flat=7.6
    )

# Visualize
m = gj.Map(width=800)
m.d_rast(map="earthworks")
m.d_legend(raster="earthworks", color="white", at=(5, 95, 1, 3))
m.show()
m.save("images/levees_06.webp")

Levees

Use a fill operation with r.earthworks to model the improved levee. The improved levee should have a crest elevation of 5.5 meters, a crest width of 1.5 meters, and 10% slopes with a ratio of 10H:1V. For r.earthworks set elevation to the earthworks raster, lines to the levee vector, z to 5.5, rate to 0.1, and flat to 1.5.

Command line
Python

r.earthworks elevation=earthworks earthworks=earthworks operation=fill lines=levees z=5.5 rate=0.1 flat=1.5 --overwrite
d.rast map=earthworks
d.legend raster=earthworks color=white at=5,95,1,3

# Model levees
gs.run_command(
    "r.earthworks",
    elevation="earthworks",
    earthworks="earthworks",
    operation="fill",
    lines="levees",
    z=5.5,
    rate=0.1,
    flat=1.5
    )

# Visualize
m = gj.Map(width=800)
m.d_rast(map="earthworks")
m.d_legend(raster="earthworks", color="white", at=(5, 95, 1, 3))
m.show()
m.save("images/levees_07.webp")

Gap Closure

Use a fill operation with r.earthworks to model gap closures with sheet pile structures. These structures are walls built from rows of interlocking vertical segments of piles that are driven into the ground. Set elevation to the earthworks raster, lines to the gaps vector, z to -0.6, rate to 1, and flat to 10. The radius of flats should be set to the region resolution to ensure that the gaps are fully closed because we test our design with a flood simulation. To better visualize the results, use r.relief and d.shade for hillshading.

Command line
Python

r.earthworks elevation=earthworks earthworks=earthworks operation=cutfill lines=gaps z=-0.6 rate=1 flat=10 --overwrite
r.earthworks elevation=earthworks earthworks=earthworks operation=cutfill lines=closures z=1.37 rate=1 flat=5 --overwrite
r.relief input=earthworks output=relief zscale=10 --overwrite
d.shade shade=relief color=earthworks brighten=36
d.legend raster=earthworks color=white at=5,95,1,3

# Model gaps
gs.run_command(
    "r.earthworks",
    elevation="earthworks",
    earthworks="earthworks",
    operation="cutfill",
    lines="gaps",
    z=-0.6,
    rate=1,
    flat=10
    )

# Model gap closures
gs.run_command(
    "r.earthworks",
    elevation="earthworks",
    earthworks="earthworks",
    operation="cutfill",
    lines="closures",
    z=1.37,
    rate=1,
    flat=5
    )

# Model relief
gs.run_command("r.relief", input="earthworks", output="relief", zscale=10)

# Visualize
m = gj.Map(width=800)
m.d_shade(shade="relief", color="earthworks", brighten=36)
m.d_legend(raster="earthworks", color="white", at=(5, 95, 1, 3))
m.show()
m.save("images/levees_08.webp")

Flood Simulation

Test proposed coastal infrastructure with simulated storm surge. Use r.lake to model inundation from storm surge over the earthworks raster. Set the water level to one meter and the coordinates to 1109000, 102750. The simulation will show that the restored ridge with closed gaps would be not overtopped by a meter of surge.

Command line
Python

r.lake elevation=earthworks water_level=1 lake=flooding coordinates=1109000,102750
d.shade shade=relief color=earthworks brighten=36
d.rast map=flooding
d.legend raster=flooding digits=1 color=white at=5,95,1,3

# Simulate flooding
gs.run_command(
    "r.lake",
    elevation="earthworks",
    water_level=1,
    lake="flooding",
    coordinates=[1109000,102750]
    )

# Visualize
m = gj.Map(width=800)
m.d_shade(shade="relief", color="earthworks", brighten=36)
m.d_rast(map="flooding")
m.d_legend(raster="flooding", digits=1, color="white", at=(5, 95, 1, 3))
m.show()
m.save("images/levees_09.webp")

--- title: "Earthworks: Modeling Coastal Infrastructure" author: "Brendan Harmon" date: 2025-07-06 date-modified: today categories: [earthworks, terrain, coast, raster, beginner] description: Model levees and alluvial ridges with r.earthworks. image: images/levees_08.webp links: r_earthworks: "[r.earthworks](https://grass.osgeo.org/grass-stable/manuals/addons/r.earthworks.html)" g_extension: "[g.extension](https://grass.osgeo.org/grass-stable/manuals/g.extension.html)" g_region: "[g.region](https://grass.osgeo.org/grass-stable/manuals/g.region.html)" r_relief: "[r.relief](https://grass.osgeo.org/grass-stable/manuals/r.relief.html)" d_shade: "[d.shade](https://grass.osgeo.org/grass-stable/manuals/d.shade.html)" r_lake: "[r.lake](https://grass.osgeo.org/grass-stable/manuals/r.lake.html)" r_mapcalc: "[r.mapcalc](https://grass.osgeo.org/grass-stable/manuals/r.mapcalc.html)" v_transform: "[v.transform](https://grass.osgeo.org/grass-stable/manuals/v.transform.html)" code-links: - text: Jupyter notebook href: levees.ipynb icon: file-code target: _blank resources: levees.ipynb format: html: toc: true code-tools: true code-copy: true code-fold: false engine: jupyter execute: eval: false jupyter: python3 --- ![Levee improvement & ridge restoration](images/levees_08.webp) Learn the how to model levees and ridges with {{< meta links.r_earthworks >}}. Download the [Bayou L'Ours Dataset](https://doi.org/10.5281/zenodo.15870441). The coordinate reference system for this project is Louisiana State Plane South in meters. We will model two of the coastal infrastructure projects in the Louisiana Coastal Protection and Restoration Authority's [2023 Coastal Masterplan](https://coastal.la.gov/wp-content/uploads/2023/06/230531_CPRA_MP_Final-for-web_spreads.pdf). Improvements are planned to the [Larose to Golden Meadow Levee System](https://coastal.la.gov/wp-content/uploads/masterplan/111_Larose-to-Golden-Meadow.pdf) which will raise existing earthen levees to an elevation between 3.6-6.4 meters NAVD88. The [Bayou L'Ours Ridge Restoration](https://coastal.la.gov/wp-content/uploads/masterplan/334_Bayou-LOurs-Ridge-Restoration.pdf) project will restore the natural levee by closing gaps, rebuilding landforms, and reforesting uplands. While the improved levee system will better protect the towns, the restored ridge will attenuate storm surge. This tutorial covers: * Visualizing existing conditions * Modeling ridge restoration * Modeling levee improvement * Modeling gap closure * Simulating flooding ::: {.callout-note title="Computational notebook"} This tutorial can be run as a [computational notebook](https://grass-tutorials.osgeo.org/content/tutorials/earthworks/levees.ipynb). Learn how to work with notebooks in the tutorial [Get started with GRASS & Python in Jupyter Notebooks](./get_started/fast_track_grass_and_python.qmd). ::: # Setup ## Project Download and unarchive the [Bayou L'Ours Dataset](https://doi.org/10.5281/zenodo.15870441). Then start a GRASS session in the Bayou L'Ours project. Since the dataset is approximately 120MB, it may take a couple minutes to download. ::: {.panel-tabset group="language"} ## Command line ```{bash} grass ~/grassdata/bayou_lours/PERMANENT ``` ## Python ```{python} # Import libraries import os import sys import subprocess from pathlib import Path import urllib.request from zipfile import ZipFile # Find GRASS Python packages sys.path.append( subprocess.check_output( ["grass", "--config", "python_path"], text=True ).strip() ) # Import GRASS packages import grass.script as gs import grass.jupyter as gj # Set GRASS database gisdbase = os.path.join(Path.home(), "grassdata") # Download dataset url = "https://zenodo.org/records/15870442/files/bayou_lours.zip?download=1" filepath = os.path.join(gisdbase, "bayou_lours.zip") try: urllib.request.urlretrieve(url, filepath) except Exception as e: print(f"Error downloading file: {e}") # Unarchive dataset with ZipFile(filepath, 'r') as archive: archive.extractall(gisdbase) # Delete archive os.remove(filepath) # Start GRASS in Bayou L'Ours project session = gj.init(Path(gisdbase, "bayou_lours")) ``` ::: ## Installation Install {{< meta links.r_earthworks >}} with {{< meta links.g_extension >}}. ::: {.panel-tabset group="language"} ## Command line ```{bash} g.extension extension=r.earthworks ``` ## Python ```{python} # Install extension gs.run_command("g.extension", extension="r.earthworks") ``` ::: ## Region Use {{< meta links.g_region >}} to set the extent and resolution of the computational region. ::: {.panel-tabset group="language"} ## Command line ```{bash} g.region n=105100 s=102600 w=1105000 e=1115000 res=5 ``` ## Python ```{python} # Set region gs.run_command("g.region", n=105100, s=102600, w=1105000, e=1115000, res=5) ``` ::: # Existing Conditions Use hillshading, flood simulations, and data from the Louisiana Coastal Protection and Restoration Authority to visualize the existing conditions and the proposed plans for coastal infrastructure. The existing ring levee system - in the west of our study area - protects the communities of Larose, Cutoff, Galliano, and Golden Meadow along Bayou Lafourche. This section of the levee is between 4.5-5 meters high. It may be raised as high as 6.4 meters to provide protection from 100-storms. The alluvial ridge along Bayou L'Ours - which runs through the middle of our study area - has eroded, subsided, and been cut apart by oil and gas exploration canals. The gaps should be closed with sheet pile structures, the ridge rebuilt to an elevation of 1.5 meters NAVD88, and its uplands reforested. Once restored this natural levee would attenuate storm surge and provide upland coastal habitat. ## Imagery Use {{< meta links.r_relief >}} for hillshading and {{< meta links.d_shade >}} to drape the imagery over the hillshade. Adjust the `brighten` parameter to better illuminate the map. ::: {.panel-tabset group="language"} ## Command line ```{bash} r.relief input=elevation output=relief zscale=10 d.shade shade=relief color=imagery brighten=36 ``` ## Python ```{python} # Model relief gs.run_command("r.relief", input="elevation", output="relief", zscale=10) # Display imagery m = gj.Map(width=800) m.d_shade(shade="relief", color="imagery", brighten=36) m.show() m.save("images/levees_01.webp") ``` ::: ![Imagery](images/levees_01.webp) ## Terrain Use {{< meta links.d_shade >}} to drape the elevation over the hillshade. ::: {.panel-tabset group="language"} ## Command line ```{bash} d.shade shade=relief color=elevation brighten=36 d.legend raster=elevation digits=0 color=white at=5,95,1,3 ``` ## Python ```{python} # Display elevation m = gj.Map(width=800) m.d_shade(shade="relief", color="elevation", brighten=36) m.d_legend(raster="elevation", digits=0, color="white", at=(5, 95, 1, 3)) m.show() m.save("images/levees_02.webp") ``` ::: ![Terrain](images/levees_02.webp) ## Flood Simulation Use {{< meta links.r_lake >}} to model inundation from storm surge. Set the water level to one meter and the coordinates to `1109000, 102750`. The simulation will show that the degraded ridge would be overtopped by a meter of surge. ::: {.panel-tabset group="language"} ## Command line ```{bash} r.lake elevation=elevation water_level=1 lake=flooding coordinates=1109000,102750 d.shade shade=relief color=elevation brighten=36 d.rast map=flooding d.legend raster=flooding digits=1 color=white at=5,95,1,3 ``` ## Python ```{python} # Simulate flooding gs.run_command( "r.lake", elevation="elevation", water_level=1, lake="flooding", coordinates=[1109000, 102750] ) # Display flooding m = gj.Map(width=800) m.d_shade(shade="relief", color="elevation", brighten=36) m.d_rast(map="flooding") m.d_legend(raster="flooding", digits=1, color="white", at=(5, 95, 1, 3)) m.show() m.save("images/levees_03.webp") ``` ::: ![Flood simulation](images/levees_03.webp) ## Coastal Restoration Map the proposed coastal infrastructure projects. Layer the vector maps of proposed ridge restoration, levee improvement, and gap closure projects over the shaded relief. ::: {.panel-tabset group="language"} ## Command line ```{bash} d.shade shade=relief color=elevation brighten=36 d.vect map=levees color=white width=4 d.vect map=ridges color=yellow width=4 d.vect map=gaps icon=basic/pin_dot size=25 color=white fill_color=white d.text text="Levee improvement" color=white size=7 at=22,10 d.text text="Ridge restoration" color=yellow size=7 at=34,65 d.text text="Gap closure" color=white size=7 at=49,10 d.legend raster=flooding digits=1 color=white at=5,95,1,3 ``` ## Python ```{python} # Display projects m = gj.Map(width=800) m.d_shade(shade="relief", color="elevation", brighten=36) m.d_vect(map="levees", color="white", width=4) m.d_vect(map="ridges", color="yellow", width=4) m.d_vect(map="gaps", icon="basic/pin_dot", size=25, color="white", fill_color="white") m.d_text(text="Levee improvement", color="white", size=7, at=(22,10)) m.d_text(text="Ridge restoration", color="yellow", size=7, at=(34,65)) m.d_text(text="Gap closure", color="white", size=7, at=(49,10)) m.show() m.save("images/levees_04.webp") ``` ::: ![Coastal restoration and protection projects](images/levees_04.webp) # Ridge Restoration Use {{< meta links.r_earthworks >}} to model the restored ridge and the borrow needed to build it. The restored ridge should have a crest elevation of 1.5 meters, a crest width of 15.2 meters, and 20% slopes with a ratio of 5H:1V, i.e. a horizontal run of 5 meters for a vertical rise of 1 meter. The soil to rebuild the ridge should be taken onsite from an adjacent borrow area that would be offset by 7.6 meters from the foot of the ridge. ## Borrow Area Start by modeling the borrow area - an excavation for sourcing material for construction - needed to restore the ridge. To model the borrow area, offset the proposed ridge and then perform a cut operation. First use {{< meta links.v_transform >}} to shift the vector map of the proposed ridgeline 36 meters to the north. Then use a cut operation with {{< meta links.r_earthworks >}} to model the borrow area. Set `elevation` to the elevation raster, `lines` to the borrow vector, `z` to -1.5, `rate` to 0.2, and `flat` to 7.6. ::: {.panel-tabset group="language"} ## Command line ```{bash} v.transform input=ridges output=borrow yshift=36 r.earthworks elevation=elevation earthworks=earthworks operation=cut lines=borrow z=-1.5 rate=0.2 flat=7.6 d.rast map=earthworks d.legend raster=earthworks color=white at=5,95,1,3 ``` ## Python ```{python} # Shift ridgeline gs.run_command("v.transform", input="ridges", output="borrow", yshift=36) # Model borrow gs.run_command( "r.earthworks", elevation="elevation", earthworks="earthworks", operation="cut", lines="borrow", z=[-1.5], rate=0.2, flat=7.6 ) # Visualize m = gj.Map(width=800) m.d_rast(map="earthworks") m.d_legend(raster="earthworks", color="white", at=(5, 95, 1, 3)) m.show() m.save("images/levees_05.webp") ``` ::: ![Borrow area](images/levees_05.webp) ## Ridge Use a fill operation with {{< meta links.r_earthworks >}} to model the restored ridge. Set `elevation` to the earthworks raster, `lines` to the ridge vector, `z` to 1.5, `rate` to 0.2, and `flat` to 7.6. ::: {.panel-tabset group="language"} ## Command line ```{bash} r.earthworks elevation=earthworks earthworks=earthworks operation=fill lines=ridges z=1.5 rate=0.2 flat=7.6 --overwrite d.rast map=earthworks d.legend raster=earthworks color=white at=5,95,1,3 ``` ## Python ```{python} # Model ridges gs.run_command( "r.earthworks", elevation="earthworks", earthworks="earthworks", operation="fill", lines="ridges", z=1.5, rate=0.2, flat=7.6 ) # Visualize m = gj.Map(width=800) m.d_rast(map="earthworks") m.d_legend(raster="earthworks", color="white", at=(5, 95, 1, 3)) m.show() m.save("images/levees_06.webp") ``` ::: ![Ridge restoration](images/levees_06.webp) # Levees Use a fill operation with {{< meta links.r_earthworks >}} to model the improved levee. The improved levee should have a crest elevation of 5.5 meters, a crest width of 1.5 meters, and 10% slopes with a ratio of 10H:1V. For {{< meta links.r_earthworks >}} set `elevation` to the earthworks raster, `lines` to the levee vector, `z` to 5.5, `rate` to 0.1, and `flat` to 1.5. ::: {.panel-tabset group="language"} ## Command line ```{bash} r.earthworks elevation=earthworks earthworks=earthworks operation=fill lines=levees z=5.5 rate=0.1 flat=1.5 --overwrite d.rast map=earthworks d.legend raster=earthworks color=white at=5,95,1,3 ``` ## Python ```{python} # Model levees gs.run_command( "r.earthworks", elevation="earthworks", earthworks="earthworks", operation="fill", lines="levees", z=5.5, rate=0.1, flat=1.5 ) # Visualize m = gj.Map(width=800) m.d_rast(map="earthworks") m.d_legend(raster="earthworks", color="white", at=(5, 95, 1, 3)) m.show() m.save("images/levees_07.webp") ``` ::: ![Levee improvement](images/levees_07.webp) # Gap Closure Use a fill operation with {{< meta links.r_earthworks >}} to model gap closures with sheet pile structures. These structures are walls built from rows of interlocking vertical segments of piles that are driven into the ground. Set `elevation` to the earthworks raster, `lines` to the gaps vector, `z` to -0.6, `rate` to 1, and `flat` to 10. The radius of flats should be set to the region resolution to ensure that the gaps are fully closed because we test our design with a flood simulation. To better visualize the results, use {{< meta links.r_relief >}} and {{< meta links.d_shade >}} for hillshading. ::: {.panel-tabset group="language"} ## Command line ```{bash} r.earthworks elevation=earthworks earthworks=earthworks operation=cutfill lines=gaps z=-0.6 rate=1 flat=10 --overwrite r.earthworks elevation=earthworks earthworks=earthworks operation=cutfill lines=closures z=1.37 rate=1 flat=5 --overwrite r.relief input=earthworks output=relief zscale=10 --overwrite d.shade shade=relief color=earthworks brighten=36 d.legend raster=earthworks color=white at=5,95,1,3 ``` ## Python ```{python} # Model gaps gs.run_command( "r.earthworks", elevation="earthworks", earthworks="earthworks", operation="cutfill", lines="gaps", z=-0.6, rate=1, flat=10 ) # Model gap closures gs.run_command( "r.earthworks", elevation="earthworks", earthworks="earthworks", operation="cutfill", lines="closures", z=1.37, rate=1, flat=5 ) # Model relief gs.run_command("r.relief", input="earthworks", output="relief", zscale=10) # Visualize m = gj.Map(width=800) m.d_shade(shade="relief", color="earthworks", brighten=36) m.d_legend(raster="earthworks", color="white", at=(5, 95, 1, 3)) m.show() m.save("images/levees_08.webp") ``` ::: ![Gap closure](images/levees_08.webp) # Flood Simulation Test proposed coastal infrastructure with simulated storm surge. Use {{< meta links.r_lake >}} to model inundation from storm surge over the earthworks raster. Set the water level to one meter and the coordinates to `1109000, 102750`. The simulation will show that the restored ridge with closed gaps would be not overtopped by a meter of surge. ::: {.panel-tabset group="language"} ## Command line ```{bash} r.lake elevation=earthworks water_level=1 lake=flooding coordinates=1109000,102750 d.shade shade=relief color=earthworks brighten=36 d.rast map=flooding d.legend raster=flooding digits=1 color=white at=5,95,1,3 ``` ## Python ```{python} # Simulate flooding gs.run_command( "r.lake", elevation="earthworks", water_level=1, lake="flooding", coordinates=[1109000,102750] ) # Visualize m = gj.Map(width=800) m.d_shade(shade="relief", color="earthworks", brighten=36) m.d_rast(map="flooding") m.d_legend(raster="flooding", digits=1, color="white", at=(5, 95, 1, 3)) m.show() m.save("images/levees_09.webp") ``` ::: ![Flood simulation](images/levees_09.webp)