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petrasovaa merged 1 commit into from
Sep 30, 2026
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r.sim.water: updated example with mannings #7975

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195 changes: 159 additions & 36 deletions raster/r.sim/r.sim.water/r.sim.water.html
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Original file line number Diff line number Diff line change
Expand Up @@ -219,6 +219,52 @@ <h3>Run summary</h3>
-t -p format=json
</pre></div>

In Python with grass.script:

<div class="code"><pre>
import grass.script as gs

summary = gs.parse_command(
"r.sim.water",
elevation="elevation",
depth="depth",
discharge="discharge",
rain_value=50,
man_value=0.05,
nwalkers=100000,
duration=20,
output_step=10,
random_seed=3,
flags="tp",
format="json",
)
print(summary["walkers_remaining"], summary["outputs"][-1]["depth"])
</pre></div>

In Python with grass.tools:

<div class="code"><pre>
from grass.tools import Tools

tools = Tools()
summary = tools.r_sim_water(
elevation="elevation",
depth="depth",
discharge="discharge",
rain_value=50,
man_value=0.05,
nwalkers=100000,
duration=20,
output_step=10,
random_seed=3,
flags="tp",
format="json",
)
print(summary["walkers_remaining"], summary["outputs"][-1]["depth"])
</pre></div>

The printed summary:

<div class="code"><pre>
{
"walkers_requested": 100000,
Expand Down Expand Up @@ -258,63 +304,140 @@ <h3>Run summary</h3>
}
</pre></div>

<h2>EXAMPLE</h2>

This example uses the
<a href="https://doi.org/10.5281/zenodo.23017720">SIMWE sample dataset</a>
of the NC State University Sediment and Erosion Control Research and
Education Facility, a 52 ha area in Raleigh, North Carolina, USA, at 1 m
resolution. It contains a lidar-based elevation map, a land cover map
and orthophoto bands.

<p>
Reading the summary in Python:
Set the computational region to the elevation map and derive the
Manning's n raster map from the land cover classes with
<em><a href="r.recode.html">r.recode</a></em>. Buildings (class 1), paved
roads (2) and compacted roads and parking lots (3) get low roughness
values, while herbaceous cover such as fields and lawns (4) and forest (5)
get high values suitable for shallow overland flow. Water (6) gets a low
value. See the
<a href="https://grass.osgeo.org/grass-stable/manuals/addons/r.manning.html">r.manning</a>
addon for an explanation of Manning's n and reference values for common
land cover classifications.

<div class="code"><pre>
g.region raster=elevation
r.recode input=landcover output=mannings rules=- &lt;&lt;EOF
1:1:0.012
2:2:0.014
3:3:0.025
4:4:0.24
5:5:0.35
6:6:0.04
EOF
</pre></div>

In Python with grass.script:

<div class="code"><pre>
import grass.script as gs

summary = gs.parse_command(
"r.sim.water",
elevation="elevation",
depth="depth",
discharge="discharge",
flags="p",
format="json",
gs.run_command("g.region", raster="elevation")
manning = {
1: 0.012, # buildings
2: 0.014, # paved roads
3: 0.025, # compacted roads and parking lots
4: 0.24, # herbaceous cover
5: 0.35, # forest
6: 0.04, # water
}
rules = "\n".join(f"{k}:{k}:{v}" for k, v in manning.items())
gs.write_command(
"r.recode", input="landcover", output="mannings", rules="-", stdin=rules
)
print(summary["walkers_remaining"], summary["outputs"][-1]["depth"])
</pre></div>

<h2>EXAMPLE</h2>
In Python with grass.tools:

<div class="code"><pre>
from io import StringIO

from grass.tools import Tools

tools = Tools()
tools.g_region(raster="elevation")
manning = {
1: 0.012, # buildings
2: 0.014, # paved roads
3: 0.025, # compacted roads and parking lots
4: 0.24, # herbaceous cover
5: 0.35, # forest
6: 0.04, # water
}
rules = "\n".join(f"{k}:{k}:{v}" for k, v in manning.items())
tools.r_recode(input="landcover", output="mannings", rules=StringIO(rules))
</pre></div>

<p>
<div align="center" style="margin: 10px;">
<img style="margin: 0.5em;" src="r_sim_water_mannings.png" alt="Manning's n derived from land cover"><br>
<i>
Figure: Manning's n derived from land cover with low values for
buildings and roads and high values for fields and forest.
</i>
</div>

Using the North Carolina full sample dataset:
<p>
Simulate 30 minutes of overland flow with a uniform rainfall excess of
20 mm/hr. The random seed makes the run reproducible.

<div class="code"><pre>
# set computational region
g.region raster=elev_lid792_1m -p
r.sim.water elevation=elevation man=mannings rain_value=20 depth=depth \
duration=30 random_seed=1
</pre></div>

# compute dx, dy
r.slope.aspect elevation=elev_lid792_1m dx=elev_lid792_dx dy=elev_lid792_dy
In Python with grass.script:

# simulate (this may take a minute or two)
r.sim.water elevation=elev_lid792_1m dx=elev_lid792_dx dy=elev_lid792_dy depth=water_depth disch=water_discharge nwalk=10000 rain_value=100 niter=5
<div class="code"><pre>
gs.run_command(
"r.sim.water",
elevation="elevation",
man="mannings",
rain_value=20,
depth="depth",
duration=30,
random_seed=1,
)
</pre></div>

Now, let's visualize the result using rendering to a file
(note the further management of computational region and
usage of <a href="d.mon.html">d.mon</a> module
which are not needed when working in GUI):
In Python with grass.tools:

<div class="code"><pre>
# increase the computational region by 350 meters
g.region e=e+350
# initiate the rendering
d.mon start=cairo output=r_sim_water_water_depth.png
# render raster, legend, etc.
d.rast map=water_depth_1m
d.legend raster=water_depth_1m title="Water depth [m]" label_step=0.10 font=sans at=20,80,70,75
d.barscale at=67,10 length=250 segment=5 font=sans
d.northarrow at=90,25
# finish the rendering
d.mon stop=cairo
tools.r_sim_water(
elevation="elevation",
man="mannings",
rain_value=20,
depth="depth",
duration=30,
random_seed=1,
)
</pre></div>

<p>
<div align="center" style="margin: 10px;">
<img style="margin: 0.5em;" src="r_sim_water_water_depth.png" alt="r.sim.water generated depth map"><br>
<i >
Figure: Simulated water depth map in the rural area of
the North Carolina sample dataset.
<img style="margin: 0.5em;" src="r_sim_water_depth.png" alt="Water depth over shaded relief"><br>
<i>
Figure: Simulated water depth in meters after 30 minutes of rainfall
shown over shaded relief.
</i>
</div>

<p>
<div align="center" style="margin: 10px;">
<img style="margin: 0.5em;" src="r_sim_water_depth_orthophoto.png" alt="Water depth over orthophoto"><br>
<i>
Figure: Water depth of at least 0.1 m shown over the orthophoto, with
flow concentrated in ditches and channels and ponding in depressions.
</i>
</div>

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