Exact answer known1D · free surfaceCandidate

Calm flow over a hump

Does the model get the dip in the water surface over a bump in the bed right?

Test A1F-040
Delestre (2013)
Version 1.0.0

Hidden at first because they are far off the exact answer: LISFLOOD-FP ACC, EPA SWMM 5.2.4, OpenSWMM 6 dynamic wave, Hydra, SWMM 5.1.15, Landlab. Click a name to show it.

What happens

Water flows steadily and calmly along a 25 m channel over a smooth 20 cm hump. The surface dips over the hump by an amount fixed exactly by conservation of flow and energy.

Why it matters

Weirs, sills, bridge piers and bed forms all change the water level this way. A model that gets the dip wrong gets every structure's backwater wrong.

A good model must

keep the average depth error under 5 mm
carry the same flow along the whole channel
get steadily more accurate on finer grids

How models did

In rank order: by each model's percent difference from the right answer, the largest over the test's accuracy checks (lower is better; results within 10 % of each other share a rank). Mass error, timing and NSE are shown beside it, not folded in. Each model runs the same inputs on 3 grids, from 0.2 m down to 0.05 m cells; the checks are scored on the 0.05 m grid, and each card lists the error on every grid. Compare them in the results viewer →

Rank	Model	Difference	Largest difference in	Run time
1	Basilisk Saint-Venant	<0.01 %	Average depth error	3.6 s
2=	LISFLOOD-FP 8.3 · FV1	0.03 %	Average depth error	10.0 s
2=	LISFLOOD-FP 8.3 · DG2	0.03 %	Average depth error	46.9 s
2=	OpenSWMM 6 finite volume	0.03 %	Average depth error	28.3 s
5	TELEMAC-2D openTELEMAC 9.1	0.047 %	Average depth error	3.8 min
6	ANUGA 4.0.1	0.12 %	Average flow error	–
7	Landlab OverlandFlow	28 %	Average depth error	16.9 s
8	LISFLOOD-FP 8.3 · ACC	64 %	Average flow error	6.3 s
9=	SWMM 5.1.15 previous release	174 %	Average flow error	1.8 min
9=	EPA SWMM 5.2.4 dynamic wave	174 %	Average flow error	1.8 min
11	Hydra urban drainage	199 %	Average flow error	1.4 min
12	OpenSWMM 6 dynamic wave	236 %	Average flow error	1.9 min

The SWMM dynamic-wave engines (EPA SWMM 5.2.4, OpenSWMM v6 dynamic wave and Hydra) never settle on this frictionless channel with 10 and 5 cm conduits, even with a 0.0025 s routing step: depths swing by more than a metre. On 20 cm conduits SWMM 5.2.4 is within 8 mm on the subcritical case. OpenSWMM v6's finite-volume routing stays close on all four bump tests.

Basilisk

Basilisk (Popinet, basilisk.fr), its second-order well-balanced Saint-Venant solver on a uniform Cartesian grid with its defaults; Manning friction added as the usual semi-implicit term (it has none built in)

#1 under 0.01 % off

Rate the error shrinks on finer grids: 2.2 (90 % range 1.9 to 2.4); the method's expected rate is 0.8 or more

Run time, finest grid: 3.6 s · Intel(R) Xeon(R) 6973P-C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: basilisk saint-venant.h (uniform Cartesian grid, defaults; a C program per test, generated by scripts/run_basilisk.py)

Average depth error

• 0.0083 mm off

<0.01 % of the exact profile's mean depth, 1.97 m

Average flow error

• < 0.0001 m²/s off

<0.01 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.000163 · 0.1 m: < 0.0001 · 0.05 m: < 0.0001

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate 2.15 (90 % range 1.94 to 2.36)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: Basilisk Saint-Venant (snapshot de851a91)

Run it yourself

Basilisk Saint-Venant (snapshot de851a91) · runs on Linux · produced by scripts/run_basilisk.py in open-solvers.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev]"
curl -O https://basilisk.fr/basilisk/basilisk.tar.gz && tar xzf basilisk.tar.gz  # a rolling snapshot: the score card names its SHA-256
(cd basilisk/src && ln -s config.gcc config && make -k; make)
python scripts/run_basilisk.py --basilisk basilisk/src A1F-040

The model files the suite wrote for this run (.zip, 0.58 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

The script writes a small Basilisk C program for the test (kept in the model files below) and compiles it with qcc.

The test itself (inputs, exact answer, checks): A1F-040.zip. Check the score card with benchmark1d2d verify-card: its signature by the suite's public development key shows the card has not changed since it was written, not who wrote it.

LISFLOOD-FP FV1

LISFLOOD-FP 8.3, first-order finite-volume solver (full shallow-water equations)

#2= 0.03 % off

Rate the error shrinks on finer grids: 1 (90 % range 0.99 to 1); the method's expected rate is 0.8 or more

Run time, finest grid: 10.0 s · AMD EPYC 7763 64-Core Processor, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: lisflood (fv1 solver, defaults; CPU, double precision)

Average depth error

• 0.58 mm off

0.03 % of the exact profile's mean depth, 1.97 m

Average flow error

• 0.000951 m²/s off

0.022 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00232 · 0.1 m: 0.00116 · 0.05 m: 0.000584

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate 1.00 (90 % range 0.99 to 1.00)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: LISFLOOD-FP 8.3 FV1

Run it yourself

LISFLOOD-FP 8.3 FV1 · runs on Linux · produced by scripts/run_lisflood.py in open-solvers.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
# LISFLOOD-FP 8.3 source (Zenodo, MD5 abaa2e41b58f6c7df4443274bf130d4a), built CPU-only in double precision:
sudo apt-get install -y libnetcdf-dev libnuma-dev
cmake -S <lisflood>/source -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build --target lisflood
python -m pip install -e ".[dev]"
python scripts/run_lisflood.py --exe build/lisflood --solver fv1 A1F-040

The model files the suite wrote for this run (.zip, 3.3 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

LISFLOOD-FP DG2

LISFLOOD-FP 8.3, second-order discontinuous Galerkin solver

#2= 0.03 % off

Rate the error shrinks on finer grids: 1 (90 % range 0.99 to 1); the method's expected rate is 0.8 or more

Run time, finest grid: 46.9 s · AMD EPYC 7763 64-Core Processor, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: lisflood (dg2 solver, defaults; CPU, double precision)

Average depth error

• 0.58 mm off

0.03 % of the exact profile's mean depth, 1.97 m

Average flow error

• 0.000951 m²/s off

0.022 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00232 · 0.1 m: 0.00117 · 0.05 m: 0.000584

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate 1.00 (90 % range 0.99 to 1.00)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: LISFLOOD-FP 8.3 DG2

Run it yourself

LISFLOOD-FP 8.3 DG2 · runs on Linux · produced by scripts/run_lisflood.py in open-solvers.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
# LISFLOOD-FP 8.3 source (Zenodo, MD5 abaa2e41b58f6c7df4443274bf130d4a), built CPU-only in double precision:
sudo apt-get install -y libnetcdf-dev libnuma-dev
cmake -S <lisflood>/source -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build --target lisflood
python -m pip install -e ".[dev]"
python scripts/run_lisflood.py --exe build/lisflood --solver dg2 A1F-040

The model files the suite wrote for this run (.zip, 7.9 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

OpenSWMM 6

OpenSWMM Engine v6 with its new explicit finite-volume routing (FLOW_ROUTING FV): one cell per conduit on the channel tests, its default cells per pipe on the drainage networks

#2= 0.03 % off

Rate the error shrinks on finer grids: 0.99 (90 % range 0.99 to 1); the method's expected rate is 0.8 or more

Run time, finest grid: 28.3 s · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (FV routing, conduit per grid cell)

Average depth error

• 0.59 mm off

0.03 % of the exact profile's mean depth, 1.97 m

Average flow error

• 0.000492 m²/s off

0.011 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00233 · 0.1 m: 0.00117 · 0.05 m: 0.000586

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate 0.99 (90 % range 0.99 to 1.00)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: OpenSWMM Engine v6 finite volume (6.0.0a4.dev1)

Run it yourself

OpenSWMM Engine v6 finite volume (6.0.0a4.dev1) · runs on Linux · produced by scripts/run_swmm_track_a.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
python -m venv .venv-openswmm
.venv-openswmm/bin/python -m pip install "openswmm==6.0.0a4.dev1" numpy
python scripts/run_swmm_track_a.py A1F-040 --engines openswmm-fv --openswmm-python .venv-openswmm/bin/python

The model files the suite wrote for this run (.zip, 19 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

TELEMAC-2D

openTELEMAC-2D 9.1.1, run with its default finite-volume settings

#5 0.047 % off

Rate the error shrinks on finer grids: 0.97 (90 % range 0.94 to 1); the method's expected rate is 0.8 or more

Run time, finest grid: 3.8 min · AMD EPYC 7763 64-Core Processor, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: telemac2d (Selafin, centreline nodes)

Average depth error

• 0.92 mm off

0.047 % of the exact profile's mean depth, 1.97 m

Average flow error

• 0.000951 m²/s off

0.022 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00355 · 0.1 m: 0.00182 · 0.05 m: 0.000918

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate 0.97 (90 % range 0.94 to 1.01)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: openTELEMAC-2D v9.1.1

Run it yourself

openTELEMAC-2D v9.1.1 · runs on Linux · produced by scripts/run_telemac.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev]"
conda create -n telemac -c conda-forge opentelemac=v9.1.1 python=3.12
python scripts/run_telemac.py --telemac-prefix "$(conda info --base)/envs/telemac" A1F-040

The model files the suite wrote for this run (.zip, 48 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

ANUGA

ANUGA 4.0.1 (Geoscience Australia / ANU), default settings on a triangular mesh

#6 0.12 % off

Rate the error shrinks on finer grids: 0.94 (90 % range 0.69 to 1.2); the method's expected rate is 0.8 or more

Settings: anuga (rectangular_cross mesh, defaults, sampled at cell centres)

Average depth error

• 0.94 mm off

0.048 % of the exact profile's mean depth, 1.97 m

Average flow error

• 0.00516 m²/s off

0.12 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00346 · 0.1 m: 0.00172 · 0.05 m: 0.000942

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate 0.94 (90 % range 0.69 to 1.19)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: ANUGA 4.0.1

Run it yourself

ANUGA 4.0.1 · runs on Linux · produced by scripts/run_anuga.py in open-solvers.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
conda create -n anuga -c conda-forge anuga=4.0.1 python=3.12 pip scipy
conda activate anuga
python -m pip install -e ".[dev]"
python scripts/run_anuga.py A1F-040

The model files for this run will be published with its next run.

ANUGA builds its mesh and boundaries in Python: the script is the model.

Landlab

Landlab 2.11 (CSDMS), its OverlandFlow component: the local-inertial 2D flood scheme of de Almeida et al. (2012), written independently in NumPy, with its defaults

#7 28 % off

Rate the error shrinks on finer grids: 0.0021 (90 % range 0.00038 to 0.0039); the method's expected rate is 0.8 or more

Run time, finest grid: 16.9 s · AMD EPYC 7763 64-Core Processor, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: landlab.components.OverlandFlow (local inertial, defaults; a node per cell centre)

Average depth error

• 56 cm off

28 % of the exact profile's mean depth, 1.97 m

Average flow error

• 1.24 m²/s off

28 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.557 · 0.1 m: 0.556 · 0.05 m: 0.555

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate 0.00 (90 % range 0.00 to 0.00)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: Landlab 2.11.0 OverlandFlow

Run it yourself

Landlab 2.11.0 OverlandFlow · runs on Linux · produced by scripts/run_landlab.py in open-solvers.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev]" "landlab==2.11.0"
python scripts/run_landlab.py A1F-040

The model files for this run will be published with its next run.

Landlab models are built in Python: the script is the model.

LISFLOOD-FP ACC

LISFLOOD-FP 8.3, the local-inertia solver it is best known for; it leaves out the advection term

#8 64 % off

Rate the error shrinks on finer grids: -0.13 (90 % range -0.27 to 0.011); the method's expected rate is 0.8 or more

Run time, finest grid: 6.3 s · AMD EPYC 7763 64-Core Processor, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: lisflood (acceleration solver, defaults; CPU, double precision)

Average depth error

• 67 cm off

34 % of the exact profile's mean depth, 1.97 m

Average flow error

• 2.82 m²/s off

64 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.562 · 0.1 m: 0.632 · 0.05 m: 0.674

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate −0.13 (90 % range −0.27 to 0.01)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: LISFLOOD-FP 8.3 ACC

Run it yourself

LISFLOOD-FP 8.3 ACC · runs on Linux · produced by scripts/run_lisflood.py in open-solvers.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
# LISFLOOD-FP 8.3 source (Zenodo, MD5 abaa2e41b58f6c7df4443274bf130d4a), built CPU-only in double precision:
sudo apt-get install -y libnetcdf-dev libnuma-dev
cmake -S <lisflood>/source -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build --target lisflood
python -m pip install -e ".[dev]"
python scripts/run_lisflood.py --exe build/lisflood --solver acc A1F-040

The model files the suite wrote for this run (.zip, 3.4 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

SWMM 5.1.15

EPA SWMM 5.1.15, the release before 5.2: on the parity tests compared with SWMM 5.2.4, on the exact tests scored like any other engine

#9= 174 % off

Rate the error shrinks on finer grids: -3.7 (90 % range -15 to 7); the method's expected rate is 0.8 or more

Run time, finest grid: 1.8 min · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (DYNWAVE routing, conduit per grid cell)

Average depth error

• 1.45 m off

74 % of the exact profile's mean depth, 1.97 m

Average flow error

• 7.71 m²/s off

174 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00809 · 0.1 m: 0.838 · 0.05 m: 1.45

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate −3.74 (90 % range −14.50 to 7.01)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: EPA SWMM 5.1.15

Run it yourself

EPA SWMM 5.1.15 · version data5.1.15 · runs on Linux · produced by scripts/run_swmm_track_a.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
python3.10 -m venv .venv-swmm51 && .venv-swmm51/bin/python -m pip install "swmm-toolkit==0.10.0"  # EPA SWMM 5.1.15
python scripts/run_swmm_track_a.py A1F-040 --engines swmm515 --swmm51-python .venv-swmm51/bin/python

The model files the suite wrote for this run (.zip, 19 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

EPA SWMM 5.2.4

EPA SWMM 5.2.4 with dynamic-wave routing; on the channel tests, the channel is written as one conduit per grid cell

#9= 174 % off

Rate the error shrinks on finer grids: -3.7 (90 % range -15 to 7); the method's expected rate is 0.8 or more

Run time, finest grid: 1.8 min · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (DYNWAVE routing, conduit per grid cell)

Average depth error

• 1.45 m off

74 % of the exact profile's mean depth, 1.97 m

Average flow error

• 7.71 m²/s off

174 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00809 · 0.1 m: 0.838 · 0.05 m: 1.45

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate −3.74 (90 % range −14.50 to 7.01)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: EPA SWMM 5.2.4

Run it yourself

EPA SWMM 5.2.4 · runs on Linux · produced by scripts/run_swmm_track_a.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
python scripts/run_swmm_track_a.py A1F-040 --engines swmm524

The model files the suite wrote for this run (.zip, 19 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

Hydra

Hydra 17 (NEER AI, Rust): its urban-drainage engine on the SWMM data model, run through its CLI with default settings

#11 199 % off

Rate the error shrinks on finer grids: -0.2 (90 % range -0.28 to -0.12); the method's expected rate is 0.8 or more

Run time, finest grid: 1.4 min · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (DYNWAVE routing, conduit per grid cell)

Average depth error

• 1.5 m off

76 % of the exact profile's mean depth, 1.97 m

Average flow error

• 8.79 m²/s off

199 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 1.14 · 0.1 m: 1.29 · 0.05 m: 1.5

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate −0.20 (90 % range −0.28 to −0.12)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: Hydra urban drainage (17.0.0/4.1.1)

Run it yourself

Hydra urban drainage (17.0.0/4.1.1) · runs on Linux · produced by scripts/run_swmm_track_a.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
HYDRA=$(python scripts/fetch_hydra.py .hydra)  # the pinned Hydra 17 CLI
python scripts/run_swmm_track_a.py A1F-040 --engines hydra-uds --hydra-exe "$HYDRA"

The model files the suite wrote for this run (.zip, 19 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

OpenSWMM 6

OpenSWMM Engine v6 (HydroCouple's SWMM 6, pre-release) with dynamic-wave routing

#12 236 % off

Rate the error shrinks on finer grids: -3.9 (90 % range -14 to 6.4); the method's expected rate is 0.8 or more

Run time, finest grid: 1.9 min · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (DYNWAVE routing, conduit per grid cell)

Average depth error

• 1.86 m off

94 % of the exact profile's mean depth, 1.97 m

Average flow error

• 10.4 m²/s off

236 % of the 4.42 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.0086 · 0.1 m: 0.893 · 0.05 m: 1.86

reported, not counted · the checks above are scored on the 0.05 m grid

Improves with finer grids

· rate −3.88 (90 % range −14.16 to 6.41)

reported, not counted · the scheme's design order is 2

Scored with method 3.0 · Score card: OpenSWMM Engine v6 dynamic wave (6.0.0a4.dev1)

Run it yourself

OpenSWMM Engine v6 dynamic wave (6.0.0a4.dev1) · runs on Linux · produced by scripts/run_swmm_track_a.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
python -m venv .venv-openswmm
.venv-openswmm/bin/python -m pip install "openswmm==6.0.0a4.dev1" numpy
python scripts/run_swmm_track_a.py A1F-040 --engines openswmm-dw --openswmm-python .venv-openswmm/bin/python

The model files the suite wrote for this run (.zip, 19 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

Not run yet: HEC-RAS 7.0, GeoClaw, TUFLOW, MIKE 21, Iber, BASEMENT, Delft3D FM

Run it and add yours →

Error shrinks as the grid gets finer

Average error on each grid. A steeper line means the model converges faster.

TELEMAC-2D · rate 0.97ANUGA · rate 0.94LISFLOOD-FP FV1 · rate 1LISFLOOD-FP DG2 · rate 1LISFLOOD-FP ACC · rate -0.13EPA SWMM 5.2.4 · rate -3.7OpenSWMM 6 · rate -3.9OpenSWMM 6 · rate 0.99Hydra · rate -0.2SWMM 5.1.15 · rate -3.7Basilisk · rate 2.2Landlab · rate 0.0021

Status of this test

Candidate. To become frozen it needs: maintainer review recorded.

Download inputs (.zip)Check your results in the browser Cite this test View on GitHub

Technical details for reviewers Checks, reference, inputs, sources

Checks and why each limit was chosen

Check	Difference measured as a percentage of	Latest	Why this check
L1_h L1 of h · in the score	of the exact profile's mean depth, 1.97 m	–	Average depth error under 5 mm (0.25 % of the depth) once steady.
L1_Q L1 of Q · in the score	of the 4.42 m²/s unit discharge	–	Discharge within 1 % of q everywhere on average: a steady state must carry the inflow along the whole channel.
convergence observed order of L1 error in h · reported, not counted	the design order, 2	–	Measured from the error on every refinement level, as in ASME V&V 20.

Reference

Exact solution from references.swashes.steady_bump.

Promotion checklist

Only frozen tests have fixed inputs and answers. This list is computed from the manifest and the published score cards; the rules are in the method.

✓Data licence verifiedfor candidateanalytic-public
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✓Reference (truth) availablefor candidateanalytic generator
✓Every tolerance justifiedfor candidateeach gating criterion has a rationale
✓A published run passesfor frozen6 passing score card(s)
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Inputs

File	Role	Format	SHA-256	Get it
`inputs/params.json`	parameters	json	`29539a588884…`	Download

About this test

Physics

A frictionless 25 m channel with a parabolic bump \(z = 0.2 - 0.05 (x - 10)^2\) on \(8 < x < 12\) carries \(q = 4.42\) m²/s, held at 2 m downstream. In steady flow the discharge is constant and so is the Bernoulli head, so at every point the depth is the subcritical root of

\[ \frac{q^2}{2 g h^2} + h + z = \frac{q^2}{2 g h_{out}^2} + h_{out}, \]

found to \(10^{-14}\) m (references/swashes.py). The surface dips to 1.71 m over the crest. The run starts from still water at 2 m and lasts 300 s.

What it checks

The balance between the bed slope and the pressure gradient in moving water, and that a steady state carries the same discharge everywhere.

Citation

Delestre, O., et al. (2013). SWASHES: a compilation of shallow water analytic solutions for hydraulic and environmental studies. Int. J. Numer. Meth. Fluids, 72(3), 269–300. doi:10.1002/fld.3741. Goutal, N., and Maurel, F. (1997), EDF report HE-43/97/016/B.

Sources

Delestre, O., Lucas, C., Ksinant, P.-A., Darboux, F., Laguerre, C., Vo, T. N. T., James, F., Cordier, S. (2013). SWASHES: a compilation of shallow water analytic solutions for hydraulic and environmental studies. doi:10.1002/fld.3741
Goutal, N., Maurel, F. (1997). Proceedings of the 2nd workshop on dam-break wave simulation (EDF HE-43/97/016/B). www.irit.fr/swashes

Test A1F-040_swashes_bump_subcritical · version 1.0.0 · data licence analytic-public