Exact answer known1D · free surfaceCandidate

Hump with a hydraulic jump

Does the jump below the hump stand in the right place?

Test A1F-042
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

A small flow goes rapid over the hump and then jumps back to calm, deeper water partway down its far side, at a point fixed exactly by the balance of momentum.

Why it matters

Jumps below weirs and sills decide where the bed scours and how big the protection must be.

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	0.076 %	Average flow error	1.7 s
2	ANUGA 4.0.1	0.11 %	Average flow error	–
3	TELEMAC-2D openTELEMAC 9.1	0.17 %	Average flow error	1.6 min
4=	OpenSWMM 6 finite volume	0.19 %	Average depth error	28.3 s
4=	LISFLOOD-FP 8.3 · DG2	0.2 %	Average depth error	21.7 s
4=	LISFLOOD-FP 8.3 · FV1	0.2 %	Average depth error	4.6 s
7	Landlab OverlandFlow	46 %	Average flow error	7.0 s
8	LISFLOOD-FP 8.3 · ACC	61 %	Average flow error	2.3 s
9	OpenSWMM 6 dynamic wave	194 %	Average flow error	54.0 s
10	Hydra urban drainage	220 %	Average flow error	1.0 min
11=	SWMM 5.1.15 previous release	322 %	Average flow error	51.3 s
11=	EPA SWMM 5.2.4 dynamic wave	322 %	Average flow error	51.2 s

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 0.076 % off

Rate the error shrinks on finer grids: 1.1 (90 % range 0.71 to 1.4); the method's expected rate is 0.8 or more

Run time, finest grid: 1.7 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

• 0.19 mm off

0.056 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.000136 m²/s off

0.076 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.000815 · 0.1 m: 0.000366 · 0.05 m: 0.000188

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

Improves with finer grids

· rate 1.06 (90 % range 0.71 to 1.41)

reported, not counted · the scheme's design order is 1

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-042

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-042.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.

ANUGA

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

#2 0.11 % off

Rate the error shrinks on finer grids: 0.98 (90 % range -1.2 to 3.1); the method's expected rate is 0.8 or more

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

Average depth error

• 0.25 mm off

0.073 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.0002 m²/s off

0.11 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.000955 · 0.1 m: 0.000731 · 0.05 m: 0.000247

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

Improves with finer grids

· rate 0.98 (90 % range −1.17 to 3.12)

reported, not counted · the scheme's design order is 1

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-042

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.

TELEMAC-2D

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

#3 0.17 % off

Rate the error shrinks on finer grids: 0.98 (90 % range 0.74 to 1.2); the method's expected rate is 0.8 or more

Run time, finest grid: 1.6 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

• 0.37 mm off

0.11 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.000305 m²/s off

0.17 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00142 · 0.1 m: 0.000754 · 0.05 m: 0.000367

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

Improves with finer grids

· rate 0.98 (90 % range 0.74 to 1.21)

reported, not counted · the scheme's design order is 1

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-042

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.

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

#4= 0.19 % off

Rate the error shrinks on finer grids: 0.89 (90 % range 0.52 to 1.3); 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

• 0.65 mm off

0.19 % of the exact profile's mean depth, 0.336 m

Average flow error

• < 0.0001 m²/s off

0.012 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00221 · 0.1 m: 0.00128 · 0.05 m: 0.000645

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

Improves with finer grids

· rate 0.89 (90 % range 0.52 to 1.25)

reported, not counted · the scheme's design order is 1

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-042 --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.

LISFLOOD-FP DG2

LISFLOOD-FP 8.3, second-order discontinuous Galerkin solver

#4= 0.2 % off

Rate the error shrinks on finer grids: 0.86 (90 % range 0.47 to 1.2); the method's expected rate is 0.8 or more

Run time, finest grid: 21.7 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

• 0.67 mm off

0.2 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.000288 m²/s off

0.16 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00219 · 0.1 m: 0.0013 · 0.05 m: 0.000667

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

Improves with finer grids

· rate 0.86 (90 % range 0.47 to 1.24)

reported, not counted · the scheme's design order is 1

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-042

The model files the suite wrote for this run (.zip, 8 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

LISFLOOD-FP FV1

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

#4= 0.2 % off

Rate the error shrinks on finer grids: 0.9 (90 % range 0.61 to 1.2); the method's expected rate is 0.8 or more

Run time, finest grid: 4.6 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

• 0.68 mm off

0.2 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.000288 m²/s off

0.16 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.00238 · 0.1 m: 0.00134 · 0.05 m: 0.000678

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

Improves with finer grids

· rate 0.90 (90 % range 0.61 to 1.20)

reported, not counted · the scheme's design order is 1

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-042

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.

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 46 % off

Rate the error shrinks on finer grids: 0.0093 (90 % range -0.019 to 0.038); the method's expected rate is 0.8 or more

Run time, finest grid: 7.0 s · AMD EPYC 7763 64-Core Processor, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

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

Average depth error

• 10 cm off

30 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.0835 m²/s off

46 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.102 · 0.1 m: 0.101 · 0.05 m: 0.101

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

Improves with finer grids

· rate 0.01 (90 % range −0.02 to 0.04)

reported, not counted · the scheme's design order is 1

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-042

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 61 % off

Rate the error shrinks on finer grids: 0.036 (90 % range -0.55 to 0.62); the method's expected rate is 0.8 or more

Run time, finest grid: 2.3 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

• 8.5 cm off

25 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.11 m²/s off

61 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.0895 · 0.1 m: 0.0975 · 0.05 m: 0.0851

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

Improves with finer grids

· rate 0.04 (90 % range −0.55 to 0.62)

reported, not counted · the scheme's design order is 1

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-042

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.

OpenSWMM 6

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

#9 194 % off

Rate the error shrinks on finer grids: -0.38 (90 % range -0.82 to 0.063); the method's expected rate is 0.8 or more

Run time, finest grid: 54.0 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 (DYNWAVE routing, conduit per grid cell)

Average depth error

• 24 cm off

70 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.349 m²/s off

194 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.14 · 0.1 m: 0.197 · 0.05 m: 0.236

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

Improves with finer grids

· rate −0.38 (90 % range −0.82 to 0.06)

reported, not counted · the scheme's design order is 1

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-042 --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.

Hydra

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

#10 220 % off

Rate the error shrinks on finer grids: -0.33 (90 % range -1 to 0.38); the method's expected rate is 0.8 or more

Run time, finest grid: 1.0 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

• 24 cm off

72 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.397 m²/s off

220 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.155 · 0.1 m: 0.221 · 0.05 m: 0.243

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

Improves with finer grids

· rate −0.33 (90 % range −1.03 to 0.38)

reported, not counted · the scheme's design order is 1

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=$(python scripts/fetch_hydra.py .hydra)  # the pinned Hydra 17 CLI
python scripts/run_swmm_track_a.py A1F-042 --engines hydra-uds --hydra-exe "$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.

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

#11= 322 % off

Rate the error shrinks on finer grids: -0.56 (90 % range -1.6 to 0.46); the method's expected rate is 0.8 or more

Run time, finest grid: 51.3 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 (DYNWAVE routing, conduit per grid cell)

Average depth error

• 29 cm off

86 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.579 m²/s off

322 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.132 · 0.1 m: 0.237 · 0.05 m: 0.287

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

Improves with finer grids

· rate −0.56 (90 % range −1.58 to 0.46)

reported, not counted · the scheme's design order is 1

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-042 --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

#11= 322 % off

Rate the error shrinks on finer grids: -0.56 (90 % range -1.6 to 0.46); the method's expected rate is 0.8 or more

Run time, finest grid: 51.2 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 (DYNWAVE routing, conduit per grid cell)

Average depth error

• 29 cm off

86 % of the exact profile's mean depth, 0.336 m

Average flow error

• 0.579 m²/s off

322 % of the 0.18 m²/s unit discharge

Error by grid (L1 h)

· 0.2 m: 0.132 · 0.1 m: 0.237 · 0.05 m: 0.287

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

Improves with finer grids

· rate −0.56 (90 % range −1.58 to 0.46)

reported, not counted · the scheme's design order is 1

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-042 --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.

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 0.98ANUGA · rate 0.98LISFLOOD-FP FV1 · rate 0.9LISFLOOD-FP DG2 · rate 0.86LISFLOOD-FP ACC · rate 0.036EPA SWMM 5.2.4 · rate -0.56OpenSWMM 6 · rate -0.38OpenSWMM 6 · rate 0.89Hydra · rate -0.33SWMM 5.1.15 · rate -0.56Basilisk · rate 1.1Landlab · rate 0.0093

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, 0.336 m	–	Average depth error under 5 mm (1.5 % of the downstream depth) once steady; a jump one cell out of place costs about 0.2 m over one cell, about 1 mm of average error on the finest grid.
L1_Q L1 of Q · in the score	of the 0.18 m²/s unit discharge	–	Discharge within 1 % of q on average.
convergence observed order of L1 error in h · reported, not counted	the design order, 1	–	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
✓All inputs availablefor candidateall committed or fetched by SHA-256
✓Reference (truth) availablefor candidateanalytic generator
✓Every tolerance justifiedfor candidateeach gating criterion has a rationale
✓A published run passesfor frozen6 passing score card(s)
✓Order of accuracy shownfor frozenobserved order meets the minimum
✓Two independent codes passfor frozenpassing codes: ANUGA 4.0.1, Basilisk Saint-Venant, LISFLOOD-FP 8.3 DG2, LISFLOOD-FP 8.3 FV1, OpenSWMM Engine v6 finite volume, openTELEMAC-2D v9.1.1
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Inputs

File	Role	Format	SHA-256	Get it
`inputs/params.json`	parameters	json	`60030aae5847…`	Download

About this test

Physics

The same channel and bump with \(q = 0.18\) m²/s and 0.33 m held downstream. The flow goes critical at the crest, supercritical down its far side and jumps back to subcritical where the Rankine–Hugoniot condition

\[ \frac{q^2}{h_1} + \frac{g h_1^2}{2} = \frac{q^2}{h_2} + \frac{g h_2^2}{2} \]

holds between the supercritical branch of the crest head and the subcritical branch of the downstream head: \(x_s = 11.6656\) m (solved to \(10^{-12}\) m).

What it checks

Shock capturing on a sloping bed: the jump's position and the conjugate depths on either side.

Citation

Delestre et al. (2013), SWASHES, doi:10.1002/fld.3741.

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-042_swashes_bump_shock · version 1.0.0 · data licence analytic-public