swmm-anywhere

Synthesize a plausible SWMM drainage network from public data (OSM streets + DEM) when no real pipe-network data exists.

Use ONLY when the user explicitly does not have pipe shapefile / CAD / GIS data, or when establishing a baseline before real data arrives. Do NOT use if `swmm-gis` or `swmm-network` can run on the user's actual data — the synthesized network is an inferred plausibility, not measured infrastructure.

For bbox-only inputs without real pipe data: this is the right skill. For inputs that include a .shp, .csv, or network.json of real pipes: route to swmm-network or swmm-gis instead.

What this skill does

Given a bounding box (and optional region name), this skill:

1. Downloads public source data via SWMManywhere: OpenStreetMap streets, a DEM tile (Planetary Computer by default), building footprints, river lines.
2. Snapshots the raw inputs under runs/<date>/<id>/00_raw/ with a SHA-256 manifest that is verified after capture (the result lands in synth_provenance.json under raw_snapshot_verified), so the exact OSM/DEM inputs that produced this run are pinned and audited (OSM/DEM otherwise drift continuously upstream).
3. Runs SWMManywhere's 24-step graph pipeline to infer subcatchment polygons, manhole nodes, pipe topology, pipe diameters, and outfall locations.
4. Writes a SWMM 5.2 `.inp` under runs/<date>/<id>/10_swmmanywhere/synth.inp, post-processed so the aiswmm swmm5 binary can run it directly (external storm.dat is copied next to the INP and its path is rewritten as relative, dodging the macOS path-with-spaces parsing bug).
5. Returns the INP path, raw-snapshot manifest path, and a structured provenance record (which graphfcns ran, parameter overrides used, upstream tool versions).

The synthesized INP is immediately runnable through swmm-runner and immediately auditable through swmm-experiment-audit.

Required inputs

• --bbox: four floats min_lon min_lat max_lon max_lat (WGS84). 1×1 km is a comfortable test size; smaller is faster and uses less RAM, larger needs more.
• --run-dir: target audit-pipeline directory; defaults to runs/<today>/<HHMMSS>_swmm_anywhere/.

Optional inputs

• --refresh-raw: reserved flag for a future cache-aware path. Today every call re-downloads OSM/DEM via SWMManywhere's own prepare_data; aiswmm does not yet replay a run from the 00_raw/ snapshot, so this flag has no effect at the aiswmm layer yet.
• --project-name: human-readable label embedded in the manifest.
• --config-overrides: JSON object of per-call SWMManywhere parameter overrides to adjust the synthesis (cure orphan nodes, tune pipe density). See Adjusting the synthesis below. The synth_swmm_from_bbox agent tool exposes the same as a config_overrides argument.

Defaults — tuned for fewer, more useful outfalls

The skill ships with outfall_derivation parameters tuned in spike 04 (A/B'd against SWMManywhere defaults on the same 1×1 km London Greenwich bbox):

On the spike bbox these defaults dropped outfalls from 50 to 33 (-34 %), grew pipes from 500 to 517 (+3.4 %), and shortened end-to-end runtime from 40 s to 32 s. The defaults can be overridden per call.

Adjusting the synthesis — orphan nodes, too many / too few pipes

When the structural QA step (network_qa.py --inp ..., step 1 of What to do next) flags isolated_node / no_outfall_path nodes, or the pipe count looks off, tune SWMManywhere's parameters and re-synthesise. Pass overrides per call — CLI --config-overrides '<json>', or the synth_swmm_from_bbox tool's config_overrides argument — shape {group: {param: value}}. The runner merges them onto the resolved config and re-runs the pipeline.

Example — more outfalls (cure orphans) and fewer pipes:

python3 skills/swmm-anywhere/scripts/synth_from_bbox.py --bbox <min_lon> <min_lat> <max_lon> <max_lat> \
  --config-overrides '{"outfall_derivation": {"outfall_length": 60}, "subcatchment_derivation": {"node_merge_distance": 25}}'

SWMManywhere is nonlinear, so expect 1–2 iterations to hit a target, and keep each value inside its valid range (synthesis errors on out-of-bounds, e.g. node_merge_distance ≥ max_street_length). Re-run the structural QA after each re-synthesis to confirm the fix.

Skill artifacts produced

runs/<date>/<id>/
├── 00_raw/                       # raw OSM/DEM/buildings snapshot
│   ├── street.json
│   ├── elevation.tif
│   ├── building.geoparquet
│   ├── river.json
│   └── raw_manifest.json         # SHA-256 of every file + source URLs
├── 10_swmmanywhere/
│   ├── synth.inp                 # the runnable SWMM 5.2 model
│   ├── storm.dat                 # copied alongside (path-with-spaces fix)
│   ├── nodes.geoparquet          # for visualization
│   ├── edges.geoparquet          # for visualization (color by outfall_id)
│   ├── subcatchments.geoparquet  # for visualization
│   └── synth_provenance.json     # parameters, tool versions, timings

Example invocation

Minimum: just give a bbox.

# Default — uses tuned outfall_derivation defaults (33 outfalls on the spike bbox)
python skills/swmm-anywhere/scripts/synth_from_bbox.py \
    --bbox 0.04020 51.55759 0.05450 51.56660 \
    --run-dir runs/2026-05-28/100000_my_first_synth

To reproduce SWMManywhere upstream extended_demo behaviour (separate-mode outfalls):

python skills/swmm-anywhere/scripts/synth_from_bbox.py \
    --bbox 0.04020 51.55759 0.05450 51.56660 \
    --upstream-defaults \
    --run-dir runs/2026-05-28/100000_upstream_replica

To use your own rainfall instead of the bundled 15-min demo storm:

python skills/swmm-anywhere/scripts/synth_from_bbox.py \
    --bbox 0.04020 51.55759 0.05450 51.56660 \
    --rain-file /path/to/your/storm.dat \
    --run-dir runs/2026-05-28/100000_custom_rain

What to do next

The skill produces a runnable SWMM .inp under <run-dir>/10_swmmanywhere/synth.inp. Chain it through aiswmm's standard audit pipeline:

# 1. Structural QA of the synthesized network (NO SWMM run needed) — flags
#    orphan/isolated nodes, junctions with no downstream path to an outfall,
#    and pipe/outfall counts straight off the synthesized INP. This is the
#    signal for whether to adjust SWMManywhere parameter overrides and
#    re-synthesize before spending a SWMM run on a structurally broken network.
python3 skills/swmm-network/scripts/network_qa.py --inp <run-dir>/10_swmmanywhere/synth.inp

# 2. Run SWMM (aiswmm's own swmm5 binary, NOT pyswmm)
aiswmm run --inp <run-dir>/10_swmmanywhere/synth.inp --run-dir <run-dir>/swmm_run

# 3. Audit the run
aiswmm audit --run-dir <run-dir>/swmm_run

# 4. Plausibility-review the synthesized model (NO observed data needed).
#    Reference-free: scores velocity / capacity / slope / roughness / diameter
#    against physical-plausibility bands → <run-dir>/swmm_run/09_review/.
#    Every finding is a WARN (flag-for-review); continuity stays the postflight gate's job.
aiswmm review --run-dir <run-dir>/swmm_run \
  --rules skills/swmm-design-review/rulebooks/synth_plausibility.yaml

# 5. Plot rain/runoff
aiswmm plot --run-dir <run-dir>/swmm_run

# 6. Plot a specific node or conduit (requires --link support; see swmm-plot SKILL.md)
aiswmm plot --run-dir <run-dir>/swmm_run --node <node_id> --node-attr Total_inflow

# 7. Pick a peak-flow conduit from the RPT Link Flow Summary and plot it
aiswmm plot --run-dir <run-dir>/swmm_run --link <conduit_id>

Constraints and known limits

• Apple Silicon (macOS arm64): SWMManywhere's pyswmm dependency triggers a SIGKILL on import because its bundled swmm.toolkit._solver.abi3.so ships its own libomp.dylib that collides with the OS OpenMP runtime. The runner module stubs pyswmm before any SWMManywhere import; aiswmm runs the resulting INP through its own swmm5 binary, so the stub is fully safe.
• OSM is mutable: the same bbox tomorrow can produce a different street graph. The 00_raw/ snapshot pins and SHA-256-verifies the exact inputs each run used, so a run stays auditable against upstream drift. (Replaying a new run from an existing snapshot — true byte-identical re-synthesis — is reserved future work; today each call re-downloads, so two runs of the same bbox can differ if OSM changed between them.)
• Plausible ≠ real: the synthesized network reflects what a sewer system might look like under these streets and this DEM. It is a starting point for calibration / sensitivity analysis, not a substitute for surveyed infrastructure data.
• Memory profile: end-to-end requires ~1–2 GB free RAM at peak (raster ops + numba JIT). On a constrained machine, close other apps before running large bboxes (> 2×2 km).

Installing — and credit where it's due

This skill leans on [SWMManywhere](https://github.com/ImperialCollegeLondon/SWMManywhere), a project from the Imperial College London team that figures out a plausible urban drainage network from OSM streets and a DEM. The clever part — graph cleanup, subcatchment delineation, pipe topology, pipe sizing — is all their work, released under BSD-3-Clause. What this skill adds is the agent-loop plumbing around it: a typed tool the LLM can call, a SKILL.md contract for context priming, a Python runner that smooths over a few macOS arm64 quirks, and the standard runs/<date>/<id>/ audit layout for the resulting INP.

To install:

pip install aiswmm[anywhere]

That brings in swmmanywhere from PyPI along with the geospatial stack it needs (geopandas, osmnx, rasterio, pyflwdir, pywbt, and ~22 others). The default pip install aiswmm stays light — the geo stack only shows up when you opt in to this extra.

If you're publishing work that uses or builds on this skill, please cite SWMManywhere and check the upstream repository at <https://github.com/ImperialCollegeLondon/SWMManywhere> for their citation guidance and the BSD-3-Clause license text. The synthesised network is upstream's intellectual contribution; this skill is just the agent-side adapter.