SWMM GIS / Preprocess

Part of Agentic SWMM — install the project first for the executable toolchain (aiswmm CLI, SWMM solver, MCP servers).

Before calling any watershed delineation tool — ask the user

When the user triggers watershed delineation (qgis_raw_to_entropy_partition or equivalent), always ask these questions first before making the tool call:

1. Delineation mode — Standard (fast, direct GRASS basins, no entropy) or Entropy-guided (paper WJE/NWJE/WFJS split-lump with sensitivity figures)?
2. Stream threshold — How many upslope cells define a stream? Default 100. Smaller = more streams = finer subcatchments.
3. If entropy mode — Delta threshold (default 0.015) and WFJS similarity threshold (default 0.95)? Use defaults unless doing sensitivity exploration.
4. Purpose — Planning / calibration exploration / sensitivity analysis / paper reproduction? This affects how strictly to apply paper-only splits and whether sensitivity figures are needed.
5. CRS normalization needed? — Are all input layers already in the same projected CRS? If uncertain, check first with qgis_load_layers + qgis_validate_crs.

Do not assume entropy mode. Do not skip the stream threshold question — it directly controls subcatchment count.

Default CRS policy: if source layers already share the same projected CRS, do not run normalize-layers. The normalization bridge reprojects, clips, and may resample raster grids, so it can change watershed structure. Only use it when layer CRS/raster alignment actually needs preprocessing. If CRS differs but geometry should be preserved, prefer a reproject-only step over clipping/resampling.

Choosing the right delineation mode

Entropy hotspot ranking

After an entropy run, audit/entropy_hotspot_ranking.json ranks subcatchments by WJE descending. Rank 1 = highest spatial heterogeneity = candidate for finer delineation in calibration. Surface this to the user if they ask "which subcatchments matter most" or "where should I refine."

What this skill provides

• Subcatchment polygon preprocessing (MVP):
  • ingest polygon GeoJSON
  • estimate area/width/slope with deterministic fallback and optional DEM-assisted metrics
  • link each subcatchment outlet to a network node ID
  • export builder-ready CSV for swmm-builder
• QGIS-oriented raw-data entrypoint:
  • validate raw/QGIS-exported layer paths and shapefile sidecars
  • inspect CRS hints from .prj and GeoJSON metadata
  • run QGIS Processing / GRASS hydrology for flow accumulation, drainage direction, stream network, and basin labels
  • compute paper-consistent WJE/NWJE/WFJS entropy diagnostics along the longest D8 flow path
  • generate entropy-guided subcatchment partitions and threshold sensitivity figures
  • extract QGIS overlay attributes into swmm-params CSV inputs
  • export standard Agentic SWMM intermediates under runs/\x3Ccase>/01_gis/, 02_params/, and 04_network/
• Clean final layer packaging:
  • keep detailed audit artifacts in 00_raw/, 01_gis/, 02_params/, audit/, and memory/
  • also create a user-facing final_layers/ folder with the SWMM/GIS layers the user needs next
  • include subcatchments.shp, flow.shp, slope_percent.tif, outfall.shp, overview.png, and manifest.json

Scripts

• scripts/preprocess_subcatchments.py

  • --subcatchments-geojson \x3Cfile>
  • --network-json \x3Cfile> (from swmm-network schema)
  • --out-csv \x3Cfile> (builder-ready CSV)
  • --out-json \x3Cfile> (assumptions + detailed metrics)
  • optional DEM mode: --dem-stats-json \x3Cfile>, --dem-stats-id-field \x3Cfield>
  • optional helpers: --id-field, --outlet-hint-field, --default-slope-pct, --min-width-m, --max-link-distance-m

• scripts/qgis_prepare_swmm_inputs.py

  • load-layers: validate source paths and shapefile sidecars
  • validate-crs: write a CRS consistency report from a layer manifest
  • normalize-layers: use QGIS Processing to reproject DEM, boundary, land-use, and soil layers to one CRS and clip them by the boundary
  • overlay-landuse-soil: convert a QGIS overlay GeoJSON into landuse.csv and soil.csv
  • export-swmm-intermediates: produce the standard data-side outputs for the modular path:
    • runs/\x3Ccase>/00_raw/qgis_layers_manifest.json
    • runs/\x3Ccase>/00_raw/qgis_crs_report.json
    • runs/\x3Ccase>/01_gis/subcatchments.{geojson,csv,json}
    • runs/\x3Ccase>/02_params/{landuse.csv,soil.csv,landuse.json,soil.json,merged_params.json}
    • runs/\x3Ccase>/04_network/{network.json,network_qa.json}
    • runs/\x3Ccase>/qgis_export_manifest.json

• scripts/flowpath_entropy_partition.py

  • computes paper-consistent spatial heterogeneity diagnostics:
    • WJE(g) over upstream contributing area U(g)
    • NWJE(g) = WJE(g) / ln(D(g))
    • upstream-averaged fuzzy memberships for soil drainage, land-use perviousness, and slope
    • WFJS_seq between adjacent cells on the longest flow path
    • WFJS_outlet relative to the outlet profile
    • delta_NWJE_seq and delta_NWJE_outlet
  • default paper split rule:
    • preserve/split where abs(delta_NWJE_seq) >= 0.015 and WFJS_seq \x3C= 0.95
    • safe lump / HP-REA interval where abs(delta_NWJE_seq) \x3C= 0.015 and WFJS_seq >= 0.95
  • --paper-only-splits disables secondary engineering split points for publication-style or paper-rule-only outputs

• scripts/cell_entropy_similarity_aggregation.py

  • non-flow-connected local aggregation diagnostic:
    • computes normalized joint entropy in a moving cell window from soil / land-use / slope triples
    • computes adjacent-cell fuzzy Jaccard similarity from soil / land-use / slope membership vectors
    • labels cells as lumpable, transitional, or preserve_discrete
  • use this before hydrologic routing as a data-side heterogeneity screen; do not treat it as upstream WJE/NWJE or a watershed delineation result

• scripts/plot_entropy_threshold_sensitivity.py

  • renders five-panel paper-rule decision-space and watershed-partition figures
  • uses Arial 12 pt styling and non-overlapping figure-level legends

• scripts/qgis_raw_to_entropy_partition.py

  • reproducible one-command cross-watershed raw GIS runner:
    • validates source GIS layers
    • optionally normalizes CRS and clips DEM / boundary / land-use / soil layers by boundary
    • calls QGIS Processing grass:r.watershed
    • runs paper-rule entropy partitioning
    • runs threshold sensitivity variants
    • writes audit manifests, command logs, figures, and run memory cards

• scripts/qgis_todcreek_raw_to_entropy_partition.py

  • compatibility wrapper around qgis_raw_to_entropy_partition.py for the committed Tod Creek case study

• scripts/qgis_package_final_layers.py

  • packages QGIS/GRASS run outputs into runs/\x3Ccase>/final_layers/
  • copies/renames the selected subcatchment shapefile to subcatchments.shp
  • copies the DEM-derived slope raster to slope_percent.tif
  • derives flow.shp from QGIS/GRASS stream_\x3Cthreshold>.tif plus acc_\x3Cthreshold>.tif
  • derives outfall.shp from the maximum flow-accumulation stream cell
  • writes overview.png using Arial, inward ticks, longitude/latitude border labels, green-low/red-high semi-transparent slope background, bold subcatchment boundaries, prominent blue flow paths, and a legend
  • writes manifest.json so users do not need to inspect the audit tree to find deliverables

• scripts/plot_qgis_standard_layers.py

  • renders the clean final_layers/overview.png
  • intended for deliverable figures, not raw audit screenshots

Explicit assumptions for subcatchment preprocessing

• Coordinates should be in one projected CRS before SWMM geometric quantities are trusted. Use qgis_normalize_layers or --normalize-layers when raw DEM / land-use / soil / boundary inputs may be mixed CRS or not clipped to the study boundary.
• Width helper priority:
  1. properties.width_m / properties.hydraulic_width_m
  2. DEM flow length (dem_flow_length_m) via area_m2 / flow_length_m
  3. fallback width_m = max(min_width_m, 2 * area_m2 / perimeter_m)
• Slope helper priority:
  1. properties.slope_pct
  2. DEM direct slope (e.g., dem_slope_pct, raster_slope_pct)
  3. DEM elevation-derived slope (e.g., dem_elev_max_m, dem_elev_min_m, dem_elev_mean_m, dem_elev_outlet_m)
  4. (properties.elev_mean_m - properties.elev_outlet_m) / flow_length_m * 100
  5. default slope
• Outlet linking priority:
  1. valid properties.outlet_hint (or configured field)
  2. nearest node ID from network coordinates (fallback with diagnostics)

DEM-assisted example

python3 skills/swmm-gis/scripts/preprocess_subcatchments.py \
  --subcatchments-geojson skills/swmm-gis/examples/subcatchments_dem_assisted.geojson \
  --network-json skills/swmm-network/examples/basic-network.json \
  --dem-stats-json skills/swmm-gis/examples/subcatchments_dem_stats_demo.json \
  --default-rain-gage RG1 \
  --out-csv runs/swmm-gis/subcatchments_dem_assisted.csv \
  --out-json runs/swmm-gis/subcatchments_dem_assisted.json

QGIS data-prep example

Use this when QGIS has already delineated subcatchments and overlaid land-use / soil attributes onto the subcatchment layer:

python3 skills/swmm-gis/scripts/qgis_prepare_swmm_inputs.py export-swmm-intermediates \
  --case-id qgis-demo \
  --run-dir runs/qgis-demo \
  --subcatchments-geojson skills/swmm-gis/examples/qgis_overlay_subcatchments.geojson \
  --network-json skills/swmm-network/examples/basic-network.json \
  --landuse-field landuse_class \
  --soil-field soil_texture \
  --default-rain-gage RG1

This bridge supports two modes. Prepared-overlay mode expects QGIS to provide delineated/overlayed polygons. Entropy-partition mode calls QGIS Processing / GRASS hydrology directly, then computes the paper-rule WJE/NWJE/WFJS split-lump partition inside Agentic SWMM.

QGIS/GRASS entropy-guided subcatchment example

Use this for the full raw GIS to paper-rule subcatchment workflow. QGIS/GRASS provides the hydrology backbone; Agentic SWMM computes the paper-consistent entropy/fuzzy split-lump logic and writes audit artifacts.

Generic form for any watershed with DEM, boundary, land-use, and soil layers:

python3 skills/swmm-gis/scripts/qgis_raw_to_entropy_partition.py \
  --case-id my-watershed-qgis-entropy \
  --case-label "My Watershed" \
  --dem path/to/dem.tif \
  --boundary path/to/boundary.shp \
  --landuse path/to/landuse.shp \
  --soil path/to/soil.shp \
  --out-dir runs/my-watershed-qgis-entropy

Use normalization when raw layers need CRS harmonization and boundary clipping before hydrology:

python3 skills/swmm-gis/scripts/qgis_raw_to_entropy_partition.py \
  --case-id my-watershed-qgis-entropy \
  --case-label "My Watershed" \
  --dem path/to/dem.tif \
  --boundary path/to/boundary.shp \
  --landuse path/to/landuse.shp \
  --soil path/to/soil.shp \
  --normalize-layers \
  --out-dir runs/my-watershed-qgis-entropy

Tod Creek case-study command:

python3 skills/swmm-gis/scripts/qgis_raw_to_entropy_partition.py \
  --case-id todcreek-qgis-entropy \
  --case-label "Tod Creek" \
  --dem data/Todcreek/Geolayer/n48_w124_1arc_v3_Clip_Projec1.tif \
  --boundary data/Todcreek/Boundary/Boundary.shp \
  --landuse data/Todcreek/Geolayer/landuse.shp \
  --soil data/Todcreek/Geolayer/soil.shp \
  --rainfall data/Todcreek/Rainfall/1984rain.dat \
  --out-dir runs/todcreek-qgis-entropy

The run writes:

runs/\x3Ccase>/00_raw/qgis_layers_manifest.json
runs/\x3Ccase>/00_raw/qgis_crs_report.json
runs/\x3Ccase>/00_raw/normalized_layers/qgis_normalized_layers_manifest.json  # if --normalize-layers
runs/\x3Ccase>/01_gis/threshold_sweep/{acc,drain,basin,stream}_100.tif
runs/\x3Ccase>/02_params/paper_entropy_partition/
runs/\x3Ccase>/02_params/threshold_sensitivity/
runs/\x3Ccase>/07_figures/paper_rule_decision_spaces_5panel.png
runs/\x3Ccase>/07_figures/paper_rule_watershed_partitions_5panel.png
runs/\x3Ccase>/audit/qgis_entropy_run_manifest.json
runs/\x3Ccase>/audit/processing_commands.json
runs/\x3Ccase>/memory/qgis_entropy_subcatchment_memory.{json,md}
runs/\x3Ccase>/final_layers/{subcatchments.shp,flow.shp,slope_percent.tif,outfall.shp,overview.png,manifest.json}  # after packaging

Evidence boundary: this workflow produces GIS-derived SWMM subcatchment spatial units and audit evidence. It does not prove calibrated hydrologic performance until the outputs are passed through swmm-builder, swmm-runner, and swmm-experiment-audit.

Cell-level entropy/similarity aggregation diagnostic

Use this when the question is local data aggregation before hydrologic routing: where can adjacent raster cells be lumped because they are information-similar, and where should local spatial heterogeneity be preserved?

python3 skills/swmm-gis/scripts/cell_entropy_similarity_aggregation.py \
  --dem data/Todcreek/Geolayer/n48_w124_1arc_v3_Clip_Projec1.tif \
  --boundary-shp data/Todcreek/Boundary/Boundary.shp \
  --landuse-shp data/Todcreek/Geolayer/landuse.shp \
  --soil-shp data/Todcreek/Geolayer/soil.shp \
  --out-dir runs/todcreek-cell-entropy-aggregation

This diagnostic does not use flow accumulation, drainage direction, or upstream contributing area U(g). It is useful for a pre-flow data heterogeneity layer, while qgis_flowpath_entropy_partition remains the hydrologically connected SWMM subcatchment partition.

MCP-facing operations

mcp/swmm-gis/server.js exposes 15 tools. They split into three families.

Subcatchment construction (start here for raw municipal data)

• basin_shp_to_subcatchments: pick polygons from any municipal basin / catchment shapefile and emit SWMM-ready subcatchments.geojson + subcatchments.csv (subcatchment_id, outlet, area_ha, width_m, slope_pct, rain_gage). Four selection modes: by_id_field (default), by_index, largest, all. Width defaults to sqrt(area_m²); slope defaults to 1%. Use this as step 1 when starting from raw shapefile data.
• gis_preprocess_subcatchments: deterministic preprocessor used by both the explicit DEM-assisted path and the legacy non-MCP scripts. Computes width/slope/area from a basin shapefile + DEM. Use when a DEM is available.

Standard QGIS data-prep chain

• qgis_load_layers: validate source files and sidecars.
• qgis_validate_crs: check that explicit CRS hints are consistent before export.
• qgis_normalize_layers: reproject DEM, boundary, land-use, and soil layers to a target CRS, then clip them by the boundary. Uses QGIS Processing native:reprojectlayer, native:clip, gdal:warpreproject, and gdal:cliprasterbymasklayer.
• qgis_overlay_landuse_soil: extract overlay attributes into the swmm-params input CSV format.
• qgis_extract_slope_area_width: call the deterministic subcatchment preprocessor.
• qgis_import_drainage_assets: copy/import a network JSON and run network QA.
• qgis_export_swmm_intermediates: run the complete MVP data-side bridge.

Entropy-guided partition (research-grade, optional)

• qgis_raw_to_entropy_partition: run the full cross-watershed raw GIS → QGIS/GRASS hydrology → paper-rule entropy subcatchment workflow with audit artifacts. Region-agnostic.
• qgis_todcreek_raw_to_entropy_partition: case-study alias for the committed Tod Creek regression. Don't use for new regions; pass your own paths to qgis_raw_to_entropy_partition instead.
• qgis_flowpath_entropy_partition: run the core paper-rule WJE/NWJE/WFJS partition from already prepared QGIS/GRASS flow accumulation and drainage rasters.
• qgis_package_final_layers: package the selected QGIS/GRASS outputs into a clean final_layers/ deliverable folder with SWMM/GIS layers, overview figure, and manifest.
• qgis_cell_entropy_similarity_aggregation: non-flow-connected local cell-level entropy/similarity aggregation diagnostic.

Area-weighted parameter mapping (core for any region)

• qgis_area_weighted_params: intersect subcatchments with land-use and soil polygons, compute area fractions, and write area-weighted weighted_params.json plus landuse_area_weights.csv and soil_area_weights.csv audit files. This is the canonical handoff into swmm-builder. Backed by skills/swmm-params/references/landuse_class_to_subcatch_params.csv (extend that lookup if your region's zoning vocabulary is unfamiliar).

Future QGIS processing should fill the same interfaces rather than changing downstream swmm-params, swmm-network, or swmm-builder contracts.

Notes

• These steps occur before generating SWMM INP.
• CSV/JSON outputs include *_source / *_method fields for auditability.