swmm-uncertainty

SWMM Uncertainty

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

Agent path without observed data

The honest split (live findings F-107 and F-109, 2026-09-03): WITH observed flow, swmm_sensitivity_oat / swmm_sensitivity_morris / swmm_sensitivity_sobol rank parameters against the data (they need an observed series and a patch map). WITHOUT observed flow, propagate_parameter_ranges is the tool for both questions: mode=one_at_a_time varies each parameter alone in one call and returns a per-parameter spread and a ranking ("which parameters matter most"); the default joint mode samples all ranges together and reports the spread ("how uncertain is the peak"). Never emulate a ranking with one sweep per parameter. Rainfall: a request to scale the observed event by factors (0.8, 1.0, 1.2) on a model with inline rain is run_climate_scenarios with those factors (live finding F-112, 2026-09-03); swmm_rainfall_ensemble needs a prepared rainfall series file and a JSON config (perturbation or IDF).

propagate_parameter_ranges is the typed tool for "how uncertain is the peak if Manning's n and imperviousness vary". It applies each named parameter globally (the same value on every subcatchment or conduit), runs SWMM once per sample through the audited runner, and writes 09_audit/parameter_sweep.json and .md with the baseline peak, the min/median/max over the samples, the spread as a percent of the baseline and the dominant parameter. Ranges are a mapping such as {"n_imperv": [0.010, 0.020], "pct_imperv": [60, 80]}; aliases manning_n, imperviousness, conduit_roughness, n_perv, s_imperv, s_perv, width, slope. It is prior sensitivity, not calibrated uncertainty; the per-object fuzzy and Monte Carlo workflows below remain the research path.

What this skill provides

• User-defined fuzzy membership functions for SWMM parameters.
• Baseline-aware triangular fuzzy numbers, where the current model value is the default triangle peak.
• Alpha-cut transformation from fuzzy membership functions to parameter intervals.
• LHS, random, or boundary sampling inside each alpha-cut interval.
• Monte Carlo parameter sampling for prior or calibration-informed probability distributions.
• Normal/lognormal/truncated-normal/uniform sampling with simple physical constraints such as bound parameters and greater-than rules.
• Batch propagation through SWMM by reusing the existing calibration patch-map convention.
• Normalized Shannon entropy metrics for output ensembles, such as hydrograph entropy over time.
• Machine-readable uncertainty summaries for output envelopes, entropy records, and failed/invalid samples.
• Sensitivity-analysis screening with three sub-methods (OAT / Morris / Sobol') sharing one entry point (scripts/sensitivity.py).
• Rainfall-forcing ensembles: time-series perturbation of an observed rainfall record (gaussian, multiplicative, AR(1), intensity_scaling) or IDF-curve sampling of design storms (Chicago / Huff / SCS Type II), with optional per-realisation SWMM runs and ensemble envelope aggregation.