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swmm-uncertainty

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Parameter and forcing uncertainty for EPA SWMM. Without observed flow, call propagate_parameter_ranges (global ranges, one SWMM run per sample, peak spread); the Morris/OAT/Sobol tools need an observed series. Use when an agent needs to (1) propagate parameter uncertainty through SWMM (fuzzy alpha-cut or Monte Carlo), (2) quantify hydrograph envelopes or output entropy without treating the run as calibration, (3) screen which parameters matter using OAT / Morris elementary-effects / Sobol' indices, (4) generate a rainfall ensemble (observed-series perturbation or IDF-curve design storms) and aggregate the resulting hydrograph envelope, or (5) build the integrated paper-reviewer-facing uncertainty source decomposition (`uncertainty_source_summary.md` + `uncertainty_source_decomposition.json`) over the raw outputs of the prior steps.

skills/swmm-uncertainty/SKILL.md

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Oct 28, 2026
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# SWMM Uncertainty

Part of [Agentic SWMM](https://github.com/Zhonghao1995/agentic-swmm-workflow) — 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
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