swmm-uncertaintylisted

# 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 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