swmm-uncertaintylisted

Parameter and forcing uncertainty propagation and sensitivity analysis for EPA SWMM. 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.
Zhonghao1995/agentic-swmm-workflow · ★ 8 · AI & Automation · score 71

Install: claude install-skill Zhonghao1995/agentic-swmm-workflow

# SWMM Uncertainty ## 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. This skill is intentionally separate from `swmm-calibration`. - `swmm-calibration` asks: which parameter set best matches observations? - `swmm-uncertainty` asks: how much output uncertainty