Claude Skills · Data & Analytics

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 a...

★ 25 Synced 2 hours ago View SKILL.md

At a glance

Data & Analytics Manual install Python Actively maintained

This skill is for Data & Analytics and helps you propagate parameter uncertainty through swmm, quantify hydrograph envelopes and output entropy, and screen critical swmm parameters.

Install git clone --depth 1 https://github.com/Zhonghao1995/agentic-swmm-workflow cp -r agentic-swmm-workflow/skills/swmm-uncertainty ~/.claude/skills/swmm-uncertainty
Can use Not declared by the author

Setup, runtime and requirements describe Zhonghao1995/agentic-swmm-workflow, the repo this skill ships in.

SwmmUncertainty QuantificationMonte CarloSensitivity AnalysisStormwaterHydrology

Also in Zhonghao1995/agentic-swmm-workflow

View the repo
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