Analyze groundwater time-series with transfer-function noise models using Pastas.
npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill pastas
Please help me install this Agent Skill. Skill: pastas Source: https://github.com/SteadfastAsArt/geoscience-skills/tree/main/pastas Command: npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill pastas
Pastas provides a structured framework for groundwater time-series analysis using transfer-function noise models to quantify how aquifers respond to external stresses, enabling fast hypothesis testing and model-based forecasting.
Create a Pastas model from head, precipitation, and evaporation time series, run calibration, and inspect diagnostics.
High-intent search queries and answers about installing and using this skill.
You can model groundwater time-series by inputting head observations alongside precipitation, evaporation, pumping, and river stress data. The framework constructs a transfer-function noise model to calibrate and infer the aquifer's behavioral response.
Transfer-function noise modeling is a structured framework for groundwater time-series analysis that quantifies aquifer responses to external stresses. It enables fast hypothesis testing, signal decomposition, and model-based forecasting of head data.
Yes, you can use pandas and matplotlib for groundwater calibration and visualization. The Skill depends on pandas for time-series data manipulation and matplotlib for plotting model diagnostics, step responses, and stress contributions.
Yes, this approach supports forecasting and signal decomposition for head data. It uses transfer-function noise models to separate individual stress contributions and generate model-based forecasts of groundwater head behavior.
A limitation of using transfer-function models for groundwater analysis is the reliance on structured linear assumptions to infer aquifer behavior. This Skill is best suited for fast hypothesis testing and calibration rather than simulating complex non-linear flow systems.
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