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Groundwater time series analysis and modelling using transfer function noise models. Use when Claude needs to: (1) Analyze groundwater level time series, (2) Model well responses to precipitation/pumping, (3) Calibrate aquifer parameters from head data, (4) Forecast or hindcast groundwater levels, (5) Decompose hydrological signals into components, (6) Compare response functions, (7) Perform model diagnostics and uncertainty analysis.

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Added on2026-05-16

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Pastas - Groundwater Time Series Analysis

Quick Reference

python

import pastas as ps
import pandas as pd

# Load data
head = pd.read_csv('well.csv', index_col=0, parse_dates=True).squeeze()
precip = pd.read_csv('precip.csv', index_col=0, parse_dates=True).squeeze()
evap = pd.read_csv('evap.csv', index_col=0, parse_dates=True).squeeze()

# Create model
ml = ps.Model(head, name='Well_001')

# Add recharge stress
sm = ps.RechargeModel(precip, evap, rfunc=ps.Gamma(), name='recharge')
ml.add_stressmodel(sm)

# Solve and plot
ml.solve()
ml.plot()

Key Classes

Class	Purpose
`ps.Model`	Main model container
`ps.StressModel`	Response to external stress (pumping, river)
`ps.RechargeModel`	Recharge from precipitation minus evaporation
`ps.Gamma`	Gamma distribution response function
`ps.Exponential`	Simple exponential response function

Essential Operations

Create and Solve Model

python

ml = ps.Model(head, name='well')
ml.add_stressmodel(ps.RechargeModel(precip, evap, rfunc=ps.Gamma(), name='recharge'))
ml.solve()

Add Pumping Well

python

pumping = pd.read_csv('pumping.csv', index_col=0, parse_dates=True).squeeze()
ml.add_stressmodel(ps.StressModel(pumping, rfunc=ps.Hantush(),
                                   name='pumping', up=False))  # up=False for drawdown

Model Diagnostics

python

print(f"EVP: {ml.stats.evp():.1f}%")      # Explained variance
print(f"RMSE: {ml.stats.rmse():.3f} m")   # Root mean square error
print(f"AIC: {ml.stats.aic():.1f}")       # Model selection criterion

ml.plots.diagnostics()                     # Diagnostic plots
ml.plots.acf()                            # Autocorrelation

Get Contributions

python

contributions = ml.get_contributions()
for name, contrib in contributions.items():
    print(f"{name}: mean={contrib.mean():.2f}")

Step and Impulse Response

python

step = ml.get_step_response('recharge')    # Step response
block = ml.get_block_response('recharge')  # Impulse response

Export and Load

python

ml.to_json('model.pas')                    # Save model
ml_loaded = ps.io.load('model.pas')        # Load model

sim = ml.simulate()
sim.to_csv('simulation.csv')               # Export results

Model Statistics

Statistic	Description	Good Value
EVP	Explained variance percentage	>70%
RMSE	Root mean square error	Low (context-dependent)
AIC	Akaike Information Criterion	Lower = better
BIC	Bayesian Information Criterion	Lower = better

Common Patterns

Compare Response Functions

python

for rfunc in [ps.Gamma(), ps.Exponential(), ps.Hantush()]:
    ml = ps.Model(head)
    ml.add_stressmodel(ps.RechargeModel(precip, evap, rfunc=rfunc, name='r'))
    ml.solve(report=False)
    print(f"{rfunc.name}: EVP={ml.stats.evp():.1f}%, AIC={ml.stats.aic():.1f}")

Forecast Future Levels

python

ml.solve()
forecast = ml.simulate(tmin='2024-01-01', tmax='2025-12-31')
ml.plot(tmax='2025-12-31')

River or Custom Stress

python

river = pd.read_csv('river_stage.csv', index_col=0, parse_dates=True).squeeze()
sm = ps.StressModel(river, rfunc=ps.Exponential(), name='river',
                    settings='waterlevel')
ml.add_stressmodel(sm)

When to Use vs Alternatives

Use Case	Tool	Why
Groundwater time series analysis	Pastas	Purpose-built transfer function models
Well response to recharge/pumping	Pastas	Built-in stress models and response functions
Numerical groundwater flow (MODFLOW)	FloPy	Full 3D finite-difference groundwater model
Simple exponential decay fitting	Custom scipy	`scipy.optimize.curve_fit` is sufficient
Regional groundwater flow modelling	FloPy	Spatially distributed parameters and boundaries
Aquifer test analysis (pumping tests)	Aqtesolv / custom	Dedicated well test interpretation
Multi-well network analysis	Pastas	Model each well independently, compare responses
Signal decomposition	Pastas	Separate recharge, pumping, and trend contributions

Choose Pastas when: You have groundwater level time series and want to model responses to precipitation, evaporation, or pumping using transfer function noise models. Excellent for rapid model building with diagnostics.

Choose FloPy when: You need spatially distributed groundwater flow modelling with MODFLOW, including multiple layers, boundary conditions, and transport.

Choose custom scipy when: You only need to fit a simple analytical model (e.g., Theis equation) to pumping test data without time series decomposition.

Common Workflows

Groundwater Response Model with Diagnostics

Load head time series and stress data (precipitation, evaporation, pumping)
Inspect data: check for gaps, outliers, and time coverage
Create
```
ps.Model(head)
```
with observation data

Add recharge stress with

ps.RechargeModel(precip, evap, rfunc=ps.Gamma())

Add pumping or river stresses if applicable
Solve model with
```
ml.solve()
```
Check EVP (>70%), RMSE, and AIC
Run
```
ml.plots.diagnostics()
```
to inspect residuals
Check residual autocorrelation; enable noise model if needed:
```
ml.solve(noise=True)
```
Compare response functions (Gamma vs Exponential vs Hantush) using AIC
Extract step/block responses to interpret aquifer behavior
Decompose signal into individual stress contributions
Export model to JSON and simulation results to CSV

Tips

Start simple - Add stresses incrementally
Check residuals - Should be white noise (use
```
ml.plots.diagnostics()
```
)
Compare response functions - Use AIC/BIC to select best model
Use daily data - Pastas works best with daily time series
Normalize units - Precipitation in mm/day, head in meters

Common Issues

Issue	Solution
Poor fit (low EVP)	Try different response functions
Residual autocorrelation	Add noise model: `ml.solve(noise=True)`
Unstable parameters	Set parameter bounds or fix values
Missing stress data	Interpolate or use `fillna()` before modeling

References

Stress Models - Available stress model types
Response Functions - Response function selection

Scripts

scripts/groundwater_model.py - Complete groundwater modeling workflow

pastas

NPX Install

Tags

SKILL.md Content

Pastas - Groundwater Time Series Analysis

Quick Reference

Key Classes

Essential Operations

Create and Solve Model

Add Pumping Well

Model Diagnostics

Get Contributions

Step and Impulse Response

Export and Load

Model Statistics

Common Patterns

Compare Response Functions

Forecast Future Levels

River or Custom Stress

When to Use vs Alternatives

Common Workflows

Groundwater Response Model with Diagnostics

Tips

Common Issues

References

Scripts