gwtransport#

gwtransport provides timeseries analysis of groundwater transport of solutes and temperature. Estimate two aquifer properties from a temperature tracer test, predict residence times and transport of other solutes, and assess pathogen removal efficiency. Alternatively, the aquifer properties can be estimated directly from the streamlines.

Testing of source code
Testing of examples
Package

What you can do with a calibrated model#

Once you have calibrated the aquifer pore volume distribution, you can:

Predict residence time distributions under varying flow conditions
Forecast contaminant arrival times and transport pathways
Design treatment systems with quantified pathogen removal efficiency
Assess groundwater vulnerability to contamination
Early warning systems as digital twin for drinking water protection

Two ways to obtain model parameters#

The aquifer pore volume distribution can be obtained using:

1. Streamline Analysis#

Compute the area between streamlines from flow field data to directly estimate the pore volume distribution parameters.

from gwtransport.advection import distribution_infiltration_to_extraction

# Measurements
cin_data = [1.0, 2.0, 3.0]  # Example concentration infiltrated water
flow_data = [100.0, 150.0, 100.0]  # Example flow rates
tedges = pd.date_range(start="2020-01-05", end="2020-01-08", freq="D")  # Example time edges

areas_between_streamlines = np.array([100.0, 90.0, 110.0])  # Example areas
depth_aquifer = 2.0  # Convert areas between 2d streamlines to 3d aquifer pore volumes.
aquifer_pore_volumes = areas_between_streamlines * depth_aquifer

cout = distribution_infiltration_to_extraction(
    cin=cin_data,
    flow=flow_data,
    tedges=tedges,
    cout_tedges=tedges,
    aquifer_pore_volumes=aquifer_pore_volumes,
    retardation_factor=1.0,
)

# Note that the first values are NaN, as no cin values have fully passed through the aquifer yet.

2. Temperature Tracer Test#

Approximate the aquifer pore volume distribution with a two-parameter gamma distribution. Estimate these parameters from the measured temperature of the infiltrated and extracted water. Temperature acts as a natural tracer, revealing how water flows through different paths in heterogeneous aquifers through calibration.

from gwtransport.advection import gamma_infiltration_to_extraction

# Measurements
cin_data = [11.0, 12.0, 13.0]  # Example temperature infiltrated water
flow_data = [100.0, 150.0, 100.0]  # Example flow rates
tedges = pd.date_range(start="2020-01-05", end="2020-01-08", freq="D")  # Example time edges

cout_data = [10.5, 11.0, 11.5]  # Example temperature extracted water. Only required for the calibration period.

cout_model = gamma_infiltration_to_extraction(
    cin=cin_data,
    flow=flow_data,
    tedges=tedges,
    cout_tedges=tedges,
    mean=200.0,  # [m3] Adjust such that cout_model matches the measured cout
    std=16.0,    # [m3] Adjust such that cout_model matches the measured cout
    retardation_factor=2.0,  # [-] Retardation factor for the temperature tracer
)

# Compare model output with measured data to calibrate the mean and std parameters. See Example 1.

Installation#

pip install gwtransport

Documentation Contents#

User Guide

Examples

API Reference

License#

GNU Affero General Public License v3.0

gwtransport

Contents