Getting Started

Getting Started#

Installation#

Install gwtransport from PyPI:

pip install gwtransport

Requirements#

  • Python 3.10 or higher

  • NumPy

  • SciPy

  • Pandas

  • Matplotlib

Basic Concepts#

gwtransport provides two main approaches to characterize groundwater systems:

  1. Temperature Tracer Test

    Use natural temperature variations as tracers to estimate aquifer properties. This approach fits a two-parameter gamma distribution to represent the pore volume distribution.

  2. Streamline Analysis

    Directly compute pore volumes from flow field data using streamline analysis. This provides more detailed spatial information about the aquifer structure.

Core Workflow#

  1. Data Collection

    Collect time series data of: - Temperature of infiltrated water - Temperature of extracted water - Flow rates - Time measurements

  2. Model Calibration

    Fit model parameters to match observed temperature breakthrough curves.

  3. Prediction

    Use calibrated model to predict: - Residence time distributions - Contaminant transport - Pathogen removal efficiency

Quick Example#

Here’s a simple example using temperature tracer test data:

import numpy as np
from gwtransport.advection import gamma_infiltration_to_extraction

# Measurement data
cin_data = [11.0, 12.0, 13.0]  # Temperature infiltrated water [°C]
flow_data = [100.0, 150.0, 100.0]  # Flow rates [m³/day]
tedges = [0, 1, 2, 3]  # Time edges [days]

# Model parameters (to be calibrated)
mean_pore_volume = 30000  # [m³]
std_pore_volume = 8100   # [m³]
retardation_factor = 2.0  # [-]

# Compute model prediction
cout_model = gamma_infiltration_to_extraction(
    cin=cin_data,
    flow=flow_data,
    tedges=tedges,
    cout_tedges=tedges,
    mean=mean_pore_volume,
    std=std_pore_volume,
    retardation_factor=retardation_factor,
)

print(f"Predicted temperature: {cout_model}")

Next Steps#

Contents