SWAT-pytools

A Python wrapper for executing and calibrating the Soil and Water Assessment Tool (SWAT) in Unix/macOS systems. A module for computing the 171 hydrologic indices reported by Henriksen et al. (2006) and the Magnificent Seven indices proposed by Archfield et al. (2014) is also included here. For single- and multi-objective calibration, we use the pymoo framework.

Installation

First, you need to have a Python 3 enviroment installed. We recommend using miniconda or anaconda. Check if conda is available in the command line:

conda --version

Create a new Python environment with Numpy, Pandas, and Matplotlib preinstalled and activate it:

conda create -n swatpy -y python==3.7 numpy pandas matplotlib
conda activate swatpy

If you are familiar with conda, you might prefer to use an existing environment that you already created.

Now, you can install the current SWAT-pytools version. cd to the directory where you normally save your repositories and execute these lines in a terminal:

git clone https://github.com/jshernandezs/swat-pytools
cd swat-pytools
pip install .

Getting started

Once you download this package, you will find the following local directory tree:

swat-pytools
├── resources
│   ├── csv_files
│   ├── figFiles
│   ├── Models
│   └── Observed
├── src
│   └── swat_utilities
└── tests

In the resources directory, you must place all the necessary files for executing and calibrating SWAT, such as the model executables, zip files containing input SWAT text files (resources/Models directory), csv files containing observed time series (resources/Observed directory), and other optional files. Regarding SWAT executables, revisions 622, 670, and 681 are available for Unix in this repository, only version 622 is available for macOS.

Executing SWAT with Python

In this example, we are using the SWAT 622 version to run a model of the Honeyoey Creek - Pine Creek Watershed located in Michigan, US. The SWAT input text files, which normally are generated inside the TxtInOut folder when using ArcSWAT, are put together in a zip file that will be handled by the Python wrapper. In this case, we are using the Honeyoy_Model.zip file placed in the resources/Observed directory.

We assume that a new Python script is created in the tests directory.

First, we import the libraries that we are going to use:

import os
from swat_utilities.swat_config import ModelSetup

Then, we define a variable with the path to the zip file containing the SWAT text files:

model_file_path = os.path.abspath('../resources/Models/Honeyoy_Model.zip')

Now, we create the model object using the ModelSetup class:

swat_model = ModelSetup(model_file_path)

We must indicate which SWAT version we are going to use, which is the property swat_exec_name of the model object created above:

swat_model.swat_exec_name = 'SWAT_Rev622'

To see the execution progress when running the model, we set the property 'verbose_swat' as True:

swat_model.verbose_swat = True

To run the model, we need to execute the prepare_swat() method followed by the run_swat() method as follows:

swat_model.prepare_swat()
swat_model.run_swat()

By default, the results are stored in the /tmp/output_swat/New_SWAT directory. The user can modify the output directory and the model folder containing the input and output SWAT text files by using the output_dir and new_model_name properties of the swat_model object, respectively.