SWATPollution Class

The SWATPollution class is designed to manage the setup and optional execution of a SWAT+ simulation for modeling contaminant transport in a specified watershed. It facilitates input preparation, parameter configuration, and integration of observational data for analysis and calibration.

This code was developed for the paper:
Integrated modeling of the generation, attenuation, and transport of point-source pollutants at the watershed-scale using SWAT+

Library used:
pyswatplus – Python interface for SWAT+ modeling and file handling.

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Features

• Supports multiple predefined watersheds.
• Integrates observational data for calibration and validation.
• Handles temporary file management and execution setup.
• Allows simulation of various contaminants.
• Customizes SWAT+ input files via user-defined modifications.

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Parameters

Parameter	Type	Description
conca	str	Name of the watershed. Valid options: 'muga', 'fluvia', 'ter', 'llobregat', 'besos', 'tordera', 'sud'.
contaminant	str	Name of the pollutant to simulate (e.g., 'Venlafaxina', 'Ciprofloxacina').
txtinout_folder	str or Path	Path to directory containing SWAT txtinout files.
channels_geom_path	str or Path, optional	Path to the shapefile representing channel geometry. If None, a default is used.
tmp_path	str or Path, optional	Temporary directory for simulation input/output files. Used only if run=True.
run	bool, optional	If True, prepares and runs the model. Default: False.
compound_features	dict, optional	Dictionary defining SWAT file modifications. Format: { 'filename': (id_col, [(id, col, value)]) }.
show_output	bool, optional	If True, prints SWAT run output logs. Default: True.
copy_txtinout	bool, optional	If True, copies txtinout files to tmp_path before execution. Used only if run=True.
overwrite_txtinout	bool, optional	If True, allows overwriting existing files in tmp_path.
observacions	pd.DataFrame, optional	Observational data for model calibration. If None, defaults are loaded.
lod_path	str or Path, optional	Path to file with limits of detection (LOD). If None, a default is used.
year_start	int, optional	Start year for the simulation. Default: 2000. Used only if run=True.
year_end	int, optional	End year for the simulation. Default: 2022. Used only if run=True.
warmup	int, optional	Number of warm-up years. Default: 1. Used only if run=True.

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Example Usage

from pathlib import Path
import pandas as pd

contaminant = 'Venlafaxina'
conca = 'besos'

cwd = Path('C:/Users/joans/OneDrive/Escriptori/icra/traca_contaminacio/traca_contaminacio')
txtinout_folder = cwd / 'data' / 'txtinouts' / 'tmp' / contaminant / conca
channels_geom_path = cwd / 'data' / 'rivs1' / 'canals_tot_ci.shp'

# Load observational data
recall_points_path = cwd.parent / 'traca' / 'traca' / 'inputs compound generator' / 'inputs' / 'recall_points.xlsx'
recall_points_df = pd.read_excel(recall_points_path)
edars = recall_points_df[recall_points_df['conca'] == conca][['lat', 'lon', 'edar_code']].dropna()

observacions = generate_pollution_observations(contaminant)
df = observacions_from_conca(channels_geom_path, observacions, conca)

first_observation = df.year.min()
year_end = 2022
year_start = max(first_observation - 3, 2000)
warmup = max(1, first_observation - year_start)

swatpy = SWATPollution(
    conca=conca,
    contaminant=contaminant,
    txtinout_folder=txtinout_folder,
    channels_geom_path=channels_geom_path,
    run=False,
    year_start=year_start,
    year_end=year_end,
    warmup=warmup,
    observacions=observacions
)