Developing and Applying a QGIS-Based Model That Accounts for Nonpoint Source Pollution Due to Domestic Animals

Watershed management must take into account both the quantity and quality of water. Therefore, many hydrological models have been developed for hydrological and water quality prediction for various purposes. The Spreadsheet Tool for Estimating Pollutant Loads (STEPL), which was developed in the Unit...

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Bibliographic Details
Published inWater (Basel) Vol. 14; no. 17; p. 2742
Main Authors Lee, Hanyong, Park, Jong-Yoon, Park, Youn Shik
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2022
Subjects
Agricultural runoff
Calibration
Domestic animals
Flow rates
Flow velocity
Hydrologic data
Hydrologic models
Hydrology
Land use
Livestock
Load distribution
Nonpoint source pollution
Pollutants
Pollution load
Pollution sources
QGIS
Rainfall
River flow
Rivers
Runoff
Simulation
STEPL
Stormwater
Water quality
Watershed management
Watersheds
United States > US
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Summary:Watershed management must take into account both the quantity and quality of water. Therefore, many hydrological models have been developed for hydrological and water quality prediction for various purposes. The Spreadsheet Tool for Estimating Pollutant Loads (STEPL), which was developed in the United States for water quality regulation, can predict both the quantity and quality of water, and has the advantage of including information on livestock. However, complex characteristics of the watershed must be generated by users for use as input data, and simulations only yield annual average values. Therefore, in this study, we developed a model that overcomes these limitations using geographic information data and enabling monthly predictions. The model developed in the study estimates monthly direct runoff and baseflow using daily rainfall data, while the STEPL model employs average annual approaches that are limited to consider seasonal variances of hydrological behaviors. It was developed for use within the QGIS software, and was applied to a watershed covering an area of 128.71 km2, considering information on livestock, soil, and land use. The model exhibited good predictive accuracy for four nonpoint source (NPS) pollutant loads and river flow, displaying acceptable criteria greater than 0.83 for river flow rates and 0.71 for all NPS pollutant load rates during calibration and validation.
ISSN:2073-4441
2073-4441
DOI:10.3390/w14172742