South African soil, land cover and weather generator file databases for SWAT applications

South Africa. The focus of the study is to develop soil, land cover and weather generator file datasets for Soil and Water Assessment Tool (SWAT) applications in South Africa. The first objective was to format national datasets for use as baseline to run the SWAT model in South Africa. The second objective was to evaluate the performance of the baseline input data by applying the national datasets in four (previously simulated) research catchments. The input datasets comprise of geo-spatial datasets at a national scale to run ArcSWAT or QSWAT (graphical user interface for SWAT in ArcGIS and SWAT+ in QGIS, respectively) in South Africa including: SWAT catchment outline data (tertiary and quaternary); Land cover maps at 20–30 m resolution including South African National Land Cover (2014, 2018, 2020) linked to SWAT land cover codes; A soil map with SWAT attribute data derived from pedotransfer functions of the Land Type Database of South Africa useable at a scale of 1:250,000; Weather statistics (WGN) files for 12 weather stations obtained from the Agricultural Research Council in South Africa. The national baseline data is an important step forward in hydrological modelling by assisting modellers to set-up and run the SWAT model in South Africa. Input databases for SWAT applications in South Africa • This study provides databases for SWAT applications in South Africa including: • SWAT catchment outline data (tertiary and quaternary) • Land cover maps (2014; 2018; 2020) (20–30 m) linked to SWAT land cover codes • A soil map with SWAT attribute data useable at a scale of 1:250,000 • Weather statistics (WGN) files for 12 weather stations

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Publication Details

Journal
Journal of Hydrology Regional Studies
Published
2025-04-16
DOI
https://doi.org/10.1016/j.ejrh.2025.102387
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
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article

South African soil, land cover and weather generator file databases for SWAT applications

Harold Weepener, Jay le Roux, Ndifelani Mararakanye, Michael van der Laan et al.
Journal of Hydrology Regional Studies
Soil Moisture and Remote Sensing
article

South African soil, land cover and weather generator file databases for SWAT applications

Harold Weepener, Jay le Roux, Ndifelani Mararakanye, Michael van der Laan, J. van Tol, Leushantha Mudaly
article en

Abstract

South Africa. The focus of the study is to develop soil, land cover and weather generator file datasets for Soil and Water Assessment Tool (SWAT) applications in South Africa. The first objective was to format national datasets for use as baseline to run the SWAT model in South Africa. The second objective was to evaluate the performance of the baseline input data by applying the national datasets in four (previously simulated) research catchments. The input datasets comprise of geo-spatial datasets at a national scale to run ArcSWAT or QSWAT (graphical user interface for SWAT in ArcGIS and SWAT+ in QGIS, respectively) in South Africa including: SWAT catchment outline data (tertiary and quaternary); Land cover maps at 20–30 m resolution including South African National Land Cover (2014, 2018, 2020) linked to SWAT land cover codes; A soil map with SWAT attribute data derived from pedotransfer functions of the Land Type Database of South Africa useable at a scale of 1:250,000; Weather statistics (WGN) files for 12 weather stations obtained from the Agricultural Research Council in South Africa. The national baseline data is an important step forward in hydrological modelling by assisting modellers to set-up and run the SWAT model in South Africa. Input databases for SWAT applications in South Africa • This study provides databases for SWAT applications in South Africa including: • SWAT catchment outline data (tertiary and quaternary) • Land cover maps (2014; 2018; 2020) (20–30 m) linked to SWAT land cover codes • A soil map with SWAT attribute data useable at a scale of 1:250,000 • Weather statistics (WGN) files for 12 weather stations

Journal of Hydrology Regional StudiesVol. 59
University of the Free State (ZA), Institute for Soil, Climate and Water (ZA), University of Pretoria (ZA)
Openalex Percentile: Top 93%
Soil Moisture and Remote Sensing
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