Predicting gully erosion susceptibility in South Africa by integrating literature directives with regional spatial data

• Published in: Earth surface processes and landforms 2023-11, Vol.48 (14), p.2661-2681
• Main Authors: Olivier, George, Van De Wiel, Marco J., Clercq, Willem P.
• Format: Article
• Language: English
• Subjects: Aggregation; climate change; Degradation; Environmental changes; Environmental degradation; frequency ratio; GIS; Gullies; Gully erosion; Land degradation; Literature reviews; Modelling; South Africa; Spatial data; Spatial discrimination; Spatial resolution; Susceptibility; Tessellation; weighted overlay
• ISSN: 0197-9337; 1096-9837
• DOI: 10.1002/esp.5653

Gully erosion has been identified as a severe land degradation process with environmental and socio‐economic consequences. Identifying areas susceptible to gully erosion will aid in developing strategies to inhibit future degradation. Various approaches have been implemented to predict and map gully erosion susceptibility but are mostly restricted to small geographical extents because of process limitations. Here, we introduce a novel method that predicts gully erosion susceptibility on a regional/national scale (1.22 million km2) by synthesising literature directives with a statistical approach. Findings from a literature review were used to extract physiographic properties associated with gully erosion that was conditioned to characterise susceptibility by using the Frequency Ratio model. The conditioned physiographic properties were aggregated by a weighted overlay procedure using an aggregation of controlling factors derived from the literature review as a weighting system. The gully susceptibility index (GSI) model was validated against a published gully inventory map (n = 163 019) and randomly generated 1‐km2 tessellation zones from which primary validation data were derived. Although uncertainties within the modelling procedure exist (e.g., gully site distribution, the spatial resolution of input data and determination of gully points), the validation shows that the GSI model is generally robust, identifying areas of contrasting susceptibilities. Furthermore, findings converge with other susceptibility metrics, which have been derived by different methodologies. Because empirical gully erosion research has been conducted worldwide, this model could be applied to regional‐scale gully susceptibility modelling assessments (as a solitary method or combined with primary data) in other parts of the world. Additionally, the GSI model can be adopted to model environmental change scenarios. A novel method is used to predict regional gully erosion with low data input. A triple validation process is implemented, using a published gully inventory map of SA and primary data. The model shows robust performance and could be used to model environmental change scenarios and used as a tool for land managers. Future research can continue to refine the model by using further literature directives as variables, but also using higher spatial resolution datasets.