Predicting gully erosion at the continental scale is challenging with current generation models. Moreover, datasets reflecting gully erosion processes are still rather scarce, especially in Africa. This study aims to bridge this gap by collecting an extensive dataset and developing a robust, empirical model that predicts gully head density at high resolution for the African continent. We developed a logistic probability model at 30 m resolution that predicts the likelihood of gully head occurrence using currently available GIS data sources. To calibrate and validate this model, we used a new database of 31,531 gully heads, mapped over 1216 sites across Africa. The exact location of all gully heads was manually mapped by trained experts using high-resolution imagery available from Google Earth. This allowed the extraction of detailed information at the gully head scale, such as the local soil surface slope. Variables included in our empirical model are topography, climate, vegetation, soil characteristics and tectonic context. They are consistent with our current process-based understanding of gully formation and evolution. The model shows that gully occurrences mainly depend on slope steepness, soil texture and vegetation cover and to a lesser extent on rainfall intensity and tectonic activity. The combination of these factors allows for robust and fairly reliable predictions of gully head occurrences, with Areas Under the Curve for validation around 0.8. Based on these results, we present the first gully head susceptibility map for Africa at a 30 m resolution.
Keywords: Continental scale; Google earth; Gully erosion modelling; Gully head density database; Land management; Logistic regression.
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