Zeinab Smillie

Soil erosion is one of the major global concerns. Such a process can occur naturally, at different rates. However, under extreme conditions, soil erosion can accelerate and present a risk to ecosystems, loss of food supply (loss of agricultural lands), and lead to episodic landslides.
The present study employs Geographic Information Systems and functions to predict possible risks connected to soil erosion based on the knowledge of topographical data, average rainfall, soil types and landcover data. The study combines different risk factors to produce different models.
Different risk maps were produced to better visualise the spatial distribution of soil erosion risk categories. The influence of ground slope, rainfall and soil texture was evident via the models. The addition of the landcover factor hugely modified the risk categories distribution. It is clear that some habitats, such as wetlands and forests, can increase soil stability and reduce soil erosion risk.