Evaluating spatio-temporal soil erosion dynamics in the Winam Gulf catchment, Kenya for enhanced decision making in the land-lake interface

Soil erosion accelerated by poor agricultural practices, land degradation, deprived infrastructure development and other anthropogenic activities has important implications for nutrient cycling, land and lake productivity, loss of livelihoods and ecosystem services, as well as socioeconomic disruption. Enhanced knowledge of dynamic factors influencing soil erosion is critical for policymakers engaged in land use decision-making. This study presents the first spatio-temporal assessment of soil erasion risk modelling in the Winam Gulf, Kenya using the Revised Universal Soil Loss Equation (RUSLE) within a geospatial framework at a monthly resolution between January 2017 and June 2020. Dynamic rainfall emsivity and land cover management factors were derived from existing datasets to determine their effect on average monthly soil kiss by water erosion. By assessing soil erasion rates with enhanced temporal resolution, it is possible to provide greater knowledge regarding months that are particularly susceptible to soil erosion and can better inform future strategies for targeted mitigation measures. Whilst the pseudo monthly average soil loss was calculated (0.80 t ha(-1) month(-1)), the application of this value would lead to misrepresentation of monthly soil loss throughout the year. Our results indicate that the highest erosion rates occur between February and April (average 0.95 t ha(-1) month(-1)). In contrast, between May and August, there is a significantly reduced risk (average 0.72 t ha(-1) month(-1)) due to the low rainfall erosivity and increased vegetation cover as a result of the long rainy season. The mean annual gross soil loss by water erosion in the Winam Gulf catchment amounts to 10.71 Mt year with a mean soil loss rate of 9.63 t ha(-1) year(-1). These findings highlight the need to consider dynamic factors within the RUSLE model and can prove vital for identifying areas of high erosion risk for future targeted investigation and conservation action.

Automated summary

Soil erosion has significant implications for nutrient cycling, land productivity, livelihoods, and ecosystem services. This study presents a spatio-temporal assessment of soil erosion risk in the Winam Gulf, Kenya using the Revised Universal Soil Loss Equation. The results show that soil erosion rates are influenced by rainfall intensity and land cover management, with different levels of susceptibility in different months.