Evaluating digital soil mapping products for modelling road-related soil erosion in Baringo County, Kenya

Roads in Sub-Saharan Africa provide essential transportation functions; however, they often cause adverse environmental impacts, such as enhanced erosion, that are frequently underestimated or overlooked during planning and implementation. Global digital soil mapping (DSM) products such as OpenLandMap and SoilGrids provide open-source soil information for large-scale ecosystem service assessment and monitoring. However, the accuracy of these modelled datasets varies spatially because the input data used for model development are unevenly distributed. Thus, their reliability and implications for erosion modelling in data-scarce semi-arid regions remain insufficiently understood.

In this study, we investigated the spatial variability of soil properties (soil texture fractions, soil organic carbon, bulk density, and pH) in Baringo County, Kenya and examined whether paved and unpaved roads influence this variability. We evaluated the accuracy of SoilGrids250m 2.0 and OpenLandMap (30 m) in representing field-measured soil properties and assessed how DSM-related uncertainties propagate into erosion estimates. Using pedotransfer functions based on soil texture fractions and soil organic carbon, we derived soil erodibility factors from both field data and OpenLandMap. The factors were subsequently used in the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Sediment Delivery Ratio model to estimate spatial patterns of soil loss.

Both DSM products underestimated topsoil silt content (RMSE = 35.6% and 36.1% for OpenLandMap and SoilGrids, respectively) and overestimated clay (RMSE = 17.8% and 22.3%) and sand contents (21.4% and 17.9%, respectively), with accuracy decreasing with depth. Field data revealed significantly lower silt content and higher sand content in topsoils near roads compared to further away with a moderate effect size. Sediment deposition and export computed using the parametrized OpenLandMap factor showed high correlation with results parametrized from field data across varying distances for both paved roads and unpaved roads (R² > 0.87). Despite high correlations, modelling results parametrized with OpenLandMap underestimated sediment deposition and export by factors of approximately 2.2 and 2.7 for paved and unpaved roads, respectively. Unpaved roads showed greater sediment export near the road corridor compared to paved roads, while paved roads were associated with greater sediment deposition.

Our results demonstrate that while global DSM products can reproduce relative spatial patterns of road-related erosion, systematic biases in soil property predictions an affect erosion estimate. This highlights the need to explicitly consider DSM uncertainty when using open soil data for erosion modelling and infrastructure, especially in data-scarce regions.