Dataset for Predicting Soil Thickness on Soil Mantled Hillslopes

Soil thickness is a fundamental variable in many earth science disciplines but difficult to predict. We find a strong inverse linear relationship between soil depth and hillslope curvature (r2=0.89, RMSE=0.17 m) at a field site in Idaho. Similar relationships are present across a diverse data set, although the slopes and y-intercepts vary widely. We show that the slopes of these functions vary with the standard deviations (SD) in catchment curvatures and that the catchment curvature distributions are centered on zero. Our simple empirical model predicts the spatial distribution of soil depth in a variety of catchments based only on high-resolution elevation data and a few soil depths. Spatially continuous soil depth datasets enable improved models for soil carbon, hydrology, weathering and landscape evolution.