A PROCESS-BASED MODEL FOR COLLUVIAL SOIL DEPTH AND SHALLOW LANDSLIDING USING DIGITAL ELEVATION DATA

A model is proposed for predicting the spatial variation in colluvial soil depth, the results of which are used in a separate model to exami ne the effects of root strength and vertically varying saturated condu ctivity on slope stability. The soil depth model solves for the mass b alance between soil production from underlying bedrock and the diverge nce of diffusive soil transport. This model is applied using high-reso lution digital elevation data of a well-studied site in northern Calif ornia and the evolving soil depth is solved using a finite difference model under varying initial conditions. The field data support an expo nential decline of soil production with increasing soil depth and a di ffusivity of about 50 cm(2)/yr. The predicted pattern of thick and thi n colluvium corresponds well with field observations. Soil thickness o n ridges rapidly obtain an equilibrium depth, which suggests that deta iled field observations relating soil depth to local topographic curva ture could further test this model. Bedrock emerges where the curvatur e causes divergent transport to exceed the soil production rate, hence the spatial pattern of bedrock outcrops places constraints on the pro duction law. The infinite slope stability model uses the predicted soi l depth to estimate the effects of root cohesion and vertically varyin g saturated conductivity. Low cohesion soils overlying low conductivit y bedrock are shown to be least stable. The model may be most useful i n analyses of slope instability associated with vegetation changes fro m either land use or climate change, although practical applications m ay be limited by the need to assign values to several spatially varyin g parameters. Although both the soil depth and slope stability models offer local mechanistic predictions that can be applied to large areas , representation of the finest scale valleys in the digital terrain mo del significantly influences local model predictions. This argues for preserving fine-scale topographic detail and using relatively fine gri d sizes even in analyses of large catchments.