As hillslope interiors are very difficult and costly to illuminate and access, the topography of the bedrock surface is largely unknown. This paper is concerned with the prediction of spatial patterns in the depth to bedrock (DTB) using high-resolution topographic data, numerical modeling and Bayesian analysis. Our DTB model predicts a thick soil mantle at the ridge top and gets progressively thinner downslope. We reconcile the DTB model with field observations using Bayesian analysis. We illustrate our method using real-world data set of bedrock depth observations collected at the Papagaio river basin in Rio de Janeiro, Brazil. Our results demonstrate that the DTB model predicts accurately the observed bedrock depth data. We also derive the posterior prediction uncertainty of the DTB model.


The depth to bedrock (DTB) controls a large array of geomorphologic, hydrologic, geochemical, ecologic and atmospheric processes, yet is large unknown as hillslope interiors are very difficult and costly to illuminate and access. The regolith thickness determines groundwater flow, infiltration and redistribution, subsurface saturation, runoff generation, storage capacity, the shape of the hydrograph, and variably saturated water flow. The bedrock topography is also of paramount importance in geotechnical engineering as it determines slope stability, pore pressure responses to infiltration, and landslide potential. An accurate characterization of the DTB is thus a prerequisite to describe adequately many different Earth-surface processes.

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