Spatial modelling and uncertainty assessment of pyroclastic cover thickness in the Sorrento Peninsula |
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| Authors: | Federica Lucà Gabriele Buttafuoco Gaetano Robustelli Antonio Malafronte |
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| Institution: | 1.DIATIC, University of Calabria,Arcavacata di Rende,Italy;2.National Research Council of Italy, Institute for Agricultural and Forest Systems in the Mediterranean (ISAFOM),Rende,Italy;3.Regione Campania, Settore Provinciale Genio Civile,Castellammare di Stabia,Italy |
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| Abstract: | Assessing spatial variability of soil thickness is a critical issue for understanding and predicting slope processes. The present work was aimed at estimating the spatial scales at which the variation of pyroclastic cover thickness occurs in a sample area in the Sorrento Peninsula (Italy). Stochastic simulation was used to understand the spatial variability of pyroclastic cover thickness on Mount Pendolo and to assess its spatial uncertainty. In the study area, covering about 0.7 km2, thickness measurements were collected using electrical resistivity tomography profiles, continuous core drillings and steel rod penetrometric tests. Variographic analysis revealed the occurrence of an anisotropic behaviour along the N50 and N140 directions. In the latter anisotropic direction, a nested variogram was fitted including (1) a long-range component which could be related to large-scale factors, like the curvature of the slope and contributing area and (2) a shorter scale variation which is probably associated with the occurrence of denudation processes or to the articulate cover/bedrock interface. To assess the spatial variability and uncertainty of pyroclastic cover thickness, a stochastic simulation algorithm was used and 500 equally probable images of cover thickness were yielded. The results showed that a better thickness distribution map can be drawn by simulating the data collected on the slope and at the footslope separately. The approach also allowed delineating the areas characterized by greater uncertainty, suggesting supplementary measurements to further improve the cover thickness distribution model, thus reducing the uncertainty. |
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