Soils are a vital part of the natural environment and one of the most important natural resources. The EuropeanMediterranean Regions are particularly sensitive to soil degradation. Especially in Italy many landscapes areprone to water related soil erosion processes such as rill-interrill erosion (sheet erosion), gullies and complexprocesses forming“calanchi”corresponding to the English term“Badlands”. Calanchi develop preferentially onPlio-Pleistocene deposits, which are highly susceptible to aquatic erosion processes. This study focusses on theassessment of rill-interrill erosion as well as calanchi erosion forms and features in previously none or very little-studied areas of the Langhe-Roero and Monferrato, located in the Liguria and Piedmont Region, NorthernApennines, Italy. In this study we assess the driving factors and the spatial distribution of rill-interrill andcalanchi erosion processes using a geostatistical/geostochastic modelling framework. Therefore, a MaximumEntropy Model, a Generalized Linear Model and a Boosted Regression Tree approach were applied. As in-dependent environmental variables we selected DEM based morphometric information as well as pedologic, andgeologic input data. Moreover, we explore remote sensing techniques to derive information on the vegetationdensity and vitality. The stochastic models applied show, that the two very different soil erosion phenomena canbe differentiated well with the chosen stochastic techniques. The main drivers controlling the spatial distributionof aquatic soil erosion in the study area are soil type, slope, elevation and topographic wetness index. The resultsof the MaxEnt and BRT model were confirmed by the GLM application in terms of the significance of predictorvariables. Generally, the models show an acceptable to excellent performance with BRT and GLM outperformingMaxEnt. The single independent variable response curves gave valuable insights into the process triggering theerosion forms. We were able to clearly differentiate between the processes looking at the response curves thatshow reasonable characteristics with all applied models.

Assessment of calanchi and rill-interrill erosion susceptibility in northern Liguria, Italy: A case study using a probabilistic modelling framework

Scopesi, Claudia;Giordani, Paolo;Firpo, Marco;Rellini, Ivano
2020

Abstract

Soils are a vital part of the natural environment and one of the most important natural resources. The EuropeanMediterranean Regions are particularly sensitive to soil degradation. Especially in Italy many landscapes areprone to water related soil erosion processes such as rill-interrill erosion (sheet erosion), gullies and complexprocesses forming“calanchi”corresponding to the English term“Badlands”. Calanchi develop preferentially onPlio-Pleistocene deposits, which are highly susceptible to aquatic erosion processes. This study focusses on theassessment of rill-interrill erosion as well as calanchi erosion forms and features in previously none or very little-studied areas of the Langhe-Roero and Monferrato, located in the Liguria and Piedmont Region, NorthernApennines, Italy. In this study we assess the driving factors and the spatial distribution of rill-interrill andcalanchi erosion processes using a geostatistical/geostochastic modelling framework. Therefore, a MaximumEntropy Model, a Generalized Linear Model and a Boosted Regression Tree approach were applied. As in-dependent environmental variables we selected DEM based morphometric information as well as pedologic, andgeologic input data. Moreover, we explore remote sensing techniques to derive information on the vegetationdensity and vitality. The stochastic models applied show, that the two very different soil erosion phenomena canbe differentiated well with the chosen stochastic techniques. The main drivers controlling the spatial distributionof aquatic soil erosion in the study area are soil type, slope, elevation and topographic wetness index. The resultsof the MaxEnt and BRT model were confirmed by the GLM application in terms of the significance of predictorvariables. Generally, the models show an acceptable to excellent performance with BRT and GLM outperformingMaxEnt. The single independent variable response curves gave valuable insights into the process triggering theerosion forms. We were able to clearly differentiate between the processes looking at the response curves thatshow reasonable characteristics with all applied models.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1005780
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