Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: A case study in the Oltrepo Pavese, Northern Apennines, Italy. Issue 12 (26th July 2020)

• Record Type: Journal Article
• Authors: Bosino, Alberto; Giordani, Paolo; Quénéhervé, Geraldine; Maerker, Michael
• Is Part Of: Earth surface processes and landforms. Volume 45:Issue 12(2020)
• Page Start: 3025
• Page End: 3041
• Publication Date: 2020-07-26
• Subjects: susceptibility map -- badlands -- calanchi -- rill–interrill erosion -- soil erosion -- Oltrepo Pavese (Italy) -- MaxEnt -- support vector machines (SVM); Geomorphology -- Periodicals; 551.4
• DOI: 10.1002/esp.4949
• Languages: English
• ISSNs: 0197-9337

Abstract

Abstract: Soil erosion is one of the most important environmental problems distributed worldwide. In the last decades, numerous studies have been published on the assessment of soil erosion and the related processes and forms using empirical, conceptual and physically based models. For the prediction of the spatial distribution, more and more sophisticated stochastic modelling approaches have been proposed – especially on smaller spatial scales such as river basins. In this work, we apply a maximum entropy model (MaxEnt) to evaluate badlands (calanchi) and rill–interrill (sheet erosion) areas in the Oltrepo Pavese (Northern Apennines, Italy). The aim of the work is to assess the important environmental predictors that influence calanchi and rill–interrill erosion at the regional scale. We used 13 topographic parameters derived from a 12 m digital elevation model (TanDEM‐X) and data on the lithology and land use. Additional information about the vegetation is introduced through the normalized difference vegetation index based on remotely sensed data (ASTER images). The results are presented in the form of susceptibility maps showing the spatial distribution of the occurrence probability for calanchi and rill–interrill erosion. For the validation of the MaxEnt model results, a support vector machine approach was applied. The models show reliable results and highlight several locations of the study area that are potentially prone to future soil erosion. Thus, coping and mitigation strategies may be developed to prevent or fight the soil erosion phenomenon under consideration. © 2020 John Wiley & Sons, Ltd. Abstract : In this study we applied the MaxEnt model to derive three susceptibility maps for calanchi and rill–interrill erosion forms and features in the Oltrepo Pavese, Northern Apennines, Italy. Our work represents the first successfully generated calanchi/rill–interrill erosion susceptibility maps at regional scale. Land use is the most influencing parameter controlling both soil erosion types (calanchi A, B and rill–interrill erosion).