Identification, characterisation and analysis of the Oltrepo Pavese calanchi in the Northern Apennines (Italy)

Bosino, Alberto; Omran, Adel; Maerker, Michael

doi:10.1016/j.geomorph.2019.05.003

Cited by 20 publications

(23 citation statements)

References 47 publications

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“…The main geomorphological processes are related to surface and subsurface runoff. In the study area, rill-interrill, gully, piping and calanchi erosion, as well as mass movements (Bosino et al, 2019a), are the dominant soil erosion processes.…”

Section: Study Areamentioning

confidence: 99%

“…The calanchi of the Oltrepo Pavese have been described by Bucciante (1922) and Bosino et al (2019a). In total, 263 calanchi forms were identified and accordingly classified in the field, following the classification system developed by Moretti and Rodolfi (2000).…”

Section: Study Areamentioning

confidence: 99%

“…However, the soil erosion and sediment dynamics due to surface and subsurface runoff in general have only scarcely been studied in the Oltrepo Pavese (i.e. Ciancetti et al, 2008;Bosino et al, 2019a).…”

Section: Introductionmentioning

confidence: 99%

“…In many Italian regions, the calanchi are directly associated with the soft unconsolidated, poorly consolidated sedimentary marine clays of Plio-Pleistocene Age (e.g. Phillips, 1998;Gallart et al, 2002;Piccarreta et al, 2006;Vergari et al, 2013;Bosino et al, 2019a). Several authors relate the calanchi formations with the lithological, mineralogical, geochemical and pedological properties of the clayey deposits (Alexander, 1982;Faulkner et al, 2000;Summa et al, 2007;Pulice et al, 2013;Vergari et al, 2013;Grubin et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Farabegoli and Agostini, 2000;Battaglia et al, 2011) and (ii) GIS-morphometrical analysis performed on high-resolution digital elevation models (DEMs) (e.g. Coco, 2010, 2013;Caraballo-Arias et al, 2014, 2015Cappadonia et al, 2016;Bosino et al, 2019a). Through morphometric indices, the connectivity of calanchi landforms was evaluated (Faulkner, 2008;Caraballo-Arias et al, 2017) using parameters directly related to the drainage density and sediment transport efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: A case study in the Oltrepo Pavese, Northern Apennines, Italy

Bosino

Giordani

Quénéhervé

et al. 2020

Earth Surf Processes Landf

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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.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: A case study in the Oltrepo Pavese, Northern Apennines, Italy

Bosino

Giordani

Quénéhervé

et al. 2020

Earth Surf Processes Landf

Self Cite

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Mediterranean badlands: Their driving processes and climate change futures

Nadal‐Romero

Rodríguez‐Caballero

Chamizo

et al. 2021

Earth Surf Processes Landf

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Badlands are landforms that occur all over the World. In the Mediterranean region, badlands are found in both dry (arid and semi‐arid) and wet (subhumid and humid) environments, and are characterized by complex hydro‐geomorphological dynamics, high intense erosion processes and extreme sediment yield. Understanding the impact of Global Change is key to predict the on‐site and off‐site effects on badland dynamics, particularly its consequences on bedrock weathering, on sediment yield and delivery and on plant colonization. Here, conducting a systematic literature review, we analyzed an extensive database and identified the main climate‐drivers affecting the hydro‐geomorphological dynamics in Mediterranean badlands (based on non‐metric multidimensional scaling and structural equation modeling analysis). Later, we examined the main impacts expected from climate change forecasting in the near future, and we explored the interactions between badlands response to climate variation. In Mediterranean badlands, weathering processes are mainly related to wetting–drying cycles and freeze–thaw cycles in dry and wet badlands, respectively. In both environments, rainfall amount appears as the main driver for runoff response, and rainfall amount and rainfall intensity for erosion dynamics. Future climate scenarios forecast a decrease in annual rainfall, number of rainfall events and frost days, and in soil moisture, and an increase in rainfall intensity. These changes will have direct hydro‐geomorphological implications with direct and indirect effects on badland dynamics. This may result in a decrease in annual runoff in dry badlands, but the occurrence of more frequent extreme events would increase soil erosion and could negatively affect biological soil crust. In wet badlands, weathering and erosion processes may decrease, and a stabilization of the slopes, with consequently improved vegetation growth, may be expected. In addition, the forecasted changes must be taken into account, especially considering the possible off‐site effects of these extreme environments.

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Badland erosion susceptibility mapping using machine learning data mining techniques, Firozkuh watershed, Iran

Mohammady

2023

Nat Hazards

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Badlands are usually uvial, with dissected V-shaped valleys, short steep slopes, and high drainage density, and results from a very important type of erosion that develops due to a complex interaction of conditioning factors, including climatic, hydrologic, geologic and soil properties, topographic characteristics, and land use. The main goals of this study were 1) create badland susceptibility maps of the Firozkuh watershed and ve machine learning algorithms (models)-functional discriminant analysis (FDA), generalized linear model (GLM), mixture discriminant analysis (MDA), multivariate adaptive regression spline (MARS) and support vector machine (SVM); and 2) compare the accuracy of these models. Sixteen conditioning factors were chosen to model and classify badland susceptibility based on a literature review, data availability, and eld surveys. Model accuracy was assessed using ROC curve and AUC analyses. The analyses showed that SVM was "excellent", MARS was "very good", MDA was "good", and GLM and FDA were "moderate" in classi cation accuracy. The land area of the very high and high classes ranged from 31-51% of the Firozkuh watershed for the GLM and SVM models, respectively. This indicates that badland erosion is a very important problem in the study area. Climatic, hydrologic, geologic, topographic and soil conditions as well as land use changes renders the Firozkuh watershed prone to badland formation and soil erosion which results in substantial socio-economic losses. Badland susceptibility mapping is an important tool that can be used to improve managing future badland erosion in the Firozkuh watershed and other areas affected by badland erosion.

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Identification, characterisation and analysis of the Oltrepo Pavese calanchi in the Northern Apennines (Italy)

Cited by 20 publications

References 47 publications

Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: A case study in the Oltrepo Pavese, Northern Apennines, Italy

Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: A case study in the Oltrepo Pavese, Northern Apennines, Italy

Mediterranean badlands: Their driving processes and climate change futures

Badland erosion susceptibility mapping using machine learning data mining techniques, Firozkuh watershed, Iran

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