Geomorphology of Cinque Terre National Park (Italy)

Raso, Emanuele; Mandarino, Andrea; Pepe, Giacomo; Calcaterra, Domenico; Cevasco, Andrea; Confuorto, Pierluigi; Napoli, Mariano Di; Firpo, Marco

doi:10.1080/17445647.2020.1837270

Cited by 15 publications

(15 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CTNP is frequently affected by rainfall-induced shallow landslides (e.g., debris slides and debris avalanches), often evolving into flows, with the failure surfaces usually set at the soil-bedrock interface . Landforms and deposits related to mass movements are extremely widespread, since gravity is one of the main geomorphologic agents [55]. The high steepness of slopes represents an important predisposing factor for landslides along with favoring areal and channelled erosion due to running water [55].…”

Section: Geological and Geomorphological Settingmentioning

confidence: 99%

“…Landforms and deposits related to mass movements are extremely widespread, since gravity is one of the main geomorphologic agents [55]. The high steepness of slopes represents an important predisposing factor for landslides along with favoring areal and channelled erosion due to running water [55].…”

Section: Geological and Geomorphological Settingmentioning

confidence: 99%

“…On average, the Cinque Terre area receives 900-1100 mm of rainfall annually [48][49][50][51][52][53][54]. Extraordinary rainfall events can occur in late summer and early autumn, especially in the ongoing climate change context [48][49][50][51][52][53][54][55]56]. During the rainfall event of 25 October 2011, a cumulative rainfall of 382 mm and maximum rainfall intensities of about 90 mm/h, 195 mm/3h and 350 mm/6h were recorded at Monterosso rain gauge [6,57].…”

Section: Geological and Geomorphological Settingmentioning

confidence: 99%

“…Indeed, wide portions of the CTNP area, from the shoreline up to 400-500 m a.s.l., have been modified through slope terracing, producing an unusual man-made coastal landscape which is perfectly integrated with the local geomorphological setting ( Figure 2). Terraced slopes cover an area of about 15 km 2 , corresponding to about 41% of the entire CTNP area; however, approximately 67% of the terraced areas have been abandoned [55]. Terranova [59] estimated that the maximum linear extension of dry-stone walls could be approximately 6000 km.…”

Section: Agricultural Terracesmentioning

confidence: 99%

“…In this study, special care has been paid to the recognition and mapping of both agricultural terraces and non-terraced areas. In particular, using data from previous studies [12,55], four classes were distinguished: 1. cultivated terraced areas; 2. abandoned terraces with poor vegetation cover (initial state of abandonment); 3. abandoned terraces with dense vegetation cover (advanced state of abandonment); 4. non-terraced areas (including either urban areas, outcropping rocks and woods). Land-use, because of its temporal and spatial variations, can greatly affect landslide occurrence as PFs [15].…”

Section: Predisposing Factorsmentioning

confidence: 99%

See 4 more Smart Citations

Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Napoli

Martire

Bausilio

et al. 2021

Water

Self Cite

View full text Add to dashboard Cite

Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed.

show abstract

Section: Geological and Geomorphological Settingmentioning

confidence: 99%

Section: Geological and Geomorphological Settingmentioning

confidence: 99%

Section: Geological and Geomorphological Settingmentioning

confidence: 99%

Section: Agricultural Terracesmentioning

confidence: 99%

Section: Predisposing Factorsmentioning

confidence: 99%

See 3 more Smart Citations

Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Napoli

Martire

Bausilio

et al. 2021

Water

Self Cite

View full text Add to dashboard Cite

show abstract

Multitemporal relative landslide exposure and risk analysis for the sustainable development of rapidly growing cities

et al. 2023

View full text Add to dashboard Cite

In recent decades, developing countries have experienced an increase in the impact of natural disasters due to ongoing climate change and the sustained expansion of urban areas. The intrinsic vulnerability of settlements, due to poverty and poor governance, as well as the lack of tools for urban occupation planning and mitigation protocols, has made such impacts particularly severe. Cuenca (Ecuador) is a significant example of a city that in recent decades has experienced considerable population growth (i.e. exposure) and an associated increase in loss due to landslide occurrence. Despite such effects, updated urban planning tools are absent, so an evaluation of multitemporal exposure to landslides and related risks is required. In this perspective, a potential urban planning tool is presented based on updated data depicting the spatial distribution of landslides and their predisposing factors, as well as population change between 2010 and 2020. In addition, a multitemporal analysis accounting for changes in exposure between 2010 and 2020 and an estimation of relative landside risk was carried out. Due to the absence of spatially distributed population data, energy supply contract data have been used as a proxy of the population. The results show that the current higher exposure and related relative risk are estimated for parishes (parroquias) located in the southern sector of the study area (i.e. Turi, Santa Ana, Tarqui, Nulti, Baños and Paccha). Moreover, the exposure multitemporal analysis indicates that most parishes located in the hilly areas bounding the city centre (i.e. Sayausi, San Joaquin, Tarqui, Sidcay, Baños, Ricaurte, Paccha and Chiquintad) are experiencing sustained population growth and will be potentially exposed to an increased risk with a consistently growing trend. The obtained relative risk map can be considered a valuable tool for guiding land planning, land management, occupation restriction and early warning strategy adoption in the area. The methodological approach used, which accounts for landslide susceptibility and population variation through proxy data analysis, has the potential to be applied in a similar context of growing population cities in low- to mid-income countries, where data usually needed for a comprehensive landslide risk analysis are non-existing or only partially available.

show abstract

On the estimation of landslide intensity, hazard and density via data-driven models

Di Napoli,

Tanyas,

Castro-Camilo

et al. 2023

Nat Hazards

View full text Add to dashboard Cite

Maps that attempt to predict landslide occurrences have essentially stayed the same since 1972. In fact, most of the geo-scientific efforts have been dedicated to improve the landslide prediction ability with models that have largely increased their complexity but still have addressed the same binary classification task. In other words, even though the tools have certainly changed and improved in 50 years, the geomorphological community addressed and still mostly addresses landslide prediction via data-driven solutions by estimating whether a given slope is potentially stable or unstable. This concept corresponds to the landslide susceptibility, a paradigm that neglects how many landslides may trigger within a given slope, how large these landslides may be and what proportion of the given slope they may disrupt. The landslide intensity concept summarized how threatening a landslide or a population of landslide in a study area may be. Recently, landslide intensity has been spatially modeled as a function of how many landslides may occur per mapping unit, something, which has later been shown to closely correlate to the planimetric extent of landslides per mapping unit. In this work, we take this observation a step further, as we use the relation between landslide count and planimetric extent to generate maps that predict the aggregated size of landslides per slope, and the proportion of the slope they may affect. Our findings suggest that it may be time for the geoscientific community as a whole, to expand the research efforts beyond the use of susceptibility assessment, in favor of more informative analytical schemes. In fact, our results show that landslide susceptibility can be also reliably estimated (AUC of 0.92 and 0.91 for the goodness-of-fit and prediction skill, respectively) as part of a Log-Gaussian Cox Process model, from which the intensity expressed as count per unit (Pearson correlation coefficient of 0.91 and 0.90 for the goodness-of-fit and prediction skill, respectively) can also be derived and then converted into how large a landslide or several coalescing ones may become, once they trigger and propagate downhill. This chain of landslide intensity, hazard and density may lead to substantially improve decision-making processes related to landslide risk.

show abstract

Geomorphology of Cinque Terre National Park (Italy)

Cited by 15 publications

References 57 publications

Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Multitemporal relative landslide exposure and risk analysis for the sustainable development of rapidly growing cities

On the estimation of landslide intensity, hazard and density via data-driven models

Contact Info

Product

Resources

About