Implications of climate change on landslide hazard in Central Italy

Alvioli, Massimiliano; Melillo, Massimo; Guzzetti, Fausto; Rossi, Mauro; Palazzi, Elisa; Hardenberg, Jost von; Brunetti, Maria Teresa; Peruccacci, Silvia

doi:10.1016/j.scitotenv.2018.02.315

Cited by 145 publications

(87 citation statements)

References 69 publications

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“…For the former, new modelling frameworks will have to be devised, and tested. For the latter, not only we lack long-term past landslide data to train sound models, but we also lack a proper understanding of how climate may change and influence future slope instabilities, in the same general area (Alvioli et al, 2018), and in other areas (Gariano and Guzzetti, 2016). The main problem to overcome both limitations lays in the lack of accurate, spatially distributed, multi-temporal landslide datasets.…”

Section: Geomorphological Considerationsmentioning

confidence: 99%

Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

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Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment in many areas. Investigators have for long attempted to estimate landslide hazard in an effort to determine where, when (or how frequently), and how large (or how destructive) landslides are expected to be in an area. This information may prove useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most of the attempts at predicting the occurrence of populations of landslides by adopting statistical approaches are based on the empirical observation that landslides occur as a result of multiple, interacting, conditioning and triggering factors. Based on this observation, and under the assumption that at the spatial and temporal scales of our investigation individual landslides are discrete "point" events in the landscape, we propose a novel Bayesian modelling framework for the prediction of the spatio-temporal occurrence of landslides of the slide type caused by weather triggers. We build our modelling effort on a Log-Gaussian Cox Process (LGCP) by assuming that individual landslides in an area are the result of a point process described by an unknown intensity function. The modelling framework has two stochastic components: (i) a Poisson component, which models the observed (random) landslide count in each terrain subdivision for a given landslide "intensity", i.e., the expected number of landslides per terrain subdivision (which may be transformed into a corresponding landslide "susceptibility"); and (ii) a Gaussian component, used to account for the spatial distribution of the local environmental conditions that influence landslide occurrence, and for the spatio-temporal distribution of "unobserved" latent environmental controls on landslide occurrence. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory covering the period from before 1941 to 2014 is available together with lithological and bedding data. We subdivided the 79 km 2 area into 889 slope units (SUs). In each SU, we computed the percentage of 16 morphometric covariates derived from a 10 m × 10 m digital elevation model, and 13 lithological and bedding attitude covariates obtained from a 1:10,000 scale geological map. We further counted how many of the 3,379 landslides in the multi-temporal inventory affect each SU and grouped them into six periods. We used this complex space-time information to prepare five models of increasing complexity. Our "baseline" model (Mod1) carries the spatial information only through the covariates mentioned above. It does not include any additional information about the spatial and temporal structure of the data, and it is therefore equivalent to a "traditional" landslide susceptibility model. The second model (Mod2) is analogous, but it allows for time-interval-specific regression constants. Our next two models are more complex. In partic...

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Section: Geomorphological Considerationsmentioning

confidence: 99%

Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Some physically based models calculate the safety factor (SF) that can be integrated in the landslide hazard assessment (Wu and Abdel-Latif 2000;Frattini et al 2004;Haneberg 2004Baum et al 2005. Advanced approaches couple the hydrological model and the infinite slope stability model and both can be implemented with various degrees of sophistication, including either steady-state or transient conditions (Montgomery and Dietrich 1994;Savage et al 2004;Baum et al 2008;Godt et al 2008;Salciarini et al 2008;Rossi et al 2013;Alvioli et al 2018). The first physically based models were twodimensional (2D), but nowadays, three-dimensional (3D) slope stability models exist (Marchesini et al 2009;Jia et al 2012;Mergili et al 2014;Raia et al 2014;Alvioli and Baum 2016).…”

Section: Introductionmentioning

confidence: 99%

Preparing first-time slope failures hazard maps: from pixel-based to slope unit-based

2019

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In this work, we present a novel quantitative geographical information system-based procedure to obtain the magnitude (area) and frequency of medium to large first-time shallow slope failures. The procedure has been set up at the Barcedana Valley, in the Tremp Basin (Eastern Pyrenees). First, pixel-based susceptibility classes were defined using a slope stability index obtained with the physically based model SINMAP. The frequency calculated from the number of firsttime failures recorded during the last 60 years was then assigned to each susceptibility class. We devised a procedure to estimate the size of potential failures by means of the aggregation of pixels within the boundaries of morphological slope units, optimized for the purpose. Finally, the landslide hazard was prepared using the magnitude-frequency matrix. Results show that a proper pixel clustering has been carried which avoids the generation of small groups of pixels with different susceptibility degrees within the same slope unit. For a given hill slope, the area of the cluster of pixels depends on the size of the slope unit, which is not unique as it depends on the criterion used to delineate them. Therefore, the latter is a key factor in the final results. In this study, we validated our results with the size distribution of the observed landslides. The methodology presented in this work can be applied using any susceptibility assessment method with a pixel-based output.

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“…As the high-resolution geographical data from multisource has become available in this decade, debris flow susceptibility assessments based on dynamic methods have been applied more widely for the detailed description of complex rainfalls that trigger debris flow [32][33][34][35]. Furthermore, dynamic methods have the potential to address the climate projection data simulated by global/regional circulation models to implicate the corresponding changes of debris flow susceptibility [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Debris Flow Risk Assessment Based on a Water–Soil Process Model at the Watershed Scale Under Climate Change: A Case Study in a Debris-Flow-Prone Area of Southwest China

Wang

et al. 2019

Sustainability

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Risk assessment lays a foundation for disaster risk reduction management, especially in relation to climate change. Intensified extreme weather and climate events driven by climate change may increase related disaster susceptibility. This may interact with exposed and vulnerable socioeconomic systems to aggravate the impacts and impede progress towards regional development. In this study, debris flow risk under climate change was assessed by an integrated debris flow mechanism model and an inclusive socioeconomic status evaluation. We implemented the method for a debris flow-prone area in the eastern part of the Qinghai-Tibet Plateau, China. Based on the analysis of three general circulation models (GCMs)-Beijing Climate Center Climate System Model version 1 (BCC_CSM), model for Interdisciplinary Research on Climate-Earth System, version 5 (MIROC5, and the Community Climate System Model version 4 (CCSM4)-the water-soil process model was applied to assess debris flow susceptibility. For the vulnerability evaluation, an index system established from the categories of bearing elements was analyzed by principle component analysis (PCA) methods. Our results showed that 432 to 1106 watersheds (accounting for 23% to 52% of the study area) were identified as debris-flow watersheds, although extreme rainfall would occur in most of the area from 2007 to 2060. The distributions of debris flow watersheds were concentrated in the north and transition zones of the study area. Additionally, the result of the index and PCA suggested that most areas had relatively low socioeconomic scores and such areas were considered as high-vulnerability human systems (accounts for 91%). Further analysis found that population density, road density, and gross domestic production made great contributions to vulnerability reduction. For practical mitigation strategies, we suggested that the enhancement of road density may be the most efficient risk reduction strategy.

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Implications of climate change on landslide hazard in Central Italy

Cited by 145 publications

References 69 publications

Space-Time Landslide Predictive Modelling

Space-Time Landslide Predictive Modelling

Preparing first-time slope failures hazard maps: from pixel-based to slope unit-based

Debris Flow Risk Assessment Based on a Water–Soil Process Model at the Watershed Scale Under Climate Change: A Case Study in a Debris-Flow-Prone Area of Southwest China

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