Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides

Lombardo, Luigi; Bakka, Haakon; Tanyaş, Hakan; Westen, C.J. van; Martin, P.; Huser, Raphaël

doi:10.1029/2019jf005056

Cited by 83 publications

(56 citation statements)

References 94 publications

(144 reference statements)

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“…In any region, landslide mapping is the fundamental task that must be completed for landslide susceptibility and hazard analyses. However, as landslide mapping requires time and effort, forward modelling and spatial analysis tools (e.g., see [33,34] for the Newmark mapping approach) have been developed to 'predict' landslide susceptibility more rapidly and without prior information on previous slope failures (as would be needed for regions hit by a severe storm or earthquake, for which a new or updated landslide susceptibility map should be rapidly created; see applications presented by [35,36]). Nonetheless, for a reliable and general (not event-based) landslide susceptibility assessment, landslide inventories provide a necessary control on such predicted susceptibility maps, as spatial analysis or forward modelling approaches cannot account for all region-specific information.…”

Section: Discussionmentioning

confidence: 99%

A New Landslide Inventory for the Armenian Lesser Caucasus: Slope Failure Morphologies and Seismotectonic Influences on Large Landslides

et al. 2020

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Landslide hazard analyses in Armenia require consideration of the seismotectonic context of the Lesser Caucasus. As it is located near the center of the Arab-Eurasian collision, the Lesser Caucasus is characterized by its complex geology, dense fault network and mountainous relief; it is marked by recent volcanic and seismic activity largely influencing slope stability at different scales. We therefore sought to identify all major landslides in the Armenian Lesser Caucasus and to understand the environmental factors contributing to regional landslide susceptibility. We performed spatial and size-frequency analyses using two landslide catalogues as inputs: “Georisk”, provided by the Georisk Scientific Research Company, and “Matossian”, herein. Our spatial analyses show that landslide susceptibility depends on many factors according to the area considered: near faults, a tectonic influence on slope stability is clearly observable, whereas high concentrations of landslides in northern mountain regions, marked by a wetter climate and far from known active faults, show that climatic factors also strongly contribute to slope-failure potential. The influence of volcanoes and volcanic deposits on the development of mass movements is unclear and requires further analysis. The aforementioned inventories do not include any records of volcanic flank collapses, although we expect at least one case in the eastern Lesser Caucasus.

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Section: Discussionmentioning

confidence: 99%

A New Landslide Inventory for the Armenian Lesser Caucasus: Slope Failure Morphologies and Seismotectonic Influences on Large Landslides

et al. 2020

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“…As for the temporal scale, due to the large time-span, the detected temporal patterns may reflect more information due to longterm climatic variations rather than specific conditions. For this reason, we are planning to extend our spatiotemporal cluster analyses to more complex models, which can concurrently capture multivariate contributions featuring environmental effects, even at the latent level (Lombardo et al, 2018(Lombardo et al, , 2019a.…”

Section: Discussionmentioning

confidence: 99%

“…This increase in probability can be captured through clustering analyses and various examples already exist in literature where this has been done at different spatial and temporal scales and via different analytical approaches. Notably, this type of application spans in many areas of natural hazards and have become mainstream in case of seismicity (e.g., Fischer and Horálek, 2003;Georgoulas et al, 2013;Varga et al, 2012;Woodward et al, 2018;Yang et al, 2019), joint sets and their orientation in rock outcrops (e.g., Tokhmechi et al, 2011;Zhan et al, 2017), groundwater monitoring (Chambers et al, 2015), wildfires (e.g., Orozco et al, 2012;Costafreda-Aumedes et al, 2016;Fuentes-Santos et al, 2013;Tonini et al, 2017), and landslides (e.g., Lombardo et al, 2018Lombardo et al, , 2019aTonini and Cama, 2019). In the specific case of flooding, Zhao et al (2014) used the projection pursuit theory to cluster spatial data and to build a dynamic risk assessment model for flood disasters.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal clustering of flash floods in a changing climate (China, 1950–2015)

Wang¹,

Lombardo²,

Tonini³

et al. 2020

Preprint

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Abstract. The persistence over space and time of flash flood disasters – flash floods that have caused either economical or life losses, or both – is a diagnostic measure of areas subjected to hydrological risk. The concept of persistence can be assessed via clustering analyses, performed here to analyse the national inventory of flash flood disasters in China occurred in the period 1950–2015. Specifically, we investigated the spatiotemporal pattern distribution of the flash flood disasters and their clustering behavior by using both global and local methods: the first, based on the Ripley's K-function, and the second on Scan Statistics. As a result, we could visualize patterns of aggregated events, estimate the cluster duration and make assumptions about their evolution over time, also with respect precipitation trend. Due to the large spatial (the whole Chinese territory) and temporal (66 years) scale of the dataset, we were able to capture whether certain clusters gather in specific locations and times, but also whether their magnitude tends to increase or decrease. Overall, the eastern regions in China are much more subjected to flash flood disasters compared to the rest of the country. Detected clusters revealed that these phenomena predominantly occur between July and October, a period coinciding with the wet season in China. The number of detected clusters increases with time, but the associated duration drastically decreases in the recent period. This may indicate a change towards triggering mechanisms which are typical of short-duration extreme rainfall events. Finally, being flash flood disasters directly linked to precipitation and their extreme realization, we indirectly assessed whether the magnitude of the trigger itself has also varied through space and time, enabling considerations in the context of climatic changes.

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“…We can interpret this setup as a random effects model of local variation on top of a broader trend (Lombardo et al . 2019). It can be shown that this combination returns yet another, additive Gaussian process (Rasmussen and Williams, 2006).…”

Section: Worked Examplesmentioning

confidence: 99%

Bayesian geomorphology

Korup

2020

Earth Surf Processes Landf

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Summary The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

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Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides

Cited by 83 publications

References 94 publications

A New Landslide Inventory for the Armenian Lesser Caucasus: Slope Failure Morphologies and Seismotectonic Influences on Large Landslides

A New Landslide Inventory for the Armenian Lesser Caucasus: Slope Failure Morphologies and Seismotectonic Influences on Large Landslides

Spatiotemporal clustering of flash floods in a changing climate (China, 1950–2015)

Bayesian geomorphology

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