Regional coseismic landslide hazard assessment without historical landslide inventories: A new approach

Kritikos, Theodosios; Robinson, Tom; Davies, Tim

doi:10.1002/2014jf003224

Cited by 125 publications

(102 citation statements)

References 101 publications

(156 reference statements)

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“…By general rules of thumb, AUC values greater than 0.8 represent good classification performance (e.g. : Mehdi, 2011), while to be successful predictions must achieve an AUC value of 0.7 (Kritikos et al, 2015). AUC values for individual events range from 0.75 to 0.88 ( Figure 5).…”

Section: Model Fitmentioning

confidence: 95%

“…Methods using data from multiple earthquakes, to derive statistical models generalized at 1 km 2 resolution (Nowicki et al, 2014), and 90 m resolution (Kritikos et al, 2015), have had some success. These models perform well in identifying the likely locations of landslides in the landscape, based upon a relatively small number of earthquakes.…”

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confidence: 99%

“…CC BY 3.0 License. Ridge to stream patterns of topographic amplification and damping Davis andWest (1973), Bouchon (1973), Wu et al (1990), Benites et al (1994, Meunier et al (2008), Densmore et al (1997), (Kritikos et al, 2015) Orientation of hillslope relative to seismic source …”

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confidence: 99%

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Spatial prediction of earthquake-induced landslide probability

Parker

Rosser

Hales

2017

Preprint

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Abstract.We developed a generalized model to describe and predict the spatial distribution of earthquake-induced landslides, based on a regression analysis of 9 co-seismic landslide inventories from different earthquakes and regions. Our model expresses the absolute spatial probability of landslides as a function of peak ground acceleration and hillslope gradient, based on data from global topographic and seismic ground motion datasets. The output from our model predicts probabilities for landslides triggered in sedimentary, meta-sedimentary, igneous and volcanic lithology, and is applicable to 10 shallow continental earthquakes of moment magnitude range 6.2 to 7.9, and depths between 10 and 21 km. To obtain absolute probability predictions, we use only landslide source areas as input data, and explicitly estimate and correct for known incompleteness in input datasets, through a novel Monte Carlo approach. We estimate the uncertainty of these predictions, through extensive testing of the performance of the model, when making out-of-sample predictions for all 9 earthquakes. Our model is notably simpler than others developed to predict spatial probability of landsliding, as we have 15 only included variables that could be constrained consistently at the global-scale, and eliminated those that did not influence landslide probability in a consistent manner across all earthquakes in our dataset. The model outputs also provide a baseline to further investigate spatial and temporal sources of unexplained variability in co-seismic landslide distributions. Using freely available topographic and ground motion data, we suggest that our model can be applied more widely, to provide landslide predictions for earthquakes with no landslide data. 20

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Section: Model Fitmentioning

confidence: 95%

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confidence: 99%

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Spatial prediction of earthquake-induced landslide probability

Parker

Rosser

Hales

2017

Preprint

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“…Most studies to date have addressed spatial correlations between the distribution of landslides and variables that provide proxies for seismic ground motions and the modelled stability of hillslopes (e.g. Dai et al, 2011;Meunier et al, 2007Meunier et al, , 2008; Kritikos et al, 2015). These studies implicitly rely upon a static model of hillslope sensitivity to landslide triggering.…”

Section: Introductionmentioning

confidence: 99%

Spatial distributions of earthquake-induced landslides and hillslope preconditioning in the northwest South Island, New Zealand

et al. 2015

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Abstract. Current models to explain regional-scale landslide events are not able to account for the possible effects of the legacy of previous earthquakes, which have triggered landslides in the past and are known to drive damage accumulation in brittle hillslope materials. This paper tests the hypothesis that spatial distributions of earthquake-induced landslides are determined by both the conditions at the time of the triggering earthquake (time-independent factors) and the legacy of past events (time-dependent factors). To explore this, we undertake an analysis of failures triggered by the 1929 Buller and 1968 Inangahua earthquakes, in the northwest South Island of New Zealand. The spatial extents of landslides triggered by these events were in part coincident. Spatial distributions of earthquake-triggered landslides are determined by a combination of earthquake and local characteristics, which influence the dynamic response of hillslopes. To identify the influence of a legacy from past events, we first use logistic regression to control for the effects of time-independent variables. Through this analysis we find that seismic ground motion, hillslope gradient, lithology, and the effects of topographic amplification caused by ridge-and slope-scale topography exhibit a consistent influence on the spatial distribution of landslides in both earthquakes. We then assess whether variability unexplained by these variables may be attributed to the legacy of past events. Our results suggest that hillslopes in regions that experienced strong ground motions in 1929 were more likely to fail in 1968 than would be expected on the basis of time-independent factors alone. This effect is consistent with our hypothesis that unfailed hillslopes in the 1929 earthquake were weakened by damage accumulated during this earthquake and its associated aftershock sequence, which influenced the behaviour of the landscape in the 1968 earthquake. While our results are tentative, they suggest that the damage legacy of large earthquakes may persist in parts of the landscape for much longer than observed subdecadal periods of post-seismic landslide activity and sediment evacuation. Consequently, a lack of knowledge of the damage state of hillslopes in a landscape potentially represents an important source of uncertainty when assessing landslide susceptibility. Constraining the damage history of hillslopes, through analysis of historical events, therefore provides a potential means of reducing this uncertainty.

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“…A rapid appraisal of the first available imagery suggested that landsliding occurred in an E-W swath located north of the Kathmandu Valley, covering a large proportion of western and central Nepal (∼ 12 000 km 2 ). Initial indications from coseismic earthquake-triggered landslide models, based on Kritikos et al (2015) and Parker et al (2017), were used to direct the mapping effort (see http://ewf.nerc.ac.uk/2015/ 04/25/nepal-earthquake-likely-areas-of-landsliding/). However, mapping efforts were constrained by widespread cloud cover that limited the availability of good-quality optical imagery.…”

Section: Initial Landslide Identification Effortsmentioning

confidence: 99%

Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

Williams

Rosser

Kincey

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 M w 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.

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Regional coseismic landslide hazard assessment without historical landslide inventories: A new approach

Cited by 125 publications

References 101 publications

Spatial prediction of earthquake-induced landslide probability

Spatial prediction of earthquake-induced landslide probability

Spatial distributions of earthquake-induced landslides and hillslope preconditioning in the northwest South Island, New Zealand

Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

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