Probabilistic seismic landslide hazard maps including epistemic uncertainty

Wang, Yubing; Rathje, Ellen M.

doi:10.1016/j.enggeo.2015.08.001

Cited by 62 publications

(19 citation statements)

References 23 publications

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“…The latter includes an analogous plug-in scheme while using fixed empirical relations derived for physically-based properties. As a result, models that feature empirical relations suffer from uncertainty in the empirical relations but also in terms of model parameterization, as demonstrated by Wang and Rathje (2015). Our work derives statistical relations instead of empirical ones and essentially translates the uncertainty estimation routine in Wang and Rathje (2015) into the binomial GAM context.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, implementing statistical simulations for earthquake scenarios was never tested so far and especially in the study area, where the main landslide trigger is due to the strong seismicity. Therefore, our proposed method may deliver a much more relevant information to local authorities compared to traditional susceptibility models, especially in the case where the scarcity of data on soil and rock characteristics limit the application of physically-based methods for earthquake-induced landslide hazard assessment, such as those presented by Wang and Rathje (2015). In fact, the usual procedure consists of building a susceptibility model trained by using past landslides and either including the responsible ground motion (thus being overly specific) or without it (thus neglecting the spatial dependence in the model induced by the shaking levels).…”

Section: Discussionmentioning

confidence: 99%

“…Rathje and Saygili (2008) developed displacement hazard curves, that show the exceedance probability of Newmark displacement levels. To account for the uncertainty in input parameters, several approaches have been proposed, such as Monte Carlo simulation (see, Refice and Capolongo 2002) or logic tree approach (see, Wang and Rathje 2015). Nevertheless, despite these advances the application of physically-based approaches for earthquakeinduced landslide susceptibility remains problematic in many areas, due to the scarcity of geotechnical data to characterize the soil materials, the lack of soil depth information, lack of landslide inventories for different earthquake events, and the limitation of the Newmark model to shallow slope failures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

Luo

Lombardo

Westen

et al. 2021

Stoch Environ Res Risk Assess

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The vast majority of statistically-based landslide susceptibility studies assumes the slope instability process to be time-invariant under the definition that “the past and present are keys to the future”. This assumption may generally be valid. However, the trigger, be it a rainfall or an earthquake event, clearly varies over time. And yet, the temporal component of the trigger is rarely included in landslide susceptibility studies and only confined to hazard assessment. In this work, we investigate a population of landslides triggered in response to the 2017 Jiuzhaigou earthquake ($$M_w = 6.5$$ M w = 6.5 ) including the associated ground motion in the analyses, these being carried out at the Slope Unit (SU) level. We do this by implementing a Bayesian version of a Generalized Additive Model and assuming that the slope instability across the SUs in the study area behaves according to a Bernoulli probability distribution. This procedure would generally produce a susceptibility map reflecting the spatial pattern of the specific trigger and therefore of limited use for land use planning. However, we implement this first analytical step to reliably estimate the ground motion effect, and its distribution, on unstable SUs. We then assume the effect of the ground motion to be time-invariant, enabling statistical simulations for any ground motion scenario that occurred in the area from 1933 to 2017. As a result, we obtain the full spectrum of potential coseismic susceptibility patterns over the last century and compress this information into a hazard model/map representative of all the possible ground motion patterns since 1933. This backward statistical simulations can also be further exploited in the opposite direction where, by accounting for scenario-based ground motion, one can also use it in a forward direction to estimate future unstable slopes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

Luo

Lombardo

Westen

et al. 2021

Stoch Environ Res Risk Assess

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show abstract

“…The empirical determination of a landslide hazard curve as outlined above is distinct from a geotechnical probabilistic approach that is primarily used for subaerial landslides. The latter relies on estimates for expected ground shaking from nearby earthquakes, slope, and physical properties including soil shear strength (e.g., Rathje et al, , Saygili & Rathje , , Wang & Rathje , ).…”

Section: Development Of Offshore Landslide Hazard Curvesmentioning

confidence: 99%

Offshore Landslide Hazard Curves From Mapped Landslide Size Distributions

Geist

Brink

2019

JGR Solid Earth

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We present a method to calculate landslide hazard curves along offshore margins based on size distributions of submarine landslides. The method utilizes 10 different continental margins that were mapped by high-resolution multibeam sonar with landslide scar areas measured by a consistent Geographic Information System procedure. Statistical tests of several different probability distribution models indicate that the lognormal model is most appropriate for these siliciclastic environments, consistent with an earlier study of the U.S. Atlantic margin , https://doi.org/10.1016/j.margeo.2008.08.007). Parameter estimation is performed using the maximum likelihood technique, and confidence intervals are determined using likelihood profiles. Pairwise comparison of size distributions for the 10 margins indicates that the U.S. Atlantic and Queen Charlotte margins are different than most other margins. These margins represent end-members, with the U.S. Atlantic margin having the highest mean scar area and the Queen Charlotte margin the lowest. We demonstrate that empirical, offshore landslide hazard curves can be developed from the landslide size distributions, if the duration of mapped landslide activity is known. This study indicates that the shape parameter of the size distribution is similar among all 10 margins, and thus, the shape of the hazard curves is also similar. Significant differences in hazard curves among the margins are therefore related to differences in mean sizes and, potentially, differences in the duration of landslide activity.

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“…Ideally, uncertainties of geospatial and hazard predictor variables could be incorporated and propagated through to probabilistic secondary hazard models. Some authors have proposed approaches to address uncertainties, such as using logic trees (Wang and Rathje, 2015), but this topic requires further development.…”

Section: Strategies and Challengesmentioning

confidence: 99%

USGS approach to real-time estimation of earthquake-triggered ground failure - Results of 2015 workshop

Allstadt¹,

Thompson²,

Wald³

et al. 2016

Open-File Report

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For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit http://www.usgs.gov/ or call 1-888-ASK-USGS (1-888-275-8747).For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprod/.Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner. AbstractThe U.S. Geological Survey (USGS) Earthquake Hazards and Landslide Hazards Programs are developing plans to add quantitative hazard assessments of earthquake-triggered landsliding and liquefaction to existing real-time earthquake products (ShakeMap, ShakeCast, PAGER) using open and readily available methodologies and products. To date, prototype global statistical models have been developed and are being refined, improved, and tested. These models are a good foundation, but much work remains to achieve robust and defensible models that meet the needs of end users. In order to establish an implementation plan and identify research priorities, the USGS convened a workshop in Golden, Colorado, in October 2015. This document summarizes current (as of early 2016) capabilities, research and operational priorities, and plans for further studies that were established at this workshop. Specific priorities established during the meeting include (1) developing a suite of alternative models; (2) making use of higher resolution and higher quality data where possible; (3) incorporating newer global and regional datasets and inventories; (4) reducing barriers to accessing inventory datasets; (5) developing methods for using inconsistent or incomplete datasets in aggregate; (6) developing standardized model testing and evaluation methods; (7) improving ShakeMap shaking estimates, particularly as relevant to ground failure, such as including topographic amplification and accounting for spatial variability; and (8) developing vulnerability functions for loss estimates.

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Probabilistic seismic landslide hazard maps including epistemic uncertainty

Cited by 62 publications

References 23 publications

From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

Offshore Landslide Hazard Curves From Mapped Landslide Size Distributions

USGS approach to real-time estimation of earthquake-triggered ground failure - Results of 2015 workshop

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