Distribution Pattern of Coseismic Landslides Triggered by the 2017 Jiuzhaigou Ms 7.0 Earthquake of China: Control of Seismic Landslide Susceptibility

Chen, Xiaoli; Shan, Xinjian; Wang, Mingming; Liu, Chunguo; Han, Na

doi:10.3390/ijgi9040198

Cited by 26 publications

(9 citation statements)

References 27 publications

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“…The complex geological environment superimposed with the impact of external factors, such as earthquake, precipitation, and human activities, can cause a chain reaction to geohazards in the Longmenshan area, which also provides the geological environment basis for constructing the geohazard susceptibility analysis model for our research [ 48 ]. Based on full consideration of geomorphology and geological structure of the study area, we selected 14 factors of five types, including topography, geology, land cover, meteorology and hydrology, and human activities, to construct an index evaluation system of geohazard susceptibility by combining the distribution characteristics of geohazards with previous research [ 49 , 50 , 51 , 52 , 53 ]. The data sources of all factors are shown in Table 2 , which are consistent with the geohazard data in time and space.…”

Section: Methodsmentioning

confidence: 99%

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Zhao

et al. 2022

IJERPH

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Multitemporal geohazard susceptibility analysis can not only provide reliable results but can also help identify the differences in the mechanisms of different elements under different temporal and spatial backgrounds, so as to better accurately prevent and control geohazards. Here, we studied the 12 counties (cities) that were severely affected by the Wenchuan earthquake of 12 May 2008. Our study was divided into four time periods: 2008, 2009–2012, 2013, and 2014–2017. Common geohazards in the study area, such as landslides, collapses and debris flows, were taken into account. We constructed a geohazard susceptibility index evaluation system that included topography, geology, land cover, meteorology, hydrology, and human activities. Then we used a random forest model to study the changes in geohazard susceptibility during the Wenchuan earthquake, the following ten years, and its driving mechanisms. We had four main findings. (1) The susceptibility of geohazards from 2008 to 2017 gradually increased and their spatial distribution was significantly correlated with the main faults and rivers. (2) The Yingxiu-Beichuan Fault, the western section of the Jiangyou-Dujiangyan Fault, and the Minjiang and Fujiang rivers were highly susceptible to geohazards, and changes in geohazard susceptibility mainly occurred along the Pingwu-Qingchuan Fault, the eastern section of the Jiangyou-Dujiangyan Fault, and the riparian areas of the Mianyuan River, Zagunao River, Tongkou River, Baicao River, and other secondary rivers. (3) The relative contribution of topographic factors to geohazards in the four different periods was stable, geological factors slowly decreased, and meteorological and hydrological factors increased. In addition, the impact of land cover in 2008 was more significant than during other periods, and the impact of human activities had an upward trend from 2008 to 2017. (4) Elevation and slope had significant topographical effects, coupled with the geological environmental effects of engineering rock groups and faults, and river-derived effects, which resulted in a spatial aggregation of geohazard susceptibility. We attributed the dynamic changes in the areas that were highly susceptible to geohazards around the faults and rivers to the changes in the intensity of earthquakes and precipitation in different periods.

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Section: Methodsmentioning

confidence: 99%

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Zhao

et al. 2022

IJERPH

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“…The ETL locations are assessed in the critical acceleration method by comparing each slope section's critical acceleration and the PGA from an earthquake. If the earthquake ground acceleration surpasses a critical acceleration, the slope may fail during shaking (Chen et al, 2020). In the context of Newmark's method, the dynamic stability of a slope is related to its static stability.…”

Section: Critical Acceleration Methodsmentioning

confidence: 99%

“…Xiaoli and Chunguo (2019) analyzed the slope stability for the 2014 Ludian earthquake in China and showed that Newmark's critical acceleration and PGA could be used to estimate accurate ETL locations. Similarly, Chen et al (2020) concluded that it is possible to quickly evaluate ETL by comparing critical acceleration and PGA.…”

Section: F Smentioning

confidence: 99%

“…The model shortcomings were attributed to large cell sizes in the digital elevation model (90 m), aspects of the ground motion spectra that PGA does not capture, and lack of spatial variability in surface material strength. Other studies have used critical acceleration, instead of Newmark's displacement, to analyze the terrain's susceptibility to ETL (Chen et al, 2014;Xiaoli and Chunguo, 2019;Chen et al, 2020). Chen et al (2014) compared the landslide distribution triggered by the 2008 Wenchuan earthquake with a critical acceleration map and found good correspondence with the actual landslide locations.…”

Section: Introductionmentioning

confidence: 99%

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Rapid Terrain Assessment for Earthquake-Triggered Landslide Susceptibility With High-Resolution DEM and Critical Acceleration

et al. 2021

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Earthquake ground motion often triggers landslides in mountainous areas. A simple, robust method to quickly evaluate the terrain’s susceptibility of specific locations to earthquake-triggered landslides is important for planning field reconnaissance and rescues after earthquakes. Different approaches have been used to estimate coseismic landslide susceptibility using Newmark’s sliding block model. This model requires an estimate of the landslide depth or thickness, which is a difficult parameter to estimate. We illustrate the use of Newmark sliding block’s critical acceleration for a glaciated valley affected by the 2015 Gorkha earthquake in Nepal. The landslide data came from comparing high-resolution pre- and post-earthquake digital elevation models (DEMs) derived from Spot 6/7 images. The areas where changes were detected provided an inventory of all the landslides triggered by the earthquake. The landslide susceptibility was modeled in a GIS environment using as inputs the pre-earthquake terrain and slope angles, the peak ground acceleration from the 2015 Gorkha earthquake, and a geological map. We exploit the depth information for the landslides (obtained by DEM difference) to apply the critical acceleration model. The spatial distribution of the predicted earthquake-triggered landslides matched the actual landslides when the assumed landslide thickness in the model is close to the median value of the actual landslide thickness (2.6 m in this case). The landslide predictions generated a map of landslide locations close to those observed and demonstrated the applicability of critical acceleration for rapidly creating a map of earthquake-triggered landslides.

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“…For instance, Cao et al (2020) has recently modeled the pre-earthquake landslide susceptibility between 2000 and 2015, whereas Yi et al (2019) modeled the coseismic landslide susceptibility. Conversely, Chen et al (2020) reconstructed the spatial distribution of coseismic unstable slopes by using the Newmark approach. Chen et al (2018) and Wang et al (2018) focused instead on studying coseismic landslide occurrences and their impact on infrastructure.…”

Section: Jiuzhaigou Earthquakementioning

confidence: 99%

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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Distribution Pattern of Coseismic Landslides Triggered by the 2017 Jiuzhaigou Ms 7.0 Earthquake of China: Control of Seismic Landslide Susceptibility

Cited by 26 publications

References 27 publications

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Rapid Terrain Assessment for Earthquake-Triggered Landslide Susceptibility With High-Resolution DEM and Critical Acceleration

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

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