Revisions of the M 8.0 Wenchuan earthquake seismic intensity map based on co-seismic landslide abundance

Xu, Chong; Xu, Xiwei; Zhou, Bengang; Yu, Guihua

doi:10.1007/s11069-013-0757-0

Cited by 50 publications

(26 citation statements)

References 23 publications

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“…Moreover, this result shows a significantly positive relationship with comparatively steep slopes (20-30 • and >30 • ) and percentage of high seismic intensity zone, indicating that a high potential energy in steep slopes induced secondary geo-hazards to destroy vegetation growing on the slope surface. This result is also consistent with the findings of Su and Cui [58] and Xu et al [30], who observed that serious damage was caused in the IX, X, and XI seismic intensity zones and the areas with unstable steep slopes.…”

Section: Grids Of Negative Residuals Of the Standard Linear Modelsupporting

confidence: 93%

“…The fault data were extracted from the map of horizontal peak ground acceleration (PGA) in the Wenchuan earthquake-affected area, created by Tang et al [29]. In addition, the seismic intensity zone data were extracted from the Ms 8.0 Wenchuan earthquake seismic intensity map created by Xu et al [30]. All data were combined with the damaged vegetation data using an overlay analysis.…”

Section: Data Collectionmentioning

confidence: 99%

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Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China

Liu

et al. 2019

Forests

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Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged vegetation are of great interest in order to improve the assessment of vegetation loss and the prediction of the spatial distribution of damaged vegetation caused by earthquakes. In this study, we used Moran’s I correlograms to study the spatial autocorrelation of damaged vegetation and its potential driving factors in the nine worst-hit Wenchuan earthquake-affected cities and counties. Both dependent and independent variables showed a positive spatial autocorrelation but with great differences at four aggregation levels (625 × 625 m, 1250 × 1250 m, 2500 × 2500 m, and 5000 × 5000 m). Shrubs can represent the characteristics of all damaged vegetation due to the significant linear relationship between their Moran’s I at the four aggregation levels. Clustering of similar high coverage of damaged vegetation occurred in the study area. The residuals of the standard linear regression model also show a significantly positive autocorrelation, indicating that the standard linear regression model cannot explain all the spatial patterns in damaged vegetation. Spatial autoregressive models without spatially autocorrelated residuals had the better goodness-of-fit to deal with damaged vegetation. The aggregation level 8 × 8 is a scale threshold for spatial autocorrelation. There are other environmental factors affecting vegetation destruction. Our study provides useful information for the countermeasures of vegetation protection and conservation, as well as the prediction of the spatial distribution of damaged vegetation, to improve vegetation restoration in earthquake-affected areas.

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Section: Grids Of Negative Residuals Of the Standard Linear Modelsupporting

confidence: 93%

Section: Data Collectionmentioning

confidence: 99%

Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China

Liu

et al. 2019

Forests

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“…Information gathering of damaged buildings can be extremely important for further studies, and helpful to guide the future design and construction of masonry structures. One of the co-authors of this paper has investigated 238 masonry buildings located at the seismic intensity zones VIII~X during the 2008 Wenchuan earthquake (Su et al 2014;Xu et al 2013), as shown in Fig. 4.…”

Section: Characteristics Of the Survey Databasementioning

confidence: 99%

Simplified Density Indexes of Walls and Tie-Columns for Confined Masonry Buildings in Seismic Zones

Cai

Tsavdaridis

et al. 2018

Journal of Earthquake Engineering

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“…Coseismic landslide hazards have drawn increasing attention in recent years (i.e. Jibson et al, 1998;Khazai and Sitar, 2004;Qi et al, 2010Qi et al, , 2011Qi et al, , 2012Xu et al, 2013;Chen et al, 2012;Yuan et al, 2014). In fact, the damage caused by seismically triggered landslides is sometimes more severe than the damage direct from the earthquake (Keefer, 1984).…”

Section: Introductionmentioning

confidence: 99%

An improved method of Newmark analysis for mapping hazards of coseismic landslides

Zang¹,

Qi²,

Sheng³

et al. 2019

Preprint

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Abstract. Coseismic landslides have been responsible for destroyed buildings and structures, dislocated roads and bridges, cut off of pipelines and lifelines, and tens of thousands of deaths. Newmark's method is widely applied to assess the permanent displacement along a potential slide surface to determine the coseismic responses of the slope. The Mw 6.1 (USGS) earthquake in Ludian, Yunnan Province, China in 2014 has caused widespread landslides and provided the ideal data sets to conduct a regional analysis of coseismic stability of slopes. The data sets include the topography, shear strength, and ground shaking of the study area. All of these data sets are digitized and rasterized at 30 m grid spacing using ArcGIS and combined in a dynamic slope model based on Newmark permanent-deformation analysis. The application of Barton model was then applied in the permanent-deformation analysis. According to a method of inexact reasoning, comparisons are made between the predicted displacements and a comprehensive inventory of landslides triggered by the Ludian earthquake to map the spatial variability in certainty factors. A coseismic landslide hazard map is then produced based on the spatial distribution of the values of certainty factors. Such map can be applied to predict the hazard zone of the region and provide guidelines for making decisions regarding infrastructure development and post-earthquake reconstruction.

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Revisions of the M 8.0 Wenchuan earthquake seismic intensity map based on co-seismic landslide abundance

Cited by 50 publications

References 23 publications

Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China

Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China

Simplified Density Indexes of Walls and Tie-Columns for Confined Masonry Buildings in Seismic Zones

An improved method of Newmark analysis for mapping hazards of coseismic landslides

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