Geological characteristics of landslides triggered by the 2016 Kumamoto earthquake in Mt. Aso volcano, Japan

Song, Kun; Wang, Fawu; Dai, Zili; Iio, Akinori; Osaka, Osamu; Sakata, S.

doi:10.1007/s10064-017-1097-1

Cited by 28 publications

(14 citation statements)

References 21 publications

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“…In general, landslides in relatively ‘soft’ soils, such as volcanic ash, occur on relatively gentle slope gradients (e.g. Hotta et al, ; Tanaka, ; Okusa et al, ; Chigira et al, ; Song et al, ). Indeed, 59% of the landslides identified in our study occurred on slopes < 30° (Figure ).…”

Section: Discussionmentioning

confidence: 99%

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Characteristics of landslides in forests and grasslands triggered by the 2016 Kumamoto earthquake

Koyanagi

Gomi

Sidle

2020

Earth Surf Processes Landf

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We examined the characteristics of landslides triggered by the 2016 Kumamoto earthquake (Mw = 7.0: focal depth=10.0 km) in forests and grasslands within two affected watersheds (Tokosegawa: 6.9 km2 and Nigorigawa: 6.1 km2) in southwestern Japan. We identified 190 landslides using aerial photographs and analyzed their sizes by geographic information system (GIS). Field investigations were conducted to obtain landslide depth, volume and residual sediment for 38 selected landslides (21 in forests and 17 in grasslands). The minimum area of detected landslides in grasslands (400 m2) was smaller than in forests (1000 m2), probably because of reduced detectability of landslides under tree cover. The ratio of total area occupied by landslides for a given range of slope gradient in the watersheds increased from 3.2% on gentle grassland slopes (10–15°) to 15.5% on steep (>45°) slopes, whereas the maximum landslide‐area ratio in forest sites (7.4%) occurred on relatively gentle slopes (25–30°). Estimated landslide volume ranged from 27 to 9622 m3, based on mean depth of each landslide measured around individual landslide scars. Moreover, the volumetric ratio of landslide deposit volume to total landslide volume exceeded 100% for 48% of the landslides within forests and 35% of the landslides within grasslands. Our findings show that land cover had extensive and recognizable effects on the characteristics of landslides and resulting in‐channel sediment accumulations. Resetting sediment dynamics after earthquakes associated with different land cover distributions needs to be considered within watersheds. © 2019 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

“…Small landslides and fissures concentrated on hillslopes covered by forests and grasslands within the central cones of Aso Volcano (Ishikawa et al, ; Miyabuchi, ; Yano et al, ). Sequences of soil layers, including tephra, were the main sliding surfaces in these areas (Song et al, ).…”

Section: Introductionmentioning

confidence: 99%

Characteristics of landslides in forests and grasslands triggered by the 2016 Kumamoto earthquake

Koyanagi

Gomi

Sidle

2020

Earth Surf Processes Landf

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“…indicates an amplification of ground motion towards the crown of the landslides. Additionally, (Sato et al, 2017) consider the tephra layers rich in halloysite to be the main sliding surfaces indicating shallow landslides (Song et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Effects of finite source rupture on landslide triggering: The 2016 <i>M<sub>W</sub></i> 7.1 Kumamoto earthquake

Specht¹,

Öztürk²,

Veh³

et al. 2018

Preprint

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Abstract. The propagation of a seismic rupture on a fault introduces spatial variations in the seismic wavefield surrounding the fault during an earthquake. This directivity effect results in larger shaking amplitudes in the rupture propagation direction. Its seismic radiation pattern also causes amplitude variations between the strike-normal and strike-parallel components of horizontal ground motion. We investigated the landslide response to these effects during the 2016 Kumamoto earthquake (MW 7.1) in central Kyūshū (Japan). Although the distribution of some 1,500 earthquake-triggered landslides as function of rupture distance is consistent with the observed Arias intensity, the landslides are more concentrated to the northeast of the southwest-northeast striking rupture. We examined several landslide susceptibility factors: hillslope inclination, median amplification factor (MAF) of ground shaking, lithology, land cover, and topographic wetness. None of these factors can sufficiently explain the landslide distribution or orientation (aspect), although the landslide headscarps coincide with elevated hillslope inclination and MAF. We propose a new physics-based ground motion model that accounts for the seismic rupture effects, and demonstrate that the low-frequency seismic radiation pattern consistent with the overall landslide distribution. The spatial landslide distribution is primarily influenced by the rupture directivity effect, whereas landslide aspect is influenced by amplitude variations between the fault-normal and fault-parallel motion at frequencies

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“…Most studies [36,37,38,39] have shown that: (1) the earthquakes were triggered by the local fracture movement in the volcanic zone; (2) the structure of the rock-soil was damaged; (3) the originally weak structural planes and joint fissures in the stratum were displaced; (4) the bedrock fissure network was penetrated, expanded, further evolved, and accumulated into loose debris under specific hydrological conditions; and (5) subsequent to steps 1–4, collapse and landslide occurred. As a result, an earthquake–collapse–landslide disaster chain was formed.…”

Section: Methodsmentioning

confidence: 99%

Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models

Han

Feng

et al. 2019

IJERPH

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Severe natural disasters and related secondary disasters are a huge menace to society. Currently, it is difficult to identify risk formation mechanisms and quantitatively evaluate the risks associated with disaster chains; thus, there is a need to further develop relevant risk assessment methods. In this research, we propose an earthquake disaster chain risk evaluation method that couples Bayesian network and Newmark models that are based on natural hazard risk formation theory with the aim of identifying the influence of earthquake disaster chains. This new method effectively considers two risk elements: hazard and vulnerability, and hazard analysis, which includes chain probability analysis and hazard intensity analysis. The chain probability of adjacent disasters was obtained from the Bayesian network model, and the permanent displacement that was applied to represent the potential hazard intensity was calculated by the Newmark model. To validate the method, the Changbai Mountain volcano earthquake–collapse–landslide disaster chain was selected as a case study. The risk assessment results showed that the high-and medium-risk zones were predominantly located within a 10 km radius of Tianchi, and that other regions within the study area were mainly associated with very low-to low-risk values. The verified results of the reported method showed that the area of the receiver operating characteristic (ROC) curve was 0.817, which indicates that the method is very effective for earthquake disaster chain risk recognition and assessment.

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Geological characteristics of landslides triggered by the 2016 Kumamoto earthquake in Mt. Aso volcano, Japan

Cited by 28 publications

References 21 publications

Characteristics of landslides in forests and grasslands triggered by the 2016 Kumamoto earthquake

Characteristics of landslides in forests and grasslands triggered by the 2016 Kumamoto earthquake

Effects of finite source rupture on landslide triggering: The 2016 <i>M<sub>W</sub></i> 7.1 Kumamoto earthquake

Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models

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Geological characteristics of landslides triggered by the 2016 Kumamoto earthquake in Mt. Aso volcano, Japan

Cited by 28 publications

References 21 publications

Characteristics of landslides in forests and grasslands triggered by the 2016 Kumamoto earthquake

Characteristics of landslides in forests and grasslands triggered by the 2016 Kumamoto earthquake

Effects of finite source rupture on landslide triggering: The 2016 &lt;i&gt;M&lt;sub&gt;W&lt;/sub&gt;&lt;/i&gt; 7.1 Kumamoto earthquake

Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models

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Effects of finite source rupture on landslide triggering: The 2016 <i>M<sub>W</sub></i> 7.1 Kumamoto earthquake