Using PCA in evaluating event-controlling attributes of landsliding in the 2005 Kashmir earthquake region, NW Himalayas, Pakistan

Basharat, Muhammad; Ali, Abid; Jadoon, Ishtiaq A. K.; Rohn, Joachim

doi:10.1007/s11069-016-2172-9

Cited by 34 publications

(31 citation statements)

References 29 publications

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“…The total landslide area mapped by Basharat et al (2016) is 33.6 km 2 (420%) larger than the area mapped by Sato et al (2007). The total landslide area mapped by Sato et al (2007) is 8.0 km 2 , and only 45% of this landslide area overlaps with the polygons delineated by Basharat et al (2016). However, Sato et al (2007) mapped 127 more landslides than did Basharat et al (2016).…”

Section: Subjectivity Of Mapping Proceduresmentioning

confidence: 85%

“…The 2005 Kashmir inventories yield the largest difference in total landslide area mapped for the same areal extent (Figure 9(d)). The total landslide area mapped by Basharat et al (2016) is 33.6 km 2 (420%) larger than the area mapped by Sato et al (2007). The total landslide area mapped by Sato et al (2007) is 8.0 km 2 , and only 45% of this landslide area overlaps with the polygons delineated by Basharat et al (2016).…”

Section: Subjectivity Of Mapping Proceduresmentioning

confidence: 86%

“…These two Kashmir inventories used a similar mapping method and the same satellite imagery (SPOT 5). In contrast, however, the Basharat et al (2016) inventory has a higher power-law exponent (β=3.01) than the Sato et al (2007) inventory (β=2.37). Their FADs also are quite different, and the rollover point is much smaller in the Sato et al (2007) inventory (~760 m 2 ) compared with the Basharat et al (2016) inventory (~8650 m 2 ).…”

Section: Subjectivity Of Mapping Proceduresmentioning

confidence: 90%

“…However, Sato et al (2007) mapped 127 more landslides than did Basharat et al (2016). Their FADs also are quite different, and the rollover point is much smaller in the Sato et al (2007) inventory (~760 m 2 ) compared with the Basharat et al (2016) inventory (~8650 m 2 ). These two inventories are quite different although they are from the same event, have the same areal extent, and used the same mapping method.…”

Section: Subjectivity Of Mapping Proceduresmentioning

confidence: 91%

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Factors controlling landslide frequency–area distributions

Tanyaş

Westen

Allstadt

et al. 2018

Earth Surf Processes Landf

118

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A power‐law relation for the frequency–area distribution (FAD) of medium and large landslides (e.g. tens to millions of square meters) has been observed by numerous authors. But the FAD of small landslides diverges from the power‐law distribution, with a rollover point below which frequencies decrease for smaller landslides. Some studies conclude that this divergence is an artifact of unmapped small landslides due to lack of spatial or temporal resolution; others posit that it is caused by the change in the underlying failure process. An explanation for this dilemma is essential both to evaluate the factors controlling FADs of landslides and power‐law scaling, which is a crucial factor regarding both landscape evolution and landslide hazard assessment. This study examines the FADs of 45 earthquake‐induced landslide inventories from around the world in the context of the proposed explanations. We show that each inventory probably involves some combination of the proposed explanations, though not all explanations contribute to each case. We propose an alternative explanation to understand the reason for the divergence from a power‐law. We suggest that the geometry of a landslide at the time of mapping reflects not just one single movement but many, including the propagation of numerous smaller landslides before and after the main failure. Because only the resulting combination of these landslides can be observed due to a lack of temporal resolution, many smaller landslides are not taken into account in the inventory. This reveals that the divergence from the power‐law is not necessarily attributed to the incompleteness of an inventory. This conceptual model will need to be validated by ongoing observation and analysis. Also, we show that because of the subjectivity of mapping procedures, the total number of landslides and total landslide areas in inventories differ significantly, and therefore the shapes of FADs also differ considerably. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

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Section: Subjectivity Of Mapping Proceduresmentioning

confidence: 85%

Section: Subjectivity Of Mapping Proceduresmentioning

confidence: 86%

Section: Subjectivity Of Mapping Proceduresmentioning

confidence: 90%

Section: Subjectivity Of Mapping Proceduresmentioning

confidence: 91%

See 2 more Smart Citations

Factors controlling landslide frequency–area distributions

Tanyaş

Westen

Allstadt

et al. 2018

Earth Surf Processes Landf

118

View full text Add to dashboard Cite

show abstract

“…seismogenic fault, the area between the two faults commonly has a high concentration of EQTLs (Basharat et al, 2016;Kamp et al, 2008;Wang et al, 2002;Xu, Xu, Yao, & Dai, 2014). Landslides triggered by strike-slip faulting tend to have a symmetric distribution around the fault (Gorum et al, 2014;Xu & Xu, 2014) but in a narrower zone ( Figure 8).…”

Section: Reviews Of Geophysicsmentioning

confidence: 99%

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Fan

Scaringi

Korup

et al. 2019

Reviews of Geophysics

614

325

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Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.

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Presentation and Analysis of a Worldwide Database of Earthquake‐Induced Landslide Inventories

et al. 2017

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Earthquake‐induced landslide (EQIL) inventories are essential tools to extend our knowledge of the relationship between earthquakes and the landslides they can trigger. Regrettably, such inventories are difficult to generate and therefore scarce, and the available ones differ in terms of their quality and level of completeness. Moreover, access to existing EQIL inventories is currently difficult because there is no centralized database. To address these issues, we compiled EQIL inventories from around the globe based on an extensive literature study. The database contains information on 363 landslide‐triggering earthquakes and includes 66 digital landslide inventories. To make these data openly available, we created a repository to host the digital inventories that we have permission to redistribute through the U.S. Geological Survey ScienceBase platform. It can grow over time as more authors contribute their inventories. We analyze the distribution of EQIL events by time period and location, more specifically breaking down the distribution by continent, country, and mountain region. Additionally, we analyze frequency distributions of EQIL characteristics, such as the approximate area affected by landslides, total number of landslides, maximum distance from fault rupture zone, and distance from epicenter when the fault plane location is unknown. For the available digital EQIL inventories, we examine the underlying characteristics of landslide size, topographic slope, roughness, local relief, distance to streams, peak ground acceleration, peak ground velocity, and Modified Mercalli Intensity. Also, we present an evaluation system to help users assess the suitability of the available inventories for different types of EQIL studies and model development.

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Using PCA in evaluating event-controlling attributes of landsliding in the 2005 Kashmir earthquake region, NW Himalayas, Pakistan

Cited by 34 publications

References 29 publications

Factors controlling landslide frequency–area distributions

Factors controlling landslide frequency–area distributions

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Presentation and Analysis of a Worldwide Database of Earthquake‐Induced Landslide Inventories

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