TY - JOUR
T1 - Variation in landslide-affected area under the control of ground motion and topography
AU - Tanyaş, H.
AU - Lombardo, L.
PY - 2019/10/3
Y1 - 2019/10/3
N2 - Earthquake-Induced Landslide (EQIL) inventories are the key to improve our understanding of the relationship between landslides and their causes, including environmental settings and ground shaking parameters. However, creating a high-quality inventory can take years. As a result, reliable information on landslide-affected areas typically remains unknown until a complete inventory is compiled. In this paper, we analyze 20 digital EQIL inventories of varying quality and completeness that represent a range of geologic and climatic settings around the globe. We examine the landslide-affected area with respect to Peak Ground Acceleration (PGA) and topography, and develop a statistical model to estimate the landslide distribution without prior knowledge of the actual landslide triggering locations.
For each EQIL inventory, we initially calculated the PGA contours where 90% of the total landslide population fell into. Subsequently, we define landslide susceptible areas as those pixels with slope > 5° and local relief>100 m. The latter is used to normalize the total landslide-affected area and to compute correlations with local PGA values. We find that the landslide-affected area may be predicted from PGA values only, with the mean error ranging from −20.0% to +7.1%, with respect to total landslide population. This relationship can be used immediately following a disaster to identify areas of greatest landslide impact and to prioritize emergency response actions, even without a landslide inventory.
AB - Earthquake-Induced Landslide (EQIL) inventories are the key to improve our understanding of the relationship between landslides and their causes, including environmental settings and ground shaking parameters. However, creating a high-quality inventory can take years. As a result, reliable information on landslide-affected areas typically remains unknown until a complete inventory is compiled. In this paper, we analyze 20 digital EQIL inventories of varying quality and completeness that represent a range of geologic and climatic settings around the globe. We examine the landslide-affected area with respect to Peak Ground Acceleration (PGA) and topography, and develop a statistical model to estimate the landslide distribution without prior knowledge of the actual landslide triggering locations.
For each EQIL inventory, we initially calculated the PGA contours where 90% of the total landslide population fell into. Subsequently, we define landslide susceptible areas as those pixels with slope > 5° and local relief>100 m. The latter is used to normalize the total landslide-affected area and to compute correlations with local PGA values. We find that the landslide-affected area may be predicted from PGA values only, with the mean error ranging from −20.0% to +7.1%, with respect to total landslide population. This relationship can be used immediately following a disaster to identify areas of greatest landslide impact and to prioritize emergency response actions, even without a landslide inventory.
KW - ITC-ISI-JOURNAL-ARTICLE
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/isi/lombardo_var.pdf
UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1016/j.enggeo.2019.105229
U2 - 10.1016/j.enggeo.2019.105229
DO - 10.1016/j.enggeo.2019.105229
M3 - Article
VL - 260
JO - Engineering geology
JF - Engineering geology
SN - 0013-7952
M1 - 105229
ER -