C. Bloom, C. Singeisen, T. Stahl, A. Howell, C. Massey, D. Mason
{"title":"2016年新西兰7.8级Kaikōura地震期间沿海地震引发的滑坡易感性","authors":"C. Bloom, C. Singeisen, T. Stahl, A. Howell, C. Massey, D. Mason","doi":"10.5194/nhess-23-2987-2023","DOIUrl":null,"url":null,"abstract":"Abstract. Coastal hillslopes often host higher concentrations of\nearthquake-induced landslides than those further inland, but few studies\nhave investigated the reasons for this occurrence. As a result, it is\nunclear if regional earthquake-induced landslide susceptibility models\ntrained primarily on inland hillslopes are effective predictors of coastal\nsusceptibility. The 2016 Mw 7.8 Kaikōura earthquake on the\nnortheastern South Island of New Zealand resulted in ca. 1600 landslides > 50 m2 on slopes > 15∘ within 1 km of\nthe coast, contributing to an order of magnitude greater landslide source\narea density than inland hillslopes within 1 to 3 km of the coast. In this\nstudy, logistic regression modelling is used to investigate how landslide\nsusceptibility differs between coastal and inland hillslopes and to determine\nthe factors that drive the distribution of coastal landslides initiated by\nthe 2016 Kaikōura earthquake. Strong model performance (area under the\nreceiver operator characteristic curve or AUC of ca. 0.80 to 0.92) was\nobserved across eight models, which adopt four simplified geology types. The\nsame landslide susceptibility factors, primarily geology, steep slopes, and\nground motion, are strong model predictors for both inland and coastal\nlandslide susceptibility in the Kaikōura region. In three geology types\n(which account for more than 90 % of landslide source areas), a 0.03 or\nless drop in model AUC is observed when predicting coastal landslides using\ninland-trained models. This suggests little difference between the features\ndriving inland and coastal landslide susceptibility in the Kaikōura\nregion. Geology is similarly distributed between inland and coastal\nhillslopes, and peak\nground acceleration (PGA) is generally lower in coastal hillslopes. Slope angle,\nhowever, is significantly higher in coastal hillslopes and provides the best\nexplanation for the high density of coastal landslides during the 2016\nKaikōura earthquake. Existing regional earthquake-induced landslide\nsusceptibility models trained on inland hillslopes using common predictive\nfeatures are likely to capture this signal without additional predictive\nvariables. Interestingly, in the Kaikōura region, most coastal\nhillslopes are isolated from the ocean by uplifted shore platforms. Enhanced\ncoastal landslide susceptibility from this event appears to be a legacy\neffect of past erosion from wave action, which preferentially steepened\nthese coastal hillslopes.\n","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":" ","pages":""},"PeriodicalIF":4.2000,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand\",\"authors\":\"C. Bloom, C. Singeisen, T. Stahl, A. Howell, C. Massey, D. Mason\",\"doi\":\"10.5194/nhess-23-2987-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. Coastal hillslopes often host higher concentrations of\\nearthquake-induced landslides than those further inland, but few studies\\nhave investigated the reasons for this occurrence. As a result, it is\\nunclear if regional earthquake-induced landslide susceptibility models\\ntrained primarily on inland hillslopes are effective predictors of coastal\\nsusceptibility. The 2016 Mw 7.8 Kaikōura earthquake on the\\nnortheastern South Island of New Zealand resulted in ca. 1600 landslides > 50 m2 on slopes > 15∘ within 1 km of\\nthe coast, contributing to an order of magnitude greater landslide source\\narea density than inland hillslopes within 1 to 3 km of the coast. In this\\nstudy, logistic regression modelling is used to investigate how landslide\\nsusceptibility differs between coastal and inland hillslopes and to determine\\nthe factors that drive the distribution of coastal landslides initiated by\\nthe 2016 Kaikōura earthquake. Strong model performance (area under the\\nreceiver operator characteristic curve or AUC of ca. 0.80 to 0.92) was\\nobserved across eight models, which adopt four simplified geology types. The\\nsame landslide susceptibility factors, primarily geology, steep slopes, and\\nground motion, are strong model predictors for both inland and coastal\\nlandslide susceptibility in the Kaikōura region. In three geology types\\n(which account for more than 90 % of landslide source areas), a 0.03 or\\nless drop in model AUC is observed when predicting coastal landslides using\\ninland-trained models. This suggests little difference between the features\\ndriving inland and coastal landslide susceptibility in the Kaikōura\\nregion. Geology is similarly distributed between inland and coastal\\nhillslopes, and peak\\nground acceleration (PGA) is generally lower in coastal hillslopes. Slope angle,\\nhowever, is significantly higher in coastal hillslopes and provides the best\\nexplanation for the high density of coastal landslides during the 2016\\nKaikōura earthquake. Existing regional earthquake-induced landslide\\nsusceptibility models trained on inland hillslopes using common predictive\\nfeatures are likely to capture this signal without additional predictive\\nvariables. Interestingly, in the Kaikōura region, most coastal\\nhillslopes are isolated from the ocean by uplifted shore platforms. Enhanced\\ncoastal landslide susceptibility from this event appears to be a legacy\\neffect of past erosion from wave action, which preferentially steepened\\nthese coastal hillslopes.\\n\",\"PeriodicalId\":18922,\"journal\":{\"name\":\"Natural Hazards and Earth System Sciences\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2023-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Natural Hazards and Earth System Sciences\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.5194/nhess-23-2987-2023\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Hazards and Earth System Sciences","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/nhess-23-2987-2023","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
Abstract. Coastal hillslopes often host higher concentrations of
earthquake-induced landslides than those further inland, but few studies
have investigated the reasons for this occurrence. As a result, it is
unclear if regional earthquake-induced landslide susceptibility models
trained primarily on inland hillslopes are effective predictors of coastal
susceptibility. The 2016 Mw 7.8 Kaikōura earthquake on the
northeastern South Island of New Zealand resulted in ca. 1600 landslides > 50 m2 on slopes > 15∘ within 1 km of
the coast, contributing to an order of magnitude greater landslide source
area density than inland hillslopes within 1 to 3 km of the coast. In this
study, logistic regression modelling is used to investigate how landslide
susceptibility differs between coastal and inland hillslopes and to determine
the factors that drive the distribution of coastal landslides initiated by
the 2016 Kaikōura earthquake. Strong model performance (area under the
receiver operator characteristic curve or AUC of ca. 0.80 to 0.92) was
observed across eight models, which adopt four simplified geology types. The
same landslide susceptibility factors, primarily geology, steep slopes, and
ground motion, are strong model predictors for both inland and coastal
landslide susceptibility in the Kaikōura region. In three geology types
(which account for more than 90 % of landslide source areas), a 0.03 or
less drop in model AUC is observed when predicting coastal landslides using
inland-trained models. This suggests little difference between the features
driving inland and coastal landslide susceptibility in the Kaikōura
region. Geology is similarly distributed between inland and coastal
hillslopes, and peak
ground acceleration (PGA) is generally lower in coastal hillslopes. Slope angle,
however, is significantly higher in coastal hillslopes and provides the best
explanation for the high density of coastal landslides during the 2016
Kaikōura earthquake. Existing regional earthquake-induced landslide
susceptibility models trained on inland hillslopes using common predictive
features are likely to capture this signal without additional predictive
variables. Interestingly, in the Kaikōura region, most coastal
hillslopes are isolated from the ocean by uplifted shore platforms. Enhanced
coastal landslide susceptibility from this event appears to be a legacy
effect of past erosion from wave action, which preferentially steepened
these coastal hillslopes.
期刊介绍:
Natural Hazards and Earth System Sciences (NHESS) is an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences. Embracing a holistic Earth system science approach, NHESS serves a wide and diverse community of research scientists, practitioners, and decision makers concerned with detection of natural hazards, monitoring and modelling, vulnerability and risk assessment, and the design and implementation of mitigation and adaptation strategies, including economical, societal, and educational aspects.