Assessment of Global Landslide Hazard Hotspots View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012-08-31

AUTHORS

Farrokh Nadim , Christian Jaedicke , Helge Smebye , Bjørn Kalsnes

ABSTRACT

In the Natural Disaster Hotspots project, Nadim et al. (2006) developed a model to identify the global distribution of landslide hazard and risk. The model was based on the global datasets of climate, lithology, earthquake activity, and topography; and it was used to identify the areas with the highest hazard, or “hotspots”. The landslide hazard assessment model developed in the Natural Disaster Hotspots project was modified and improved to provide a better basis for making predictions of the global risk associated with landslides in the Global Risk Update (GRU) study of UNISDR (UNISDR 2009). This paper describes the updated model for global landslide hazard assessment. The major triggering factors for landslides are extreme precipitation events, strong earthquakes, and human activity. Precipitation-induced landslide hazard and earthquake-induced landslide hazard are assessed independently in the update model. Landslides induced by human activity are not addressed in this paper. The updating of the model was done by the landslide research team at the ICL World Centre of Excellence, the International Centre for Geohazards (ICG) in Oslo, Norway, and the work is designated as project C102 in the International Programme for Landslides (IPL). More... »

PAGES

59-71

Book

TITLE

Landslides: Global Risk Preparedness

ISBN

978-3-642-22086-9
978-3-642-22087-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-22087-6_4

DOI

http://dx.doi.org/10.1007/978-3-642-22087-6_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018536907


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0404", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geophysics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway", 
          "id": "http://www.grid.ac/institutes/grid.425894.6", 
          "name": [
            "Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nadim", 
        "givenName": "Farrokh", 
        "id": "sg:person.010641334307.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010641334307.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway", 
          "id": "http://www.grid.ac/institutes/grid.425894.6", 
          "name": [
            "Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jaedicke", 
        "givenName": "Christian", 
        "id": "sg:person.016006532433.89", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016006532433.89"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway", 
          "id": "http://www.grid.ac/institutes/grid.425894.6", 
          "name": [
            "Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Smebye", 
        "givenName": "Helge", 
        "id": "sg:person.014042060650.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014042060650.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway", 
          "id": "http://www.grid.ac/institutes/grid.425894.6", 
          "name": [
            "Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kalsnes", 
        "givenName": "Bj\u00f8rn", 
        "id": "sg:person.016547025107.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016547025107.15"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2012-08-31", 
    "datePublishedReg": "2012-08-31", 
    "description": "In the Natural Disaster Hotspots project, Nadim et al. (2006) developed a model to identify the global distribution of landslide hazard and risk. The model was based on the global datasets of climate, lithology, earthquake activity, and topography; and it was used to identify the areas with the highest hazard, or \u201chotspots\u201d. The landslide hazard assessment model developed in the Natural Disaster Hotspots project was modified and improved to provide a better basis for making predictions of the global risk associated with landslides in the Global Risk Update (GRU) study of UNISDR (UNISDR 2009). This paper describes the updated model for global landslide hazard assessment. The major triggering factors for landslides are extreme precipitation events, strong earthquakes, and human activity. Precipitation-induced landslide hazard and earthquake-induced landslide hazard are assessed independently in the update model. Landslides induced by human activity are not addressed in this paper. The updating of the model was done by the landslide research team at the ICL World Centre of Excellence, the International Centre for Geohazards (ICG) in Oslo, Norway, and the work is designated as project C102 in the International Programme for Landslides (IPL).", 
    "editor": [
      {
        "familyName": "Sassa", 
        "givenName": "Kyoji", 
        "type": "Person"
      }, 
      {
        "familyName": "Rouhban", 
        "givenName": "Badaoui", 
        "type": "Person"
      }, 
      {
        "familyName": "Brice\u00f1o", 
        "givenName": "S\u00e1lvano", 
        "type": "Person"
      }, 
      {
        "familyName": "McSaveney", 
        "givenName": "Mauri", 
        "type": "Person"
      }, 
      {
        "familyName": "He", 
        "givenName": "Bin", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-22087-6_4", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-22086-9", 
        "978-3-642-22087-6"
      ], 
      "name": "Landslides: Global Risk Preparedness", 
      "type": "Book"
    }, 
    "keywords": [
      "landslide hazard", 
      "human activities", 
      "earthquake-induced landslide hazard", 
      "extreme precipitation events", 
      "landslide hazard assessment", 
      "hazard assessment model", 
      "earthquake activity", 
      "precipitation events", 
      "strong earthquakes", 
      "hazard assessment", 
      "global dataset", 
      "landslides", 
      "global distribution", 
      "higher hazard", 
      "hazards", 
      "international programs", 
      "update study", 
      "lithology", 
      "hotspots", 
      "geohazards", 
      "earthquakes", 
      "climate", 
      "International Centre", 
      "topography", 
      "Norway", 
      "events", 
      "model", 
      "assessment model", 
      "area", 
      "et al", 
      "good basis", 
      "distribution", 
      "datasets", 
      "al", 
      "UNISDR", 
      "center", 
      "project", 
      "update model", 
      "prediction", 
      "global risk", 
      "assessment", 
      "Oslo", 
      "activity", 
      "C102", 
      "world center", 
      "basis", 
      "study", 
      "factors", 
      "updating", 
      "research team", 
      "paper", 
      "work", 
      "risk", 
      "program", 
      "team", 
      "excellence", 
      "Natural Disaster Hotspots project", 
      "Disaster Hotspots project", 
      "Hotspots project", 
      "Nadim et al", 
      "landslide hazard assessment model", 
      "Global Risk Update (GRU) study", 
      "Risk Update (GRU) study", 
      "global landslide hazard assessment", 
      "Precipitation-induced landslide hazard", 
      "landslide research team", 
      "ICL World Centre", 
      "project C102", 
      "Global Landslide Hazard Hotspots", 
      "Landslide Hazard Hotspots", 
      "Hazard Hotspots"
    ], 
    "name": "Assessment of Global Landslide Hazard Hotspots", 
    "pagination": "59-71", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1018536907"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-22087-6_4"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-22087-6_4", 
      "https://app.dimensions.ai/details/publication/pub.1018536907"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2021-12-01T20:09", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/chapter/chapter_41.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-22087-6_4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-22087-6_4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-22087-6_4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-22087-6_4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-22087-6_4'


 

This table displays all metadata directly associated to this object as RDF triples.

172 TRIPLES      23 PREDICATES      96 URIs      89 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-22087-6_4 schema:about anzsrc-for:04
2 anzsrc-for:0404
3 schema:author N4509d5f8a3f549d7955e68f08e0bc5d2
4 schema:datePublished 2012-08-31
5 schema:datePublishedReg 2012-08-31
6 schema:description In the Natural Disaster Hotspots project, Nadim et al. (2006) developed a model to identify the global distribution of landslide hazard and risk. The model was based on the global datasets of climate, lithology, earthquake activity, and topography; and it was used to identify the areas with the highest hazard, or “hotspots”. The landslide hazard assessment model developed in the Natural Disaster Hotspots project was modified and improved to provide a better basis for making predictions of the global risk associated with landslides in the Global Risk Update (GRU) study of UNISDR (UNISDR 2009). This paper describes the updated model for global landslide hazard assessment. The major triggering factors for landslides are extreme precipitation events, strong earthquakes, and human activity. Precipitation-induced landslide hazard and earthquake-induced landslide hazard are assessed independently in the update model. Landslides induced by human activity are not addressed in this paper. The updating of the model was done by the landslide research team at the ICL World Centre of Excellence, the International Centre for Geohazards (ICG) in Oslo, Norway, and the work is designated as project C102 in the International Programme for Landslides (IPL).
7 schema:editor N13279b144f0940708ae5c9f4a04ecc7e
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N0e08cfcdf79248a59252894d78b08f0f
12 schema:keywords C102
13 Disaster Hotspots project
14 Global Landslide Hazard Hotspots
15 Global Risk Update (GRU) study
16 Hazard Hotspots
17 Hotspots project
18 ICL World Centre
19 International Centre
20 Landslide Hazard Hotspots
21 Nadim et al
22 Natural Disaster Hotspots project
23 Norway
24 Oslo
25 Precipitation-induced landslide hazard
26 Risk Update (GRU) study
27 UNISDR
28 activity
29 al
30 area
31 assessment
32 assessment model
33 basis
34 center
35 climate
36 datasets
37 distribution
38 earthquake activity
39 earthquake-induced landslide hazard
40 earthquakes
41 et al
42 events
43 excellence
44 extreme precipitation events
45 factors
46 geohazards
47 global dataset
48 global distribution
49 global landslide hazard assessment
50 global risk
51 good basis
52 hazard assessment
53 hazard assessment model
54 hazards
55 higher hazard
56 hotspots
57 human activities
58 international programs
59 landslide hazard
60 landslide hazard assessment
61 landslide hazard assessment model
62 landslide research team
63 landslides
64 lithology
65 model
66 paper
67 precipitation events
68 prediction
69 program
70 project
71 project C102
72 research team
73 risk
74 strong earthquakes
75 study
76 team
77 topography
78 update model
79 update study
80 updating
81 work
82 world center
83 schema:name Assessment of Global Landslide Hazard Hotspots
84 schema:pagination 59-71
85 schema:productId N03f4177649134346af6669ab97725f69
86 Nc0ce1edf44ef43b4ae735162c05c5878
87 schema:publisher Na6bcfd178b934c908d2443c8c20bc9b0
88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018536907
89 https://doi.org/10.1007/978-3-642-22087-6_4
90 schema:sdDatePublished 2021-12-01T20:09
91 schema:sdLicense https://scigraph.springernature.com/explorer/license/
92 schema:sdPublisher N52dac76ed72c40859ac0db24cb5863cc
93 schema:url https://doi.org/10.1007/978-3-642-22087-6_4
94 sgo:license sg:explorer/license/
95 sgo:sdDataset chapters
96 rdf:type schema:Chapter
97 N004a9bc1c76c4c99b0fbb78ae1e27dd0 schema:familyName Sassa
98 schema:givenName Kyoji
99 rdf:type schema:Person
100 N03f4177649134346af6669ab97725f69 schema:name dimensions_id
101 schema:value pub.1018536907
102 rdf:type schema:PropertyValue
103 N0e08cfcdf79248a59252894d78b08f0f schema:isbn 978-3-642-22086-9
104 978-3-642-22087-6
105 schema:name Landslides: Global Risk Preparedness
106 rdf:type schema:Book
107 N0f4ca245b4324fdc980cac7589e44faa rdf:first sg:person.016547025107.15
108 rdf:rest rdf:nil
109 N13279b144f0940708ae5c9f4a04ecc7e rdf:first N004a9bc1c76c4c99b0fbb78ae1e27dd0
110 rdf:rest N8cb68b33de5548c7a5fcdbf357567d27
111 N22f7895807c8474ea644d0bbf44ca8e1 schema:familyName Briceño
112 schema:givenName Sálvano
113 rdf:type schema:Person
114 N4509d5f8a3f549d7955e68f08e0bc5d2 rdf:first sg:person.010641334307.02
115 rdf:rest N8983c475aed849faafb02d3cc01a1cfd
116 N52dac76ed72c40859ac0db24cb5863cc schema:name Springer Nature - SN SciGraph project
117 rdf:type schema:Organization
118 N6ec2e27e96fd4ae08280aa00dcac6809 schema:familyName Rouhban
119 schema:givenName Badaoui
120 rdf:type schema:Person
121 N82368083d6b3474ea38d2f8f0dd4707c rdf:first sg:person.014042060650.22
122 rdf:rest N0f4ca245b4324fdc980cac7589e44faa
123 N869fd2a1218645cd847c3b4142066a0d rdf:first N90c82a0fc1d342e3a3b6bf0114d22d8c
124 rdf:rest rdf:nil
125 N8983c475aed849faafb02d3cc01a1cfd rdf:first sg:person.016006532433.89
126 rdf:rest N82368083d6b3474ea38d2f8f0dd4707c
127 N8cb68b33de5548c7a5fcdbf357567d27 rdf:first N6ec2e27e96fd4ae08280aa00dcac6809
128 rdf:rest Naf1816617f0a490193a6dc3a762167ba
129 N90c82a0fc1d342e3a3b6bf0114d22d8c schema:familyName He
130 schema:givenName Bin
131 rdf:type schema:Person
132 Na6bcfd178b934c908d2443c8c20bc9b0 schema:name Springer Nature
133 rdf:type schema:Organisation
134 Naf1816617f0a490193a6dc3a762167ba rdf:first N22f7895807c8474ea644d0bbf44ca8e1
135 rdf:rest Nca61ec0caf354850b413a7708d857e60
136 Nc0ce1edf44ef43b4ae735162c05c5878 schema:name doi
137 schema:value 10.1007/978-3-642-22087-6_4
138 rdf:type schema:PropertyValue
139 Nca61ec0caf354850b413a7708d857e60 rdf:first Nd10955282e17456abd9fe0c4e2ac2c71
140 rdf:rest N869fd2a1218645cd847c3b4142066a0d
141 Nd10955282e17456abd9fe0c4e2ac2c71 schema:familyName McSaveney
142 schema:givenName Mauri
143 rdf:type schema:Person
144 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
145 schema:name Earth Sciences
146 rdf:type schema:DefinedTerm
147 anzsrc-for:0404 schema:inDefinedTermSet anzsrc-for:
148 schema:name Geophysics
149 rdf:type schema:DefinedTerm
150 sg:person.010641334307.02 schema:affiliation grid-institutes:grid.425894.6
151 schema:familyName Nadim
152 schema:givenName Farrokh
153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010641334307.02
154 rdf:type schema:Person
155 sg:person.014042060650.22 schema:affiliation grid-institutes:grid.425894.6
156 schema:familyName Smebye
157 schema:givenName Helge
158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014042060650.22
159 rdf:type schema:Person
160 sg:person.016006532433.89 schema:affiliation grid-institutes:grid.425894.6
161 schema:familyName Jaedicke
162 schema:givenName Christian
163 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016006532433.89
164 rdf:type schema:Person
165 sg:person.016547025107.15 schema:affiliation grid-institutes:grid.425894.6
166 schema:familyName Kalsnes
167 schema:givenName Bjørn
168 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016547025107.15
169 rdf:type schema:Person
170 grid-institutes:grid.425894.6 schema:alternateName Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway
171 schema:name Norwegian Geotechnical Institute (NGI), International Centre for Geohazards (ICG), Oslo, Norway
172 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...