Description
This is a global liquefaction susceptibility map (EPSG:4326) based on the geospatial liquefaction prediction models of Zhu et al.[1] .
The coastal model was applied for areas <20km from the coast. The inland model was applied elsewhere. Refer to Zhu et al. [1] in the first instance for further methodology and descriptions.
Input data used in part, or their entirety may comprise (amongst others):
rivers [2-4]
depth to ground water [5]
precipitation [6]
land [7]
Vs30 [8]
Cell values are based on Zhu et al. [1] susceptibility classes where:
very low,
low,
moderate,
high,
very high
0 refers to no data -- typically water bodies.
While this data may be useful as preliminary information for regional-scale planning, a PGV intensity term is required for probability maps. Again, see Zhu et al. [1] for a discussion here.
An application using this dataset is seen in Koks et al. [9].
[1] Zhu et al. (2017) An updated geospatial liquefaction model for global application. Bull. Seismol. Soc. Am. 107, 1365–1385.
[2] Lehner et al. (2006) HydroSHEDS: Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales, Version 1.0.
[3] Vogt et al. (2008) CCM River and Catchment Database, version 2.1.
[4] Wessel et al. (1992) Digital Chart of the World: Inland Water.
[5] Fan et al. (2013) Global patterns of groundwater table depth. Science (80)339, 940–943.
[6] Fick et al. (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315.
[7] Wessel et al. (2017) LGSHHG: A Global Self-consistent, Hierarchical, High-resolution Geography Database Version 2.3.7.
[8] Worden et al. (2017) Development of an Open-Source Hybrid Global Vs30 Model, Seismological Society of America Annual Meeting, 21-23 April, Pasadena, CA.
[9] Koks et al. (2019) A global multi-hazard risk analysis of road and railway infrastructure assets Nature Communications 10 (2677)
The coastal model was applied for areas <20km from the coast. The inland model was applied elsewhere. Refer to Zhu et al. [1] in the first instance for further methodology and descriptions.
Input data used in part, or their entirety may comprise (amongst others):
rivers [2-4]
depth to ground water [5]
precipitation [6]
land [7]
Vs30 [8]
Cell values are based on Zhu et al. [1] susceptibility classes where:
very low,
low,
moderate,
high,
very high
0 refers to no data -- typically water bodies.
While this data may be useful as preliminary information for regional-scale planning, a PGV intensity term is required for probability maps. Again, see Zhu et al. [1] for a discussion here.
An application using this dataset is seen in Koks et al. [9].
[1] Zhu et al. (2017) An updated geospatial liquefaction model for global application. Bull. Seismol. Soc. Am. 107, 1365–1385.
[2] Lehner et al. (2006) HydroSHEDS: Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales, Version 1.0.
[3] Vogt et al. (2008) CCM River and Catchment Database, version 2.1.
[4] Wessel et al. (1992) Digital Chart of the World: Inland Water.
[5] Fan et al. (2013) Global patterns of groundwater table depth. Science (80)339, 940–943.
[6] Fick et al. (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315.
[7] Wessel et al. (2017) LGSHHG: A Global Self-consistent, Hierarchical, High-resolution Geography Database Version 2.3.7.
[8] Worden et al. (2017) Development of an Open-Source Hybrid Global Vs30 Model, Seismological Society of America Annual Meeting, 21-23 April, Pasadena, CA.
[9] Koks et al. (2019) A global multi-hazard risk analysis of road and railway infrastructure assets Nature Communications 10 (2677)
| Date made available | 7 Mar 2019 |
|---|---|
| Publisher | Unknown Publisher |