Global liquefaction susceptibility map

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]
• V_s30 ^[8]

Cell values are based on Zhu et al. ^[1] susceptibility classes where: 

1. very low,
2. low,
3. moderate,
4. high,
5. 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)