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 &lt;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] <em>V_s30 </em>^[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. <em>Bull. Seismol. Soc. Am.</em> 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. <em>Science </em>(80)339, 940–943. ^[6] Fick et al. (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. <em>Int. J. Climatol. </em>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) <em>Development of an Open-Source Hybrid Global Vs30 Model,</em> 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 <em>Nature Communications</em> 10 (2677)