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Published April 17, 2019 | Published + Supplemental Material
Journal Article Open

Simple rules to minimise exposure to coseismic landslide hazard

Abstract

Landslides constitute a hazard to life and infrastructure and their risk is mitigated primarily by reducing exposure. This requires information on landslide hazard on a scale that can enable informed decisions. Such information is often unavailable to, or not easily interpreted by, those who might need it most (e.g. householders, local governments and non-governmental organisations). To address this shortcoming, we develop simple rules to minimise exposure to coseismic landslide hazard that are understandable, communicable and memorable, and that require no prior knowledge, skills or equipment to apply. We examine rules based on two common metrics of landslide hazard, (1) local slope and (2) upslope contributing area as a proxy for hillslope location relative to rivers or ridge crests. In addition, we introduce and test two new metrics: the maximum angle to the skyline and the hazard area, defined as the upslope area with slope >40∘ from which landslide debris can reach a location without passing over a slope of <10∘. We then test the skill with which each metric can identify landslide hazard – defined as the probability of being hit by a landslide – using inventories of landslides triggered by six earthquakes that occurred between 1993 and 2015. We find that the maximum skyline angle and hazard area provide the most skilful predictions, and these results form the basis for two simple rules: "minimise your maximum angle to the skyline" and "avoid steep (>10∘) channels with many steep (>40∘) areas that are upslope". Because local slope alone is also a skilful predictor of landslide hazard, we can formulate a third rule as "minimise the angle of the slope under your feet, especially on steep hillsides, but not at the expense of increasing skyline angle or hazard area". In contrast, the upslope contributing area has a weaker and more complex relationship to hazard than the other predictors. Our simple rules complement but do not replace detailed site-specific investigation: they can be used for initial estimations of landslide hazard or to guide decision-making in the absence of any other information.

Additional Information

© 2019 Author(s). This work is distributed under the Creative Commons Attribution 4.0 License. Received: 18 Sep 2018; Discussion started: 13 Nov 2018; Revised: 28 Feb 2019; Accepted: 11 Mar 2019; Published: 17 Apr 2019. Data availability: Landslide inventories for Northridge (Harp and Jibson, 1996), Haiti (Harp et al., 2017), Wenchuan (Li et al., 2014; Xu et al., 2014b) and Gorkha (Roback et al., 2018) were retrieved from the online USGS ScienceBase-Catalog: (https://www.sciencebase.gov/catalog/item/586d824ce4b0f5ce109fc9a6, last access: 29 March 2019). Landslide inventories for Finisterre (Meunier et al., 2007) and Chi-Chi (Dadson et al., 2004) were obtained from the authors. The SRTM-30 m DEM data (NASA JPL, 2013) are available from the online Global Data Explorer (https://gdex.cr.usgs.gov/gdex/, last access: 29 March 2019). The lidar DEM data (NCALM, 2015) are available from OpenTopography (http://opentopo.sdsc.edu/datasetMetadata?otCollectionID=OT.072016.32611.1, last access: 29 March 2019). Supplement: The supplement related to this article is available online at: https://doi.org/10.5194/nhess-19-837-2019-supplement. Author contributions: DM and ALD conceived the study. DB and DM conceived the modified SHALRUN model. DM collated and analysed all data. DM and ALD wrote the paper with assistance and input from DB, NR, JW, GL and KO. The authors declare that they have no conflict of interest. This work was financially supported by grants from the NERC/ESRC Increasing Resilience to Natural Hazards programme (NE/J01995X/1) and the NERC/ESRC/NNSFC Increasing Resilience to Natural Hazards in China programme (NE/N012216/1). We thank (1) colleagues at the National Society for Earthquake Technology-Nepal (NSET) who have helped to shape our thinking on landslide hazard and the challenge of risk communication; (2) those responsible for collecting the landslide inventories used in this study, particularly Niels Hovius and contributors to the ScienceBase-Catalog; and (3) William Dietrich and Niels Hovius for helpful comments on an earlier draft. Gianvito Scaringi, Odin Marc and an anonymous reviewer provided constructive and illuminating comments and suggestions that considerably refined our thinking. Lidar data acquisition and processing were completed by the National Center for Airborne Laser Mapping (NCALM). NCALM funding was provided by NSF's Division of Earth Sciences, Instrumentation and Facilities Program (EAR-1043051). MATLAB code for the computation of skyline angles is available at https://github.com/DavidMilledge (last access: 29 March 2019). Review statement: This paper was edited by Thom Bogaard and reviewed by Odin Marc and one anonymous referee.

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Created:
August 19, 2023
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