From spatio-temporal landslide susceptibility to landslide risk forecast

Abstract

The literature on landslide susceptibility is rich with examples that span a wide range of topics. However, the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored. This statement holds true, particularly in the context of landslide risk, where few scientific contributions investigate risk dynamics in space and time. This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years (from 2013 to 2021). For the analyses, the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit, resulting in a total of 236,997 units. This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature (e.g., variable interaction plots). However, the main innovative effort is in the subsequent phase of the protocol we propose, as we used climate projections of the main trigger (rainfall) to obtain future estimates of yearly susceptibility patterns. These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model, assuming vulnerability = 1. Overall, this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.

Keywords

Space-time statistics / Dynamic landslide susceptibility / Landslide risk / Future projections

CRediT authorship contribution statement

Tengfei Wang: Data curation, Investigation, Methodology, Writing. Ashok Dahal: Software, Writing – original draft. Zhice Fang: Software. Cees van Westen: Investigation, Visualization, Writing – review & editing. Kunlong Yin: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft. Luigi Lombardo: Experiment, Writing – original draft.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research was supported by the National Natural Science Foundation of China – Young Scientist Funds (No.42207174). The first author wishes to thank the China Scholarship Council (CSC) for funding his research period at the University of Twente.

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