TY - JOUR
T1 - From spatio-temporal landslide susceptibility to landslide risk forecast
AU - Wang, Tengfei
AU - Dahal, Ashok
AU - Fang, Zhice
AU - van Westen, Cees
AU - Yin, Kunlong
AU - Lombardo, Luigi
N1 - Publisher Copyright:
© 2023 China University of Geosciences (Beijing) and Peking University
PY - 2024/3
Y1 - 2024/3
N2 - 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.
AB - 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.
KW - Dynamic landslide susceptibility
KW - Future projections
KW - Landslide risk
KW - Space-time statistics
KW - ITC-GOLD
KW - ITC-ISI-JOURNAL-ARTICLE
U2 - 10.1016/j.gsf.2023.101765
DO - 10.1016/j.gsf.2023.101765
M3 - Article
AN - SCOPUS:85179037677
SN - 1674-9871
VL - 15
JO - Geoscience Frontiers
JF - Geoscience Frontiers
IS - 2
M1 - 101765
ER -