Surface temperature controls the pattern of post-earthquake landslide activity

Abstract

The patterns and controls of the transient enhanced landsliding that follows strong earthquakes remain elusive. Geostatistical models can provide clues on the underlying processes by identifying relationships with a number of physical variables. These models do not typically consider thermal information, even though temperature is known to affect the hydro-mechanical behavior of geomaterials, which, in turn, controls slope stability. Here, we develop a slope unit-based multitemporal susceptibility model for the epicentral region of the 2008 Wenchuan earthquake to explore how land surface temperature (LST) relates to landslide patterns over time. We find that LST can explain post-earthquake landsliding while it has no visible effect on the coseismic scene, which is dominated by the strong shaking. Specifically, as the landscape progressively recovers and landslide rates decay to pre-earthquake levels, a positive relationship between LST and landslide persistence emerges. This seems consistent with the action of healing processes, capable of restoring the thermal sensitivity of the slope material after the seismic disturbance. Although analyses in other contexts (not necessarily seismic) are warranted, we advocate for the inclusion of thermal information in geostatistical modeling as it can help form a more physically consistent picture of slope stability controls.

Space-time landslide susceptibility modelling in Taiwan

“…So far, most susceptibility models have been framed within the generally accepted assumption that the past is the key to the future (Guzzetti et al, 1999;Van Westen et al, 2008). This assumption has been recently challenged in the context of rainfall-induced landslides because global warming is now changing the spatio-temporal patterns of some predictors (e.g., Loche et al, 2022). Or, because the human intervention is actively modifying the slope equilibrium, either through land use changes (Hao et al, 2020) or road cuts (Tanyaş et al, 2022).…”

Call for collaboration: Benchmark datasets for landslide susceptibility zonation

“…For illustrative purposes, we őtted the benchmark dataset with a simple generalized additive model (GAM; (Goetz et al 2015, 2011, Loche et al 2022b)). This results, in the present form, is only intended to provide a graphical result ś we did not attempt a calibration/validation attempt, no randomization of the input data, and we did not calculate performance metrics.…”

Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

“…This topic is hardly addressed in the literature as it requires an understanding not only of the evolution of the triggers, but also of the predisposing or preparatory conditions. These may behave differently in time [4], reflecting changes in mechanical [14], hydrological and hydraulic [15], or thermal conditions [13,16].…”

“…Seismically active areas may contain many landslide bodies. The seismic shaking likely triggers their first movement, while subsequent remobilisations become increasingly related to different triggers and predisposing factors as time goes by [16,19]. Different triggers can also produce distinct patterns in space [20].…”