  The vast majority of landslide susceptibility studies assumes the slope
instability process to be time-invariant under the definition that "the past
and present are keys to the future". This assumption may generally be valid.
However, the trigger, be it a rainfall or an earthquake event, clearly varies
over time. And yet, the temporal component of the trigger is rarely included in
landslide susceptibility studies and only confined to hazard assessment. In
this work, we investigate a population of landslides triggered in response to
the 2017 Jiuzhaigou earthquake ($M_w = 6.5$) including the associated ground
motion in the analyses, these being carried out at the Slope Unit (SU) level.
We do this by implementing a Bayesian version of a Generalized Additive Model
and assuming that the slope instability across the SUs in the study area
behaves according to a Bernoulli probability distribution. This procedure would
generally produce a susceptibility map reflecting the spatial pattern of the
specific trigger and therefore of limited use for land use planning. However,
we implement this first analytical step to reliably estimate the ground motion
effect, and its distribution, on unstable SUs. We then assume the effect of the
ground motion to be time-invariant, enabling statistical simulations for any
ground motion scenario that occurred in the area from 1933 to 2017. As a
result, we obtain the full spectrum of potential susceptibility patterns over
the last century and compress this information into a susceptibility model/map
representative of all the possible ground motion patterns since 1933. This
backward statistical simulations can also be further exploited in the opposite
direction where, by accounting for scenario-based ground motion, one can also
use it in a forward direction to estimate future unstable slopes.
