Temporal variations in landslide distributions following extreme events:
implications for landslide susceptibility modelling
Abstract
Landslide susceptibility models are fundamental components of landslide
risk management strategies. These models typically assume that landslide
occurrence is time-independent, even though processes including
earthquake preconditioning and landslide path dependency transiently
impact landslide occurrence. Understanding the temporal characteristics
of landslide occurrence remains limited by a lack of systematic
investigation into how landslide distributions vary through time, and
how this impacts landslide susceptibility. Here, we apply
Kolmogorov-Smirnoff and Chi-2 statistics to a 30-year inventory of
monsoon-triggered landslides from Nepal to systematically quantify how
landslide spatial distributions vary through time in ‘normal’ years and
years impacted by extreme events. We then develop Binary Logistic
Regression (BLR) susceptibility models for 12 years in our inventory
with > 400 landslides and use Area Under Receiver Operator
Curve (AUROC) validation to assess how well these models can hindcast
landslide occurrence in other years. Landslide distributions are found
to vary through time, particularly in years impacted by storms (1993 and
2002), earthquakes (2015) and floods (2017). Notably, Gorkha earthquake
landscape preconditioning shifted 2015 monsoon-triggered landslides to
higher slopes, reliefs and excess topographies. These variations
significantly impact BLR susceptibility modelling, with models trained
on extreme years unable to consistently hindcast landslide occurrence in
other years. However, developing BLR models using increasingly long
historical inventories shows that susceptibility models developed using
> 6 - 8 years of landslide data provide consistently good
hindcasting accuracy. Overall, our results challenge time-independent
assumptions of landslide susceptibility approaches, highlighting the
need for time-dependent modelling techniques or historical inventories
for landslide susceptibility modelling.