Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations

van Zadelhoff, Feiko Bernard; Albaba, Adel; Cohen, Denis; Phillips, Chris; Schaefli, Bettina; Dorren, Luuk; Schwarz, Massimiliano (2022). Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations. Natural Hazards and Earth System Sciences, 22(8), pp. 2611-2635. Copernicus Publications 10.5194/nhess-22-2611-2022

Preview

Text vanZadelhoff_nhess-22-2611-2022.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (6MB) | Preview

Shallow landslides pose a risk to infrastructure and
residential areas. Therefore, we developed SlideforMAP, a
probabilistic model that allows for a regional assessment of
shallow-landslide probability while considering the effect of
different scenarios of forest cover, forest management and
rainfall intensity. SlideforMAP uses a probabilistic approach
by distributing hypothetical landslides to uniformly random-
ized coordinates in a 2D space. The surface areas for these
hypothetical landslides are derived from a distribution func-
tion calibrated on observed events. For each generated land-
slide, SlideforMAP calculates a factor of safety using the
limit equilibrium approach. Relevant soil parameters are as-
signed to the generated landslides from log-normal distribu-
tions based on mean and standard deviation values represen-
tative of the study area. The computation of the degree of
soil saturation is implemented using a stationary flow ap-
proach and the topographic wetness index. The root rein-
forcement is computed by root proximity and root strength
derived from single-tree-detection data. The ratio of unstable
landslides to the number of generated landslides, per raster
cell, is calculated and used as an index for landslide proba-
bility. We performed a calibration of SlideforMAP for three
test areas in Switzerland with a reliable landslide inventory
by randomly generating 1000 combinations of model param-
eters and then maximizing the area under the curve (AUC)
of the receiver operation curve. The test areas are located
in mountainous areas ranging from 0.5–7.5 km2 with mean
slope gradients from 18–28◦. The density of inventoried his-
torical landslides varies from 5–59 slides km−2. AUC values
between 0.64 and 0.93 with the implementation of single-tree
detection indicated a good model performance. A qualitative
sensitivity analysis indicated that the most relevant param-
eters for accurate modelling of shallow-landslide probabil-
ity are the soil thickness, soil cohesion and the precipitation
intensity / transmissivity ratio. Furthermore, we show that
the inclusion of single-tree detection improves overall model
performance compared to assumptions of uniform vegeta-
tion. In conclusion, our study shows that the approach used in
SlideforMAP can reproduce observed shallow-landslide oc-
currence at a catchment scale.

Interest & Impact

Downloads

295 since deposited on 11 Nov 2022
248 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item