Abstract
We introduce and compare two approaches to consistently combine release and runout in GIS-based landslide susceptibility modeling. The computational experiments are conducted on data from the Schnepfau investigation area in western Austria, which include a high-quality landslide inventory and a landslide release susceptibility map. The two proposed methods use a constrained random walk approach for downslope routing of mass points and employ the probability density function (PDF) and the cumulative density function (CDF) of the angles of reach and the travel distances of the observed landslides. The bottom-up approach (A) produces a quantitative spatial probability at the cost of losing the signal of the release susceptibility, whereas the top-down approach (B) retains the signal and performs better, but results in a semi-quantitative score. Approach B also reproduces the observed impact area much better than a pure analysis of landslide release susceptibility. The levels of performance and conservativeness of the model results also strongly depend on the choice of the PDF and CDF (angle of reach, maximum travel distance, or a combination of both).
Highlights
Overviews of spatial landslide probability at local or regional scales are useful to support hazard indication zonation and for prioritizing target areas for risk mitigation
General model layout We introduce a statistical-stochastic framework to compute the spatial pattern of the likelihood of landslide impact within a given hilly or mountainous landscape, which is subdivided into a regular grid of geographic information systems (GIS) raster cells
probability density function (PDF) and cumulative density function (CDF) of angle of reach and travel distance Figure 5 illustrates the PDFs and CDFs of the observed angle of reach and the observed travel distance (LOT) (Fig. 2), thereby depicting the details derived for the subset XYZ applied as training area (TA), and the variations observed among the results obtained for the various subsets
Summary
Overviews of spatial landslide probability (susceptibility) at local or regional scales are useful to support hazard indication zonation and for prioritizing target areas for risk mitigation. Computer models that utilize geographic information systems (GIS) are commonly employed to produce such overviews (Van Westen et al 2006). Heuristic models, based on the opinion of experts, are useful for larger areas, but they often provide qualitative results only. For these reasons, statistical methods—often coupled with stochastic concepts—are commonly employed to relate the spatial patterns of landslide occurrence to those of environmental variables such as slope, vegetation, or lithology, and applying these relationships to estimate landslide susceptibility (Guzzetti 2006). A broad array of statistical methods for landslide susceptibility analysis has been developed, documented by a large number of publications
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