MM

Quantified estimates of landslide consequences in space and time (risk) facilitate rational land use decisions such as zoning for new development, protecting existing communities, allocating finite resources, designing mitigation works, and educating the public about natural hazards. Probabilistic landslide risk analysis (PLRA) should include all landslide modes, magnitudes, and triggering scenarios that could credibly cause harm and is most useful on a regional scale where landslide risk at a location can be compared across a broader area and in the context of other natural and anthropogenic sources of risk. However, to date, no readily transferable, regional-scale method for PLRA exists. In this work, we expand an existing deterministic multimodal method for landslide risk analysis developed in the country of Lebanon into a linked framework of code-based modules that are location-agnostic and computationally efficient for regional end-to-end risk estimation.•Use of near-global, remote-sensing-based inputs enables risk estimates almost anywhere in the world•Modular computational framework facilitates upgrades of component models as new research becomes available•Probabilistic implementation through a Monte Carlo approach.