摘要：Climate change is set to increase landslide frequency around the globe, thus increasing the potential exposure of people and material assets to these disturbances. Landslide hazard is commonly modelled from terrain and precipitation parameters, assuming that shorter, more intense rain events require less precipitation volume to trigger a slide. Given the extent of non-catastrophic slides, an operable vulnerability mapping requires high spatial resolution. We combined heterogeneous regional slide inventories with long-term meteorological records and small-scale spatial information for hazard modelling. Slope, its (protective) interaction with forest cover, and altitude were the most influential terrain parameters. A widely used exponential threshold to estimate critical precipitation was found to incorrectly predict meteorological hazard to a substantial degree and, qualitatively, delineate the upper boundary of natural conditions rather than a critical threshold. Scaling rainfall parameters from absolute values into local probabilities (per km²) however revealed a consistent pattern across datasets, with the transition from normal to critical rain volumes and durations being gradual rather than abrupt thresholds. Scaled values could be reverted into site-specific nomograms for easy appraisal of critical rain conditions by local stakeholders. An overlay of terrain-related hazard with infrastructure yielded local vulnerability maps, which were verified with actual slide occurrence. Multiple potential for observation bias in ground-based slide reporting underlined the value of complementary earth observation data for slide mapping and early warning.
