Abstract. We developed a new approach for mapping landslide hazards by combining
probabilities of landslide impacts derived from a data-driven statistical
approach and a physically based model of shallow landsliding. Our
statistical approach integrates the influence of seven site attributes (SAs) on
observed landslides using a frequency ratio (FR) method. Influential attributes
and resulting susceptibility maps depend on the observations of landslides
considered: all types of landslides, debris avalanches only, or source areas
of debris avalanches. These observational datasets reflect the detection of
different landslide processes or components, which relate to different
landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and
physically based probabilities as indices and calculates a joint probability
of landsliding at the intersections of probability bins. A ratio of the
joint probability and the physically based model bin probability is used as
a weight to adjust the original physically based probability at each grid
cell given empirical evidence. The resulting integrated probability of
landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially
unstable areas with the proposed integrated model are statistically
quantified. We provide multiple landslide hazard maps that land managers can
use for planning and decision-making, as well as for educating the public
about hazards from landslides in this remote high-relief terrain.
Smarticles: A Method for Identifying and Correcting Instability and Error Caused by Explicit Integration Techniques in Physically Based Simulations