(Van Westen et al., 2006 Sidle and Ochiai, 2006). Susceptibility or spatial probabilities applicable to the study location andĬannot represent causal factors or triggering conditions that change in time On prevailing conditions that predispose hillslopes to failure (Hungr etĪl., 2014), typically providing general indices of relative landslide Landslide type and locations (e.g., Dai and Lee, 2002 Gupta and Joshi,ġ990 Pachauri and Pant, 1992 Kirschbaum et al., 2012). Geology, and topography) and an inventory of past landslides that includes Inherent or quasi-static stability of hillslopes derived from statisticalĪssociations (e.g., correlations) between site attributes (SAs) (e.g., soil, The proposed approach is illustrated in the North Cascades region of the state of Washington, USA.ĭata-driven statistical landslide susceptibility approaches assess the In this paper we develop a statistical approach to combine the probability of landslide initiation obtained from an observation-based statistical mapping method and a physically based model. There is a need for unifying these two lines of research to provide regional-scale landslide prediction for resource management and hazard mitigation strategies. Toĭate, data-driven empirical research on landslide hazard mapping (CorominasĮt al., 2014 Lee et al., 2007 Chung and Fabbri, 2002) has been typically conducted independently from hydroclimate-driven modeling of landslides that largely focus on hydrologic controls on landsliding (Wooten et al., 2016 Cevasco et al., 2014). Such as bedrock, faulting, and complexities of microclimatic conditions. Physically based models are limited to geotechnical stability analysesĭriven by soil pore water pressure, and they often neglect geological factors While detailed quantitative and categorical climatic, geologic, ecologic,Īnd pedologic information can be used in statistical models, Slope stability equations driven by hydro-climatic inputs (Bordoni et al.,Ģ015 Mancini et al., 2010 Sidle and Ochiai, 2006 El-Ramly et al., 2002). Statistically relate the location of existing landslides to otherĮnvironmental variables and physically based models based on geotechnical Initiation or impact, can be obtained using empirical methods that Maps of landslide hazards, quantified as a probability of landslide With growing climatic extremes (Coe, 2016 Haeberli et al., 2017 Landslide hazards are expected to increase globally (Ghirotti, 2012 Baum et al., 2008), and harm people (Wartman et al., 2016 Pollock, 1998), damage infrastructure such as roads, utilities, and dams Landslides disrupt aquatic habitats (May et al., 2009 Geomorphology, loose-soil development, geology, and high precipitation Most mountain ranges are susceptible to landsliding due to their steep Use for planning and decision-making, as well as for educating the publicĪbout hazards from landslides in this remote high-relief terrain. We provide multiple landslide hazard maps that land managers can Unstable areas with the proposed integrated model are statistically Improvements in distinguishing potentially Landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Joint probability and the physically based model bin probability is used asĪ weight to adjust the original physically based probability at each gridĬell given empirical evidence. Of landsliding at the intersections of probability bins. Physically based probabilities as indices and calculates a joint probability A two-dimensional binning method employs empirical and The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. 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. These observational datasets reflect the detection ofĭifferent landslide processes or components, which relate to different Influential attributesĪnd resulting susceptibility maps depend on the observations of landslidesĬonsidered: all types of landslides, debris avalanches only, or source areas Observed landslides using a frequency ratio (FR) method. Statistical approach integrates the influence of seven site attributes (SAs) on Probabilities of landslide impacts derived from a data-driven statisticalĪpproach and a physically based model of shallow landsliding. We developed a new approach for mapping landslide hazards by combining