Abstract
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.
Highlights
Most mountain ranges are susceptible to landsliding due to their steep geomorphology, loose-soil development, geology, and high precipitation (e.g., Coe, 2016)
This paper describes research designed to address the following questions: (1) How can we quantify the relative contributions of local topography, geology, and ecology on landslide frequency and derive spatial probabilities of landsliding using a statistical model? (2) How would probabilities of landslide initiation derived from empirical observations compare with those derived from a physically based model? (3) How can we combine empirical and physically based models for landslide susceptibility to improve the prediction of landslide hazards?
The empirically based probability model based on source areas was used to adjust a previously developed physically based probabilistic model through a calculated weighting term developed from a joint spatial probability
Summary
Most mountain ranges are susceptible to landsliding due to their steep geomorphology, loose-soil development, geology, and high precipitation (e.g., Coe, 2016). Maps of landslide hazards, quantified as a probability of landslide initiation or impact, can be obtained using empirical methods that statistically relate the location of existing landslides to other environmental variables and physically based models based on geotechnical slope stability equations driven by hydro-climatic inputs (Bordoni et al, 2015; Mancini et al, 2010; Sidle and Ochiai, 2006; El-Ramly et al, 2002). R. Strauch et al.: A new approach to mapping landslide hazards ited to geotechnical stability analyses driven by soil pore water pressure, and they often neglect geological factors such as bedrock, faulting, and complexities of microclimatic conditions. 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. The proposed approach is illustrated in the North Cascades region of the state of Washington, USA
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