Quantitative spatial distribution and human vulnerability assessment for site-specific loess landslide

Landslides are associated with severe losses on the Loess Plateau of China. Providing hazard mitigation decision support for stakeholders and ensuring the safety of personnel play essential roles in risk management for landslides. Although early warning systems and escape guidelines have mitigated the risk to some extent, most methods are qualitative or semi-quantitative in the sitescale. Therefore, we propose a quantitative simulated-based spatial distribution model and scenario‐based human vulnerability probabilistic model for site-specific loess landslide risk assessment. For spatial distribution, coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides have occurred since 2004 on the Heifangtai terrace (North-West China) were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter analysis. The centroid distance and overlapping area can quantitatively evaluate the accuracy of the simulation results. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume. Different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. Following the obtained hazard zone, a scenario-based model for evaluating the escape behavior and human vulnerability was proposed using a Python platform. Based on sampling surveys and field investigations, a database that includes detailed information for the hazard zone’s demographic structure and behavioral characteristics were established. The probability of scenario input parameters, such as the escape route and speed, were calculated and quantified by classic probability theory. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. The registered population over 65 years old accounted for 13.46% of the total, and most residents had no more than a primary school education background. Older adults were inclined to escape a moving landslide by running parallel to the sliding direction, although the public considers this direction to be the most dangerous. The model simulation revealed that cumulative mortality could be significantly reduced by promoting disaster prevention awareness and improving the advance warning time. The developed quantitative hazard and human vulnerability framework provide a useful reference for local disaster reduction and disaster prevention rehearsal guidelines.