Modeling shallow landslides and root reinforcement: A review

Abstract

Slope Stability Models (SSMs) are valuable tools used as decision support in land management to mitigate catastrophic effects caused by rainfall-induced shallow landslides. In particular, SSMs incorporating the presence and influence of vegetation allow for the evaluation of how trees influence relative slope stability and how forest management could ensure the root reinforcement effect in space and time. By implementing empirical knowledge about complex mechanical and hydrological processes, SSMs have been realized by employing different modeling approaches and methods, becoming suitable for different contexts and scales of analysis. Recent SSMs increasingly consider vegetation both as a mechanism to counteract the triggering process of shallow landslides and as a manageable and modifiable tool for mitigating hazards.This review aims to analyze the state-of-the-art of SSMs applicable to vegetated slope areas, investigating those that consider root reinforcement and some of the most cited SSMs in the literature that neglect this effect instead. After classification and exposition on the spatial and temporal dimension of the analysis, modeling approaches, and complexity, we discuss the identification of the most suitable Slope Stability Model (SSM) for individual applications considering four fundamental aspects: modeling approaches, the analysis scale, and purpose, and the output data. Although all SSMs allow for risk analysis by quantifying the factor of safety, only a few allow for an accurate assessment of how changes in vegetation structure, due to the occurrence of natural and human disturbances, also affect the stability of a studied area. Such information is critical to identifying land management criteria to preserve and enhance the protection effect.The improvement of data collection and measurement techniques to obtain parameters for stability analysis required the development of new SSMs able to exploit the improved detail of information, thus allowing for increasingly accurate analyses.