Treefall risk assessment in an urban green area for a hypothetical 30-year return period storm using damage data from Typhoons Faxai and Hagibis in 2019

Abstract

Abstract Urban trees support human well-being, yet their benefits must be balanced against the risk of tree failure. Here, we propose a simple quantitative approach that practitioners can use to assess and manage urban treefall risk. We developed a simple mechanical model based on readily measured trunk diameter and height to calculate a hypothetical maximum compressive stress in the trunk for a given wind speed. This stress-based index was treated as a latent variable linking the mechanical and stochastic components of our framework. We then fitted a statistical model that predicts treefall probability from the latent stress index using empirically observed treefall records from Typhoons Faxai and Hagibis (2019). Unmodeled sources of variability and other unaccounted-for factors were implicitly incorporated through statistical calibration. The fitted model showed a statistically supported association between the latent stress index and treefall probability (broadleaved tree AUC = 0.94, conifer AUC = 0.77). Using extreme value analysis, we estimated the 30-year return level of maximum wind speed at the study site and assessed treefall risk for individual trees under this scenario. The model indicated higher risk for tall non-native trees (relative to local native species) and for conifers. Although available treefall data remain limited, to our knowledge, this study is among the first to predict future treefall probability using empirically observed treefall records and to provide a quantitative framework for practitioners to manage treefall risk.