Changes in landslide susceptibility due to canopy loss scenarios in Pittsburgh, PA
Human health and economic sustainability are coupled to the provisions provided by urban forests through services such as air pollution reduction, storm water management, shade, and hillslope stabilization. Despite the importance of urban forests to cities, this coupling is not well understood. This research develops tools to predict the effect of ash and oak loss on urban landslide susceptibility. Emerald ash borer and oak wilt are responsible for substantial tree loss in urban areas. For example, large stands of red oak forest (>100 trees) have been cleared in Pittsburgh, PA to control oak wilt outbreaks and thousands of ash trees are predicted to be lost. A spatial model was built using the SINMAP (Stability INdex MAPping) equation and soil, hydrological, and topographical features. The cohesion term in the SINMAP equation was manipulated based on tree loss scenarios (0%, 25%, 50%, and 75% loss) and the probability of ash and oak presence. Tree presence probability was determined via Monte Carlo methods using iTree plot data provided by Tree Pittsburgh and Treevitalize. The model outputs were assessed with a stability index scaled 0 -10 (stability thresholds: unstable(<1), quasi-unstable(1-1.5), and stable(1.5–10)).
Monte Carlo results indicate ash and oak are most likely to be located on the steeper slopes (50 – 70 degrees) of Pittsburgh, due to human occupation of flatter areas. Given the prevalence of these species on steep slopes, there was increased instability of steep areas as each tree loss scenario was applied to the model. The 25% loss of ash and oak resulted in 14,626 newly “unstable” and “quasi-unstable” 10m pixels. Based on the estimated costs of landslide damage, the realization of a 25% loss of ash and oak could result in remediation costs in the billions of dollars in Pittsburgh, while also negatively impacting the environment and human health. A sensitivity analysis of model parameters revealed that variations in rainfall rates, root cohesion, and friction angle produce only minor changes to model outputs, indicating the robust nature of predictions despite substantial model parameter uncertainties. The results not only demonstrate the importance of large canopy species to urban hillslope stability, but also the need to better understand the role of urban forests in coupled human-natural systems. This modeling framework will enable assessment of changes in landslide risk due to tree mortality, providing knowledge important to sustainability, human health, and economics.