402 Norris and Moore: How Tree Risk Assessment Methods Work Arboriculture & Urban Forestry 2020. 46(6):402–431 URBAN FORESTRY ARBORICULTURE Scientific Journal of the International Society of Arboriculture & How Tree Risk Assessment Methods Work: Sensitivity Analyses of Sixteen Methods Reveal the Value of Quantification and the Impact of Inputs on Risk Ratings By M.B. Norris and G.M. Moore Abstract. Sixteen tree risk assessment methods were subjected to sensitivity analysis to determine which factors most influenced the output of each method. The analyses indicate the relative influence that the input variables exert on the final risk value. Excel was used to create a simple ± 25% or ± 1 rank change (depending on the method) for each criterion, with the change to the output recorded as a percentage. Palisade’s @Risk software was used to undertake a Monte Carlo (with Latin Hypercube sampling) simulation of 5000 iterations based on the input vari- ables and output formula. From the simulation, multivariate stepwise regression was undertaken to determine the influence of each method’s input variables in determining the output values. Results from the sensitivity analysis indicate some clear and strong differences amongst the 16 methods, reflecting that the underlying mathematics, input categories, ranges, and scaling influence the way that different methods process and express risk. It is not surprising that methods perform differently in different circumstances and express risk level differently. The analyses demonstrated that most methods placed too great an emphasis on limited aspects of risk assessment. Most methods strongly focused on the haz- ard or defect aspects of assessment and the likelihood of failure rather than the consequence aspect of an assessment. While methods were uniquely different, they could be placed into 3 broad groups: Group 1 methods produced a normal distribution with most values around the mean; Group 2 methods produced outputs at the lower end of the risk scale; and Group 3 methods produced outputs evenly if not continuously across the risk scale. Users of tree risk assessment should understand the strengths and weaknesses of any method used, as it could be relatively simple to challenge the results of a risk assessment based on limitations inherent in the underlying methodology. Keywords. Risk Assessment; Risk Consequence; Risk Likelihood; Sensitivity Analysis; Tree Risk. INTRODUCTION Prior to the 1980s, with the exception of work by the USDA Forestry Service (USDAFS), few tree risk assessment methods existed (Paine 1971; Johnson 1981; Robbins 1986). It is now commonplace for arborists to undertake tree risk assessments, which are significant elements of urban tree management, particularly for community-owned trees growing in public open spaces, such as parks, road reserves, and around public buildings (Koeser et al. 2016; Smiley et al. 2017). Based on the definitions used in Austra- lian Standard AS/NZS 4360:2004 and adopted for this research, risk management is defined as: The sys- tematic application of management policies, proce- dures, and practices to the tasks of communicating, ©2020 International Society of Arboriculture establishing the context, identifying, analysing, estimat- ing, evaluating, treating, monitoring, and reviewing risk. Confusion can arise over the use of hazard and risk, but this paper defines hazard as being associated with the source of danger, such as a rotten branch, and risk being associated with elements of uncertainty, such as the likelihood of failure, the consequences of failure, and the time of exposure to the hazard. In some parts of the world, local government agen- cies may be protected from legal action by an extension of sovereign immunity, but even in the USA, this may not completely exclude actions for damages caused by the negligence of an agency or its employees (Cornell Law School 2020). In Australia, tree risk management occurs largely because insurers of local
November 2020
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