168 Stewart et al.: QTRA and Risk-based Cost-benefit of Tree Assessment Figure 3. Risk assessment process. sequences is the loss or consequence if failure occurs. The reduction in risk (∆R) is the degree to which the decision or risk mitigating measures reduces the likelihood of failure and/or the losses sustained in a failure. The cost of reduc- ing the risk, including opportunity costs, is C∆R . This equa- tion can be generalized for more detailed costs and damages. It is important to recognize the issue of risk aversion (Faber and Stewart 2003; Stewart et al. 2011), and peer review—particularly for decisions that have the poten- tial for political and public repercussions or where the as- sets have heritage, ecological, or cultural significance. QUANTIFIED TREE RISK ASSESSMENT Calculation of Risk of Harm The probability of failure is the most subjective parameter to quantify when estimating Risk of Harm. According to the QTRA User Manual V3.06 (2012), there is an order or more of magnitude between each probability of failure range used on the QTRA calculator, starting from 1/1 and rang- ing to 1/1,000,000. The QTRA User Manual (2007) points out that trees that have a low probability of failure “are at significant risk of failure only during very extreme weather events”; a statement supported by the observations of Gug- genmoos (2009) in relation to damage of overhead power lines following catastrophic storm events in North America. There seems to be little guidance for QTRA practitio- ners on how to assess the probability of failure. There are no instructions on how to undertake a statistical analysis of tree failures, how likelihoods may be estimated from wind speed and rainfall records (if these are the hazards contributing to failure), how mode of failure affects fail- ure probabilities, how to estimate 90th or 95th percentile confidence intervals on predictions, or how to extrapolate probability of failure per tree to a group of trees (see Equa- tion 2 to Equation 5). At this moment, the QTRA process of estimating probability of failure is subject to a degree of ©2013 International Society of Arboriculture uncertainty because of the complex variables involved in tree biomechanics and tree failure, variable weather con- ditions, and varying levels of expertise of the assessors. There is also a need to compile datasets on tree fail- ures and failure rates in a format suitable for benchmark- ing. The International Tree Failure Database, for example, may be very useful for determining the number of failed trees, but a calculation of probability of failure (failure rate) requires also the number of unfailed trees, and the time pe- riod over which failures were observed (see Equation 10). Benchmarking allows the predicted probability of failure to be compared to known statistics of failure obtained from rel- evant datasets. For example, is the assessed tree likely to be 10 times more likely to fail than an average tree, about the same, or 10 times less? Probability of failure may also in- crease with time if deterioration is observed, or perhaps those that have failed were weaker and so remaining trees are less vulnerable. Calculated risks will be most sensitive to prob- ability of failure, and more scientific approaches and de- tailed guidelines are needed to better estimate this parameter. The QTRA guidelines provide significantly more detailed instructions on quantifying target value or likelihood of im- pact. This is to be expected since it is not particularly dif- ficult to assess the likelihood that people or vehicles will be under a tree at any point in time. In a busy street, it will be approximately 50%–100%, and for a park, maybe 10%–20%. Either way, any estimate will be accurate to ±10%–20%. One issue that needs further elaboration is that trees are most vulnerable during periods of high wind and/or rain. These are circumstances where many people avoid the outdoors, thus re- ducing the target value considerably. Although the influence of weather is not discussed by Ellison (2005), it is discussed and some guidance is provided by the QTRA User Manual (2012). The guidelines for impact potential are quite prescriptive, and are based on size of tree part likely to impact the target. Impact potentials vary from 1/1 (100%) for a 450 mm diam- eter tree part to 1/2,500 for a 10–25 mm tree part. If a tree or tree part were to fall, and a person was under the tree at the time, there will not be 100% surety of harm to the person no matter how large the tree-part. A tree has a large canopy, and a tree could fall away, or a tree-part could be on the opposite side from where a person is standing. Therefore, the upper limit of 100% seems too high and a more reasonable upper limit may be, say, 25% or 50%, depending on the size of the tree. Impact potentials would seem to be over-estimated. Finally, there is some evidence that results from a QTRA are highly subjective. Norris (2007) asked twelve experienced arborists to assess eight trees using eight different risk assess- ment methods (Figure 4). For QTRA, the Risk of Harm ranged from 1/19 to 1/128 million. The lowest and highest values were obtained from QTRA licensed practitioners. The range in probability of failure was 1/2 to 1/50,000. Such a large discrepancy should be a concern, as should the discrepancies between the two licensed QTRA practitioners and Ellison, who developed QTRA, in the case of the Laman Street fig trees. Risk Acceptance Criteria If the ROH exceeds 1 in 10,000, then the QTRA process deems that the risk is “unacceptable,” and remedial action is needed to reduce the risk to an acceptable level (Ellison
July 2013
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