Arboriculture & Urban Forestry 48(4): July 2022 windthrow and stem weight or root-soil plate weight (Gardiner et al. 2008). The resistance to breakage of a tree is related to the diameter of the stem and the tree species and must be greater than the bending moment required to exceed the MOR or stem failure will occur (Gardiner et al. 2008). “These relations can be simplified to state that the stem volume best predicts the resistance to uprooting, whereas dbh³ best pre- dicts resistance to stem breakage” (Quine and Gar- diner 2007; Gardiner et al. 2008). The second stage of the mechanistic modeling of windthrow risk to trees is predicting the probability of the CWS being exceeded (Gardiner et al. 2008). The primary method to predict the local wind climate is to use the airflow model, Wind Atlas Analysis and Application Program (WAsP)(Mortensen et al. 2005; Gardiner et al. 2008). Although in settings with more complex terrain or wind climates the use of Weibull parameters from highly accurate weather forecast data may be required for accurate airflow modeling (Gar- diner et al. 2008; Mitchell et al. 2008). The GALES model utilizes tree height, diameter, current tree spacing, soil type, cultivation, drainage, and tree species to determine the CWS (Gardiner et al. 2008). GALES was originally designed to calcu- late the CWS at 10 m above the zero-plane displace- ment height for even-aged conifer monocultures. To consider mixed-species stands, the simulation must be run for each species in turn and all trees in the stand must be considered to be of that species (Gar- diner et al. 2008). GALES can be utilized to calculate the risk at any distance from a newly created edge and for any size of upwind gap (Gardiner et al. 2008). For existing edges, the risk is considered constant from the edge due to the effects of adaptive growth by trees (Telewski 1995; Gardiner et al. 2008). Additionally, GALES requires tree-pulling data, MOR for the green timber of the tree species of interest, and descriptive measures of the crown characteristics (Gardiner et al. 2008). When using GALES, it has been found that an increase of the predicted CWS by an additional fixed value of 1 m/s improves the accuracy of the model’s predictions (Gardiner et al. 2008). The HWIND model was developed by Peltola et al. (1999) for the description of the mechanistic behavior of monocultures of Scots pine, Norway spruce, and birch under wind and snow loading (Peltola et al. 1999; Gardiner et al. 2008). While originally designed for calculations of the CWS of trees at newly created 247 edges of stands, HWIND has now been adapted for the calculation of CWS at different distances from the upwind gap and for different sizes of upwind gap (Gardiner et al. 2008). HWIND predicts the mean CWS over a 10-minute time period at 10 m above ground level (Gardiner et al. 2008). This model requires knowledge of tree species, tree height, DBH, stand density, distance to the stand edge, and gap size (Gardiner et al. 2008). HWIND, like GALES, is sen- sitive to any inaccuracies of the inputs, especially DBH, which determines the amount of wind load a tree can experience before failure and the expected amount of wind load a tree will experience (Gardiner et al. 2008). Thus, any inaccuracy can have a signifi- cant influence on the predicted CWS (Gardiner et al. 2008). The FOREOLE model developed by Ancelin et al. (2004) was the first attempt to contend with complex stand structure within predictive mechanis- tic models (Gardiner et al. 2008). FOREOLE assumes an empirical wind profile within the canopy and cal- culates the horizontal wind loading on each individ- ual tree (Gardiner et al. 2008). Reasonable agreement between the predictions made by GALES, HWIND, and FOREOLE have been noted when compared (Gardiner et al. 2008). While FOREOLE has yet to be entirely validated, its predicted CWSs have aligned with the wind speeds required to cause damage to trees (Gardiner et al. 2008). To quantify wind loading, GALES may use either a “roughness method,” where a wind-induced stress distribution of trees in a forest is calculated, or a pre- dicted wind profile within or at the forest front (Gar- diner et al. 2008). In contrast, HWIND and FOREOLE both utilize only the latter method (Gardiner et al. 2008). An early limitation of CWS-based models was that they were originally built to represent the risk to a “mean tree” within a stand, not to consider the risk posed to individual trees (Gardiner et al. 2008). How- ever, recently Suzuki et al. (2016) determined CWS for individual trees as well as demonstrated a quanti- tative risk-management evaluation for individual trees (Suzuki et al. 2016). Most of these CWS-based models are limited because they do not account for variations in wind from different directions (Gardiner et al. 2008). While Ancelin et al. (2004) demonstrated a first attempt to deal with complex stand structure, their approach has not yet been validated against data from complex stand structures (Gardiner et al. 2008). Additionally, ©2022 International Society of Arboriculture
July 2022
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