Comparison and validation of three versions of a forest wind risk model. (June 2015)

• Record Type: Journal Article
• Authors: Hale, Sophie E.; Gardiner, Barry; Peace, Andrew; Nicoll, Bruce; Taylor, Philip; Pizzirani, Stefania
• Is Part Of: Environmental modelling & software. Volume 68(2015:Jun.)
• Page Start: 27
• Page End: 41
• Publication Date: 2015-06
• Subjects: Risk modelling -- Decision support system -- Wind damage -- Forest disturbance -- Mechanistic modelling -- Validation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.01.016
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Predicting the probability of wind damage in both natural and managed forests is important for understanding forest ecosystem functioning, the environmental impact of storms and for forest risk management. We undertook a thorough validation of three versions of the hybrid-mechanistic wind risk model, ForestGALES, and a statistical logistic regression model, against observed damage in a Scottish upland conifer forest following a major storm. Statistical analysis demonstrated that increasing tree height and local wind speed during the storm were the main factors associated with increased damage levels. All models provided acceptable discrimination between damaged and undamaged forest stands but there were trade-offs between the accuracy of the mechanistic models and model bias. The two versions of the mechanistic model with the lowest bias gave very comparable overall results at the forest scale and could form part of a decision support system for managing forest wind damage risk. Highlights: Comprehensive description and validation of three versions of a mechanistic wind risk model for even-aged forests. Analysis of the performance of mechanistic and logistic regression models against measured damage in a conifer forest. All models provided acceptable discrimination between damaged and undamaged forest stands. The two version of the mechanistic model with the lowest bias gave very comparable overall results at the forest scale. Statistical analysis showed that increasing tree height and local wind speed were the main factors associated with damage.