Variance-based sensitivity analysis of a wind risk model - Model behaviour and lessons for forest modelling. (January 2017)

• Record Type: Journal Article
• Authors: Locatelli, Tommaso; Tarantola, Stefano; Gardiner, Barry; Patenaude, Genevieve
• Is Part Of: Environmental modelling & software. Volume 87(2017)
• Page Start: 84
• Page End: 109
• Publication Date: 2017-01
• Subjects: Method of Soboĺ -- Assessment of model performance -- Copula method -- Correlated variables; 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.2016.10.010
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We submitted the semi-empirical, process-based wind-risk model ForestGALES to a variance-based sensitivity analysis using the method of Soboĺ for correlated variables proposed by Kucherenko et al. (2012). Our results show that ForestGALES is able to simulate very effectively the dynamics of wind damage to forest stands, as the model architecture reflects the significant influence of tree height, stocking density, dbh, and size of an upwind gap, on the calculations of the critical wind speeds of damage. These results highlight the importance of accurate knowledge of the values of these variables when calculating the risk of wind damage with ForestGALES. Conversely, rooting depth and soil type, i.e. the model input variables on which the empirical component of ForestGALES that describes the resistance to overturning is based, contribute only marginally to the variation in the outputs. We show that these two variables can confidently be fixed at a nominal value without significantly affecting the model's predictions. The variance-based method used in this study is equally sensitive to the accurate description of the probability distribution functions of the scrutinised variables, as it is to their correlation structure. Highlights: The Soboĺ method for correlated variables is applied to a complex wind-risk model. The results are interpreted from the viewpoints of model users and modellers. The variance-based approach is sensitive to the variables correlation structure. Rooting depth and soil type provide minor contribution to the outputs variance. ForestGALES models the dynamics of wind damage to forest stands very effectively.