Overview
Under climate change, extreme wind events are predicted to become both more common and more severe, increasing the vulnerability of plantation forests. In February 2023, ex-tropical Cyclone Gabrielle caused widespread wind damage to radiata pine (Pinus radiata D. Don) forests across the North Island of New Zealand, providing a rare opportunity to evaluate mechanistic wind-risk modelling under extreme storm conditions. This study assessed the performance of the ForestGALES model in predicting wind damage within the Rangipo genetic accelerator trial and examined the influence of stocking and cultivation on wind vulnerability. Using detailed pre-cyclone field measurements and high-resolution unmanned aerial vehicle light detection and ranging (ULS) data, ForestGALES was parameterised for the Rangipo trial and applied at both individual-tree and stand scales. Model predictions were compared with observed post-cyclone damage using balanced area under the receiver operating characteristic curve (AUC), accounting for strong class imbalance.