Large-scale modelling wind damage vulnerability through combination of high-resolution forest resources maps and ForestGALES
Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution model...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-12-01
|
| Series: | Forest Ecosystems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2197562025000703 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution modelling of wind damage vulnerability in Norway. The hybrid mechanistic wind damage model, ForestGALES, was adapted to map the critical wind speeds (CWS) of damage across Norway using a national forest attribute map at a 16 m × 16 m spatial resolution. Parametrization of the model for the Norwegian context was done using the literature and the National Forest Inventory data. This new parametrization of the model for Norwegian forests yielded estimates of CWS significantly different from the default parametrization. Both parametrizations fell short of providing acceptable discrimination of the damaged area following the storm of November 19, 2021 in the central southern region of Norway when using unadjusted CWS. After adjusting the CWS and the storm wind speeds by a constant factor, the Norwegian parametrization provided acceptable discrimination and was thus defined as suitable to use in future studies, despite the lack of field- and laboratory experiments to directly derive parameters for Norwegian forests. The windstorm event used for model validation in this study highlighted the challenges of predicting wind damage to forests in landscapes with complex topography. Future studies should focus on further developing ForestGALES and new datasets describing extreme wind climates to better represent the wind and tree interactions in complex topography, and predict the level of risk in order to develop local climate-smart forest management strategies. |
|---|---|
| ISSN: | 2197-5620 |