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Modelling damage occurrence by snow and wind in forest ecosystems

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  • Díaz-Yáñez, Olalla
  • Mola-Yudego, Blas
  • González-Olabarria, José Ramón

Abstract

Snow and wind damages are one of the major abiotic disturbances playing a major role in forest ecosystems and affecting both stand dynamics and forest management decisions. This study analyses the occurrence of wind and snow damage on Norwegian forests, based on data from four consecutive forest inventories (1995–2014). The methodological approach is based on boosted regression trees, a machine learning method aiming to demonstrate the effects of different variables on damage probability and their interactions as well as to spatialize damage occurrence to make predictions. In total, 313 models are fitted to detect trends, interactions and effects among the variables. The main variables associated with damage occurrence are consistent across all the models and include: latitude, altitude and slope (related to site and location), and tree density, mean diameter and height (related to forest characteristics). The results show that stand dominant height is a key variable in explaining damage probability, whereas stand slenderness has a limited effect. More heterogeneous forest structures make birch dominated stands more resistant to damage. Finally, the models are translated into occurrence maps, to provide landscape-level information on snow and wind damage hazard. Further application of the models can be oriented towards assessing the probability of damage for alternate stand management scenarios.

Suggested Citation

  • Díaz-Yáñez, Olalla & Mola-Yudego, Blas & González-Olabarria, José Ramón, 2019. "Modelling damage occurrence by snow and wind in forest ecosystems," Ecological Modelling, Elsevier, vol. 408(C), pages 1-1.
    • Handle: RePEc:eee:ecomod:v:408:y:2019:i:c:6
    DOI: 10.1016/j.ecolmodel.2019.108741
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    References listed on IDEAS

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    1. Seidl, Rupert & Fernandes, Paulo M. & Fonseca, Teresa F. & Gillet, François & Jönsson, Anna Maria & Merganičová, Katarína & Netherer, Sigrid & Arpaci, Alexander & Bontemps, Jean-Daniel & Bugmann, Hara, 2011. "Modelling natural disturbances in forest ecosystems: a review," Ecological Modelling, Elsevier, vol. 222(4), pages 903-924.
    2. Aertsen, Wim & Kint, Vincent & van Orshoven, Jos & Özkan, Kürşad & Muys, Bart, 2010. "Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests," Ecological Modelling, Elsevier, vol. 221(8), pages 1119-1130.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    4. Schelhaas, M.J. & Kramer, K. & Peltola, H. & van der Werf, D.C. & Wijdeven, S.M.J., 2007. "Introducing tree interactions in wind damage simulation," Ecological Modelling, Elsevier, vol. 207(2), pages 197-209.
    5. C. J. Randall & R. van Woesik, 2015. "Contemporary white-band disease in Caribbean corals driven by climate change," Nature Climate Change, Nature, vol. 5(4), pages 375-379, April.
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    Cited by:

    1. Petri P. Kärenlampi, 2024. "Mortality of Boreal Trees," Sustainability, MDPI, vol. 16(15), pages 1-17, July.
    2. Ali Jahani & Maryam Saffariha, 2022. "Tree failure prediction model (TFPM): machine learning techniques comparison in failure hazard assessment of Platanus orientalis in urban forestry," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 881-898, January.
    3. Félix Bastit & Marielle Brunette & Claire Montagne-Huck, 2021. "Earth, wind and fire: A multi-hazard risk review for natural disturbances in forests," Working Papers of BETA 2021-25, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

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