A review on the resilience of temperate forests to extreme precipitation and wind events - Kuzee, Tijs; Locatelli, Tommaso; Robinson, Suzanne; Perks, Mike P.; Burgess, Paul J.; Khouakhi, Abdou (Frontiers, 2026)
Temperate forests which provide vital ecosystem functions through the provision of timber resources, carbon sequestration, and recreational value are increasingly affected by extreme weather events, with wind and precipitation extremes (drought and excessive rainfall) posing significant challenges to forest resilience. This review synthesizes current knowledge on the impacts of wind and precipitation extremes on temperate forests, focusing on compound disturbance interactions, vulnerability factors, and recovery processes through a systematic review of 248 sources. Research concentrated on single disturbances, with drought and wind most frequently studied. Moreover, there is a focus on short-term resistance and recovery, with limited evidence on reorientation (i.e., transition to a new ecosystem state). Furthermore, we assess recent advancements in disturbance modeling, remote sensing, and machine learning for detecting and forecasting damage from these events. The key observation is that remote sensing and disturbance models are rapidly growing areas of study, but they are skewed toward single disturbance types and are highly specific to particular ecosystems. Machine learning has reduced this specificity and allowed for more data integration in recent years, although small-scale disturbance detection in remote sensing remains challenging owing to data availability limitations. By integrating climate, ecological, and management perspectives, this review concludes that future research and practice must explicitly integrate compound events into multi-hazard models, supported by strengthened long-term (remote sensing) monitoring networks, and adopt adaptive silvicultural strategies. Improved monitoring and multi-hazard modeling will enhance early warning, attribution, and predictive capacity, thereby supporting risk-informed decision-making and the design of targeted adaptive management interventions. Such shifts are essential to sustain ecosystem services and enhance forest resilience under increasing climate extremes.
Keywords: 30 Agricultural, Veterinary and Food Sciences; Machine Learning and Artificial Intelligence; 15 Life on Land; 13 Climate Action; 3007 Forestry sciences