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Recent climate change reduced Spanish forests' carbon sink capacity
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Abstract
Forests play a crucial role as carbon sinks and are central to climate mitigation strategies, yet their long-term reliability in this function is increasingly uncertain under climate change. Using deep learning techniques on a multi-source dataset—combining multi-spectral satellite data, airborne laser scanning, and ground-based measurements—we produced the most up-to-date high-resolution maps of woody carbon storage in continental Spain on a yearly basis from 2017 to 2024. Our analysis revealed substantial forest carbon losses, with Spanish forests and woodlands functioning as a net carbon source during this period, losing approximately 12% of their biomass carbon stocks, with the sharpest decline occurring during 2022 following the most intense drought of the century. Eucalyptus plantations and conifer forests experienced the greatest relative losses, 30% and 22% respectively, while low-density woodlands and agroforestry systems showed the greater resilience, with woody carbon stocks dropping only 5\% in these areas. Our results support structural overshoot, where periods of favorable growing conditions promote vegetation growth that subsequently becomes vulnerable during drier conditions. Specifically, large rainfall and cooler summer temperatures were strongly correlated with subsequent time-lagged carbon losses. These findings reveal a critical challenge for climate policy as forest mitigation potential is hampered by climate change impacts, especially in Mediterranean regions where drought intensity is increasing. This highlights the urgent need to align adaptation and mitigation policies to enhance forest resilience and its long-term climate mitigation capacity.
DOI
https://doi.org/10.31223/X5ST8V
Subjects
Artificial Intelligence and Robotics, Ecology and Evolutionary Biology, Environmental Sciences, Forest Sciences, Life Sciences
Keywords
Artificial Intelligence, biomass dynamics, carbon sink, climate change, Climate change mitigation, ecological modelling, forest carbon, Neural Networks, remote sensing, structural overshoot
Dates
Published: 2025-11-13 16:22
Last Updated: 2025-11-13 16:22
License
CC BY Attribution 4.0 International
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Conflict of interest statement:
None
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