Can we use critical thresholds to better inform drought damage modeling in forests?

In 2018, northern Europe was hit with an extreme summer drought event whose impacts on regional forest carbon cycling remain poorly understood and sparsely documented.  Using southern Norway as a case study, we developed a remote sensing-based algorithm to investigate the relationship between drought severity and forest resilience, identify critical thresholds, characterize damages, and study the factors shaping them.  We then trained stastical models to predict damages upon critical threshold exceedance and found that the event reduced gross primary productivity (GPP) by 4.55 Mt-CO₂ yr⁻¹ between 2018-2023, driven by loss of resistance (66%) with prolonged recovery and mortality (34%).  This reduction explains 26% of the observed weakening in Norway’s forest carbon sink over the same period.  Site-level features were the strongest predictors of damage.  Our results highlight extreme drought's role as a major carbon cycle disturbance in northern European forests and provides a framework to inform adaptive management strategies and benchmark other, more mechanistic modeling tools.