Assessing the ability of LPJ-GUESS-HYD to predict water stress responses in boreal forests

Boreal forests are increasingly exposed to extreme heat and altered precipitation patterns, leading to periods of water stress that threaten their capacity to provide important ecosystem services. Management interventions can improve the resilience of forests to water stress, but our ability to implement such adaptation methods is contingent upon accurate identification of areas most susceptible to the adverse effects of this water stress. Recent advances in dynamic vegetation modelling have improved our ability to predict water stress responses in forests, including the integration of plant hydraulic processes into the ecosystem model LPJ-GUESS. Here, we evaluate the ability of this new adaptation, LPJ-GUESS-HYD, to detect water stress in three forests across Sweden. We identified periods of moderate, severe and extreme drought based on the Standardized Precipitation-Evapotranspiration Index (SPEI), and compared LPJ-GUESS-HYD carbon flux simulations with ICOS eddy-covariance flux data and satellite-based vegetation indices in drought and non-drought periods from 2015 to 2022. We found that LPJ-GUESS-HYD could accurately capture many water-stress-induced shifts in carbon fluxes and vegetation indices. However, its ability to detect these water stress responses varied largely between sites and years, and depended on the duration and intensity of the water stress. Our results provide insight into the factors determining the efficacy of LPJ-GUESS-HYD for predicting water stress responses, and highlight areas where improvement is needed.