A Novel Wavelet Approach to Understanding Interannual Variations in a Young Beech Forest Gross Primary Productivity: Influence of Driving Meteorological Variables Over 24 Years

The rise in extreme events over recent decades has led to observable eco-physiological changes across diverse ecosystems. These changes introduce uncertainties in our understanding of ecosystem processes, challenging the accuracy of predictive models on ecosystem behavior. To address these uncertainties, long-term studies offer a unique perspective for unraveling the intricate dynamics of ecosystems and their relationship with climatic fluctuations. Among these ecosystems, forests hold particular importance due to their role as significant carbon sinks and providers of numerous ecosystem services. In this context, this study provides insights on a 24-year (1997-2020) dataset of continuous CO2 turbulent flux measurements from ICOS Hesse site, a beech-dominated forest under temperate conditions in north-eastern France. We introduce a novel approach using the continuous wavelet transform, a time-frequency analysis tool, to define indicators of Gross Primary Productivity (GPP) intra-annual dynamics. Our study uncovers critical temporal windows during which current or previous year meteorological conditions significantly impact beech photosynthetic activity and eco-physiological behavior. Notably, precipitations during July-August emerges as a pivotal phase for next year’s GPP dynamics. Furthermore, radiation, air temperature, vapor pressure deficit, precipitations and soil water availability exhibit both short and long-term effects on GPP. Our proposed approach disentangles these influences, identifying dominant periods for each variable and their localized impact on GPP dynamics. By unraveling these correlations, our study provides insights supporting a comprehensive understanding of forest eco-physiological response to shifting climate patterns, yielding critical information for modelling purposes.