Disentangling the Effects of Forest Structural Heterogeneity on Observed Ecosystem Carbon and Water Fluxes

Eddy covariance (EC) measurements are essential to characterising biosphere–atmosphere exchanges of carbon (Net Ecosystem Exchange, NEE) and water vapour (Evapotranspiration, ET). However, their interpretation of structurally complex forest canopies remains challenging. EC fluxes integrate spatially variable source areas that are commonly treated as functionally homogeneous, neglecting the role of vegetation structural heterogeneity in regulating observed NEE and ET. Addressing this limitation is critical for improving flux interpretation and land-surface model parameterisation across heterogeneous forest ecosystems. We present a transferable, footprint-based framework. It integrates half-hourly EC fluxes with high-resolution Aerial Laser Scanning (ALS) data to explicitly resolve within-footprint vegetation structural heterogeneity. Using a two-dimensional flux footprint model (Kljun et al., 2015), EC fluxes were assigned according to the spatial contribution of distinct vegetation structural classes. This enables analysis of functional relationships between fluxes and the environment under comparable atmospheric forcing. The approach revealed substantial and systematic differences in both flux magnitude and functional responses among vegetation structural classes. Median differences reached up to 20 µmol m⁻² s⁻¹ for NEE and up to 5 mmol m⁻² s⁻¹ for ET. Light-response parameters and water-use efficiency varied consistently between structural groups. Our results underscore the importance of footprint heterogeneity characterisation for interpreting functional relationships in structurally complex forest ecosystems. By explicitly accounting for spatial heterogeneity within EC footprints, this framework provides a scalable pathway to link vegetation structure with ecosystem-scale carbon and water fluxes. The proposed framework is transferable to other EC sites. It offers the potential to improve the parameterisation of land-surface and dynamic global vegetation models, and ultimately to enhance predictions of biosphere–atmosphere exchange of matter and energy.