Airborne eddy covariance (EC) systems can measure turbulent fluxes by rapidly sampling the atmospheric boundary layer over complex terrain at the regional scale, revealing turbulent structures across multiple spatial scales. Over a heterogeneous surface, however, their application is complicated by non‑stationary conditions, and by the need to choose averaging windows that balance mitigating random errors against preserving the imprint of land‑surface heterogeneity on the measured fluxes. In this study, we developed a wavelet-based adaptive method to determine averaging windows. Window boundaries along flight lines are delineated from the spectral wavelet analysis of the target covariances, so that the averaging windows explicitly follow the underlying turbulent structures. A central part of our evaluation is a systematic analysis of the relative random errors associated with these shorter, turbulence-guided windows. The results show that the adaptive windows keep relative random errors small, with median values of around 20% for latent heat flux (LE) and 15% for sensible heat flux (H), despite the substantially shorter averaging length. This suggests that they sample segments over which the turbulent statistics are more internally consistent. By reducing random error without lengthening the window, the method resolves the trade-off that forces fixed-window processing to sacrifice spatial resolution for statistical reliability, while it preserves the spatial imprint of land-surface heterogeneity on the fluxes. This is illustrated by many instances in the fragmented irrigated oasis of the study area. As an additional evaluation, we examined how the Bowen ratio derived from our flux estimates relates to footprint-weighted land-surface properties and found overall trends consistent with the expected controls by land surface properties. Overall, the proposed adaptive window approach enhances the reliability (in terms of random error) of airborne flux observations in resolving spatial patterns in surface heat fluxes, which supports a better understanding of the dominant energy exchanges between the land surface and the atmosphere.
Topographic heterogeneity strongly shapes tree growth in oceanic island forests by modulating local microclimatic conditions and buffering climatic stress. However, climate warming threatens this buffering capacity by increasing atmospheric evaporative demand and intensifying drought constraints. We combined tree-ring chronologies, high-resolution dendrometer measurements, stable carbon isotope (δ¹³C) analyses, and Vaganov–Shashkin (VS) model simulations to investigate Pinus canariensis growth dynamics across a topoclimatic gradient spanning contrasting slope aspects and elevations. North-facing stands maintained comparatively stable growth under more favourable hydrological conditions and exhibited higher drought resistance and resilience than south-facing stands. In contrast, south-facing stands showed stronger moisture limitation and long-term growth declines, greater drought sensitivity, higher interannual growth variability, increasingly negative growth trends, and sustained years-long stem water deficits lasting nearly two years. VS simulations indicated that temperature primarily controls growth onset, whereas soil moisture increasingly regulates growth duration and continuity, particularly on south-facing slopes, where water availability is lower and evaporative stress is greater. Intrinsic water-use efficiency (iWUE) increased over time across all stands, with a steeper increase on south-facing slopes, where it was also associated with declining growth. Our results demonstrate that fine-scale topoclimatic heterogeneity shapes drought limitation in P. canariensis forests, with slope aspect emerging as a major driver of differences in growth and drought resilience. By combining long-term growth records with physiological indicators and process-based simulations, this study provides a multi-proxy assessment of drought vulnerability that can help identify exposed stands for targeted monitoring and support adaptive conservation and forest management in these endemic island ecosystems.
Plants allocate photosynthetically fixed carbon (C) between metabolic needs, biomass production (BP), and non-structural carbon (NSC), a buffer between carbon supply and demand. While conceptual representation of alternative regulatory mechanisms of NSC has advanced significantly in recent decades, quantitative assessments of NSC's contribution to the ecosystem C budget have not been possible. We estimated C flux to or from the NSC pool (FNSC) as the difference between gross primary productivity (GPP), plant respiration (Rp), and BP in a shortleaf pine forest. Annual GPP remained relatively stable, but FNSC varied during the 3-year study period: 77-350 g C m⁻² yr⁻¹ in dry years (2022-2023) and 41 g C m⁻² yr⁻¹ in the wet year (2024), representing 2-18% of annual GPP. In contrast, C allocation to BP was ∼43% higher in the wet year. Flux-based FNSC (37 ± 84 g C m⁻² yr⁻¹) closely matched FNSC estimates from monthly tissue NSC content (65 ± 77 g C m⁻² yr⁻¹) during 2024 (Feb-Dec). FNSC was inversely related to BP, Biomass Production Efficiency, and plant water status. These findings are consistent with the surplus carbon hypothesis, which posits sink strength-driven C allocation, and can serve as the conceptual foundation for next-generation carbon allocation algorithms.
