Holger Lange
Research Professor
Authors
Giacomo Nicolini David Durden Luca Di Fiore Christopher Florian Simone Sabbatini Bert Gielen Arne Iserbyt Benjamin Loubet Ivan Mammarella Adriana Mariotti Maarten Op de Beeck Caleb Slemmons Carlo Trotta Adam Young Abad Chabbi Iris Feigenwinter Bernard Heinesch Natalia Kowalska Matthias Mauder Ladislav Šigut Michiel van der Molen Flavio Bastos Campos Daniel Berveiller Christian Brümmer Matthias Cuntz Jean‐Christophe Domec Benjamin Dumont Silvano Fares Damiano Gianelle Rasmus Jensen Carmen Kalalian Natascha Kljun Holger Lange Jean‐Marc Limousin Erik Lundin Antonio Manco Leonardo Montagnani Eiko Nemitz Matthias Peichl Erkka Rinne Marilyn Roland Marius Schmidt Guillaume Simioni Abin Thomas Caroline Vincke Dario PapaleAbstract
The lack of energy balance closure in Eddy‐Covariance (EC) measurements is a well‐known, still unresolved challenge in micrometeorology, with energy balance closure (EBC) rates typically ranging between 60% and 80%. While numerous hypotheses have been proposed to explain this imbalance, the relative contributions of neglected energy storage terms, data quality and flux processing options remain insufficiently disentangled. Using standardized ICOS and NEON datasets, we show that a significant portion of the observed energy imbalance can be attributed to overlooked or inconsistently handled energy components and turbulent flux quality control. Using data drawn from 84 sites, we show that comprehensive energy accounting—including soil heat flux, storage terms (soil, air, biomass), photosynthetic energy demand, and strict quality filtering of turbulent fluxes—improved EBC by 16% on average, with site‐specific gains up to 40%. However, we also identify a persistent residual imbalance that is unlikely to be resolved through methodological refinements or additional measurements alone, pointing to fundamental physical processes that are not accounted for in the standard measurement and processing. We argue that this unresolved imbalance should be explicitly acknowledged and bounded, rather than implicitly absorbed into correction schemes, and we outline practical guidance for diagnosing and interpreting EBC in standardized flux networks. This perspective evaluates methodological advances and residual uncertainties, providing an actionable framework for the appropriate use of EC energy fluxes in carbon, water, and climate research.
Abstract
Vi undersøker trends i karbonopptak av skog basert på områder utstyrt med flukstårn
Abstract
Introduction Leaf area index (LAI) estimation is sensitive to sensor field of view (FOV), within-plot spatial heterogeneity, and sampling layout. Because LAI influences canopy radiation transmission, microclimate and vegetation–atmosphere exchange, robust field estimation is important for biometeorological and ecosystem research. Methods We evaluated these effects in mature Norway spruce [ Picea abies (L.) H. Karst] stands in the Czech Republic across 15 sites at elevations of 407–1,019 m a.s.l., using a combined gap-fraction approach based on LAI-2200 PCA measurements and digital hemispherical photography. At each site, a measured 9 × 9 grid of 81 below-canopy measurement points with 2-m spacing was used as an operational within-plot benchmark for mean optically derived LAI and spatial structure. Monte Carlo subsampling was then used to compare how reduced layouts, including random, row-wise, column-wise, block-wise, and spatially balanced block–row–column layouts, reproduced the full-grid benchmark mean. Results Narrower FOVs produced higher stand-level optically derived LAI estimates and greater within-plot variability. The full grids also showed directional spatial structure, with stronger autocorrelation along the north–south column direction than along the west–east row direction, although this contrast weakened under the narrowest FOV. Reduced layouts with more even spatial coverage outperformed random sampling. The spatially balanced block–row–column layout performed most consistently, whereas the row-wise layout provided little improvement. Approximately 25–36 well-distributed below-canopy measurements were sufficient to keep reduced-layout LAI deviations within 0.15 m 2 m −2 of the full-grid benchmark, whereas 7–9 measurements were enough to remain within 5% of this benchmark in most stands. Discussion Spatially balanced sampling can therefore improve the robustness and efficiency of stand-level optically derived LAI estimation, with relevance to forest biometeorology, ecosystem monitoring, and the validation of satellite-derived LAI products.