Does increased spatial replication above heterogeneous agroforestry improve the representativeness of eddy covariance measurements?

Abstract. Spatial heterogeneity in terrestrial ecosystems compromises the accuracy of eddy covariance measurements. Examples of heterogeneous ecosystems are temperate agroforestry systems, which have been poorly studied by eddy covariance. Agroforestry systems have been getting increasing attention due to their potential environmental benefits, e.g. a higher carbon sequestration, improved microclimate and erosion reduction compared to open-cropland agricultural systems. Lower-cost eddy covariance setups might offer an opportunity to better capture spatial heterogeneity by allowing for more spatial replicates of flux towers. The aim of this study was to quantify the spatial variability of carbon dioxide (FC), latent heat (LE) and sensible heat (H) fluxes above a heterogeneous agroforestry system in northern Germany using a distributed network of three lower-cost eddy covariance setups across the agroforestry system. Fluxes from the three towers in the agroforestry were further compared to fluxes from an adjacent open-cropland site. The campaign took place from March 2023 until September 2024. The results indicated that the spatial variability of fluxes was largest for FC, attributed to the effect of different crops (rapeseed, corn and barley) within the flux footprints contributing to the measured fluxes. Differences between fluxes across towers were enhanced after harvest events. However, the temporal variability due to the seasonality and diurnal cycles during the campaign was larger than the spatial variability across the three towers. When comparing fluxes between the agroforestry and the open-cropland systems, weekly sums of carbon and evapotranspiration fluxes followed similar seasonality, with peak values of −50 g C m−2 week−1 and 40 mm week−1 during the growing season, respectively. The variation of the magnitude depended on the phenology of the different crops. The effect size, which is an indicator of the representativeness of the fluxes across the distributed network of three eddy covariance towers compared to only one, showed, in conjunction with the other results, that the spatial heterogeneity across the agroforestry was better captured by the network of three stations. This supports previous findings that spatial heterogeneity should be taken into account in eddy covariance studies and that lower-cost setups may offer the opportunity to bridge this gap and improve the accuracy of eddy covariance measurements above heterogeneous ecosystems.

Authors

Publication Details

Journal
Biogeosciences
Published
2025-09-10
DOI
https://doi.org/10.5194/bg-22-4507-2025
Citations
1
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
1.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Does increased spatial replication above heterogeneous agroforestry improve the representativeness of eddy covariance measurements?

Ivan Mammarella, Olli Peltola, Timo Vesala, Christian Markwitz et al.
1 citations
Biogeosciences
Plant Water Relations and Carbon Dynamics
1.11
article

Does increased spatial replication above heterogeneous agroforestry improve the representativeness of eddy covariance measurements?

Ivan Mammarella, Olli Peltola, Timo Vesala, Christian Markwitz, Alexander Knohl, José Ángel Callejas-Rodelas
article en
1 citations

Abstract

Abstract. Spatial heterogeneity in terrestrial ecosystems compromises the accuracy of eddy covariance measurements. Examples of heterogeneous ecosystems are temperate agroforestry systems, which have been poorly studied by eddy covariance. Agroforestry systems have been getting increasing attention due to their potential environmental benefits, e.g. a higher carbon sequestration, improved microclimate and erosion reduction compared to open-cropland agricultural systems. Lower-cost eddy covariance setups might offer an opportunity to better capture spatial heterogeneity by allowing for more spatial replicates of flux towers. The aim of this study was to quantify the spatial variability of carbon dioxide (FC), latent heat (LE) and sensible heat (H) fluxes above a heterogeneous agroforestry system in northern Germany using a distributed network of three lower-cost eddy covariance setups across the agroforestry system. Fluxes from the three towers in the agroforestry were further compared to fluxes from an adjacent open-cropland site. The campaign took place from March 2023 until September 2024. The results indicated that the spatial variability of fluxes was largest for FC, attributed to the effect of different crops (rapeseed, corn and barley) within the flux footprints contributing to the measured fluxes. Differences between fluxes across towers were enhanced after harvest events. However, the temporal variability due to the seasonality and diurnal cycles during the campaign was larger than the spatial variability across the three towers. When comparing fluxes between the agroforestry and the open-cropland systems, weekly sums of carbon and evapotranspiration fluxes followed similar seasonality, with peak values of −50 g C m−2 week−1 and 40 mm week−1 during the growing season, respectively. The variation of the magnitude depended on the phenology of the different crops. The effect size, which is an indicator of the representativeness of the fluxes across the distributed network of three eddy covariance towers compared to only one, showed, in conjunction with the other results, that the spatial heterogeneity across the agroforestry was better captured by the network of three stations. This supports previous findings that spatial heterogeneity should be taken into account in eddy covariance studies and that lower-cost setups may offer the opportunity to bridge this gap and improve the accuracy of eddy covariance measurements above heterogeneous ecosystems.

BiogeosciencesVol. 22(17)
Openalex Percentile: Top 20%
Plant Water Relations and Carbon Dynamics
1.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.