► A comprehensive quality assessment strategy for long-term eddy-covariance measurement is developed. ► Fundamentally based tests and algorithms are applied. ► This strategy comprises tests on ...high-frequency data, tests on fluxes and quantification of uncertainty. ► Its robustness and effectiveness is demonstrated for five test data sets. ► The resulting data quality assessment parameters are compared with alternative approaches.
Eddy-covariance measurements are performed at several hundred sites all over the world on a long-term basis. The gathered data are used to characterise ecosystem exchanges of trace gases, water and energy and to validate or constrain process-based models. There is an increasing demand on standardised and comprehensive quality flagging and uncertainty quantification of these fluxes. In this paper, we review established quality assessment procedures and present a comprehensive newly composed strategy emphasising tests on high-frequency raw data, expanding existing tests on statistics, fluxes and corrections, plus quantification of errors. Moreover, representativity of fluxes is checked by footprint analysis. This strategy is applied within the recently launched TERENO network of ecosystem observatories, and its robustness is demonstrated for data acquired with different measurement set-ups. Four test data sets from TERENO and one data set from CarboEurope-IP were subjected to this quality assessment. The presented strategy is compared with established quality assessment schemes, and it is demonstrated that unrealistic fluxes are now efficiently excluded while retaining the largest possible amount of high quality data. Additionally, the algorithms applied provide comprehensive, reproducible, qualitative and quantitative uncertainty estimates for users of eddy-covariance flux data.
Forests play a major role in the global carbon cycle, and droughts have been shown to explain much of the interannual variability in the terrestrial carbon sink capacity. The quantification of ...drought legacy effects on ecosystem carbon fluxes is a challenging task, and research on the ecosystem scale remains sparse. In this study we investigate the delayed response of an extreme drought event on the carbon cycle in the mixed deciduous forest site 'Hohes Holz' (DE-HoH) located in Central Germany, using the measurements taken between 2015 and 2020. Our analysis demonstrates that the extreme drought and heat event in 2018 had strong legacy effects on the carbon cycle in 2019, but not in 2020. On an annual basis, net ecosystem productivity was Formula: see text higher in 2018 (Formula: see text) and Formula: see text lower in 2019 (Formula: see text) compared to pre-drought years (Formula: see text). Using spline regression, we show that while current hydrometeorological conditions can explain forest productivity in 2020, they do not fully explain the decrease in productivity in 2019. Including long-term drought information in the statistical model reduces overestimation error of productivity in 2019 by nearly Formula: see text. We also found that short-term drought events have positive impacts on the carbon cycle at the beginning of the vegetation season, but negative impacts in later summer, while long-term drought events have generally negative impacts throughout the growing season. Overall, our findings highlight the importance of considering the diverse and complex impacts of extreme events on ecosystem fluxes, including the timing, temporal scale, and magnitude of the events, and the need to use consistent definitions of drought to clearly convey immediate and delayed responses.
We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of ...both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an ‘extra’ day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.
Forests play an important role in climate regulation due to carbon sequestration. However, a deeper understanding of forest carbon flux dynamics is often missing due to a lack of information about ...forest structure and species composition, especially for non-even-aged and species-mixed forests. In this study, we integrated field inventory data of a species-mixed deciduous forest in Germany into an individual-based forest model to investigate daily carbon fluxes and to examine the role of tree size and species composition for stand productivity. This approach enables to reproduce daily carbon fluxes derived from eddy covariance measurements (R2 of 0.82 for gross primary productivity and 0.77 for ecosystem respiration). While medium-sized trees (stem diameter 30–60 cm) account for the largest share (66%) of total productivity at the study site, small (0–30 cm) and large trees (>60 cm) contribute less with 8.3% and 25.5% respectively. Simulation experiments indicate that vertical stand structure and shading influence forest productivity more than species composition. Hence, it is important to incorporate small-scale information about forest stand structure into modelling studies to decrease uncertainties of carbon dynamic predictions.
Trees concentrate rainfall to near-stem soils via stemflow. When canopy structures are organized appropriately, stemflow can even induce preferential flow through soils, transporting nutrients to ...biogeochemically active areas. Bark structure significantly affects stemflow, yet bark-stemflow studies are primarily qualitative. We used a LaserBark to compute bark microrelief (MR), ridge-to-furrow amplitude (R) and slope (S) metrics per American Society of Mechanical Engineering standards (ASME-B46.1-2009) for two morphologically contrasting species (Fagus sylvatica L. (European beech), Quercus robur L. (pendunculate oak)) under storm conditions with strong bark water storage capacity (BWSC) influence in central Germany. Smaller R and S for F. sylvatica significantly lowered BWSC, which strongly and inversely correlated to maximum funnelling ratios and permitted stemflow generation at lower rain magnitudes. Larger R and S values in Q. robur reduced funnelling, diminishing stemflow drainage for larger storms. Quercus robur funnelling and stemflow was more reliant on intermediate rain intensities and intermittency to maintain bark channel-dependent drainage pathways. Shelter provided by Q. robur's ridged bark also appears to protect entrained water, lengthening mean intrastorm dry periods necessary to affect stemflow. Storm conditions where BWSC plays a major role in stemflow accounted for much of 2013's rainfall at the nearest meteorological station (Wulferstedt).
Editor M.C. Acreman; Associate editor not assigned
Ectomycorrhizal (EcM) and saprotrophic fungi interact in the breakdown of organic matter, but the mechanisms underlying the EcM role on organic matter decomposition are not totally clear. We ...hypothesized that the ecological relations between EcM and saprotroph fungi are modulated by resources availability and accessibility, determining decomposition rates. We manipulated the amount of leaf litter inputs (No-Litter, Control Litter, Doubled Litter) on Trenched (root exclusion) and Non-Trenched plots (with roots) in a temperate deciduous forest of EcM-associated trees. Resultant shifts in soil fungal communities were determined by phospholipid fatty acids and DNA sequencing after 3 years, and CO
2
fluxes were measured throughout this period. Different levels of leaf litter inputs generated a gradient of organic substrate availability and accessibility, altering the composition and ecological relations between EcM and saprotroph fungal communities. EcM fungi dominated at low levels of fresh organic substrates and lower organic matter quality, where short-distances exploration types seem to be better competitors, whereas saprotrophs and longer exploration types of EcM fungi tended to dominate at high levels of leaf litter inputs, where labile organic substrates were easily accessible. We were, however, not able to detect unequivocal signs of competition between these fungal groups for common resources. These results point to the relevance of substrate quality and availability as key factors determining the role of EcM and saprotroph fungi on litter and soil organic matter decay and represent a path forward on the capacity of organic matter decomposition of different exploration types of EcM fungi.
The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an ...unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.
Ambient atmospheric concentrations of isoprene and monoterpenes were measured at two forest sites, one deciduous and one coniferous, over the year 2022. Both sites in a regional area were sampled ...monthly between April and September. The samples were taken using sorbent tubes and analyzed with thermal desorption–gas chromatography–mass spectrometry. The highest concentrations were determined in August at both sites. While isoprene is the most abundant compound at the deciduous forest with an average concentration of 5.59 µg m−3 in August, α-pinene and β-pinene dominate throughout the year at the coniferous forest with the highest concentrations also in August (3.44 µg m−3 and 1.51 µg m−3). Because other monoterpenes (camphene, sabinene, 3-carene, p-cymene and limonene) are also emitted in significant amounts, the total concentration measured in the coniferous forest is higher (7.96 µg m−3 in August) in comparison to the deciduous forest (6.08 µg m−3). Regarding the detected monoterpenes in the deciduous forest, sabinene is the dominant monoterpene in addition to α-pinene and is sometimes present in higher (July) or equal (August) concentrations. The seasonal and diurnal concentrations of all monoterpenes correlate very well with each other at both sites. An exception is sabinene with a diurnal concentration profile similar to isoprene.
Soil respiration is a combination of CO
2 fluxes derived from a diversity of belowground sources, many depending directly on the input of carbon from living plants. Here we present data from two ...different forest ecosystems, a beech and a spruce forest, where a partitioning of soil respiration was carried out. We used soil cores inside micro-pore meshes together with periodic chamber-based measurements to estimate rhizosphere, mycorrhizal fungal and microbial heterotrophic respiration. Calculated mycorrhizal mycelium respiration was 8% at the spruce forest and 3% at the beech forest. Given the nature of the partitioning method these values represent minimum estimates. The ratio of root-derived carbon respiration to heterotrophic respiration was about 1:1 at both forest types. The relationship of each source with temperature and photosynthesis, measured as gross primary productivity derived from eddy covariance measurements, was subsequently explored. Both factors revealed effects specific to the respiration source and the forest type. A response to temperature was evident in all cases except for mycorrhizal mycelium respiration at the spruce forest (
R
2
=
0.06,
p
=
0.41). Significant correlations of photosynthesis with rhizosphere and mycorrhizal fungal respiration were found in all cases. Peaks in correlation values showed time lags between photosynthetic activity and a respiration response ranging from 1 day for the fungal component and 4 days for the rhizosphere component at the beech forest (
R
2
=
0.70,
p
<
0.01 and
R
2
=
0.42,
p
<
0.05, respectively) to 5 days for both fluxes at the spruce forest (
R
2
=
0.44,
p
<
0.01 and
R
2
=
0.72,
p
<
0.01, respectively). Results show that respiration of the mycorrhizal component cannot be predicted by common temperature driven models in some ecosystems. They also indicate a strong influence of forest canopy processes on the activity of roots and associated organisms. The specific response in each vegetation type should be ideally explained by physiological mechanisms inherent to different species as a next step towards understanding belowground carbon dynamics.
The eddy covariance (EC) method is a standard micrometeorological technique for monitoring the exchange rate of the main greenhouse gases across the interface between the atmosphere and ecosystems. ...One of the first EC data processing steps is the temporal alignment of the raw, high frequency measurements collected by the sonic anemometer and gas analyser. While different methods have been proposed and are currently applied, the application of the EC method to trace gases measurements highlighted the difficulty of a correct time lag detection when the fluxes are small in magnitude. Failure to correctly synchronise the time series entails a systematic error on covariance estimates and can introduce large uncertainties and biases in the calculated fluxes. This work aims at overcoming these issues by introducing a new time lag detection procedure based on the assessment of the cross-correlation function (CCF) between variables subject to (i) a pre-whitening based on autoregressive filters and (ii) a resampling technique based on block-bootstrapping. Combining pre-whitening and block-bootstrapping facilitates the assessment of the CCF, enhancing the accuracy of time lag detection between variables with correlation of low order of magnitude (i.e. lower than
-
1
) and allowing for a proper estimate of the associated uncertainty. We expect the proposed procedure to significantly improve the temporal alignment of the EC time-series measured by two physically separate sensors, and to be particularly beneficial in centralised data processing pipelines of research infrastructures (e.g. the Integrated Carbon Observation System, ICOS-RI) where the use of robust and fully data-driven methods, like the one we propose, constitutes an essential prerequisite.
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