Eddy Covariance Flux Research Articles

Rising Water Levels and Vegetation Shifts Drive Substantial Reductions in Methane Emissions and Carbon Dioxide Uptake in a Great Lakes Coastal Freshwater Wetland.

Coastal freshwater wetlands are critical ecosystems for both local and global carbon cycles, sequestering substantial carbon while also emitting methane (CH4) due to anoxic conditions. Estuarine freshwater wetlands face unique challenges from fluctuating water levels, which influence water quality, vegetation, and carbon cycling. However, the response of CH4 fluxes and their drivers to altered hydrology and vegetation remains unclear, hindering mechanistic modeling. To address these knowledge gaps, we studied an estuarine freshwater wetland in the Great Lakes region, where rising water levels led to a vegetation shift from emergent Typha dominance in 2015-2016 to floating-leaved species in 2020-2022. Using eddy covariance flux measurements during the peak growing season (June-September) of both periods, we observed a 60% decrease in CH4 emissions, from 81 ± 4 g C m-2 in 2015-2016 to 31 ± 3 g C m-2 in 2020-2022. This decline was driven by two main factors: (1) higher water levels, which suppressed ebullitive fluxes via increased hydrostatic pressure and extended CH4 residence time, enhancing oxidation potential in the water column; and (2) reduced CH4 conductance through plants. Net carbon dioxide (CO2) uptake decreased by 90%, from -267 ± 26 g C m-2 in 2015-2016 to -27 ± 49 g C m-2 in 2020-2022. Additionally, diel CH4 flux patterns shifted, with a distinct morning peak observed in 2015-2016 but absent in 2020-2022, suggesting changes in plant-mediated transport and a potential decoupling from photosynthesis. The dominant factors influencing CH4 fluxes shifted from water temperature and gross primary productivity in 2015-2016 to atmospheric pressure in 2020-2022, suggesting an increased role of ebullition as a primary transport pathway. Our results demonstrate that changes in water levels and vegetation can substantially alter CH4 and CO2 fluxes in coastal freshwater wetlands, underscoring the critical role of hydrological shifts in driving carbon dynamics in these ecosystems.

Potential for Augmenting Water Yield by Restoring Longleaf Pine (Pinus palustris) Forests in the Southeastern United States

AbstractOver 95% of original longleaf pine (Pinus palustris) (LLP) forests have been converted to other land uses, including loblolly pine (Pinus taeda L) (LOP), croplands, urban uses during the past two centuries in the southeastern United States (U.S.) for socioeconomic developments. Restoring the LLP forests represents a contemporary forest management objective to improve wildlife habitat, water yield, and overall ecosystem services and resilience to a changing climate. Given the importance of understanding ecohydrological processes for guiding restoration efforts, this study compared evapotranspiration (ET) measurements at eight eddy covariance flux sites dominated by LLP or LOP forests in the southeastern U.S. In addition, we developed a “paired stands” approach to compare remote sensing based ET estimates and associated site biophysical properties for approximately 1,600 LLP‐LOP pairs. We found significant differences in ET, ET/Precipitation ratio, and water yield/precipitation ratio between the two types of pine forests, and these differences are explained by surface properties and management histories. Compared to LOP, the LLP forests generally had lower ET due to their significantly (p < 0.05) lower leaf area index but higher land surface temperature and albedo. Regionally, forest ET differences increased with the increase in atmospheric dryness index (reference ET/precipitation ratio). Therefore, we conclude that large‐scale restoration of LLP forests has the potential to reduce ET and augment water yield in the long run, especially in relatively drier watersheds. Maintaining low stand tree density and understory leaf area characteristic of natural LLP ecosystems through active forest management is critical for enhancing forest water supply. Our study provides the scientific basis for large scale restoration of a diminishing ecosystem for benefiting water resources in the southeastern U.S.

Towards Mobile Wind Measurements Using Joust Configured Ultrasonic Anemometer for Applications in Gas Flux Quantification

Small uncrewed aerial systems (sUASs) can be used to quantify emissions of greenhouse and other gases, providing flexibility in quantifying these emissions from a multitude of sources, including oil and gas infrastructure, volcano plumes, wildfire emissions, and natural sources. However, sUAS-based emission estimates are sensitive to the accuracy of wind speed and direction measurements. In this study, we examined how filtering and correcting sUAS-based wind measurements affects data accuracy by comparing data from a miniature ultrasonic anemometer mounted on a sUAS in a joust configuration to highly accurate wind data taken from a nearby eddy covariance flux tower (aka the Tower). These corrections had a small effect on wind speed error, but reduced wind direction errors from 50° to >120° to 20–30°. A concurrent experiment examining the amount of error due to the sUAS and the Tower not being co-located showed that the impact of this separation was 0.16–0.21 ms−1, a small influence on wind speed errors. Lower wind speed errors were correlated with lower turbulence intensity and higher relative wind speeds. There were also some loose trends in diminished wind direction errors at higher relative wind speeds. Therefore, to improve the quality of sUAS-based wind measurements, our study suggested that flight planning consider optimizing conditions that can lower turbulence intensity and maximize relative wind speeds as well as include post-flight corrections.

Proximal remote sensing: an essential tool for bridging the gap between high-resolution ecosystem monitoring and global ecology.

A new proliferation of optical instruments that can be attached to towers over or within ecosystems, or 'proximal' remote sensing, enables a comprehensive characterization of terrestrial ecosystem structure, function, and fluxes of energy, water, and carbon. Proximal remote sensing can bridge the gap between individual plants, site-level eddy-covariance fluxes, and airborne and spaceborne remote sensing by providing continuous data at a high-spatiotemporal resolution. Here, we review recent advances in proximal remote sensing for improving our mechanistic understanding of plant and ecosystem processes, model development, and validation of current and upcoming satellite missions. We provide current best practices for data availability and metadata for proximal remote sensing: spectral reflectance, solar-induced fluorescence, thermal infrared radiation, microwave backscatter, and LiDAR. Our paper outlines the steps necessary for making these data streams more widespread, accessible, interoperable, and information-rich, enablingus to address key ecological questions unanswerable from space-based observations alone and, ultimately, to demonstrate the feasibility of these technologies to address critical questions in local and global ecology.

Enhancing evapotranspiration estimates in composite terrain through the integration of satellite remote sensing and eddy covariance measurements.

Accurate evaluation of water resource systems is essential for informed planning and decision-making. Evapotranspiration (ET), a key component of water resource management, is often estimated using remote sensing techniques; however, such estimates can be subject to significant uncertainties under certain conditions. In this study, we present a novel approach to improving the accuracy of ET estimates in composite terrains. The methodology involves optimizing the Surface Energy Balance Algorithm for Land (SEBAL-OPT) by integrating ground-based eddy covariance (EC) flux tower data into the satellite-based ET retrieval process. The approach was evaluated at four sites in California, each representing different land uses. Parameter optimization was achieved through Bayesian inference using the Differential Evolution Adaptive Metropolis (DREAM) algorithm, which minimized discrepancies between ET estimates derived from Landsat 8 and 9 imagery and the observed ET from EC measurements. Results from the global sensitivity analysis identified solar radiation and hot/cold pixel selection as the most sensitive parameters in the SEBAL algorithm, highlighting their critical role in reducing uncertainty in ET estimates. SEBAL-OPT demonstrated significantly improved accuracy, with root mean square error (RMSE) values ranging from 0.72mm to 1.33mm, compared to the original SEBAL parameterization (SEBAL-ORG), which produced RMSE values between 1.03mm and 2.14mm. This approach highlights that, when properly calibrated, the model can be effectively applied across diverse agricultural landscapes, regardless of the specific land use at individual sites. These findings have significant implications for water resource planning, agricultural water management, and water rights adjudication and could be applied to other remote sensing of ET models.

Air temperature and precipitation constraining the modelled wetland methane emissions in a boreal region in northern Europe

Abstract. Wetland methane responses to temperature and precipitation are studied in a boreal wetland-rich region in northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5, and CLM5) are compared to multi-model means of ecosystem models and atmospheric inversions from the Global Carbon Project and upscaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, are used as priors in atmospheric inversions with Carbon Tracker Europe–CH4 (CTE-CH4) in order to find out how the assimilation of atmospheric concentration data changes the flux estimates and how this alters the interpretation of the flux responses to temperature and precipitation. Inversion moves wetland emissions of both models towards co-limitation by temperature and precipitation. Between 2000 and 2018, periods of high temperature and/or high precipitation often resulted in increased emissions. However, the dry summer of 2018 did not result in increased emissions despite the high temperatures. The process models show strong temperature and strong precipitation responses for the region (51 %–91 % of the variance explained by both). The month with the highest emissions varies from May to September among the models. However, multi-model means, inversions, and upscaled eddy covariance flux observations agree on the month of maximum emissions and are co-limited by temperature and precipitation. The setup of different emission components (peatland emissions, mineral land fluxes) has an important role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the regional representation of wet and dry mineral soils and periodic flooding which contribute to the seasonality and magnitude of methane fluxes. The realistic representation of temperature dependence of the peat soil fluxes is also important. Furthermore, it is important to use process-based descriptions for both mineral and peat soil fluxes to simulate the flux responses to climate drivers.

A flux tower site attribute dataset intended for land surface modeling

Abstract. Land surface models (LSMs) require reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are widely regarded as the benchmark for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets frequently lack site-observed attribute data, such as fractional vegetation cover and leaf area index, which limits their utility as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, energy balance closure (EBC), and external disturbances such as irrigation and deforestation, leading to 90 high-quality sites. For these sites, we collected vegetation, soil, and topography data as well as wind speed, temperature, and humidity measurement heights from literature; regional networks; and Biological, Ancillary, Disturbance, and Metadata (BADM) files. We then compiled the final flux tower attribute dataset by filling in missing attributes with global data and classifying plant functional types (PFTs). This dataset is provided in NetCDF (Network Common Data Form) format with necessary descriptions and reference sources. Model simulations revealed substantial disparities in the output between the attribute data observed at the site and those commonly used by LSMs, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of the site attribute to some extent, reduces uncertainty in LSM data source, and aids in diagnosing parameter and process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.12596218 (Shi et al., 2024).

Differences in the Sensitivity of Gross Primary Productivity and Ecosystem Respiration to Precipitation

The spatiotemporal variability of precipitation profoundly influences terrestrial carbon fluxes, driving shifts between carbon source and sink dynamics through gross primary productivity (GPP) and ecosystem respiration (ER). As a result, the sensitivities of GPP and ER to precipitation (SGPP and SER), along with their differential responses, are pivotal for understanding ecosystem reactions to precipitation changes and predicting future ecosystem functions. However, comprehensive evaluations of the spatiotemporal variability and differences in SGPP and SER remain notably scarce. In this study, we utilized eddy covariance flux data to investigate the spatial patterns, temporal dynamics, and differences in SGPP and SER. Spatially, SGPP and SER were generally strongly correlated. Among different ecosystems, the correlation between SGPP and SER was lowest in mixed forest and highest in broadleaf and needleleaf forest. Within the same ecosystem, SGPP and SER exhibited considerable variation but showed no significant differences. In contrast, they differed significantly across ecosystems, with pronounced variability in their magnitudes. For example, shrubland exhibited the highest values for SGPP, whereas needleleaf forest showed the highest values for SER. Temporally, SER demonstrated more pronounced changes than SGPP. Different ecosystems displayed distinct trends: shrubland exhibited an upward trend for both metrics, while grassland showed a downward trend in both SGPP and SER. Forest, on the other hand, maintained stable SGPP but displayed a downward trend in SER. Additionally, SGPP and SER exhibited a notable non-linear response to changes in the aridity index (AI), with both showing a rapid decline followed by stabilization. However, SER demonstrated a wider adaptive range to precipitation changes. Generally, this research enhances our understanding of the spatiotemporal variations in ecosystem carbon fluxes under changing precipitation patterns.

An eco-physiological model of forest photosynthesis and transpiration under combined nitrogen and water limitation.

Although the separate effects of water and nitrogen (N) limitations on forest growth are well known, the question of how to predict their combined effects remains a challenge for modeling of climate change impacts on forests. Here, we address this challenge by developing a new eco-physiological model that accounts for plasticity in stomatal conductance and leaf N concentration. Based on optimality principle, our model determines stomatal conductance and leaf N concentration by balancing carbon uptake maximization, hydraulic risk and cost of maintaining photosynthetic capacity. We demonstrate the accuracy of the model predictions by comparing them against gross primary production estimates from eddy covariance flux measurements and sap-flow measurement scaled canopy transpiration in a long-term fertilized and an unfertilized Scots pine (Pinus sylvestris L.) forest in northern Sweden. The model also explains the response to N fertilization as a consequence of (i) reduced carbon cost of N uptake and (ii) increased leaf area per hydraulic conductance. The results suggest that leaves optimally coordinate N concentration and stomatal conductance both on short (weekly) time scales in response to weather conditions and on longer time scales in response to soil water and N availabilities.

Impact of Atmospheric Stability on Swell‐Induced Perturbations in Wave Boundary Layer

AbstractWind stress deviates both in magnitude and direction under light wind conditions due to the modulation effect of swell waves. Eddy covariance flux measurements were conducted from an offshore wind tower in the northern South China Sea to investigate the influence of swell‐induced perturbations on the evolution of wind velocity fluctuations and wind stress reductions. Direct evidence of the wind field being affected by dominant swell waves was observed at an observational height of 20 m, indicating that swell‐induced perturbations can penetrate the wave boundary layer to at least this height. Time series analysis implies that a stable atmosphere helps preserve the observed prominent peak in wind velocity spectra, extending it to a higher range under large wave age. Therefore, swell‐induced perturbations are more easily observed at night. Additionally, our results show that swell wave modulation on wind stress is not merely restricted to moments with pronounced peaks in velocity spectra, indicating that wind stress reduction is not an exception. The most significant reduction occurs at wind speeds around 2–4 m/s, leading to negative wind stress, which implies momentum transfer from the ocean to the atmosphere. Our conclusion illustrates that the deviation of wind stress magnitude increases under stable atmospheric conditions; however, with a simple correction model, the modified wind stress is comparable to the observed values.

Comparing the performance of vegetation indices for improving urban vegetation GPP estimation via eddy covariance flux data and Landsat 5/7 data

Comparing the performance of vegetation indices for improving urban vegetation GPP estimation via eddy covariance flux data and Landsat 5/7 data

Random Forest Development and Modeling of Gross Primary Productivity in the Hudson Bay Lowlands

Peatlands are a critical component of the global carbon cycle. Within Canada, the Hudson Bay Lowlands (HBL) has accumulated an estimated 33 Gt of carbon as peat because of a small but persistent difference between gross primary productivity (GPP) and ecosystem respiration over millennia. However, the impacts of disturbance and climate change on GPP are difficult to monitor across the HBL due to its vast size and remote location. This study evaluates the potential for random forest regression models to estimate GPP at five HBL eddy covariance flux monitoring sites using only optical data from MODIS (500 m, 8 day) or harmonized Landsat/Sentinel (HLS; 30 m, 16 day or more frequent). The results show that spatial resolution has less impact on modeled daily GPP compared to temporal resolution across model configurations. Using MODIS data, individual sites’ daily GPP could be simulated with minimal bias, R2 up to 0.89 and mean absolute error as low as 0.37 g C m−2 day−1. For annual GPP, MODIS (R2 = 0.84; mean absolute error 40.5 g C m−2 year−1) also outperformed the HLS models (R2 = 0.46; mean absolute error 86.4 g C m−2 year−1).

Sea Ice Modulates Air–Sea Methane Flux in the Southern Ocean

AbstractThe Southern Ocean (SO) is predicted to be a weak sink for atmospheric CH4, although the magnitude is uncertain due to a lack of observations of the marginal ice zone (MIZ). Using both eddy covariance and bulk formula flux measurements from the icebreaker R/V Xuelong2, we found that the eastern SO during an austral summer was a sink for CH4. The strongest downward CH4 fluxes occurred in areas of low sea ice concentration (10%–40%), where sea‐ice melting resulted in low temperature and salinity, increasing CH4 solubility. The CH4 fluxes are weak in regions of high sea ice concentration (>50%) due to the blocking effect of sea ice. We estimate that the uptake of CH4 during one summer month in the study region offsets 1.2%–2.6% of annual global oceanic CH4 emissions. Suggesting that the Antarctic MIZ is more important in the global CH4 budget than previously thought.

When is a trend meaningful? Insights to carbon cycle variability from an initial-condition large ensemble

Internal climate variability (ICV) creates a range of climate trajectories, which are superimposed upon the forced response. A single climate model realization may not represent forced change alone and may diverge from other realizations, as well as observations, due to ICV. We use an initial-condition large ensemble of simulations with the Community Earth System Model (CESM2) to show that ICV produces a range of outcomes in the terrestrial carbon cycle. Trends in gross primary production (GPP) from 1991 to 2020 differ among ensemble members due to the different climate trajectories resulting from ICV. We quantify how ICV imparts on GPP trends and apply our methodology to the observational record. Observed changes in GPP at two long-running eddy covariance flux towers are consistent with ICV, challenging the understanding of forced changes in the carbon cycle at these locations. A probabilistic framework that accounts for ICV is needed to interpret carbon cycle trends.

Evaluating the Two-Source Energy Balance Model Using MODIS Data for Estimating Evapotranspiration Time Series on a Regional Scale

Estimating daily continuous evapotranspiration (ET) can significantly enhance the monitoring of crop stress and drought on regional scales, as well as benefit the design of agricultural drought early warning systems. However, there is a need to verify the models’ performance in estimating the spatiotemporal continuity of long-term daily evapotranspiration (ETd) on regional scales due to uncertainties in satellite measurements. In this study, a thermal-based two-surface energy balance (TSEB) model was used concurrently with Terra/Aqua MODIS data and the ERA5 atmospheric reanalysis dataset to calculate the surface energy balance of the soil–canopy–atmosphere continuum and estimate ET at a 1 km spatial resolution from 2000 to 2022. The performance of the model was evaluated using 11 eddy covariance flux towers in various land cover types (i.e., savannas, woody savannas, croplands, evergreen broadleaf forests, and open shrublands), correcting for the energy balance closure (EBC). The Bowen ratio (BR) and residual (RES) methods were used for enforcing the EBC in the EC observations. The modeled ET was evaluated against unclosed ET and closed ET (ETBR and ETRES) under clear-sky and all-sky observations as well as gap-filled data. The results showed that the modeled ET presented a better agreement with closed ET compared to unclosed ET in both Terra and Aqua datasets. Additionally, although the model overestimated ETd across all different land cover types, it successfully captured the spatiotemporal variability in ET. After the gap-filling, the total number of days compared with flux measurements increased substantially, from 13,761 to 19,265 for Terra and from 13,329 to 19,265 for Aqua. The overall mean results including clear-sky and all-sky observations as well as gap-filled data with the Aqua dataset showed the lowest errors with ETRES, by a mean bias error (MBE) of 0.96 mm.day−1, an average mean root square (RMSE) of 1.47 mm.day−1, and a correlation (r) value of 0.51. The equivalent figures for Terra were about 1.06 mm.day−1, 1.60 mm.day−1, and 0.52. Additionally, the result from the gap-filling model indicated small changes compared with the all-sky observations, which demonstrated that the modeling framework remained robust, even with the expanded days. Hence, the presented modeling framework can serve as a pathway for estimating daily remote sensing-based ET on regional scales. Furthermore, in terms of temporal trends, the intra-annual and inter-annual variability in ET can be used as indicators for monitoring crop stress and drought.

Micrometeorological drivers affecting the variability of CO2 uptake in the Himalayan Oak and Pine dominated ecosystems: An assessment of causal relationships

Micrometeorological variability significantly impacts the structures, functions, and dynamics of ecosystems. However, the assessment of feedback and causal relationships among microclimatic drivers and various ecosystems in the Himalayan region is rarely evaluated. Here, we studied the micrometeorological drivers controlling the variability in the net ecosystem exchange (NEE) of Himalayan Oak (Banj-Oak/Quercus leucotrichophora) and Pine (Chir-Pine/Pinus roxburghii) dominated ecosystems, as NEE is an indicator of ecosystem functioning. We used half-hourly eddy covariance flux data of CO2 fluxes from two sites established over Pine and Oak dominated ecosystems in Uttarakhand, India. We conducted the analysis with the information theory-based Temporal Information Partitioning Networks (TIPNets) approach to generate weekly process networks. TIPNets represent directed lag-structured causal graphs to identify the causal relationships and capture the temporal association among the variables. Our analysis aimed to capture fluctuations in variables with up to 6 h of memory. Based on the data availability, we generated the weekly networks at both the sites for the monsoon and post-monsoon seasons of 2016 and 2017. In both ecosystems, the sub-daily scale variations among the micrometeorological variables are responsible for the fluctuations in NEE. The Pine ecosystem is found to be more sensitive to changes in air temperature (TA) and uptakes more CO2 as compared to the Oak ecosystem throughout the study period. The transfer entropy links show that the NEE of the Oak ecosystem is moisture-driven (precipitation and relative humidity), while the Pine ecosystem is heat-driven (TA and net solar radiation) in both seasons. The influence of precipitation is not observed within a short memory of 6 h in the Pine ecosystem. This is because lesser fine roots take time to show the precipitation signature on NEE through infiltration, soil moisture, and root water uptake, compared to Oak. However, the impacts of moisture stress are evident in the network structure of both ecosystems, with more causal links observed in the network during dry periods compared to wet periods.

A Bowen ratio-informed method for coordinating the estimates of air-sea turbulent heat fluxes

Abstract Accurate quantification of turbulent heat fluxes [THF, comprising sensible heat flux (SHF) and latent heat flux (LHF)] and Bowen ratio (β, ratio of SHF and LHF) is essential for understanding the air-sea interaction. However, the biased estimates of SHF and LHF by widely applied bulk aerodynamic models, and the separate estimates of SHF and LHF by data-driven models, both may result in unrealistic β estimates. This study for the first time innovatively proposes a Bowen ratio-informed data-driven model (BrTHF) for coordinating the estimations of THF and β using the multivariate random forest technique and a combination of eddy covariance flux observations and meteorological and oceanic observations collected from 53 historical cruises. The result shows that the BrTHF model could not only achieve high-accuracy SHF and LHF estimates, but also avoid the outliers of β estimates that were commonly found in un-synergistic random forest and well-known COARE3.5 models.

Quality control of eddy covariance fluxes of two ecosystem types with local flux-variance similarity functions in West Africa

Ecosystem capacity studies for carbon sequestration and water extraction are largely conditioned by Eddy Covariance (EC) measurement availability and quality. Our study aims at evaluating several procedures of EC data qualification. Indeed, quality control of EC data is generally ensured by stationarity and well-developed turbulence tests. The latter is based on integral turbulence characteristic (ITC) models. In this study, we revisited the quality control procedure using locally established ITC models for wind speed components and atmospheric scalars (temperature, humidity and carbon dioxide molar densities) in unstable and stable conditions. We also considered the interdependence of flux qualities because of the corrections applied to fluxes during their preprocessing. The analyses were carried out using one year of data collected above a woodland forest and mixed crop sites, both situated in Northern Benin, West Africa. From these analyzes we note that the quality flags vary depending on the use of local or existing ITC models. Also, the test of turbulence development on atmospheric scalars, previously neglected, is of great importance in the quality control of data. Thus, taking into account the ITC models associated with the atmospheric scalars established locally in the well-developed turbulence test as well as taking into account the interdependence of the flux quality have considerably improved the standard quality control procedure used in effectively filtering unrealistic flux data while keeping a considerable percentage of good and medium quality data.

Satellite remote sensing reveals the footprint of biodiversity on multiple ecosystem functions across the NEON eddy covariance network

Abstract Biodiversity relates to ecosystem functioning by modulating biogeochemical cycles of carbon, water, energy, and nutrients within and between multiple biotic and abiotic components of the ecosystems. However, large-scale, systematic measurements of plant biodiversity are still lacking, and the effects of biodiversity on measured biogeochemical processes are understudied. Here, we combined alpha (α) and beta (β) taxonomic measurements, spectral diversity from satellite observations, structural properties of the vegetation, and climatic drivers to assess the effect of biodiversity on ecosystem functional properties. Ecosystem functional properties were computed from eddy-covariance fluxes at 44 sites of the National Ecological Observatory Network. Based on the spectral variation hypothesis, we used the near-infrared reflectance of vegetation (NIRv) derived from Sentinel-2 satellite imagery to compute Rao’s quadratic entropy (Rao Q), a distance metric related to spatial heterogeneity. Using an automatic model averaging technique, we found that biodiversity proxies hold substantial explanatory power when predicting several ecosystem functions related to carbon and water exchange. In particular, NIRv-based Rao Q (RaoQNIRv) reflected positive biodiversity effects on productivity, as expected from the literature. In contrast, traditional taxonomic α-diversity indices were generally not selected as relevant predictors of the ecosystem functional properties. Yet, β-diversity strongly contributed to the prediction of carbon use efficiency, surface conductance, and water use efficiency. We also found that the RaoQNIRv is less affected by issues of saturation and bare soil contribution compared to RaoQNDVI. We show that spectral heterogeneity based on remotely sensed NIRv holds the potential for globally characterizing the biodiversity-ecosystem functioning relationship (BEF). While systematic measurements of taxonomic diversity co-located at biogeochemical measurement stations could reduce the uncertainty surrounding the BEF relationship at whole-ecosystem scale, remotely- sensed metrics characterizing important functional and structural diversity aspects of the landscape will be crucial for continuous spatiotemporal monitoring of biodiversity with relevant implications for ecosystem services to humankind.

Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry

Abstract. Eddy covariance (EC) measurements can provide direct and non-invasive ecosystem measurements of the exchange of energy, water (H2O) and carbon dioxide (CO2). However, conventional eddy covariance (CON-EC) setups (ultrasonic anemometer and infrared gas analyser) can be expensive, which recently led to the development of lower-cost eddy covariance (LC-EC) setups (University of Exeter). In the current study, we tested the performance of an LC-EC setup for CO2 and H2O flux measurements at an agroforestry and adjacent grassland site in a temperate ecosystem in northern Germany. The closed-path LC-EC setup was compared with a CON-EC setup using an enclosed-path gas analyser (LI-7200, LI-COR Inc., Lincoln, NE, USA). The LC-EC CO2 fluxes were lower compared to CON-EC by 4 %–7 % (R2=0.91–0.95), and the latent heat (LE) fluxes were higher by 1 %–5 % in 2020 and 23 % in 2021 (R2=0.84–0.91). The large difference between latent heat fluxes in 2021 seemed to be a consequence of the lower LE fluxes measured by the CON-EC. Due to the slower response sensors of the LC-EC setup, the (co)spectra of the LC-EC were more attenuated in the high-frequency range compared to the CON-EC. The stronger attenuation of the LC-EC led to larger cumulative differences between spectral methods of 0.15 %–38.8 % compared to 0.02 %–11.36 % of the CON-EC. At the agroforestry site where the flux tower was taller compared to the grassland, the attenuation was lower because the cospectrum peak and energy-containing eddies shift to lower frequencies which the LC-EC can measure. It was shown with the LC-EC and CON-EC systems that the agroforestry site had a 105.6 g C m−2 higher carbon uptake compared to the grassland site and 3.1–14.4 mm higher evapotranspiration when simultaneously measured for 1 month. Our results show that LC-EC has the potential to measure EC fluxes at a grassland and agroforestry system at approximately 25 % of the cost of a CON-EC system.