Eddy Covariance Flux Measurements Research Articles

Evapotranspiration uncertainty at micrometeorological scales: the impact of the eddy covariance energy imbalance and correction methods

Under ideal conditions, evapotranspiration (ET) fluxes derived through the eddy covariance (EC) technique are considered a direct measure of actual ET. Eddy covariance flux measurements provide estimates at a temporal frequency that allows examining sub-daily, daily, and seasonal scale processes and relationships between different surface fluxes. The Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data to ground-truth new remote sensing tools for fine-tuning vineyard irrigation management across numerous sites since 2013. This rich dataset allows us to quantify the impact of different approaches to estimate daily ET fluxes, while accounting for energy imbalance. This imbalance results from the lack of agreement between the total available energy and turbulent fluxes derived by the EC technique. We found that different approaches to deal with this energy imbalance can lead to uncertainty in daily ET estimates of up to 50%. Over the growing season, this uncertainty can lead to considerable biases in crop water use estimates, which in some cases were equivalent to ~ 1/3rd of the total growing season applied irrigation We analyzed ET uncertainty relative to atmospheric meteorological, stability, and advective conditions, and highlight the importance of recognizing limitations of micrometeorological observational techniques, considered state of the art, to quantify ET for model validation and field-scale monitoring. This study provides a framework to quantify daily ET estimates’ uncertainty and expected reliability when using the eddy covariance technique for ground-truthing or model validation purposes.

Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data

Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration (ET) measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this study, an innovative deep learning (DL) gap-filling method was tested on a database comprising six datasets from different crops (cotton, tomato, and wheat). For various gap scenarios, the performance of the method was compared with the common gap-filling technique, marginal distribution sampling (MDS), which is based on lookup tables. Furthermore, a predictor importance analysis was performed to evaluate the importance of the different meteorological inputs in estimating ET. On the half-hourly time scale, the deep learning method showed a significant 13.5% decrease in nRMSE (normalized root mean square error) throughout all datasets and gap durations. A substantially smaller standard deviation of mean nRMSE, compared with marginal distribution sampling, was also observed. On the whole-gap time scale (half a day to six days), average nMBE (normalized mean bias error) was similar to that of MDS, whereas standard deviation was improved. Using only air temperature and relative humidity as input variables provided an RMSE that was significantly smaller than that resulting from the MDS method. These results suggest that the deep learning method developed here is reliable and more consistent than the standard gap-filling method and thereby demonstrates the potential of advanced deep learning techniques for improving dynamic time series modeling.

Eucalyptus obliqua tall forest in cool, temperate Tasmania becomes a carbon source during a protracted warm spell in November 2017

Tasmania experienced a protracted warm spell in November 2017. Temperatures were lower than those usually characterising heatwaves. Nonetheless the warm spell represented an extreme anomaly based on the historical local climate. Eddy covariance measurements of fluxes in a Eucalyptus obliqua tall forest at Warra, southern Tasmania during the warm spell were compared with measurements in the same period of the previous year when temperatures were closer to average. Compared with previous year, the warm spell resulted in 31% lower gross primary productivity (GPP), 58% higher ecosystem respiration (ER) and the forest switching from a carbon sink to a source. Significantly higher net radiation received during the warm spell was dissipated by increased latent heat flux, while canopy conductance was comparable with the previous year. Stomatal regulation to limit water loss was therefore unlikely as the reason for the lower GPP during the warm spell. Temperatures during the warm spell were supra-optimal for GPP for 75% of the daylight hours. The decline in GPP at Warra during the warm spell was therefore most likely due to temperatures exceeding the optimum for GPP. All else being equal, these forests will be weaker carbon sinks if, as predicted, warming events become more common.

Partitioning evapotranspiration in a tallgrass prairie using micrometeorological and water use efficiency approaches under contrasting rainfall regimes

Partitioning evapotranspiration (ET) into evaporation (E) and plant transpiration (T) is key to understanding ecosystem responses to rainfall variability resulting from climate change. The goal of this study was to quantify T and E using eddy covariance (EC) flux measurements in a tallgrass prairie in consecutive growing seasons with contrasting rainfall regimes. The field measurements were conducted at the National Ecological Observatory Network (NEON) KONZ site, in Kansas, U.S., during the growing seasons of 2017, 2018 and 2019. The ET partitioning was performed using an approach based on the concept of the underlying water use efficiency (uWUE). To evaluate the uWUE approach, we compared daily E estimates obtained from the uWUE with E observations provided by microlysimeters (ML). Green chromatic coordinate (GCC) was used to monitor the vegetation dynamics. In the 2017 growing season, the total rainfall was 23.1% below the site’s long-term average cumulative precipitation. On the other hand, in 2018 and 2019 the accumulated growing season precipitations were 7.2% and 40.2%, respectively, above the long-term precipitation average. The relationship between uWUE approach and ML E measurements showed a Pearson correlation coefficient (r) of 0.42 and a root mean square error (RMSE) of 0.58 mm d−1. The lowest T/ET average value (0.50) was observed in the 2017 growing season, while the largest T/ET average (0.65) was observed in 2018. The correlations between GCC and T/ET were reduced during the growing seasons that experienced drought periods. Air temperature was the main environmental driver of T/ET during the wet growing seasons (r = 0.49 and 0.72). The subsurface soil moisture (0.45 m) was the main environmental driver of T/ET during a dry growing season (r = 0.41). These results demonstrate that the precipitation variability not only has a direct impact on the ET components but also modulates the response of those components to other environmental drivers. Since ET partitioning studies at the ecosystem scale are still scarce, our results can improve T/ET and water use efficiency estimates in long-term modelling studies. This will help to better understand how ET in ecosystems will respond to global warming and increased CO2 concentration during wet and dry growing seasons.

Heat and drought impact on carbon exchange in an age-sequence of temperate pine forests

BackgroundMost North American temperate forests are plantation or regrowth forests, which are actively managed. These forests are in different stages of their growth cycles and their ability to sequester atmospheric carbon is affected by extreme weather events. In this study, the impact of heat and drought events on carbon sequestration in an age-sequence (80, 45, and 17 years as of 2019) of eastern white pine (Pinus strobus L.) forests in southern Ontario, Canada was examined using eddy covariance flux measurements from 2003 to 2019.ResultsOver the 17-year study period, the mean annual values of net ecosystem productivity (NEP) were 180 ± 96, 538 ± 177 and 64 ± 165 g C m–2 yr–1 in the 80-, 45- and 17-year-old stands, respectively, with the highest annual carbon sequestration rate observed in the 45-year-old stand. We found that air temperature (Ta) was the dominant control on NEP in all three different-aged stands and drought, which was a limiting factor for both gross ecosystem productivity (GEP) and ecosystems respiration (RE), had a smaller impact on NEP. However, the simultaneous occurrence of heat and drought events during the early growing seasons or over the consecutive years had a significant negative impact on annual NEP in all three forests. We observed a similar trend of NEP decline in all three stands over three consecutive years that experienced extreme weather events, with 2016 being a hot and dry, 2017 being a dry, and 2018 being a hot year. The youngest stand became a net source of carbon for all three of these years and the oldest stand became a small source of carbon for the first time in 2018 since observations started in 2003. However, in 2019, all three stands reverted to annual net carbon sinks.ConclusionsOur study results indicate that the timing, frequency and concurrent or consecutive occurrence of extreme weather events may have significant implications for carbon sequestration in temperate conifer forests in Eastern North America. This study is one of few globally available to provide long-term observational data on carbon exchanges in different-aged temperate plantation forests. It highlights interannual variability in carbon fluxes and enhances our understanding of the responses of these forest ecosystems to extreme weather events. Study results will help in developing climate resilient and sustainable forestry practices to offset atmospheric greenhouse gas emissions and improving simulation of carbon exchange processes in terrestrial ecosystem models.

First eddy covariance flux measurements of semi-volatile organic compounds with the PTR3-TOF-MS

Abstract. We present first eddy covariance flux measurements with the PTR3-TOF-MS, a novel proton transfer time of flight reaction mass spectrometer. During 3 weeks in spring 2016, the instrument recorded 10 Hz data of biogenic volatile organic compounds above a boreal forest, on top of a measurement tower at the SMEAR (Station for Measuring Ecosystem –Atmosphere Relations) II station in Hyytiälä, Finland. Flux and concentration data of isoprene, monoterpenes, and sesquiterpenes were compared to the literature. Due to the improved instrument sensitivity and a customized wall-less inlet design, we could detect fluxes of semi-volatile and low-volatility organic compounds with less than single-digit picomol per square meter per second (pmolm-2s-1) values for the first time. These compounds include sesquiterpene oxidation products and diterpenes. Daytime diterpene fluxes were in the range of 0.05 to 0.15 pmolm-2s-1, which amounts to about 0.25 % to 0.5 % of the daytime sesquiterpene flux above canopy.

Energy Exchange and Evapotranspiration over the Ejina Oasis Riparian Forest Ecosystem with Different Land-Cover Types

Investigating the energy and water vapor exchange in oasis riparian forest ecosystems is of significant importance to improve scientific understanding of land surface processes in extreme arid regions. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) provided many observations of water vapor and heat fluxes from riparian forest ecosystem by using a network of eddy-covariance (EC) systems installed over representative surfaces in the Ejina Oasis, which is located in the downstream areas of the Heihe River Basin, northwestern China. Based on EC flux measurements and meteorological data performed at five stations and covering representative surface types of Populus euphratica tree with associated Tamarix chinensis shrub, Tamarix chinensis shrubland, cantaloupe cropland, and barren-land, this study explored the spatio-temporal patterns of heat and water vapor fluxes over the Ejina Oasis riparian forest ecosystem with five different surface types over the course of a growing season in 2014. Energy balance closure of the flux data was evaluated; footprint analysis for each EC site was also performed. Results showed that energy balance closure for the flux data was reasonably good, with average energy balance ratio (EBR) of 1.03. The seasonal variations in net radiation (Rn), latent (LE), and sensible heat flux (H) over the five contrasting surfaces were similar, and a reverse seasonal change was observed in energy partitioning into LE and H. Remarkable differences in Rn, LE, and H between the five surfaces were explored preliminarily, associated closely with the soil properties and foliage phenology. Over the growing season (May–October) in 2014, the total ET ranged 622–731 mm for mixed forest of P. euphratica trees with associated T. chinensis shrubs with average daily ET of 3.6–4.2 mm; ET from T. chinensis shrubland was about 541 mm, with average daily ET of 3.6 mm. ET for barren-land was 195 mm. The total ET in irrigated cantaloupe cropland with plastic mulch was 431 mm for its four-month growing period with a total average of 3.8 mm d−1. Determination of ET over riparian forest ecosystem helps to improve reasonable use of limited water resource in the Ejina Oasis.

A new open-path eddy covariance method for nitrous oxide and other trace gases that minimizes temperature corrections.

Low-power, open-path gas sensors enable eddy covariance (EC) flux measurements in remote areas without line power. However, open-path flux measurements are sensitive to fluctuations in air temperature, pressure, and humidity. Laser-based, open-path sensors with the needed sensitivity for trace gases like methane (CH4 ) and nitrous oxide (N2 O) are impacted by additional spectroscopic effects. Corrections for these effects, especially those related to temperature fluctuations, often exceed the flux of gases, leading to large uncertainties in the associated fluxes. For example, the density and spectroscopic corrections arising from temperature fluctuations can be one or two orders of magnitude greater than background N2 O fluxes. Consequently, measuring background fluxes with laser-based, open-path sensors is extremely challenging, particularly for N2 O and gases with similar high-precision requirements. We demonstrate a new laser-based, open-path N2 O sensor and a general approach applicable to other gases that minimizes temperature-related corrections for EC flux measurements. The method identifies absorption lines with spectroscopic effects in the opposite direction of density effects from temperature and, thus, density and spectroscopic effects nearly cancel one another. The new open-path N2 O sensor was tested at a corn (Zea mays L.) field in Southwestern Michigan, United States. The sensor had an optimal precision of 0.1ppbv at 10Hz and power consumption of 50W. Field trials showed that temperature-related corrections were 6% of density corrections, reducing EC random errors by 20-fold compared to previously examined lines. Measured open-path N2 O EC fluxes showed excellent agreement with those made with static chambers (m=1.0±0.3; r2 =.96). More generally, we identified absorption lines for CO2 and CH4 flux measurements that can reduce the temperature-related corrections by 10-100 times compared to existing open-path sensors. The proposed method provides a new direction for future open-path sensors, facilitating the expansion of accurate EC flux measurements.

Assessing and Modeling Ecosystem Carbon Exchange and Water Vapor Flux of a Pasture Ecosystem in the Temperate Climate-Transition Zone

The rising frequency of extreme weather events and global warming are greatly challenging pastoral ecosystem productivity, particularly in the temperate climate-transition regions. While this could cause greater gross primary production (GPP) mainly contributed by the warm-season vegetation, the consequences for the dynamics of net ecosystem exchange (NEE) and hydrological responses (e.g., evapotranspiration, ET) on an ecosystem level are poorly known. Here, we investigated the evolution of plant phenology, nutritive value, energy balance, and carbon/water budgets of a cool-season dominated pastoral ecosystem in the temperate zone; integrating both eddy covariance (EC) flux measurement and simulation modeling-based uncertainty analysis. Throughout the two-year duration (2017–2018) of this study, the entire pasture ecosystem remained a strong carbon sink (NEE = −1.23 and −1.95 kg C m−2, respectively) with 74% and 62% of available energy loss explained by EC fluxes, respectively. The cumulative ET was 735.8 and 796.8 mm, respectively; and the overall ecosystem water use efficiency (EWUE) were calculated as 6.5 g C kg−1 water across both growing seasons. The above-ground biomass yield agreed with the cumulative GPP and was inversely correlated with grass nutritive value. The uncertainty analysis indicated that accurate EC flux gap-filling models could be constructed using support vector machine trained time-series models (NEE, R2 = 0.77, RMSE = 11.8; ET, R2 = 0.90, RMSE = 73.8). The performance benchmarking tests indicated that REddyProc-based gap-filling performance was very limiting and highly variable (NEE, R2 = 0.21–0.64; ET, R2 = 0.79–0.87), particularly for estimating NEE. Overall, the warm-season vegetation encroachment greatly filled the production gap of cool-season grasses, leading to greater cumulative NEE and EWUE on a system level, compared with those from many other reported field-crop or grassland studies using EC approaches. The complex and dynamic nature of grassland ecosystems greatly challenged the conventional REddyProc-based EC flux gap-filling performance. However, accurate machine learning models could be constructed for error/uncertainty control purposes and, thus, should be encouraged in future studies.

Small precipitation events enhance the Eurasian grassland carbon sink

Different sizes of precipitation are specifically essential in controlling plant physiological processes in ecosystems and extremely important for predicting the effects of precipitation on arid and semiarid region carbon cycle. However, the sizes of precipitation events and their role in carbon flux processes remain unclear. A consecutive three years (2014–2016) eddy covariance flux measurements were conducted to investigate the changes in carbon fluxes under different sizes of precipitation in Mongolia – typical steppe (TPL), meadow steppe (MDW) and shrubland (SHB). Most of the precipitation distributions were dominated by small size events over all sites of the study area. Gross primary productivity reached highest on days with 2 mm d−1 and 5 mm d−1 of precipitation in 2015 and 2014, respectively in TPL, whereas the highest gross primary productivity observed at 10–25 mm d−1 precipitation size in both MDW and SHB. The ecosystem respiration changed positively with different sizes of precipitation events, except for the 5 mm d−1 and 10 mm d−1 precipitation size in the study sites. There existed positive relationship between net ecosystem CO2 exchange (NEE) and small size precipitation event in both TPL and MDW. The slight association was detected between NEE and small size precipitation (<2 mm) events in SHB during the study years. By contrast, the strong relationship was observed between large precipitation sizes and NEE (p < 0.01) in SHB. Overall, this study highlights the different responses of carbon flux to changes in the event size distribution of precipitation in grassland ecosystems which may help to predict how grassland structure and characteristic will respond to climatic change.

Carbon balance of a Finnish bog: temporal variability and limiting factors based on 6 years of eddy-covariance data

Abstract. Pristine boreal mires are known as substantial sinks of carbon dioxide (CO2) and net emitters of methane (CH4). Bogs constitute a major fraction of pristine boreal mires. However, the bog CO2 and CH4 balances are poorly known, having been largely estimated based on discrete and short-term measurements by manual chambers and seldom using the eddy-covariance (EC) technique. Eddy-covariance (EC) measurements of CO2 and CH4 exchange were conducted in the Siikaneva mire complex in southern Finland in 2011–2016. The site is a patterned bog having a moss–sedge–shrub vegetation typical of southern Eurasian taiga, with several ponds near the EC tower. The study presents a complete series of CO2 and CH4 EC flux (FCH4) measurements and identifies the environmental factors controlling the ecosystem–atmosphere CO2 and CH4 exchange. A 6-year average growing season (May–September) cumulative CO2 exchange of −61 ± 24 g C m−2 was observed, which partitions into mean total respiration (Re) of 167 ± 33 (interannual range 146–197) g C m−2 and mean gross primary production (GPP) of 228 ± 46 (interannual range 193–257) g C m−2, while the corresponding FCH4 amounts to 7.1 ± 0.7 (interannual range 6.4–8.4) g C m−2. The contribution of October–December CO2 and CH4 fluxes to the cumulative sums was not negligible based on the measurements during one winter. GPP, Re and FCH4 increased with temperature. GPP and FCH4 did not show any significant decline even after a substantial water table drawdown in 2011. Instead, GPP, Re and FCH4 were limited in the cool, cloudy and wet growing season of 2012. May–September cumulative net ecosystem exchange (NEE) of 2013–2016 averaged at about −73 g C m−2, in contrast with the hot and dry year 2011 and the wet and cool year 2012. Suboptimal weather likely reduced the net sink by about 25 g C m−2 in 2011 due to elevated Re, and by about 40 g C m−2 in 2012 due to limited GPP. The cumulative growing season sums of GPP and CH4 emission showed a strong positive relationship. The EC source area was found to be comprised of eight distinct surface types. However, footprint analyses revealed that contributions of different surface types varied only within 10 %–20 % with respect to wind direction and stability conditions. Consequently, no clear link between CO2 and CH4 fluxes and the EC footprint composition was found despite the apparent variation in fluxes with wind direction.

An open-path ammonia analyzer for eddy covariance flux measurement

Although biosphere-atmosphere exchanges of ammonia (NH3) are of great environmental importance regarding, e.g., eutrophication and aerosol-related human health problems, their magnitudes are not well constrained due to the challenges in in-situ measurements of NH3 fluxes. In this paper we introduce a portable, low-power and quantum cascade laser-based open-path analyzer for eddy covariance (EC) measurement of NH3 fluxes particularly at sites without grid power. This analyzer minimizes the sampling artifacts due to surface adsorption of NH3 by using an open-path optical Herriott cell configuration, while also increases its portability and convenience in field deployment by assembling most instrumental components in a highly integrated and compact enclosure. Its performance was tested through laboratory experiments and one-week EC measurements at a subtropical rice paddy. Its uncertainty in measuring NH3 concentration is ± 15%, being comparable to those of the currently available high-sensitivity NH3 analyzers. The flux random error averaged at 26 ± 28% during the entire experimental period. The instrumental noise of the analyzer with a sampling frequency of 10 Hz is estimated to be 0.286 and 0.302 nmol mol‒1 (ppbv) under the laboratory and field conditions, respectively. Under the given test conditions, the detection limit of the EC system is 7.1 ± 1.1 μg N m−2 h−1 for half-hourly NH3 flux measurement. Nearly 91% of the half-hourly fluxes observed during the experimental period were larger than the mean detection limit. Such instrumental sensitivity is adequate for quantifying NH3 emissions from most source ecosystems, and also possible to monitor deposition fluxes at ecosystems away from strong sources with low NH3 concentrations. Availability of this open-path NH3 analyzer provides an access to a better understanding of the biosphere-atmosphere NH3 exchanges and to a more reliable estimate of the NH3 inventory at ecosystem or larger scales.

The high-frequency response correction of eddy covariance fluxes – Part 2: An experimental approach for analysing noisy measurements of small fluxes

Abstract. Fluxes measured with the eddy covariance (EC) technique are subject to flux losses at high frequencies (low-pass filtering). If not properly corrected for, these result in systematically biased ecosystem–atmosphere gas exchange estimates. This loss is corrected using the system's transfer function which can be estimated with either theoretical or experimental approaches. In the experimental approach, commonly used for closed-path EC systems, the low-pass filter transfer function (H) can be derived from the comparison of either (i) the measured power spectra of sonic temperature and the target gas mixing ratio or (ii) the cospectra of both entities with vertical wind speed. In this study, we compare the power spectral approach (PSA) and cospectral approach (CSA) in the calculation of H for a range of attenuation levels and signal-to-noise ratios (SNRs). For a systematic analysis, we artificially generate a representative dataset from sonic temperature (T) by attenuating it with a first order filter and contaminating it with white noise, resulting in various combinations of time constants and SNRs. For PSA, we use two methods to account for the noise in the spectra: the first is the one introduced by Ibrom et al. (2007a) (PSAI07), in which the noise and H are fitted in different frequency ranges, and the noise is removed before estimating H. The second is a novel approach that uses the full power spectrum to fit both H and noise simultaneously (PSAA21). For CSA, we use a method utilizing the square root of the H with shifted vertical wind velocity time series via cross-covariance maximization (CSAH,sync). PSAI07 tends to overestimate the time constant when low-pass filtering is low, whilst the new PSAA21 and CSAH,sync successfully estimate the expected time constant regardless of the degree of attenuation and SNR. We further examine the effect of the time constant obtained with the different implementations of PSA and CSA on cumulative fluxes using estimated time constants in frequency response correction. For our example time series, the fluxes corrected using time constants derived by PSAI07 show a bias between 0.1 % and 1.4 %. PSAA21 showed almost no bias, while CSAH,sync showed bias of ±0.4 %. The accuracies of both PSA and CSA methods were not significantly affected by SNR level, instilling confidence in EC flux measurements and data processing in set-ups with low SNR. Overall we show that, when using power spectra for the empirical estimation of parameters of H for closed-path EC systems the new PSAA21 outperforms PSAI07, while when using cospectra the CSAH,sync approach provides accurate results. These findings are independent of the SNR value and attenuation level.

Surface energy balance and flux partitioning of annual crops in southwestern France

In the micrometeorology community, it is well known that the turbulent fluxes measured with eddy covariance (EC) systems do not usually equal the available energy. Hence, qualitative knowledge of the impact of different vegetation types, and climatic variables on this ‘nonclosure’ is essential. This study analyzed a unique database of EC flux measurements covering 8 growing seasons of 3 crops (maize, wheat, and rapeseed) cultivated over two close agricultural sites (FR-Lam and FR-Aur) in southwestern France. For data analysis, some dry and wet cropping seasons of the same crop type were selected; then, their phenological stages were identified to investigate their effect on the energy balance closure (EBC), and flux partitioning. The results showed that the systematic effect of each site on the EBC was stronger than the influence of crop type and stage, as EBC was generally higher at FR-Aur (82%) than at FR-Lam (67%), even for the same crop type. The assessed effect of rainfall, and phenological stages on energy partitioning revealed that during the wet seasons, over 42% of the net radiation (Rn) was accounted for by the latent heat flux (LE), which was 9% higher than the recorded LE in the dry year during the active vegetation period. Similarly, the ground heat flux (G) was observed to be very sensitive to vegetation; G accounted for 30% of Rn when vegetation was low, whereas at the peak of vegetation, it fell below 16% due to canopy shading. Closure was also assessed under various atmospheric stability conditions and wind sectors, and it was observed to be higher under unstable conditions, and in prevailing wind directions. Analysis of the sensible heat advection (AH) revealed that AH accounts for more than half of the imbalance at both sites.

Atmospheric CO2 Exchange of a Small Mountain Lake: Limitations of Eddy Covariance and Boundary Layer Modeling Methods in Complex Terrain

AbstractLakes receive and transform significant amounts of terrestrial carbon and are often considered a source of atmospheric carbon dioxide (CO2). Yet, continuous direct measurements of lake‐atmosphere CO2 exchange with high temporal resolution are sparse. In this study, we measured the CO2 exchange of a mountain lake in the eastern Austrian Alps continuously for one year using the eddy covariance (EC) and the boundary layer model (BLM) approaches. Results from both the EC and the BLM methods indicated the lake to be a small source of atmospheric CO2 with highest emissions in fall. EC flux measurements were affected by low‐frequency contributions especially during low wind conditions. The CO2 concentration gradient at the air‐water interface decreased during night‐time due to an increase in atmospheric CO2 above the lake, likely caused by cold and CO2‐rich air draining from the surrounding land. Consequently, BLM fluxes were lower during night‐time than during daytime. This diel pattern was lacking in the EC flux measurements because the EC instruments deployed at the shore of the lake did not capture low nocturnal lake CO2 fluxes due to the local wind regime. Overall, this study illustrates the effect of the surrounding landscape on lake‐atmosphere flux measurements. We conclude that estimating CO2 evasion from lakes situated in complex topography needs to explicitly account for biases in EC flux measurements caused by low‐frequency contributions and local wind regimes.

Carbon dioxide fluxes and carbon balance of an agricultural grassland in southern Finland

Abstract. A significant proportion of the global carbon emissions to the atmosphere originate from agriculture. Therefore, continuous long-term monitoring of CO2 fluxes is essential to understand the carbon dynamics and balances of different agricultural sites. Here we present results from a new eddy covariance flux measurement site located in southern Finland. We measured CO2 and H2O fluxes at this agricultural grassland site for 2 years, from May 2018 to May 2020. In particular the first summer experienced prolonged dry periods, which affected the CO2 fluxes, and substantially larger fluxes were observed in the second summer. During the dry summer, leaf area index (LAI) was notably lower than in the second summer. Water use efficiency increased with LAI in a similar manner in both years, but photosynthetic capacity per leaf area was lower during the dry summer. The annual carbon balance was calculated based on the CO2 fluxes and management measures, which included input of carbon as organic fertilizers and output as yield. The carbon balance of the field was −57 ± 10 and −86 ± 12 g C m−2 yr−1 in the first and second study years, respectively.

A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests

Abstract. Tree phenology is a major driver of forest–atmosphere mass and energy exchanges. Yet, tree phenology has rarely been monitored in a consistent way throughout the life of a flux-tower site. Here, we used seasonal time series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (greenness chromatic coordinate), broadband NDVI, LAI (leaf area index), fAPAR (fraction of absorbed photosynthetic active radiation), CC (canopy closure), fRvis (fraction of reflected radiation) and GPP (gross primary productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest using an asymmetric double sigmoid function (ADS) fitted to the time series. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured the interannual variability of spring phenology very well (R2&gt;0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) of less than 4 d. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 d. The ADS method used to derive the phenological markers provides the most biased estimates for the GPP and GCC. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2=0.63 and 0.80 for NDVI and CC, respectively), with an MAD of less than 7 d. Our results show that proximal-sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at eddy covariance (EC) flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.

Non-growing season carbon emissions in a northern peatland are projected to increase under global warming

Peatlands are important ecosystems that store approximately one third of terrestrial organic carbon. Non-growing season carbon fluxes significantly contribute to annual carbon budgets in peatlands, yet their response to climate change is poorly understood. Here, we investigate the governing environmental variables of non-growing season carbon emissions in a northern peatland. We develop a support-vector regression model using a continuous 13-year dataset of eddy covariance flux measurements from the Mer Blue Bog, Canada. We determine that only seven variables were needed to reproduce carbon fluxes, which were most sensitive to net radiation above the canopy, soil temperature, wind speed and soil moisture. We find that changes in soil temperature and photosynthesis drove changes in net carbon flux. Assessing net ecosystem carbon exchange under three representative concentration pathways, we project a 103% increase in peatland carbon loss by 2100 under a high emissions scenario. We suggest that peatland carbon losses constitute a strong positive climate feedback loop.

Mapping CO2 fluxes of cypress swamp and marshes in the Greater Everglades using eddy covariance measurements and Landsat data

Wetlands play a critical role in the global carbon (C) budget by sequestering carbon dioxide (CO2) and storing significant amounts of C, but climate change is altering C dynamics in these ecosystems. Therefore, predicting and mapping net ecosystem exchange (NEE) of CO2 in wetlands has emerged as a focus of research to better understand where, how, and when biogeochemical cycles will be modified by human and natural disturbances like climate change. To address this, we developed an object-based machine learning ensemble approach to upscale eddy covariance (EC) CO2 flux measurements across two major wetland ecosystems (cypress swamp and freshwater marshes) in the Greater Everglades of south Florida. We linked 30-m Landsat, water depth, and air temperature data with EC flux measurements using the temporally composite concept for model development, and mapped fluxes within Big Cypress National Preserve (BCNP) and Everglades National Park (ENP) to characterize two distinct seasonal flux patterns. Using an ensemble analysis of three machine learning model outputs (Artificial Neural Network, Support Vector Machine, and Random Forest), our models of NEE at BCNP had a high predictive power with a R2 greater than 0.8, while NEE was predicted at ENP with a R2 greater than 0.5. We also generated an uncertainty map to quantify the prediction diversity caused by application of multiple models in upscaling. This uncertainty map identifies regions that are either easy or challenging to predict from model based analyses. We conclude that integration of Landsat, water depth, and air temperature data is valuable for quantifying CO2 exchange between the atmosphere and Everglades ecosystems, and the developed paradigm is promising for upscaling EC flux measurements in wetlands to study and mitigate disturbances to wetland ecosystem services such as hurricanes, fires, water management and/or climate change.

Methane emissions from an oil sands tailings pond: a quantitative comparison of fluxes derived by different methods

Abstract. Tailings ponds in the Alberta oil sands region are significant sources of fugitive emissions of methane to the atmosphere, but detailed knowledge on spatial and temporal variabilities is lacking due to limitations of the methods deployed under current regulatory compliance monitoring programs. To develop more robust and representative methods for quantifying fugitive emissions, three micrometeorological flux methods (eddy covariance, gradient, and inverse dispersion) were applied along with traditional flux chambers to determine fluxes over a 5-week period. Eddy covariance flux measurements provided the benchmark. A method is presented to directly calculate stability-corrected eddy diffusivities that can be applied to vertical gas profiles for gradient flux estimation. Gradient fluxes were shown to agree with eddy covariance within 18 %, while inverse dispersion model flux estimates were 30 % lower. Fluxes were shown to have only a minor diurnal cycle (15 % variability) and were weakly dependent on wind speed, air, and water surface temperatures. Flux chambers underestimated the fluxes by 64 % in this particular campaign. The results show that the larger footprint together with high temporal resolution of micrometeorological flux measurement methods may result in more robust estimates of the pond greenhouse gas emissions.