Long-term Eddy Covariance Measurements Research Articles

Global warming impacts of carbon dioxide, methane, and albedo in an island forest nature reserve

Abstract Forest ecosystems influence climate by sequestering carbon from the atmosphere and by altering the surface energy balance. However, the combined global warming impacts (GWIs), contribution from carbon dioxide (CO2) fluxes, methane (CH4) fluxes, and albedo changes (Δα) remain poorly understood. Here, we reported the combined GWIs of CO2, CH4, and albedo with eddy covariance (EC) measurements during 2020–2022 in a subtropical island forest located in the Nanji Islands National Marine Protected Area in Southern China. We suggested that the island forest acted as a significant carbon sink, with annual CO2 and CH4 fluxes of −548.6 ± 11.1 and −5.67 ± 1.1 g C m−2 yr−1, respectively, while the daily albedo varied within the range of 0.03–0.15. By converting the radiative forcing induced by CH4 and albedo change in the forest to CO2 equivalents, we analyzed the three contributors to the combined GWI. The annual averages GWI of CO2 uptake, CH4 uptake, and Δα were −2 011.6 ± 40.6, −211.3 ± 1.1, and 0.03 ± 4.5 g CO2-eq m−2 yr−1, respectively, with a mean combined GWI of −2 223 ± 40.8 g CO2-eq m−2 yr−1. During 2020–2022, the contributions of CO2 uptake, CH4 uptake, and Δα to the combined GWI were 89.7% to 91.4%, 9.4% to 9.6% and −1.0%–0.9%, respectively. Nanji Island forest had a strong positive effect on climate change mitigation, with CO2 and CH4 uptake greatly enhancing its cooling benefits. Using Pearson correlation and path analysis, we found photosynthetically active radiation, precipitation, soil water content were the primary factors controlling the GWI dynamics, mainly driving the changes in CO2 fluxes. This study provided novel insights into the establishment of the overall evaluation framework for ecosystem-scale GWIs of CO2 and CH4 fluxes, and albedo based on long-term EC measurements in an island forest.

Divergent environmental responses of long-term variations in evapotranspiration over four grassland ecosystems in China based on eddy-covariance measurements

Understanding the long-term variation in evapotranspiration (ET) for the spatially distributed grasslands is crucial for the accurate prediction of ET response to climate change. In this study, we analyzed the interannual variability (IAV) of ET and its responses to environmental conditions at four grassland ecosystems across a wide range of climatic and biome conditions based on the long-term (9–11 years) eddy-covariance measurements. The four ecosystems encompassed the most prevalent grassland vegetations in China, containing a typical temperate steppe, an alpine meadow-steppe, an alpine shrubland meadow, and an alpine marsh meadow. The IAVs of annual ET at the typical temperate steppe and the alpine meadow-steppe were primarily affected by either change in precipitation (P) or relative humidity (RH). Leaf area index (LAI) was the dominant factor controlling the IAVs of annual and growing-season ET at the alpine shrubland meadow, and the IAV of LAI was significantly correlated with P variation. As to the alpine marsh meadow, net radiation turned to be the dominant factor for the IAV of annual ET, additionally with significant effects from water supply condition (P and RH) on the IAV of growing-season ET. Similar environmental responses were also found for the IAVs of mean surface conductance (gs) across the sites. Specifically, annual and growing-season mean gs significantly increased with increases in LAI at the alpine shrubland meadow, and appeared to be more sensitive to changes in water availability and VPD at the typical temperate steppe and the alpine meadow-steppe. Significant linear relationships were also observed among the IAVs of mean Priestley-Taylor coefficient (α = ET/ETeq, where ETeq is the equilibrium evaporation), decoupling coefficient (Ω), and gs on both the annual and growing-season basis in this study. Moreover, the variabilities of annual mean gs, Ω, and α further demonstrated the energy-limited conditions at the alpine marsh meadow, and the overall water-limited conditions at the other three grasslands. This study reveals the divergent environmental responses of long-term ET variations over grassland ecosystems, and contributes to the comprehensive understanding on the ET process and modeling efforts as well.

High urban NOx triggers a substantial chemical downward flux of ozone.

Nitrogen oxides (NOx) play a central role in catalyzing tropospheric ozone formation. Nitrogen dioxide (NO2) has recently reemerged as a key target for air pollution control measures, and observational evidence points toward a limited understanding of ozone in high-NOx environments. A complete understanding of the mechanisms controlling the rapid atmospheric cycling between ozone (O3)-nitric oxide (NO)-NO2 in high-NOx regimes at the surface is therefore paramount but remains challenging because of competing dynamical and chemical effects. Here, we present long-term eddy covariance measurements of O3, NO, and NO2, over an urban area, that allow disentangling important physical and chemical processes. When generalized, our findings suggest that the depositional O3 flux near the surface in urban environments is negligible compared to the flux caused by chemical conversion of O3. This leads to an underestimation of the Leighton ratio and is a key process for modulating urban NO2 mixing ratios. As a consequence, primary NO2 emissions have been significantly overestimated.

Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half.

The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide (CO2 ) and mitigating climate change has received increasing attention in recent years. While recent studies have shown that methane (CH4 ) emissions can potentially offset the carbon burial rates in low-salinity coastal wetlands, there is hitherto a paucity of direct and year-round measurements of ecosystem-scale CH4 flux (FCH4 ) from mangrove ecosystems. In this study, we examined the temporal variations and biophysical drivers of ecosystem-scale FCH4 in a subtropical estuarine mangrove wetland based on 3years of eddy covariance measurements. Our results showed that daily mangrove FCH4 reached a peak of over 0.1gCH4 -Cm-2 day-1 during the summertime owing to a combination of high temperature and low salinity, while the wintertime FCH4 was negligible. In this mangrove, the mean annual CH4 emission was 11.7±0.4gCH4 -Cm-2 year-1 while the annual net ecosystem CO2 exchange ranged between -891 and -690gCO2 -Cm-2 year-1 , indicating a net cooling effect on climate over decadal to centurial timescales. Meanwhile, we showed that mangrove FCH4 could offset the negative radiative forcing caused by CO2 uptake by 52% and 24% over a time horizon of 20 and 100years, respectively, based on the corresponding sustained-flux global warming potentials. Moreover, we found that 87% and 69% of the total variance of daily FCH4 could be explained by the random forest machine learning algorithm and traditional linear regression model, respectively, with soil temperature and salinity being the most dominant controls. This study was the first of its kind to characterize ecosystem-scale FCH4 in a mangrove wetland with long-term eddy covariance measurements. Our findings implied that future environmental changes such as climate warming and increasing river discharge might increase CH4 emissions and hence reduce the net radiative cooling effect of estuarine mangrove forests.

Dead Sea evaporation by eddy covariance measurements vs. aerodynamic, energy budget, Priestley–Taylor, and Penman estimates

Abstract. The Dead Sea is a terminal lake, located in an arid environment. Evaporation is the key component of the Dead Sea water budget and accounts for the main loss of water. So far, lake evaporation has been determined by indirect methods only and not measured directly. Consequently, the governing factors of evaporation are unknown. For the first time, long-term eddy covariance measurements were performed at the western Dead Sea shore for a period of 1 year by implementing a new concept for onshore lake evaporation measurements. To account for lake evaporation during offshore wind conditions, a robust and reliable multiple regression model was developed using the identified governing factors wind velocity and water vapour pressure deficit. An overall regression coefficient of 0.8 is achieved. The measurements show that the diurnal evaporation cycle is governed by three local wind systems: a lake breeze during daytime, strong downslope winds in the evening, and strong northerly along-valley flows during the night. After sunset, the strong winds cause half-hourly evaporation rates which are up to 100 % higher than during daytime. The median daily evaporation is 4.3 mm d−1 in July and 1.1 mm d−1 in December. The annual evaporation of the water surface at the measurement location was 994±88 mm a−1 from March 2014 until March 2015. Furthermore, the performance of indirect evaporation approaches was tested and compared to the measurements. The aerodynamic approach is applicable for sub-daily and multi-day calculations and attains correlation coefficients between 0.85 and 0.99. For the application of the Bowen ratio energy budget method and the Priestley–Taylor method, measurements of the heat storage term are inevitable on timescales up to 1 month. Otherwise strong seasonal biases occur. The Penman equation was adapted to calculate realistic evaporation, by using an empirically gained linear function for the heat storage term, achieving correlation coefficients between 0.92 and 0.97. In summary, this study introduces a new approach to measure lake evaporation with a station located at the shoreline, which is also transferable to other lakes. It provides the first directly measured Dead Sea evaporation rates as well as applicable methods for evaporation calculation. The first one enables us to further close the Dead Sea water budget, and the latter one enables us to facilitate water management in the region.

Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation

With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems. Both ANFIS and ELM methods were further compared with the most widely used artificial neural network (ANN) and support vector machine (SVM) methods. Moreover, we also focused on probing into the effects of internal parameters on their corresponding approaches. Our estimates showed that most variation in each carbon flux could be effectively explained by the developed models at almost all the sites. Moreover, the forecasting accuracy of each method was strongly dependent upon their respective internal algorithms. The best training function for ANN model can be acquired through the trial and error procedure. The SVM model with the radial basis kernel function performed considerably better than the SVM models with the polynomial and sigmoid kernel functions. The hybrid ELM models achieved similar predictive accuracy for the three fluxes and were consistently superior to the original ELM models with different transfer functions. In most instances, both the subtractive clustering and fuzzy c-means algorithms for the ANFIS models outperformed the most popular grid partitioning algorithm. It was demonstrated that the newly proposed ELM and ANFIS models were able to produce comparable estimates to the ANN and SVM models for forecasting terrestrial carbon fluxes.

Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.

Ozone flux in plant ecosystems: new opportunities for long-term monitoring networks to deliver ozone-risk assessments.

Ozone (O3) is a photochemically formed reactive gas responsible for a decreasing carbon assimilation in plant ecosystems. Present in the atmosphere in trace concentrations (less than 100ppbv), this molecule is capable of inhibiting carbon assimilation in agricultural and forest ecosystems. Ozone-risk assessments are typically based on manipulative experiments. Present regulations regarding critical ozone levels are mostly based on an estimated accumulated exposure over a given threshold concentration. There is however a scientific consensus over flux estimates being more accurate, because they include plant physiology analyses and different environmental parameters that control the uptake-that is, not just the exposure-of O3. While O3 is a lot more difficult to measure than other non-reactive greenhouse gases, UV-based and chemiluminescence sensors enable precise and fast measurements and are therefore highly desirable for eddy covariance studies. Using micrometeorological techniques in association with latent heat flux measurements in the field allows for the partition of ozone fluxes into the stomatal and non-stomatal sinks along the soil-plant continuum. Long-term eddy covariance measurements represent a key opportunity in estimating carbon assimilation at high-temporal resolutions, in an effort to study the effect of climate change on photosynthetic mechanisms. Our aim in this work is to describe potential of O3 flux measurement at the canopy level for ozone-risk assessment in established long-term monitoring networks.

Impact of canopy aerodynamic distance spatial and temporal variability on long term eddy covariance measurements

Understanding if and how the spatial and temporal variability of the surrounding environment affects turbulence is essential for long-term eddy covariance measurements. It requires characterizing the surrounding environment. One way to achieve this is to analyse the canopy aerodynamic distance (Δ), which is the difference between measurement height (zm) and displacement height (d).In this work, an original method to estimate the canopy aerodynamic distance at a fine spatial (30° sectors) and temporal (one year) resolution was proposed. It was based on sensible heat cospectra analysis, calibrated on a measurement height change and validated using canopy height inventories.This method was applied to 20 years of eddy covariance measurements from the Vielsalm Terrestrial Observatory (VTO), a site located in a mixed temperate forest. The method allowed Δ spatio-temporal variability due to changes in canopy or measurement height to be detected.Relationships between Δ and turbulence statistics were then analysed: the momentum correlation coefficient (ruw) was found to be dependent on Δ, confirming that the measurements were made in the roughness sublayer of the atmospheric surface layer. In contrast, no such relationship was found sensible heat, CO2 or water vapour correlation coefficients, suggesting that the Δ variability did not affect significantly these fluxes. There were significant differences, however, between azimuthal directions, suggesting that these scalars were affected by forest heterogeneity in a different way. Various hypotheses were put forward to explain the differences and their relevance was evaluated.This study highlighted the need to consider the spatial and temporal variability of the surrounding environment in order to verify the consistency of long-term eddy covariance datasets.

Carbon and Water Budgets in Multiple Wheat-Based Cropping Systems in the Inland Pacific Northwest US: Comparison of CropSyst Simulations with Eddy Covariance Measurements

Accurate carbon and water flux simulations for croplands are greatly dependent on high quality representation of management practices and meteorological conditions, which are key drivers of the surface-atmosphere exchange processes. Fourteen site-years of carbon and water fluxes were simulated using the CropSyst model over four agricultural sites in the inland Pacific Northwest US from October 1, 2011 to September 30, 2015. Model performance for field-scale net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) was evaluated by comparing simulations with long-term eddy covariance measurements. The model captured the temporal variations of NEE and ET reasonably well with an overall r of 0.78 and 0.80, and a low RMSE of 1.82 g C m-2 d-1 and 0.84 mm d-1 for NEE and ET, respectively. The model slightly underestimated NEE and ET by 0.51 g C m-2 d-1 and 0.09 mm d-1, respectively. ET simulations showed better agreement with eddy covariance measurements than NEE. The model performed much better for the sites with detailed initial conditions (e.g. SOC content) and management practice information (e.g. tillage type). The CropSyst results showed that the winter wheat fields could be annual net carbon sinks or close to neutral with the net ecosystem carbon balance (NECB) ranging from 92 to -17 g C m-2, while the spring crop fields were net carbon sources or neutral with an annual NECB of -327 to -3 g C m-2. Simulations for the paired tillage sites showed that the no-till site resulted in lower CO2 emissions for the crop rotations of winter wheat-spring garbanzo, but had higher carbon loss into the atmosphere for spring canola compared to the conventional tillage site. Water budgets did not differ significantly between the two tillage systems. Winter wheat in the high-rainfall area had higher crop yields and water use efficiency but emitted larger amounts of CO2 into the atmosphere than in the low-rainfall area. Based on model evaluations in this study, CropSyst appears promising as a tool to simulate field-scale carbon and water budgets and assess the effects of different management practices and local meteorological conditions for the wheat-based cropping systems in this region.

Precipitation Pattern Determines the Inter-annual Variation of Herbaceous Layer and Carbon Fluxes in a Phreatophyte-Dominated Desert Ecosystem

Arid and semi-arid ecosystems dominated by shrubby species are an important component in the global carbon cycle but are largely under-represented in studies of the effect of climate change on carbon flux. This study synthesizes data from long-term eddy covariance measurements and experiments to assess how changes in ecosystem composition, driven by precipitation patterns, affect inter-annual variability of carbon flux and their components in a halophyte desert community dominated by deep-rooted shrubs (phreatophytes, which depend on groundwater as their primary water source). Our results demonstrated that the carbon balance of this community responded strongly to precipitation variations. Both pre-growing season precipitation and growing season precipitation frequency significantly affected inter-annual variations in ecosystem carbon flux. Heavy pre-growing season precipitation (November–April, mostly as snow) increased annual net ecosystem carbon exchange, by facilitating the growth and carbon assimilation of shallow-rooted annual plants, which used spring and summer precipitation to increase community productivity. Sufficient pre-growing season precipitation led to more germination and growth of shallow-rooted annual plants. When followed by high-frequency growing season precipitation, community productivity of this desert ecosystem was lifted to the level of grassland or forest ecosystems. The long-term observations and experimental results confirmed that precipitation patterns and the herbaceous component were dominant drivers of the carbon dynamics in this phreatophyte-dominated desert ecosystem. This study illustrates the importance of inter-annual variations in climate and ecosystem composition for the carbon flux in arid and semi-arid ecosystems. It also highlights the important effect of changing frequency and seasonal pattern of precipitation on the regional and global carbon cycle in the coming decades.

Comparative estimates of anthropogenic heat emission in relation to surface energy balance of a subtropical urban neighborhood

Abstract Long-term eddy covariance measurements have been conducted in a subtropical urban area, an older neighborhood north of downtown Houston. The measured net radiation ( Q* ), sensible heat flux (H) and latent heat flux (LE) showed typical seasonal diurnal variations in urban areas: highest in summer; lowest in winter. From an analysis of a subset of the first two years of measurements, we find that approximately 42% of Q* is converted into H, and 22% into LE during daytime. The local anthropogenic heat emissions were estimated conventionally using the long-term residual method and the heat emission inventory approach. We also developed a footprint-weighted inventory approach, which combines the inventory approach with flux footprint calculations. The results show a range of annual anthropogenic heat fluxes from 20 W m −2 to 30 W m −2 within the study domain. Possibly as a result of local radiation versus heat flux footprint mismatches, the mean value of surface heat storage ( ΔQs ) was relatively large, approximately 43% and 34% of Q* in summer and winter, respectively, during daytime.

Long-term eddy covariance measurements of the isotopic composition of the ecosystem–atmosphere exchange of CO2 in a temperate forest

Measurements of the isotopic composition of the net ecosystem–atmosphere exchange of CO2 (NEE) have been desired as a means to probe ecosystem carbon cycling and in particular to partition NEE into gross ecosystem photosynthesis and respiration. Several attempts at such measurements have combined eddy covariance (EC) measurements of the total net CO2 flux with flask measurements of the isotopic composition of CO2 in ambient air – an indirect method that has never been validated. Direct EC measurements of the isotopic composition of NEE (i.e. of the net exchanges of 12C16O2, 13C16O2, and 18O12C16O) have been made only twice, in short-term (2-month and 1-month) campaigns.Here we present a full growing season of direct EC measurements of the isotopic composition of NEE in a temperate deciduous forest, and we use these data: (1) to rigorously assess their limiting sources of error, (2) to test the indirect EC/flask method, and (3) to give an indication of the potential for ecological analyses. We describe the method and instrumentation, including the new cryogen-free, continuous-wave, quantum cascade laser spectrometer, which can determine δ13C and δ18O in ambient CO2 with unprecedented noise levels of ±0.02‰ and ±0.03‰ (1 standard deviation), respectively, for a 100s integration time and a 1-h calibration interval. We find: (1) that precision is jointly limited by the instrumentation and by horizontal ecosystem or landscape heterogeneity, so that there is little to be gained by further improvements to instrument performance; (2) that the isotopic composition of NEE obtained by the EC/flask method can be biased, on a monthly timescale, by 2‰; and (3) that the present measurements are precise enough to elucidate biological mechanisms controlling the ecosystem-scale carbon balance.

A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements

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.

Identification of Micro-scale Anthropogenic CO2, heat and moisture sources – Processing eddy covariance fluxes for a dense urban environment

Anthropogenic emissions of heat and exhaust gases play an important role in the atmospheric boundary layer, altering air quality, greenhouse gas concentrations and the transport of heat and moisture at various scales. This is particularly evident in urban areas where emission sources are integrated in the highly heterogeneous urban canopy layer and directly linked to human activities which exhibit significant temporal variability. It is common practice to use eddy covariance observations to estimate turbulent surface fluxes of latent heat, sensible heat and carbon dioxide, which can be attributed to a local scale source area. This study provides a method to assess the influence of micro-scale anthropogenic emissions on heat, moisture and carbon dioxide exchange in a highly urbanized environment for two sites in central London, UK. A new algorithm for the Identification of Micro-scale Anthropogenic Sources (IMAS) is presented, with two aims. Firstly, IMAS filters out the influence of micro-scale emissions and allows for the analysis of the turbulent fluxes representative of the local scale source area. Secondly, it is used to give a first order estimate of anthropogenic heat flux and carbon dioxide flux representative of the building scale. The algorithm is evaluated using directional and temporal analysis. The algorithm is then used at a second site which was not incorporated in its development. The spatial and temporal local scale patterns, as well as micro-scale fluxes, appear physically reasonable and can be incorporated in the analysis of long-term eddy covariance measurements at the sites in central London. In addition to the new IMAS-technique, further steps in quality control and quality assurance used for the flux processing are presented. The methods and results have implications for urban flux measurements in dense urbanised settings with significant sources of heat and greenhouse gases.

Assessment and up-scaling of CO 2 exchange by patches of the herbaceous vegetation mosaic in a Portuguese cork oak woodland

Long-term eddy covariance measurements over a montado oak woodland in southern Portugal have documented a vulnerability to predicted decreases in springtime rainfall, since water availability during spring limits annual CO 2 gain, the growth of fodder for animals, and the production of cork by Quercus suber. The current study examined CO 2 exchange of three different herbaceous vegetation components distributed over montado landscapes and within the footprint of long-term landscape eddy covariance monitoring studies. Simultaneous measurements with eddy covariance at two sites and with manually operated chambers at multiple locations revealed that slow drainage of shallow basins, the onset of drying at higher sites and a high release of CO 2 below tree canopies significantly influenced the overall course of montado ecosystem gas exchange during the spring. Hyperbolic light response models were employed to up-scale and compare herbaceous gas exchange with landscape net ecosystem CO 2 flux. The up-scaling demonstrates the importance of the herbaceous understory in determining annual carbon balance of the montado and suggests a relatively small additional CO 2 uptake by the tree canopies and boles, i.e., by the aboveground tree compartment, during springtime. Annual flux totals obtained during the extremely dry year 2005 and a normal precipitation year 2006 for the oak woodland and a nearby grassland were essentially the same, indicating that both ecosystems similarly exploit available resources. Based on comparisons with additional temperate grasslands, we can visualize the montado herbaceous cover as a typical European grassland canopy, but where temperature fluctuations in winter control uptake, and where total production depends on springtime rainfall as it controls phenological events and eventually dieback of the vegetation. On the other hand, tree canopies remain active longer during late spring and early summer, modifying the montado response from that of grassland. Uncertainties in flux estimates via both chamber and eddy covariance methodologies currently prevent a full understanding of vegetation/atmosphere coupling, of the recycling of CO 2 between the understory communities and trees, and of relationships between exchange rates of individual components of the vegetation mosaic and overall carbon and water balances in montado landscapes.

Comparing spectra and cospectra of turbulence over different surface boundary conditions

Long-term eddy-covariance measurements from the LBA-ECO Project were used to study the behavior of turbulence spectra and cospectra above the Amazon rainforest as well as over a nearby deforested area in the central part of Amazon region. Results are separated according to the wind speed and stability conditions. Under stable low wind speed conditions the Kolmogorov inertial subrange law is not observed. For all other situations simple empirical relations are obtained and length scales associated with the more energetic eddies are calculated. Above the forest canopy, the cospectra are more peaked than those observed over the deforested area, confirming previous observations made over a midlatitude forest.

An assessment of observed vertical flux divergence in long-term eddy-covariance measurements over two Midwestern forest ecosystems

Vertical divergence of CO 2 fluxes is observed over two Midwestern AmeriFlux forest sites. The differences in ensemble averaged hourly CO 2 fluxes measured at two heights above canopy are relatively small (0.2–0.5 μmol m −2 s −1), but they are the major contributors to differences (76–256 g C m −2 or 41.8–50.6%) in estimated annual net ecosystem exchange (NEE) in 2001. A friction velocity criterion is used in these estimates but mean flow advection is not accounted for. This study examines the effects of coordinate rotation, averaging time period, sampling frequency and co-spectral correction on CO 2 fluxes measured at a single height, and on vertical flux differences measured between two heights. Both the offset in measured vertical velocity and the downflow/upflow caused by supporting tower structures in upwind directions lead to systematic over- or under-estimates of fluxes measured at a single height. An offset of 1 cm s −1 and an upflow/downflow of 1° lead to 1% and 5.6% differences in momentum fluxes and nighttime sensible heat and CO 2 fluxes, respectively, but only 0.5% and 2.8% differences in daytime sensible heat and CO 2 fluxes. The sign and magnitude of both offset and upflow/downflow angle vary between sonic anemometers at two measurement heights. This introduces a systematic and large bias in vertical flux differences if these effects are not corrected in the coordinate rotation. A 1 h averaging time period is shown to be appropriate for the two sites. In the daytime, the absolute magnitudes of co-spectra decrease with height in the natural frequencies of 0.02–0.1 Hz but increase in the lower frequencies (<0.01 Hz). Thus, air motions in these two frequency ranges counteract each other in determining vertical flux differences, whose magnitude and sign vary with averaging time period. At night, co-spectral densities of CO 2 are more positive at the higher levels of both sites in the frequency range of 0.03–0.4 Hz and this vertical increase is also shown at most frequencies lower than 0.03 Hz. Differences in co-spectral corrections at the two heights lead to a positive shift in vertical CO 2 flux differences throughout the day at both sites. At night, the vertical CO 2 flux differences between two measurement heights are 20–30% and 40–60% of co-spectral corrected CO 2 fluxes measured at the lower levels of the two sites, respectively. Vertical differences of CO 2 flux are relatively small in the daytime. Vertical differences in estimated mean vertical advection of CO 2 between the two measurement heights generally do not improve the closure of the 1D (vertical) CO 2 budget in the air layer between the two measurement heights. This may imply the significance of horizontal advection. However, a reliable assessment of mean advection contributions in annual NEE estimate at these two AmeriFlux sites is currently an unsolved problem.

Filling gaps in evapotranspiration measurements for water budget studies: Evaluation of a Kalman filtering approach

Missing data in long-term eddy covariance measurements of latent heat flux produce errors in the estimation of evapotranspiration and the water budget. Because no standard method of gap filling has been widely accepted, identification of optimal filling methods for gaps is crucial for determining total evapotranspiration. In this study we evaluate the application of a Kalman filter for filling gaps in latent heat flux data collected from an agricultural research station. The filtering approach was compared with several gap-filling methods including mean diurnal variation, multiple regressions, 2-week average Priestley–Taylor coefficient, and multiple imputation. The results demonstrated that a Kalman filtering approach developed using the relationship between latent heat flux, available energy, and vapour pressure deficit provides a closer approximation of the original data and introduces smaller errors than the other methods evaluated. Evaluation of the Kalman filter approach demonstrates the efficiency of this technique in replacing data in both small and large gaps of up to several days.

Spectral Characteristics and Correction of Long-Term Eddy-Covariance Measurements Over Two Mixed Hardwood Forests in Non-Flat Terrain

We present turbulence spectra and cospectra derived from long-term eddy-covariancemeasurements (nearly 40,000 hourly data over three to four years) and the transferfunctions of closed-path infrared gas analyzers over two mixed hardwood forests inthe mid-western U.S.A. The measurement heights ranged from 1.3 to 2.1 times themean tree height, and peak vegetation area index (VAI) was 3.5 to 4.7; the topographyat both sites deviates from ideal flat terrain. The analysis follows the approach ofKaimal et al. (Quart. J. Roy. Meteorol. Soc. 98, 563–589, 1972) whose results were based upon 15 hours of measurements atthree heights in the Kansas experiment over flatter and smoother terrain. Both thespectral and cospectral constants and stability functions for normalizing and collapsingspectra and cospectra in the inertial subrange were found to be different from those ofKaimal et al. In unstable conditions, we found that an appropriate stabilityfunction for the non-dimensional dissipation of turbulent kinetic energy is of the form Φe(ζ) = (1 - b-ζ)-1/4 - c-ζ, where ζ representsthe non-dimensional stability parameter. In stable conditions, a non-linear functionGxy(ζ) = 1 + bxyζc xy (cxy < 1) was found to benecessary to collapse cospectra in the inertial subrange. The empirical cospectralmodels of Kaimal et al. were modified to fit the somewhat more (neutraland unstable) or less (stable) sharply peaked scalar cospectra observed over forestsusing the appropriate cospectral constants and non-linear stability functions. Theempirical coefficients in the stability functions and in the cospectral models varywith measurement height and seasonal changes in VAI. The seasonal differencesare generally larger at the Morgan Monroe State Forest site (greater peak VAI) andcloser to the canopy. The characteristics of transfer functions of the closed-path infrared gas analysersthrough long-tubes for CO2 and water vapour fluxes were studied empirically. This was done by fitting the ratio between normalized cospectra of CO2 or watervapour fluxes and those of sensible heat to the transfer function of a first-order sensor.The characteristic time constant for CO2 is much smaller than that for water vapour. The time constant for water vapour increases greatly with aging tubes. Three methods were used to estimate the flux attenuations and corrections; from June through August, the attenuations of CO2 fluxes are about 3–4% during the daytime and 6–10% at night on average. For the daytime latent heat flux (QE), the attenuations are foundto vary from less than 10% for newer tubes to over 20% for aged tubes. Correctionsto QE led to increases in the ratio (QH + QE)/(Q* - QG) by about 0.05 to0.19 (QH is sensible heat flux, Q* is net radiation and QG is soil heat flux),and thus are expected to have an important impact on the assessment of energy balanceclosure.