A b s t r a c t. Methane emission from a wetland was measured with the eddycovariance system. The location of the system allow- ed observation of methane efflux from areas that were covered by different vegetation types. The data presented in this paper were collected in the period between the13th of June and the 31st of August 2012. During the warmest months of the summer, there was no strong correlation between methane emissions and either the water table depth or peat temperature. The presence of reed and cattail contributed to a pronounced diurnal pattern of the flux and lower methane emission, while areas covered by sedges emitted higher amounts more with no clear diurnal pattern.
there has been significant confusion around the importance of that role, largely because the community studying sea ice gas fluxes has been unable to reconcile large fluxes measured by eddycovariance with significantly smaller fluxes mea- sured by enclosure methods (Table 1). This problem is anal- ogous to the problem faced by researchers studying open wa- ter gas exchange, whereby for several decades EC measure- ments could not be reconciled with tracer-based measure- ments (e.g., Broecker et al., 1986). The open water problem was eventually resolved by using closed-path EC systems with a dried sample airstream (Miller et al., 2010), and EC measurements are now better aligned with other techniques.
Fig. 6. Model results for the modified Hybrid at UBT for 10 July 2009 (a–b), 27 July 2009 (c–d), 5 August 2009 (e–f) and 6 August 2009 (g–h). Left column: latent heat flux (Q E ); right column: sensible heat flux (Q H ) and surface temperature T 0 [ ◦ C]. L and W refer to “land” and “water” as origin of the fluxes. L+W is the complete available time series. The subscripts Hyb,mod and Hyb,org refer to fluxes from the modified and original Hybrid and COARE are fluxes from the lake derived by TOGA-COARE whereas SEWAB is a SVAT model and HM refers to a hydrodynamic multi-layer lake model after Foken (1984) and Panin et al. (2006). EC and EC,EBC refer to measurements by eddycovariancemethod where in the latter the energy balance has been closed by distributing the residual according to Bowen-ratio (this requires good data quality and fluxes and can only be done for fluxes that are attributed to land). The circles indicate poor data quality of the EC system according to Foken et al. (2004). Gray shading indicates times where the flux footprint of UBT was over the lake.
0.176 to 6.61 mm, averaged in 1 m s 1 wind speed bins, are shown in Figure 10. There is a general increase in the esti- mated fluxes with wind speed up to about 13 m s 1 , with a flattening off or even decrease at higher wind speeds for particles larger than 0.9 mm. This flattening off of the flux is also seen in the mean particle number concentrations as functions of wind speed (not shown). Pant et al.  also reported a leveling off of particle concentration with wind speed for wind speeds between 16 and 22 m s 1 , as did Exton et al.  between 14 and 19 m s 1 . Pant et al.  suggested that the feature might be an inlet sam- pling efficiency issue at high wind speeds. Another possi- bility is that it results from a sampling bias due to the small number of observations at these higher wind speeds within each study. During an extended series of measurements in the Outer Hebrides, almost 30 years ago [Smith et al., 1989], one of us (MHS) was struck by a leveling off of the particle concentrations at the highest wind speeds recorded; how- ever, when further measurements extended to higher wind speeds, the earlier leveling off was not observed but a similar feature occurred at the new high wind speed limit. Although the issue was never pursued, a possible explanation was considered. In any given sample interval the mean wind speed and particle concentration are obtained; the equilib- rium particle concentration will typically lag changes in the wind speed. For the majority of the wind speed range a more or less equal number of samples will be obtained from intervals with increasing/decreasing wind, and the effect of the lag on particle concentration is averaged out. At the Figure 9. A comparison at 80% humidity of the source function from the eddycovariancemethod (solid
Abstract. A comparison of two popular eddy-covariance software packages is presented, namely, EddyPro and TK3. Two approximately 1-month long test data sets were processed, representing typical instrumental setups (i.e., CSAT3/LI-7500 above grassland and Solent R3/LI-6262 above a forest). The resulting fluxes and quality flags were compared. Achieving a satisfying agreement and understand- ing residual discrepancies required several iterations and in- terventions of different nature, spanning from simple soft- ware reconfiguration to actual code manipulations. In this pa- per, we document our comparison exercise and show that the two software packages can provide utterly satisfying agree- ment when properly configured. Our main aim, however, is to stress the complexity of performing a rigorous comparison of eddy-covariance software. We show that discriminating ac- tual discrepancies in the results from inconsistencies in the software configuration requires deep knowledge of both soft- ware packages and of the eddy-covariancemethod. In some instances, it may be even beyond the possibility of the inves- tigator who does not have access to and full knowledge of the source code. Being the developers of EddyPro and TK3, we could discuss the comparison at all levels of details and this proved necessary to achieve a full understanding. As a result, we suggest that researchers are more likely to get compara- ble results when using EddyPro (v5.1.1) and TK3 (v3.11) – at least with the setting presented in this paper – than they are when using any other pair of EC software which did not undergo a similar cross-validation.
momentum, and heat at sites around the world (http://fluxnet. fluxdata.org/data/fluxnet2015-dataset; last access: 24 Febru- ary 2017). Measurements are made with the eddycovariancemethod on towers above vegetation canopies (Baldocchi et al., 2001; Anderson et al., 1984; Goldstein et al., 2000) with consistent gap filling (Reichstein et al., 2005; Vuichard and Papale, 2015) and quality control across sites (Pastorello et al., 2014). Flux and meteorological quantities are reported in half-hour intervals. We analyze data from all sites in the United States and Europe in the FLUXNET2015 Tier 1 dataset. This analysis is restricted to the US and Europe be- cause these regions have dense O 3 monitoring networks, de-
The investigations were was conducted at Rzecin wet- land 70 km NW of Poznañ (Western Poland). The eddycovariance (EC) tower was erected there (52° 45’ 44” N / 16° 18’ 34” E) at the end of 2003. The instruments installed on the tower enable one to carry out the comprehensive studies of mass and energy exchange between the wetland eco- system surface and the atmosphere (Lund et al., 2010; Owen et al., 2007). The results of the analysis presented in this paper were based on the data collected during the period from the 1st of January to the 31st December 2009. A pair of CM3 pyranometers was applied for shortwave radiation flux density measurements. The upward facing pyranometer was used for measurements of shortwave radiation flux (global radiation) (Rs in ) that reaches the ecosystem vegetation
and tipping bucket amounts. Precipitation was measured by six different lysimeters, and yearly amounts for individual lysimeters showed variations of −3.0 to 1.0 % compared to the yearly precipitation mean over all lysimeters. An addi- tional comparison with corrected tipping bucket precipita- tion measurements according to the method of Richter (1995) shows in general a decrease of the monthly and yearly dif- ference, which was 3 % after correction. In order to explain the differences in precipitation between the devices, the con- tribution of dew, rime, and fog to the yearly precipitation was analyzed. This was done by filtering the data for typi- cal weather conditions like high relative humidity, low wind speed, and negative net radiation, which promote the devel- opment of dew and rime. For the identified cases a check was made with a visual surveillance system as to whether dew/rime was visible. During these conditions the lysime- ter shows clearly larger precipitation amounts than the TB, which explains 16.9 % of the yearly precipitation difference. Fog and drizzling rain conditions, additionally identified with the help of the on-site camera system, explain another 5.5 % of the yearly precipitation differences. These findings indi- cate an improved ability of the lysimeters to measure dew and rime as well as fog and drizzling rain. The remaining 78 % of the precipitation difference between lysimeters and tipping bucket is strongly related to snowfall events, as under those conditions large differences were found. Lysimeter precipi- tation measurements are affected by a relatively high mea- surement uncertainty during winter weather conditions, sim- ilar to TB and other common measurement methods. Thus, the limitations for the lysimeter precipitation measurements during those periods require further investigation. We found that, during conditions where the lysimeters were completely covered by snow, lysimeter records were unreliable and con- tributed 36 % of the total precipitation difference.
It is recommended that surface fluxes measured using the eddycovariance (EC) technique are done in the inertial sub- layer and free from obstructions (Roth, 2000). These assump- tions are often easy to meet over natural surfaces but can be challenging for EC systems above cities. Often the EC mea- surements are made within or in the vicinity of the roughness sublayer, the adjacent layer to the surface with height of 2–5 times the mean building height (Raupach et al., 1991). In this layer, turbulence is not homogeneous but rather varies greatly in space, and the Monin–Obukhov similarity theory (MOST) is no longer strictly valid. Despite the non-ideal conditions, EC measurements from urban areas are needed for the pur- poses of wind engineering, understanding the urban surface– atmosphere interactions, in the estimation of urban carbon budgets (Christen et al., 2011; Nordbo et al., 2012a), and in order to improve the description of urban areas in numerical weather and air quality predictions via the measured turbu- lent fluxes of heat (Grimmond et al., 2010; Karsisto et al., 2015; Demuzere et al., 2017). In order for the urban EC sys- tems to meet the requirements of the technique, we are of- ten forced to conduct the measurements on top of buildings or other platforms such as telecommunication towers (Wood et al., 2010; Liu et al., 2012; Brümmer et al., 2013; Nordbo et al., 2013; Keogh et al., 2012; Ao et al., 2016) instead of narrow lattice masts, which would minimise the effect of the structure itself on the EC measurements. Thus strictly speak- ing, the measurements are not necessarily made completely free of the impact of roughness elements even if the measure- ment height is sufficiently above the surrounding roughness elements. The interaction between the EC measurements and the measurement platform itself causes challenges for obtain- ing high-quality EC data sets, and special attention should be paid to the effect of the so-called flow distortion area on the measurements (Barlow et al., 2011). Urban EC mea- surements have furthermore raised the need for local scal- ing of mean turbulent properties with minor deviations from inertial-sublayer scaling (Rotach, 1993; Roth, 2000; Vesala et al., 2008; Wood et al., 2010) and corrections for local-scale anthropogenic sources (Kotthaus and Grimmond, 2012).
Hensen, A., Nemitz, E., Flynn, M. J., Blatter, A., Jones, S. K., Sørensen, L. L., Hensen, B., Pryor, S. C., Jensen, B., Otjes, R. P., Cobussen, J., Loubet, B., Erisman, J. W., Gallagher, M. W., Nef- tel, A., and Sutton, M. A.: Inter-comparison of ammonia fluxes obtained using the Relaxed Eddy Accumulation technique, Bio- geosciences, 6, 2575–2588, doi:10.5194/bg-6-2575-2009, 2009. H¨ortnagl, L., Clement, R., Graus, M., Hammerle, A., Hansel, A., and Wohlfahrt, G.: Dealing with disjunct concentration measurements in eddycovariance applications: A compari- son of available approaches, Atmos. Environ, 44, 2024–2032, doi:10.1016/j.atmosenv.2010.02.042, 2010.
Cumulative fluxes derived from non-gap-filled chamber measurement data are smaller (84 %) than the ones derived from gap-filled data. This shows that the integration method can introduce a bias in the estimate of cumulative fluxes and therefore emission factors. In theory the arithmetic mean of a flux dataset provides an actual integration over time. However, if large fluxes are measured only for a short term, e.g. after N applications, peak values may be over repre- sented, leading to a biased cumulative flux. Indeed cham- bers data from our comparison periods showed a positively skewed distribution due to large flux values immediately af- ter N application, with the exception of June 2003, where the data distribution was negatively skewed due to large negative fluxes. EC fluxes overall led to smaller cumulative estimates when compared to chambers, but the values were actually higher in the lower range of emission, and lower in the higher range. A possible explanation for the high-end of the emis- sion range would be the sampling of hot spots of emissions by chambers that get smoothed by the EC integration over a larger surface.
The eddycovariance (EC) technique provides one of the most direct measures of energy and mass exchanges between the land surface and the atmosphere (Baldoc- chi, 2008, 2014). A major strength of EC is its unique ability to provide a time series of spatially integrated flux estimates at the footprint scale. In recent decades, EC has become the de facto approach for estimating land–atmosphere fluxes of terrestrial ecosystems. Col- laboration between researchers has resulted in global networks (Baldocchi et al., 2001; Baldocchi, 2008, 2014), and these have provided numerous invaluable advances in our understanding of – for example – ecosystem dynamics (Lasslop et al., 2010; Migliavacca et al., 2011; Raczka et al., 2013; Stoy et al., 2013).
often selected because of the presence of seafloor gas seeps, the tem- poral dataset could potentially be biased toward near-seep measure- ments. Therefore, we spatially normalized the dataset, following the method described in (4). Briefly, GPS latitudes and longitudes were rounded to the nearest 0.001°, and all measurements within that grid cell were combined. A 0.001° × 0.001° grid cell is approximately 111 × 102 m at 75°N latitude; for comparison, Oden is 108 m in length with a beam of 31 m. Because of the long duration (20 min) of the EC averaging period, the overall difference between the temporally and spatially normalized datasets was small; the spatially normalized dataset contains 2499 total EC flux measurements (compared to 2642 in the temporal dataset). A total of 46 additional measurements were removed because of CH 4 flux <−6 mg m −2 day −1 and wind
perature. We interpret this as an inertia effect of the ther- mohygrometer. So, if the thermohygrometer complex has a higher thermal mass than the ambient air, the temperature measurements taken by the thermohygrometer are attenuated in the high-frequency range. As the attenuation effect was not found in the relative humidity measurements, we assume that the relative humidity measurements were independent of temperature measurements, and therefore relative humidity was not attenuated in the same way as air temperature. Sub- sequently, relative humidity fluctuations were conserved and could be used for the calculations of the water vapour mole fraction. In general, the deviation from the mean is of higher interest than the mean itself for the EC method (Baldocchi, 2014). As long as the relative humidity fluctuations are con- served in the calculations of the water vapour mole fraction, a plausible covariance between the water vapour mole fraction and the vertical velocity can be calculated.
This research demonstrates the first direct covariance mea- surements of air-sea ozone flux from a ship platform. This task was accomplished by integrating a fast-response chemiluminescence sensor into the NOAA/ESRL ship-based flux system. Under the operational conditions described here, the instrument was found to have a sensitivity of ∼ 2800 counts s −1 ppbv −1 , which yielded a high enough signal-to-noise ratio to measure ozone fluxes at the ambi- ent levels and deposition rates observed over the ocean. A number of data filters and corrections were applied to reduce errors and uncertainties in the ozone flux determination. At- tenuation in covariance caused by the sampling manifold and the reaction chamber was described, and a cutoff frequency of ∼ 0.4 Hz was determined for a 11 l min −1 sampling line purge flow rate. The time lag between the ozone and tur- bulent vertical wind speed was first determined by the cross correlation method, and subsequently a “puff-system” was developed for a more accurate and reliable method for the lag time determination. Quenching and density variations caused by water vapor were found to contribute errors in the ozone flux determination. A Nafion membrane dryer was shown to reduce fast water fluctuations to levels where cor- rections were no longer required. Use of the dryer eliminates uncertainties from the water vapor interferences.
chronicity as per the requirements of the EC method, for a select few anemometer–analyzer pairs. However, most of such solutions are not scalable to other hardware models or gas species because the required instrumentation does not necessarily support the same connectivity technology and specifications. Therefore, it is generally very challenging, for example, to simply replace a gas analyzer with another one from another manufacturer and keep the same synchro- nization performance. Furthermore, it is customary for many research groups to assemble EC systems “in-house”, espe- cially when addressing gas species that have not been pop- ular enough to grow strong commercial interest. Typically, in these systems, data collection is performed with industrial data loggers or computers via serial or Ethernet connectivity, using custom-built logging software. In such cases, it is par- ticularly important to verify that various types of data mis- alignment are not being introduced by the data logging sys- tem and data collection strategy to assure minimal or no bias in resulting fluxes.
EC data were processed using EddyUH software (Mam- marella et al., 2016) according to the approaches in Mam- marella et al. (2015). Briefly, spikes in the data were removed on the basis of a maximum difference being allowed between two adjacent points, and 2-D coordinate rotation was done so that the wind component u is directed parallel to the mean horizontal wind. Linear detrending was used for calculating the turbulent fluctuations. Lag time was determined from the maximum of the cross-covariance function and cross-wind correction was applied to sonic temperature data (Liu et al., 2001). High-frequency spectral corrections were calculated according to Mammarella et al. (2009).
nitude than the instrument precision. For future studies, in- lets below the canopy could be installed farther from the soils (> 0.5 m) and placed closer together (1z < 3 m) so as to still measure statistically significant gradients that may be more linear than observed here. For studies with taller tow- ers extending beyond the vegetative canopy, a greater dis- tance between the inlets (1z > 4 m) could increase the mole fraction gradient signal-to-noise ratio, but should not exceed relevant eddy length scales, which can range from the me- chanical eddy size forced by obstruction of the wind by the trees ( ∼ 5 m) to the lower planetary boundary layer buoyant eddy size (∼ 100 m). At Harvard Forest, the dominant flux- carrying eddy frequency is between 0.01 and 0.2 Hz, which corresponds to eddy scales of 10 to 200 m for mean winds around 2 m s −1 (Goulden et al., 1996)
In general, drip irrigation systems deliver the limited amount of water directly to the plant root zone; consequently, the soil water content (SWC) in the inter-film zone is very low (Bonachela et al., 2001). In addition, the mulched film elimi- nates soil evaporation in the wide-row and narrow-row zones (Wang et al., 2001). Therefore, the soil evaporation is ex- pected to be a small portion of ET under mulched drip irri- gation, especially when irrigation is stopped for a long time. In this study, LCpro + measurements were used to measure the bare soil evaporation in the inter-film zone when the soil pot was substituted for the leaf chamber on 20 September (2 days after SP3, no irrigation for 23 days, SWC = 15.5 % within a depth of 20 cm). The measured value was only 0.04 mm day −1 . Therefore, we assume that soil evaporation was sufficiently small in SP3 so that it can be neglected. In other words, evapotranspiration measured by eddy co- variance in SP3 contained the transpiration component only. Thus, in this study, SP3 was chosen as the period for transpi- ration comparison at the field scale.