Mander, Ülo; Krasnova, Alisa; Escuer-Gatius, Jordi; Espenberg, Mikk; Schindler, Thomas; Machacova, Katerina; Pärn, Jaan; Maddison, Martin; Megonigal, Patrick; Pihlatie, Mari; Kasak, Kuno; Niinemets, Ülo; Junninen, Heikki; Soosaar, Kaido (2021): Nitrous oxide observations from the soil, stems and ecosystem (eddy covariance), meteorological and soil chemical measurements in the Agali experimental forest, Estonia, 2017-2019. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.926331 (dataset in review)
1 Study site and set-up
The studied hemiboreal riparian forest is a 40-year old Filipendula type grey alder (Alnus incana (L.) Moench) forest stand grown on a former agricultural agricultural land. It is situated in the Agali Village (58o17' N; 27o17' E) in eastern Estonia within the Lake Peipsi Lowland (Varep 1964).
The area is characterized by a flat relief with an average elevation of 32m a.s.l., formed from the bottom of former periglacial lake systems, it is slightly inclined (1%) towards a tributary of the Kalli River. The soil is Gleyic Luvisol. The thickness of the humus layer was 15-20 cm. The content of total carbon (TC), total nitrogen (TN), nitrate (NO3- -N), ammonia NH4+-N, Ca and Mg per dry matter in 10cm topsoil was 3.8 and 0.33 %, and 2.42, 2.89, 1487 and 283 mg kg-1, respectively, which was correspondingly 6.3, 8.3, 4.4, 3.6, 2.3, and 2.0 times more than those in 20cm deep zone.
The long-term average annual precipitation of the region is 650 mm, and the average temperature is 17.0 °C in July and -6.7 °C in January. The duration of the growing season is typically 175-180 days from mid-April to October (Kupper et al. 2011).
The mean height of the forest stand is 17.5 m, the mean stem diameter at breast height 15.6 cm and the growing stock 245 m3 ha−1 (based on Uri et al 2014 and Becker et al 2015). In the forest floor, the following herbs dominate: Filipendula ulmaria (L.) Maxim., Aegopodium podagraria L., Cirsium oleraceum (L.) Scop., Geum rivale L., Crepis paludosa (L.) Moench,), shrubs (Rubus idaeus L., Frangula alnus L., Daphne mezereum L.) and young trees (A. incana, Prunus padus (L.)) dominate. In moss-layer Climacium dendroides (Hedw.) F. Weber & D. Mohr, Plagiomnium spp and Rhytidiadelphus triquetrus (Hedw.) Warnst.
2 Soil flux measurements
Soil fluxes were measured using 12 automatic dynamic chambers located close to each studied tree and installed in June 2017. The chambers were made from polymethyl methacrylate (Plexiglas) covered with non-transparent plastic film. Each soil chamber (volume of 0.032 m³) covered a 0.16 m² soil surface. To avoid stratification of gas inside the chamber, air with a constant flow rate of 1.8 L min-1 was circulated within a closed loop between the chamber and gas analyzer unit during the measurements by a diaphragm pump. The air sample was taken from the top of the chamber headspace and pumped back by distributing it to each side of the chamber. For the measurements, the soil chambers were closed automatically for 9 minutes each. Flushing time of the whole system with ambient air between measurement periods was 1 minute. Thus, there were approximately 12 measurements per chamber per day. A Picarro G2508 (Picarro Inc., Santa Clara, CA, USA) gas analyzer using cavity ring-down spectroscopy (CRDS) technology was used to monitor N2O gas concentrations in the frequency of approximately 1.17 measurements per second. The chambers were connected to the gas analyzer using a multiplexer.
Since the 9 minutes of closing each soil chamber for measurements consisted of two minutes for stabilization the trend in the beginning and about two minutes unstable fluctuations at the end, for soil flux calculations, only 5 minutes of the linear trend of N2O concentration change has been used for soil flux calculations.
After the quality checking 105,830 flux values (98.7% of total possible) of soil N2O fluxes could be used during the whole study period.
3 Stem flux measurements
The tree stem fluxes were measured manually with frequency 1-2 times per week from September 2017 until December 2018. Twelve representative mature grey alder trees were selected for stem flux measurements and equipped with static closed tree stem chamber systems for stem flux measurements (Machacova et al 2016). Soil fluxes were investigated close to each selected tree. The tree chambers were installed in June 2017 in following order: at the bottom part of the tree stem (approximately 10 cm above the soil) and at 80 and 170 cm above the ground. The rectangular shape stem chambers were made of transparent plastic containers, including removable airtight lids (Lock & Lock Co Ltd, Seoul, Republic of Korea). For chamber preparation see Schindler et al. (2020). Two chambers per profile were set randomly across 180° and interconnected with tubes into one system (total volume of 0.00119 m³) covering 0.0108 m² of stem surface. A pump (model 1410VD, 12 V; Thomas GmbH, Fürstenfeldbruck, Germany) was used to homogenize the gas concentration prior to sampling. Chamber systems remained open between each sampling campaign. During 60 measurement campaigns, four gas samples (each 25 ml) were collected from each chamber system via septum in a 60 min interval: 0/60/120/180 min sequence (sampling time between 12:00 and 16:00) and stored in pre-evacuated (0.3 bar) 12 ml coated gas-tight vials (LabCo International, Ceregidion, UK). The gas samples were analysed in the laboratory at University of Tartu within a week using gas chromatograph (GC-2014; Shimadzu, Kyoto, Japan) equipped with an electron capture detector for detection of N2O and a flame ionization detector for CH4. The gas samples were injected automatically using Loftfield autosampler (Loftfield Analytics, Göttingen, Germany). For gas-chromatographical settings see Soosaar et al. (2011).
4 Soil and stem flux calculation
Fluxes were quantified on a linear approach according to change of CH4 and N2O concentrations in the chamber headspace over time, using the equation according to Livingston & Hutchison (1995).
Stem fluxes were quantified on a linear approach according to change of N2O concentrations in the chamber headspace over time. A data quality control was applied based on R2 values of linear fit for CO2 measurements. When the R2 value for CO2 efflux was above 0.9, the conditions inside the chamber were applicable, and the calculations for N2O gases were also accepted in spite of their R2 values.
To compare the contribution of soil and stems, the stem fluxes were upscaled to hectare of ground area based on average stem diameter, tree height, stem surface area, tree density, and stand basal area estimated for each period. A cylindric shape of tree stem was assumed. To estimate average stem emissions per tree, fitted regression curves for different periods were made between the stem emissions and height of the measurements as previously done by Schindler et al. (2020).
5 Eddy covariance instrumentation
Eddy-covariance system was installed on a 21 m height scaffolding tower. Fast 3-D sonic anemometer Gill HS-50 (Gill Instruments Ltd., Lymington, Hampshire, UK) was used to obtain 3 wind components. CO2 fluxes were measured using the Li-Cor 7200 analyser (Li-Cor Inc., Lincoln, NE, USA). Air was sampled synchronously with the 30 m teflon inlet tube and analyzed by a quantum cascade laser absorption spectrometer (QCLAS) (Aerodyne Research Inc., Billerica, MA, USA) for N2O concentrations. The Aerodyne QCLAS was installed in the heated and ventilated cottage near the tower base. A high-capacity free scroll vacuum pump (Agilent, Santa Clara, CA, USA) guaranteed air flow rate 15 L min-1 between the tower and gas analyzer during the measurements. Air was filtered for dust and condense water. All measurements were done at 10Hz and the gas-analyzer reported concentrations per dry air (mixing ratios).
6 Eddy-covariance flux calculation and data quality control
The fluxes of N2O were calculated using the EddyPro software (v.6.0-7.0, Li-Cor) as a covariance of the gas mixing ratio with the vertical wind component over 30-minute periods. Despiking of the raw data was performed following Mauder (2013). Anemometer tilt was corrected with the double axis rotation. Linear detrending was chosen over block averaging to minimize the influence of a possible fluctuations of a gas analyser. Time lags were detected using covariance maximisation in a given time window (5±2s was chosen based on the tube length and flow rate). While WPL-correction is typically performed for the closed-path systems, we did not apply it as water correction was already performed by the Aerodyne and the software reported mixing ratios. Both low and high frequency spectral corrections were applied using fully analytic corrections (Moncrieff et al. 1997, 2004).
Calculated fluxes were filtered out in case they were coming from the half-hour averaging periods with at least one of the following criteria: more than 1000 spikes, half-hourly averaged mixing ratio out of range (300-350 ppb), quality control (QC) flags higher than 7 (Foken et al, 2004).
Footprint area was estimated using Kljun et al (2015) implemented in TOVI software (Li-Cor Inc.). Footprint allocation tool was implemented to flag the non-forested areas within the 90% cumulative footprint and fluxes appointed to these areas were removed from the further analysis.
Storage fluxes were estimated using point concentration measurements from the eddy system, assuming the uniform change within the air column under the tower during every 30 min period (calculated in EddyPro software). In the absence of a better estimate or profile measurements, these estimates were used to correct for storage change. Total flux values that were higher than eight times the standard deviation were additionally filtered out (following Wang et al., 2013). Overall, the quality control procedures resulted in 61% data coverage.
While friction velocity (u*) threshold is used to filter eddy fluxes of CO2 (Papale et al. 2006), visual inspection of the friction velocity influence on N2O fluxes demonstrated no effect. Thus, we decided not to apply it, taking into account that 1-9 QC flag system already marks the times when the turbulence is not sufficient.
To obtain the continuous time-series and to enable the comparison to chamber estimates over hourly time scales, gap-filling of N2O fluxes was performed using marginal distribution sampling method implemented in ReddyProcWeb online tool (https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb) (described in detail in Wutzler et al 2018).
MATLAB (ver. 2018a-b, Mathworks Inc., Natick, MA, USA) was used for all the eddy fluxes data analysis.
7 Ancillary measurements
Air temperature and relative humidity were measured within the canopy at 10m height using the HC2A-S3 - Standard Meteo Probe / RS24T (Rotronic AG, Bassersdorf, Switzerland) and Campbell CR100 data logger (Campbell Scientific Inc., Logan, UT, USA). Based on these data, dew point depression was calculated to characterise chance of fog formation within the canopy. The incoming solar radiation data were obtained from the SMEAR Estonia station located at 2 km from the study site (Noe et al 201587) using the Delta-T-SPN-1 sunshine pyranometer (Delta-T Devices Ltd., Cambridge, UK). The cloudiness ratio was calculated based on radiation data.
Near-ground air temperature, soil temperature (Campbell Scientific Inc.) and soil water content sensors (ML3 ThetaProbe, Delta-T Devices, Burwell, Cambridge, UK) were installed directly on the ground and 0-10 cm soil depth close to the studied tree spots. During six campaigns from August to November 2017 composite topsoil samples were taken with a soil corer from a depth of 0-10 cm for physical and chemical analysis using standard methods (APHA-AWWA-WEF, 2005).
Mander, Ülo; Krasnova, Alisa; Escuer-Gatius, Jordi; Espenberg, Mikk; Schindler, Thomas; Machacova, Katerina; Pärn, Jaan; Maddison, Martin; Megonigal, Patrick; Pihlatie, Mari; Kasak, Kuno; Niinemets, Ülo; Junninen, Heikki; Soosaar, Kaido (submitted): Nitrous oxide observations from the soil, stems and ecosystem (eddy covariance), meteorological and soil chemical measurements in the Agali experimental forest, Estonia, 2017-2019.
Latitude: 58.283333 * Longitude: 27.283333
Date/Time Start: 2017-07-23T00:00:00 * Date/Time End: 2019-12-31T00:00:00
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Datasets listed in this bundled publication
- Mander, Ü; Krasnova, A; Escuer-Gatius, J et al. (2021): Soil chemistry measurements in the Agali experimental forest, Estonia, 2018. https://doi.pangaea.de/10.1594/PANGAEA.926314
- Mander, Ü; Krasnova, A; Escuer-Gatius, J et al. (2021): Hourly flux nitrous oxide measurements from the soil, stems and ecosystem in the Agali experimental forest, Estonia, 2017-2019. https://doi.pangaea.de/10.1594/PANGAEA.926302
- Mander, Ü; Krasnova, A; Escuer-Gatius, J et al. (2021): Meteorological data from the Agali experimental forest, Estonia, 2017-2019. https://doi.pangaea.de/10.1594/PANGAEA.926306
- Mander, Ü; Krasnova, A; Escuer-Gatius, J et al. (2021): Nitrogen dioxide wind drought onset from the Agali experimental forest, Estonia, 2017-2019. https://doi.pangaea.de/10.1594/PANGAEA.926310