Study site and set-up
The studied hemiboreal riparian forest is a 40-year old Filipendula type gray alder (Alnus incana (L.) Moench) forest stand grown on a former agricultural land. It is situated in the Agali Village (58°17′N; 27°17′E) in eastern Estonia within the Lake Peipsi Lowland50 (Supplementary Figs. 12 and 13).
The area is characterized by a flat relief with an average elevation of 32 m 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 10 cm 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 20 cm deep zone (Supplementary Table 3).
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 October51.
The mean height of the forest stand is 17.5 m, stand density 1520 trees per ha, the mean stem diameter at breast height 15.6 cm, basal area 30.5 m2 ha−1, the growing stock 245 m3 ha−1, and the current annual increment of stems 12.0 m3 ha−1 year−1 (based on Uri et al.52 and Becker et al.53). 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 the moss-layer Climacium dendroides (Hedw.) F. Weber & D. Mohr, Plagiomnium spp and Rhytidiadelphus triquetrus (Hedw.) Warnst are overwhelming.
Environmental characteristics of hot moments
Based on high emissions of N2O, dynamics of SWC, and near-ground air temperature, we identified four hot moments and related them to soil and environmental variables (see numbers in Fig. 1): wet (1), dry (2) with drought onset (2a), freeze-thaw (3), and dry-minor (4). The main criterion for the hot moments was a rapid increase in N2O emissions of any source.
Anomalies from the mean of each hot moment period illustrate the pattern of fluxes during the hot moments (Supplementary Fig. 2). At the end of the freeze-thaw period, the rising SWC is driven by snowmelt became a leading determinant (Supplementary Fig. 2). During the wet period, the rise in soil emissions was accompanied by a remarkable increase in the EC-based ecosystem fluxes. However, all the other hot moments were isolated to soil surfaces.
Soil flux measurements
Soil fluxes were measured using 12 automatic dynamic chambers located at 1–2 m distance from each studied tree and installed in June 2017 (Supplementary Fig. 11, see also54). 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 min each. The flushing time of the whole system with ambient air between measurement periods was 1 min. Thus, there were ~12 measurements per chamber per day, making a total of 144 flux measurements 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 ~1.17 measurements per second. The chambers were connected to the gas analyzer using a multiplexer allowing a sequent practically continuous measurement.
To account for initial stabilization after chamber closing and flushing time, we used 5 min out of the total 9 min closing time (~350 concentration measurements) to estimate slope change of N2O concentration, which was the basis for soil flux calculations.
After the quality check, 105,830 flux values (98.7% of total possible) of soil N2O fluxes could be used during the whole study period.
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 gray alder trees were selected for stem flux measurements and equipped with static closed tree stem chamber systems for stem flux measurements20. Soil fluxes were investigated close to each selected tree. The tree chambers were installed in June 2017 in the following order: at the bottom part of the tree stem (~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 the chamber, preparation see Schindler et al.54. 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 analyzed in the laboratory at the University of Tartu within a week using gas chromatography (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.55.
Soil and stem flux calculation
Fluxes were quantified on a linear approach according to the change of CH4 and N2O concentrations in the chamber headspace over time, using the equation according to Livingston and Hutchison56.
Stem fluxes were quantified on a linear approach according to the change of N2O concentrations in the chamber headspace over time. A data quality control (QC) 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 hectares 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 the 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.54.
EC instrumentation
EC 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 three 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 an airflow rate of 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 10 Hz and the gas-analyzer reported concentrations per dry air (dry mixing ratios).
Eddy-covariance flux calculation and data QC
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-min periods. Despiking of the raw data was performed following Mauder et al.57. Anemometer tilt was corrected with the double-axis rotation. Linear detrending was chosen over block averaging to minimize the influence of possible fluctuations of a gas analyzer. Time lags were detected using covariance maximization in a given time window (5 ± 2 s was chosen based on the tube length and flow rate). While Webb-Pearman-Leuning (WPL) correction58 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 dry mixing ratios. Both low and high-frequency spectral corrections were applied using fully analytic corrections59,60.
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), QC flags higher than 761.
The footprint area was estimated using Kljun et al.62 implemented in TOVI software (Li-Cor Inc.). A 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 concentration measurements from the eddy system (Eq. (1)), assuming the uniform change within the air column under the tower during every 30 min period63,64:
$${mathrm{S}} = {Delta}{mathrm{C}}/{Delta}{mathrm{t}} ast {mathrm{z}}_{mathrm{m}},$$
(1)
where S is storage, ΔC is change in the dry mixing ratio of N2O, Δt is time period (30 min), zm is measurement height (21 m).
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.36). Overall, the QC procedures resulted in 61% data coverage.
While friction velocity (u*) threshold is used to filter eddy fluxes of CO265, visual inspection of the friction velocity influence on N2O fluxes demonstrated no effect. Thus, we decided not to apply it, taking into account that the 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.66).
MATLAB (ver. 2018a-b, Mathworks Inc., Natick, MA, USA) was used for all the eddy fluxes data analysis.
Ancillary measurements
Air temperature, relative and absolute humidity were measured within the canopy at 10 m height using the HC2A-S3—Standard Meteo Probe/RS24T (Rotronic AG, Bassersdorf, Switzerland) and Campbell CR100 data logger (Campbell Scientific Inc., Logan, UT, USA). The potential amount of dissolved N2O in the atmospheric water was calculated based on the absolute humidity and the maximum solubility of N2O in water67. DPD was calculated from air temperature and estimated dew point temperature to characterize the chance of fog formation within the canopy. The solar radiation data were obtained from the SMEAR Estonia station located at 2 km from the study site68 using the Delta-T-SPN-1 sunshine pyranometer (Delta-T Devices Ltd., Cambridge, UK). The cloudiness ratio was calculated as the ratio of diffuse solar radiation to total solar radiation.
Near-ground air temperature, soil temperature (Campbell Scientific Inc.), and SWC 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 methods69.
Statistical analysis
R version 4.0.2 (R Development Core Team, 2020) was used to examine, analyze and visualize the data. The significance level (alpha) considered for all the tests was 0.05. The “akima” package version 0.6–2.1 was used to create interpolated contour plots representing a three-dimensional surface70 by plotting soil temperature and SWC against soil N2O emissions as the independent variable. Linear regression models were fitted and Spearman’s rank correlation coefficients were shown for change of SWC and soil N2O flux in period drought onset and air temperature and soil N2O flux in period freeze-thaw. Spearman’s rank correlation coefficients were also shown characterizing the relationship between the monthly average number of days with a high chance of sunshine and fog formation and the difference between the N2O flux from soil and ecosystem. Regarding all measurements of soil temperature, SWC, and soil N2O flux, relationships were better represented by nonlinear than linear models. In addition, the Bragg equation with four parameters71 was used for describing the relationship between SWC and soil N2O flux in the dry period. A workflow for the nonlinear regression analysis was used72 and regression models were fitted in R using functions lm, nls, or loess.
Source: Ecology - nature.com
