Persistent carbon sink at a boreal drained bog forest

Biogeosciences, 15, 3603–3624, 2018 https://doi.org/10.5194/bg-15-3603-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Persistent carbon sink at a boreal drained bog forest Kari Minkkinen1, Paavo Ojanen1, Timo Penttilä2, Mika Aurela3, Tuomas Laurila3, Juha-Pekka Tuovinen3, and Annalea Lohila3 1Department of Forest Sciences, P.O. Box 27, 00014 University of Helsinki, Helsinki, Finland 2Natural Resources Institute Finland, P.O. Box 2, 00791 Helsinki, Finland 3Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland Correspondence: Kari Minkkinen ([email protected]) Received: 8 December 2017 – Discussion started: 2 January 2018 Revised: 5 April 2018 – Accepted: 7 May 2018 – Published: 15 June 2018 Abstract. Drainage of peatlands is expected to turn these ecosystems into carbon sources to the atmosphere. We measured carbon dynamics of a drained forested peatland in southern Finland over 4 years, including one with severe drought during growing season. Net ecosystem exchange (NEE) of carbon dioxide (CO2) was measured with the eddy covariance method from a mast above the forest. Soil and forest floor CO2and methane (CH4) fluxes were measured from the strips and from ditches with closed chambers. Biomass and litter production were sampled, and soil subsidence was measured by repeated levellings of the soil surface. The drained peatland ecosystem was a strong sink of carbon dioxide in all studied years. Soil CO2balance was estimated by subtracting the carbon sink of the growing tree stand from NEE, and it showed that the soil itself was a carbon sink as well. A drought period in one summer significantly decreased the sink through decreased gross primary production. Drought also decreased ecosystem respiration. The site was a small sink for CH4, even when emissions from ditches were taken into account. Despite the continuous carbon sink, peat surface subsided slightly during the 10-year measurement period, which was probably mainly due to compaction of peat. It is concluded that even 50 years after drainage this peatland site acted as a soil C sink due to relatively small changes in the water table and in plant community structure compared to similar undrained sites, and the significantly increased tree stand growth and litter production. Although the site is currently a soil C sink, simulation studies with process models are needed to test whether such sites could remain C sinks when managed for forestry over several tree-stand rotations. 1 Introduction Peatlands worldwide contain 500–600Pgcarbon (C) (Gorham, 1991; Yu et al., 2010; Page et al., 2011) that has been fixed from the atmosphere. Wet, anoxic conditions constrain the decomposition of organic matter and thus enable the accumulation of carbon as peat. Since wet conditions are a prerequisite for peat accumulation, drying of peatlands through drainage or climate change has been assumed to result in the release of sequestered carbon back to the atmosphere. The effect of drainage of forested peatlands on carbon stocks has been under debate at least since the 1980s, when large carbon dioxide (CO2) emissions were reported from drained peatlands in Finland (Silvola, 1986). Studies from agricultural peat soils show that carbon stocks are usually greatly diminished under efficient drainage (e.g. Oleszczuk et al., 2008; Tiemeyer et al., 2016), with some exceptions (e.g. Merbold et al., 2009; Fleischer et al., 2016). Similar C loss has often been assumed for all drained peatlands, including those drained for forestry. However, in some peatlands, soil has been reported to sequester carbon even after drainage (e.g. Lohila et al., 2011; Turetsky et al., 2011). Minkkinen and Laine (1998a) and Minkkinen et al. (1999) showed, based on peat C stock measurements, that many nutrient-poor peatland sites remained C sinks after drainage. Later, Ojanen et al. (2013) showed the same relation with site type and soil C balance, nutrient-poor ones being sinks and fertile ones sources. The continued C sequestration on relatively nutrient-poor sites has been related to the increased litter production and changes in litter quality (Laiho et al., 2003; Straková et al., 2012) vs. only moderately increased Published by Copernicus Publications on behalf of the European Geosciences Union. 3604 K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest decomposition of old peat (Minkkinen and Laine, 1998a). This view has, however, still been challenged (e.g. Simola et al., 2012), and, for example according to IPCC guidance, drained peatlands are assumed to be C sources (Drösler et al., 2014). Climate warming, in addition to drainage, has been predicted to increase C loss from peatlands because of increased soil temperatures and droughts (e.g. Moore, 2002). In warmer and drier conditions the decomposition of soil organic matter (SOM) is expected to increase, although increased primary production and a possible long-term shift towards more shrub and tree dominated vegetation communities (Laiho et al., 2003; Tahvanainen, 2011; Straková et al., 2010, 2012) may partly compensate for the increased decomposition rates (Flanagan and Syed, 2011). The reported impacts of droughts on ecosystem CO2fluxes are, however, variable. Droughts have been shown to decrease photosynthesis and increase ecosystem respiration, especially on wet and nutrient-rich fens (Bubier et al., 2003; Adkinson et al., 2011), while on naturally drier bogs, the effects may be reversed (Sulman et al., 2010). CO2emissions from the decomposition of peat are often shown to increase linearly with water level drawdown (e.g. Silvola et al., 1996; Jauhiainen 2012) but there are also indications of an optimum water table depth in boreal peatlands below which soil respiration would not further increase (Mäkiranta et al., 2009). Thus, in some cases decomposition of SOM might even decrease during droughts (Sulman et al., 2010). Our study site, the forestry-drained peatland Kalevansuo in southern Finland, was earlier reported to be a strong C sink in terms of net ecosystem CO2exchange (NEE) during 2004–2005 (Lohila et al., 2011). The magnitude of the C sink was remarkably higher than the estimated tree stand C pool increment, which led us to the conclusion that the soil must also act as a C sink. Whether this was just a single-year result or whether it holds through several years with varying weather conditions will be investigated in this paper. The aims of this study were to estimate the full C balance of a drained peatland forest ecosystem over 4 years, and to analyse the impact of seasonal drought on the C fluxes. We measured the C pools in the ecosystem (peat soil, vegetation above and below ground), CO2fluxes between the ecosystem and atmosphere, namely NEE, gross primary production (GPP), ecosystem respiration (RECO)and forest floor respiration (RFF)divided to component fluxes (peat, litter, roots, ground vegetation) and the C flux in litter (L). We complemented the results with measurements of methane (CH4) fluxes and peat subsidence. 2 Material and methods 2.1 Site The measurements were carried out in a drained peatland forest, Kalevansuo, in southern Finland (60◦3804900 N, 24◦2102300 E, 123m.a.s.l.). The peatland was drained by digging open ditches in 1971. Kalevansuo is a typical dwarfshrub type peatland forest according to the classification of Vasander and Laine (2008). The dominant tree species is Scots pine (Pinus sylvestris L.), comprising 98% of the stand volume and 53% of the stem number. Pubescent birch (Betula pubescens Roth) and Norway spruce (Picea abies L.) form the sparse understorey. The site has been naturally forested since long before drainage as evidenced by very old scattered stumps of Scots pine found in all parts of the site. Tree ages of the present Scots pine stand, as determined in 2005 from increment cores of sample trees (n=7), varied from 67 to 179 years with an average of 120 years. In 2008, the stand stem volume was 130m3ha−1, basal area 18 m2ha−1, dominant height 16m and stem number 1670ha−1. Microtopographically the site is rather even (lawn level), with small hummocks covering about 25% of the area. A more detailed description of the stand is given by Lohila et al. (2011). Following drainage, the tree stand has grown bigger and the coverage of mire species has decreased and forest species increased in the bottom and field layers. However, many mire species are still present at the peatland. Forest floor vegetation consists mainly of forest and mire dwarf shrubs (Vaccinium myrtillus L., V. vitis idaea L., V. uliginosum L., Ledum palustre L.), with patches of cottongrass (Eriophorum vaginatum L.) and cloudberry (Rubus chamaemorus L.). The dominant moss species are Pleurozium schreberi (Brid.) Mitt., covering 48% of the study area, and Dicranum polysetum (37%), but Sphagnum mosses such as S. angustifolium (Russ.) C. Jens., S. russowii Warnst., and S. magellanicum Brid. are also abundant in moist patches (coverage 15 %; Badorek et al., 2011). The ditches have not been cleaned since digging in 1971 and are nowadays totally vegetated, mainly with Sphagnum riparium (and S. russowii, S. angustifolium), some cottongrass (Eriophorum vaginatum) and sporadic dwarf shrubs (Ledum palustre). Peat depth, measured from the 33 sample plots, varies from 1.3 to 3.0m, the average being 2.2m. Mean peat bulk density is 94kg m−3in the 0–20cm layer. The peat accumulated prior to drainage at the study area is composed mainly of the remains of Sphagna (Sphagnum fuscum, S. magellanicum), Ericaceous shrubs and cottongrass (Eriophorum vaginatum) (Mathijssen et al., 2017). After drainage the remains of forest mosses and woody roots have increased their share in surface peat. Drainage has increased surface peat oxidation which is seen as a shallow layer of more decomposed peat about 10–20cm below the surface. Remains of several forest fires are also present, especially in the surface layers Biogeosciences, 15, 3603–3624, 2018 www.biogeosciences.net/15/3603/2018/ K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest 3605 30–50cm, where a charcoal layer is clearly visible, but also in the deeper layers from 70 to 180cm. The mean air temperature in 2005–2008 was 15.3◦C during summer months (June–August) and −3.8◦C during winter (December–March). The annual mean air temperature was 5.1◦C and temperature sum (>5 ◦C) 1356 degree-days. Annual average precipitation was 722mm and maximum wintertime snow depth 20–60cm. 2.2 Measurement setup The site was set up for C flux measurements in June–August 2004. The micrometeorological eddy covariance (EC) measurements were conducted from a mast, erected in August 2004, in the centre of the peatland at a 200–250m distance from an upland forest in the north-west and from a small lake in the south-west. To the north-east the homogenous fetch was longer, about 600m. The EC footprint was thus concentrated to the fairly homogenous peatland pine forest with at least 200m radius (Lohila et al., 2011). The chamber measurements of CO2and CH4fluxes were conducted at four plots, located 50–100m from the mast. The measurement collars were inserted and litter and plants removed from the treated collar plots in June 2004. As every plot consisted of 16 measurement points (collars), the whole setup contained 4×16, i.e. 64 measurement points. In addition, CH4fluxes from ditches were measured in 2011 at four points on two parallel ditches located on both sides of the mast. The depth of the water table (WT) was manually measured from two perforated plastic pipes at each plot, along with chamber measurements. The WT was also continuously recorded close to the EC mast by a PDCR 830 (Druck Messtechnik GmbH, in 2004–2006), and a Hobo U20-00101 (Onset Computer Corporation, MA, USA, in 2007–2009). Soil temperatures were recorded with temperature loggers (i-Button DS1921G, Maxim Integrated Products) from the depths of 5cm (T5)and 30 cm (T30)below soil surface at intervals of 1–3h. In 2005–2006, T5was recorded from every measurement point and T30 from 16 points (four per plot). In 2007–2008, T5recordings were taken from two points per plot and T30 recordings from two points in total. The tree stand, ground vegetation and soil properties were measured on 33 plots located evenly along 8 radial transects extending 160m from the mast (the centre plot). Four transects with plots spaced at 20, 60, 100 and 140m distances from the mast were alternated with four other transects with plots spaced at 40, 80, 120 and 160m distances from the mast. The area of each plot was 200m2. 2.3 Measurements 2.3.1 Ecosystem–atmosphere exchange of CO2 The turbulent fluxes of CO2, water vapour (H2O), sensible heat and momentum were measured with the eddy covariance technique on top of the 21.5m telescopic mast (17.5 m from April 2005 to April 2006). Supporting meteorological measurements included, for example, relative humidity (RH), photosynthetic photon flux density (PPFD), air and soil temperatures, and soil moisture (see Lohila et al., 2011, for closer description). Measurements were carried out from August 2004 to March 2009. Here we report results for the full years 2005–2008. We used an SATI-3SX (Applied Technologies, Inc.) sonic anemometer/thermometer from 2004 to November 2006, after which a METEK USA-1 (METEK GmbH, Elmshorn, Germany) was used. The atmospheric concentrations of CO2and H2O were measured with an LI-7000 (LI-COR, Inc.) analyser. This instrument was calibrated bimonthly to monthly with two known CO2concentrations [CO2] (0 and 421ppm). CO2-free synthetic dry air was used as a reference gas. The heated inlet tube (3.1mm Bevaline IV) for the LI7000 was 17m long, and a flow rate of 6Lmin−1was used. The signals were sampled at a frequency of 10Hz, and the turbulent fluxes were calculated on-line as 30 min averages, applying standard EC procedures. The effect of density fluctuations related to the water vapour flux (Webb et al., 1980) was included in the calculations, and the fluxes were corrected for systematic losses using the transfer function method of Moore (1986), including the losses due to autoregressive running mean filtering and the imperfect highfrequency response of the measurement system. Details of the flux calculation and correction procedures can be found in Pihlatie et al. (2010) and Lohila et al. (2011). To estimate the storage fluxes of CO2, the mean [CO2] observed at a height of 4m with a LI-820 CO2analyser and the [CO2] measured at the top of the mast were used. The storage term was calculated with the central difference method from the mean concentration during the subsequent and preceding 30 min periods and added to the measured turbulent flux. Hereafter NEE refers to the sum of turbulent and storage fluxes. In this paper, we use the convention that a positive value of NEE indicates a flux from the ecosystem to the atmosphere. 2.3.2 Forest floor CO2efflux CO2efflux from the forest floor was measured with an opaque closed steady state chamber (diameter 31.5cm, height 14.9cm) attached to a portable infrared gas analyser (EGM-4, PP-Systems, Hitchin, UK; NSF11 in Pumpanen et al., 2004). Chamber closure time was 81s. Measuring points were delimited with permanent collars and had four different treatments including the following respiration components: www.biogeosciences.net/15/3603/2018/ Biogeosciences, 15, 3603–3624, 2018 3606 K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest (A) peat soil (including cut roots), (B) A+above-ground litter, (C) B +living roots and (D) C+ground vegetation. In plots with vascular plants, extra collars of 5–10cm height were used to fit the plants inside the chamber. The chamber volume was corrected accordingly. In order to exclude autotrophic respiration, treatments A and B involved trenching with 30cm deep collars and removing above ground parts of living vegetation by repeated clippings every time before measurements if new plant growth had emerged. From treatment A, the above-ground litter was also removed every time before measurements. From treatment C, only the above-ground parts of plants were removed and treatment D was left intact. Collar depth in treatments C and D was only 2–3cm to minimise disturbance to roots. Treatment D (RD)thus includes all respiration components of forest floor respiration (RFF)and treatment A respiration from peat soil only (RPEAT). Respiration from treatment B (RB)equals heterotrophic respiration (RHET), and autotrophic respiration (RAUT)is calculated as RD−RB. Autotrophic respiration of above-ground vegetation (RGV)is defined as RD−RC, root respiration (RROOT)equals RC−RB and RLITTER equals RB−RA. CO2fluxes from treatments A and D were measured during the whole period 2005–2008, while treatments B and C were only measured from 2005 to 2006. 2.3.3 Forest floor and ditch CH4fluxes Soil CH4fluxes from the strips between ditches were measured with static chambers from the D points and reported by Lohila et al. (2011). To complement the CH4flux estimate for the whole area, fluxes from ditches were measured with the same equipment and methods as earlier. Fluxes were measured from four points on two parallel ditches on the both sides of the mast, altogether 7 times between 28 June and 8 December 2011. The annual flux was estimated as 365× daily mean flux. 2.3.4 Organic carbon pools and fluxes The carbon stock in peat, and biomasses and litter production of the tree stand and ground vegetation, were measured to estimate organic carbon pools and fluxes in the peatland. Peat C stock was estimated based on average peat layer thickness on the tree stand transects (Lohila et al., 2011) and average carbon density in peat (Mathijssen et al., 2017). Tree stand properties were measured in spring 2005 and autumn 2008. In 2005, the sample trees were cored to estimate diameter increment during the previous 5 years. Tree stand biomasses and C pools for years 2000, 2005 and 2008 were then estimated from these data using models of Repola (2008, 2009) and Laiho and Finér (1996) for pine below-ground biomasses (root d>1 cm), as described in detail by Ojanen et al. (2012). In all biomass C stock and flux calculations, C content of 50% was assumed. Above-ground biomass of ground vegetation vascular plants was sampled along the tree stand transects (n plots=39), from an area of 0.25 m2per plot. Moss samples (n=64) were collected from the same sites using corers with a diameter of 93 or 125mm. In the lab, the dead part of the moss was cut and removed, based on ocular assessment (colour change of the moss). The samples were separated by species, and dry mass (105◦C) was determined for each sample. The biomass of roots (and rhizomes of shrubs) were determined by taking a soil sample of 15×15 ×20 cm (width×length ×depth) along the tree stand transects, adjacent to the mid-points of the tree sample plots (n=32). In the laboratory all roots were carefully separated from peat, divided according to species or functional groups (pine, spruce, birch, shrubs, grasses and herbs) and diameter (below and over 2mm), dried in 105◦C and weighed. According to Bhuiyan et al. (2016), 15% of the fine roots in Kalevansuo are located deeper than 20cm. The biomasses estimated here were corrected accordingly. C flux in above-ground litter was estimated with 14 litter traps (20×20 cm) per chamber plot (i.e. altogether 56 traps). Litter was collected 2–3 times per year, separated by species, dried in 105◦C and weighed. As moss litter is not captured by litter traps, moss litter production was estimated by harvesting moss biomass production over 2 and 5 years (Ojanen et al., 2012). As the whole moss biomass eventually dies and forms litter on site, annual moss biomass growth equals annual litter production. Coarse root (>2 mm) litter production was estimated as biomass×turnover rate (0.12 for pine, 0.08 for shrub rhizomes; Finer and Laine, 1998). Fine root litter production was estimated with root-ingrowth cores by Bhuiyan et al. (2016). Sixty cores (diameter 3cm, length 50 cm) filled with Sphagnum peat were installed into soil in October 2009, and 20 cores were collected every year for 3 years. The fine root production rate was calculated as the average fine root mass (live+dead) in the cores divided by incubation years (average for 2nd and 3rd years). 2.3.5 Change in peat layer thickness To survey the changes in peat layer thickness, caused by compaction and decomposition of soil organic matter, litter production and moss height growth, soil surface around the mast was levelled in 2004, 2011 and 2014. In the beginning of measurements in 2004, a 20mm thick steel rod was hammered through the peat layer firmly to the subsoil, serving as a stable benchmark. The soil surface at the undisturbed chamber measurement points (D collars), was repeatedly levelled in relation to the benchmark. A manual levelling instrument with a levelling rod was used and the readings were recorded with the precision of ±0.5cm. Biogeosciences, 15, 3603–3624, 2018 www.biogeosciences.net/15/3603/2018/ K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest 3607 2.4 Gas flux calculations 2.4.1 NEE The NEE data obtained from the EC measurements were screened as described by Lohila et al. (2011). In short, screening criteria were applied to remove spikes in the 10Hz anemometer data and to discard poor-quality 30 min data. For the latter, the criteria were based on the expected range of the mean [CO2] and air temperature (from the sonic anemometer), and of the variances of [CO2], vertical wind speed and air temperature. In addition, a cumulative flux footprint of 70% was required, and a threshold of 0.1m s−1 was set to the friction velocity (Lohila et al., 2011). The procedures of gap-filling of the EC flux data and partitioning of NEE to the GPP and RECO components are described in Appendix A. The estimation of uncertainties in annual NEE is described in Appendix B. 2.4.2 Forest floor CO2efflux CO2efflux from the forest floor is a result of heterotrophic and autotrophic processes from different layers (vegetation and soil), which have different temperature dynamics. Therefore an additive, layerwise model was used, in which soil temperatures T5and T30 predict fluxes from different layers, with different temperature dynamics. An Arrhenius-type function (Lloyd and Taylor, 1994) was fitted to the measured CO2efflux (gCO2m−2h−1)from the forest floor: CO2efflux =RREF5 expE05 1 TREF −T0 −1 T5−T0 +RREF30 expE030 1 TREF −T0 −1 T30 −T0,(1) where RREF5 and RREF30 are respirations at reference temperatures (TREF =10◦C), and E05 and E030 describe temperature sensitivities of respiration in 5 and 30cm peat depths, respectively. T0= −46.02 ◦C is a constant. Parameter values were estimated separately for different treatments (A–D) representing different components of RFF, the four gas measurement plots and two groups of years (2005–2006 and 2007–2008; Appendix C), as the decomposability of soil organic matter changes in time at A collars. The WT was also tested as an explanatory variable, but as it predicted the temporal flux variation poorly, it was not included in the final models. The models were used with measured soil temperature data to simulate the temporal dynamics and annual fluxes of different flux components. 2.5 Modelling of the tree stand CO2fluxes To analyse the contribution of the tree stand (above ground) to the ecosystem CO2exchange, we used the GPP and shoot respiration (R) models in the Stand Photosynthesis Program (SPP). SPP predicts canopy light interception, photosynthesis and shoot respiration in half-hourly time steps (Mäkelä Table 1. Meteorological parameters for the full years and summer months, June–August. T=mean air temperature, P=precipitation sum, PPFD=mean daily sum of photosynthetic photon flux density, RH=mean relative humidity, VPD=mean vapour pressure deficit in the afternoon (12:00–16:00 LT). Year June–August Year T P T P PPFD RH VPD (◦C) (mm) (◦C) (mm) (molm−2(%) (kPa) d−1) 2005 4.7 725 15.1 285 35.0 76.0 0.89 2006 5.1 600 16.5 95 37.6 66.9 1.35 2007 4.9 724 15.1 234 35.1 75.4 0.93 2008 5.6 839 14.3 237 31.3 73.2 0.91 et al., 2006). PPFD, air CO2concentration, air temperature and relative air humidity measured at the site were used as inputs for SPP. The photosynthesis model used was OPAC (Mäkelä et al., 2006). Tree stand was described as three size classes (Ojanen et al., 2012), foliar masses for each class were estimated using the models of Repola (2009), and these were converted to leaf area index with specific leaf area of 11m2kg−1(Luoma, 1997). Stem respiration was estimated with the model of Zha et al. (2004). 3 Results 3.1 Meteorological conditions Of the studied years, 2008 was the warmest, especially during the winter months January–March, which were almost snowless. It was also the rainiest year. The summer (June to August) of 2008 was significantly cooler, but otherwise similar to the other summers. In contrast, the year 2006 was exceptionally dry from January until the end of September, including a severe drought during the growing season. In summer 2006, air temperature and PPFD were higher than in other years, whereas relative humidity and the water table were lower (Table 1, Figs. 1 and 2). The dry and warm growing season 2006 was preceded by a cold winter, which is why soil surface temperatures (T5)were much below average in the spring, and down at 30cm stayed below average until September, i.e. for almost the whole growing season (Fig. 2). In September–October the deeper peat layers finally warmed up and stayed warmer than average for the rest of the year. The WT typically fluctuated between −30 and −50cm in a year, being on average 42cm below ground surface during the snow-free season (April–November) and only about 5cm higher during the winters (December–March) (Figs. 1 and 2). The WT varied also spatially (mean range between waterwells 24cm), being deeper in the hummocks (−49 cm) compared to the lawns (−35cm). During the drought in 2006, the WT started dropping down in July, reached −79 cm in the www.biogeosciences.net/15/3603/2018/ Biogeosciences, 15, 3603–3624, 2018 3608 K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest –70 –60 –50 –40 –30 –20 –10 0 10 20 30 –80 –60 –40 –20 0 20 40 60 80 100 120 Temperature (°C) WT (cm) Precipitaon (mm day ) WT Precipitaon Tair T5 T30 2005 2006 2007 2008 -1 Figure 1. Daily weather data: precipitation (average from nearby weather stations), air (Tair)and soil temperatures at 5cm (T5; d collars) and 30cm (T30)depths, and average water table (WT) level at Kalevansuo peatland. end of September, and rose again after heavy rainfalls in the beginning of October. The average WT in 2006 was 10cm deeper than in other years. 3.2 Ecosystem CO2exchange According to the EC flux measurements, the site acted as a CO2source typically during the winter months (October– March) and a sink during the growing season (April– September) (Figs. 3, 4a). The variation in NEE during winter was small, ranging from about −0.1 to 0.1mgm−2s−1 (Fig. 3). While there were occasional warm days with net CO2uptake during the winter, the actual spring recovery of photosynthesis seemed to occur typically in the beginning of April, the only exception being the spring after the warm winter of 2006–2007, when the recovery started already in March. In summer (June–August), the highest nighttime CO2emission values, representing RECO, were on average 0.35mg m−2s−1, and the highest day-time CO2uptake typically fluctuated around −0.75mg m−2s−1. Only in summer 2006 the amplitude in the diurnal dynamics was smaller. The site was a sink of CO2in all years, NEE varying between −520 and −990g CO2m−2a−1(Table 2). The average NEE for the 4 years was −860gCO2(i.e. −234g Cm−2a−1). With the exception of the dry year of 2006, the annual NEE was surprisingly similar in other years, varying from −950 to −990gCO2m−2a−1. The estimated uncertainty in the annual budget, including the random measurement and gap-filling error and the uncertainty in the high-frequency loss correction and gap-filling of the gaps longer than 2 days, varied from 35 to 114g m−2a−1, corresponding to 3.6 to 22% of the respective annual balance (Appendix B). The drought during the spring and the growing season of 2006 was clearly reflected in the CO2exchange. The (gapfilled) NEE and GPP were markedly less negative in June and July 2006, indicating lower CO2uptake by photosynthesis compared to the other years (Fig. 4b, c). However, in July and August RECO was also clearly suppressed (Fig. 4d), thus decreasing the net loss of CO2from the peatland (NEE). In October 2006, GPP had fully recovered to the level of other years, but RECO stayed at a slightly higher level during the rest of the year, leading to clearly higher NEE during the last months of the year. After the first week of June until the end of July 2006, there were only a few days with accepted NEE observations (Fig. 3), so the results shown for these months (Fig. 4) largely depend on gap-filling. However, the main parameters of respiration and photosynthesis (RREF =respiration at 10◦C, GPMAX =photosynthesis in optimal light conditions; see Appendix A) indicate that both the photosynthetic capacity and ecosystem respiration were reduced in the summer of 2006 (Fig. 5). The data coverage was considerably better in August, making it possible to reliably study the impact of drought on NEE. In August 2006, both RREF and GPMAX had values that were significantly different from the other years (Fig. 5). While typically RREF reached its maximum in August and decreased thereafter (GPMAX having similar but opposite dynamics), in 2006 the trend was reversed and both RREF and GPMAX increased towards October. This suggests that the ecosystem was affected by the drought in August and September 2006 and slowly recovering in October. Thus, the distinct decrease in the annual net CO2uptake in 2006 (Table 2) was likely to be caused by the GPP decrease during the summertime, although RECO decreased during the drought as well. In addition to the summer depression in net CO2 uptake, the higher RECO in autumn months after the drought and heavy rains in October (Fig. 2) furthermore increased the difference to other years: the cumulative NEE in OctoberDecember in 2006 was as high as 320g CO2m−2, while in other years it varied from 130 to 190gCO2m−2. Biogeosciences, 15, 3603–3624, 2018 www.biogeosciences.net/15/3603/2018/ K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest 3609 –15 –10 –5 0 5 10 15 20 123456789101112 °C Month Air temperature 2005 2006 2007 2008 0 200 400 600 800 1000 1200 1400 1600 1800 123456789101112 d.d. >5 °C Month Temperature sum 0 5 10 15 20 25 30 35 40 45 50 123456789101112 mol m--2 d--1 Month Mean daily PPFD 0 20 40 60 80 100 120 140 160 180 200 123456789101112 mm month--1 Month Precipitaon –5 0 5 10 15 20 123456789101112 °C Month Soil T5 –2 0 2 4 6 8 10 12 14 123456789101112 °C Month Soil T30 –80 –70 –60 –50 –40 –30 –20 –10 0 123456789101112 cm Month Water table depth 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 123456789101112 kPa Month Vapour pressure deficit Figure 2. Weather variables by year and month, measured at Kalevansuo, except precipitation, which is an average from nearby weather stations. Mean daily air temperature (◦C) and temperature sum (>5 ◦C d.d.) at 2m height, soil temperatures (◦C) at 5 and 30 cm depths, mean daily PPFD (molm−2d−1)at 21.5 m. height, monthly precipitation sum (mmmonth−1), water table depth (cm) and vapour pressure deficit (kPa) at 21.5m height in the afternoon (12:00–16:00 LT). 3.3 Forest floor CO2flux The measured instantaneous CO2fluxes from the forest floor (RFF)varied between −0.02 and 1.80gCO2m−2h−1 (Fig. 6), following the dynamics in soil temperature. For the treatments A, B, C and D, the mean±SD respiration fluxes in non-winter seasons (April–November) 2005–2006 were 0.23±0.11, 0.31 ±0.13, 0.38±0.22 and 0.42±0.24 gCO2m−2h−1, respectively. In winter (i.e. over a snowpack or frozen ground between December and March), the mean fluxes were almost the same in the different treatments (0.022, 0.019, 0.022 and 0.035g CO2m−2h−1 from A to D, respectively). The regression models with T5and T30 as explanatory variables (Eq. 2) explained 70% (46–90 %) of the variation in the fluxes of the entire dataset (Appendix C). Respiration rates at 10◦C (RREF5 and RREF30)increased from A to D collars, i.e. as respiration components were added, and decreased at A collars with time since the beginning of the study (05–06 to 07–08). The modelled annual respiration ranged during the first 2 years from 1233g CO2m−2a−1in A collars (RPEAT) www.biogeosciences.net/15/3603/2018/ Biogeosciences, 15, 3603–3624, 2018 3610 K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest Table 2. EC-measured (and gap-filled and partitioned) annual net ecosystem exchange (NEE±error; Appendix B), gross primary production (GPP) and ecosystem respiration (RECO)of the Kalevansuo peatland, in comparison with the simulated tree stand GPP (GPPTREES), tree stand above-ground respiration (RTREES_AG)and forest floor respiration (RFF). Unit: g CO2m−2a−1. EC measurements+gap-filling Model simulations +flux partitioning Year NEE GPP RECO 1GPPTREES 1RTREES_AG RTREES_AG +2RFF 2005 −991±37 −3816 2821 −2311 1033 3345 2006 −516±114 −3231 2725 −2590 1160 3468 2007 −952±35 −4149 3207 −2530 1010 3122 2008 −970±35 −4023 3089 −2463 1007 3063 Mean −857 −3805 2961 −2473 1053 3250 as C −234 −1038 807 −674 287 886 1SPP model (Mäkelä et al., 2006). 2Eq. (1) (App. 3, treatment D). Table 3. Modelled annual forest floor CO2effluxes (mean±SEM; gCO2m−2a−1)in the four treatments at Kalevansuo peatland. A = peat, B =peat+litter, C =peat+litter+roots and D =peat+litter+ roots+ground vegetation.SEM is the standard error between the four plot means. Year A B C D 2005 1233±48 1745 ±121 2118 ±67 2312 ±170 2006 1233±48 1741 ±108 2117 ±76 2308 ±179 2007 822±40 No data No data 2112±141 2008 835±49 No data No data 2056±143 to 2312g CO2m−2a−1in D collars (RFF, Table 3). During 2007–2008, RPEAT clearly decreased from the previous years, to ca. 830g CO2m−2a−1, whereas RFF varied little between the studied years. In A collars the decomposability of organic matter is likely gradually decreased when the labile components are decomposed and the recalcitrant ones are enriched. Also, as we had to remove a newly grown moss layer from A collars in spring 2007 (inevitably with some soil organic matter attached), this procedure probably decreased the proportion of labile components on the soil surface. Based on the modelled fluxes of the first 2 years, RHET contributed 75% and RAUT 25 % to the mean annual RFF (Table 3). RPEAT comprised 53 % of the flux, RLITTER 22%, RROOT 16 % and RGV 8 %. The 4-year mean of RFF was 2197gCO2m−2a−1, i.e. ca. 600g Cm−2a−1. Using this mean value with the proportions from 2005–2006, we get an estimate for RHET of 450g C m−2a−1and RAUT 150g Cm−2a−1. In 2006, the main part of summertime (15 June to 12 September) measurements were lost due to instrument failure. Thus, we cannot reliably analyse the impact of 2006 summer drought on forest floor respiration. The existing soil CO2efflux data from September 2006, when the WT was extremely low, do show higher effluxes than those in early June 2006, although soil surface temperatures (T5)were lower in September. However, at the same time T30 was much higher (10.7◦C) than in June (5.4 ◦C), explaining the increased efflux. Compared to the other years, soil temperatures in September were at their highest in 2006 (Fig. 2), and the temperature response models thus predicted higher fluxes for September 2006 than for the other years. Following the heavy rains in the beginning of October, respiration decreased at the same time with the rise of the WT – and the decrease in T5. The impact of the WT on forest floor respiration was ambiguous. Correlations between the WT and CO2efflux were weak and variable by year and treatment. The residuals of the model (Eq. 1) estimates vs. The WT indicated a positive response especially in D collars (lower RFF with lower WT). However, this effect was caused mostly by spatial variation, as measurement points in hummocks generally had a lower WT and lower respiration than the points in the lawn level. Since the models were used for predicting temporal dynamics, the WT was not included in the models. 3.4 Simulated tree stand CO2flux The SPP model simulated the tree stand GPP and respiration well. For the year 2008 with the most complete NEE data, the RECO, derived from the gap-filling and partitioning of the EC measurements, matched very well (0.8% difference) the model-derived sum of RFF and above-ground tree respiration (RTREE, Fig. 7a, Table 2). Not surprisingly, the model was not able to simulate the suppression of respiration in 2006 (Fig. 7b), apparently since it does not have linkages to soil moisture. The simulated 4-year average was 9% higher than the EC-derived RECO (Table 2). The simulated 4-year average GPP of the tree stand was 2473g CO2m−2a−1(675g C). The GPP for the ground vegetation, measured by manual flux chambers in another camBiogeosciences, 15, 3603–3624, 2018 www.biogeosciences.net/15/3603/2018/ K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest 3611 mg CO2 m–2 s–1 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 2005 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec mg CO2 m–2 s–1 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 2008 mg CO2 m–2 s–1 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 2007 mg CO2 m–2 s–1 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 2006 Figure 3. Quality-controlled half-hourly NEE measured with the eddy covariance method at Kalevansuo peatland in 2005–2008. paign, was 1040gCO2m−2a−1(Badorek et al., 2011). Altogether the tree stand and the ground vegetation GPP sum up to 3513g CO2m−2a−1, which is relatively close (92%) to the ecosystem GPP obtained from the partitioning of the EC fluxes (3805g CO2m−2a−1). These independent findings suggest that the tree stand contributes about 70% and the ground vegetation 30% of the GPP at Kalevansuo. 3.5 CH4fluxes CH4flux from ditches was very variable, especially spatially but also temporally. The instantaneous fluxes varied between −0.098 and 1.757mg CH4m−2h−1. The wettest plot, with cottongrass (Eriophorum vaginatum), emitted on average 0.936mg CH4m−2h−1, significantly more (p<0.001) than the other three, slightly drier plots (mainly Sphagnum riparium), with mean fluxes of 0.006, 0.056 and −0.006mg m−2h−1. Temporal variation was high but no clear seasonality was observed. At the wettest plot, fluxes had similar temporal pattern with the WT, i.e. the highest flux took place in September during the highest WT. The average flux from ditch plots was 0.248mg CH4m−2h−1, which calculated for the whole drained area (ditches 2.5% of the area) increased the estimated total flux by 0.006mg CH4m−2h−1. As the flux at the strips was on average −0.015mgm−2h−1 (Lohila et al., 2011), the site would therefore remain as a small sink for CH4. The annual areally weighted flux was −0.06g CH4m−2a−1(i.e. −0.12g CH4m−2a−1×0.975 (strips)+2.2 gCH4m−2a−1×0.025 (ditches)). 3.6 Change in peat layer thickness The soil surface on the undisturbed D collars had subsided on average by 1.4cm in 10 years from 2004 to 2014, i.e. 1.4mm a−1(Fig. 8). There was considerable variability between points from an increase in elevation by 2cm to a subsidence of 5cm, so that the change was not quite statistically significant (p=0.067). Also, some back and forth variation in peat thickness between years was observed: in August 2011 all but four points had lower elevation than in 2014. This can be either a measurement error or real shrink–swell behaviour (breathing) of the peatland. 3.7 Carbon balance The biggest carbon pool at Kalevansuo (Fig. 9.) was the 2.2m thick peat layer making up 95.3 % of the total carbon pool. Tree stand (without fine roots) comprised 4.3 % and ground vegetation only 0.4%. Fine roots comprised 0.2%. The total C pool in vegetation in 2008 was 5.5kg m−2, which corresponds to about 10cm layer of peat. Above-ground parts comprised 62% of the total biomass. Of the moss biomass, Sphagna comprised 20% and forest mosses 80 %. The tree stand volume increased from 90m3ha−1in 2000 to 130m3ha−1in 2008, i.e. on average by 5m3ha−1a−1. The corresponding carbon pool was 4.6kgm−2in 2008 and 3.2kg m−2in 2000. The tree stand thus sequestered ca. 170g Cm−2a−1. This made 74 % of the carbon accumulation at Kalevansuo, while the rest was attributed to peat soil (Fig. 9). Total litter production was estimated at 437gC m−2a−1. Of this, mosses comprised 20% and vascular plants 80%. Of the litter production by vascular plants, trees comprised 79% (above ground) and 66% (below ground). Fine root production was estimated at 120gC m−2a−1(Bhuiyan et al., 2016), comprising 76% of the below-ground litter. As the average of the 4 years, the Kalevansuo peatland ecosystem fixed ca. 1040g Cm−2a−1through photosynthesis, 70% of which was attributed to the tree stand. Simultaneously it lost 810g Cm−2a−1through RECO. Ca. 50 % of www.biogeosciences.net/15/3603/2018/ Biogeosciences, 15, 3603–3624, 2018 3618 K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest Appendix A: Gap-filling and partitioning of net ecosystem exchange The gap-filling of the net ecosystem exchange (NEE) data obtained from the eddy covariance measurements was performed with the procedures incorporated into the FluxPartFill.py program developed at the Finnish Meteorological Institute. The gap-filling algorithm is based on empirical functions for total ecosystem respiration (RECO)and gross primary production (GPP) and thus additionally provides the partitioning of NEE into the RECO and GPP components. NEE =RECO +GPP (A1) Ecosystem respiration was assumed to respond to temperature according to the Arrhenius-type relationship suggested by Lloyd and Taylor (1994): RECO (T)=RREF expE01 TREF −T0 −1 T−T0,(A2) where Tis temperature, RREF is the reference respiration (Rat TREF =283.15K), E0describes the temperature sensitivity of Rto T, and T0=227.13K is a constant. Equation (A2) is fitted to nocturnal (photosynthetic photon flux density PPFD< 5µmol m−2s−1)flux data by optimising the parameters RREF and E0. Gross primary production was assumed to depend on PPFD according to a rectangular hyperbola that is multiplied by a function fVPD representing the reduction of GPP with increasing water vapour pressure deficit (VPD): GPP(PPFD,VPD)=αPPFD GPmax αPPFD+GPmax fVPD (VPD),(A3) where αis the apparent quantum yield and GPMAX is the maximum asymptotic GPP when f=1 (GPP → GPmax,as PPFD → ∞). For fVPD, we adopted a form that results in fVPD =1 for VPD ≤VPD1,fVPD =f0for VPD ≥ VPD0and a linear reduction from fVPD =1 to f0between VPD1and VPD0: fVPD (VPD)=max(f0, min1−VPD −VPD1 VPD0−VPD1(1−f0).(A4) Equation (A3) is fitted to day-time (PPFD >20 µmolm−2s−1)net flux data from which the respiration flux, calculated using Eq. (A2) with the optimized parameters, has been subtracted. The Levenberg–Marquardt algorithm as implemented in the LMFIT package (Newville et al., 2014) is used for both RECO and GPP fits. In FluxPartFill.py, the model parameters are calculated for each day with a centred multi-day data window. The length of this window can be made variable (within a specified range) by defining the minimum number of flux and meteorological data that must be available for the fit. In this study, the parameters were fitted using fixed 21and 11-day windows for RECO and GPP, respectively. To avoid unrealistic fluctuations in the parameter values due to the multidimensionality of the fitting problem, FluxPartFill.py makes it possible to apply an iterative process in which a varying subset of parameters is fitted in subsequent runs, with an option for manually adjusted parameter time series. In the present study, E0was set constant at 200K for the whole period, based on an initial fit to the all the 4-year data. Air temperature measured at 2m was used for T. For GPP, we fitted αand GPMAX, while fVPD was based on fixed parameter values: VPD1=10hPa and VPD0=25hPa (Lohila et al., 2011), and f0=0.4. Based on the gap-filled time series, using linearly interpolated parameter values where necessary, daily balances are calculated for NEE, RECO and GPP. Biogeosciences, 15, 3603–3624, 2018 www.biogeosciences.net/15/3603/2018/ K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest 3619 Table B1. Uncertainty analysis of the annual CO2balance, NEE (gCO2m−2a−1). Error components are explained in the text, and nobs denotes the number of accepted flux observations in 2005– 2008. 2005 2006 2007 2008 EMEAS 5.8 6.3 12.9 13.0 EGAPS 6.2 6.5 7.3 6.0 EHFL 29.7 15.5 28.6 29.1 ELONGGAPS 20.8 113 13.2 13.3 nobs 7868 7240 11678 12842 (44.9%) (41.3%) (66.7 %) (73.3 %) NEE±error −991±37 −516 ±114 −952 ±35 −970±35 Appendix B: Uncertainty analysis of NEE The uncertainty of the annual CO2balance was estimated separately for each year. We followed here the approaches presented by Aurela et al. (2002), Lohila et al. (2011) and Räsänen et al. (2017). The random error arising from the stochastic variability of turbulent fluxes (EMEAS)was estimated, similarly to Räsänen et al. (2017), from the difference between the measurements and the corresponding values obtained from the gap-filling model fits (Eqs. A1– A4 in Appendix A). This error varied between 5.8 and 13g CO2m−2a−1in 2005–2008 (Table A1). The same approach was applied to the random error arising from the gap-filling of the data (EGAPS), which ranged from 6.0 to 7.3g CO2m−2a−1in 2005–2008 (Table A1). The uncertainty associated with the corrections for the high-frequency flux loss (EHFL)was estimated at 3% of the annual balance (Lohila et al., 2011). For the uncertainty due to the gap-filling of the longer (>2 days) gaps in the flux data (ELONGGAPS), we adopted a new approach: as the year 2008 had only a few gaps, we simulated the impact of longer gaps in other years by assuming similar data gaps in the time series of 2008 and then ran the gap-filling procedures for these compromised data. For each gap, a cumulative CO2balance was estimated from two differently gap-filled datasets, i.e. the original and the simulated, and the difference of these was assumed to represent the error. The annual error was calculated by assuming that the errors obtained this way for separate gaps were independent of each other. For 2006, a similar simulation was also run with the data of 2005 and 2007, and the average of the three annual estimates obtained was taken as the total error related to the gap-filling of long gaps. In 2008, there was only one longer (10 day) gap in December. The uncertainty due to this gap was calculated by adopting the daily errors estimated for December 2007, resulting in an ELONGGAPS of 13.3g CO2m−2a−1for 2008. The total uncertainty in the annual balance was calculated by assuming that EMEAS,EGAPS and ELONGGAPS are independent. The total error of ±37g CO2m−2a−1in 2005 was significantly reduced from that (±100g m−2a−1)reported by Lohila et al. (2011). This was mainly due to the different approach adopted for the error estimates for the compensation of long data gaps: for this, Lohila et al. (2011) shifted model parameters 2 weeks forward and backward, which resulted in a relative error of 10.7% of the annual balance. We consider the present approach more realistic, as it is based on assessing the effect of realised gaps on actual measurement data. However, it is obvious that the uncertainty estimate for 2006 is limited by the fact that the summer of that year was exceptionally dry, and the changes in NEE induced by the drought cannot be accurately estimated based on the data of 2008. It is likely that the dynamics of photosynthesis and respiration during a dry summer are different from a normal year. This hypothesis gains support from the observation that the RECO and GPP parameters in August 2006 differed markedly from those estimated for the other years (Fig. 5). As a further sensitivity test, we estimated the most conservative range for the NEE uncertainty in summer 2006. Because our gap-filling model fills the long gaps by linear interpolation, the outcome depends on the selection of the start and end points of the interpolation. The sensitivity test was performed by assuming different parameter scenarios during the longest gap in the parameters (13 June–4 August, during which there were only 212 valid NEE observations available). In one scenario, a parameter had the starting point value during the whole gap, while in the other it dropped immediately to the level at the end point and stayed there over the whole gap. The annual NEE was calculated for each combination of different RREF and GPMAX dynamics. The two most extreme cases (either both RREF and GPMAX were reduced for the whole gap, or both were increased) produced an annual NEE of −377 and −844g CO2m−2s−1, respectively. These can be considered as the most conservative estimates of annual NEE, and thus we can safely conclude that the annual NEE was negative in 2006, i.e. the ecosystem acted as a CO2sink even during the exceptionally dry year. Despite the large gaps during the growing season of 2006, and the large uncertainty resulting from these, the annual NEE balance of 2006 differed significantly from the other years. This difference between two annual balances (NEEi and NEEi+1)was considered significant if the 95% confidence interval of the difference, defined as (NEEi+1−NEEi)±2qSE2 i+1+SE2 i(B1) where SEiis the standard error of NEEi, did not cross zero. www.biogeosciences.net/15/3603/2018/ Biogeosciences, 15, 3603–3624, 2018 3620 K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest Appendix C Table C1. Parameter values of forest floor CO2efflux models (Eq. 1) for different collar treatments (A=peat, B =peat+litter, C =peat + litter+roots and D =peat+litter +roots+ground vegetation) and years. RREF5 and RREF30 are respirations at 10 ◦C for the 5 and 30 cm depths (gCO2m−2h−1),E05 and E030 are is temperature sensitivities of respiration for the same layers, r2is coefficient of determination of the model, and n is the number of observations. Treatment Plot Years RREF5 E05 RREF30 E030 r2n A 1 2005–2006 0.151 302.4 0.088 716.4 0.76 127 A 1 2007–2008 0.124 319.5 0.027 755.2 0.78 108 A 2 2005–2006 0.138 359.7 0.072 754.5 0.77 105 A 2 2007–2008 0.104 431.5 0.021 418.2 0.90 108 A 3 2005–2006 0.127 324.6 0.087 1011.4 0.77 113 A 3 2007–2008 0.120 299.6 0.015 508.6 0.79 107 A 4 2005–2006 0.086 432.6 0.123 297.7 0.62 114 A 4 2007–2008 0.112 331.1 0.027 511.7 0.64 102 B 1 2005–2006 0.175 284.7 0.142 660.2 0.75 135 B 2 2005–2006 0.159 282.6 0.199 560.7 0.72 121 B 3 2005–2006 0.183 244.6 0.146 1053.0 0.67 125 B 4 2005–2006 0.102 303.7 0.183 584.6 0.82 127 C 1 2005–2006 0.237 338.0 0.163 942.3 0.77 145 C 2 2005–2006 0.152 193.3 0.217 532.9 0.49 131 C 3 2005–2006 0.139 311.9 0.228 605.9 0.58 124 C 4 2005–2006 0.204 240.8 0.261 824.9 0.46 137 D 1 2005–2006 0.165 339.3 0.202 741.3 0.50 130 D 1 2007–2008 0.171 360.5 0.146 885.8 0.54 107 D 2 2005–2006 0.192 361.4 0.295 593.3 0.70 129 D 2 2007–2008 0.195 383.2 0.254 518.9 0.71 105 D 3 2005–2006 0.050 581.4 0.319 411.4 0.56 129 D 3 2007–2008 0.123 261.0 0.300 586.8 0.58 106 D 4 2005–2006 0.265 280.4 0.280 1115.6 0.62 121 D 4 2007–2008 0.229 367.7 0.146 550.1 0.56 103 Biogeosciences, 15, 3603–3624, 2018 www.biogeosciences.net/15/3603/2018/ K. Minkkinen et al.: Persistent carbon sink at a boreal drained bog forest 3621 Data availability. The flux data are available at http://www. europe-fluxdata.eu/ Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. We thank Tiina Badorek for biomass measurements, Markku Koskinen for measuring ditch CH4emissions, Tea Thum helping in flux measurements and Timo Haikarainen and Inkeri Suopanki for help in tree stand measurements and computations. Funding for setting up the site was received from Ministry of Agriculture and Forestry and Ministry of Trade and Industry of Finland. The Kolli Foundation (Marjatta ja Eino Kollin Säätiö) funded the biomass measurements. Edited by: Fortunat Joos Reviewed by: three anonymous referees References Adkinson, A. C., Syed, K. H., and Flanagan L. B.: Contrasting responses of growing season ecosystem CO2exchange to variation in temperature and water table depth in two peatlands in northern Alberta, Canada, J. 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