Methane and carbon dioxide fluxes of a temperate mire in Central Europe

Fortuniak, Krzysztof; Pawlak, Włodzimierz; Bednorz, Leszek; Grygoruk, Mateusz; Siedlecki, Mariusz; Zieliński, Mariusz

doi:10.1016/j.agrformet.2016.08.023

Cited by 56 publications

(18 citation statements)

References 76 publications

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“…The study area was not a highly productive ecosystem (annual GEP = 415 ± 28.8 g CO 2 -C m −2 yr −1 ) but exhibited low R e (annual R e = 236 ± 16.4 g CO 2 -C m −2 yr −1 ), likely due to oxygen limitations. The annual CO 2 fluxes reported here from a restored and rewetted peatland are comparable with data reported from pristine temperate peatlands in temperate mid latitudes (Campbell et al, 2014;Flanagan and Syed, 2011;Fortuniak et al, 2017;Lund et al, 2010). The study area sequestered less CO 2 than the few other restored wetlands reported in the literature (Badiou et al, 2011;Hendriks et al, 2007;Herbst et al, 2013;Knox et al, 2015).…”

Section: Discussionsupporting

confidence: 76%

“…Only Herbst et al (2013) reported an annual CH 4 flux from a restored wetland in Denmark that was lower than in this study (9 to 13 g CH 4 -C m −2 yr −1 ). Our annual CH 4 flux at 17 ± 1.0 g CH 4 -C m −2 yr −1 was comparable to an average natural temperate wetland CH 4 flux, which is typically around 15 g CH 4 -C m −2 yr −1 (Abdalla et al, 2016;Fortuniak et al, 2017;Nicolini et al, 2013;Turetsky et al, 2014). The CH 4 fluxes from a number of temperate and tropical pristine wetlands exceeded the CH 4 fluxes reported in this study, including emissions from marshes in the southwestern US (130 g CH 4 -C m −2 yr −1 ; Whiting and Chanton, 2001), tropical wetlands in Costa Rica (82 g CH 4 -C m −2 yr −1 ; Nahlik and Mitsch, 2010), marshes in the midwestern US (50 g CH 4 -C m −2 yr −1 , Koh et al, 2009), all three studies based on chamber measurements, and an ombrotrophic bog in New Zealand (29 and 21 g CH 4 -C m −2 yr −1 based on EC measurements; Goodrich et al, 2015).…”

Section: Ch 4 Exchange 441 Annual and Seasonal Ch 4 Budgetsmentioning

confidence: 71%

“…Values that are comparable to the current restored wetland were reported in five pristine temperate wetlands: −248 g CO 2 -C m −2 yr −1 (Lafleur et al, 2001), −234 g CO 2 -C m −2 yr −1 (Campbell et al, 2014), −210 g CO 2 -C m −2 yr −1 (Fortuniak et al, 2017), −189 g CO 2 -C m −2 yr −1 (Flanagan and Syed, 2011), and −103 g CO 2 -C m −2 yr −1 (Lund et al, 2010). The few datasets in the literature for NEE of restored wetlands showed a wide range of values.…”

Section: Co 2 Exchangementioning

confidence: 73%

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Annual greenhouse gas budget for a bog ecosystem undergoing restoration by rewetting

et al. 2017

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Abstract. Many peatlands have been drained and harvested for peat mining, agriculture, and other purposes, which has turned them from carbon (C) sinks into C emitters. Rewetting of disturbed peatlands facilitates their ecological recovery and may help them revert to carbon dioxide (CO 2 ) sinks. However, rewetting may also cause substantial emissions of the more potent greenhouse gas (GHG) methane (CH 4 ). Our knowledge of the exchange of CO 2 and CH 4 following rewetting during restoration of disturbed peatlands is currently limited. This study quantifies annual fluxes of CO 2 and CH 4 in a disturbed and rewetted area located in the Burns Bog Ecological Conservancy Area in Delta, BC, Canada. Burns Bog is recognized as the largest raised bog ecosystem on North America's west coast. Burns Bog was substantially reduced in size and degraded by peat mining and agriculture. Since 2005, the bog has been declared a conservancy area, with restoration efforts focusing on rewetting disturbed ecosystems to recover Sphagnum and suppress fires. Using the eddy covariance (EC) technique, we measured year-round (16 June 2015 to 15 June 2016) turbulent fluxes of CO 2 and CH 4 from a tower platform in an area rewetted for the last 8 years. The study area, dominated by sedges and Sphagnum, experienced a varying water table position that ranged between 7.7 (inundation) and −26.5 cm from the surface during the study year. The annual CO 2 budget of the rewetted area was −179 ± 26.2 g CO 2 -C m −2 yr −1 (CO 2 sink) and the annual CH 4 budget was 17 ± 1.0 g CH 4 -C m −2 yr −1 (CH 4 source). Gross ecosystem productivity (GEP) exceeded ecosystem respiration (R e ) during summer months (June-August), causing a net CO 2 uptake. In summer, high CH 4 emissions (121 mg CH 4 -C m −2 day −1 ) were measured. In winter (December-February), while roughly equal magnitudes of GEP and R e made the study area CO 2 neutral, very low CH 4 emissions (9 mg CH 4 -C m −2 day −1 ) were observed. The key environmental factors controlling the seasonality of these exchanges were downwelling photosynthetically active radiation and 5 cm soil temperature. It appears that the high water table caused by ditch blocking suppressed R e . With low temperatures in winter, CH 4 emissions were more suppressed than R e . Annual net GHG flux from CO 2 and CH 4 expressed in terms of CO 2 equivalents (CO 2 eq.) during the study period totalled −22 ± 103.1 g CO 2 eq. m −2 yr −1 (net CO 2 eq. sink) and 1248 ± 147.6 g CO 2 eq. m −2 yr −1 (net CO 2 eq. source) by using 100-and 20-year global warming potential values, respectively. Consequently, the ecosystem was almost CO 2 eq. neutral during the study period expressed on a 100-year time horizon but was a significant CO 2 eq. source on a 20-year time horizon.

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Section: Discussionsupporting

confidence: 76%

Section: Ch 4 Exchange 441 Annual and Seasonal Ch 4 Budgetsmentioning

confidence: 71%

Section: Co 2 Exchangementioning

confidence: 73%

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Annual greenhouse gas budget for a bog ecosystem undergoing restoration by rewetting

et al. 2017

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“…The methane emissions from wetlands have been intensively studied by chamber techniques (e.g., Bubier et al, 2005;Riutta et al, 2007;Ström et al, 2015;Whiting et al, 1991;Whiting & Chanton, 1992), and more recently also by eddy covariance method, measuring the ecosystem scale gas exchange (e.g., Brown et al, 2014;Fortuniak et al, 2017;Hargreaves et al, 2001;Hommeltenberg et al, 2014;Jackowicz-Korczyński et al, 2010;Kim, Verma, Billesbach, & Clement, 1998;Kowalska et al, 2013;Mikhaylov et al, 2015;Nadeau et al, 2013;Rinne et al, 2007). While chamber measurements provide insight to many processes and to microtopography-scale spatial variability, they usually lack the temporal resolution of eddy covariance data.…”

Section: Introductionmentioning

confidence: 99%

Temporal Variation of Ecosystem Scale Methane Emission From a Boreal Fen in Relation to Temperature, Water Table Position, and Carbon Dioxide Fluxes

Rinne

Tuittila

Peltola

et al. 2018

Global Biogeochemical Cycles

103

109

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We have analyzed decade‐long methane flux data set from a boreal fen, Siikaneva, together with data on environmental parameters and carbon dioxide exchange. The methane flux showed seasonal cycle but no systematic diel cycle. The highest fluxes were observed in July–August with average value of 73 nmol m−2 s−1. Wintertime fluxes were small but positive, with January–March average of 6.7 nmol m−2 s−1. Daily average methane emission correlated best with peat temperatures at 20–35 cm depths. The second highest correlation was with gross primary production (GPP). The best correspondence between emission algorithm and measured fluxes was found for a variable‐slope generalized linear model (r2 = 0.89) with peat temperature at 35 cm depth and GPP as explanatory variables, slopes varying between years. The homogeneity of slope approach indicated that seasonal variation explained 79% of the sum of squares variation of daily average methane emission, the interannual variation in explanatory factors 7.0%, functional change 5.3%, and random variation 9.1%. Significant correlation between interannual variability of growing season methane emission and that of GPP indicates that on interannual time scales GPP controls methane emission variability, crucially for development of process‐based methane emission models. Annual methane emission ranged from 6.0 to 14 gC m−2 and was 2.7 ± 0.4% of annual GPP. Over 10‐year period methane emission was 18% of net ecosystem exchange as carbon. The weak relation of methane emission to water table position indicates that space‐to‐time analogy, used to extrapolate spatial chamber data in time, may not be applicable in seasonal time scales.

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“…Coupling cutting-edge hydrological models with advanced support originating from geographic information systems (GIS) and remote sensing (RS) that are integrated with ecological indicators allows the field of model-based analyses to expand into new dimensions, providing new and detailed information, which is required to broaden scientific knowledge of ecosystem functions (Berezowski et al 2015;Grygoruk et al 2014;Zhou et al 2008). A particularly broad field in this regard covers the protection, management and restoration of mires and riparian wetlands, where strict hydrological criteria (water level distribution, duration of water levels, flood extents and volumes) must be fulfilled to assure sustainable habitat conditions or keep greenhouse gases emissions at an acceptable level (Chormański et al 2009;Fortuniak et al 2017;Grygoruk et al 2015;Koreny et al 1999;Mirosław-Ś wiątek et al 2016c;Vepraskas and Caldwell 2008). Special attention is paid to the requirements and tolerance of wetland vegetation species to a variability of water levels in a classical (phytosociological) approach (Caldwell et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Too wet and too dry? Uncertainty of DEM as a potential source of significant errors in a model-based water level assessment in riparian and mire ecosystems

Mirosław‐Świątek

Kiczko

Szporak-Wasilewska

et al. 2017

Wetlands Ecol Manage

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Modelling groundwater depths in floodplains and peatlands remains a basic approach to assessing hydrological conditions of habitats. Groundwater flow models used to compute groundwater heads are known for their uncertainties, and the calibration of these models and the uncertainty assessments of parameters remain fundamental steps in providing reliable data. However, the elevation data used to determine the geometry of model domains are frequently considered deterministic and hence are seldom considered a source of uncertainty in model-based groundwater level estimations. Knowing that even the cutting-edge laser-scanning-based digital elevation models have errors due to vegetation effects and scanning procedure failures, we provide an assessment of uncertainty of water level estimations that remain basic data for wetland ecosystem assessment and management. We found that the uncertainty of the digital elevation model (DEM) significantly influenced the results of the assessment of the habitat's hydrological conditions expressed as groundwater depths. In extreme cases, although the average habitat suitability index (HSI) assessed in a deterministic manner was defined as 'unsuitable', in a probabilistic approach (grid-cell-scale estimation), it reached a value of 40% probability, signifying 'optimum' or 'tolerant'. For the 24 habitats analysed, we revealed vast differences between HSI scores calculated for individual grid cells of the model and HSI scores computed as average values from the set of grid cells located within the habitat patches. We conclude that groundwater-modelling-based decision support approaches to wetland assessment can result in incorrect management if the quality of DEM has not been addressed in studies referring to groundwater depths.

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Methane and carbon dioxide fluxes of a temperate mire in Central Europe

Cited by 56 publications

References 76 publications

Annual greenhouse gas budget for a bog ecosystem undergoing restoration by rewetting

Annual greenhouse gas budget for a bog ecosystem undergoing restoration by rewetting

Temporal Variation of Ecosystem Scale Methane Emission From a Boreal Fen in Relation to Temperature, Water Table Position, and Carbon Dioxide Fluxes

Too wet and too dry? Uncertainty of DEM as a potential source of significant errors in a model-based water level assessment in riparian and mire ecosystems

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