Auxiliary Material

Multi-scale temporal variation of methane flux and its controls in a subtropical tidal salt marsh in eastern China

Hong Li1, 2, Shengqi Dai1, Zutao Ouyang3, Xiao Xie1, Haiqiang Guo1, Caihong Gu4, Xiangming Xiao1, 5, Zhenming Ge6, Changhui Peng2, Bin Zhao1

1 Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, and Coastal Ecosystems Research Station of the Yangtze River Estuary, Fudan University, Shanghai 200433, China

2 Department of Biology Science, Institute of Environment Sciences, University of Quebec at Montreal, Montreal C3H 3P8, Canada

3 Department of Geography, Michigan State University, East Lansing, MI, United States of America

4 Hydrological Station of Chongming County, Shanghai, China

5 Department of Botany and Microbiology, Center for Spatial Analysis, University of Oklahoma, Norman, Oklahoma, United States of America

6 State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China

Correspondence to: Bin Zhao, School of Life Science, Institute of Biodiversity Science, Fudan University (Jiangwan Campus), # 2005 Songhu Road, Shanghai, 200438, China

Email: , Phone: 86-021-51630762

Section S1: Advances setting of flux calculations in Eddypro, and quality control

The Angle of Attack corrections for Gill WindMaster Pro firmware were used (Nakai and Shimoyama 2012). The block average method was used for detrending of raw data. The time lag detection method used was covariance maximization with default. The double coordinate rotation method was used to ensure the mean vertical wind speed was zero, averaged over 30min. Compensation of densityfluctuations (WPL terms) was implemented according to Webb et al. (1980). The steady state test and the well-developed turbulence test provide a quality flag (1~9) (Foken et al., 2004). We applied spike detection of raw data after Vickers and Mahrt (1997). Spectral correction was performed after Moncrieff et al. (1997) (high-frequency).

The relative signal strength indicator (RSSI) was adopted to filter for the periods when the mirror of LI7700 was contaminated by rainfall or dust (RSSI20%). Data were removed when rainfall occurred. In addition, to ensure well developed mixing conditions, we used friction velocity (u*) as a criterion for atmospheric mixing according toReichstein et al. (2005), and applied a threshold of u*0.15ms−1. The steady state test and the well-developed turbulence test were used as quality flags (Foken et al. 2004). The test (Foken et al. 2004) (1~9 system) provides the flag “1~3” for high quality fluxes, “4~6” for intermediate quality fluxes, and “7~9” for poor quality fluxes. Thus, onlydata for which the quality flag was <7 were used for further analysis. These quality criteria and occasionally occurring sensor failures led to gaps ofdifferent duration. For the entire observation period, the remaining data coverage was 46% for FCO2 and 44% for FCH4.

The co-spectra of the fluxes of sensible heat (H) and methane (Fig.S1) showed the characteristic features of the surface-layer turbulence spectrum, which closely followed the theoretical spectrum for sensible heat flux under unstable atmospheric conditions (Moore 1986). The co-spectrum of sensible heat followed the expected power law in the inertial sub range, whereas for the co-spectra of methane flux, the attenuation of low frequencies due to the limited frequency response of the gas analyzer and experimental setup was visible. The high-frequency correction was performed Moncrieff et al. (1997). The high-frequency loss occurred mainly at frequencies higher than 1 Hz and the frequency correction was around 10-15%.

References

Burba GG, McDermitt DK, Grelle A, Anderson DJ, Xu L (2008) Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers Global Change Biology 14:1854-1876 doi:10.1111/j.1365-2486.2008.01606.x %/ WILEY-BLACKWELL

Foken T, Gockede M, Mauder M, Mahrt L, Amiro B, Munger W (2004) Post-field data quality control Handbook of Micrometeorology: a Guide for Surface Flux Measurement and Aanlysis 29:181-208

Moncrieff J, Clement R, Finnigan J, Meyers T (2004) Averaging, detrending, and filtering of eddy covariance time series Handbook of Micrometeorology: a Guide for Surface Flux Measurement and Aanlysis 29:7-31

Moncrieff J, Valentini R, Greco S, Guenther S, Ciccioli P (1997) Trace gas exchange over terrestrial ecosystems: methods and perspectives in micrometeorology Journal of experimental botany 48:1133--1142

Moore CJ (1986) Frequency response corrections for eddy correlation systems Boundary-Layer Meteorology 37:17-35

Nakai T, Van der Molen M, Gash J, Kodama Y (2006) Correction of sonic anemometer angle of attack errors Agricultural and Forest Meteorology 136:19-30

Reichstein M et al. (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm Global Change Biology 11:1424--1439 %\ 2015-1409-1415 1410:1412:1400

Vickers D, Mahrt L (1997) Quality control and flux sampling problems for tower and aircraft data Journal of Atmospheric and Oceanic Technology 14:512-526

Webb EK, Pearman GI, Leuning R (1980) Correction of flux measurements for density effects due to heat and water vapour transfer Quarterly Journal of the Royal Meteorological Society 106:85-100

Section S2: ANN gap-filling for FCH4

FCH4 was gap-filled using the artificial neural network (ANN) approach, which is an expanded version of the Matlab ‘nnstart’. For training the network, we used the following variables as potential drivers of CH4:four seasonal fuzzy sets, Ta (air temperature), PAR (photosynthetically active radiation), RH (relative humidity), rainfall, Pa (atmospheric pressure), TH (tide height), u* (friction velocity) and u (u wind speed). Season was codedusing four fuzzy variables (winter, spring, summer and fall) each having a value between 0 and 1 depending on the time of year (Dengel et al. 2013; Papale and Valentini 2003). The meteorological variables (Ta, RH, precipitation) were gap-filled using data from the regression of the data of adjacent sites (CMW1, 1,000 m distant) or linear interpolation for short data gaps within 24h.In addition, u* and u were not gap filled.

A two-layer feed-forward network with sigmoid hidden neurons and linear output neurons was used to obtain a fitting mode. The neural network first normalizes all predictor variables between 0 and 1, following which it uses combinations of these variables to minimize the error of the modeled flux. The architecture for our neural network used a hyperbolic tangent sigmoid transfer function to produce 6hidden nodes from the input predictor variables. The Levenberg–Marquardt backpropagation algorithm was adopted in the training (Beale et al. 2011). We implemented a loop of 10,000 repeats to export high quality models. The models with Pearson r and mean standard error (MSE) were output if r0.8. Then we choose 10 best (referring minimum MSE and maximum r) models whose average Pearson r was 0.85and average MSE was 0.0007 for prediction. The mean of 10 predictions was then used for calculation of the annual balance. And the standard deviation was used to estimatethe uncertainty of in the gap-filling.

References

Beale MH, Hagan MT, Demuth HB (1992) Neural Network Toolbox™ User's Guide MathWorksInc

Dengel S et al. (2013) Testing the applicability of neural networks as a gap-filling method using CH 4 flux data from high latitude wetlands Biogeosciences 10:8185-8200

Papale D, Valentini R (2003) A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization Global Change Biology 9:525-535

Section S3: Uncertainty analysis for annual budgets

The uncertainties of annual FCH4 and FCO2 were obtained following Aurelaet al. (2002). The random error for the flux was estimated according to Finkelstein and Sims (2001). The uncertainties originated from gap filling and flux partitioning of FCO2 referred to Desai et al., (2008) and Richardson and Hollinger, (2005).The gap-filling method of ANN for FCH4 also gave an uncertainty.

References

Aurela M, Laurila T, Tuovinen JP (2002) Annual CO2 balance of a subarctic fen in northern Europe: importance of the wintertime efflux Journal of Geophysical Research: Atmospheres 107

Finkelstein PL, Sims PF (2001) Sampling error in eddy correlation flux measurements Journal of Geophysical Research-Atmospheres 106:3503-3509 doi:10.1029/2000jd900731 %/ amer geophysical union

Desai AR et al. (2008) Cross-site evaluation of eddy covariance GPP and RE decomposition techniques agricultural and forest meteorology 148:821-838

Richardson AD, Hollinger DY (2005) Statistical modeling of ecosystem respiration using eddy covariance data: maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three simple models Agricultural and Forest Meteorology 131:191-208

Supplementary Tables

Table S1Controls on CH4 emissions in wetlands and specific time scales of variation compiled from the literature. Headnotes indicate the flux measurement approach and the wetland type covered in the field studies. EB: enclosure-based field study; TB: tower-based field study; Exp: experimental study; Re: Review; BoM: boreal mire; PaR: paddy rice field; TeF: temperate fen; TeB: temperate bog; BoL: Boreal lake; TrL: tropical lake; BRL: bluff road landfill; FM: freshwater marsh; SM: salt marsh; SA: spectral analysis. As the tidal fluctuations include more than water table and salinity variations, tide was particularly considered as a potential factor of methane flux (FCH4). This table is modified based on Koebsch et al. (2015).

Variable / Timescale of variation
Day / Weeks / Months-seasonal / Year
Atmospheric pressure / Sachs et al. (2008) TB, BoM
Sturtevant et al. (2015) TB，FM / Xu et al. (2014)TB, BRL,SA / Yamamoto et al. (2011)EB,SM
Near-surface atmospheric turbulence / Sachs et al. (2008) TB, BoM
Poindexter and Variano (2013)Exp
Koebsch et al. (2015) TB, TeF, SA / Wille et al. (2008)TB, BoM
Plant-mediated transport triggered by stomatal conductance and PAR / Van Der Nat et al. (1998)
Hendriks et al. (2010)TB, TeF
Meijide et al. (2011)TB, PaR
Chu et al. (2014)TB, FM
Long et al. (2010)TB, TeB
Koebsch et al. (2015) TB, TeF, SA
Sturtevant et al. (2015) TB, TeF, SA / Meijide et al. (2011)TB, PaR
Root exudation enhanced by canopy photosynthesis / Hatala et al. (2012)TB, PaR, SA
Koebsch et al. (2015) TB, TeF, SA / Leppälä et al. (2011)EB, BM
Chu et al. (2014)TB,FM
Air temperature / Koebsch et al., (2015)TB,TeF,SA / Treat et al. (2007)EB, TeF
Homineltenberg et al. (2014) TB, TeB
Yvon-Durocher et al. (2014) RE
Koebsch et al. (2015) TB, TeF, SA / Treat et al. (2007)EB, TeF
Soil temperature / Meijide et al. (2011)TB, PaR
Yamamoto et al. (2009) EB,SM
Hatala et al. (2012)TB, PaR, SA
Tong et al. (2013)EB,SM / Koebsch et al. (2015)TB, TeF, SA
Hatala et al. (2012)TB, PaR, SA / Wille et al. (2008)TB, BoM
Sachs et al. (2008)TB, BoM
Leppälä et al. (2011) EB, BM
Hendriks et al. (2010)TB, TeF
Meijide et al. (2011)TB, PaR
Chu et al. (2014)TB,FM
Koebsch et al. (2015) TB, TeF, SA
Yamamoto et al. (2011)EB,SM
Water table level / Yamamoto et al. (2009)EB,SM / Homineltenberg et al. (2014)TB,TeB
Treat et al. (2007) EB, TeF / Treat et al. (2007)EB, TeF
Leppälä et al. (2011)TB, BoM;
Meijide et al. (2011) TB, PaR
Koebsch et al. (2015) TB, TeF, SA
Yamamoto et al. (2011)EB, SM
Salinity / Bu et al. (2015)EB, SM
Neubauer et al. (2013)TB/EB, FM
Chambers et al. (2011)Exp / Neubauer et al. (2013)TB/EB, FM / Poffenbarger et al. (2011)Re,SM
Neubauer et al. (2013)TB/EB, FM
Tide / Yamamoto et al. (2009)EB,SM / Bu et al. (2015)EB, SM
Tong et al. (2013)EB, SM

References

Bu N-S et al. (2015) Effects of semi-lunar tidal cycling on soil CO2 and CH4 emissions: a case study in the Yangtze River estuary, China Wetlands Ecology and Management:1-10 doi:10.1007/s11273-015-9415-5 %/ Springer Netherlands

Chambers LG, Reddy KR, Osborne TZ (2011) Short-Term Response of Carbon Cycling to Salinity Pulses in a Freshwater Wetland Soil Science Society of America Journal 75:2000-2007 doi:10.2136/sssaj2011.0026 %/ SOIL SCI SOC AMER

Chu H, Chen J, Gottgens JF, Ouyang Z, John R, Czajkowski K, Becker R (2014) Net ecosystem methane and carbon dioxide exchanges in a Lake Erie coastal marsh and a nearby cropland Journal of Geophysical Research- Biogeosciences 119:722-740 doi:10.1002/2013jg002520

Hatala JA, Detto M, Baldocchi DD (2012) Gross ecosystem photosynthesis causes a diurnal pattern in methane emission from rice Geophysical Research Letters 39

Hendriks D, Van Huissteden J, Dolman AJ (2010) Multi-technique assessment of spatial and temporal variability of methane fluxes in a peat meadow Agricultural and Forest Meteorology 150:757--774

Hendriks D, Van Huissteden J, Dolman AJ, Van der Molen MK (2007) The full greenhouse gas balance of an abandoned peat meadow Biogeosciences 4

Homineltenberg J, Mauder M, Droesler M, Heidbach K, Werle P, Schmid HP (2014) Ecosystem scale methane fluxes in a natural temperate bog-pine forest in southern Germany Agricultural and Forest Meteorology 198:273-284 doi:10.1016/j.agrformet.2014.08.017

Koebsch F, Jurasinski G, Koch M, Hofmann J, Glatzel S (2015) Controls for multi-scale temporal variation in ecosystem methane exchange during the growing season of a permanently inundated fen Agricultural and Forest Meteorology 204:94-105Leppälä, M., J. Oksanen, and E.-S. Tuittila (2011), Methane flux dynamics during mire succession, Oecologia, 165(2), 489-499.

Long KD, Flanagan LB, Cai T (2010) Diurnal and seasonal variation in methane emissions in a northern Canadian peatland measured by eddy covariance Global Change Biology 16:2420--2435 %\ 2014-2407-2407 2417:2428:2400

Meijide A, Manca G, Goded I, Magliulo V, Tommasi Pd, Seufert G, Cescatti A (2011) Seasonal trends and environmental controls of methane emissions in a rice paddy field in Northern Italy Biogeosciences 8:3809-3821

Neubauer SC, Franklin RB, Berrier DJ (2013) Saltwater intrusion into tidal freshwater marshes alters the biogeochemical processing of organic carbon Biogeosciences 10:8171-8183 doi:10.5194/bg-10-8171-2013 %/ COPERNICUS GESELLSCHAFT MBH

Poffenbarger HJ, Needelman BA, Megonigal JP (2011) Salinity influence on methane emissions from tidal marshes Wetlands 31:831—842

Poindexter CM, Variano EA (2013) Gas exchange in wetlands with emergent vegetation: The effects of wind and thermal convection at the air‐water interface Journal of Geophysical Research: Biogeosciences 118:1297-1306

Sachs T, Giebels M, Boike J, Kutzbach L (2010) Environmental controls on CH4 emission from polygonal tundra on the microsite scale in the Lena river delta, Siberia Global Change Biology 16:3096-3110

Sturtevant C, Ruddell BL, Knox SH, Verfaillie J, Matthes JH, Oikawa PY, Baldocchi D (2015) Identifying scale‐emergent, non‐linear, asynchronous processes of wetland methane exchange Journal of Geophysical Research: Biogeosciences

Tong C, Huang JF, Hu ZQ, Jin YF (2013) Diurnal Variations of Carbon Dioxide, Methane, and Nitrous Oxide Vertical Fluxes in a Subtropical Estuarine Marsh on Neap and Spring Tide Days Estuaries and Coasts 36:633-642 doi:10.1007/s12237-013-9596-1 %/ SPRINGER

Treat CC, Bubier JL, Varner RK, Crill PM (2007) Timescale dependence of environmental and plant‐mediated controls on CH4 flux in a temperate fen Journal of Geophysical Research: Biogeosciences (2005–2012) 112

Van Der Nat F-FW, Middelburg JJ, Van Meteren D, Wielemakers A (1998) Diel methane emission patterns from Scirpus lacustris and Phragmites australis Biogeochemistry 41:1-22

Wille C, Kutzbach L, Sachs T, Wagner D, Pfeiffer EM (2008) Methane emission from Siberian arctic polygonal tundra: eddy covariance measurements and modeling Global Change Biology 14:1395-1408 doi:10.1111/j.1365-2486.2008.01586.x %/ WILEY-BLACKWELL PUBLISHING, INC

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Yamamoto A, Hirota M, Suzuki S, Zhang P, Mariko S (2011) Surrounding pressure controlled by water table alters CO2 and CH4 fluxes in the littoral zone of a brackish-water lake Applied Soil Ecology 47:160-166

Yvon-Durocher G et al. (2014) Methane fluxes show consistent temperature dependence across microbial to ecosystem scales Nature 507:488-491

Table S2. List of micrometeorological sensors at the tidal salt marsh site.

Variable / Sensor / Company / Location (m)
Long-/short-wave radiation, / CNR1 / Kipp and Zonnen, Inc., Netherlands / 5.0
Solar radiation / CMP3 / Kipp and Zonnen, Inc., Netherlands / 5.0
Photosynthetically active radiation / PQS1 / Kipp and Zonnen, Inc., Netherlands / 5.0
Air temperature, Relative humidity / HMP155 / Vaisala, Finland / 2.0
Precipitation / TE525 / Texas Electronics, USA / 2.5

Table S3. Reported studies of wetland methane flux (FCH4). The units of salinity* and conductivity# were ppt (parts per thousand) and mScm−1,respectively.

Wetland types / Climate / Dominant vegetation / Soil salinity*/
conductivity# / Method / FCH4 (nmol m-2 s-1) / Annual emissions (g C-CH4 m−2 year−1) / Reference
Tidal brackish water marshes / Subtropics / Cyperusmalaccensis and P. australis / 3.11~4.22 # / CB / 14.6~111.3 / NA / (Wang et al. 2015)
Tidal freshwater marsh / Subtropics / Cyperusmalaccensis / 0.31~0.50 # / CB / 256.1 ± 40.3 / 96.9 / (Wang et al. 2015)
Coastal marsh / Subtropics / S. salsa / 8.04~18.91# / CB / −0.6~1.0 / NA / (Sun et al. 2013b)
Coastal marsh / Temperate continental / S. salsa,
P. australisand
T. chinensis / 22~31* / CB / -0.09~0.1 / 0.02 / (Sun et al. 2013a)
Freshwater estuarine wetland / Subtropics / Phragmitesaustralis / <0.5 for most time, MAX<1.5 (ppt) / CB / 23.0~202.4 / NA / (Ma et al. 2012)
Tidal salt marsh / Subtropics / Phragmitesaustralis / 2.52~4.98 ppt / CB / 13.9~229.2 / 24.4 / (Tong et al. 2010)
Tidal salt marsh / Subtropics / Spartinaalterniflora, Phragmitesaustralis / 0.6~21.1*a / CB / 0.03~1.6 / 6.57 / (Wang et al.2009)b
Tidal salt marsh / Subtropics / Spartinaalterniflora, Phragmitesaustralis / 1.5~4.11 # / CB / 2.1~118.1 / NA / (Bu, 2013)b
Ombrotrophic bog / Temperate continental / Eriophorum spp., Sphagnum spp. / NA / CB / Approximately 13.0~127.3 / 7.9 / (Lai et al. 2014)
Atlantic blanket bog / Temperate maritime / Sphagnum spp. / NA / CB / 2.2~38.3 / 3.8 / (Laine et al. 2007)
Acid moorland / Temperate / Deschampsiaflexuosa, Moliniacaerulea, Festucaovina et al. / NA / CB / −0.7~10.5 / 0.23 / (Skiba et al. 2013)
Tidal salt marsh / Subtropics / Partinaalterniflora, Phragmitesaustralis / Approximately 0~25 * / EC / −102.9~1525.1 / 17.8 / This study
Brackish marsh / Subtropics / Spartina patens, Schoenoplectusamericanus / Approximately 1~17 * / EC / 0~150.0 / 10.35 / (Holm et al. 2016)
Fresh marsh / Subtropics / Sagittarialancifolia, Leersiaoryzoides / Approximately 0.1~0.4 * / EC / −50~300 / 46.7 / (Holm et al. 2016)
Grazed degraded peatland / Mediterranean climate / Mouse barley(Hordeummurinum L.) and pepperweed (Lepidiumlatifolium L.) / NA / EC / 0.0~21.7 / 3.3 / (Hatala et al. 2012)
Rice paddy / Mediterranean climate / Rice / NA / EC / Approximately −3.5~57.3 / 2.5–6.5 / (Hatala et al. 2012)
Rice paddy / Temperate / Rice / NA / EC / Approximately −20.0~980.0 / 27.8 / (Meijide et al. 2011)
Cropland / Temperate / Soybean / NA / EC / Approximately −225.7~868.1 / 2.3 / (Chu et al. 2014)
Freshwater marsh / Temperate / Emergent and floatingleaved plants / NA / EC / Approximately
−138.9~434.0 / 49.7 / (Chu et al. 2014)
Bog forest site / Temperate and humid climate / Bog-pinesPinusmugo ssp. Rotundata,peat mosses / NA / EC / Approximately −29.5~100.7 / 5.3 / (Homineltenberg et al. 2014)
Ombrotrophic bog / Temperate continental / Carex spp., Sphagnum spp. / NA / EC / Approximately 21.7~115.8 / 12–14.6 / (Shurpali et al. 1993)
Moderately rich treed fen / Temperate / Trees, shrub, moss species / NA / EC / 0~79.9 / NA / (Long et al. 2010)
Fen / Temperate / Sphagnum moss, vascular plants sedges (Carex spp.)et al. / NA / EC / −39.9~119.8 / 16.3 / (Olson et al. 2013)
Restored wetland / Temperate / Schoenoplectusacutus, Typhalatifolia / NA / EC / 0~400.0 / 8.7 / (Baldocchi et al. 2012)
Restored peat meadow / Temperate / Phragmitesaustralis, Holcuslanatus, Agrostis stolonifera / NA / EC / Approximately −25.3~1122.7 / 31.8 / (Hendriks et al. 2007)
Palsa mire / Subarctic / Vascular plantEriophorum, dwarf shrubs Eriophorumvaginatum et al. / NA / EC / Approximately
0~347.2 / 20.3 / (Jackowicz-Korczyński et al. 2010)
Mixed forest/wetland / Temperate / Multiple mixed northern hardwood species / NA / EC / −3.5~10.4 / 0.78 / (Desai et al. 2015)

a The salinity of tide water.

b the same study area of ours.

References

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Bu N-S (2013) Effects of semi-lunar tidal cycling on soil CO2 and CH4 emissions: a case study in the Yangtze River estuary, China. Fudan University

Desai AR et al. (2015) Landscape-level terrestrial methane flux observed from a very tall tower Agricultural and Forest Meteorology 201:61-75

Hatala JA, Detto M, Sonnentag O, Deverel SJ, Verfaillie J, Baldocchi DD (2012) Greenhouse gas (CO2, CH4, H2O) fluxes from drained and flooded agricultural peatlands in the Sacramento-San Joaquin Delta Agriculture, Ecosystems & Environment 150:1--18

Hendriks D, Van Huissteden J, Dolman AJ, Van der Molen MK (2007) The full greenhouse gas balance of an abandoned peat meadow Biogeosciences 4

Holm GO, Perez BC, McWhorter DE, Krauss KW, Johnson DJ, Raynie RC, Killebrew CJ (2016) Ecosystem Level Methane Fluxes from Tidal Freshwater and Brackish Marshes of the Mississippi River Delta: Implications for Coastal Wetland Carbon Projects Wetlands 36:401-413 doi:10.1007/s13157-016-0746-7

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