Articles | Volume 19, issue 21
https://doi.org/10.5194/bg-19-5041-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-5041-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Pore network modeling as a new tool for determining gas diffusivity in peat
School of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland
Marjo Palviainen
Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland
Arianna Marchionne
Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland
Tiia Grönholm
Finnish Meteorological Institute (FMI), Erik Palménin aukio 1, 00560 Helsinki, Finland
Maarit Raivonen
Institute for Atmospheric and Earth System Research (INAR)/Physics, Faculty of Science, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland
Lukas Kohl
Department of Agricultural Sciences, University of Helsinki, P.O. Box 56, 00014 Helsinki, Finland
Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, P.O. Box 56, 00014 Helsinki, Finland
Department of Environmental and Biological Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
Annamari Laurén
School of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland
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Cited articles
Abdalla, M., Hastings, A., Truu, J., Espenberg, M., Mander, Ü., and Smith,
P.: Emissions of methane from northern peatlands: a review of management
impacts and implications for future management options, Ecol. Evol., 6,
7080–7102, https://doi.org/10.1002/ece3.2469, 2016. a, b
Akaike, H.: A new look at the statistical model identification, IEEE T.
Automat. Contr., 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974. a
Bakker, J. W. and Hidding, A.: The influence of soil structure and air content
on gas diffusion in soils, Neth. J. Agr. Sci., 18, 37–48,
https://doi.org/10.18174/njas.v18i1.17354, 1970. a
Ball, B. C. and Smith, K. A.: Gas movement and air-filled porosity, in: Soil
and Environmental Analysis: Physical Methods, 2nd Edn., edited by: Smith,
K. A. and Mullins, C. E., 499–538, Marcel Dekker, New York, NY, ISBN
978-0-8247-0414-8, 2001. a
Beven, K. and Germann, P.: Macropores and water flow in soils, Water Resour.
Res., 18, 1311–1325, https://doi.org/10.1029/WR018i005p01311, 1982. a
Blagodatsky, S. and Smith, P.: Soil physics meets soil biology: Towards better
mechanistic prediction of greenhouse gas emissions from soil, Soil Biol.
Biochem., 47, 78–92, https://doi.org/10.1016/j.soilbio.2011.12.015, 2012. a, b
Bland, J. M. and Altman, D. G.: Measuring agreement in method comparison
studies, Stat. Methods Med. Res., 8, 135–160,
https://doi.org/10.1177/096228029900800204, 1999. a
Blunt, M. J., Jackson, M. D., Piri, M., and Valvatne, P. H.: Detailed physics,
predictive capabilities and macroscopic consequences for pore-network models
of multiphase flow, Adv. Water Resour., 25, 1069–1089,
https://doi.org/10.1016/S0309-1708(02)00049-0, 2002. a
Blunt, M. J., Bijeljic, B., Dong, H., Gharbi, O., Iglauer, S., Mostaghimi, P.,
Paluszny, A., and Pentland, C.: Pore-scale imaging and modelling, Adv. Water
Resour., 51, 197–216, https://doi.org/10.1016/j.advwatres.2012.03.003, 2013. a, b, c, d
Boon, A., Robinson, J. S., Nightingale, P. D., Cardenas, L., Chadwick, D. R.,
and Verhoef, A.: Determination of the gas diffusion coefficient of a peat
grassland soil, Eur. J. Soil Sci., 64, 681–687, https://doi.org/10.1111/ejss.12056,
2013. a, b, c, d
Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K., and Zhuang, Q.: Methane
emissions from wetlands: biogeochemical, microbial, and modeling perspectives
from local to global scales, Glob. Change Biol., 19, 1325–1346,
https://doi.org/10.1111/gcb.12131, 2013. a
Burnham, K. P. and Anderson, D. R.: Multimodel inference: Understanding AIC
and BIC in model selection, Sociol. Method. Res., 33, 261–304,
https://doi.org/10.1177/0049124104268644, 2004. a
Currie, J. A.: Gaseous diffusion in porous media. Part 2. – Dry granular
materials, Brit. J. Appl. Phys., 11, 318–324,
https://doi.org/10.1088/0508-3443/11/8/303, 1960. a, b, c, d
de Vries, E. T., Raoof, A., and van Genuchten, M. T.: Multiscale modelling
of dual-porosity porous media; a computational pore-scale study for flow and
solute transport, Adv. Water Resour., 105, 82–95,
https://doi.org/10.1016/j.advwatres.2017.04.013, 2017. a
Dhanoa, M. S., Lister, S. J., France, J., and Barnes, R. J.: Use of mean square
prediction error analysis and reproducibility measures to study near infrared
calibration equation performance, J. Near Infrared Spec., 7, 133–143,
https://doi.org/10.1255/jnirs.244, 1999. a
Dong, H. and Blunt, M. J.: Pore-network extraction from
micro-computerized-tomography images, Phys. Rev. E, 80, 036307,
https://doi.org/10.1103/PhysRevE.80.036307, 2009. a
Dong, L., Zhang, W., Xiong, Y., Zou, J., Huang, Q., Xu, X., Ren, P., and Huang,
G.: Impact of short-term organic amendments incorporation on soil structure
and hydrology in semiarid agricultural lands, Int. Soil Water Conserv. Res.,
10, 457–469, https://doi.org/10.1016/j.iswcr.2021.10.003, 2022. a
Edling, P.: Soil air. Volume and gas exchange mechanisms, Report 151, Swedish
University of Agricultural Sciences, Department of Soil Sciences, Uppsala,
Sweden, ISBN 91-576-2764-9, 1986. a
Estop-Aragonés, C., Knorr, K.-H., and Blodau, C.: Controls on in situ
oxygen and dissolved inorganic carbon dynamics in peats of a temperate fen,
J. Geophys. Res., 117, G02002, https://doi.org/10.1029/2011JG001888, 2012. a
Fan, Z., McGuire, A. D., Turetsky, M. R., Harden, J. W., Waddington, J. M., and
Kane, E. S.: The response of soil organic carbon of a rich fen peatland in
interior Alaska to projected climate change, Glob. Change Biol., 19,
604–620, https://doi.org/10.1111/gcb.12041, 2013. a
Fan, Z., Neff, J. C., Waldrop, M. P., Ballantyne, A. P., and Turetsky, M. R.:
Transport of oxygen in soil pore-water systems: implications for modeling
emissions of carbon dioxide and methane from peatlands, Biogeochemistry, 121,
455–470, https://doi.org/10.1007/s10533-014-0012-0, 2014. a
Frolking, S., Talbot, J., Jones, M. C., Treat, C. C., Kauffman, J. B.,
Tuittila, E.-S., and Roulet, N.: Peatlands in the Earth's 21st century
climate system, Environ. Rev., 19, 371–396, https://doi.org/10.1139/a11-014, 2011. a
Gharedaghloo, B., Price, J. S., Rezanezhad, F., and Quinton, W. L.: Evaluating
the hydraulic and transport properties of peat soil using pore network
modeling and X-ray micro computed tomography, J. Hydrol., 561, 494–508,
https://doi.org/10.1016/j.jhydrol.2018.04.007, 2018. a, b, c, d
Giavarina, D.: Understanding Bland Altman analysis, Biochem. Med. (Zagreb), 25,
141–151, https://doi.org/10.11613/BM.2015.015, 2015. a
Gostick, J., Aghighi, M., Hinebaugh, J., Tranter, T., Hoeh, M. A.,
Day, H., Spellacy, B., Sharqawy, M. H., Bazylak, A., Burns, A.,
Lehnert, W., and Putz, A.: OpenPNM: A pore network modeling package,
Comput. Sci. Eng., 18, 60–74, https://doi.org/10.1109/MCSE.2016.49, 2016. a
Gostick, J. T.: Versatile and efficient pore network extraction method using
marker-based watershed segmentation, Phys. Rev. E, 96, 023307,
https://doi.org/10.1103/PhysRevE.96.023307, 2017. a, b, c
Gostick, J. T., Khan, Z. A., Tranter, T. G., Kok, M. D. R., Agnaou, M.,
Sadeghi, M., and Jervis, R.: PoreSpy: A Python toolkit for quantitative
analysis of porous media images, J. Open Source Softw., 4, 1296,
https://doi.org/10.21105/joss.01296, 2019. a
Günther, A., Barthelmes, A., Huth, V., Joosten, H., Jurasinski, G.,
Koebsch, F., and Couwenberg, J.: Prompt rewetting of drained peatlands
reduces climate warming despite methane emissions, Nat. Commun., 11, 1644,
https://doi.org/10.1038/s41467-020-15499-z, 2020. a
Hamamoto, S., Dissanayaka, S. H., Kawamoto, K., Nagata, O., Komtatsu, T., and
Moldrup, P.: Transport properties and pore-network structure in
variably-saturated Sphagnum peat soil, Eur. J. Soil Sci., 67, 121–131,
https://doi.org/10.1111/ejss.12312, 2016a. a, b, c, d
Hamamoto, S., Moldrup, P., Kawamoto, K., Sakaki, T., Nishimura, T., and
Komatsu, T.: Pore network structure linked by X-ray CT to particle
characteristics and transport parameters, Soils Found., 56, 676–690,
https://doi.org/10.1016/j.sandf.2016.07.008, 2016b. a
Helliwell, J. R., Sturrock, C. J., Grayling, K. M., Tracy, S. R., Flavel,
R. J., Young, I. M., Whalley, W. R., and Mooney, S. J.: Applications of X-ray
computed tomography for examining biophysical interactions and structural
development in soil systems: a review, Eur. J. Soil Sci., 64, 279–297,
https://doi.org/10.1111/ejss.12028, 2013. a
Hillel, D.: Introduction to Environmental Soil Physics, Academic Press, San
Diego, California, ISBN 978-0-12-348525-0, 1998. a
Jin, Y. and Jury, W. A.: Characterizing the dependence of gas diffusion
coefficient on soil properties, Soil Sci. Soc. Am. J., 60, 66–71,
https://doi.org/10.2136/sssaj1996.03615995006000010012x, 1996. a, b, c, d
Jokinen, P., Pirinen, P., Kaukoranta, J.-P., Kangas, A., Alenius, P., Eriksson,
P., Johansson, M., and Wilkman, S.: Climatological and oceanographic
statistics of Finland 1991–2020, Reports 2021:8, Finnish Meteorological
Institute, Helsinki, Finland, https://doi.org/10.35614/isbn.9789523361485, 2021. a
King, J. A. and Smith, K. A.: Gaseous diffusion through peat, J. Soil Sci., 38,
173–177, https://doi.org/10.1111/j.1365-2389.1987.tb02134.x, 1987. a, b
Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G.,
Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., Bruhwiler,
L., Cameron-Smith, P., Castaldi, S., Chevallier, F., Feng, L., Fraser, A.,
Heimann, M., Hodson, E. L., Houweling, S., Josse, B., Fraser, P. J., Krummel,
P. B., Lamarque, J.-F., Langenfelds, R. L., Le Quéré, C., Naik, V.,
O'Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B., Prinn,
R. G., Rigby, M., Ringeval, B., Santini, M., Schmidt, M., Shindell, D. T.,
Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo, K., Szopa,
S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R. F.,
Williams, J. E., and Zeng, G.: Three decades of global methane sources and
sinks, Nat. Geosci., 6, 813–823, https://doi.org/10.1038/ngeo1955, 2013. a
Kiuru, P., Palviainen, M., Kohl, L., Marchionne, A., and Laurén, A.: Pore
network modeling as a new tool for determining gas diffusivity in peat,
Zenodo [code, data set], https://doi.org/10.5281/zenodo.7193268, 2022b. a, b
Kiuru, P., Palviainen, M., and Laurén, A.: Peat macropore networks – new
insights into episodic and hotspot methane emission, Zenodo [data set],
https://doi.org/10.5281/zenodo.6327112, 2022c. a
Kleimeier, C., Rezanezhad, F., Cappellen, P. V., and Lennartz, B.: Influence of
pore structure on solute transport in degraded and undegraded fen peat soils,
Mires Peat, 19, 18, https://doi.org/10.19189/MaP.2017.OMB.282, 2017. a
Koestel, J., Larsbo, M., and Jarvis, N.: Scale and REV analyses for porosity
and pore connectivity measures in undisturbed soil, Geoderma, 366, 114206,
https://doi.org/10.1016/j.geoderma.2020.114206, 2020. a
Lai, D. Y. F.: Methane dynamics in northern peatlands: A review, Pedosphere,
19, 409–421, https://doi.org/10.1016/S1002-0160(09)00003-4, 2009. a
Laine, J. and Vasander, H.: Ecology and vegetation gradients in peatlands, in:
Peatlands in Finland, edited by: Vasander, H., 10–19, Finnish Peatland
Society, Helsinki, Finland, ISBN 952-90-7971-0, 1996. a
Leifeld, J. and Menichetti, L.: The underappreciated potential of peatlands in
global climate change mitigation strategies, Nat. Commun., 9, 1071,
https://doi.org/10.1038/s41467-018-03406-6, 2018. a
Leifeld, J., Wüst-Galley, C., and Page, S.: Intact and managed peatland
soils as a source and sink of GHGs from 1850 to 2100, Nat. Clim. Change, 9,
945–947, https://doi.org/10.1038/s41558-019-0615-5, 2019. a
Lennartz, B. and Liu, H.: Hydraulic functions of peat soils and ecosystem
service, Front. Environ. Sci., 7, 92, https://doi.org/10.3389/fenvs.2019.00092, 2019. a
Likos, W. J., Lu, N., and Godt, J. W.: Hysteresis and uncertainty in soil
water-retention curve parameters, J. Geotech. Geoenviron., 140,
04013050, https://doi.org/10.1061/(ASCE)GT.1943-5606.0001071, 2014. a
Limpens, J., Berendse, F., Blodau, C., Canadell, J. G., Freeman, C., Holden, J., Roulet, N., Rydin, H., and Schaepman-Strub, G.: Peatlands and the carbon cycle: from local processes to global implications – a synthesis, Biogeosciences, 5, 1475–1491, https://doi.org/10.5194/bg-5-1475-2008, 2008. a
Lin, L. I.-K.: A concordance correlation coefficient to evaluate
reproducibility, Biometrics, 45, 255–268, https://doi.org/10.2307/2532051, 1989. a
Liu, H. and Lennartz, B.: Hydraulic properties of peat soils along a bulk
density gradient – A meta study, Hydrol. Process., 33, 101–114,
https://doi.org/10.1002/hyp.13314, 2019. a
Maier, M. and Schack-Kirchner, H.: Using the gradient method to determine soil
gas flux: A review, Agr. Forest Meteorol, 192–193, 78–95,
https://doi.org/10.1016/j.agrformet.2014.03.006, 2014. a
Maier, M., Gartiser, V., Schengel, A., and Lang, V.: Long term soil gas
monitoring as tool to understand soil processes, Appl. Sci., 10, 8653,
https://doi.org/10.3390/app10238653, 2020. a
McCarter, C. P. R., Rezanezhad, F., Quinton, W. L., Gharedaghloo, B., Lennartz,
B., Price, J., Connon, R., and Van Cappellen, P.: Pore-scale controls on
hydrological and geochemical processes in peat: Implications on interacting
processes, Earth-Sci. Rev., 207, 103227,
https://doi.org/10.1016/j.earscirev.2020.103227, 2020. a, b, c, d
Merey, Ş.: Prediction of transport properties for the Eastern
Mediterranean Sea shallow sediments by pore network modelling, J. Petrol.
Sci. Eng., 176, 403–420, https://doi.org/10.1016/j.petrol.2019.01.081, 2019. a
Millington, R.: Gas diffusion in porous media, Science, 130, 100–102,
https://doi.org/10.1126/science.130.3367.100.b, 1959. a
Millington, R. J. and Quirk, J.: Permeability of porous solids, T. Faraday
Soc., 57, 1200–1207, https://doi.org/10.1039/TF9615701200, 1961. a, b, c, d
Moldrup, P., Olesen, T., Schjønning, P., Yamaguchi, T., and Rolston, D. E.:
Predicting the gas diffusion coefficient in undisturbed soil from soil water
characteristics, Soil Sci. Soc. Am. J., 64, 94–100,
https://doi.org/10.2136/sssaj2000.64194x, 2000. a, b, c
Moldrup, P., Olesen, T., Komatsu, T., Schjønning, P., and Rolston, D. E.:
Tortuosity, diffusivity, and permeability in the soil liquid and gaseous
phases, Soil Sci. Soc. Am. J., 65, 613–623, https://doi.org/10.2136/sssaj2001.653613x,
2001. a
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel,
R. D., and Veith, T. L.: Model evaluation guidelines for systematic
quantification of accuracy in watershed simulations, T. ASABE, 50,
885–900, https://doi.org/10.13031/2013.23153, 2007. a
Mostaghimi, P., Blunt, M. J., and Bijeljic, B.: Computations of absolute
permeability on micro-CT images, Math. Geosci., 45, 103–125,
https://doi.org/10.1007/s11004-012-9431-4, 2013. a
Nimmo, J. R.: Porosity and pore-size distribution, in: Encyclopedia of Soils in
the Environment, Vol. 3, edited by: Hillel, D., 295–303, Elsevier,
Oxford, UK, ISBN 978-0-12-348530-4, 2005. a
Ojanen, P. and Minkkinen, K.: The dependence of net soil CO2 emissions on
water table depth in boreal peatlands drained for forestry, Mires Peat, 24,
27, https://doi.org/10.19189/MaP.2019.OMB.StA.1751, 2019. a
Otsu, N.: A threshold selection method from gray-level histograms, IEEE T.
Syst. Man Cyb., 9, 62–66, https://doi.org/10.1109/TSMC.1979.4310076, 1979. a
Paavilainen, E. and Päivänen, J. Eds.: Peatland Forestry: Ecology and
Principles, Springer-Verlag, Berlin, Germany, ISBN 978-3-642-08198-9,
1995. a
Päivänen, J.: Hydraulic conductivity and water retention in peat soils,
Acta For. Fenn., 129, 1–70, https://doi.org/10.14214/aff.7563, 1973. a, b
Penman, H. L.: Gas and vapour movements in the soil: I. The diffusion of
vapours through porous solids, J. Agr. Sci., 30, 437–462,
https://doi.org/10.1017/S0021859600048164, 1940. a
Qiu, C., Zhu, D., Ciais, P., Guenet, B., and Peng, S.: The role of northern
peatlands in the global carbon cycle for the 21st century, Glob. Ecol.
Biogeogr., 29, 956–973, https://doi.org/10.1111/geb.13081, 2020. a
Rabot, E., Wiesmeier, M., Schlüter, S., and Vogel, H.-J.: Soil structure as
an indicator of soil functions: A review, Geoderma, 314, 122–137,
https://doi.org/10.1016/j.geoderma.2017.11.009, 2018. a
Raivonen, M., Smolander, S., Backman, L., Susiluoto, J., Aalto, T., Markkanen, T., Mäkelä, J., Rinne, J., Peltola, O., Aurela, M., Lohila, A., Tomasic, M., Li, X., Larmola, T., Juutinen, S., Tuittila, E.-S., Heimann, M., Sevanto, S., Kleinen, T., Brovkin, V., and Vesala, T.: HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands, Geosci. Model Dev., 10, 4665–4691, https://doi.org/10.5194/gmd-10-4665-2017, 2017. a
Redding, T. E. and Devito, K. J.: Particle densities of wetland soils in
northern Alberta, Canada, Can. J. Soil Sci., 86, 57–60,
https://doi.org/10.4141/S05-061, 2006. a
Reddy, K. R. and DeLaune, R. D.: Biogeochemistry of Wetlands: Science and
Applications, CRC Press, Boca Raton, Florida, ISBN 978-1-56670-678-0,
2008. a
Rezanezhad, F., Price, J. S., Quinton, W. L., Lennartz, B., Milojevic, T., and
Van Cappellen, P.: Structure of peat soils and implications for water
storage, flow and solute transport: A review update for geochemists, Chem.
Geol., 429, 75–84, https://doi.org/10.1016/j.chemgeo.2016.03.010, 2016. a, b, c
Sadeghi, M. A., Agnaou, M., Barralet, J., and Gostick, J.: Dispersion modeling
in pore networks: A comparison of common pore-scale models and alternative
approaches, J. Contam. Hydrol., 228, 103578,
https://doi.org/10.1016/j.jconhyd.2019.103578, 2020. a
Sarkkola, S., Hökkä, H., Koivusalo, H., Nieminen, M., Ahti, E.,
Päivänen, J., and Laine, J.: Role of tree stand evapotranspiration in
maintaining satisfactory drainage conditions in drained peatlands, Can. J.
Forest Res., 40, 1485–1496, https://doi.org/10.1139/X10-084, 2010. a
Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., Castaldi, S., Chandra, N., Crevoisier, C., Crill, P. M., Covey, K., Curry, C. L., Etiope, G., Frankenberg, C., Gedney, N., Hegglin, M. I., Höglund-Isaksson, L., Hugelius, G., Ishizawa, M., Ito, A., Janssens-Maenhout, G., Jensen, K. M., Joos, F., Kleinen, T., Krummel, P. B., Langenfelds, R. L., Laruelle, G. G., Liu, L., Machida, T., Maksyutov, S., McDonald, K. C., McNorton, J., Miller, P. A., Melton, J. R., Morino, I., Müller, J., Murguia-Flores, F., Naik, V., Niwa, Y., Noce, S., O'Doherty, S., Parker, R. J., Peng, C., Peng, S., Peters, G. P., Prigent, C., Prinn, R., Ramonet, M., Regnier, P., Riley, W. J., Rosentreter, J. A., Segers, A., Simpson, I. J., Shi, H., Smith, S. J., Steele, L. P., Thornton, B. F., Tian, H., Tohjima, Y., Tubiello, F. N., Tsuruta, A., Viovy, N., Voulgarakis, A., Weber, T. S., van Weele, M., van der Werf, G. R., Weiss, R. F., Worthy, D., Wunch, D., Yin, Y., Yoshida, Y., Zhang, W., Zhang, Z., Zhao, Y., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: The Global Methane Budget 2000–2017, Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, 2020. a
Schlegel, A.: hypothetical – Hypothesis and statistical testing in Python,
Github, https://github.com/aschleg/hypothetical (last
access: 13 October 2022), 2020. a
Schlüter, S., Sammartino, S., and Koestel, J.: Exploring the relationship
between soil structure and soil functions via pore-scale imaging, Geoderma,
370, 114370, https://doi.org/10.1016/j.geoderma.2020.114370, 2020. a
Seabold, S. and Perktold, J.: Statsmodels: Econometric and statistical modeling
with Python, in: Proceedings of the 9th Python in Science
Conference, edited by: van der Walt, S. and Millman, J., Austin, Texas, 28
June–3 July 2010, 92–96, https://doi.org/10.25080/Majora-92bf1922-011, 2010. a
Soinne, H., Keskinen, R., Räty, M., Kanerva, S., Turtola, E., Kaseva, J.,
Nuutinen, V., Simojoki, A., and Salo, T.: Soil organic carbon and clay
content as deciding factors for net nitrogen mineralization and cereal yields
in boreal mineral soils, Eur. J. Soil Sci., 72, 1497–1512,
https://doi.org/10.1111/ejss.13003, 2021. a
Steele, D. D. and Nieber, J. L.: Network modeling of diffusion coefficients for
porous media: I. Theory and model development, Soil Sci. Soc. Am. J., 58,
1337–1345, https://doi.org/10.2136/sssaj1994.03615995005800050008x, 1994. a
Stock, S. R.: Recent advances in X-ray microtomography applied to materials,
Int. Mater. Rev., 53, 129–181, https://doi.org/10.1179/174328008X277803, 2008. a
Sullivan, B. W., Dore, S., Kolb, T. E., Hart, S. C., and Montes-Helu, M. C.:
Evaluation of methods for estimating soil carbon dioxide efflux across a
gradient of forest disturbance, Glob. Change Biol., 16, 2449–2460,
https://doi.org/10.1111/j.1365-2486.2009.02139.x, 2010. a
Tozzi, R., Masci, F., and Pezzopane, M.: A stress test to evaluate the
usefulness of Akaike information criterion in short-term earthquake
prediction, Sci. Rep.-UK, 10, 21153, https://doi.org/10.1038/s41598-020-77834-0, 2020. a
Tsuruta, A., Aalto, T., Backman, L., Krol, M. C., Peters, W., Lienert, S.,
Joos, F., Miller, P. A., Zhang, W., Laurila, T., Hatakka, J., Leskinen, A.,
Lehtinen, K. E. J., Peltola, O., Vesala, T., Levula, J., Dlugokencky, E.,
Heimann, M., Kozlova, E., Aurela, M., Lohila, A., Kauhaniemi, M., and
Gomez-Pelaez, A. J.: Methane budget estimates in Finland from the
CarbonTracker Europe-CH4 data assimilation system, Tellus B, 71,
1565030, https://doi.org/10.1080/16000889.2018.1565030, 2019. a
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F.,
Warner, J. D., Yager, N., Gouillart, E., Yu, T., and the scikit-image
contributors: scikit-image: image processing in Python, PeerJ, 2, e453,
https://doi.org/10.7717/peerj.453, 2014. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng,
Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors:
SciPy 1.0: fundamental algorithms for scientific computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Walczak, R., Rovdan, E., and Witkowska-Walczak, B.: Water retention
characteristics of peat and sand mixtures, Int. Agrophys., 16, 161–165,
2002. a
Washington, J. W., Rose, A. W., Ciolkosz, E. J., and Dobos, R. R.: Gaseous
diffusion and permeability in four soil profiles in central Pennsylvania,
Soil Sci., 157, 65–76, https://doi.org/10.1097/00010694-199402000-00001, 1994. a
Weber, T. K. D., Iden, S. C., and Durner, W.: A pore-size classification for peat bogs derived from unsaturated hydraulic properties, Hydrol. Earth Syst. Sci., 21, 6185–6200, https://doi.org/10.5194/hess-21-6185-2017, 2017.
a
Xiong, Q., Baychev, T. G., and Jivkov, A. P.: Review of pore network modelling
of porous media: Experimental characterisations, network constructions and
applications to reactive transport, J. Contam. Hydrol., 192, 101–117,
https://doi.org/10.1016/j.jconhyd.2016.07.002, 2016. a, b
Xu, X., Yuan, F., Hanson, P. J., Wullschleger, S. D., Thornton, P. E., Riley, W. J., Song, X., Graham, D. E., Song, C., and Tian, H.: Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems, Biogeosciences, 13, 3735–3755, https://doi.org/10.5194/bg-13-3735-2016, 2016. a
Yang, Y., Wang, K., Zhang, L., Sun, H., Zhang, K., and Ma, J.: Pore-scale
simulation of shale oil flow based on pore network model, Fuel, 251,
683–692, https://doi.org/10.1016/j.fuel.2019.03.083, 2019. a
Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W., and Hunt, S. J.: Global
peatland dynamics since the Last Glacial Maximum, Geophys. Res. Lett.,
37, L13402, https://doi.org/10.1029/2010GL043584, 2010. a
Zhao, J., Qin, F., Derome, D., Kang, Q., and Carmeliet, J.: Improved pore
network models to simulate single-phase flow in porous media by coupling with
lattice Boltzmann method, Adv. Water Resour., 145, 103738,
https://doi.org/10.1016/j.advwatres.2020.103738, 2020. a
Short summary
Peatlands are large carbon stocks. Emissions of carbon dioxide and methane from peatlands may increase due to changes in management and climate. We studied the variation in the gas diffusivity of peat with depth using pore network simulations and laboratory experiments. Gas diffusivity was found to be lower in deeper peat with smaller pores and lower pore connectivity. However, gas diffusivity was not extremely low in wet conditions, which may reflect the distinctive structure of peat.
Peatlands are large carbon stocks. Emissions of carbon dioxide and methane from peatlands may...
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