Articles | Volume 23, issue 2
https://doi.org/10.5194/bg-23-683-2026
© Author(s) 2026. 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-23-683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Upscaling of soil methane fluxes from topographic attributes derived from a digital elevation model in a cold temperate mountain forest
Graduate School of Agriculture, Kyoto University, 606-8502, Japan
Keisuke Yuasa
Graduate School of Agriculture, Kyoto University, 606-8502, Japan
Masako Dannoura
Graduate School of Agriculture, Kyoto University, 606-8502, Japan
Daniel Epron
CORRESPONDING AUTHOR
Graduate School of Agriculture, Kyoto University, 606-8502, Japan
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Biogeosciences, 22, 4013–4033, https://doi.org/10.5194/bg-22-4013-2025, https://doi.org/10.5194/bg-22-4013-2025, 2025
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The rapid expansion of rubber cultivation constitutes a significant land-use change in Southeast Asia. Despite fertilization being a common practice in rubber plantations, its impact on soil methane (CH4) dynamics has remained poorly understood. Our study demonstrates that fertilization not only reduces soil CH4 consumption but also increases CH4 production, transforming rubber plantations from a net CH4 sink to a source. Implementing rational fertilization could enhance atmospheric CH4 removal.
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The rapid expansion of rubber cultivation constitutes a significant land-use change in Southeast Asia. Despite fertilization being a common practice in rubber plantations, its impact on soil methane (CH4) dynamics has remained poorly understood. Our study demonstrates that fertilization not only reduces soil CH4 consumption but also increases CH4 production, transforming rubber plantations from a net CH4 sink to a source. Implementing rational fertilization could enhance atmospheric CH4 removal.
Jun Murase, Kannika Sajjaphan, Chatprawee Dechjiraratthanasiri, Ornuma Duangngam, Rawiwan Chotiphan, Wutthida Rattanapichai, Wakana Azuma, Makoto Shibata, Poonpipope Kasemsap, and Daniel Epron
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Tropical forest soils are vital for methane uptake, but deforestation and agriculture can alter soil methane oxidation. An experiment in Thailand shows that fertilization significantly suppresses methane oxidation in rubber plantation soils, affecting depths up to 60 cm. Without fertilization, deeper soil layers (below 10 cm) actively oxidize methane. These findings suggest that fertilization negatively impacts the methane uptake capacity of deep-layer soils in rubber plantations.
Cited articles
Ågren, A. M., Lidberg, W., Strömgren, M., Ogilvie, J., and Arp, P. A.: Evaluating digital terrain indices for soil wetness mapping – a Swedish case study, Hydrol. Earth Syst. Sci., 18, 3623–3634, https://doi.org/10.5194/hess-18-3623-2014, 2014.
Angel, R., Claus, P., and Conrad, R.: Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions, ISME J., 6, 847–862, https://doi.org/10.1038/ismej.2011.141, 2012.
Apley, D. W. and Zhu, J.: Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models, J. Roy. Stat. Soc. B, 82, 1059–1086, https://doi.org/10.1111/rssb.12377, 2020.
Aronson, E. L. and Helliker, B. R.: Methane flux in non-wetland soils in response to nitrogen addition: a meta-analysis, Ecology, 91, 3242–3251, https://doi.org/10.1890/09-2185.1, 2010.
Bartoñ, K.: MuMIn: Multi-Model Inference, CRAN [code], https://doi.org/10.32614/CRAN.package.MuMIn, 2010.
Bates, D., Mächler, M., Bolker, B., and Walker, S.: Fitting linear mixed-effects models using lme4, J. Stat. Soft., 67, https://doi.org/10.18637/jss.v067.i01, 2015.
Bell, J. C., Cunningham, R. L., and Havens, M. W.: Calibration and validation of a soil-landscape model for predicting soil drainage class, Soil Sci. Soc. Am. J., 56, 1860–1866, https://doi.org/10.2136/sssaj1992.03615995005600060035x, 1992.
Ben-Shachar, M. S., Makowski, D., Lüdecke, D., Patil, I., Wiernik, B. M., Thériault, R., and Waggoner, P.: effectsize: Indices of Effect Size, CRAN [code], https://doi.org/10.32614/CRAN.package.effectsize, 2019.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant, Hydrol. Sci. Bull., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979.
Bock, M. and Köthe, R.: Predicting the depth of hydromorphic soil characteristics influenced by ground water, SAGA-Seconds Out, 19, 13–22, 2008.
Bodelier, P. L. E. and Laanbroek, H. J.: Nitrogen as a regulatory factor of methane oxidation in soils and sediments, FEMS Microbiol. Ecol., 47, 265–277, https://doi.org/10.1016/S0168-6496(03)00304-0, 2004.
Börjesson, G. and Nohrstedt, H.-Ö.: Fast recovery of atmospheric methane consumption in a Swedish forest soil after single-shot N-fertilization, For. Ecol. Manage., 134, 83–88, https://doi.org/10.1016/S0378-1127(99)00249-2, 2000.
Borken, W., Xu, Y., and Beese, F.: Conversion of hardwood forests to spruce and pine plantations strongly reduced soil methane sink in Germany, Glob. Change Biol., 9, 956–966, https://doi.org/10.1046/j.1365-2486.2003.00631.x, 2003.
Breiman, L.: Random Forest, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brumme, R. and Borken, W.: Site variation in methane oxidation as affected by atmospheric deposition and type of temperate forest ecosystem, Glob. Biogeochem. Cycles, 13, 493–501, https://doi.org/10.1029/1998GB900017, 1999.
Burt, R., Reinsch, T. G., and Miller, W. P.: A micro-pipette method for water dispersible clay, Commun. Soil Sci. Plant Anal., 24, 2531–2544, https://doi.org/10.1080/00103629309368975, 1993.
Christiansen, J. R., Levy-Booth, D., Prescott, C. E., and Grayston, S. J.: Microbial and environmental controls of methane fluxes along a soil moisture gradient in a pacific coastal temperate rainforest, Ecosystems, 19, 1255–1270, https://doi.org/10.1007/s10021-016-0003-1, 2016.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015.
Courtois, E. A., Stahl, C., Van Den Berge, J., Bréchet, L., Van Langenhove, L., Richter, A., Urbina, I., Soong, J. L., Peñuelas, J., and Janssens, I. A.: Spatial Variation of Soil CO2, CH4 and N2O Fluxes Across Topographical Positions in Tropical Forests of the Guiana Shield, Ecosystems, 21, 1445–1458, https://doi.org/10.1007/s10021-018-0232-6, 2018.
Du, Z., Riveros-Iregui, D. A., Jones, R. T., McDermott, T. R., Dore, J. E., McGlynn, B. L., Emanuel, R. E., and Li, X.: Landscape position influences microbial composition and function via redistribution of soil water across a watershed, Appl. Environ. Microbiol., 81, 8457–8468, https://doi.org/10.1128/AEM.02643-15, 2015.
Dutaur, L. and Verchot, L. V.: A global inventory of the soil CH4 sink, Glob. Biogeochem. Cycles, 21, 2006GB002734, https://doi.org/10.1029/2006GB002734, 2007.
Epron, D. and Paul, S. K.: Data related to Upscaling of soil methane fluxes from topographic attributes derived from a digital elevation model in a cold temperate mountain forest, Kyoto University Research Information Repository [data set], https://doi.org/10.57723/kds605755, 2025.
Epron, D., Plain, C., Ndiaye, F.-K., Bonnaud, P., Pasquier, C., and Ranger, J.: Effects of compaction by heavy machine traffic on soil fluxes of methane and carbon dioxide in a temperate broadleaved forest, For. Ecol. Manage., 382, 1–9, https://doi.org/10.1016/j.foreco.2016.09.037, 2016.
Epron, D., Mochidome, T., Tanabe, T., Dannoura, M., and Sakabe, A.: Variability in stem methane emissions and wood methane production of different tree species in a cold temperate mountain forest, Ecosystems, 26, 784–799, https://doi.org/10.1007/s10021-022-00795-0, 2023.
Freeman, T. G.: Calculating catchment area with divergent flow based on a regular grid, Comput. Geosci., 17, 413–422, https://doi.org/10.1016/0098-3004(91)90048-i, 1991.
Genuer, R., Poggi, J.-M., and Tuleau-Malot, C.: Variable selection using random forests, Pattern Recognit. Lett., 31, 2225–2236, 2010.
Genuer, R., Poggi, J.-M., and Tuleau-Malot, C.: VSURF: An R package for variable selection using random forests, The R Journal, 7, 19, https://doi.org/10.32614/RJ-2015-018, 2015.
Gomez, J., Vidon, P., Gross, J., Beier, C., Caputo, J., and Mitchell, M.: Estimating greenhouse gas emissions at the soil–atmosphere interface in forested watersheds of the US Northeast, Environ. Monit. Assess., 188, 295, https://doi.org/10.1007/s10661-016-5297-0, 2016.
Greenwell, B. M. and Boehmke, B. C.: Variable importance plots – an introduction to the vip package, The R Journal, 12, 343, https://doi.org/10.32614/RJ-2020-013, 2020.
Guckland, A., Flessa, H., and Prenzel, J.: Controls of temporal and spatial variability of methane uptake in soils of a temperate deciduous forest with different abundance of European beech (Fagus sylvatica L.), Soil Biol. Biochem., 41, 1659–1667, https://doi.org/10.1016/j.soilbio.2009.05.006, 2009.
Hakamada, R. E., Hubbard, R. M., Moreira, G. G., Stape, J. L., Campoe, O., and Ferraz, S. F. D. B.: Influence of stand density on growth and water use efficiency in Eucalyptus clones, Forest Ecology and Management, 466, 118125, https://doi.org/10.1016/j.foreco.2020.118125, 2020.
Harrell Jr., F. E.: Hmisc: Harrell Miscellaneous, 5.2–4, CRAN [code], https://doi.org/10.32614/CRAN.package.Hmisc, 2003.
Hirai, H., Araki, S., and Kyuma, K.: Characteristics of brown forest soils developed on the paleozoic shale in northern Kyoto with special reference to their pedogenetic process, Soil Sci. Plant Nutr., 34, 157–170, https://doi.org/10.1080/00380768.1988.10415670, 1988.
Holwerda, F., Scatena, F. N., and Bruijnzeel, L. A.: Throughfall in a Puerto Rican lower montane rain forest: A comparison of sampling strategies, J. Hydrol., 327, 592–602, https://doi.org/10.1016/j.jhydrol.2005.12.014, 2006.
Hu, R., Hirano, T., Sakaguchi, K., Yamashita, S., Cui, R., Sun, L., and Liang, N.: Spatiotemporal variation in soil methane uptake in a cool-temperate immature deciduous forest, Soil Biol. Biochem., 184, 109094, https://doi.org/10.1016/j.soilbio.2023.109094, 2023.
Hütsch, B. W.: Tillage and land use effects on methane oxidation rates and their vertical profiles in soil, Biol. Fertil. Soils., 27, 284–292, https://doi.org/10.1007/s003740050435, 1998.
IPCC: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st edn., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2023.
Ishihara, M. I., Suzuki, S. N., Nakamura, M., Enoki, T., Fujiwara, A., Hiura, T., Homma, K., Hoshino, D., Hoshizaki, K., Ida, H., Ishida, K., Itoh, A., Kaneko, T., Kubota, K., Kuraji, K., Kuramoto, S., Makita, A., Masaki, T., Namikawa, K., Niiyama, K., Noguchi, M., Nomiya, H., Ohkubo, T., Saito, S., Sakai, T., Sakimoto, M., Sakio, H., Shibano, H., Sugita, H., Suzuki, M., Takashima, A., Tanaka, N., Tashiro, N., Tokuchi, N., Yakushima Forest Environment Conservation Center, Yoshida, T., and Yoshida, Y.: Forest stand structure, composition, and dynamics in 34 sites over Japan, Ecol. Res., 26, 1007–1008, https://doi.org/10.1007/s11284-011-0847-y, 2011.
Ishizuka, S., Sakata, T., and Ishizuka, K.: Methane oxidation in Japanese forest soils, Soil Biol. Biochem., 32, 769–777, https://doi.org/10.1016/S0038-0717(99)00200-X, 2000.
Ishizuka, S., Sakata, T., Sawata, S., Ikeda, S., Sakai, H., Takenaka, C., Tamai, N., Onodera, S., Shimizu, T., Kan-na, K., Tanaka, N., and Takahashi, M.: Methane uptake rates in Japanese forest soils depend on the oxidation ability of topsoil, with a new estimate for global methane uptake in temperate forest, Biogeochemistry, 92, 281–295, https://doi.org/10.1007/s10533-009-9293-0, 2009.
Itoh, M., Ohte, N., and Koba, K.: Methane flux characteristics in forest soils under an East Asian monsoon climate, Soil Biol. Biochem., 41, 388–395, https://doi.org/10.1016/j.soilbio.2008.12.003, 2009.
Jacinthe, P. A., Vidon, P., Fisher, K., Liu, X., and Baker, M. E.: Soil methane and carbon dioxide fluxes from cropland and riparian buffers in different hydrogeomorphic settings, J. Environ. Qual., 44, 1080–1090, https://doi.org/10.2134/jeq2015.01.0014, 2015.
Jensen, S., Priemé, A., and Bakken, L.: Methanol improves methane uptake in starved methanotrophic microorganisms, Appl. Environ. Microbiol., 64, 1143–1146, https://doi.org/10.1128/AEM.64.3.1143-1146.1998, 1998.
Jeong, G., Oeverdieck, H., Park, S. J., Huwe, B., and Ließ, M.: Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain, Catena, 154, 73–84, https://doi.org/10.1016/j.catena.2017.02.006, 2017.
Jevon, F. V., Gewirtzman, J., Lang, A. K., Ayres, M. P., and Matthes, J. H.: Tree species effects on soil CO2 and CH4 fluxes in a mixed temperate forest, Ecosystems, 26, 1587–1602, https://doi.org/10.1007/s10021-023-00852-2, 2023.
Kagotani, Y., Hamabata, E., and Nakajima, T.: Seasonal and spatial variations and the effects of clear-cutting in the methane absorption rates of a temperate forest soil, Nutr. Cycl. Agroecosystems, 59, 169–175, https://doi.org/10.1023/A:1017554031367, 2001.
Kaiser, K. E., McGlynn, B. L., and Dore, J. E.: Landscape analysis of soil methane flux across complex terrain, Biogeosciences, 15, 3143–3167, https://doi.org/10.5194/bg-15-3143-2018, 2018.
Kemppinen, J., Niittynen, P., Riihimäki, H., and Luoto, M.: Modelling soil moisture in a high-latitude landscape using LiDAR and soil data, Earth Surf. Processes Landf., 43, 1019–1031, https://doi.org/10.1002/esp.4301, 2018.
King, G. M. and Schnell, S.: Ammonium and nitrite inhibition of methane oxidation by Methylobacter albus BG8 and Methylosinus trichosporium OB3b at low methane concentrations, Appl. Environ. Microbiol., 60, 3508–3513, https://doi.org/10.1128/aem.60.10.3508-3513.1994, 1994.
Kohler, M. A. and Linsley, R. K.: Predicting the Runoff from Storm Rainfall, US department of commerce, Weather Bureau, 20, 1951.
Kravchenko, A. N., Bollero, G. A., Omonode, R. A., and Bullock, D. G.: Quantitative mapping of soil drainage classes using topographical data and soil electrical conductivity, Soil Sci. Soc. Am. J., 66, 235–243, https://doi.org/10.2136/sssaj2002.2350, 2002.
Kruse, C. W., Moldrup, P., and Iversen, N.: Modeling diffusion and reaction in soils: II. atmospheric methane diffusion and consumption in a forest soil, Soil Sci., 161, 355–365, https://doi.org/10.1097/00010694-199606000-00002, 1996.
Kuhn, M. and Johnson, K.: Applied Predictive Modeling, Springer New York, New York, NY, https://doi.org/10.1007/978-1-4614-6849-3, 2013.
Kuznetsova, A., Brockhoff, P. B., and Christensen, R. H. B.: lmerTest package: tests in linear mixed effects models, J. Stat. Soft., 82, https://doi.org/10.18637/jss.v082.i13, 2017.
Lee, J., Oh, Y., Lee, S. T., Seo, Y. O., Yun, J., Yang, Y., Kim, J., Zhuang, Q., and Kang, H.: Soil organic carbon is a key determinant of CH4 sink in global forest soils, Nat. Commun., 14, 3110, https://doi.org/10.1038/s41467-023-38905-8, 2023.
Lenth, R. V.: emmeans: Estimated Marginal Means, aka Least-Squares Means, CRAN [code], https://doi.org/10.32614/cran.package.emmeans, 2017.
Lundbäck, M., Persson, H., Häggström, C., and Nordfjell, T.: Global analysis of the slope of forest land, Forestry, 94, 54–69, https://doi.org/10.1093/forestry/cpaa021, 2021.
Luo, G. J., Kiese, R., Wolf, B., and Butterbach-Bahl, K.: Effects of soil temperature and moisture on methane uptake and nitrous oxide emissions across three different ecosystem types, Biogeosciences, 10, 3205–3219, https://doi.org/10.5194/bg-10-3205-2013, 2013.
Martinson, G. O., Müller, A. K., Matson, A. L., Corre, M. D., and Veldkamp, E.: Nitrogen and phosphorus control soil methane uptake in tropical montane forests, J. Geophys. Res.-Biogeo., 126, e2020JG005970, https://doi.org/10.1029/2020JG005970, 2021.
Meinshausen, N.: Quantile Regression Forests, J. Mach. Learn. Res., 7, 983–999, 2006.
Meinshausen, N.: quantregForest: Quantile Regression Forests, CRAN [code], https://doi.org/10.32614/CRAN.package.quantregForest, 2017.
Miller, B. A., Koszinski, S., Wehrhan, M., and Sommer, M.: Impact of multi-scale predictor selection for modeling soil properties, Geoderma, 239–240, 97–106, https://doi.org/10.1016/j.geoderma.2014.09.018, 2015.
Mochizuki, Y., Koba, K., and Yoh, M.: Strong inhibitory effect of nitrate on atmospheric methane oxidation in forest soils, Soil Biol. Biochem., 50, 164–166, https://doi.org/10.1016/j.soilbio.2012.03.013, 2012.
Murphy, P. N. C., Ogilvie, J., Meng, F.-R., White, B., Bhatti, J. S., and Arp, P. A.: Modelling and mapping topographic variations in forest soils at high resolution: A case study, Ecol. Model., 222, 2314–2332, https://doi.org/10.1016/j.ecolmodel.2011.01.003, 2011.
Nakamura, Y. and Krestov, P. V.: Coniferous forests of the temperate zone of asia, in: Ecosystems of the world. Conifferous forests, vol. 6, edited by: Andersson, F. A., Elsevier, 163–220, 2005.
Osborne, B. B., Nasto, M. K., Asner, G. P., Balzotti, C. S., Cleveland, C. C., Sullivan, B. W., Taylor, P. G., Townsend, A. R., and Porder, S.: Climate, topography, and canopy chemistry exert hierarchical control over soil N cycling in a neotropical lowland forest, Ecosystems, 20, 1089–1103, https://doi.org/10.1007/s10021-016-0095-7, 2017.
Pachepsky, Ya. A., Timlin, D. J., and Rawls, W. J.: Soil Water Retention as Related to Topographic Variables, Soil Sci. Soc. Am. J., 65, 1787–1795, https://doi.org/10.2136/sssaj2001.1787, 2001.
Pinheiro, J., R Core Team, and Bates, D.: nlme: Linear and Nonlinear Mixed Effects Models, CRAN [code],
doi10.32614/cran.package.nlme, 1999.
doi10.32614/cran.package.nlme, 1999.
Praeg, N., Wagner, A. O., and Illmer, P.: Plant species, temperature, and bedrock affect net methane flux out of grassland and forest soils, Plant Soil., 410, 193–206, https://doi.org/10.1007/s11104-016-2993-z, 2017.
Quebbeman, A. W., Menge, D. N. L., Zimmerman, J., and Uriarte, M.: Topography and tree species improve estimates of spatial variation in soil greenhouse gas fluxes in a subtropical forest, Ecosystems, 25, 648–660, https://doi.org/10.1007/s10021-021-00677-x, 2022.
Räsänen, A., Manninen, T., Korkiakoski, M., Lohila, A., and Virtanen, T.: Predicting catchment-scale methane fluxes with multi-source remote sensing, Landscape Ecol., 36, 1177–1195, https://doi.org/10.1007/s10980-021-01194-x, 2021.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 20 November 2024), 2024.
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., and Dormann, C. F.: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure, Ecography, 40, 913–929, https://doi.org/10.1111/ecog.02881, 2017.
Schoener, G. and Stone, M. C.: Monitoring soil moisture at the catchment scale – A novel approach combining antecedent precipitation index and radar-derived rainfall data, J. Hydrol., 589, 125155, https://doi.org/10.1016/j.jhydrol.2020.125155, 2020.
Semrau, J. D., DiSpirito, A. A., and Vuilleumier, S.: Facultative methanotrophy: false leads, true results, and suggestions for future research: Facultative methanotrophy, FEMS Microbiol. Lett., 323, 1–12, https://doi.org/10.1111/j.1574-6968.2011.02315.x, 2011.
Sidle, R. C., Tsuboyama, Y., Noguchi, S., Hosoda, I., Fujieda, M., and Shimizu, T.: Stormflow generation in steep forested headwaters: a linked hydrogeomorphic paradigm, Hydrol. Process., 14, 369–385, https://doi.org/10.1002/(SICI)1099-1085(20000228)14:3<369::AID-HYP943>3.0.CO;2-P, 2000.
Tateno, R. and Takeda, H.: Forest structure and tree species distribution in relation to topography-mediated heterogeneity of soil nitrogen and light at the forest floor, Ecol. Res., 18, 559–571, https://doi.org/10.1046/j.1440-1703.2003.00578.x, 2003.
Ueda, S., Ando, M., and Kanzaki, K.: Forest soil surveys of the Kyoto University Forest in Ashiu. II. Soil types, grain size, and chemical and physical properties of soils, Bulletin of the Kyoto University Forests, 65, 94–112, 1993.
Vainio, E., Peltola, O., Kasurinen, V., Kieloaho, A.-J., Tuittila, E.-S., and Pihlatie, M.: Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux, Biogeosciences, 18, 2003–2025, https://doi.org/10.5194/bg-18-2003-2021, 2021.
Vanclay, J. K.: Managing water use from forest plantations, Forest Ecology and Management, 257, 385–389, https://doi.org/10.1016/j.foreco.2008.09.003, 2009.
Veldkamp, E., Koehler, B., and Corre, M. D.: Indications of nitrogen-limited methane uptake in tropical forest soils, Biogeosciences, 10, 5367–5379, https://doi.org/10.5194/bg-10-5367-2013, 2013.
Virkkala, A.-M., Niittynen, P., Kemppinen, J., Marushchak, M. E., Voigt, C., Hensgens, G., Kerttula, J., Happonen, K., Tyystjärvi, V., Biasi, C., Hultman, J., Rinne, J., and Luoto, M.: High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra, Biogeosciences, 21, 335–355, https://doi.org/10.5194/bg-21-335-2024, 2024.
Wang, J. M., Murphy, J. G., Geddes, J. A., Winsborough, C. L., Basiliko, N., and Thomas, S. C.: Methane fluxes measured by eddy covariance and static chamber techniques at a temperate forest in central Ontario, Canada, Biogeosciences, 10, 4371–4382, https://doi.org/10.5194/bg-10-4371-2013, 2013.
Wang, L. and Liu, H.: An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling, Int. J. Geogr. Inf. Sci., 20, 193–213, https://doi.org/10.1080/13658810500433453, 2006.
Warner, D. L., Vargas, R., Seyfferth, A., and Inamdar, S.: Transitional slopes act as hotspots of both soil CO2 emission and CH4 uptake in a temperate forest landscape, Biogeochemistry, 138, 121–135, https://doi.org/10.1007/s10533-018-0435-0, 2018.
Warner, D. L., Guevara, M., Inamdar, S., and Vargas, R.: Upscaling soil-atmosphere CO2 and CH4 fluxes across a topographically complex forested landscape, Agric. For. Meteorol., 264, 80–91, https://doi.org/10.1016/j.agrformet.2018.09.020, 2019.
West, A. E. and Schmidt, S. K.: Acetate stimulates atmospheric CH4 oxidation by an alpine tundra soil, Soil Biol. Biochem., 31, 1649–1655, https://doi.org/10.1016/S0038-0717(99)00076-0, 1999.
Yamao, M., Sidle, R. C., Gomi, T., and Imaizumi, F.: Characteristics of landslides in unwelded pyroclastic flow deposits, southern Kyushu, Japan, Nat. Hazards Earth Syst. Sci., 16, 617–627, https://doi.org/10.5194/nhess-16-617-2016, 2016.
Yu, L., Huang, Y., Zhang, W., Li, T., and Sun, W.: Methane uptake in global forest and grassland soils from 1981 to 2010, Sci. Total Environ., 607–608, 1163–1172, https://doi.org/10.1016/j.scitotenv.2017.07.082, 2017.
Yu, L., Zhu, J., Ji, H., Bai, X., Lin, Y., Zhang, Y., Sha, L., Liu, Y., Song, Q., Dörsch, P., Mulder, J., and Zhou, W.: Topography-related controls on N2O emission and CH4 uptake in a tropical rainforest catchment, Sci. Total Environ., 775, 145616, https://doi.org/10.1016/j.scitotenv.2021.145616, 2021.
Zevenbergen, L. W. and Thorne, C. R.: Quantitative analysis of land surface topography, Earth Surf. Processes Landf., 12, 47–56, https://doi.org/10.1002/esp.3290120107, 1987.
Short summary
We used a machine learning approach to upscale CH4 fluxes over time on non-waterlogged soil in a topographically complex mountain forest. Predicted CH4 fluxes varied significantly across topographic positions, with greater uptake on ridges and slopes than on the plain and foot slopes. Recent past precipitations significantly influenced seasonal CH4 uptake. Our findings highlight the role of topography and the potential of remote sensing and machine learning to map CH4 fluxes.
We used a machine learning approach to upscale CH4 fluxes over time on non-waterlogged soil in a...
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