90 citations as recorded by crossref.

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4. An assessment of natural methane fluxes simulated by the CLASS-CTEM model V. Arora et al. https://doi.org/10.5194/bg-15-4683-2018
5. Quantification and uncertainty of global upland soil methane sinks: Processes, controls, model limitations, and improvements H. Song et al. https://doi.org/10.1016/j.earscirev.2024.104758
6. Detectability of Arctic methane sources at six sites performing continuous atmospheric measurements T. Thonat et al. https://doi.org/10.5194/acp-17-8371-2017
7. Land use cover changes abated terrestrial ecosystem carbon sink in China during the past four decades X. Jiang et al. https://doi.org/10.1080/27669645.2025.2450943
8. Assessment of Satellite-Derived FAPAR Products With Different Spatial Resolutions for Gross Primary Productivity Estimation X. Zhang et al. https://doi.org/10.1109/JSTARS.2024.3522938
9. Spatial and temporal variations of N2O emissions from global forest and grassland ecosystems K. Zhang et al. https://doi.org/10.1016/j.agrformet.2018.12.011
10. The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2017 A. Petrescu et al. https://doi.org/10.5194/essd-13-2307-2021
11. Uncertainty Quantification of Global Net Methane Emissions From Terrestrial Ecosystems Using a Mechanistically Based Biogeochemistry Model L. Liu et al. https://doi.org/10.1029/2019JG005428
12. Net exchanges of methane and carbon dioxide on the Qinghai-Tibetan Plateau from 1979 to 2100 Z. Jin et al. https://doi.org/10.1088/1748-9326/10/8/085007
13. Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three long-term grassland sites D. Wu et al. https://doi.org/10.5194/bg-15-3421-2018
14. Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0 X. Yue et al. https://doi.org/10.5194/gmd-17-4621-2024
15. Global Methane Budget 2000–2020 M. Saunois et al. https://doi.org/10.5194/essd-17-1873-2025
16. Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations O. Peltola et al. https://doi.org/10.5194/essd-11-1263-2019
17. Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC J. Susiluoto et al. https://doi.org/10.5194/gmd-11-1199-2018
18. Importance of vegetation classes in modeling CH4 emissions from boreal and subarctic wetlands in Finland T. Li et al. https://doi.org/10.1016/j.scitotenv.2016.08.020
19. Reviews and syntheses: The role of process-based modeling of the CO2 : CH4 production ratio in predicting future terrestrial Arctic methane emissions M. Moser et al. https://doi.org/10.5194/bg-23-605-2026
20. Challenges Regionalizing Methane Emissions Using Aquatic Environments in the Amazon Basin as Examples J. Melack et al. https://doi.org/10.3389/fenvs.2022.866082
21. Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates K. Chang et al. https://doi.org/10.1111/gcb.16755
22. Effect of varying soil water potentials on methanogenesis in aerated marshland soils D. Wagner https://doi.org/10.1038/s41598-017-14980-y
23. Global vegetation biomass production efficiency constrained by models and observations Y. He et al. https://doi.org/10.1111/gcb.14816
24. Enhanced understanding of soil methane processes through modeling microbial kinetics and taxonomy S. Zhou et al. https://doi.org/10.1016/j.soilbio.2025.109838
25. Process-based modelling of the methane balance in periglacial landscapes (JSBACH-methane) S. Kaiser et al. https://doi.org/10.5194/gmd-10-333-2017
26. Wetland CH4 and CO2 emissions show opposite temperature dependencies along global climate gradients B. Jiang et al. https://doi.org/10.1016/j.catena.2024.108557
27. Sustained‐Flux Global Warming Potential Driven by Nitrogen Inflow and Hydroperiod in a Model of Great Lakes Coastal Wetlands Y. Yuan et al. https://doi.org/10.1029/2021JG006242
28. Interannual variation in methane emissions from tropical wetlands triggered by repeated El Niño Southern Oscillation Q. Zhu et al. https://doi.org/10.1111/gcb.13726
29. Winter microbial community structure and methane-cycling potential in constructed agricultural wetlands across regions and microhabitats T. Liu et al. https://doi.org/10.1093/femsec/fiaf086
30. Spatiotemporal dynamics of CO2 and CH4 fluxes in China's mariculture ponds: Application of a biogeochemical model H. Ma et al. https://doi.org/10.1016/j.jclepro.2025.147340
31. Assessing methane emissions for northern peatlands in ORCHIDEE-PEAT revision 7020 E. Salmon et al. https://doi.org/10.5194/gmd-15-2813-2022
32. Modeling Global Soil Carbon and Soil Microbial Carbon by Integrating Microbial Processes into the Ecosystem Process Model TRIPLEX‐GHG K. Wang et al. https://doi.org/10.1002/2017MS000920
33. Evaluation of CH4MODwetland and Terrestrial Ecosystem Model (TEM) used to estimate global CH4 emissions from natural wetlands T. Li et al. https://doi.org/10.5194/gmd-13-3769-2020
34. Hiatus of wetland methane emissions associated with recent La Niña episodes in the Asian monsoon region X. Zhang et al. https://doi.org/10.1007/s00382-020-05219-0
35. Estimating global natural wetland methane emissions using process modelling: spatio‐temporal patterns and contributions to atmospheric methane fluctuations Q. Zhu et al. https://doi.org/10.1111/geb.12307
36. Spatial methane emission modelling from wetlands using geospatial tools S. Bansal et al. https://doi.org/10.1080/01431161.2018.1513182
37. Decadal trends in the seasonal-cycle amplitude of terrestrial CO₂ exchange resulting from the ensemble of terrestrial biosphere models A. Ito et al. https://doi.org/10.3402/tellusb.v68.28968
38. Global responses of wetland methane emissions to extreme temperature and precipitation M. Xu et al. https://doi.org/10.1016/j.envres.2024.118907
39. Accuracy analysis in CH4MODwetland in the simulation of CH4 emissions from Chinese wetlands Q. ZHANG et al. https://doi.org/10.1016/j.accre.2020.06.003
40. Estimating natural nitrous oxide emissions from the Qinghai–Tibetan Plateau using a process-based model: Historical spatiotemporal patterns and future trends K. Zhang et al. https://doi.org/10.1016/j.ecolmodel.2022.109902
41. Variability and quasi-decadal changes in the methane budget over the period 2000–2012 M. Saunois et al. https://doi.org/10.5194/acp-17-11135-2017
42. High carbon losses from oxygen‐limited soils challenge biogeochemical theory and model assumptions W. Huang et al. https://doi.org/10.1111/gcb.15867
43. The Global N2O Model Intercomparison Project H. Tian et al. https://doi.org/10.1175/BAMS-D-17-0212.1
44. Central Role of Nitrogen Fertilizer Relative to Water Management in Determining Direct Nitrous Oxide Emissions From Global Rice‐Based Ecosystems H. Song et al. https://doi.org/10.1029/2023GB007744
45. Aquatic carbon cycling in the conterminous United States and implications for terrestrial carbon accounting D. Butman et al. https://doi.org/10.1073/pnas.1512651112
46. Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades Q. Zhu et al. https://doi.org/10.1038/srep38020
47. Satellite data based estimation of methane emissions from rice paddies in the Sanjiang Plain in northeast China M. Sun et al. https://doi.org/10.1371/journal.pone.0176765
48. Interannual Variability of Global Wetlands in Response to El Niño Southern Oscillations (ENSO) and Land-Use S. Ilyas et al. https://doi.org/10.3389/feart.2019.00289
49. Evaluating Cumulative Drought Effect on Global Vegetation Photosynthesis Using Numerous GPP Products C. Wu & T. Wang https://doi.org/10.3389/fenvs.2022.908875
50. The Kulbäcksliden Research Infrastructure: a unique setting for northern peatland studies K. Noumonvi et al. https://doi.org/10.3389/feart.2023.1194749
51. MATHEMATICAL MODELS OF METHANE CONSUMPTION BY SOILS: A REVIEW M. Glagolev et al. https://doi.org/10.18822/edgcc622937
52. Interplay between temperature and redox conditions regulates wetland biogeochemistry and greenhouse gas emissions L. Ho et al. https://doi.org/10.1016/j.scitotenv.2025.180413
53. The Global Methane Budget 2000–2017 M. Saunois et al. https://doi.org/10.5194/essd-12-1561-2020
54. A New Process‐Based Soil Methane Scheme: Evaluation Over Arctic Field Sites With the ISBA Land Surface Model X. Morel et al. https://doi.org/10.1029/2018MS001329
55. Plant‐mediated methane transport in emergent and floating‐leaved species of a temperate freshwater mineral‐soil wetland J. Villa et al. https://doi.org/10.1002/lno.11467
56. Data‐Constrained Projections of Methane Fluxes in a Northern Minnesota Peatland in Response to Elevated CO2 and Warming S. Ma et al. https://doi.org/10.1002/2017JG003932
57. Estimating wetland CH4 emissions on the Qinghai-Tibetan Plateau based on a processed model coupled with inundation dynamics between 1960 and 2100 J. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.177169
58. Process‐basedTRIPLEX‐GHGmodel for simulatingN2Oemissions from global forests and grasslands:Model development and evaluation K. Zhang et al. https://doi.org/10.1002/2017MS000934
59. Year-round simulated methane emissions from a permafrost ecosystem in Northeast Siberia K. Castro-Morales et al. https://doi.org/10.5194/bg-15-2691-2018
60. A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland C. Zhang et al. https://doi.org/10.1029/2020JG006002
61. Temporal and Spatial Variation of Wetland CH4 Emissions from the Qinghai–Tibet Plateau under Future Climate Change Scenarios X. Zhang et al. https://doi.org/10.3390/atmos13060854
62. Contribution of Incorporating the Phosphorus Cycle into TRIPLEX-CNP to Improve the Quantification of Land Carbon Cycle J. Ding et al. https://doi.org/10.3390/land11060778
63. An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California B. M. Sleeter et al. https://doi.org/10.3934/environsci.2015.3.577
64. The ecology of methane in streams and rivers: patterns, controls, and global significance E. Stanley et al. https://doi.org/10.1890/15-1027
65. Seasonal dynamics of Arctic soils: Capturing year-round processes in measurements and soil biogeochemical models Z. Lyu et al. https://doi.org/10.1016/j.earscirev.2024.104820
66. The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019 A. Petrescu et al. https://doi.org/10.5194/essd-15-1197-2023
67. Agriculture-informed Neural Networks for Predicting Nitrous Oxide Emissions C. Lin et al. https://doi.org/10.1145/3696113
68. Terrestrial Ecosystem Modeling with IBIS: Progress and Future Vision L. Jinxun et al. https://doi.org/10.5814/j.issn.1674-764x.2022.01.001
69. China's greenhouse gas budget during 2000–2023 W. Yuan et al. https://doi.org/10.1093/nsr/nwaf069
70. The global methane budget 2000–2012 M. Saunois et al. https://doi.org/10.5194/essd-8-697-2016
71. Attribution of changes in global wetland methane emissions from pre-industrial to present using CLM4.5-BGC R. Paudel et al. https://doi.org/10.1088/1748-9326/11/3/034020
72. Identifying the main drivers of the spatiotemporal variations in wetland methane emissions during 2001–2020 Y. Hu et al. https://doi.org/10.3389/fenvs.2023.1275742
73. A review on solar-induced chlorophyll fluorescence of vegetation and its ecological process modeling J. Qin et al. https://doi.org/10.1016/j.ecofro.2025.09.015
74. Variability in methane emissions from West Siberia's shallow boreal lakes on a regional scale and its environmental controls A. Sabrekov et al. https://doi.org/10.5194/bg-14-3715-2017
75. Evaluating alternative ebullition models for predicting peatland methane emission and its pathways via data–model fusion S. Ma et al. https://doi.org/10.5194/bg-19-2245-2022
76. Cold‐Season Methane Fluxes Simulated by GCP‐CH4 Models A. Ito et al. https://doi.org/10.1029/2023GL103037
77. Performance of CH4MODwetland for the case study of different regions of natural Chinese wetland T. Li et al. https://doi.org/10.1016/j.jes.2017.01.001
78. An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau Q. Zhu et al. https://doi.org/10.1038/s41467-023-42099-4
79. Field-scale simulation of methane emissions from coastal wetlands in China using an improved version of CH4MOD wetland T. Li et al. https://doi.org/10.1016/j.scitotenv.2016.03.186
80. Modelling water and energy fluxes with an explicit representation of irrigation under mulch in a maize field C. Wang et al. https://doi.org/10.1016/j.agrformet.2022.109145
81. Integrating major agricultural practices into the TRIPLEX-GHG model v2.0 for simulating global cropland nitrous oxide emissions: Development, sensitivity analysis and site evaluation H. Song et al. https://doi.org/10.1016/j.scitotenv.2022.156945
82. HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands M. Raivonen et al. https://doi.org/10.5194/gmd-10-4665-2017
83. Uncertainty analysis of multiple global GPP datasets in characterizing the lagged effect of drought on photosynthesis X. Xie et al. https://doi.org/10.1016/j.ecolind.2020.106224
84. Parameter interactions and sensitivity analysis for modelling carbon heat and water fluxes in a natural peatland, using CoupModel v5 C. Metzger et al. https://doi.org/10.5194/gmd-9-4313-2016
85. Changes in soil organic carbon and microbial carbon storage projected during the 21st century using TRIPLEX-MICROBE K. Wang et al. https://doi.org/10.1016/j.ecolind.2018.10.045
86. Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region E. Cui et al. https://doi.org/10.1029/2018GB005909
87. Opposing seasonal temperature dependencies of CO2 and CH4 emissions from wetlands J. Li et al. https://doi.org/10.1111/gcb.16528
88. Multiscale assessment of North American terrestrial carbon balance K. Foster et al. https://doi.org/10.5194/bg-21-869-2024
89. Multi-model ensemble successfully predicted atmospheric methane consumption in soils across the complex landscape M. Glagolev et al. https://doi.org/10.18822/edgcc625761
90. Climate warming and forest expansion significantly enhance China’s forest methane sink Y. Deng et al. https://doi.org/10.1016/j.agrformet.2025.110935