52 citations as recorded by crossref.

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5. Assessing Forest Ecosystems across the Vertical Edge of the Mid-Latitude Ecotone Using the BioGeoChemistry Management Model (BGC-MAN) C. Song et al. https://doi.org/10.3390/f10060523
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10. Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output R. Raj et al. https://doi.org/10.5194/gmd-11-83-2018
11. Terrestrial ecosystem process model Biome-BGCMuSo v4.0: summary of improvements and new modeling possibilities D. Hidy et al. https://doi.org/10.5194/gmd-9-4405-2016
12. Global sensitivity analysis of a modified CENTURY model for simulating impacts of harvesting fine woody biomass for bioenergy F. Wang et al. https://doi.org/10.1016/j.ecolmodel.2013.03.008
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17. Modeling grassland net primary productivity and water-use efficiency along an elevational gradient of the Northern Tianshan Mountains Q. Han et al. https://doi.org/10.1007/s40333-013-0182-y
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20. 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
21. A Case Study of the Performance of Different Detrending Methods in Turbulent-Flux Estimation A. Donateo et al. https://doi.org/10.1007/s10546-017-0243-4
22. Ecological risk assessment of ecosystem services in the Taihu Lake Basin of China from 1985 to 2020 X. Xu et al. https://doi.org/10.1016/j.scitotenv.2016.02.120
23. A test of BIOME-BGC with dendrochronology for forests along the altitudinal gradient of Mt. Changbai in northeast China Y. Wu et al. https://doi.org/10.1093/jpe/rtw076
24. Impacts of land use changes on net ecosystem production in the Taihu Lake Basin of China from 1985 to 2010 X. Xu et al. https://doi.org/10.1002/2016JG003444
25. Terrestrial ecosystem model studies and their contributions to AsiaFlux A. ITO & K. ICHII https://doi.org/10.2480/agrmet.D-20-00024
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27. Terrestrial Ecosystem‐Atmosphere Exchange of CO2, Water and Energy from FLUXNET; Review and Meta‐Analysis of a Global in‐situ Observatory A. Oliphant https://doi.org/10.1111/gec3.12009
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33. Growing season carbon exchange and driving mechanism in a boreal larch forest of China S. Zhang et al. https://doi.org/10.1007/s11676-026-02066-3
34. Energy balance and evapotranspiration changes in a larch forest caused by severe disturbance during an early secondary succession T. Hirano et al. https://doi.org/10.1016/j.agrformet.2016.10.003
35. Assessing the spatiotemporal dynamics of water and carbon fluxes in subtropical forest of Xin’an River Basin using an improved Biome-BGC model Z. Feng et al. https://doi.org/10.1016/j.jhydrol.2024.131201
36. Deriving forest fire ignition risk with biogeochemical process modelling C. Eastaugh & H. Hasenauer https://doi.org/10.1016/j.envsoft.2014.01.018
37. Lessons learned from more than a decade of greenhouse gas flux measurements at boreal forests in eastern Siberia and interior Alaska T. Hiyama et al. https://doi.org/10.1016/j.polar.2020.100607
38. Changes in Mortality in a Deciduous Broadleaf Forest Including Old White Birches: Y. Mizoguchi et al. https://doi.org/10.4005/jjfs.103.291
39. Estimation of net ecosystem production in Asia using the diagnostic‐type ecosystem model with a 10 km grid‐scale resolution T. Sasai et al. https://doi.org/10.1002/2015JG003157
40. Soil Fungal Community Differences in Manual Plantation Larch Forest and Natural Larch Forest in Northeast China M. Wang et al. https://doi.org/10.3390/microorganisms12071322
41. Evapotranspiration comparisons between eddy covariance measurements and meteorological and remote‐sensing‐based models in disturbed ponderosa pine forests W. Ha et al. https://doi.org/10.1002/eco.1586
42. Estimating spatial variation in the effects of climate change on the net primary production of Japanese cedar plantations based on modeled carbon dynamics J. Toriyama et al. https://doi.org/10.1371/journal.pone.0247165
43. Automatic calibration of the Biome-BGC model with the PEST software to simulate the forest and farmland ecosystems of the Qinling Mountains in China K. Gong et al. https://doi.org/10.1016/j.agrformet.2025.110868
44. The uncertainty of biomass estimates from modeled ICESat-2 returns across a boreal forest gradient P. Montesano et al. https://doi.org/10.1016/j.rse.2014.10.029
45. Carbon Flux Modeling with the Calibrated Biome-BGCMuSo in China’s Tropical Forests: Natural and Rubber-Planted Forests F. Yang et al. https://doi.org/10.3390/f16040661
46. Investigation into climate change effects on carbon and water fluxes, and water use efficiency of the temperate grassland ecosystems in Inner Mongolia of China Z. Wu et al. https://doi.org/10.2166/ws.2023.115
47. Spatial and seasonal variations of CO 2 flux and photosynthetic and respiratory parameters of larch forests in East Asia K. Takagi et al. https://doi.org/10.1080/00380768.2014.990349
48. Integrating Remotely Sensed Leaf Area Index with Biome-BGC to Quantify the Impact of Land Use/Land Cover Change on Water Retention in Beijing B. Huang et al. https://doi.org/10.3390/rs14030743
49. The application of a coupled hydrological and biogeochemical model (CHANGE) for modeling of energy, water, and CO2exchanges over a larch forest in eastern Siberia H. Park et al. https://doi.org/10.1029/2010JD015386
50. Estimating carbon fixation of plant organs for afforestation monitoring using a process‐based ecosystem model and ecophysiological parameter optimization T. Miyauchi et al. https://doi.org/10.1002/ece3.5328
51. Growing season and spatial variations of carbon fluxes of Arctic and boreal ecosystems in Alaska (USA) M. Ueyama et al. https://doi.org/10.1890/11-0875.1
52. Evaluation of the Terrestrial Ecosystem Model Biome-BGCMuSo for Modelling Soil Organic Carbon under Different Land Uses M. Ostrogović Sever et al. https://doi.org/10.3390/land10090968