53 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
6. A long-term simulation of forest carbon fluxes over the Qilian Mountains M. Yan et al. https://doi.org/10.1016/j.jag.2016.07.009
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8. Temporospatial patterns of human appropriation of net primary production in Central Asia grasslands X. Huang et al. https://doi.org/10.1016/j.ecolind.2018.04.045
9. Modeling of evapotranspiration changes with forest management practices: A genealogical review H. Komatsu & T. Kume https://doi.org/10.1016/j.jhydrol.2020.124835
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. Attribution of changes in vegetation structure and carbon–water fluxes in the Ji-shaped bend of the Yellow River based on process-guided machine learning C. Li et al. https://doi.org/10.1016/j.ecolind.2026.115705
12. 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
13. 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
14. Satellite-Based Modeling of the Carbon Fluxes in Mature Black Spruce Forests in Alaska: A Synthesis of the Eddy Covariance Data and Satellite Remote Sensing Data M. Ueyama et al. https://doi.org/10.1175/2010EI319.1
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17. Simulation of forest carbon fluxes by integrating remote sensing data into biome-BGC model R. Srinet et al. https://doi.org/10.1016/j.ecolmodel.2022.110185
18. 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
19. The impact of climate variation and disturbances on the carbon balance of forests in Hokkaido, Japan R. Hirata et al. https://doi.org/10.5194/bg-11-5139-2014
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21. 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
22. 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
23. 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
24. 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
25. 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
26. 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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28. 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
29. The role of carbon flux and biometric observations in constraining a terrestrial ecosystem model: a case study in disturbed forests in East Asia M. Kondo et al. https://doi.org/10.1007/s11284-013-1072-7
30. Variations in diurnal and seasonal net ecosystem carbon dioxide exchange in a semiarid sandy grassland ecosystem in China's Horqin Sandy Land Y. Niu et al. https://doi.org/10.5194/bg-17-6309-2020
31. Estimation of Forest NPP and Carbon Sequestration in the Three Gorges Reservoir Area, Using the Biome-BGC Model Y. Chen & W. Xiao https://doi.org/10.3390/f10020149
32. An approach for improving soil water content for modeling net primary production on the Qinghai-Tibetan Plateau using Biome-BGC model C. Li et al. https://doi.org/10.1016/j.catena.2019.104253
33. Exploring the response of net ecosystem CO2 exchange to precipitation events in a wet meadow ecosystem by using an improved semi-empirical physical model G. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133398
34. 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
35. 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
36. 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
37. Deriving forest fire ignition risk with biogeochemical process modelling C. Eastaugh & H. Hasenauer https://doi.org/10.1016/j.envsoft.2014.01.018
38. 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
39. Changes in Mortality in a Deciduous Broadleaf Forest Including Old White Birches: Y. Mizoguchi et al. https://doi.org/10.4005/jjfs.103.291
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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