Repository for CLM-Microbe and other associated modeling work within the Ecological Modeling and Integration Laboratory at SDSU. https://xulab.sdsu.edu/. The CLM-Microbe is branched from CLM4.5 in 2013. It is developed under an open-source license, all codes are publically accessible. Anyone who wants to use this model needs to contact Xiaofeng Xu at xxu@sdsu.edu for coordinating different efforts on model development and application. Over the past decade, the funding supports for the development and application of the CLM-Microbe model include Oak Ridge National Lab, University of Texas - El Paso, San Diego State University, CSU Program for Education and Research in Biotechnology, NSF projects (1702797, 2154746, 2208656), and an NSF CAREER project (2145130); we appreciate all those supports.
The CLM-Microbe V1 was released at https://zenodo.org/records/7439312#.Y5y_vOzMKgW.
Publications with the CLM-Microbe model.
1). Xu X., Schimel J. P., Thornton P. E., Song X., Yuan F.H., Goswami S., (2014) Substrate and environmental controls on microbial assimilation of soil organic carbon: a framework for Earth system models. Ecology Letters 17:547-555. https://doi.org/10.1111/ele.12254 (The modeling framework for microbial carbon assimilation)
2). Xu X., Elias D. A., Graham D. E., Phelps T. J., Carrol S. L., Wullschleger S. D., Thornton P. E. (2015) A microbial functional group based module for simulating methane production and consumption: application to an incubation permafrost soil. Journal of Geophysical Research-Biogeosciences 120:1315-1333. https://doi.org/10.1002/2015JG002935 (The microbial functional-group-based methane module)
3). Xu X., Yuan F., Hanson P.J., Wullschleger S.D., Thornton P.E., Riley W.J., Song X., Graham D.E., Song C.C., Tian H. (2016) Review and Synthesis: Four decades of modeling methane cycling within terrestrial ecosystems. Biogeosciences 13:3735-3755. https://doi.org/10.5194/bg-13-3735-2016 (A review paper on different types of methane models including the CLM-Microbe; the CLM-Microbe represents the new direction of microbial models on methane processes)
4). Wang Y., Yuan F.M., Yuan F.H., Gu B., Hahn M.S., Torn M.S., Ricciuto D.M., Kumar J., He L., Zona D., Lipson D.L., Wagner R., Oechel W.C., Wullschleger S.D., Thornton P.E., Xu X. (2019) Mechanistic Modeling of microtopographic impact on CH4 processes in an Alaskan tundra ecosystem using the CLM-Microbe model. Journal of Advances in Modeling Earth Systems 11:4228-4304. https://doi.org/10.1029/2019MS001771 (Site-level application of the CLM-Microbe in simulating methane flux in the Arctic)
5). He L., Lipson D.L., Rodrigues J.L., Mayes M.A., Bjork R.G., Glaser B., Thornton P.E., Xu X. (2021). Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site-level Applications of the CLM-Microbe Model. Journal of Advance in Modeling Earth Systems, https://doi.org/10.1029/2020MS002283 (Site-level application of the CLM-Microbe in simulating bacterial and fungal biomass in major biomes)
6). He, L, Lai C.T., Mayes M.A., Murayama S., Xu X. (2021) Microbial seasonality promotes soil respiratory carbon emission in natural ecosystems: a modeling study. Global Change Biology. https://doi.org/10.1111/gcb.15627 (Site-level model application to confirm microbial seasonality has stimulation impacts on soil carbon emission)
7). Zuo Y.J., Wang Y.H., He L.Y., Wang N.N., Liu J.Z., Yuan F.H., Li K.X., Guo Z.Y., Sun Y., Zhu X.H., Zhang L.H., Song C.C., Sun L., Xu X.F. (2022) Modeling methane dynamics in three wetlands in northeastern China by using the CLM-Microbe model. Ecosystem Health and Sustainability. https://doi.org/10.1080/20964129.2022.2074895. (Site-level application of the CLM-Microbe model to CH4 dynamics in Asian temperate wetlands)
8). Wang, Y., Yuan F.M., Anrt K., Liu J.Z., He L.Y., Zuo Y.J., Zona D., Lipson D.A., Oechel W.C., Ricciuto D.M., Wullschleger S.D., Thornton P.E., Xu X.F. (2022) Upscaling methane flux from plot-level to eddy covariance tower domains by combining the CLM-Microbe model with three footprint algorithms. Frontier in Environmental Sciences. 10， https://doi.org/10.3389/fenvs.2022.939238 (Regional model application to simulate methane flux within eddy tower domain in the Arctic tundra ecosystems)
9). He, Liyuan. (2022) Multi-scale modeling of soil microbial control on terrestrial carbon cycle. Joint doctoral program - San Diego State University & University of California Davis. Dissertation https://escholarship.org/uc/item/5pm4n94j
10). Wang, Yihui. (2022) Multi-scale modeling of Arctic methane cycling using the CLM-Microbe model. Joint doctoral program - San Diego State University & University of California Davis. Dissertation https://escholarship.org/uc/item/6f7932vq
11). He, L., N. Viovy, and X. Xu. 2023. Microecology differentiation between bacteria and fungi in topsoil across the United States. Global Biogeochemical Cycles 37:e2023GB007706. https://doi.org/10.1029/2023GB007706 (Site-level application of the CLM-Microbe to investigate the bacterial and fungal macroecology across the US)
12). He L., Rodrigues J., Mayes M., Lao C.T., Xu X.F. (2023) Historical dynamics of terrestrial carbon during 1901-2016 as simulated by the CLM-Microbe model. Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-15, 2024. (Global application of the CLM-Microbe model to estimate the historical dynamics of terrestrial carbon, including the carbon components in vegetation, soils, and microbes)
13). Wang Y.H., He L.Y., Liu J.Z., Arndt K.A., Rodrigues J.L.M., Zona D., Lipson D.A., Oechel W.C., Ricciuto D.M., Wullschleger S.D., Xu X.F. (2024) Intensified positive Arctic-methane feedback under IPCC climate scenarios in the 21st century. Ecosystem Health and Sustainability, 10. https://doi/10.34133/ehs.0185 (Regional application of the CLM-Microbe model in projecting methane flux within five eddy covariance domains at a super-high resolution (4 meter x 4 meter))
14). Zuo Y.J., He L.Y., Wang Y.H., Liu J.Z., Wang N.N., Li K.X., Guo Z.Y., Zhang L.H., Chen N., Song C.C., Yuan F.H., Sun L., Xu X.F. (2024) Genomie-enabled parameterizaton enhances model simulation of CH4 cycling in four natural wetlands. Journal of Advances in Modeling Earth System. (In revision). (A modeling study to show genomic data can be used to parameterize the CLM-Microbe model to better simulate methane cycling at four natural wetland ecosystems)
15). He L.Y., Lipson D.A., Cleland E.E., Xu X.F. (2024) Reduce revenue vs. increase expenditure: fires and plant invasion drive soil carbon loss with different mechanisms in a Mediterranean shrubland. Journal of Geophysical Research - Biogeosciences. (In revision) (A data-model integration study to investigate wildfire and plant invasion impacts on microbial carbon cycling in a Mediterranean shrubland ecosystem)