MOFLUX Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: Modeling Archive

Liang, Junyi; Wang, Gangsheng; Singh, Shikha; Jagadamma, Sindhu; Gu, Lianhong; Schadt, Christopher W.; Wood, Jeffrey D.; Hanson, Paul J.; Mayes, Melanie A.

doi:10.25581/ornlsfa.023/1804106

Title: MOFLUX Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: Modeling Archive

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Abstract

This Modeling Archive is in support the publication “Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: A Mechanistic Modeling Analysis” (Liang et al., 2021). Here we provide model code, inputs, outputs and evaluation datasets for the Microbial ENzyme Decomposition (MEND) model for the Missouri Ozarks AmeriFlux eddy covariance measurement site (MOFLUX) near Ashland, Missouri USA. The MEND model was developed with explicit representation of microbial and enzyme pools to mechanistically simulate the role of microbial organisms and extracellular enzymes in soil organic carbon (SOC) decomposition. Long-term SOC dynamics under intensified moisture extremes are studied using the MEND model that is parameterized with 11 years of measurements from the MOFLUX forest. The model explicitly represents microbial dormancy and resuscitation, different types of SOC-degrading enzymes, and how they vary with changes in soil moisture (Wang et al. 2015, 2019). A combination of two levels of frequency and severity of soil moisture, as well as a control with normal interannual variability, are used to simulate a range of moisture scenarios over 100 years. The code of Microbial-ENzyme Decomposition (MEND) as well as the input and output data are included in the archive. A user’s manual (MEND_Readme.pdf) is included with instructions for compilingmore »« less

Authors:

;

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Univ. of Oklahoma, Norman, OK (United States)
Univ. of Tennessee, Knoxville, TN (United States)
Univ. of Missouri, Columbia, MO (United States)

Publication Date:: Fri Jan 01 00:00:00 UTC 2021

Other Number(s):: ornlsfa.023

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Artificial Intelligence and Technology (AITO); USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

Subject:: 54 ENVIRONMENTAL SCIENCES; MEND model; Microbial-ENzyme Decomposition model; Missouri Ozarks AmeriFlux (MOFLUX) site; evaluation datasets; model code; soil organic carbon (SOC) decomposition

OSTI Identifier:: 1804106

DOI:: https://doi.org/10.25581/ornlsfa.023/1804106

Citation Formats


                    Liang, Junyi, Wang, Gangsheng, Singh, Shikha, Jagadamma, Sindhu, Gu, Lianhong, Schadt, Christopher W., Wood, Jeffrey D., Hanson, Paul J., and Mayes, Melanie A. MOFLUX Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: Modeling Archive.  United States: N. p., 2021. 
        Web.  doi:10.25581/ornlsfa.023/1804106.

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                    Liang, Junyi, Wang, Gangsheng, Singh, Shikha, Jagadamma, Sindhu, Gu, Lianhong, Schadt, Christopher W., Wood, Jeffrey D., Hanson, Paul J., & Mayes, Melanie A. MOFLUX Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: Modeling Archive.  United States.  doi:https://doi.org/10.25581/ornlsfa.023/1804106

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                    Liang, Junyi, Wang, Gangsheng, Singh, Shikha, Jagadamma, Sindhu, Gu, Lianhong, Schadt, Christopher W., Wood, Jeffrey D., Hanson, Paul J., and Mayes, Melanie A. 2021.  
"MOFLUX Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: Modeling Archive".  United States.  doi:https://doi.org/10.25581/ornlsfa.023/1804106.  https://www.osti.gov/servlets/purl/1804106. Pub date:Fri Jan 01 00:00:00 UTC 2021

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@article{osti_1804106,

  title        = {MOFLUX Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: Modeling Archive},

  author       = {Liang, Junyi and Wang, Gangsheng and Singh, Shikha and Jagadamma, Sindhu and Gu, Lianhong and Schadt, Christopher W. and Wood, Jeffrey D. and Hanson, Paul J. and Mayes, Melanie A.},

  abstractNote = {This Modeling Archive is in support the publication “Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: A Mechanistic Modeling Analysis” (Liang et al., 2021). Here we provide model code, inputs, outputs and evaluation datasets for the Microbial ENzyme Decomposition (MEND) model for the Missouri Ozarks AmeriFlux eddy covariance measurement site (MOFLUX) near Ashland, Missouri USA. The MEND model was developed with explicit representation of microbial and enzyme pools to mechanistically simulate the role of microbial organisms and extracellular enzymes in soil organic carbon (SOC) decomposition. Long-term SOC dynamics under intensified moisture extremes are studied using the MEND model that is parameterized with 11 years of measurements from the MOFLUX forest. The model explicitly represents microbial dormancy and resuscitation, different types of SOC-degrading enzymes, and how they vary with changes in soil moisture (Wang et al. 2015, 2019). A combination of two levels of frequency and severity of soil moisture, as well as a control with normal interannual variability, are used to simulate a range of moisture scenarios over 100 years. The code of Microbial-ENzyme Decomposition (MEND) as well as the input and output data are included in the archive. A user’s manual (MEND_Readme.pdf) is included with instructions for compiling and running the model to simulate soil organic carbon decomposition under various moisture scenarios. This dataset contains the modelling archive contained within a compressed (*.zip) file, a file-level metadata file in comma separate (*.csv) format, and two instructional files in PDF (*.pdf) format.},

  doi          = {10.25581/ornlsfa.023/1804106},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Jan 01 00:00:00 UTC 2021},

  month        = {Fri Jan 01 00:00:00 UTC 2021}

}

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DOI: https://doi.org/10.25581/ornlsfa.023/1804106

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