IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v496y2024ics0304380024002199.html

Coastal generalized ecosystem model (CGEM) 1.0: Flexible model formulations for simulating complex biogeochemical processes in aquatic ecosystems

Author

Listed:
  • Jarvis, Brandon M.
  • Lehrter, John C.
  • Lowe, Lisa
  • Penta, Bradley
  • Wan, Yongshan
  • Duvall, Melissa
  • Simmons, Cody
  • Melendez, Wilson
  • Ko, Dong S.

Abstract

The Coastal Generalized Ecosystem Model (CGEM) is a biogeochemical model developed to study regulating processes of water-column optical properties, water-column and benthic carbon, oxygen, and nutrient cycles, and phytoplankton and zooplankton dynamics. CGEM offers numerous formulations for important rate processes, providing users flexibility in altering model structure. This flexibility also provides a means for evaluating model structural uncertainty and impacts on simulations, which are rarely evaluated with numerical ecosystem models. As an open-source model, CGEM also offers users the option to implement new formulations or modify existing routines. We also provide a full description of the model formulations, state variables, and model parameters in CGEM. Using two published case studies, we explore how different formulations for light attenuation, phytoplankton temperature growth response, and sediment processes impact simulations. We discuss CGEM's role as a new ecosystem model within the modeling community and opportunities to address current and future water quality issues.

Suggested Citation

  • Jarvis, Brandon M. & Lehrter, John C. & Lowe, Lisa & Penta, Bradley & Wan, Yongshan & Duvall, Melissa & Simmons, Cody & Melendez, Wilson & Ko, Dong S., 2024. "Coastal generalized ecosystem model (CGEM) 1.0: Flexible model formulations for simulating complex biogeochemical processes in aquatic ecosystems," Ecological Modelling, Elsevier, vol. 496(C).
    • Handle: RePEc:eee:ecomod:v:496:y:2024:i:c:s0304380024002199
    DOI: 10.1016/j.ecolmodel.2024.110831
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380024002199
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2024.110831?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Zhonglong & Sun, Bowen & Johnson, Billy E., 2015. "Integration of a benthic sediment diagenesis module into the two dimensional hydrodynamic and water quality model – CE-QUAL-W2," Ecological Modelling, Elsevier, vol. 297(C), pages 213-231.
    2. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    3. Stephanie A. Henson & B. B. Cael & Stephanie R. Allen & Stephanie Dutkiewicz, 2021. "Future phytoplankton diversity in a changing climate," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    4. Beck, Marcus W. & Lehrter, John C. & Lowe, Lisa L. & Jarvis, Brandon M., 2017. "Parameter sensitivity and identifiability for a biogeochemical model of hypoxia in the northern Gulf of Mexico," Ecological Modelling, Elsevier, vol. 363(C), pages 17-30.
    5. Eldridge, Peter M. & Roelke, Daniel L., 2010. "Origins and scales of hypoxia on the Louisiana shelf: Importance of seasonal plankton dynamics and river nutrients and discharge," Ecological Modelling, Elsevier, vol. 221(7), pages 1028-1042.
    6. Priyadarshi, Anupam & Chandra, Ram & Kishi, Michio J. & Smith, S.Lan & Yamazaki, Hidekatsu, 2022. "Understanding plankton ecosystem dynamics under realistic micro-scale variability requires modeling at least three trophic levels," Ecological Modelling, Elsevier, vol. 467(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Claudia García-García & Catalina B. García-García & Román Salmerón, 2021. "Confronting collinearity in environmental regression models: evidence from world data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 895-926, September.
    2. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    3. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    4. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    5. Little, Andrew T. & Moore, Don A & Augenblick, Ned & Backus, Matthew, 2025. "Assumptions, Disagreement, and Overprecision: Theory and Evidence," OSF Preprints mnv4k_v1, Center for Open Science.
    6. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
    7. Johan Verbeeck & Martin Geroldinger & Konstantin Thiel & Andrew Craig Hooker & Sebastian Ueckert & Mats Karlsson & Arne Cornelius Bathke & Johann Wolfgang Bauer & Geert Molenberghs & Georg Zimmermann, 2023. "How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?," Biometrics, The International Biometric Society, vol. 79(4), pages 3998-4011, December.
    8. Coleman, Stephen, 2005. "Testing Theories with Qualitative and Quantitative Predictions," MPRA Paper 105171, University Library of Munich, Germany.
    9. Ewout W. Steyerberg, 2005. "Local Applicability of Clinical and Model-Based Probability Estimates," Medical Decision Making, , vol. 25(6), pages 678-680, November.
    10. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    11. Sauterey, Boris & Gland, Guillaume Le & Cermeño, Pedro & Aumont, Olivier & Lévy, Marina & Vallina, Sergio M., 2023. "Phytoplankton adaptive resilience to climate change collapses in case of extreme events – A modeling study," Ecological Modelling, Elsevier, vol. 483(C).
    12. Simon Cornée & Damien Rousselière & Véronique Thelen, 2025. "The environmental benefits of grassroots cooperatives in agriculture," Post-Print hal-04881201, HAL.
    13. Brooks, Jeremy S., 2010. "The Buddha mushroom: Conservation behavior and the development of institutions in Bhutan," Ecological Economics, Elsevier, vol. 69(4), pages 779-795, February.
    14. Ebersberger, Bernd & Galia, Fabrice & Laursen, Keld & Salter, Ammon, 2021. "Inbound Open Innovation and Innovation Performance: A Robustness Study," Research Policy, Elsevier, vol. 50(7).
    15. Brian Knaeble & Seth Dutter, 2017. "Reversals of Least-Square Estimates and Model-Invariant Estimation for Directions of Unique Effects," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 97-105, April.
    16. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2021. "Stochastic coherency in forecast reconciliation," International Journal of Production Economics, Elsevier, vol. 240(C).
    17. Steven M. Shugan, 2002. "In Search of Data: An Editorial," Marketing Science, INFORMS, vol. 21(4), pages 369-377.
    18. Fletcher, David & Dillingham, Peter W., 2011. "Model-averaged confidence intervals for factorial experiments," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 3041-3048, November.
    19. Liu, Min & He, Honglin & Ren, Xiaoli & Sun, Xiaomin & Yu, Guirui & Han, Shijie & Wang, Huimin & Zhou, Guoyi, 2015. "The effects of constraining variables on parameter optimization in carbon and water flux modeling over different forest ecosystems," Ecological Modelling, Elsevier, vol. 303(C), pages 30-41.
    20. Fang, Jiali & Jacobsen, Ben & Qin, Yafeng, 2014. "Predictability of the simple technical trading rules: An out-of-sample test," Review of Financial Economics, Elsevier, vol. 23(1), pages 30-45.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:496:y:2024:i:c:s0304380024002199. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.