IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v297y2015icp196-200.html
   My bibliography  Save this article

An algorithmic calibration approach to identify globally optimal parameters for constraining the DayCent model

Author

Listed:
  • Rafique, Rashad
  • Kumar, Sandeep
  • Luo, Yiqi
  • Kiely, Gerard
  • Asrar, Ghassem

Abstract

The accurate calibration of complex biogeochemical models is essential for the robust estimation of soil greenhouse gases (GHG) as well as other environmental conditions and parameters that are used in research and policy decisions. DayCent is a popular biogeochemical model used both nationally and internationally for this purpose. Despite DayCent's popularity, its complex parameter estimation is often based on experts’ knowledge which is somewhat subjective. In this study we used the inverse modelling parameter estimation software (PEST), to calibrate the DayCent model based on sensitivity and identifiability analysis. Using previously published N2O and crop yield data as a basis of our calibration approach, we found that half of the 140 parameters used in this study were the primary drivers of calibration differences (i.e. the most sensitive) and the remaining parameters could not be identified given the data set and parameter ranges we used in this study. The post calibration results showed improvement over the pre-calibration parameter set based on, a decrease in residual differences 79% for N2O fluxes and 84% for crop yield, and an increase in coefficient of determination 63% for N2O fluxes and 72% for corn yield. The results of our study suggest that future studies need to better characterize germination temperature, number of degree-days and temperature dependency of plant growth; these processes were highly sensitive and could not be adequately constrained by the data used in our study. Furthermore, the sensitivity and identifiability analysis was helpful in providing deeper insight for important processes and associated parameters that can lead to further improvement in calibration of DayCent model.

Suggested Citation

  • Rafique, Rashad & Kumar, Sandeep & Luo, Yiqi & Kiely, Gerard & Asrar, Ghassem, 2015. "An algorithmic calibration approach to identify globally optimal parameters for constraining the DayCent model," Ecological Modelling, Elsevier, vol. 297(C), pages 196-200.
    • Handle: RePEc:eee:ecomod:v:297:y:2015:i:c:p:196-200
    DOI: 10.1016/j.ecolmodel.2014.11.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030438001400595X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2014.11.022?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Cheng, Kun & Ogle, Stephen M. & Parton, William J. & Pan, Genxing, 2013. "Predicting methanogenesis from rice paddies using the DAYCENT ecosystem model," Ecological Modelling, Elsevier, vol. 261, pages 19-31.
    2. Lamers, Marc & Ingwersen, Joachim & Streck, Thilo, 2007. "Modelling N2O emission from a forest upland soil: A procedure for an automatic calibration of the biogeochemical model Forest-DNDC," Ecological Modelling, Elsevier, vol. 205(1), pages 52-58.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liang, Hao & Hu, Kelin & Batchelor, William D. & Qin, Wei & Li, Baoguo, 2018. "Developing a water and nitrogen management model for greenhouse vegetable production in China: Sensitivity analysis and evaluation," Ecological Modelling, Elsevier, vol. 367(C), pages 24-33.
    2. Myrgiotis, Vasileios & Rees, Robert M. & Topp, Cairistiona F.E. & Williams, Mathew, 2018. "A systematic approach to identifying key parameters and processes in agroecosystem models," Ecological Modelling, Elsevier, vol. 368(C), pages 344-356.

    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. He, Hongxing & Jansson, Per-Erik & Svensson, Magnus & Meyer, Astrid & Klemedtsson, Leif & Kasimir, Åsa, 2016. "Factors controlling Nitrous Oxide emission from a spruce forest ecosystem on drained organic soil, derived using the CoupModel," Ecological Modelling, Elsevier, vol. 321(C), pages 46-63.
    2. Zhao, Zheng & Cao, Linkui & Deng, Jia & Sha, Zhimin & Chu, Changbin & Zhou, Deping & Wu, Shuhang & Lv, Weiguang, 2020. "Modeling CH4 and N2O emission patterns and mitigation potential from paddy fields in Shanghai, China with the DNDC model," Agricultural Systems, Elsevier, vol. 178(C).
    3. Gilhespy, Sarah L. & Anthony, Steven & Cardenas, Laura & Chadwick, David & del Prado, Agustin & Li, Changsheng & Misselbrook, Thomas & Rees, Robert M. & Salas, William & Sanz-Cobena, Alberto & Smith, , 2014. "First 20 years of DNDC (DeNitrification DeComposition): Model evolution," Ecological Modelling, Elsevier, vol. 292(C), pages 51-62.
    4. Treydte, A.C. & Salvatierra, A. & Sauerborn, J. & Lamers, M., 2011. "Modelling llama population development under environmental and market constraints in the Bolivian highlands," Ecological Modelling, Elsevier, vol. 222(17), pages 3157-3165.
    5. Grossel, A. & Nicoullaud, B. & Bourennane, H. & Rochette, P. & Guimbaud, C. & Chartier, M. & Catoire, V. & Hénault, C., 2014. "Simulating the spatial variability of nitrous oxide emission from cropped soils at the within-field scale using the NOE model," Ecological Modelling, Elsevier, vol. 288(C), pages 155-165.
    6. Khadiza Begum & Matthias Kuhnert & Jagadeesh Yeluripati & Stephen Ogle & William Parton & Md Abdul Kader & Pete Smith, 2018. "Model Based Regional Estimates of Soil Organic Carbon Sequestration and Greenhouse Gas Mitigation Potentials from Rice Croplands in Bangladesh," Land, MDPI, vol. 7(3), pages 1-18, July.

    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:297:y:2015:i:c:p:196-200. 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.