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Fast calibration of a dynamic vegetation model with minimum observation data

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  • Lehmann, Sebastian
  • Huth, Andreas

Abstract

The estimation and uncertainty analysis of parameters for dynamic vegetation models is a complex process. If one is mainly interested in parameter estimation, this can be done with simple global stochastic search methods, while uncertainty analysis is carried out with traditional first-order analysis, which significantly reduces the number of needed model evaluations. Within a nonlinear regression framework, where the misfit between model and observations is expressed as a sum of weighted squares, we model the dynamics of tropical forest with a size-structured Sinko–Streifer model and demonstrate the general calibration procedure on a virtual data set. A second case study on real data for a single species shows that surprisingly total stem number, basal area and aboveground biomass are the minimum observations needed for successful calibration. A third case study on real data for a three species group shows the prediction of successional states while only using the former reduced set of observations for calibration. The methodology is well suited for time consuming models, where only limited amount of forest site observations are available.

Suggested Citation

  • Lehmann, Sebastian & Huth, Andreas, 2015. "Fast calibration of a dynamic vegetation model with minimum observation data," Ecological Modelling, Elsevier, vol. 301(C), pages 98-105.
    • Handle: RePEc:eee:ecomod:v:301:y:2015:i:c:p:98-105
    DOI: 10.1016/j.ecolmodel.2015.01.013
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    References listed on IDEAS

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    2. Rommel Regis & Christine Shoemaker, 2005. "Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions," Journal of Global Optimization, Springer, vol. 31(1), pages 153-171, January.
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    Citations

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    Cited by:

    1. Hetzer, Jessica & Huth, Andreas & Taubert, Franziska, 2021. "The importance of plant trait variability in grasslands: a modelling study," Ecological Modelling, Elsevier, vol. 453(C).
    2. Ribeiro, N.S. & Armstrong, Amanda Hildt & Fischer, Rico & Kim, Yeon-Su & Shugart, Herman Henry & Ribeiro-Barros, Ana I. & Chauque, Aniceto & Tear, T. & Washington-Allen, Robert & Bandeira, Romana R., 2021. "Prediction of forest parameters and carbon accounting under different fire regimes in Miombo woodlands, Niassa Special Reserve, Northern Mozambique," Forest Policy and Economics, Elsevier, vol. 133(C).
    3. Fischer, Rico & Bohn, Friedrich & Dantas de Paula, Mateus & Dislich, Claudia & Groeneveld, Jürgen & Gutiérrez, Alvaro G. & Kazmierczak, Martin & Knapp, Nikolai & Lehmann, Sebastian & Paulick, Sebastia, 2016. "Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests," Ecological Modelling, Elsevier, vol. 326(C), pages 124-133.
    4. Rau, E-Ping & Fischer, Fabian & Joetzjer, Émilie & Maréchaux, Isabelle & Sun, I Fang & Chave, Jérôme, 2022. "Transferability of an individual- and trait-based forest dynamics model: A test case across the tropics," Ecological Modelling, Elsevier, vol. 463(C).
    5. Grimm, Volker & Berger, Uta, 2016. "Structural realism, emergence, and predictions in next-generation ecological modelling: Synthesis from a special issue," Ecological Modelling, Elsevier, vol. 326(C), pages 177-187.
    6. Armstrong, A.H. & Huth, A. & Osmanoglu, B. & Sun, G. & Ranson, K.J. & Fischer, R., 2020. "A multi-scaled analysis of forest structure using individual-based modeling in a costa rican rainforest," Ecological Modelling, Elsevier, vol. 433(C).

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