02 Nov 2022
 | 02 Nov 2022
Status: a revised version of this preprint is currently under review for the journal BG.

A differentiable ecosystem modeling framework for large-scale inverse problems: demonstration with photosynthesis simulations

Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Alex W. Jones, Chris Rackauckas, Kathryn E. Lawson, and Chaopeng Shen

Abstract. Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that are obtained from limited in-situ measurements and applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data and faced issues like overfitting or parameter non-uniqueness. Here we developed a programmatically differentiable (meaning gradients of outputs to variables used in the model can be obtained efficiently and accurately) version of the photosynthesis process representation within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. This model is coupled to neural networks that learn parameterization from observations of photosynthesis rates. We first demonstrated that the framework was able to recover multiple assumed parameter values concurrently using synthetic training data. Then, using a real-world dataset consisting of many different plant functional types, we learned parameters that performed substantially better and dramatically reduced biases compared to literature values. Further, the framework allowed us to gain insights at a large scale. Our results showed that the carboxylation rate at 25 °C (Vc,max25), was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. This framework could potentially enable a substantial improvement in our capability to learn parameters and reduce biases for ecosystem modeling at large scales.

Doaa Aboelyazeed et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2022-211', Anonymous Referee #1, 14 Dec 2022
    • AC2: 'Reply on RC1', Chaopeng Shen, 26 Jan 2023
      • AC4: 'Reply on AC2', Chaopeng Shen, 31 Jan 2023
  • RC2: 'Comment on bg-2022-211', Anonymous Referee #2, 23 Jan 2023
    • AC1: 'Reply on RC2', Chaopeng Shen, 23 Jan 2023
    • AC3: 'Reply on RC2', Chaopeng Shen, 31 Jan 2023
      • RC3: 'Reply on AC3', Anonymous Referee #2, 01 Feb 2023
        • AC5: 'Reply on RC3', Chaopeng Shen, 01 Feb 2023
        • AC6: 'Reply on RC3', Chaopeng Shen, 08 Feb 2023

Doaa Aboelyazeed et al.

Doaa Aboelyazeed et al.


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Short summary
Photosynthesis is critical for life and is affected by a changing climate. Many parameters come into play when modeling, but traditional calibration approaches have faced many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.