EGU23-16693, updated on 21 Sep 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Doaa Aboelyazeed1, Chonggang Xu2, Forrest M. Hoffman3,4, Alex W. Jones5, Chris Rackauckas6, Kathryn Lawson1, and Chaopeng Shen1
Doaa Aboelyazeed et al.
  • 1Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA
  • 2Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87544, USA
  • 3Computational Sciences & Engineering Division and the Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
  • 4Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee, USA
  • 5SciML Open Source Software Organization,
  • 6Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

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.

How to cite: Aboelyazeed, D., Xu, C., Hoffman, F. M., Jones, A. W., Rackauckas, C., Lawson, K., and Shen, C.: A differentiable ecosystem modeling framework for large-scale inverse problems: demonstration with photosynthesis simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16693,, 2023.