@article{shadaydeh2022partitioning,
    type = {article},
    key = {shadaydeh2022partitioning},
    title = {Partitioning of Net Ecosystem Exchange Using Dynamic Mode Decomposition and Time Delay Embedding},
    author = {Maha Shadaydeh and Joachim Denzler and Mirco Migliavacca},
    journal = {Engineering Proceedings},
    year = {2022},
    number = {1},
    volume = {18},
    abstract = {Ecosystem respiration (Reco) represents a major component of the global carbon cycle. An accurate estimation of Reco dynamics is necessary for a better understanding of ecosystem-climate interactions and the impact of climate extremes on ecosystems. This paper proposes a new data-driven method for the estimation of the nonlinear dynamics of Reco using the method of dynamic mode decomposition with control input (DMDc). The method is validated on the half-hourly Fluxnet 2015 data. The model is first trained on the night-time net ecosystem exchange data. The day-time Reco values are then predicted using the obtained model with future values of a control input such as air temperature and soil water content. To deal with unobserved drivers of Reco other than the user control input, the method uses time-delay embedding of the history of Reco and the control input. Results indicate that, on the one hand, the prediction accuracy of Reco dynamics using DMDc is comparable to state-of-the-art deep learning-based methods, yet it has the advantages of being a simple and almost hyper-parameter-free method with a low computational load. On the other hand, the study of the impact of different control inputs on Reco dynamics showed that for most of the studied Fluxnet sites, air temperature is a better long-term predictor of Reco, while using soil water content as control input produced better short-term prediction accuracy.},
    doi = {10.3390/engproc2022018013},
    issn = {2673-4591},
    url = {https://www.mdpi.com/2673-4591/18/1/13},
}