EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Predicting energy and carbon fluxes using LSTM networks

Claire Brenner1, Jonathan Frame2, Grey Nearing3, and Karsten Schulz1
Claire Brenner et al.
  • 1Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
  • 2Department of Geological Sciences, University of Alabama, Tuscaloosa, AL USA
  • 3Google Research, Mountain View, CA USA

Global land-atmosphere energy and carbon fluxes are key drivers of the Earth’s climate system. Their assessment over a wide range of climates and biomes is therefore essential (i) for a better understanding and characterization of land-atmosphere exchanges and feedbacks and (ii) for examining the effect of climate change on the global water, energy and carbon cycles. 

Large-sample datasets such as the FLUXNET2015 dataset (Pastorello et al., 2020) foster the use of machine learning (ML) techniques as a powerful addition to existing physically-based modelling approaches. Several studies have investigated ML techniques for assessing energy and carbon fluxes, and while across-site variability and the mean seasonal cycle are typically well predicted, deviations from mean seasonal behaviour remains challenging (Tramontana et al., 2016). 

In this study we examine the importance of memory effects for predicting energy and carbon fluxes at half-hourly and daily temporal resolutions. To this end, we train a Long Short-Term Memory (LSTM, Hochreiter and Schmidthuber, 1997), a recurrent neural network with explicit memory, that is particularly suited for time series predictions due to its capability to store information over longer (time) sequences. We train the LSTM on a large number of FLUXNET sites part of the FLUXNET2015 dataset using local meteorological forcings and static site attributes derived from remote sensing and reanalysis data. 

We evaluate model performance out-of-sample (leaving out individual sites) in a 10-fold cross-validation. Additionally, we compare results from the LSTM with results from another ML technique, XGBoost (Chen and Guestrin, 2016), that does not contain system memory. By analysing the differences in model performances of both approaches across various biomes, we investigate under which conditions the inclusion of memory might be beneficial for modelling energy and carbon fluxes.



Chen, Tianqi, and Carlos Guestrin. "Xgboost: A scalable tree boosting system." Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016.

Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780.

Pastorello, Gilberto, et al. "The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data." Scientific data 7.1 (2020): 1-27

Tramontana, Gianluca, et al. "Predicting carbon dioxide and energy fluxes across global   FLUXNET sites with regression algorithms." Biogeosciences 13.14 (2016): 4291-4313.

How to cite: Brenner, C., Frame, J., Nearing, G., and Schulz, K.: Predicting energy and carbon fluxes using LSTM networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14781,, 2021.