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Abstract: Describing ecosystem carbon fluxes is essential for deepening the understanding of the Earth system. However, partitioning net ecosystem exchange (NEE), i.e. the sum of ecosystem respiration (R eco ) and gross primary production (GPP), into these summands is ill-posed since there can be infinitely many mathematically-valid solutions. We propose a novel data-driven approach to NEE partitioning using a deep state space model which combines the interpretability and uncertainty analysis of state space models with the ability of recurrent neural networks to learn the complex functions governing the data. We validate our proposed approach on the FLUXNET dataset. We suggest using both the past and the future of R eco ’s predictors for training along with the nighttime NEE (NEE night ) to learn a dynamical model of R eco . We evaluate our nighttime R eco forecasts by comparing them to the ground truth NEE night and obtain the best accuracy with respect to other partitioning methods. The learned nighttime R eco model is then used to forecast the daytime R eco conditioning on the future observations of different predictors, i.e., global radiation, air temperature, precipitation, vapor pressure deficit, and daytime NEE (NEE day ). Subtracted from the NEE day , these estimates yield the GPP, finalizing the partitioning. Our purely data-driven daytime R eco forecasts are in line with the recent empirical partitioning studies reporting lower daytime R eco than the Reichstein method, which can be attributed to the Kok effect, i.e., the plant respiration being higher at night. We conclude that our approach is a good alternative for data-driven NEE partitioning and complements other partitioning methods. |
Cottbus, Brandenburgische Technische Universität Cottbus - Senftenberg Universitätsbibliothek - Fernleihe - <DE-634>
