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Conference Paper

Causality analysis of ecological time series: a time-frequency approach


Reichstein,  Markus
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Shadaydeh, M., Garcia, Y. G., Mahecha, M. D., Reichstein, M., & Denzler, J. (2018). Causality analysis of ecological time series: a time-frequency approach. In C. Chen, D. Cooley, J. Runge, & E. Szekely (Eds.), 8th International Workshop on Climate Informatics: CI 2018 (pp. 111-114).

Cite as: https://hdl.handle.net/21.11116/0000-0003-3FF2-6
Attribution in ecosystems aims to identify
the cause-effect relationships between the variables involved.
Time series of ecological variables most often
contain multiple periodical components, e.g. daily and
seasonal cycles, induced by the meteorological forcing
variables. Such components can significantly mask the
underling endogenous causality structure of the biogeochemical
cycle when using time domain analysis. This
motivates the use of time-frequency analysis techniques
such as short time Fourier transform or wavelet where
causality inference can be investigated at different frequency
bands or different time scales. In this study,
we use the parametric spectral representation of the
multivariate autoregressive Granger causality based on
the generalized Partial Directed Coherence (gPDC) to
investigate the cause-effect relationships between the
meteorological observations of global radiation and air
temperature, and the CO2 land fluxes of gross primary
productivity and ecosystem respiration, at Hainich
National Park-Germany. Preliminary results show that
spectral domain causality analysis based on gPDC has
promising potential in handling the presence of periodic
components and in identifying the time variant causeeffect
intensities between these variables at different time scales.