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Volume 10, issue 5 | Copyright
Geosci. Model Dev., 10, 1945-1960, 2017
https://doi.org/10.5194/gmd-10-1945-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model evaluation paper 17 May 2017

Model evaluation paper | 17 May 2017

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

Christina Papagiannopoulou1, Diego G. Miralles2,3, Stijn Decubber1, Matthias Demuzere2, Niko E. C. Verhoest2, Wouter A. Dorigo4, and Willem Waegeman1 Christina Papagiannopoulou et al.
  • 1Depart. of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent, Belgium
  • 2Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
  • 3Depart. of Earth Sciences, VU University Amsterdam, Amsterdam, the Netherlands
  • 4Depart. of Geodesy and Geo-Information, Vienna University of Technology, Vienna, Austria

Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate–vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate–vegetation dynamics.

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Global satellite observations provide a means to unravel the influence of climate on vegetation. Common statistical methods used to study the relationships between climate and vegetation are often too simplistic to capture the complexity of these relationships. Here, we present a novel causality framework that includes data fusion from various databases, time series decomposition, and machine learning techniques. Results highlight the highly non-linear nature of climate–vegetation interactions.
Global satellite observations provide a means to unravel the influence of climate on vegetation....
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