Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2016-266
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Model evaluation paper
16 Nov 2016
Review status
A revision of this discussion paper was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.
A non-linear Granger causality framework to investigate climate–vegetation dynamics
Christina Papagiannopoulou1, Diego G. Miralles2,3, Niko E. C. Verhoest3, Wouter A. Dorigo4, and Willem Waegeman1 1Depart. of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium
2Depart. of Earth Sciences, VU University Amsterdam, the Netherlands
3Laboratory of Hydrology and Water Management, Ghent University, Belgium
4Depart. of Geodesy and Geo-Information, Vienna University of Technology, Austria
Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These take the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to unravel the influence of climate on vegetation dynamics. However, as advocated in this article, existing statistical methods are often too simplistic to represent complex climate–vegetation relationships due to the assumption of linearity of these relationships. 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 also discuss extensive experimental results that highlight the importance of considering the non-linear aspect of climate–vegetation dynamics.

Citation: Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate–vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.
Christina Papagiannopoulou et al.
Christina Papagiannopoulou et al.

Viewed

Total article views: 232 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
141 82 9 232 12 11

Views and downloads (calculated since 16 Nov 2016)

Cumulative views and downloads (calculated since 16 Nov 2016)

Viewed (geographical distribution)

Total article views: 232 (including HTML, PDF, and XML)

Thereof 231 with geography defined and 1 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 27 Apr 2017
Publications Copernicus
Download
Short summary
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....
Share