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

Christina Papagiannopoulou,Willem Waegeman,Niko E C Verhoest,Matthias Demuzere,Stijn Decubber,Diego G Miralles,Wouter A Dorigo

doi:10.5194/gmd-10-1945-2017

Christina Papagiannopoulou, Willem Waegeman + Show 5 more

Open Access

https://doi.org/10.5194/gmd-10-1945-2017

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Abstract

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.

Highlights

Vegetation dynamics and the distribution of ecosystems are largely driven by the availability of light, temperature, and water; they are mostly sensitive to climate conditions (Nemani et al, 2003; Seddon et al, 2016; Papagiannopoulou et al, 2017)
While the model explains more than 40 % of the variability in NDVI anomalies in some regions (R2 > 0.4), this is by itself not necessarily indicative of climate Granger causing the vegetation anomalies, as it may reflect simple correlations
We introduced a novel framework for studying Granger causality in climate–vegetation dynamics

Summary

Introduction

Vegetation dynamics and the distribution of ecosystems are largely driven by the availability of light, temperature, and water; they are mostly sensitive to climate conditions (Nemani et al, 2003; Seddon et al, 2016; Papagiannopoulou et al, 2017). Simple correlation statistics and multilinear regressions using some of these data sets have led to important steps forward in understanding the links between vegetation and climate (e.g. Nemani et al, 2003; Barichivich et al, 2014; Wu et al, 2015). These methods in general are insufficient when it comes to assessing causality, in systems like the land– atmosphere continuum in which complex feedback mechanisms are involved. A commonly used alternative consists of Granger-causality modelling (Granger, 1969)

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