Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Volume 10, issue 5
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.
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 Papagiannopoulou et al.
Viewed  
Total article views: 2,235 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,278 896 61 2,235 83 96
  • HTML: 1,278
  • PDF: 896
  • XML: 61
  • Total: 2,235
  • BibTeX: 83
  • EndNote: 96
Views and downloads (calculated since 16 Nov 2016)
Cumulative views and downloads (calculated since 16 Nov 2016)
Viewed (geographical distribution)  
Total article views: 2,119 (including HTML, PDF, and XML) Thereof 2,105 with geography defined and 14 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved (final revised paper)  
No saved metrics found.
Saved (discussion paper)  
No saved metrics found.
Discussed (final revised paper)  
No discussed metrics found.
Discussed (discussion paper)  
No discussed metrics found.
Latest update: 20 Sep 2019
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....
Citation