Articles | Volume 9, issue 2
https://doi.org/10.5194/essd-9-389-2017
https://doi.org/10.5194/essd-9-389-2017
Review article
 | 
03 Jul 2017
Review article |  | 03 Jul 2017

A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

Jaap Schellekens, Emanuel Dutra, Alberto Martínez-de la Torre, Gianpaolo Balsamo, Albert van Dijk, Frederiek Sperna Weiland, Marie Minvielle, Jean-Christophe Calvet, Bertrand Decharme, Stephanie Eisner, Gabriel Fink, Martina Flörke, Stefanie Peßenteiner, Rens van Beek, Jan Polcher, Hylke Beck, René Orth, Ben Calton, Sophia Burke, Wouter Dorigo, and Graham P. Weedon

Viewed

Total article views: 7,576 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,785 2,558 233 7,576 534 197 233
  • HTML: 4,785
  • PDF: 2,558
  • XML: 233
  • Total: 7,576
  • Supplement: 534
  • BibTeX: 197
  • EndNote: 233
Views and downloads (calculated since 06 Dec 2016)
Cumulative views and downloads (calculated since 06 Dec 2016)

Viewed (geographical distribution)

Total article views: 7,576 (including HTML, PDF, and XML) Thereof 7,141 with geography defined and 435 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (preprint)

Discussed (final revised paper)

Latest update: 15 Jul 2024
Download
Short summary
The dataset combines the results of 10 global models that describe the global continental water cycle. The data can be used as input for water resources studies, flood frequency studies etc. at different scales from continental to medium-scale catchments. We compared the results with earth observation data and conclude that most uncertainties are found in snow-dominated regions and tropical rainforest and monsoon regions.
Altmetrics
Final-revised paper
Preprint