Articles | Volume 14, issue 6
https://doi.org/10.5194/esd-14-1239-2023
https://doi.org/10.5194/esd-14-1239-2023
Research article
 | 
29 Nov 2023
Research article |  | 29 Nov 2023

Interannual land cover and vegetation variability based on remote sensing data in the HTESSEL land surface model: implementation and effects on simulated water dynamics

Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, Emanuele Di Carlo, Franco Catalano, Souhail Boussetta, Gianpaolo Balsamo, and Andrea Alessandri

Viewed

Total article views: 1,002 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
772 182 48 1,002 90 36 43
  • HTML: 772
  • PDF: 182
  • XML: 48
  • Total: 1,002
  • Supplement: 90
  • BibTeX: 36
  • EndNote: 43
Views and downloads (calculated since 22 May 2023)
Cumulative views and downloads (calculated since 22 May 2023)

Viewed (geographical distribution)

Total article views: 1,002 (including HTML, PDF, and XML) Thereof 992 with geography defined and 10 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 May 2024
Download
Short summary
Vegetation largely controls land hydrology by transporting water from the subsurface to the atmosphere through roots and is highly variable in space and time. However, current land surface models have limitations in capturing this variability at a global scale, limiting accurate modeling of land hydrology. We found that satellite-based vegetation variability considerably improved modeled land hydrology and therefore has potential to improve climate predictions of, for example, droughts.
Altmetrics
Final-revised paper
Preprint