Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 22, issue 4
Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-22-1325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 4
Research article
 | 
12 Apr 2022
Research article |  | 12 Apr 2022

Estimating soil moisture conditions for drought monitoring with random forests and a simple soil moisture accounting scheme

Yves Tramblay and Pere Quintana Seguí

Viewed

Total article views: 1,770 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,268 459 43 1,770 28 34
  • HTML: 1,268
  • PDF: 459
  • XML: 43
  • Total: 1,770
  • BibTeX: 28
  • EndNote: 34
Views and downloads (calculated since 23 Dec 2021)
Cumulative views and downloads (calculated since 23 Dec 2021)

Viewed (geographical distribution)

Total article views: 1,770 (including HTML, PDF, and XML) Thereof 1,700 with geography defined and 70 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 04 Dec 2023
Download
Short summary
Monitoring soil moisture is important during droughts, but very few measurements are available. Consequently, land-surface models are essential tools for reproducing soil moisture dynamics. In this study, a hybrid approach allowed for regionalizing soil water content using a machine learning method. This approach proved to be efficient, compared to the use of soil property maps, to run a simple soil moisture accounting model, and therefore it can be applied in various regions.
Read more
Special issue
Hydrological cycle in the Mediterranean (ACP/AMT/GMD/HESS/NHESS/OS...
Altmetrics
Final-revised paper
Preprint