Articles | Volume 22, issue 4
Nat. Hazards Earth Syst. Sci., 22, 1325–1334, 2022
https://doi.org/10.5194/nhess-22-1325-2022

Special issue: Hydrological cycle in the Mediterranean (ACP/AMT/GMD/HESS/NHESS/OS...

Nat. Hazards Earth Syst. Sci., 22, 1325–1334, 2022
https://doi.org/10.5194/nhess-22-1325-2022
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: 1,278 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
992 256 30 1,278 16 22
  • HTML: 992
  • PDF: 256
  • XML: 30
  • Total: 1,278
  • BibTeX: 16
  • EndNote: 22
Views and downloads (calculated since 23 Dec 2021)
Cumulative views and downloads (calculated since 23 Dec 2021)

Viewed (geographical distribution)

Total article views: 1,278 (including HTML, PDF, and XML) Thereof 1,225 with geography defined and 53 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Nov 2022
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.
Altmetrics
Final-revised paper
Preprint