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

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-391', Anonymous Referee #1, 26 Jan 2022
    • AC1: 'Reply on RC1', Yves Tramblay, 02 Mar 2022
  • RC2: 'Comment on nhess-2021-391', Anonymous Referee #2, 17 Feb 2022
    • AC2: 'Reply on RC2', Yves Tramblay, 02 Mar 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (11 Mar 2022) by Christian Barthlott
AR by Yves Tramblay on behalf of the Authors (11 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (11 Mar 2022) by Christian Barthlott
RR by Anonymous Referee #1 (27 Mar 2022)
ED: Publish as is (28 Mar 2022) by Christian Barthlott
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