Articles | Volume 9, issue 2
https://doi.org/10.5194/soil-9-411-2023
https://doi.org/10.5194/soil-9-411-2023
Original research article
 | 
13 Jul 2023
Original research article |  | 13 Jul 2023

Mapping land degradation risk due to land susceptibility to dust emission and water erosion

Mahdi Boroughani, Fahimeh Mirchooli, Mojtaba Hadavifar, and Stephanie Fiedler

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1511', Anonymous Referee #1, 26 Jan 2023
    • AC1: 'Reply on RC1', Mahdi Boroughani, 24 Mar 2023
  • RC2: 'Comment on egusphere-2022-1511', Anonymous Referee #2, 15 Feb 2023
    • AC2: 'Reply on RC2', Mahdi Boroughani, 24 Mar 2023
  • RC3: 'Mapping land degradation risk due to wind and water erosion', Anonymous Referee #3, 19 Feb 2023
    • AC3: 'Reply on RC3', Mahdi Boroughani, 24 Mar 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Revision (30 Mar 2023) by Carolina Boix-Fayos
AR by Mahdi Boroughani on behalf of the Authors (02 Apr 2023)  Author's response   Author's tracked changes 
EF by Sarah Buchmann (04 Apr 2023)  Manuscript 
ED: Publish subject to technical corrections (30 May 2023) by Carolina Boix-Fayos
ED: Publish subject to technical corrections (31 May 2023) by Engracia Madejón Rodríguez (Executive editor)
AR by Mahdi Boroughani on behalf of the Authors (03 Jun 2023)  Manuscript 
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Short summary
The present study used several different datasets, conducted a field survey, and paired the data with three different machine learning algorithms to construct spatial maps for areas at risk of land degradation for the Lut watershed in Iran. According to the land degradation map, almost the entire study region is at risk. A large fraction of 43 % of the area is prone to both high wind-driven and water-driven soil erosion.