Articles | Volume 24, issue 11
Research article
12 Nov 2020
Research article |  | 12 Nov 2020

Mapping groundwater abstractions from irrigated agriculture: big data, inverse modeling, and a satellite–model fusion approach

Oliver Miguel López Valencia, Kasper Johansen, Bruno José Luis Aragón Solorio, Ting Li, Rasmus Houborg, Yoann Malbeteau, Samer AlMashharawi, Muhammad Umer Altaf, Essam Mohammed Fallatah, Hari Prasad Dasari, Ibrahim Hoteit, and Matthew Francis McCabe


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (09 Jun 2020) by Lixin Wang
AR by Oliver Miguel Lopez Valencia on behalf of the Authors (15 Jun 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (08 Jul 2020) by Lixin Wang
RR by Anonymous Referee #1 (07 Sep 2020)
ED: Publish as is (02 Oct 2020) by Lixin Wang
Short summary
The agricultural sector in Saudi Arabia has expanded rapidly over the last few decades, supported by non-renewable groundwater abstraction. This study describes a novel data–model fusion approach to compile national-scale groundwater abstractions and demonstrates its use over 5000 individual center-pivot fields. This method will allow both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.