Articles | Volume 26, issue 20
Hydrol. Earth Syst. Sci., 26, 5373–5390, 2022
https://doi.org/10.5194/hess-26-5373-2022

Special issue: Experiments in Hydrology and Hydraulics

Hydrol. Earth Syst. Sci., 26, 5373–5390, 2022
https://doi.org/10.5194/hess-26-5373-2022
Research article
27 Oct 2022
Research article | 27 Oct 2022

FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms

Sara Sadri et al.

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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 hess-2022-96', Anonymous Referee #1, 29 Apr 2022
  • RC2: 'Comment on hess-2022-96', Geoff Pegram, 06 May 2022
  • RC3: 'Comment on hess-2022-96', Anonymous Referee #3, 10 May 2022
  • RC4: 'Comment on hess-2022-96', Anonymous Referee #4, 12 May 2022
    • AC5: 'Reply on RC4', Sara Sadri, 23 Sep 2022
  • RC5: 'Comment on hess-2022-96', Anonymous Referee #5, 12 May 2022
    • AC6: 'Reply on RC5', Sara Sadri, 23 Sep 2022
  • RC6: 'Comment on hess-2022-96', Anonymous Referee #6, 13 May 2022
    • AC7: 'Reply on RC6', Sara Sadri, 23 Sep 2022
  • RC7: 'Comment on hess-2022-96', Anonymous Referee #7, 13 May 2022
    • AC8: 'Reply on RC7', Sara Sadri, 23 Sep 2022
  • RC8: 'Comment on hess-2022-96', Anonymous Referee #8, 14 May 2022
    • AC9: 'Reply on RC8', Sara Sadri, 23 Sep 2022
  • RC9: 'Comment on hess-2022-96', Anonymous Referee #9, 15 May 2022
    • AC10: 'Reply on RC9', Sara Sadri, 23 Sep 2022
  • RC10: 'Comment on hess-2022-96', Anonymous Referee #10, 16 May 2022
    • AC11: 'Reply on RC10', Sara Sadri, 23 Sep 2022
  • RC11: 'Comment on hess-2022-96', Anonymous Referee #11, 17 May 2022
    • AC12: 'Reply on RC11', Sara Sadri, 23 Sep 2022
  • RC12: 'Comment on hess-2022-96', Anonymous Referee #12, 18 May 2022
    • AC13: 'Reply on RC12', Sara Sadri, 23 Sep 2022
  • CC1: 'Comment on hess-2022-96', Nils-Otto Kitterød, 18 May 2022
    • AC14: 'Reply on CC1', Sara Sadri, 24 Sep 2022
  • CC2: 'Comment on hess-2022-96', Panayiotis Dimitriadis, 22 May 2022
    • AC15: 'Reply on CC2', Sara Sadri, 24 Sep 2022
    • AC16: 'Reply on CC2', Sara Sadri, 24 Sep 2022
  • EC1: 'Editor comment', Daniel Green, 08 Jun 2022
    • AC1: 'Reply on EC1', Sara Sadri, 21 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish as is (04 Oct 2022) by Daniel Green
ED: Publish as is (04 Oct 2022) by Louise Slater(Executive Editor)
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Short summary
A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.