Articles | Volume 29, issue 6
https://doi.org/10.5194/hess-29-1759-2025
https://doi.org/10.5194/hess-29-1759-2025
Research article
 | 
28 Mar 2025
Research article |  | 28 Mar 2025

From hydraulic root architecture models to efficient macroscopic sink terms including perirhizal resistance: quantifying accuracy and computational speed

Daniel Leitner, Andrea Schnepf, and Jan Vanderborght

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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-2024-1319', Anonymous Referee #1, 29 Jul 2024
    • AC1: 'Reply on RC1', Daniel Leitner, 21 Sep 2024
  • RC2: 'Comment on egusphere-2024-1319', Anonymous Referee #2, 30 Jul 2024
    • AC2: 'Reply on RC2', Daniel Leitner, 21 Sep 2024
  • AC3: 'Comment on egusphere-2024-1319', Daniel Leitner, 23 Sep 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (30 Sep 2024) by Loes van Schaik
AR by Daniel Leitner on behalf of the Authors (11 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (25 Nov 2024) by Loes van Schaik
RR by Anonymous Referee #1 (01 Dec 2024)
ED: Publish subject to minor revisions (review by editor) (20 Dec 2024) by Loes van Schaik
AR by Daniel Leitner on behalf of the Authors (30 Dec 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (15 Jan 2025) by Loes van Schaik
AR by Daniel Leitner on behalf of the Authors (24 Jan 2025)  Manuscript 
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Short summary
Root water uptake strongly affects plant development and soil water balance. We use novel upscaling methods to develop land surface and crop models from detailed mechanistic models. We examine the mathematics behind this upscaling, pinpointing where errors occur. By simulating different crops and soils, we found that the accuracy loss varies based on root architecture and soil type. Our findings offer insights into balancing model complexity and accuracy for better predictions in agriculture.
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