Articles | Volume 26, issue 1
Research article
12 Jan 2022
Research article |  | 12 Jan 2022

Parsimonious statistical learning models for low-flow estimation

Johannes Laimighofer, Michael Melcher, and Gregor Laaha


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-481', Anonymous Referee #1, 15 Oct 2021
    • AC1: 'Reply on RC1', Johannes Laimighofer, 08 Nov 2021
  • RC2: 'Comment on hess-2021-481', Kolbjorn Engeland, 29 Oct 2021
    • AC2: 'Reply on RC2', Johannes Laimighofer, 08 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by editor) (19 Nov 2021) by Rohini Kumar
AR by Johannes Laimighofer on behalf of the Authors (26 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (27 Nov 2021) by Rohini Kumar
Short summary
This study aims to predict long-term averages of low flow on a hydrologically diverse dataset in Austria. We compared seven statistical learning methods and included a backward variable selection approach. We found that separating the low-flow processes into winter and summer low flows leads to good performance for all the models. Variable selection results in more parsimonious and more interpretable models. Linear approaches for prediction and variable selection are sufficient for our dataset.