Articles | Volume 27, issue 12
Research article
28 Jun 2023
Research article |  | 28 Jun 2023

Study on a mother wavelet optimization framework based on change-point detection of hydrological time series

Jiqing Li, Jing Huang, Lei Zheng, and Wei Zheng


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-540', Geoff Pegram, 16 Oct 2022
    • AC1: 'Reply on RC1', Jing Huang, 20 Feb 2023
  • RC2: 'Comment on egusphere-2022-540', Anonymous Referee #2, 28 Jan 2023
    • AC2: 'Reply on RC2', Jing Huang, 20 Feb 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (05 Mar 2023) by Carlo De Michele
AR by Jing Huang on behalf of the Authors (04 Apr 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (26 Apr 2023) by Carlo De Michele
RR by Mohammad Nazeri Tahroudi (29 Apr 2023)
ED: Publish as is (17 May 2023) by Carlo De Michele
AR by Jing Huang on behalf of the Authors (24 May 2023)
Short summary
Under the joint action of climate–human activities the use of runoff data whose mathematical properties have changed has become the key to watershed management. To determine whether the data have been changed, the number and the location of changes, we proposed a change-point detection framework. The problem of determining the parameters of wavelet transform has been solved by comparing the accuracy of identifying change points. This study helps traditional models adapt to environmental changes.