Articles | Volume 22, issue 2
Research article
12 Feb 2018
Research article |  | 12 Feb 2018

Evaluation of statistical methods for quantifying fractal scaling in water-quality time series with irregular sampling

Qian Zhang, Ciaran J. Harman, and James W. Kirchner


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) (12 Oct 2017) by Erwin Zehe
AR by Qian Zhang on behalf of the Authors (21 Nov 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (24 Nov 2017) by Erwin Zehe
RR by Anonymous Referee #2 (18 Dec 2017)
ED: Publish subject to technical corrections (19 Dec 2017) by Erwin Zehe
AR by Qian Zhang on behalf of the Authors (27 Dec 2017)  Author's response    Manuscript
Short summary
River water-quality time series often exhibit fractal scaling, which here refers to autocorrelation that decays as a power law over some range of scales. This paper provides a comprehensive overview of the various approaches for quantifying fractal scaling in irregularly sampled data and provides new understanding and quantification of the methods’ performances. More generally, the findings and approaches may be broadly applicable to irregularly sampled data in other scientific disciplines.