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On the impact of gaps on trend detection in extreme streamflow time series

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journal contribution
posted on 02.02.2017, 14:33 by Louise Slater, Gabriele Villarini
Streamflow time series often contain gaps of varying length and location. However, the influence of these gaps on trend detection is poorly understood and cannot be estimated a priori in trend-detection studies. We simulated the effects of varying gap size (1, 2, 5, and 10 years) and location (one quarter, one third, and half of the way) on the detection rate of significant monotonic trends in annual maxima and peaks-over-threshold, based on the most commonly-used trend tests in time series of varying length (from 15 to 150 years) and trend magnitude (β1). Results show that, in comparison with the complete time series, the loss in trend detection rate tends to grow with (i) increasing gap size, (ii) increasing gap distance from the middle of the time series, (iii) decreasing β1 slope, and (iv) decreasing time series length. Based on these findings, we provide objective recommendations and cautionary remarks for maximal gap allowance in trend detection in extreme streamflow time series.

Funding

This study was supported by the Broad Agency Announcement (BAA) Program and the Engineer Research and Development Center (ERDC)–Cold Regions Research and Engineering Laboratory CRREL) under Contract No. W913E5-16-C-0002.

History

School

  • Social Sciences

Department

  • Geography and Environment

Published in

International Journal of Climatology

Citation

SLATER, L. and VILLARINI, G., 2017. On the impact of gaps on trend detection in extreme streamflow time series. International Journal of Climatology, 37(10), pp. 3976-3983.

Publisher

© Royal Meteorological Society. Published by Wiley

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

05/11/2016

Publication date

2017

Notes

This is the peer reviewed version of the following article: SLATER, L. and VILLARINI, G., 2017. On the impact of gaps on trend detection in extreme streamflow time series. International Journal of Climatology, 37(10), pp. 3976-3983, which has been published in final form at http://dx.doi.org/10.1002/joc.4954. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

ISSN

1097-0088

Language

en