Slater Villarini - IJOC - Gaps - Revised manuscript.pdf (7.66 MB)
On the impact of gaps on trend detection in extreme streamflow time series
journal contribution
posted on 2017-02-02, 14:33 authored by Louise Slater, Gabriele VillariniStreamflow 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 ClimatologyCitation
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 WileyVersion
- 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
2016-11-05Publication date
2017Notes
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
1097-0088Publisher version
Language
- en