Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 2.281 IF 2.281
  • IF 5-year value: 2.693 IF 5-year
    2.693
  • CiteScore value: 2.43 CiteScore
    2.43
  • SNIP value: 1.193 SNIP 1.193
  • SJR value: 0.965 SJR 0.965
  • IPP value: 2.31 IPP 2.31
  • h5-index value: 40 h5-index 40
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 73 Scimago H
    index 73
Discussion papers
https://doi.org/10.5194/nhess-2019-107
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-107
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 16 Apr 2019

Research article | 16 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Have trends changed over time? A study of UK peak flow data and sensitivity to observation period

Adam Griffin, Gianni Vesuviano, and Elizabeth Stewart Adam Griffin et al.
  • Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK

Abstract. Classical statistical methods for flood frequency estimation assume stationarity in the gauged data. However, recent focus on climate change and, within UK hydrology, severe floods in 2009 and 2015 have raised the profile of statistical analyses that include trends.

This paper considers how parameter estimates for the Generalised Logistic distribution (standard for UK annual maximum flows) vary through time, using the UK Benchmark Network (UKBN2) to separate the effects of land-use change from climate change. We focus on the sensitivity of parameter estimates to adding data, through fixed-width moving window and fixed-start extending window approaches, and on whether parameter trends are more prominent in specific geographical regions.

Under stationary assumptions, the addition of new data tends to further the convergence of parameters to some final value. However, addition of a single new data point can vastly change non-stationary parameter estimates. Little spatial correlation is seen in the magnitude of trends in peak flow data, potentially due to the spatial clustering of catchments in the UKBN2. In many places, the ratio between the 50-year and 100-year flood is decreasing, whereas the ratio between the 2-year and 30-year flood is increasing, presenting as a flattening of the flood frequency curve.

Adam Griffin et al.
Interactive discussion
Status: open (until 23 Jun 2019)
Status: open (until 23 Jun 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Adam Griffin et al.
Adam Griffin et al.
Viewed  
Total article views: 222 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
162 58 2 222 1 2
  • HTML: 162
  • PDF: 58
  • XML: 2
  • Total: 222
  • BibTeX: 1
  • EndNote: 2
Views and downloads (calculated since 16 Apr 2019)
Cumulative views and downloads (calculated since 16 Apr 2019)
Viewed (geographical distribution)  
Total article views: 98 (including HTML, PDF, and XML) Thereof 95 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 19 May 2019
Publications Copernicus
Download
Short summary
Classical statistical methods for flood frequency estimation assume that flooding characteristics do not change over time. Recent focus on climate change has raised questions of the validity of such assumptions. Near-rural catchments are used to focus on climate change (not land use change), investigating the sensitivity of trend estimates to record period. Some key statistics were very sensitive, but conclusive spatial patterns were not found. Smaller floods were most affected by these trends.
Classical statistical methods for flood frequency estimation assume that flooding...
Citation