Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-381
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 11 Sep 2018

Research article | 11 Sep 2018

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

Wavelet and index methods for the identification of pool–riffle sequences

Mounir Mahdade1, Nicolas Le Moine1, and Roger Moussa2 Mounir Mahdade et al.
  • 1Sorbonne Université, CNRS, EPHE, Milieux environnementaux, transferts et interaction dans les hydrosystèmes et les sols, METIS, F-75005 Paris, France
  • 2INRA, UMR LISAH, 2 Place Pierre Viala, 34060 Montpellier, France

Abstract. The accuracy of hydraulic models depends on the quality of the bathymetric data they are based on, whatever the scale at which they are applied (e.g., 2D or 3D reach-scale modeling for local flood studies or 1D modeling for network-scale flood routing). The along-stream (longitudinal) and cross-sectional geometry of natural rivers is known to vary at the scale of the hydrographic network (e.g., generally decreasing slope, increasing width, etc.), allowing parameterizations of main cross-sectional parameters with proxy such as drainage area or a reference discharge quantile (an approach coined downstream hydraulic geometry, DHG). However, higher-frequency morphological variability is known to occur for many stream types, associated with varying flow conditions along a given reach: alternate bars, pool-riffle sequences, meanders, etc. To better take this high-frequency variability in bedforms into account in hydraulic models, a first step is to design robust methods to characterize the scales at which it occurs. In this paper, we propose and benchmark several methods to identify bedform sequences in pool-riffle morphology, for six small French rivers: the first one called the index method, based on three morphological and hydraulic descriptors; the second one called wavelet ridge extraction, performed on the continuous wavelet transform (CWT) of bed elevation. Finally, these new methods are compared with the bedform differencing technique (BDT, O’Neill and Abrahams (1984)), compared by computing a score that gives a percentage of agreement along the total surveyed length and by calculating the number of bedforms and the pool spacings for each method. The three methods were found to give similar results on average for wavelength estimation, with agreement from 64% to 84% and a similar number of bedforms identified. The filter-like behavior of the wavelet ridge analysis tends to give more robust results for the estimation of mean bedform amplitude, which varies from 0.30 to 0.81 with an SNR (signal-to-noise ratio) from 2.68 to 7.91. Otherwise, BDT gives higher mean bedform amplitude but lower SNR values from 0.85 to 1.73.

Download & links
Mounir Mahdade et al.
Interactive discussion
Status: open (until 06 Nov 2018)
Status: open (until 06 Nov 2018)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Mounir Mahdade et al.
Mounir Mahdade et al.
Viewed
Total article views: 228 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
186 39 3 228 0 0
  • HTML: 186
  • PDF: 39
  • XML: 3
  • Total: 228
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 11 Sep 2018)
Cumulative views and downloads (calculated since 11 Sep 2018)
Viewed (geographical distribution)
Total article views: 228 (including HTML, PDF, and XML) Thereof 226 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited
Saved
No saved metrics found.
Discussed
No discussed metrics found.
Latest update: 24 Sep 2018
Publications Copernicus
Download
Short summary
We benchmark three methods to identify the location of pools and riffles in a river. We find a robust extracting method that provides more information about these bedforms. This information could be used to build a simplified geometrical model of a given river reach for flood modeling, and extrapolated where detailed data is not available.
We benchmark three methods to identify the location of pools and riffles in a river. We find a...
Citation
Share