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

Abstract

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.

Upscaling of Surface Water and Groundwater Interactions in Hyporheic Zone from Local to Regional Scale

“…Recent studies tried to identify in-channel geomorphic structures with different methodologies. Mahdade et al [26] identified pool-riffle sequences at a spatial scale using wavelet and index methods, limited for low discharge rivers and large-scale undulation in the streambed. An increment in hydraulic resistance and SW-GW interaction due to the presence of in-channel logjams creates variations in the hydraulic head along the streambed.…”

Statistically derived morphological signatures of large river channels extracted from topo‐bathymetric LiDAR data

“…For instance, a high variability indicates a heterogeneous MU (e.g., a riffle) while a low variability indicates a relatively smooth bed (e.g., a pool). Based on this concept, methods using wavelet analysis were employed to document roughness and bedform organization along longitudinal profiles in channels (Adams & Zampiron, 2020; Mahdade, Moine, & Moussa, 2018) or using spatial data (Cavalli et al, 2008). Other contributions integrate parameters extracted from cross‐sections or digital elevation models (DEMs) such as width, depth, flow velocity to achieve the identification of coherent reaches from morphological and dynamical perspectives (Gonzalez & Pasternack, 2015).…”

Reducing the conflict of interest in the optimal operation of reservoirs by linking mesohabitat hydraulic modeling and metaheuristic optimization

“…Objectively identification of pools, riffles and runs has been reviewed in the literature. Some studies identified mesohabitats without hydraulic criteria (More details are reviewed by Mahdade et al 2018). However, other studies provided quantified criteria to recognize mesohabitats.…”