A reliable estimation of sediment transport in gravel‐bed streams is important for various practical engineering and biological studies (e.g., channel stability design, bed degradation/aggradation, restoration of spawning habitat). In the present work, we report original laboratory experiments investigating the transport of gravel particles at low bed shear stresses. The laboratory tests were conducted under unsteady flow conditions inducing low bed shear stresses, with detailed monitoring of the bed topography using a laser scanner. Effects of bed surface arrangements were documented by testing loose and packed bed configurations. Effects of fine sediments were examined by testing beds with sand, artificial fine sand or cohesive silt infiltrated in the gravel matrix. Analysis of the experimental data revealed that the transport of gravel particles depends upon the bed arrangement, the bed material properties (e.g., size and shape, consolidation index, permeability) and the concentration of fine sediments within the surface layer of moving grains. This concentration is directly related to the distribution of fine particles within the gravel matrix (i.e., bottom‐up infiltration or bridging) and their transport mode (i.e., bedload or suspended load). Compared to loose beds, the mobility of gravel is reduced for packed beds and for beds clogged from the bottom up with cohesive fine sediments; in both cases, the bed shear stress for gravel entrainment increases by about 12%. On the other hand, the mobility of gravel increases significantly (bed shear stress for particle motion decreasing up to 40%) for beds clogged at the surface by non‐cohesive sand particles. Copyright © 2017 John Wiley & Sons, Ltd.
Bedload transport is a complex phenomenon that is not well understood, especially for poorly sorted sediment and low transport rates, which is what is typically found in alpine gravel‐bed rivers. In this paper, the interaction between bedload rate, bed stability and flow is investigated using flume experiments. Significant differences in bedload rates were observed for experiments conducted on beds formed with the same gravel material but presenting diverse arrangements and bedforms. Tests were performed under regimes of low transport rate, which are mainly controlled by gravel‐bed roughness. Different scales of roughness were identified using the statistical characteristics of detailed bed elevation measurements: grain, structure and large bedform scales. The role played by these different roughness scales in bedload dynamics was examined. For quasi‐flat beds, bed stability was quantified using a combination of bed surface criteria describing grain and structure scales. It was found that bed stability affects the bedload rate directly and not only through its influence on the flow or on the incipient motion. For beds with large bedforms, the analysis of bedload dynamics also showed the importance of accounting for effective bed shear stress distributions. An empirical bedload model for low transport regimes was suggested. Compared with previous formulae developed for alpine rivers, this model accounts for bed stability and distribution of effective bed shear stress. It significantly improves the understanding of gravel dynamics over complex beds such as arranged beds or those with large bedforms. However, further tests are needed to use the model outside the range of conditions of this study. © 2019 John Wiley & Sons, Ltd.
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Introduction 1.1. Rating Curve Management Establishing the streamflow time series and its uncertainty is a priority for most hydrological studies and water management applications. The streamflow record is commonly computed by transforming a continuous water level record into water discharge using a stage-discharge relation, also known as a rating curve (World Meteorological Organization, 2010). Rating curves are site-specific and are usually built using occasional measurements of water discharge Q and stage h (i.e., gaugings), combined with information about hydraulic controls (section or channel controls) at the hydrometric station (Rantz, 1982a, 1982b). The process of building a rating curve is subject to a large uncertainty that needs to be quantified in order to make important water-related decisions (McMillan et al., 2017). Many methods exist to estimate rating curve uncertainty and have been applied with increasing frequency (Kiang et al., 2018). One of the main problems for rating curve management is to deal with rating changes or "shifts" (Mansanarez et al., 2019). Indeed, stage-discharge relations are not always stable and can evolve in time, which leads to non-unique h-Q relations. Rating instabilities can be caused by sudden changes (e.g., morphological evolution induced by a flood, human constructions that affect the hydraulic control) and/or by transient changes (e.g., vegetation growth, accumulation of debris or ice). The challenge is to detect these changes and update the curve accordingly, for example, modifying the rating curve model and/or re-estimating the model parameters. Some procedures exist to deal with sudden changes and are reported in Mansanarez et al. (2019). However, these methods are not suitable for transient changes due to aquatic vegetation. In that case, the stage-discharge relation varies continuously in time according to the vegetation evolution. Water discharge therefore needs to be estimated from the stage and other variables depending on time. To the best of our knowledge, no rating curve models accounting for aquatic vegetation exist in the literature.
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