# Methodological Evaluation of Riverbed Clogging-Insight From Field Measurements

J. Deng, B. Camenen, D. Hernandez, J. Liégeois, A. Bonnefoy

► To cite this version:

J. Deng, B. Camenen, D. Hernandez, J. Liégeois, A. Bonnefoy. Methodological Evaluation of Riverbed Clogging-Insight From Field Measurements. River Research and Applications, 2025, 41 (6), pp.1282-1299. 10.1002/rra.4442. hal-05525801

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RESEARCH ARTICLE

OPEN ACCESS

# Methodological Evaluation of Riverbed Clogging—Insight From Field Measurements

J. Deng

<figure><img src="image_3.png" /></figure>

B. Camenen

<figure><img src="image_4.png" /></figure>

D. Hernandez

<figure><img src="image_5.png" /></figure>

J. Liégeois | A. Bonnefoy

INRAE, UR RiverLy, Villeurbanne, France

**Correspondence:** B. Camenen (benoit.camenen@inrae.fr)

**Received:** 29 October 2024

| **Revised:**

13 March 2025

| **Accepted:** 17 March 2025

**Funding:** This work was supported by Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Agence de l'Eau Rhône-Méditerrannée-Corse (French Water Agency) and Office Français de la Biodiversité.

**Keywords:** Bou-Rouch pump | clogging | dissolved oxygen | grain size distribution | gravel-bed river | hydraulic conductivity | interstitial fine sediment concentration | McNeil sampler

## ABSTRACT

Clogging of gravel-bed rivers is a major issue affecting fish and macro-invertebrate habitats as well as groundwater-river ex-
changes. River clogging consists mainly of the deposition and infiltration of fine sediments within the bed matrix, which is a
natural phenomenon but can be exacerbated by human activities. Although there are several methods for estimating the degree
of clogging, quantitative assessment of clogging remains challenging, not to mention the lack of comparison between these meth-
ods. To assess temporal and spatial clogging, such methods also need to be quick and easy to implement. To address this gap,
we implemented three quantitative methods to estimate clogging (bed material sampling, infiltration test and interstitial water
sampling) and evaluated their suitability for long-term monitoring. These methods were applied to a natural river bed charac-
terised by high spatial heterogeneity of clogging. The results show relatively good correlations between the different methods,
and some equations are suggested to relate the different parameters (fine sediment content, hydraulic conductivity, interstitial
fine sediment concentration and interstitial dissolved oxygen). However, some data variability is observed, likely due to meth-
odological limitations related to sampling techniques or sampling depth constraints. The bed material sample obtained using a
McNeil sampler allows an evaluation of the fine content, but remains very time-consuming and limited to the first 15 cm below
the surface. In contrast, the infiltration test and interstitial water sampling using a standpipe and a Bou-Rouch pump allow
deeper assessments, beyond 15 cm. The infiltration test appears to be effective for relatively low clogging, while interstitial water
sampling is more efficient for high clogging. The results also show that the interstitial dissolved oxygen measurement alone may
not be sufficient to accurately estimate physical clogging, particularly in cases of low clogging, but it appears to be a good and
simple complementary method. Overall, the infiltration test and interstitial water-sediment sampling are simple, quick and prac-
tical methods. Their combined application provides a reliable framework for assessing spatial clogging variations, enabling the
investigation of a broad range of clogging intensities over time.

## 1 | Introduction

The clogging or colmation of the riverbed can be defined as
the creation of a layer of deposited fine sediments on top or
inside the riverbed substrate (Schälchli 1992; Dubuis and De

Cesare 2023). Fine sediments involved in the clogging process
are generally considered to be particles smaller than 2 mm.
This clogging phenomenon should be distinguished from a
poorly sorted bed mixture deposited after a flood. Typically,
the process begins with an initially well-sorted gravel bed that

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is
properly cited.</footnote>

<footnote>© 2025 The Author(s). *River Research and Applications* published by John Wiley & Sons Ltd.</footnote>
becomes filled with fine sediments over time (Schälchli 1992). As a consequence, a clogged bed layer presents a very low porosity and permeability, which could strongly affect the habitats for fish (Pulg et al. 2013), benthos and hyporheos (Bo et al. 2007; Nogaro et al. 2010) by reducing surface and subsurface habitat availability.

Clogging of a streambed is generally a natural phenomenon, which consists of the deposition and infiltration of fine particles into the bed matrix, but it has been largely exacerbated by human activities (Hancock 2002; Wharton et al. 2017). Although there are several biological and chemical effects on clogging (such as biofilms, mentioned by (Fu et al. 2013)), physical clogging remains the main process of streambed clogging (Schälchli 1992). The main mechanisms of fine sediment penetration into the streambed are mostly controlled by flow turbulence, sediment concentration, grain size and shape of both fine and coarse sediments (Beschta and Jackson 1979; Gibson et al. 2009). Three different types of clogging are usually identified as physical clogging (Schälchli 1992; Dubuis and De Cesare 2023):

* Surface clogging, on top of the riverbed substrate

* Subsurface clogging, in the subsurface of the riverbed

* Unimpeded static percolation, deeper into the riverbed

Surface clogging corresponds to the deposition of suspended particles on top of the riverbed due to low velocity of water flow and insufficient porous space in the subsurface gravel framework preventing fine particle infiltration (Schälchli 1992). For subsurface clogging, a fraction of infiltrated fine sediment is stopped through bridging limiting the vertical fine sediment flux, forming a clogged layer, usually under the surface armour layer (Beschta and Jackson 1979; Diplas and Parker 1992; Gibson et al. 2010; Huston and Fox 2015). Unlike subsurface clogging, unimpeded static percolation takes place when the gravel-bed pores are sufficiently large to let fine sediments infiltrate and settle down by gravity until they reach a physical barrier (Gibson et al. 2009; Hamm et al. 2011; Herrero and Berni 2016).

Early developed methods to assess surface or subsurface clogging were often based on visual estimates and so remain qualitative (Sennatt et al. 2006; Descloux et al. 2010). A quantitative estimation of clogging involves measuring the grain size distribution (GSD) of a matrix sample (e.g., freeze-coring), which directly estimates the fine sediment fraction. Since the fine sediment fraction within the riverbed defines physical clogging, GSD results should be considered a reference standard. Freeze-coring is one of the sampling methods for the bed GSD. It consists of retrieving frozen streambed sediment cores obtained by injecting liquid nitrogen to freeze the surrounding saturated substrate and processing the retrieved core back in the laboratory (Stocker and Williams 1972; Carling and Reader 1981; Petts et al. 1989; Lisle and Eads 1991; Descloux et al. 2010; Negreiros et al. 2023). It was developed to quantify fine sediment contents in the different layers of the gravel bed and to study the vertical distribution of invertebrates inside river sediment together with grain size characteristics, organic matter content and porosity of subsurface sediments. This method is, however, cumbersome and time-consuming, which does not appear to be adapted for a

long-term solution for managing streambed clogging monitoring (Marmonier et al. 2004). Other simpler sampling methodologies were proposed to sample a volume of the gravel bed by Deng et al. (2024) for dried emerged patches or using a McNeil sampler (McNeil and Ahnell 1964) for underwater samples. One drawback of the McNeil methodology is its inability to clearly distinguish between surface and subsurface layers, which may exhibit significantly different levels of clogging. Some authors have noted a potential bias towards fine sediments, as they may be disturbed and pushed deeper into the streambed during insertion (Rood and Church 1994; Bunte and Abt 2001). However, several studies have demonstrated that McNeil samples remain quite accurate and reproducible (Young et al. 1991; Rex and Carmichael 2002).

An important and interesting parameter for determining clogging is the porosity *p* of the bed. Maridet and Philippe (1995) found a porosity threshold for clogging when *p* ≈ 0.06. However, there exists no simple method for determining the porosity. This can be evaluated from the freeze core but requires an accurate estimation of the sampled volume. Negreiros et al. (2023) suggested using the SfM (Structure From Motion; Ullman 1979) method to measure the volume of the core, which is quite heavy to apply. Tabesh et al. (2022) introduced a method based on nuclear density gauging to measure porosity. However, this latter cannot be applied to the submerged bed and is limited to low-flow periods when most of the bed is above the water surface.

Apart from direct measurements, clogging can also be indirectly evaluated by estimating hydraulic conductivity. However, classic infiltrometers are generally not well suited for gravel-bed matrices (Johnson 1963). Nevertheless, hydraulic conductivity can be easily estimated by measuring the infiltration duration of a slug of water inside a piezometer driven to a certain depth into the river bed (Descloux et al. 2010). This method was later improved by Datry et al. (2015). More recently, Negreiros et al. (2023) introduced the VertiCO (Vertical profiles of hydraulic Conductivity and dissolved Oxygen) technique, which consists of using a longitudinally perforated standpipe hammered into the submerged riverbed. This system allows for measuring both hydraulic conductivity and interstitial dissolved oxygen saturation (IDOS) at different positions. Indeed, benthos influence IDOS levels in river beds through their biological activities, including respiration, bioturbation and organic matter decomposition. Since clogging can limit benthic activity, a correlation exists between IDOS and clogging. Finally, following this method, the Bou-Rouch pump system (Bou and Rouch 1967) could be applied to any standpipe such as the one suggested by Datry et al. (2015) to sample interstitial water and measure its fine sand concentration and IDOS.

Although there are already some assessments of clogging using some of the techniques presented above (Descloux et al. 2010), a lack of comparisons between different methods exists, especially to evaluate their efficiency to characterise different levels of clogging and their ease to use to be applied as a long-term solution for monitoring streambed clogging. Many studies are unable to determine a level of clogging and instead limit their results to a single criterion or threshold value to differentiate an unclogged bed from a clogged bed (Nogaro et al. 2010; Negreiros et al. 2023). In addition, there is still a lack of indices to correlate
these different approaches to assessing clogging. The key sci-
tific question, therefore, is whether a simple method (or a
combination of methods) exists for quantitatively assessing
fine clogging in a gravel matrix, suitable for long-term clogging
monitoring. More specifically, how capable are these methods
in characterising specific levels of clogging, as well as the types
or effects of clogging? How easily can they be applied to char-
acterise spatial and temporal variations of clogging? To address
these questions, we conducted a series of tests in a section of the
River Rhône, where a gradient of clogging is a priori observed
(i.e., from clean gravel matrices to heavily clogged matrices)
due to significant variability in hydraulic constraints. The GSD,
hydraulic conductivity (K), interstitial sediment concentration
(ISC) and IDOS of the test points were measured to evaluate
the riverbed clogging integrally. These parameters were evalu-
ated based on underwater gravel-bed samples using a McNeil
sampler for GSD, infiltration tests for K, and interstitial water-
sediment mixture samples using a Bou-Rouch pump for ISC
and IDOS. We first presented the results regarding the ability
of these different methods and their potential complementary
nature in characterising clogging across various zones with
differing constraints. In the second step, we proposed potential
correlations between these methods. Finally, we discussed the
interest of these methods and correlation equations for long-
term clogging monitoring.

2 | Materials and Methods

## 2.1 | Study Site

The River Rhône is one of the major rivers in Europe. It flows
816 km from its source in Valais, in the Swiss Alps to the
Mediterranean Sea in the South of France. The River Rhône
catchment has an area of 97,800 km² and is characterised by a
large geological and climatic heterogeneity, and by an intense
series of hydropower schemes (Launay et al. 2019).

This study focused on a 4.5 km-long river reach located in the
north of the Lyon metropolitan area (Figure 1), which corre-
sponds to an Old-Rhône reach between the Miribel and Jonage
canals built during the 19th century (Petit et al. 1996). The mean
annual discharge of the River Rhône in the upstream of Miribel-
Jonage canals is around 600 m³/s. This discharge is then divided
by the Jons regulation dam: a stable discharge between 550 and
600 m³/s is distributed into the Jonage canal to support the hy-
droelectric production at the Cusset power plant and the rest of
the water flows into the Miribel canal with a minimum flow be-
tween 60 and 90 m³/s depending on seasons.

The Miribel canal is part of one of the last remaining reaches
of the River Rhône, presenting some gravel transport (Petit
et al. 1996; Vázquez-Tarrio et al. 2019). Most of the gravels come
from the River Ain, which confluences with the River Rhône
10 km upstream. The bed of the system is thus made of grav-
els with a mean diameter of several centimetres approximately.
Also, the Jonage canal creates a backwater that can reach the up-
stream part of the Old-Rhône reach. This subsequently induces
a strong hydraulic gradient. A first important consequence is
the large gravel deposits in the upstream part of the Old-Rhône
reach (see Figure 1c).

The suspended sediment supply of the upper Rhône catchment to the Jons dam varies from 0.18 to 0.92 Mt/yr and contributes on average 11% of the total annual suspended sediment load of the River Rhône (Poulier et al. 2019). The separation of the River Rhône into two canals plays an important role in the sediment dynamics. The frequent discharge adjustments for the Miribel-Jonage canals at the Jons dam result in active erosion of sediments in the Miribel canal, and a general deposition and a significant clogging of the riverbed in the Old-Rhône reach, mainly due to the backwater effect of the Jonage canal.

In addition, the Miribel canal and Old-Rhône reach are situated
in the strategic drinking water well fields. 95% of the drinking
water of the city of Lyon is provided from water abstraction in
this region (Figure 1). A concern about water quality and pump-
ing efficiency is raised by stakeholders (Grand-Lyon) due to the
progressive clogging of the riverbed by fine sediment particles.
On the other hand, the pumping itself leads to a deepening of
the clogging.

The studied area is divided into four zones based on different hydraulic and morphological conditions. These zones can be classified, a priori, from the least clogged to the most clogged, according to the authors' expertise. The location of each zone is shown in Figure 1. Zone 1 and Zone 4 correspond to the main channel of the Old-Rhône River, with a river width varying from 70 m on average in Zone 1–50 m on average in Zone 4. The limit between these two zones corresponds to a slope break-point (from 0.0022 to 0.0001), which corresponds to the end of emerged gravel bars (see Figure 1c) The water velocity in Zone 4 is significantly smaller than that in Zone 1 due to the back-water effect of the Jonage canal leading to a possible clogging gradient. During low-flow periods, the water depth in Zone 1 remains generally lower than 1 m, whereas it is often deeper than 2 m in Zone 4. Zone 2 is a secondary channel inundated for large flows only, with a width of 15–20 m. Zone 3 is an artificial flood control basin with very low velocities, as it is not connected downstream, and significant deposits. Compared to the other zones, Zone 3 is also characterised by the presence of vegetation. Both Zones 2 and 3 present some sporadic patches with permanent water.

## 2.2 | Methods

2.2.1 | Bed Material Sampling

The GSD of a bed sample can be considered as the direct pa-
rameter to define clogging. In this study, we consider fine
sediments to be particles smaller than 2 mm. Our definition
therefore includes the sand fraction. The McNeil sampler
(McNeil and Ahnell 1964) was used to collect bed sediments
underwater (Figure 2a). The sampler is made of a 65.5 cm high
metallic cylinder with a diameter of φ<sub>mn</sub> = 14.5 cm and a re-
movable rod to facilitate its penetration into the riverbed. This
sampling technique is similar to the one proposed by Lambert
and Walling (1988), and consists of collecting the bed materi-
als with a hand inside the cylinder inserted at around 15–20 cm
into the riverbed. A sample of the remaining water within the
sampler was collected using a 0.5 L flask after having homo-
genised the mixture to measure the mass of fine sediments.
<figure><img src="image_6.png" /><figcaption>**FIGURE 1** | Location of the study site and of the four zones of interest (sampled points are marked as red triangles). [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**FIGURE 2** | McNeil sampler (a) and field sieve structure (b). [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

The collected samples were then analysed in the field using a column of sieves (Figure 2b) including two classes of gravel (sieves of 10 and 2 mm), and two classes of sand (sieves of 500 and 100 µm) using a volume $V_{\text{sieve}}$ of water taken from the river. If there were particles with a diameter larger than 50 mm, they were manually selected using a Wolman frame to create a third class of gravel ($d > 50$ mm). The choice of these sieves was made to optimise field measurements, while focusing on the fine fractions (Deng et al. 2024). In particular, gravel classes were weighed in the field whereas sand classes, because of their high water content, were dried and weighed in the laboratory to obtained their dry masses ($M_{\text{Sand}}$). The limit size between silt and sand was chosen as $d = 100$ µm as a simplification, since the 63 µm-sieve was too easily clogged and therefore not practical for field use. The silt-clay class ($< 100$ µm) was subsequently analysed in the laboratory using the filtration protocol compiled with the European standard NF EN
<figure><img src="image_8.png" /><figcaption>FIGURE 3 | The two standpipes used (a) and zoom of their perforated area (HC18 on the left and HC25 on the right) (b); experimental setup for the hydraulic conductivity measurement using a funnel on the standpipe (c); experimental setup for the interstitial water-sediment pumping with a Bou-Rouch hand piston pump (in green) installed on the top of the standpipe (d). [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

872. The mass of the silt-clay class ($M_{Silt-Clay}$) was determined by multiplying the concentration obtained from the filtration process by the sample volume. This calculation was performed for both samples taken from the McNeil sampler (with a sample volume $V_{mn} = \pi\phi^2/4 \cdot h_{mn}$ where $h_{mn}$ is the water depth in the McNeil sampler) and samples taken from the basket under sieves (with a sample volume $V_{sieve}$ corresponding to the volume of water used in the sieving process). Using the McNeil sampler, the fine sediment content ($\mu_{f}$) could be obtained as the sum of the silt-clay content ($\mu_{Silt-Clay} = M_{Silt-Clay}/M_{t}$) and the sand content ($\mu_{Sand} = M_{Sand}/M_{t}$), where $M_{t}$ is the total mass of sediments sampled.

Some limitations of the McNeil sampling method are already known. During the field experiments, fine sediment was resuspended while inserting the McNeil tube into the riverbed, potentially leading to the loss of fine particles (Rood and Church 1994; Bunte and Abt 2001; Masquelier 2022). This loss of fine sediment can introduce a bias in the sample collection, causing an underestimation of the fine content. This underestimation may also be linked to the difficulty of retrieving fine sediments from the bed underwater; however, the water sample taken at the end of the operation should provide a correct estimate of the remaining fine sediments. In addition, it should be noted that a possible bias may also occur for the coarsest particles due to the relatively small sample volume collected (on average $M_{t} = 6.7$ kg). The largest particles should not represent more than 5% of the total mass (Church et al. 1987). However, this criteria was quite difficult to maintain, especially in Zone 4, where coarse particles could reach up to 50% of the total sample mass, as observed in Z4-4 (see Table 1).

### 2.2.2 | Saturated Sediment Hydraulic Conductivity Measurement (Infiltration Test)

The simplified falling head slug test proposed by Datry et al. (2015) was used to estimate the saturated hydraulic conductivity of the

riverbed (Figure 3a-c). Two sets of standpipes were used in this study: the first one corresponds to a 120 cm long metallic standpipe with an internal diameter of 2 cm and an 18 cm perforated area (hereinafter referred to 'HC18') and the second one corresponds to a standpipe with the same length and diameter but with a 25 cm perforated area (hereinafter referred to 'HC25'). The HC18 is the one initially proposed in the study of Datry et al. (2015) with four rows of four 0.4 cm evenly spaced holes (16 holes in total). On the other hand, the HC25 has four rows of six 0.4 cm holes and four rows of five 0.4 cm holes cross-arranged and evenly spaced as well (44 holes in total) as shown in Figure 3b.

The hydraulic conductivity was calculated using the formula proposed by Hvorslev (1951) and Chapuis (1989):

$$q = C_p \cdot K \cdot h = - \frac{\pi}{4} \cdot d^2 \cdot \frac{dh}{dt} \quad (1)$$

where $q$ (m$^3$/s) is the infiltration discharge, $C_p$ (m) is a shape factor related to the standpipe design, $K$ (m/s) is the saturated sediment hydraulic conductivity, $h$ (m) is the water level above equilibrium (i.e., the active head) and $d$ (m) is the funnel diameter at the air-water interface at time $t$. The shape factor $C_p$ is calculated using the formula proposed by Chapuis (1989) for a cylindrical injection zone with an impervious bottom:

$$C_p = \frac{2\pi L_p}{\ln\left[\frac{L_p}{D_p} + \sqrt{1 + \left(\frac{L_p}{D_p}\right)^2}\right]} - 2.75D_p \quad (2)$$

where $L_p$ (m) and $D_p$ (m) are the height and diameter of the perforated zone, respectively. Characteristics of the two standpipes are provided in the Supporting Information. Eventually, one has $C_p = 0.51$ for HC25 and $C_p = 0.40$ for HC18.

The field protocol consists of pouring a volume of water $V$ into a plastic funnel inserted at the head of the standpipe previously
hammered into the riverbed (Figure 3c). The elapsed time $T_{exp}$ that passed between removing the cap and when there was no more water in the funnel was recorded. The integration of Equation (1) in each of the compartments of the funnel provides the total infiltration time $T_{exp}$ and so the hydraulic conductivity can be obtained below:

$$K = \frac{\pi}{4 \cdot C_p \cdot T_{exp}} \sum_i f_i \quad (3)$$

where $f_i$ correspond to the geometry function of each compartment of the funnel for a given initial water level (Datry et al. 2015). In several of the experiments, a pressure gauge was also inserted at the bottom of the pipe to measure the time series of water depth, which allowed a direct application of Equation (1) as a comparison with the Datry protocol.

The hydraulic conductivity measurements were conducted at 25, 35 and 55cm below the surface riverbed using the HC18 and at 35, 45 and 55cm depth using the HC25, respectively. At each depth, five tests were conducted successively, and the mean value was calculated. All infiltration tests were achieved in the vicinity of the bed material sampling using the McNeil sampler for comparison. We assumed that the clogging was homogeneous at these sampling points (see Section 2.2.4 for details about field sampling and strategy).

### 2.2.3 | Interstitial Water Pumping and Dissolved Oxygen Measurement

The Bou-Rouch method (Bou and Rouch 1967) was used in this study to collect interstitial water-sediment mixtures from the riverbed using a hand piston pump loaded on the top of a standpipe hammered into the riverbed (see Figure 3d). This method is commonly used for studies on assemblage composition in the hypoxic zone because of its relatively little labour and inexpensive equipment (Hunt and Stanley 2000; Boulton et al. 2004).

In this study, the same standpipes (HC25 and HC18) for the hydraulic conductivity measurement were used for sampling. The manual pump was first primed with clean water. Three consecutive samples were then collected after evacuating the clean water. Each sample has an approximate volume $V_{br}$ of 1 L. The silt-clay mass $M_{Silt-Clay}$ of these samples were subsequently obtained in the laboratory using the standard filtration method after sieving at 100 µm (Dramais et al. 2018). We used the 100 µm sieve instead of the 63 µm sieve to stay consistent with the in situ protocol for the bed GSD using the McNeil sampler. The remaining sand on the sieve was then dried in the oven and weighted ($M_{Sand}$). Both silt-clay and sand concentrations were then calculated such as $C_{Silt-Clay} = M_{Silt-Clay} / V_{br}$ and $C_{Sand} = M_{Sand} / V_{br}$.

<figure><img src="image_9.png" /><figcaption>FIGURE 4 | The grain size distribution curve for all the sample points (a), $d_{16}$, $d_{50}$ and $d_{84}$-values in each zone (b), silt-clay content ($\mu_{Silt-Clay}$) (c) and sand content ($\mu_{Sand}$) in each zone (d). [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
The ISC from the Bou-Rouch test was obtained by averaging the concentrations measured for the three consecutive samples taken for both sand and silt-clay fractions. One observed, in general, a decreasing concentration from the first to the third sample due to a run-out of the material around the standpipe inlet. The first sample was sometimes less concentrated than the second one, potentially due to some residual clear water used to prime the pump. Even if we did not test the effect of the pumping rate on the results (one needs to be very active to prevent the pump from de-priming), we expect some potential influence that adds some uncertainty to the results.

The dissolved oxygen and temperature were measured directly in the field using an optode sensor (i.e., HACH HQ30d) soaked in the samples. Of course, although a possible bias of the measurements may occur due to the pumping which may bring atmospheric air into interstitial water-sediment mixture, previous studies still show the feasibility of this methodology and suggest that the dissolved oxygen may also be a good proxy for clogging (Marmonier et al. 2004). The IDOS ($\mu_{IDOS}$) of samples was evaluated as the ratio between the measured interstitial dissolved oxygen concentration ($C_{DO}$) and the maximum concentration that can be dissolved at a given temperature of water ($C_{max,DO}$):

$$ \mu_{IDOS} = \frac{C_{DO}}{C_{max,DO}} \qquad (4) $$

$C_{max,DO}$ was calculated using the formula of Baca and Arnett (1976).

Samples of interstitial water-sediment mixture were generally collected at 35, 45 and 55 cm below the surface of the riverbed, respectively as listed in Table 1 to be compared with infiltration tests. Some additional samples were also taken at 25 and 30 cm using HC18 standpipe (see Supporting Information). In a similar manner as for the ISC, IDOS was obtained by averaging the $\mu_{IDOS}$-values measured for the three consecutive samples.

### 2.2.4 | Sampling Strategy

To compare the different methods, one chose a patch visually homogeneous at the surface of approximately 4m². Each patch was selected in a manner that various morphological units such as ripples, pools, gravel bars, secondary channels and river banks could be covered to obtain varying degrees of clogging, including within the four selected zones.

First, one sample using the McNeil sampler was collected. Then, for the infiltration test and Bou-Rouch method, the standpipe was hammered at different positions around the position of the McNeil sample, at a distance of approximately 50 cm, one for each test at a specific depth. This methodology was applied to avoid any influence from a previous test since both the infiltration test and Bou-Rouch method modify the structure of the bed by removing at least part of the finest fraction, if not all.

Based on the definition of physical clogging, the GSD obtained from the McNeil sample, or more precisely the fine contents, should correspond to our reference for riverbed clogging. However, one should keep in mind that a bias exists from the McNeil samples since they mix sediments from both surface and subsurface. Also, infiltration tests and interstitial sediment pumping were achieved at different (deeper) depths. This adds an assumption of vertical homogeneity in clogging, which is not always fully verified. Also, although we made the different tests in a relatively small and homogeneous zone, there are some possible heterogeneities in the subsurface that can bias the results.

## 3 | Results

### 3.1 | Overview of Field Experiments

All performed field experiments using the HC25 standpipe are listed in Table 1. Additional data set using the HC18 is presented in the Supporting Information. At each sample location, three aforementioned methods were carried out. In total, 175/127/112 infiltration tests were performed using the HC25 at 35/45/55 cm, respectively. 108/57/75 water-sediment samples were collected at 35/45/55 cm using the Bou-Rouch pump loaded on the HC25. For Zone 4, the number of infiltration tests at 55 cm is comparatively low. This is mainly due to the very low hydraulic conductivity. For several cases (Z1-11, Z4-3 and Z4-7), no water level change in the funnel could be observed for a long time (more than 5 min); and the test was therefore not conclusive. One test only was performed with an infiltration time over 5 min: Z4-2 at an infiltration depth of 55 cm. For this test, an infiltration time of 24 min was necessary to empty the funnel.

Some of the tests could not be classified in any of the four zones either because the location was too specific (side deposit) or because results were used for instrument comparison only. The sample index for these tests was set to Z5.

### 3.2 | GSD of the McNeil Samples

The results of the GSD analysis on the McNeil samples are shown in Figure 4. As expected, a relative homogeneity in the coarse grain sizes is observed for the study site with $d_{50} \approx 1$ and $d_{84} \approx 2$ cm as shown in Figure 4a,b. One can observe that Zone 1 presents the coarsest GSD and the lowest silt-clay and sand contents suggesting a very low clogging level. Indeed, Zone 1 includes the main channel with higher water velocities and bed shear stresses compared to other zones. It thus formed a coarser bed and hindered fine sediment deposition. On the contrary, large silt-clay and sand contents are observed in Zone 4, which affects the $d_{50}$-value. This zone is indeed located in the backwater of the Jonage canal and inside the drinking water well field where flow velocities are significantly lower than those in other zones, which promotes sedimentation. In a similar way, Zone 3, which corresponds to a flood control basin, presents very low velocities and significant deposits leading to higher sand and silt-clay contents. Such significant fine sediment contents affect therefore $d_{50}$ as shown in Figure 4b.
<table><caption>**TABLE 1** | List of infiltration tests using the 44-hole standpipe (HC25) with standard Datry protocol, and associated pumping and bed sampling at sample points. [Color table can be viewed at wileyonlinelibrary.com]</caption><thead><tr><th rowspan="2">Sample index</th><th colspan="3">Infiltration test</th><th colspan="2">Pumping</th><th>McNeil sample</th></tr><tr><th>Sampling depth<sup>a</sup> (cm)</th><th>Nb. of tests<sup>b</sup></th><th>Hydraulic head (cm)</th><th>Sampling depth<sup>a</sup> (cm)</th><th>Nb. of samples</th><th>Insert depth<sup>a</sup> (cm)</th></tr></thead><tbody><tr><td>Z1-1</td><td>35/45/55</td><td>5/5/5</td><td>60/44/37</td><td>35/45/55</td><td>6/3/6</td><td>15.5</td></tr><tr><td>Z1-2</td><td>30/45/55</td><td>5/5/5</td><td>45/35/23</td><td>35/55</td><td>6/6</td><td>12.7</td></tr><tr><td>Z1-3</td><td>35/45/55</td><td>5/5/5</td><td>91/72/65</td><td>35</td><td>3</td><td>16.5</td></tr><tr><td>Z1-4</td><td>35/45/55</td><td>5/5/5</td><td>69/63/44</td><td>35/45/55</td><td>6/3/6</td><td>15.0</td></tr><tr><td>Z1-5</td><td>35/55</td><td>5/5</td><td>87/71</td><td>35</td><td>3</td><td>19.5</td></tr><tr><td>Z1-6</td><td>35/55</td><td>5/5</td><td>83/55</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z1-7<sup>c</sup></td><td>35/55</td><td>4/5</td><td>84/83</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z1-8</td><td>35/55</td><td>6/7</td><td>87/72</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z1-9</td><td>35/55</td><td>5/5</td><td>66/43</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z1-10</td><td>35/55</td><td>5/5</td><td>71/50</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z1-11<sup>c</sup></td><td>35/55</td><td>5/5</td><td>64/44</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z2-1</td><td>45</td><td>11</td><td>77</td><td>45</td><td>9</td><td>23.5</td></tr><tr><td>Z2-2</td><td>30/45</td><td>5/10</td><td>107/97</td><td>45</td><td>6</td><td>18.0</td></tr><tr><td>Z2-3</td><td>35/45</td><td>10/10</td><td>98/81</td><td>35/45</td><td>3/3</td><td>20.0</td></tr><tr><td>Z2-4</td><td>35/45</td><td>5/5</td><td>84/74</td><td>35/45</td><td>3/3</td><td>23.5</td></tr><tr><td>Z2-5</td><td>35/45</td><td>5/5</td><td>80/66</td><td>35/45</td><td>3/3</td><td>16.5</td></tr><tr><td>Z2-6<sup>c</sup></td><td>35/45</td><td>5/5</td><td>71/70</td><td>35/45</td><td>3/3</td><td>15.0</td></tr><tr><td>Z2-7</td><td>35/45</td><td>5/5</td><td>55/52</td><td>35/45</td><td>3/3</td><td>15.0</td></tr><tr><td>Z3-1</td><td>35/45/55</td><td>10/10/10</td><td>68/49/46</td><td>35/45/55</td><td>3/3/3</td><td>20.0</td></tr><tr><td>Z3-2</td><td>35/45/55</td><td>5/5/10</td><td>87/75/62</td><td>35/45/55</td><td>3/3/3</td><td>20.0</td></tr><tr><td>Z3-3</td><td>35/45/55</td><td>15/15/15</td><td>76/63/51</td><td>35/45/55</td><td>6/6/6</td><td>20.0</td></tr><tr><td>Z3-4</td><td>35/45/55</td><td>5/5/5</td><td>54/44/31</td><td>35/45/55</td><td>3/3/3</td><td>15.0</td></tr><tr><td>Z3-5<sup>c</sup></td><td>35/45</td><td>5/5</td><td>38/35</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z4-1</td><td>25/35/40</td><td>5/5/5</td><td>95/97/90</td><td>35</td><td>3</td><td>28.5</td></tr><tr><td>Z4-2</td><td>35/55</td><td>10/1</td><td>56/44</td><td>35/55</td><td>6/6</td><td>15.0</td></tr><tr><td>Z4-3</td><td>30/35/45/55</td><td>10/10/5/1</td><td>72/66/62/53</td><td>35/45/55</td><td>6/6/6</td><td>15.0</td></tr><tr><td>Z4-4</td><td>35</td><td>5</td><td>89</td><td>35</td><td>3</td><td>22.5</td></tr><tr><td>Z4-5</td><td>35/45/55</td><td>5/6/5</td><td>66/63/53</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z4-6<sup>c</sup></td><td>35/55</td><td>5/5</td><td>84/57</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z4-7<sup>c</sup></td><td>35/55</td><td>5/5</td><td>63/43</td><td>35/55</td><td>3/3</td><td>15.0</td></tr><tr><td>Z5-1</td><td>35</td><td>9</td><td>79</td><td>35</td><td>3</td><td>20.0</td></tr></tbody></table>

<sup>a</sup>All depths indicate the vertical distance from the measuring point to the bed surface.

<sup>b</sup>The number indicates the number of tests performed at respective sampling depth; <sup>1</sup> indicates that the infiltration test was conducted at the given sampling test several times but none succeeded in having a value of K due to no water level change within more than 5 min.

<sup>c</sup>Infiltration test used for the comparison between the Datry protocol and the pressure gauge protocol to estimate the hydraulic conductivity K (See S1).

One should note a large variability of $d_{16}$ especially in Zone 3 and Zone 4 where the riverbed quite often includes a significant amount of fine particles. This high variability is expected

first due to the spatial variability of fine content in the riverbed in these two zones and second due to the uncertainty in field sieving. The latter point was detailed in a previous study
(Masquelier 2022), where the scatter in $d_{16}$ due to the sieving process can reach up to 25% while $d_{50}$ varies only within the order of 2%. One should also have in mind that uncertainties in the estimation of the GSD and fine content using our McNeil sampler are not negligible due to the size of the sample and the potential bias in collecting fines as discussed in Section 2.2.1.

The silt-clay fraction plays an important role in the variability of $d_{16}$ in Zone 4. As shown in Figure 4c,d, the silt-clay class can be very significant in samples of Zone 4 and its content fluctuates considerably, from 0.1% to 26.2%. For the bed sample at Z4-4, we observed silt-clay content of 26.2%, which finally skewed $d_{16}$ towards $3.1 \times 10^{-5}$ m. On the other hand, the high variability of $d_{16}$ in Zone 3 is mainly due to the high variability in sand content. As shown in Figure 4d, a significant spatial variability is observed for the sand content for all zones, especially Zone 3. This spatial variability in Zone 3 was also observed during the field experiments. At the point Z3-2 close to the main channel of Zone 1, large sand deposits were observed compared to other points located inside the flood control basin of Zone 3. Indeed, sand particles coming from upstream can settle down quickly once entering the basin since velocities in Zone 3 are always very low. On the other hand, silt and clay particles, which have a much smaller settling velocity, can be more easily transported further inside the basin.

In average, sand content varies from 3% to 22% depending on the zone, while the silt-clay varies from 0% to 6%. There is a correlation between silt-clay content and sand content with a factor varying from 2 to 5. It indicates that the sand fraction represents generally a predominant portion of fine sediment inside the gravel matrix and might potentially play an important role in filling the inter-gravel spaces, which led to clogging. Indeed, based on Naganna et al. (2017) classification, the bed starts to be clogged for a silt-clay content $\mu_{fsc} > 5\%$, a criteria that could be easily extend to the fine content with $\mu_{ft} > 10 - 20\%$. Results from Figure 4c,d also confirm our initial expertise with an increasing fine content from Zone 1 to Zone 4.

Results of the GSD analysis on the McNeil samples highlight the ability of this test to estimate riverbed clogging, with some limitations on the estimation of the silt-clay fraction and on its application to the surface layer only. Direct estimation of the fine content is the real strength of this method. However, it remains a time-consuming method that is difficult to apply to the spatial assessment of clogging.

## 3.3 | Hydraulic Conductivity (K)

### 3.3.1 | Comparisons of Methods to Derive the Hydraulic Conductivity

The two standpipes were both tested at Z2-1, Z2-2, Z2-4, Z3-4 and Z4-2 at 25, 35 and 55 cm below the riverbed surface. The K-values measured by the two different standpipes are in a very good agreement (see Figure S1a in Supporting Information). The infiltration time for the test using the HC25 is in general shorter than the one for the test using the HC18. This is mainly due to the fact that the HC25 has a larger perforated zone allowing a higher intake discharge into the riverbed. These results indicate that Equation (2) seems to well consider the different standpipe design and limits

the potential bias of the K-value related to the various standpipe models. Some tests on other types of standpipes could be helpful to confirm our conclusion. Given the shorter infiltration time using the HC25, the standpipe with a large perforated zone (HC25 in this case) could be more efficient in the field for relatively low hydraulic conductivity, and is therefore recommended. However, a long-perforated zone imposes a deeper minimum insert depth. If a surface clogging is observed, such long-perforated zone standpipe design might not be adapted despite its efficiency.

Since a pressure gauge was inserted at the bottom of the pipe for some of the experiments (See Table 1), a comparison between results from Equation (3) (referred to $K_{Datry}$) and from Equation (1) (referred to $K_{Hvorslev}$) can be done. Again, a correct agreement is observed between these two methodologies whatever the measuring depth, even if some scatter can be observed (see Figure S1b in Supporting Information). This comparison highlights the interest of using a pressure gauge to observe small variations of water level, which could be used to quantify low hydraulic conductivities.

### 3.3.2 | Spatial Variability of Hydraulic Conductivity

The hydraulic conductivity K measured at different depths in different zones is shown in Figure 5. The mean K gradually decreases from $8.9 \times 10^{-4}$ m/s at 30 cm to $5.0 \times 10^{-4}$ m/s at 55 cm in Zone 2 and decreases from $9.7 \times 10^{-4}$ m/s at 20 cm to $1.4 \times 10^{-4}$ m/s at 55 cm in Zone 4, as shown in Figure 5b,d, respectively. According to the reference values of K for clogging (Blaschke et al. 2003; Datry et al. 2015), the riverbed in Zone 4 is slightly clogged close to the bed surface and heavily clogged deep inside the bed. The riverbed in Zone 2 remained slightly clogged from the top of the bed to the bottom of the bed despite the decreasing trend of K vertically.

In contrast, the value of K is gradually increasing from 25 cm to 55 cm below the bed surface in Zone 3 and is almost constant in Zone 1 meaning that the riverbed in these zones is less clogged from the top to the bottom. The mean K-value for these zones ranges from $7.1 \times 10^{-4}$ to $1.05 \times 10^{-3}$ m/s (see Figure 5c), which is under the range of ‘no physical clogging’ proposed by Datry et al. (2015) and under the upper range of ‘physical clogging’ proposed by Blaschke et al. (2003). Large variation can be observed in Zone 1 and Zone 3. For Zone 1, it is mainly related to the specific points Z1-5 and Z1-11, which are points located closed to the pool unit between Zone 1 and Zone 4. For Zone 3, Z3-2 is located in a fine deposit zone, which also biases the mean K-value. The K-values at 35 cm are very similar (from $6.5 \times 10^{-4}$ to $1.2 \times 10^{-3}$ m/s) in all zones with the exception of Zone 4, where K is slightly lower.

It is worth noting that the Datry test could not be performed in very clogged zones due to a too long infiltration time (≈ 5 min). At each point, we tried to conduct the same number of tests at each depths. However, as shown in Table 1, we only successfully conducted 112 tests at 55 cm compared to 175 tests at 35 cm using HC25 standpipe. This indication showed that some tests at 55 cm could not be performed, especially in Zone 4 with points Z4-3 and Z4-7, and in Zone 1 with the point Z1-11 (closed to Zone 4). This clearly biases the results in Figure 5, where several points with a much smaller hydraulic conductivity were encountered and
<figure><img src="image_10.png" /></figure>

**FIGURE 5** | Vertical profiles of hydraulic conductivity in the four different zones: Zone 1 (a), zone 2 (b), zone 3 (c) and zone 4 (d) (the green triangle in each boxplot represents the mean value from the subset; the box in the bottom-left corner indicates the number of tests that were unsuccessful, that is, with a hydraulic conductivity *K* < 10⁻⁵ m/s; the delimitation of these four zones is presented in Figure 1). [Color figure can be viewed at wileyonlinelibrary.com]

could not be represented. For the point Z4-2, a very low *K*-value of 3.63×10⁻⁶ m/s has been obtained at a depth of 55 cm, after a very long infiltration time of 1457 s (24 min). Therefore, it appears that one may have difficulties in providing a valid *K*-value using the Datry test for a highly clogged riverbed as in Zone 4.

The infiltration test appears to be an interesting method to estimate riverbed clogging, easy to apply in the field. It is, however, limited to relatively high values of conductivity to avoid too long measurement, but could be improved using a pressure gauge and different funnels or caps.

## 3.4 | Interstitial Water Samples

### 3.4.1 | Interstitial Sediment Concentration

Zone 3 yields the highest concentration of silt-clay at 35 and 45 cm sampling depths while the silt-clay concentration decreases at 55 cm as shown in Figure 6a. The high concentration of silt-clay in Zone 3 is consistent with field observations that the bed was often covered with fine sediments that were easily resuspended. However, the GSD of Zone 3 is close to the one in Zone 2 and

coarser than Zone 4 suggesting a similar clogging level as Zone 2 and a lower clogging level compared to Zone 4. The relatively high *K*-values in Zone 3 confirm this observation that the bed may be not as clogged as the bed in Zone 4. Indeed, the main reason may be that the bed in Zone 3 has relative low sand content, which is closer to the one in Zone 2 and much smaller than that in Zone 4. Despite the significant high values of silt-clay concentration in Zone 3 at 35–45 cm depth, the lack of sand fraction in the bed matrix of Zone 3 may result in insufficient fines to adequately fill the voids between the gravels, as is the case in Zone 4 with a high level of clogging. Our data therefore indicate that clogging may be a combined effect of both silt-clay and sand fractions and also depends on the structure of the bed matrix and the GSD. Also, the decrease in concentration of silt-clay and sand at 55 cm in Zone 3 suggests that bridging processes (Gibson et al. 2010; Huston and Fox 2015) may exist to explain the limited infiltration depth for fine particles observed from our data. In general, the sand concentration is approximately 4–10 times larger than the silt-clay concentration, which is in agreement with the bed GSD shown in Figure 4c,d. The sand concentration is clearly higher in Zone 4 than in other zones. This high concentration of sand along with high concentration of silt-clay results in a higher clogging level in Zone 4, that is also confirmed by
<figure><img src="image_11.png" /><figcaption>**FIGURE 6** | Concentration of the silt-clay class (a) and concentration of the sand class (b) measured in the 'Bou-Rouch' samples for each zone and at different measuring depths. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**FIGURE 7** | Interstitial dissolved oxygen saturation from the Bou-Rouch samples in each zone and at different measuring depths. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

the measured K-values. In addition, both silt-clay and sand concentrations increase with sampling depths in Zone 4 as shown in Figure 6d. This observation indicates that the bed in Zone 4 may be filled with fine particles from the bottom to the top of the bed (unimpeded static percolation, Gibson et al. 2009). This is however unexpected since the GSD is similar to the three other zones. This may also be an effect of the pumping system for the drinking water well fields, which potentially promotes deeper fine particle infiltration.

Interstitial sediment analysis seems to be a well-suited method to assess high riverbed clogging, providing a quantitative estimation of fine content, while being easy to apply. However, it requires relatively cumbersome laboratory filtrations.

### 3.4.2 | Interstitial Dissolved Oxygen Saturation

The IDOS in each zone is shown in Figure 7. Zone 4 has a very low μIDOS-value at 55 cm (only 44.3%). Such low IDOS suggests very poor hyporheic exchanges inside Zone 4, which could result from the highly clogged riverbed. Zone 1 and Zone

2 appear to be more oxygenated suggesting a good hyporheic exchange and should therefore correspond to a low clogging. Zone 3 presents an interesting variation with low μIDOS-values at 45 cm and almost the same level of IDOS at 35 and 55 cm. By combining this result with the concentration of fine sediments shown in Figure 6, it seems that this decrease of μIDOS at 45 cm could be due to the high fine content in the riverbed that prevents the oxygen exchange from the water body to the interstitial space. However, we could not relate this decrease of μIDOS directly to the clogging since the high K-value at 45 cm indicates that the bed in Zone 3 should not be clogged. Therefore, the generally lower IDOS in Zone 3 might not result from the clogging. By contrast, Zone 3 is a flood retention basin where the flow velocity is relatively low. Such a low-moving water body induces less mixing of the surface water, which could limit the diffusion of oxygen and, therefore, could limit the overall IDOS.

## 3.5 | Comparison Between the Different Types of Measurements

### 3.5.1 | Hydraulic Conductivity and GSD From Bed Material Samples

The hydraulic conductivity K measured from the Datry infiltration test is correlated with the fine sediment content (including sand, i.e., particles smaller than 2 mm) μft obtained from McNeil samples, despite a significant scatter. Negative correlations are obtained indicating that the hydraulic conductivity decreases with the amount of fine sediments inside the riverbed (Figure 8a). This is consistent with previous findings from the literature (Descloux et al. 2010). As a first guest, an exponential relationship between K and μft could be obtained:

$$K = K_{0,\mu_{ft}} \exp(-\alpha_{\mu_{ft}} \mu_{ft}) \quad (5)$$

with K0,μft the hydraulic conductivity for the clean bed (μft = 0) and αμft the slope of the relationship. For our specific data set, we have K0,μft ≈ 2 × 10⁻³ m/s. However, there is a significant scatter for the effect of the fine sediment content, which appear to vary depending on the depth of the infiltration test. Indeed, we
<figure><img src="image_13.png" /><figcaption>**FIGURE 8** | Comparison between hydraulic conductivity K and fine sediment content (from clay to sand) μ<sub>ft</sub> (a), concentration of fine sediments including sand C<sub>ft</sub> (b) and interstitial dissolved oxygen saturation μ<sub>IDOS</sub> (c) using the present dataset. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

obtained α<sub>μft</sub> = 0.100 at 55 cm, α<sub>μft</sub> = 0.075 at 45 cm and α<sub>μft</sub> = 0.01 at 35 cm (Figure 8a).

One general explanation of the scatter is that the GSD from the McNeil samples, corresponding to the 15–20 first centimetres below the surface, may be not representative of the GSD at deeper depths. There would be an effect of the surface layer that biases the GSD of the McNeil sample but also potentially a vertical variability of the fine content. Also, the K-values at 35 cm appear quite high even for large fine content μ<sub>ft</sub>. We suspect some limit of the infiltration test for relatively low insertion depths and low water depths. Indeed, for high hydraulic head H, the vertical flux through the perforated holes could create a strong upward water flow capable of triggering the quicksand phenomenon (H > H<sub>c</sub>, where H<sub>c</sub> is the critical hydraulic head; see Supporting Information), able to loosen the bed matrix and to create leakage. In the field, we have sometimes observed water leaking from the riverbed during the infiltration test, which obviously biased the result towards higher hydraulic conductivity. As observed in Table 1, there are a few cases where H > 0.8 m close to the quicksand criteria. This suggests that the infiltration test may be inaccurate for small insertion depth of the standpipe. At least, a system with a shorter height of the perforated zone (such as the HC18 for our case) should be used.

3.5.2 | Hydraulic Conductivity and Interstitial Sediment Concentration

We compared K-values from infiltration tests with the total fine sediment concentrations C<sub>ft</sub> = C<sub>Silt-Clay</sub> + C<sub>Sand</sub> from the interstitial water samples obtained using the Bou-Rouch pump (Figure 8b). Although only a weak statistical significance is found whatever the measuring depths, a general negative correlation between K and C<sub>ft</sub> is observed. This observation indicates that a high interstitial fine sediment concentration results in a low hydraulic conductivity indicating a riverbed clogging (Bou and Rouch 1967; Datry et al. 2015; Negreiros et al. 2023). In addition, our dataset suggests that the hydraulic conductivity K tends to be stable for a concentration lower than 100g/L and decreases quickly when the concentration is larger than 100g/L. This threshold behaviour seems to be sensitive to the measuring depths as the decreasing rate of K differs at each measuring depth. One should note that we could not obtain K-value lower than 10<sup>-5</sup> due to the limit of measuring time (assuming a maximum test duration of 5min) using the Datry protocol. Such a limit hinders us from verifying this threshold behaviour for low K cases. Based on our current data, it is still possible to suggest a relationship between K and C<sub>ft</sub> in a similar way to the one between K and μ<sub>ft</sub>:

$$
K = K_{0,C_{ft}} \exp(-\alpha_{C_{ft}} C_{ft}) \quad (6)
$$
with $K_{0, C_n} = 1.0 \times 10^{-3}$ m/s and $\alpha_{C_n} = -5.0 \times 10^{-3}$ based on data with an insertion depth of 45 and 55 cm. Indeed, as discussed above, we suspect some potential bias in the hydraulic conductivity measurements for low insertion depths (at 35 cm). This equation provides an alternative of Equation (5) when the GSD of the riverbed is unknown. The latest usually requires a lot of labour and is more difficult to estimate than the concentration measurement using the Bou-Rouch pumping.

### 3.5.3 | Hydraulic Conductivity and Interstitial Dissolved Oxygen Saturation

We observed a positive correlation between IDOS and K (Figure 8c), which is in agreement with the observations by Negreiros et al. (2023). For our specific data, one observes nevertheless a significant scatter for zone 1, 2 and 3. We obtained a good correlation for Zone 4 only ($R^2 = 0.88$). Following these results, a linear relationship can be provided between $\mu_{IDOS}$ and $\log(K)$ or between $\mu_{IDOS}$ and $\mu_{ft}$ (following Equation 5) or between $\mu_{IDOS}$ and $C_{ft}$ (following Equation 6):

$$ \mu_{IDOS} = \mu_{IDOS,0,K} + \alpha_K \log(K) = \mu_{IDOS,0,\mu_n} + \alpha_{\mu_n} \cdot \mu_{ft} = \mu_{IDOS,0,C_n} + \alpha_{C_n} \cdot C_{ft} \quad (7) $$
with $\mu_{IDOS,0,K} = 200$ and $\alpha_K = 15$ s/m based on data from Zone 4.

Considering our dataset, some questions arise following the large scatter. First, one should observe the highest hydraulic conductivity for the large $\mu_{IDOS}$-values, which is not always the case here. This may be due to potential bias in the hydraulic conductivity measurements at low insertion depths as discussed before. For Zone 3, one also observes a large scatter in the K-values, especially for low $\mu_{IDOS}$ ($\mu_{IDOS} \approx 25\%$). This result indicates that the IDOS is impacted by other factors such as the number of organisms and organic matter, which can vary with depth. This is particularly expected for Zone 3, where the presence of vegetation may affect the quantity of organic matter and organisms. Organic matter can be trapped into fine sediments and continue decomposing and consuming oxygen from water. This could explain the relative low level of IDOS in Zone 3. These factors cannot be identified using the hydraulic

conductivity only. For $\mu_{IDOS} > 80\%$, the scatter is much lower indicating that the hydraulic conductivity is better correlated to the IDOS when a good hyporheic exchange exists. As a consequence, the IDOS test can be considered as complementary to the hydraulic conductivity test to define a clogging state as it is sensitive to the benthos activity. Even if large uncertainties were observed in our measurements for high K-values, this result also shows that the IDOS can be impacted by other factors than hydraulic conductivity.

One also could expect some correlation between $\mu_{IDOS}$ and $C_{ft}$, or more specifically $C_{Silt-Clay}$ since these particles have a higher oxygen demand. Indeed, high Silt-Clay contents often correspond to low $\mu_{IDOS}$ (Zones 3 and 4). However, even if both measurements are made based on the same samples, one observes a poor correlation between both parameters (not shown here). Several parameters can affect IDOS as discussed above.

### 3.5.4 | Fine Content in Bed Material Samples and Interstitial Sediment Concentration

The comparison between the sand/silt-clay concentrations from the Bou-Rouch samples and sand/silt-clay contents measured with the McNeil sampler is shown in Figure 9. Although a scatter is observed, the interstitial sand and silt-clay concentrations tend to increase with the sand and silt-clay contents in the matrix, respectively, with a relatively good correlation for the sand fraction ($R^2 > 0.8$). On the other hand, while high concentrations of silt-clay can be obtained from Bou-Rouch samples ($C_{Silt-Clay} > 20$ g/L), a low silt-clay content is often observed from the McNeil samples. Such discrepancy between the two methods could be related to the McNeil protocol, because of the difficulty of extracting the silt-clay fraction from the tube (suspension, overflow) leading to an underestimation of silt-clay content. It could also come from an overestimation in the concentration of silt-clay from the Bou-Rouch sampling. In fact, the Bou-Rouch pumping system may be so efficient that most of the silt-clay sized particles in the vicinity of the measurement point, or even further, may be extracted. In addition, one should remember that the McNeil samples corresponds to the surface

<figure><img src="image_14.png" /><figcaption>FIGURE 9 | Comparison between sediment fraction obtained from bed material samples ($\mu_{Sand}$ or $\mu_{Silt-Clay}$) and the concentration obtained from pumping ($C_{Sand}$ or $C_{Silt-Clay}$): (a) for the sand fraction and (b) for the silt and clay fraction. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>**FIGURE 10** | Comparison between hydraulic conductivity K and fine sediment content (from clay to sand) μ<sub>ft</sub> (a) and between K and μ<sub>IDOS</sub> (b) using data from the literature. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

layer (15–20 first centimetres) while Bou-Rouch samples were obtained at deeper positions. Therefore, a bias exists due to the difference in sampling depth.

According to these data, a linear relationship between μ<sub>Sand</sub> and C<sub>Sand</sub> or μ<sub>Silt-Clay</sub> and C<sub>Silt-Clay</sub> can be obtained:

$$C_{\text{Sand/Silt-Clay}} = \alpha_{C_{\mu, \text{Sand/Silt-Clay}}} \cdot \mu_{\text{Sand/Silt-Clay}} \quad (8)$$

with α<sub>C<sub>μ, Sand</sub></sub> ≈ 9 and α<sub>C<sub>μ, Silt-Clay</sub></sub> ≈ 7. This result, though quite uncertain, indicates a similar efficiency of the Bou-Rouch pump for both classes, with actually a potential slightly higher efficiency for the sand fraction.

# 4 | Discussion

## 4.1 | Discussion on the Relationships Between Hydraulic Conductivity, Fine Content and Interstitial Dissolved Oxygen Saturation

There exist other studies in which measurements of hydraulic conductivity were combined with GSD measurements and the induced fine sediment content (Descloux et al. 2010; Gomez et al. 2014), or with IDOS (Negreiros et al. 2023). The advantage of these datasets is that fine sediment content, hydraulic conductivity and IDOS were evaluated at the same depths.

In Figure 10a, we present a comparison between hydraulic conductivity K and fine sediment content μ<sub>ft</sub> using data from Descloux et al. (2010); Gomez et al. (2014); Negreiros et al. (2023). In the specific case of Negreiros et al. (2023), the depth-averaged value of hydraulic conductivity was compared to the fine sediment content measured for the same bed layer. Based on the data from Descloux et al. (2010), the fit for Equation (5) yields K<sub>0,μ<sub>ft</sub></sub> = 3 × 10<sup>-4</sup> m/s et α<sub>μ<sub>ft</sub></sub> = 0.25. A similar slope, α<sub>μ<sub>n</sub></sub>, is observed for the data from Gomez et al. (2014), although K<sub>0,μ<sub>n</sub></sub> is one order of magnitude smaller. This difference can be explained by the fact that K<sub>0,μ<sub>ft</sub></sub> should represent the hydraulic conductivity of the ‘clean’ matrix without fine sediments, which can vary from one experiment to another depending on the matrix characteristics. Data from Negreiros

<figure><img src="image_16.png" /><figcaption>**FIGURE 11** | Estimation of the hydraulic conductivity using the Datry protocol for different values of height between the top of the standpipe and the water surface h<sub>a</sub>. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

et al. (2023) show a larger hydraulic conductivity but a relatively poor correlation, likely due to the fact that they correspond to three different experimental sites.

In Figure 10b, we present a comparison between hydraulic conductivity K and IDOS (μ<sub>IDOS</sub>) using data from Negreiros et al. (2023). The fit of Equation (7) to field data from Negreiros et al. (2023) yields μ<sub>IDOS,0,K</sub> = 230% and α<sub>K</sub> = 25 s/m, which is close to our own measurements in Zone 4. However, for a fixed value of μ<sub>IDOS</sub>, the hydraulic conductivity is generally higher than our data, by up to one order of magnitude, especially for μ<sub>IDOS</sub> > 80%. The data from Negreiros et al. (2023) appear more realistic for the higher μ<sub>IDOS</sub> values, as one would expect the highest hydraulic conductivity values to correspond to the highest μ<sub>IDOS</sub>-values.

Strengthened by observations in the literature, the correlation between hydraulic conductivity and IDOS confirms the interest of measuring IDOS for a large-scale clogging assessment,
potentially thanks to interstitial water samples using a Bou-Rouch pump.

## 4.2 | Limitations of the Current Infiltration Test

It is important to note that the Datry protocol is limited to relatively high hydraulic conductivities ($K \ge 10^{-5}$ m/s if the test duration is restricted to a maximum of 5 min; see Figure 11), thus allowing for the characterisation of relatively moderated clogging only. For heavily clogged beds, this protocol provides only an indication of $K$, that is, $K < 10^{-5}$ m/s, rather than an exact value. As indicated in Table 2, which compiles data from the literature, the clogging characterisation using the Datry protocol is limited to riverbed with low or moderate clogging. On the other hand, the $K$ value becomes quite uncertain when the test duration is less than 5 s ($T_{exp} < 5$ s), which corresponds to $K \ge 10^{-3}$. This limitation is the main drawback of this simple and fast protocol.

Care should also be taken when estimating hydraulic conductivity at relatively shallow water depths to avoid quicksand conditions if the hydraulic head reaches $H \approx 1$ m. In such situations, a relatively shallow sinking depth can lead to leakage along the standpipe, which can bias the measurement and lead to an overestimation of the hydraulic conductivity values (See Section 2 in Supporting Information).

The addition of a pressure gauge at the bottom of the pipe is very useful for checking a posteriori the test duration. As shown in Supporting Information, the pressure gauge data seem to correlate well with the Datry protocol data for hydraulic conductivities between $K = 10^{-5}$ and $K = 10^{-4}$ m/s, while the data become scattered for $K > 10^{-4}$ m/s. Additionally, by directly applying Equation (1), it should be possible to detect a small decrease in water level during the slug test, which could help estimate

hydraulic conductivity in more clogged beds. However, our experiences showed that this improvement was not very significant. Another potential solution is to use a small transparent pipe instead of the funnel, as the slug test duration is proportional to the flow diameter. This approach should allow for the measurement of much lower hydraulic conductivities.

It is also challenging to assess the area of influence of this measurement and how it might affect measurements taken close by. However, this issue becomes even more pronounced with the Bou-Rouch method, which can heavily modify the bed structure around the measuring point.

## 4.3 | Synthesis of the Different Methods to Evaluate Clogging

Table 2 provides some typical values for the hydraulic conductivity $K$ following the type of bed based on Naganna et al. (2017) and Datry et al. (2015) works. We added some corresponding values for the fine content and fine interstitial concentration based on the present study and Equations (5–7). These indicators, combined together, may complement each other and should provide a good description of the clogging level of a river bed as suggested by Negreiros et al. (2023).

Our data from the McNeil sampler show significant variation of $d_{16}$ and fine content, which is generally associated with clogging. However, the limited sampling depth (around 15–20 cm) and potential underestimation of fine sediment due to resuspension should be considered, which may explain the scatter observed in the correlation between the McNeil data and the hydraulic conductivity data. The Datry protocol is suitable for quick quantitative clogging assessments due to its simplicity. Our data confirms its effectiveness in measuring hydraulic conductivity in the low to moderate clogging range (i.e., for $K > 10^{-5}$ m/s), but

<table><caption>TABLE 2 | Correspondence between types of beds, hydraulic conductivity and clogging (Naganna et al. 2017) ($\mu_{fsc}$: Percentage of fines including silt and clay only).</caption><thead><tr><th>Type of beds</th><th>Hydraulic cond.<br>K (m/s)</th><th>Permeability</th><th>Clogging</th><th>Fine content<br>μ<sub>ft</sub> (%)</th><th>Fine conc.<br>C<sub>ft</sub> (g/L)</th><th>IDOS<br>μ<sub>IDOS</sub> (%)</th></tr></thead><tbody><tr><td>Gravel and sand<br>without fine<br>(μ<sub>fsc</sub> ≈ 0%)</td><td>10<sup>-3</sup> &lt; K &lt; 10<sup>-2</sup></td><td>Very high</td><td>Inexistent</td><td>μ<sub>ft</sub> &lt; 1</td><td>C<sub>ft</sub> &lt; 5</td><td>μ<sub>IDOS</sub> &gt; 100</td></tr><tr><td>Gravel and sand<br>without fine<br>(μ<sub>fsc</sub> &lt; 5%)</td><td>10<sup>-4</sup> &lt; K &lt; 10<sup>-3</sup></td><td>High</td><td>Low</td><td>1 ≤ μ<sub>ft</sub> &lt; 5</td><td>5 ≤ C<sub>ft</sub> &lt; 20</td><td>20 &lt; μ<sub>IDOS</sub> ≤ 100</td></tr><tr><td>Gravel and<br>sand with fine<br>(5 &lt; μ<sub>fsc</sub> &lt; 15%)</td><td>10<sup>-6</sup> &lt; K &lt; 10<sup>-4</sup></td><td>Moderate</td><td>Moderate</td><td>5 ≤ μ<sub>ft</sub> &lt; 20</td><td>20 ≤ C<sub>ft</sub> &lt; 100</td><td>μ<sub>IDOS</sub> ≤ 20</td></tr><tr><td>Silt and silty<br>sand</td><td>10<sup>-8</sup> &lt; K &lt; 10<sup>-6</sup></td><td>Low</td><td>High</td><td>20 ≤ μ<sub>ft</sub> &lt; 40</td><td>100 ≤ C<sub>ft</sub> &lt; 300</td><td>—</td></tr><tr><td>Clay silt et clay<br>sand</td><td>10<sup>-10</sup> &lt; K &lt; 10<sup>-8</sup></td><td>Very low,<br>quasi-<br>impermeable</td><td>Very high</td><td>40 ≤ μ<sub>ft</sub> &lt; 60</td><td>300 ≤ C<sub>ft</sub> &lt; 1000</td><td>—</td></tr><tr><td>Clay</td><td>10<sup>-12</sup> &lt; K &lt; 10<sup>-10</sup></td><td>Impermeable</td><td>—</td><td>μ<sub>ft</sub> &gt; 60</td><td>C<sub>ft</sub> &gt; 1000</td><td>—</td></tr></tbody></table>
also highlights the difficulty in obtaining a value of *K* for high to very high clogging, even when using a pressure gauge. For very clogged bed, we recommend using the Bou-Rouch protocol with IDOS measurement to complement the data. In a similar way as the freeze-coring, interstitial water sample obtained using a Bou-Rouch pump can also provide valuable insights into streambed biochemistry (Hunt and Stanley 2000; Comer-Warner et al. 2020). Another advantage of the infiltration test and interstitial water sampling, compared to the McNeil sampler, is that both methods allow the characterisation of clogging at different depths, enabling the distinction between subsurface and deeper clogging (unimpeded static percolation). It also appeared interesting to separate the silt and clay fraction from the sand fraction in the interstitial water samples. As observed in Figure 4, both classes may have distinct effects on clogging, potentially cumulating to reduce porosity (Yu and Standish 1987). And in Figure 9, it appeared that fine contents in interstitial water samples from the Bou-Rouch should be well correlated to the fine contents in the streambed.

Following Table 2 and the above-mentioned advantages and limitations of the different tests, we suggest an integrated method using both infiltration test and Bou-Rouch sampling to monitor clogging efficiently. The use of a standpipe with a short perforated length such as the HC18 eventually appears more useful since it allows evaluating clogging closer to the surface, which corresponds to the richest part of the hyporheic zone in terms of activity for various stages of aquatic organisms (Woessner 2017) and for which, clogging may be the most impacting. Such methodology also allows a better spatialisation of clogging, including along the vertical axis. Easy to apply, it is suitable for long-term and large-scale monitoring.

## 5 | Conclusion

In this paper, we present a comparison of different methods that are relatively simple to deploy in the field and to process data, aimed at quantitatively evaluating the clogging level of a gravel bed. These methods and related conclusions are as follows:

*   **McNeil Tube Sampling:** This method allows for determining the fine sediment content $\mu_{ft}$ by calculating the GSD, which can be considered as the reference parameter for physical clogging. However, this method is time-consuming, limited to shallow water, and provides only a surface bed sample (partly mixed with the subsurface). Additionally, we observed a potential underestimation of the silt-clay fraction. It is thus not recommended for straightforward clogging assessments.

*   **Datry protocol:** This protocol provides an interesting indirect evaluation of the clogging state of the bed through the estimation of hydraulic conductivity *K*. It is simple, quick and effective but limited to low and moderate clogging environments. A potential adaptation of the protocol using a pressure gauge and a small pipe instead of the funnel could extend its use to measure lower hydraulic conductivities.

*   **Bou-Rouch protocol:** This protocol provides both the interstitial fine sediment concentration $C_{ft}$ and an IDOS evaluation $\mu_{IDOS}$.

Using tools similar to the Datry protocol, this protocol offers complementary results for a quantitative assessment of clogging, which appears very efficient, especially for heavily clogged gravel beds. However, additional data are needed to better characterise the results obtained with this protocol.

Our comparisons highlighted the interest of separating the fine fraction into two classes: silt-clay and sand classes. Indeed, for the same total fine fraction content, different proportions of these fractions may have distinct impacts on porosity and clogging. We also explored possible relationships between hydraulic conductivity, fine content in the bed matrix, interstitial fine concentrations and IDOS. These relationships are proposed and discussed in more detail, leading to a better characterization of clogging, as summarised in Table 2. In general, our data showed significant scatter, indicating some limitations in these relatively simple protocols but yet showed some interesting correlations.

Finally, we suggest that the combination of the Datry (infiltration test) and Bou-Rouch (interstitial water sample) protocols should provide a comprehensive overview of the different levels of clogging and should be used jointly for long-term and large-scale monitoring. Such a protocol would make it possible to study the spatial variability of clogging, including in the vertical axis, which can be highly variable in environments like the Miribel-Jonage system, characterised by the presence of dams, a headrace canal, a channel with compensation flow and a drinking water well field.

## Acknowledgements

This work has been supported by INRAE, OFB (Office Français de la Biodiversité, action 2021–2024 no. 3) and Agence de l'Eau Rhône-Méditerranée-Corse (French Water Agency, Pinthe2 Project 2024–2025). The authors are deeply grateful to the two anonymous reviewers for their constructive comments and advices.

## Data Availability Statement

The data that support the findings of this study are openly available in https://entrepot.recherche.data.gouv.fr/dataverse/riverly at https://doi.org/10.57745/WN3HNM.

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### Supporting Information

Additional supporting information can be found online in the Supporting Information section.