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# Past hydraulics influence microhabitat selection by invertebrates and fish in hydropeaking rivers

Clarisse Judes, Hervé Capra, Véronique Gouraud, Hervé Pella, Nicolas Lamouroux

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## ► To cite this version:

Clarisse Judes, Hervé Capra, Véronique Gouraud, Hervé Pella, Nicolas Lamouroux. Past hydraulics influence microhabitat selection by invertebrates and fish in hydropeaking rivers. River Research and Applications, 2023, 39, pp.375-388. 10.1002/rra.3981. hal-03910907

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Submitted on 12 Sep 2023

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1 Past hydraulics influence microhabitat

2 selection by invertebrates and fish in

3 hydropeaking rivers

4 Clarisse Judes¹,²,³*, Hervé Capra², Véronique Gouraud¹,³, Hervé Pella², Nicolas Lamouroux²

5 1: EDF R&D LNHE - Laboratoire National d'Hydraulique et Environnement, Chatou, France.

6 2: INRAE RiverLy, Villeurbanne, France.

7 3: HYNES team (INRAE-EDF E&D), Chatou, France.

8 *Corresponding author, clarisse.judes@gmail.com.

9

10 Abstract

11 Hydropeaking hydropower plants are the major source of renewable energy meeting sub-daily
12 peaks in electricity demand. They induce rapid artificial flow variations, highly variable
13 velocities, drift and stranding risks for aquatic organisms. In hydropeaking reaches,
14 microhabitat selection likely depends on both present and past hydraulics (flow velocity and
15 water depth); this study aims to assess their relative impact. For this purpose, we used
16 observations of fish abundance in 1,180 microhabitats (507 sampled by electrofishing, 673 by
17 snorkeling) and of invertebrate abundance in 36 microhabitats (hyporheic and benthic) in a
18 medium-sized hydropeaking river. We described past hydraulics of microhabitats over the 15
19 days preceding sampling, using a 2D hydrodynamic model, by identifying microhabitats
20 dewatering (drying during > 10h) or with high-velocity conditions (>1.3 m s⁻¹ during > 10h).

21 Invertebrates guilds (defined based on their selection of present hydraulics in rivers without
22 hydropeaking) responded significantly to past hydraulics, with abundances 3.5-15.3 times
23 lower in dewatering habitats. Selection for present hydraulics by invertebrates was different
24 from that observed in rivers without hydropeaking. For more mobile fish, responses were
25 weaker and different, with a "bank" guild selecting dewatering microhabitats and, secondarily,
26 a "midstream" guild avoiding them. Selection of present hydraulics by fish was similar to that
27 observed in rivers without hydropeaking. Overall, past hydraulics influenced microhabitat
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selection, with stronger effects on invertebrates and stronger effects of dewatering than of
high past velocities. However, high past velocities force fish to move and invertebrates to
experience a large range of velocity.

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Keywords

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Habitat preferences, Flow variations, 2D hydrodynamic model, Behavior, Dewatering responses, Hydropower

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Introduction

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Hydropeaking hydropower plants are the major source of renewable energy that meets sub-daily peaks in electricity demand. By producing electricity on demand, they create frequent rapid flow variations known as hydropeaking. Hydropeaking influences the habitats of aquatic organisms, with strong spatial and temporal variations in point flow velocity, water depth and shear stress (hydraulic conditions). In particular, near-shore areas may be subject to dewatering during base flow (i.e., become temporarily dry when turbines are shut down), which may result in stranding of individuals using shallow and slow-flowing habitats (Halleraker et al., 2003; Saltveit et al., 2001). Midstream habitats may be subject to high flow velocity during peak flow (when turbines are on), which may cause forced drift of individuals with low swimming ability (Bruno et al., 2013).

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Hydropeaking often results in a decrease in abundance, biomass and species diversity in both fish and invertebrate communities (Kjaerstad et al., 2018; Schmutz et al., 2015; Vila-Martínez et al., 2019). However, these negative effects are not systematic and may depend on hydropeaking hydraulic characteristics. For example, Judes et al. (2021) showed, in rivers with moderate hydraulic variations, that hydropeaking could have weaker effects than floods on fish community dynamics and did not greatly modify the organization of fish communities along longitudinal gradients. To improve our mechanistic understanding of hydropeaking influences on biota, we need to better understand individual responses to changes in the spatial and temporal variations of point hydraulic conditions (i.e. microhabitat scale). The microhabitat scale (here corresponding to fixed patches of ~7m² for fish, 0.05 m² for benthic invertebrates
and 6 L of hyporheic water for hyporheic invertebrates following Stubbington et al., 2016) is
particularly suited for identifying the key hydraulic drivers of biological response to
hydropeaking. Several studies quantified key hydraulic conditions in microhabitats for fish and
invertebrates drifting and stranding processes (Auer et al., 2017; Halleraker et al., 2003; Saltveit
et al., 2001). For example, Halleraker et al., (2003) showed that stranding of juvenile brown
trout (*Salmo trutta*) occurs with dewatering vertical water level gradients higher than 0.10 cm
h⁻¹. By contrast, little is known concerning how microhabitat selection of fish and invertebrates
is influenced by variations in point hydraulic conditions.

In rivers without hydropeaking and associated sub-daily variations, microhabitat selection by fish and invertebrates is usually predicted from present hydraulic conditions and substrate size (Forcellini et al., in press.; Lamouroux et al., 1999; Plichard et al., 2020). In hydropeaking rivers, past hydraulic variability during the weeks preceding sampling is expected to influence microhabitat selection by aquatic organisms. Indeed, experiments on fish individual behavior (Green, 1971; Roy & Bhat, 2018) indicated that fish can memorize environmental conditions over durations up to two weeks. Individuals perceive temporal variations in hydraulic conditions (Patton & Braithwaite, 2015) and may avoid habitats that are frequently dewatered or have major variations in flow velocity. For example, using telemetry in the Rhône River (France) to monitor 18 individual fish, Capra et al. (2017) suggested that fish memorize spatial and temporal environmental variations during the two weeks preceding sampling so as to use the “least constraining” microhabitats. Organisms may also have strong habitat fidelity even after their habitats become unsuitable (Kemp et al., 2003). Several studies showed that some fish species are reluctant to move across the river during sudden flow increases and consequently use habitats with higher flow velocity and/or deeper habitats (Kemp et al., 2003; Pert & Erman, 1994; Shirvell, 1994). This behavior may be explained by the energy cost associated with displacement and/or a higher risk of predation. Less mobile taxa, which include many invertebrates, may be unable to respond instantaneously to a rapid shift in habitat conditions (Blinn et al., 1995). Some invertebrate taxa may also be removed from hydropeaking reaches by passive or active drift during periods of low or high flow, altering the taxonomic and functional composition of benthic assemblages in hydropeaking reaches (Bruno et al., 2013). However, in adapting to naturally variable microhabitat conditions, some taxa have developed morphological and behavioral adaptations to maximize colonization of
available habitat and can withstand extreme habitat conditions. For example, due to behavior or/and morphological adaptation to high current velocity Simuliidae and Hydropsychidae are less likely to passive drift due to hydropeaking and can be better able to use microhabitat undergoing variable current velocity (Bruno et al. 2010, 2016; Mochizuki et al. 2006, Sidler et al. 2018). Other invertebrate taxa may also colonize subsurface hyporheic habitats to escape from variable benthic hydraulic conditions (Dole-Olivier et al., 1997; Vadher et al., 2017).

Studying the effects of past hydraulics on microhabitat selection by aquatic organisms from field data is challenging. A first challenge is to describe past hydraulic conditions in microhabitats. Spatially explicit hydrodynamic models, such as two-dimensional (2D) models that map vertically averaged flow velocities at various discharge rates, are useful for this purpose. However, calibrating and testing 2D models is time-consuming, expensive, and requires an appropriate amount of quality field measurements. A second challenge is to sample organisms at several discharge rates in order to have a sufficient number of microhabitat samples with various combinations of past and present hydraulic conditions. Addressing this challenge requires extensive fieldwork.

In this study, we combined extensive biological sampling in microhabitats at various discharge rate with results of a calibrated 2D hydrodynamic model in a hydropeaking reach to weight the relative effects of past and present hydraulic conditions on microhabitat selection. Specifically, we recorded fish taxa abundance in 1,187 microhabitats and the abundance of benthic and hyporheic invertebrate taxa in 36 microhabitats along a 6 km reach of a hydropeaking reach of a medium-sized French river (the Ain River). Due to a limited statistical power, we studied the responses of selected taxonomic guilds that had significant and contrasting microhabitat selection in rivers without hydropeaking, according to the literature. The 2D hydrodynamic model was used to estimate past hydraulics at each microhabitat during the 15 days preceding sampling (Capra et al., 2017).

For fish, we expected that fish using shallow and slow-flowing habitats along the banks would be forced to use dewatering habitats at high flows. We expected "midstream" fish taxa, using fast-flowing and deep habitats, to be less forced to use risky dewatering areas at high flows because they should sustain higher flow velocity variations.
For invertebrates, given their reduced mobility, we expected that past hydraulic conditions would have a stronger influence on their microhabitat than for fish. They should be disfavored by past dewatering and high flow velocity periods. Consequently, we also expected invertebrate selection for present hydraulics to be different in hydropeaking rivers than those without hydropeaking. Finally, we expected different responses in benthic and in hyporheic invertebrates, with a weaker effect of dewatering and high flow velocities on hyporheic invertebrates, the hyporheic area acting as shelter.

## 122 Materials and methods

### 123 Study reach

The Ain River in eastern France flows from the Jura mountains to the Rhône River, with a catchment area of 3,630 km². The study reach is 6 km long and 110 m wide, with a mean discharge of 103 m³ s⁻¹, an altitude of 230 m and a Strahler order of 5. It includes a diversity of morphological units (runs, riffles and pools), secondary channels and tributaries. Substrate is dominated by cobbles and the reach contains a few woody debris but little macrophyte cover. The reach is located 20 km downstream from the Allement hydropower plant (46° 06′ 44″ N, 5° 25′ 20″ E), the last of a series of five hydropeaking hydropower plants along the river between Vouglans and the Rhône confluence. According to data measured by DREAL Rhône-Alpes (between 1960 and 2019) at Pont-d'Ain (7.5 km upstream of the study reach), mean daily discharge is generally between 13.5 m³ s⁻¹ (exceeded 95% of the time) and 278 m³ s⁻¹ (exceeded 5% of the time). The study reach is subject to frequent sub-daily discharge variations, with base flow generally between 14 m³ s⁻¹ and (occasionally) 150 m³ s⁻¹ and peak flow generally between 40 m³ s⁻¹ and 200 m³ s⁻¹ (Figure 1). Discharge is more stable during weekends and low-flow periods (June-September).

### 138 Reach hydraulics: the 2D model

A 2D unsteady hydrodynamic model (mapping flow velocities averaged over the vertical; Rubar 2D; Bazin et al. 2017) was built, based on a digital elevation model obtained from topographic and bathymetric LiDAR surveys carried out in 2015 and 2016. Model calibrations used water surface levels derived from the LiDAR surveys at 16 m³ s⁻¹ and from field surveys at 90 m³ s⁻¹.
Calibrations were made so that the absolute difference between the water level simulated by the model and that measured in the field was less than 10 cm at any measuring point. We used the model to translate the hourly flow time-series given by the gauging station of Pont d'Ain, seven kilometers upstream of the study site (from http://hydro.eaufrance.fr), into maps of hourly hydraulic time-series (flow velocity, water level and water depth) over the 403,591 nodes of the 2 m-edge rectangular mesh of the model.

## 149 Sampling

### 150 Fish sampling

Fish species abundance was estimated by electrofishing in 507 microhabitats and by snorkeling in 673 microhabitats. With both protocols, the sampled surface area of microhabitats was approximately 7 m², fish being attracted (electrofishing) or observable (snorkeling) within a radius of about 1.5 m (although this distance can depend on fish size; Regis et al., 1981).

Electrofishing was conducted by a team of three or four operators from a motorboat in deep areas and by wading in shallow areas, approaching the microhabitat as discreetly as possible to minimize fish escape. An anode was immersed and held steady, and all fish around the anode were captured with a landing net, identified, measured and released. Microhabitat positions were recorded by GPS with a precision of 5 m. Electrofishing surveys covered different flow conditions (Figure 2) and microhabitat locations were chosen to cover diverse combinations of past and present hydraulic conditions. In practice, the 2D model was used to draw maps of simulated past hydraulic conditions corresponding to a typical hydropeaking week, to help finding these different combinations of past and present hydraulics in the field. Sampling at different flows also reduced the correlation between present and past hydraulic conditions (e.g., the expected negative correlation between dewatering frequency and water depth).

Snorkeling observations targeted only large individuals of species that are difficult to sample by electrofishing (estimated length > 25 cm) (Plichard et al., 2017). Observations were made along six longitudinal transects, regularly spaced laterally across the reach, in the direction of flow over the entire study reach, during base flow only to ensure fish identification (~16 m³ s⁻¹). Two persons drifted along the transect in a downstream direction: (1) an
172 experienced snorkeler detecting and identifying fish, followed by (2) an hydrospeeder
173 recording the observations and GPS positions with a waterproof recorder. As reported by
174 others (Chamberland et al., 2014), we did not frequently observed strong escape behavior
175 while snorkeling and judged that most fish were observed by snorkelers. Underwater visibility,
176 estimated as the distance at which snorkelers could see their bright swimming fins, was 5 m.
177 Fish were observed and identified when in a 1.5 m radius around the snorkeler, and several fish
178 individuals were associated to the same GPS position when observed simultaneously.
179 Therefore, each GPS position in the dataset was considered as a microhabitat of ~7m²,
180 containing one or several fish. When drifting, snorkelers observed large fish (estimated length >
181 25 cm) of eight species (Barbus barbus, Squalius cephalus, Chondrostoma nasus, Thymallus
182 thymallus, Perca fluviatilis, Esox lucius, Cyprinus carpio, Salmo trutta fario) and smaller
183 individuals of Thymallus thymallus only (estimated length < 25 cm).

184 All snorkeling microhabitats were presence-only (microhabitats with fish presence),
185 unlike the electrofishing and invertebrate microhabitats. To enable common statistical
186 processing of all data, we created fictive snorkeling microhabitats with fish absence ("pseudo-
187 absence microhabitats"; Manly et al. 2002). In practice, for each taxon × microhabitat
188 combination, we randomly picked n =10 pseudo-absence microhabitats among the nodes of
189 the 2D model network. At this step, we considered only nodes with water depth >30 cm, as
190 snorkeling was not conducted in shallower habitats. The choice of n = 10 pseudo-absence
191 microhabitats was intended to provide a global prevalence comparable with that of the
192 electrofishing data. We tested the sensitivity of results to this choice by repeating the analysis
193 for n=20.

194 Invertebrate sampling

195 We sampled benthic and hyporheic invertebrates in 36 microhabitats, under different flow
196 conditions (Figure 2). Similarly to the procedure used for fish, we used the 2D model to draw
197 maps of simulated past hydraulic conditions corresponding to a typical hydropeaking week, to
198 help finding different combinations of past and present hydraulics in the field. We chose
199 microhabitats in six cross-sections with low and six with high velocity, distributed along the
200 reach to have different present and past flow velocity. Within each cross-section, we selected
three points with low (0≤depth<0.2 m), medium (0.2≤depth<0.5 m) and high water depth (0.5≤depth<0.8 m) (and thus different dewatering frequencies).

We sampled benthic invertebrates with a Hess sampler (surface area 0.05 m², 250 µm mesh size) and recorded their positions by GPS. Then, within a radius of one meter around the Hess sampler, hyporheic invertebrates were sampled with a Bou-Rouch pump (Bou & Rouch, 1967; Dole-Olivier et al., 2014), which extracted 6L of water from 30 cm below the stream bed (using a planted steel pipe) to catch organisms living in the interstices of substrate particles. Benthic samples were filtered through a sieve with 250 µm mesh size to be consistent with the benthic sampling. Invertebrates were preserved in 96% ethanol and were sorted, counted and identified to the lowest practical taxonomic level using a microscope in the laboratory (list of references used for invertebrate identification in Supporting Information, list S1).

## Classification in species guilds

We grouped fish and invertebrate individuals into a few habitat guilds with comparable habitat selection according to Plichard et al. (2020) and Forcellini et al. (in press), who synthesized a large part of the available information on fish and invertebrate microhabitat selection in a wide range of mostly non-hydropeaking European rivers. This grouping by guilds was used to increase the statistical power of our analyses and the potential transferability of our results in rivers with other species composition.

Fish at electrofishing points were first grouped by size class, using the taxa definitions of Plichard et al. (2020). These taxa combined a specific code and one or several size classes (1: <8 cm; 2: 8-18 cm, 3:18-30 cm; 4: >30 cm; Table 1). For example, taxon Lel_cl123 corresponds to *Leuciscus Leuciscus* (Lel) fish with sizes < 30 cm. We defined two fish guilds for which we expected different influence of past hydraulics on microhabitat selection. The 'bank' guild grouped together taxa selecting shallow and slow-flowing habitats, thus incurring high dewatering risk. Conversely, the 'midstream' guild grouped together species selecting deep and fast-flowing habitats, typically found in the center channel, which should better sustain high flow velocity variations. Observed graylings (*Thymallus thymallus*) were also assigned to the midstream guild (Mallet et al. 2000). Plichard et al. (2020) summarized the microhabitat selection by fish taxa for microhabitat velocity V using the statistic AGV_V, which in short
corresponds to the average microhabitat velocity that would be used by the taxa if velocities between 0 and 0.94 m s⁻¹ were uniformly available in the river. Similarly, AVG_D summarizes selection for depth D over the depth range 0-3 m. Our midstream guild comprised the nine taxa with the highest value for the product AVG_V*AVG_D, and our bank guild the eight taxa with the lowest value. The number of taxa per guild was a compromise between guild abundance and magnitude of habitat selection.

For invertebrates, we defined two guilds according to selection for bottom shear stress reported in Forcellini et al. (in press), where shear stress was measured using hemispheres (FST, Statzner & Müller, 1989) numbered from 0 to 19 (low to high shear stress). As done by Plichard et al. (2020) for fish, Forcellini et al. (in press) summarized microhabitat selection by invertebrates, using AVG_V and AVG_FST, which is the equivalent of AVG_V but over the FST range 0-19. The AVG_FST given by Forcellini et al. (in press) were calculated by species or higher levels, without accounting for variations in habitat use with individual size. Therefore, we used the same guild definitions for both benthic and hyporheic taxa, although individuals in hyporheic samples were generally smaller. The 'limnophilic' guild grouped together taxa selecting low shear stress (AVG_FST < 5), and thus expected to be negatively affected by high flow velocity variations. Conversely, the 'rheophilic' guild corresponded to taxa selecting high shear stress (AVG_FST > 8).

## Present and past microhabitat hydraulics

In each electrofishing and invertebrates sampling microhabitat (i.e. a couple benthic/hyporheic), we measured the present water depth (D in m), and present flow velocity (V₄₀ in m s⁻¹; measured at 40% of water depth from the bottom with an electromagnetic 30 Hz current meter and averaged over 10 seconds, using a Marsh McBirney FLO MATE 2000).

We described past hydraulic conditions using the 2D hydraulic model simulations for flows observed during the 15 days preceding sampling. The duration of 15 days was consistent with previous experiments on fish individual learning (Green, 1971; Roy & Bhat, 2018) and previous field studies on fish in hydropeaking reaches (Capra et al., 2017). This 15 days duration also allowed to account for typical discharge variations observed in the Ain River (Figure 2).
Nevertheless, we tested the sensitivity of results to our choice of a duration of 15 days, by repeating our analyses for durations of 5 and 10 days before sampling.

For estimating past hydraulics at a given microhabitat (electrofishing point or invertebrate sample), we associated the sampled microhabitat to a node of the 2D model that (1) had comparable water depth, and (2) was as close as possible from the sampled microhabitat. We judged the primary use of a "depth similarity criteria" as essential for an optimum estimation of the dewatering frequency of the microhabitat. Indeed, for a given measurement discharge, water depth and past dewatering frequency are expected to be strongly correlated. In addition, even if the morphology of the river had changed locally between the 2D model calibration and the biological survey, we expected that velocity variations for a given water depth was well represented by the 2D model. In the Ain River, mesohabitats (e.g., riffles, run, pools) have surface areas of several hundred m², and the 2D model is expected to well reflect hydraulic behavior of these mesohabitats, even if it can be locally imprecise (Guay et al., 2000). In practice, we selected the closest 2D node that had a modeled depth differing from the measured depth at the microhabitat by less than 10 cm. For snorkeling observations, depth measurement was not available and we assigned snorkeling microhabitats to the closest 2D node.

The past hydraulic conditions during the 15 days preceding sampling were described with two variables coded as boolean categories:

(1) Duration of dewatering: we considered the microhabitat as "dewatering" if the dewatering time (depth = 0 cm) over the 15-days period before sampling was >10 hours, and as "non-dewatering" if ≤10 hours. We tested the sensitivity of results to our choice of a duration of 10 hours, by repeating our analyses for a duration of 1 hour.

(2) Duration of high flow velocity conditions (>1.3 m s⁻¹): We considered that microhabitats had "high past velocities" if the total duration with velocity >1.3 m s⁻¹ was >10 hours, and "low past-velocities" if ≤10 hours. We chose a threshold of 1.3 m·s⁻¹ because this value is above the velocity used by most aquatic organisms (Plichard et al., 2020; Forcellini et al., in press), and is also frequently reached in many microhabitats of the Ain River. Here again, we tested the sensitivity
of results to our choice of a threshold of 1.3 m.s⁻¹, by repeating our analyses for a threshold of 286
0.7 m.s⁻¹. 287

288 Microhabitat selection models by guild

For the two fish (electrofishing, snorkeling) datasets and the benthic invertebrates dataset, we 289 related guild abundance to past and present microhabitat characteristics (measured when 290 available, modeled otherwise). We derived similar microhabitat selection models based on the 291 hyporheic dataset, for testing if the influence of past hydraulics observed on benthic 292 invertebrates was still observed in hyporheic samples, or if the hyporheic area played a 293 particular role for these guilds. For each dataset, following Plichard et al. (2020) and Forcellini 294 et al. (in press), we used GLMs with B-spline transformations of the present hydraulic variable 295 (enabling non-linear responses) and assuming a negative binomial distribution of abundance 296 (accounting for abundance overdispersion). We used splines with two degrees of freedom, with 297 a single knot positioned at the median value (Plichard et al., 2020). To avoid 298 overparameterization and due to limited statistical power, we had to consider models involving 299 combinations of a single present hydraulic variable (PresHyd = V₄₀ or D) and a single past 300 hydraulic variable (PastHyd= Dew or HV). Consistently, we looked at the inter-correlations 301 between present and past hydraulic variables to interpret the results. 302

For each *PresHyd* × *PastHyd* combination, the abundance *Y*<sub>*i*</sub> of a guild in microhabitat *i* was 303 assumed to follow a negative binomial distribution of mean *µ*<sub>*i*</sub> and dispersion parameter Θ: 304

Y_i ~ NB(μ_i, Θ)

Three GLMs of increasing complexity were compared to explain μᵢ as a function of hydraulics:

M0 (no microhabitat selection): log(μᵢ) = β₀

M1 (habitat selection with present hydraulic conditions only): log(μᵢ) = β₀ + f(PresHydᵢ)

M2 (habitat selection with present and past hydraulic conditions): log(μᵢ = β₀ + f(PresHydᵢ) + β₁
PastHydᵢ

where β₀ is the intercept, f() is a spline transformation of the present hydraulic variable
PresHydᵢ, and β₁ the coefficient of the past hydraulics effect. In M0, microhabitat variables have
no influence on abundance, in M1 present hydraulic conditions have an influence, and in M2
there is an additive effect of past hydraulics.

We fitted all models using the R software (R Development Core Team 2018) and the
`glm.nb` function of the 'MASS' package (Venables & Ripley, 2002), combined with spline
functions of the 'splines' package. We used likelihood ratio tests comparing M0 vs. M1 to test
the influence of present hydraulics on microhabitat selection, and tests comparing M1 vs. M2
to assess for an additive effect of past hydraulics. Following Plichard et al. (2020), we used non-
parametric Spearman Rho rank correlation (Spearman, 1904) to assess fits; this is an
appropriate statistic for overdispersed data such as our abundance data, based on the
correlation between the ranks of predicted and observed abundance.

## Influence of hydropeaking on taxa selection for present hydraulics

For the fish electrofishing dataset and benthic invertebrates, we computed an average
preferred velocity AGV_V and depth AVG_D as defined by Plichard et al. (2020), but
corresponding to the fits of our M1 model by taxa. We then compared our values with those of
Plichard (2020) for fish and Forcellini et al. (in press) for invertebrates, to estimate how
hydropeaking modifies the selection of present microhabitat hydraulics generally observed in
rivers without hydropeaking. We used only electrofishing data for the fish comparison, because
fish size was not available in snorkeling data, preventing comparison with the size classes of
Plichard et al. (2020). We used only the benthic invertebrate data, to be consistent with
Forcellini et al. (in press).

## Results

### Biological assemblages

We sampled 3,642 fish of 23 species by electrofishing. The bank guild contained 2,344
individuals and was composed mainly of *Phoxinus phoxinus* cl12 (N=1,785). The midstream
guild contained 73 individuals, mainly *Barbus barbus* cl34 (N=27) (Table 1). We sampled 1,610
fish of 12 species by snorkeling (mainly *Thymallus thymallus*, N=444; *Barbus barbus*, N=437 and
*Chondrostoma nasus*, N=238). In the snorkeling dataset, large *Barbus barbus*, large
340 *Chondrostoma nasus* and all *Thymallus thymallus* belonged to the midstream guild, with many
341 more individuals sampled than with electrofishing (N =356) (Table 1).

342 We sampled 48,275 invertebrates: 38,196 in benthic samples and 10,079 in hyporheic
343 samples. The full list of taxa of both datasets are available as Supporting Information (Table S1).
344 For the benthic dataset, the limnophilic guild contained 687 individuals (principally composed
345 of *Chironomini*, N=612) and the rheophilic guild contained 2988 individuals (principally
346 composed of *Esolus*, N=2,021) (Table 2). For the hyporheic dataset, the limnophilic guild
347 contained 170 individuals (principally composed of *Chironomini*, N=140) and the rheophilic
348 guild contained 961 individuals (principally composed of *Esolus*, N=754) (Table S2). Note that
349 only 10% of all benthic individuals and 11% of all hyporheic individuals belong to a habitat guild
350 (i.e. documented and significant habitat selection).

351 Present and past hydraulic variables and their intercorrelation (Figure 3)

352 Present depth and velocity typically ranged between 0-4 m and 0-2 m s⁻¹ in the fish
353 datasets (Figure 3A). By contrast, depth was generally shallower than 0.8 m and velocity lower
354 than 1 m s⁻¹ in the invertebrate dataset, due to sampling limits. Water depth and flow velocity
355 were poorly correlated (and with different directions) for the fish data (electrofishing: r=0.13,
356 P<0.05; snorkeling: r=-0.48, P<0.05) (Figure 3A). The correlation was greater in the invertebrate
357 dataset (r=0.57; P<0.05).

358 When calculating past hydraulics using the 2D hydrodynamic model, 76% of the
359 electrofishing microhabitats and 75% of the invertebrate microhabitats were associated with a
360 model node within a radius of 5.5 m around the sampled microhabitat. In the electrofishing
361 dataset, 102/507 microhabitats were dewatering habitats and 255/507 had high past velocities
362 (Figure 3B). The snorkeling dataset contained no dewatering microhabitats and 342/673
363 microhabitats with high past velocities. A little less than half of the invertebrate samples
364 (32/72) were dewatering and a higher proportion (44/72) had past low velocities. For fish
365 (electrofishing) and invertebrates, all combinations of dewatering × high past velocity habitats
366 were sampled (Figure 3B). Nevertheless, dewatering habitats were generally those with low
367 past velocity for both fish and invertebrates (Xhi2 test, P<0.05).
Correlations between present and past hydraulics were limited ($r^2$ < 0.30 in all cases). The strongest correlation was between flow velocity and high past velocities (Figure 3C), especially for the electrofishing and the invertebrate datasets. Dewatering habitats also often had lower flow velocities and depths (Figure 3D).

## Microhabitat selection models by guilds

As expected from the fish guild definitions, the midstream guild used higher present velocities and depths than the bank guild, according to both electrofishing and snorkeling data. The bank guild significantly avoided fast-flowing and deep microhabitats (Table 3, Figure 4). Differences in response to flow velocity between guilds, however, were less clear for benthic invertebrates than for fish (Table 3, Figure 4A). The rheophilic benthic invertebrate guild significantly avoided shallow microhabitats (Table 3, Figure 4A). Similar patterns were observed for hyporheic invertebrates (Table S3, Figure S1).

Several significant additive effects of past hydraulics were observed (indicated by asterisks in Figure 4). Benthic and hyporheic invertebrates responded more significantly to past hydraulics, all invertebrate guilds being 3.5-15.3 times less abundant in dewatering habitats (Figure 4A, Figure S1). Consistently, Spearman Rho values for benthic invertebrate M2 models involving dewatering (between 0.27 and 0.61, Table 3) were much higher than the corresponding values for M1 models (between 0.04 and 0.35, Table 3). Again, similar patterns were observed for hyporheic invertebrates (Table S3). Secondarily, benthic rheophilic invertebrates selected microhabitats with high past velocities (3.9 times more abundant in high past velocity microhabitats in average) and hyporheic limnophilic invertebrates tend to avoid them (0.4 times less abundant, only significant with the model including water depth as present hydraulic variable) (Figure 4A, Figure S1). The influence of high past velocities was not significant for hyporheic rheophilic invertebrates and benthic limnophilic invertebrates. For fish (electrofishing), responses were weaker and clearly different, with the bank guild selecting dewatering microhabitats (Figure 4A) and, less evidently, the midstream guild avoiding dewatering microhabitats. Midstream fish of the snorkeling dataset selected microhabitats with high past velocities.
396

The sensitivity tests concerning the choice of a 15 days duration for calculating past
hydraulics and a 10 hours duration for defining dewatering microhabitats did not modify any of
our results (significance tests in Table 3 and Figure 4).

397

398

399

By contrast, a threshold of 0.7 m.s⁻¹ instead of 1.3 m.s⁻¹ for defining microhabitats with
high past velocities and, a number of 20 instead of 10 pseudo-absence when building model on
the snorkeling dataset modified some of our (secondary) results on the effects of high past
velocities. With a lower 0.7 m.s⁻¹ threshold, the bank fish guild and hyporheic rheophilic
invertebrates significantly selected high past velocities (M2 models) and the midstream fish
(electrofishing) guild avoided them (M2 model with velocity). In addition, the selection of high
past velocities by midstream fish (snorkeling) was no longer significant. The selection of high
past velocities by midstream fish (snorkeling) was also no longer significant with a number of
20 instead of 10 pseudo-absence when building models.

400

401

402

403

404

405

406

407

408

Influence of hydropeaking on taxa selection for present hydraulics

409

Microhabitat selection for present hydraulics could be compared between the present study
and the literature for nine fish taxa and thirteen benthic invertebrate taxa. Present velocity and
water depth selection by fish taxa corresponded to those reported in the literature (Figure 5A)
and had comparable Spearman Rho values (Figure 5B). By contrast, present velocity and water
depth selection by benthic invertebrate taxa did not match values reported in the literature
(Figure 5C) and had lower Spearman Rho values here (Figure 5D).

410

411

412

413

414

415

Discussion

416

By studying the combined influence of present and past hydraulics on microhabitat selection,
we improved our mechanistic understanding of the impact of hydropeaking on fish and
invertebrate guilds. We found that both benthic limnophilic and rheophilic invertebrates were
less abundant in dewatering microhabitats. This confirms findings such as those of Blinn et al.
(1995), who reported a four-fold lower invertebrate biomass in dewatering habitats of the
Colorado River. Consistently with this result, we also showed that the average velocity and
water depth preferences of some invertebrate taxa differed from those previously established
in mainly non-hydropeaking rivers (Figure 5). This suggests that invertebrates are not able to

417

418

419

420

421

422

423
maintain habitat preferences when flow changes too rapidly or frequently: they likely stay at the same location, undergoing different hydraulic conditions according to flow conditions.

Similarly, to benthic invertebrates, we found that hyporheic invertebrates are also less abundant in dewatering microhabitat. Yet, we expected the opposite since we suggested that hyporheic area provide shelter under dewatering conditions in the Ain River (Williams & Hynes, 1974). Others studies find some comparable results suggested that benthic macroinvertebrates may not always use the hyporheic area as shelter in response to a discharge reduction (del Rosario and Resh 2000; James et al. 2008). In our case, since some microhabitats could have been dewatered for several days before sampling, we cannot exclude that in some case the hyporheic area undergone dewatering or low oxygen levels resulting in death of some individuals. Furthermore, invertebrates may have migrate rapidly through the benthic area just after flow increases and before sampling (Bruno et al., 2020). Sampling during the dry period could allow to test if the hyporheic area plays the role of shelter.

In contrast to invertebrates, fish followed their preferred habitat preferences observed in other rivers and were less influenced by past hydraulics. Nevertheless, fish from the "bank" guild probably stayed along the bank and moved to dewatering microhabitats when flow increased, in order to remain within their strict range of preferred hydraulic conditions. This result contrasts with many studies concerning salmonid juveniles, also living near the banks, and indicating that only a fraction of individuals relocate when flow changes (Kemp et al., 2003; Korman & Campana, 2009; Shirvell, 1994). High velocities in the Ain River and the different species considered here (mostly cyprinids) may explain these differences. In the Ain River, fish from the bank guild are probably exposed to a high risk of stranding, particularly because cobble bars have very low slopes (Tuhtan et al., 2012). It would be interesting to quantify the influence of horizontal down-ramping rate (water surface retreat rate) on fish stranding near these cobble bars. The fish "midstream" guild also followed their preferred range of habitat, and tended to avoid dewatering and stressful microhabitats (Capra et al., 2017; Reebs, 1996). Although it concerned other species (large barbel, catfish and chub), the telemetry experiment by Capra et al. (2017) also reported that fish could avoid dewatering microhabitats. However, due to the small number (N=73) of fish involved in this present result, it is not possible to generalize.
Observed microhabitat selection by fish and invertebrates was not negatively affected by high past velocities, suggesting a weaker influence of high past velocities compared to dewatering. In addition, the statistical significance of the weak responses to high past velocities depended on the threshold chosen for defining high past velocities, indicating unstable results. However, considering that hydropeaking has occurred since the 1930s in the Ain River, environmental filtering may have selected only species that can cope with rapid and frequent variations in hydraulic conditions. Furthermore, our results suggest that high velocities force fish to move (sticking to their general hydraulic preference) and invertebrates to find flow-shelter or attach to the substrate (e.g. with silk draglines, tarsal claws, or a muscular foot; Holomuzki et al. 2000). Moving (fish) or staying in sub-optimal habitat conditions (invertebrates) may create energetic costs that influence community structure on the long term and explain reach-scale effects of hydropeaking (Judes et al., 2021). More detailed observations of behavior at high flows (e.g. by snorkeling or with cameras), experimental/physiologic experiments in flumes, or additional comparisons between reaches subjected to different degrees of hydropeaking (Schmutz et al., 2015; Judes et al., 2021) will be useful to confirm this interpretation.

We identify five major limits and ways of improvement in our study. First, we pooled microhabitat samples under low and high flow conditions due to a limited statistical power. However, habitat availability strongly changes according to flow conditions. For example, in the Ain, 77% of the wetted surface is less than 1 m deep at $14 \text{ m}^3 \text{ s}^{-1}$ versus 33% at $214 \text{ m}^3 \text{ s}^{-1}$. Such differences in habitat availability could influence species microhabitat selection (Arthur et al., 1996; Schooley, 1994). Further sampling could target a more detailed analysis of the effects of habitat availability. Similarly, improved analyses should also consider the effects of woody debris, macrophyte cover, tributary proximity and/or thermal refugia. Secondly, here, guilds are composed only of taxa with significant habitat selection documented in the literature. In the future, it would be interesting to be more representative of the community in place. In particular, it would be interesting to study ubiquitous taxa, to account for size distributions within taxa, or to analyze responses of functional traits. Thirdly, discharge before and during our sampling period combined hydropeaks and artificial releases of intermediate flows (around $100 \text{ m}^3 \text{ s}^{-1}$, Figure 2) to compensate for low discharge in the Rhône River. The duration of intermediate flow may influence observed fish and invertebrates microhabitat selection. Future
sampling will need to be implemented during different hydropeaking regimes, especially for invertebrates sampling, which was conducted at only three flow conditions here. Fourthly, because our results were little sensitive to a number of our methodological choices (duration considered for past hydraulics, threshold used for defining dewatering habitats and high past velocities), additional experiments/data would be useful to specify the key quantitative drivers of biological responses. Finally, although the 2D hydrodynamic model allowed us to estimate past hydraulics at each microhabitat, its accuracy and the methods used for associating field observations with model nodes would deserve further investigation (see Guay et al., 2000).

In conclusion, our microhabitat-scale study shows a stronger effect of dewatering than high past velocities on invertebrates, and secondarily on fish. Although this study failed to identify critical past velocity thresholds for hydropeaking management, the results suggests that fish are forced to move and invertebrate to hide due to these high past velocities. Comparing our results with data collected in other rivers and at a larger variety of flows, combined with experimental studies, should help further identifying the key hydropeaking characteristics to consider in mitigation policies.

## 500 Acknowledgments

We are grateful for the financial support of EDF-Hydro Alpes (Gérald Ramos) for the LiDAR and water line surveys. We thank EDF-CIH (Le Bourget du Lac, France) and Dimitri Lague (Géosciences Rennes, UMR 6118, France) for collaboration in developing the Digital Elevation Model derived from LiDAR surveys. We gratefully acknowledge all the people who contributed to the field work.

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## Figure legends

Figure 1. Location of the Ain River study reach and maps of its flow velocity and water depth for the maximum and the minimum flow discharge encountered over the study period.

Figure 2. Hourly discharge at Pont d'Ain (7.5 km upstream the study reach) during the 15 days preceding sampling and during sampling (2018). The vertical lines represent sampling days at noon.

Figure 3. Relationship (A) among present hydraulic variables, (B) among past hydraulic variables and (C,D) between present and past hydraulic variables for invertebrates (→*), one value for a benthic/hyporheic pair), fish sampled by electrofishing (●), and fish sampled by snorkeling (OTO). Points represent microhabitats.

Figure 4. Fits of M2 models relating guild abundance to present hydraulics and including an additive effect of dewatering (A) or past velocity (B). Red and green curves reflect the effect of past hydraulic variables, when significant. Graphs are shown for the electrofishing (●) snorkeling (OTO) and benthic invertebrate (→*) datasets. Stars show the significance of the past hydraulics effect (**) P<0.01; (*) P<0.05.

Figure 5. Comparison of the average preferred velocity (AVG_V, corresponding to model M3) obtained in this study with data from Plichard et al. (2020) for fish and from Forcellini et al. (in press) for benthic invertebrates. Axis regressions (full lines) are shown as well as the y = x lines (dotted lines). For Plichard et al. (2020), AVG_V: y= -0.76x-0.09, R² =0.76, P<0.01; Spearman Rho: -0.48x+0.33, R² =0.13, P=0.34. AVG_D: y=0.54x+0.08, R² =0.21, P=0.22; Spearman Rho: -0.13x+0.26, R² =0.03, P=0.66. For Forcellini et al. (in press), AVG_V: y= 0.02x+0.68, R² =0.01, p-value=0.92; Spearman Rho: 0.56x+0.091, R² =0.03, P=0.63, AVG_D: y= -0.09x+0.52, R² =0.01, P=0.84; Spearman Rho: 0.05x+0.20, R² =0.00, P=0.79. GPUL:
653 *Gammarus pulex*; HYDROP: *Hydropsyche contubernalis*; TANYT: *Tanytarsini* (tribe); LIMN: *Limnomysis*

654 *benedeni*; OVIL: *Orectochilus villosus*; VCRI: *Valvata cristata*; PISI: *Pisidium* (genus); TIPUL: *Tipula*

655 (genus). See table 1 and 2 for other taxa names.

656
657

658

<figure><img src="image_3.png" /><figcaption>Figure 1.</figcaption></figure>

659

660
<figure><img src="image_4.png" /><figcaption>Figure 2.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>(B) M2 models relating guild abundance to present hydraulics and including an additive effect of past velocity</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 4.</figcaption></figure>
Electrofishing data

<figure><img src="image_8.png" /><figcaption>(A)</figcaption></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>(B)</figcaption></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

Macroinvertebrate data

<figure><img src="image_13.png" /><figcaption>(C)</figcaption></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>(D)</figcaption></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<caption>Figure 5</caption>
<table><caption>Table 1. Fish guilds, species, taxa codes, with total abundance and occurrence in microhabitats.</caption><thead><tr><th>Dataset</th><th>Guild</th><th>Family</th><th>Scientific name</th><th>Common name</th><th>Taxa code</th><th>Abundance</th><th>Occurence</th></tr></thead><tbody><tr><td rowspan="12">Electrofishing</td><td rowspan="12">Bank N=2344</td><td>Cyprinidae</td><td>Phoxinus phoxinus</td><td>Minnow</td><td>PhP_cl12</td><td>1785</td><td>134</td></tr><tr><td>Cyprinidae</td><td>Squalius cephalus</td><td>Chub</td><td>SqC_cl1</td><td>217</td><td>47</td></tr><tr><td>Cyprinidae</td><td>Rutilus rutilus</td><td>Roach</td><td>RuR_cl1</td><td>122</td><td>15</td></tr><tr><td>Balitoridae</td><td>Barbatula<br>barbatula</td><td>Stone loach</td><td>BaBa_cl12</td><td>116</td><td>57</td></tr><tr><td>Cyprinidae</td><td>Telestes soufia</td><td>Blageon</td><td>TeS_cl1</td><td>57</td><td>17</td></tr><tr><td>Cyprinidae</td><td>Gobio gobio</td><td>Gudgeon</td><td>GoG_cl1</td><td>30</td><td>16</td></tr><tr><td>Percidae</td><td>Perca fluviatilis</td><td>Perch</td><td>PER_cl12</td><td>11</td><td>11</td></tr><tr><td>Centrarchidae</td><td>Lepomis gibbosus</td><td>Pumpkinseed</td><td>LeG_cl123</td><td>6</td><td>5</td></tr><tr><td rowspan="10">Midstream N=73</td><td>Cyprinidae</td><td>Barbus barbus</td><td>Barbel</td><td>BaBu_cl34</td><td>27</td><td>17</td></tr><tr><td>Salmonidae</td><td>Thymallus<br>thymallus</td><td>Grayling</td><td>ThT_cl2</td><td>15</td><td>10</td></tr><tr><td>Cyprinidae</td><td>Chondrostoma<br>nasus</td><td>Nase</td><td>ChN_cl34</td><td>12</td><td>7</td></tr><tr><td>Cyprinidae</td><td>Barbus barbus</td><td>Barbel</td><td>BaBu_cl2</td><td>8</td><td>8</td></tr><tr><td>Cyprinidae</td><td>Alburnus alburnus</td><td>Bleak</td><td>AlA_cl23</td><td>4</td><td>5</td></tr><tr><td>Salmonidae</td><td>Thymallus<br>thymallus</td><td>Grayling</td><td>ThT_cl4</td><td>3</td><td>2</td></tr><tr><td>Salmonidae</td><td>Thymallus<br>thymallus</td><td>Grayling</td><td>ThT_cl3</td><td>2</td><td>3</td></tr><tr><td>Cyprinidae</td><td>Rutilus rutilus</td><td>Roach</td><td>RuR_cl34</td><td>2</td><td>2</td></tr><tr><td rowspan="3">Snorkeling</td><td rowspan="3">Midstream N=1119</td><td>Salmonidae</td><td>Thymallus<br>thymallus</td><td>Grayling</td><td>ThT_cl234</td><td>444</td><td>347</td></tr><tr><td>Cyprinidae</td><td>Barbus barbus</td><td>Barbel</td><td>BaBu_cl34</td><td>437</td><td>164</td></tr><tr><td>Cyprinidae</td><td>Chondrostoma<br>nasus</td><td>Common<br>nase</td><td>ChN_cl34</td><td>238</td><td>43</td></tr></tbody></table>
<table><caption>Table 2. Benthic macroinvertebrate guilds, family, genus, taxa codes, with total abundance and occurrence in microhabitats.</caption><thead><tr><th>Guild</th><th>Family</th><th>Tribe</th><th>Genus</th><th>Taxa</th><th>Code taxa</th><th>Abundance</th><th>Occurrence</th></tr></thead><tbody><tr><td rowspan="4">Limnophili<br>c<br>N=687</td><td>Chironomidae</td><td>Chironomini</td><td></td><td></td><td>CHIR</td><td>612</td><td>23</td></tr><tr><td>Sphaeriidae</td><td></td><td>Pisidium spp.</td><td></td><td>PISI</td><td>70</td><td>15</td></tr><tr><td>Tabanidae</td><td></td><td></td><td></td><td>TABA</td><td>3</td><td>3</td></tr><tr><td>Baetidae<br>(non Baetis)</td><td></td><td>Procloeon spp.</td><td>Procloeon<br>bifidum</td><td>PBIF</td><td>2</td><td>1</td></tr><tr><td rowspan="10">Rheophilic<br>N=2988</td><td>Elmidae</td><td></td><td>Esolus spp.</td><td></td><td>ESOL</td><td>2021</td><td>34</td></tr><tr><td>Hydropsychidae</td><td></td><td>Cheumatopsyche<br>spp.</td><td>Cheumatopsyche<br>lepida</td><td>CLEP</td><td>393</td><td>14</td></tr><tr><td>Psychomyiidae</td><td></td><td>Psychomyia spp.</td><td>Psychomyia<br>pusilla</td><td>PPUS</td><td>214</td><td>14</td></tr><tr><td>Elmidae</td><td></td><td>Elmis spp.</td><td></td><td>ELMI</td><td>192</td><td>17</td></tr><tr><td>Baetidae</td><td></td><td>Baetis spp.</td><td>Baetis fuscatus</td><td>BFUS</td><td>102</td><td>14</td></tr><tr><td>Hydropsychidae</td><td></td><td>Hydropsyche<br>spp.</td><td>Hydropsyche<br>incognita</td><td>HINC</td><td>48</td><td>10</td></tr><tr><td>Psychodidae</td><td></td><td></td><td></td><td>PSYC</td><td>7</td><td>4</td></tr><tr><td>Heptageniidae</td><td></td><td>Heptagenia spp.</td><td>Heptagenia<br>sulphurea</td><td>HSUL</td><td>6</td><td>3</td></tr><tr><td>Baetidae</td><td></td><td>Baetis spp.</td><td>Baetis<br>vardarensis</td><td>BVAR</td><td>3</td><td>1</td></tr><tr><td>Hydropsychidae</td><td></td><td>Hydropsyche<br>spp.</td><td>Hydropsyche<br>exocellata</td><td>HEXO</td><td>1</td><td>1</td></tr><tr><td>Baetidae</td><td></td><td>Baetis spp.</td><td>Baetis rhodani</td><td>BRHO</td><td>1</td><td>1</td></tr></tbody></table>
<table><caption>Table 3. Fitting statistics for models M0 (no microhabitat selection), M1 (effects of present hydraulics) and M2 (additional effect of past hydraulics), including AIC and Spearman Rho values. M2 models correspond to those shown in Figure 4. Asterisks indicate significance of likelihood ratio tests comparing M1 vs. M0 or M2 vs. M1, with (*) for P<0.05 and (**) for P<0.01. In bold: abundance is significantly higher in past high velocity or dewatering habitat; Underlined: abundance is lower.</caption><thead><tr><th rowspan="3">Model type</th><th rowspan="3">Hydraulic variables involved</th><th colspan="8">Dataset</th></tr><tr><th colspan="4">Electrofishing data</th><th colspan="2">Snorkeling data</th><th colspan="4">Benthic macroinvertebrate data</th></tr><tr><th colspan="2">Bank guild</th><th colspan="2">Midstream guild</th><th colspan="2">Midstream guild</th><th colspan="2">Limnophilic guild</th><th colspan="2">Rheophilic guild</th></tr><tr><th></th><th></th><th>AIC</th><th>Spea. Rho</th><th>AIC</th><th>Spea. Rho</th><th>AIC</th><th>Spea. Rho</th><th>AIC</th><th>Spea. Rho</th><th>AIC</th><th>Spea. Rho</th></tr></thead><tbody><tr><td>M0</td><td></td><td>1780</td><td></td><td>392</td><td></td><td>4108</td><td></td><td>256</td><td></td><td>273</td><td></td></tr><tr><td>M1</td><td>Flow velocity</td><td>1670**</td><td>0.41</td><td>395</td><td>0.11</td><td>4847*</td><td>0.16</td><td>258</td><td>0.09</td><td>370*</td><td>0.13</td></tr><tr><td rowspan="2">M2</td><td>Flow velocity + Dewatering</td><td>1650**</td><td>0.44</td><td>392*</td><td>0.14</td><td></td><td></td><td>256*</td><td>0.27</td><td>353*</td><td>0.61</td></tr><tr><td>Flow velocity+ Past velocity</td><td>1672</td><td>0.40</td><td>395</td><td>0.11</td><td>4843*</td><td>0.15</td><td>259</td><td>0.09</td><td>369*</td><td>0.31</td></tr><tr><td>M1</td><td>Water depth</td><td>1734**</td><td>0.43</td><td>385**</td><td>0.10</td><td>4933</td><td>0.04</td><td>259</td><td>0.04</td><td>370*</td><td>0.35</td></tr><tr><td rowspan="2">M2</td><td>Water depth+ Dewatering</td><td>1725**</td><td>0.43</td><td>386</td><td>0.13</td><td></td><td></td><td>258*</td><td>0.44</td><td>358*</td><td>0.53</td></tr><tr><td>Water depth+ Past velocity</td><td>1734</td><td>0.44</td><td>384</td><td>0.12</td><td>4931*</td><td>0.03</td><td>259</td><td>0.15</td><td>366*</td><td>0.46</td></tr></tbody></table>