<figure><img src="image_100.png" /><figcaption>Fig. 3. Probability of detection of drags data as a function of six environmental variables, for the eight combinations of user-defined variables. We fixed the limits of environmental categories so that their sizes were balanced for all combinations, taking into account that some discrete values had a high frequency. The average class size across combinations and classes was 2429 (standard deviation = 1051). Note that the x-axis was log-transformed for the P-RR variable.</figcaption></figure>

data) and 1140 tag emissions (banks data) were considered in our analyses.

## 3.2. Environmental effects on detection probability within combinations

A number of environmental variables influenced the probability of detection consistently within the eight combinations of user-defined variables (Fig. 3 and Fig. 4). Detection probability was mainly influenced by configuration variables. Detection probability increased with C-NH within all combinations, for banks and drags data: from < 10% for C-NH < 6 to about 70% for C-NH > 12 for drags data (Fig. 3a) and from 0% for C-NH < 6 to about 30% for C-NH > 14 for banks data (Fig. 4a). The detection probability also increased with

C-VA from less than 5% (drags and banks, Fig. 3b and Fig. 4b) to about 55% for drags (Fig. 3b) and 20% for banks (Fig. 4b). Physical variables also influenced the probability of detection, since very high P-CV (>1.44 m s⁻¹) reduced the probability of detection from around 50% to 20% for drags (Fig. 3c), whereas low P-CV (<0.52 m s⁻¹) reduced the probability of detection from around 30% to less than 10% for banks (Fig. 4c). Low P-Dp (<2.31 m) were also associated with reduced detection probabilities, for drags and banks (Fig. 3d and Fig. 4d). High P-RR values (>2.8%, Fig. 3f) could reduce about twofold the probability of detection for drags, and to a lesser extent for banks (Fig. 4f). An intermediate temperature category was also associated with higher detection probabilities, but this temperature differed for drags (21.1 °C, Fig. 3e) and banks (17.44 °C, Fig. 4e). Banks data generally covered shallower and slower-flowing
<figure><img src="image_101.png" /><figcaption>Fig. 4. Probability of detection of banks data as a function of six environmental variables, for the eight combinations of user-defined variables. We fixed the limits of environmental categories so that their sizes were balanced for all combinations, taking into account that some discrete values had a high frequency. The average class size across combinations and classes was 228 (standard deviation = 16). Note that the x-axis was log-transformed for the P-RR variable.</figcaption></figure>

habitats (compare Figs. 3 and 4). They had a lower detection prob-
ability than drags on average, consistently with their low C-VA
and high P-RR. However, detection probability was lower for banks
data, even for comparable values of environmental variables.

Maps of detection probability showed similar spatial patterns
among combinations (see Fig. 5A for an example). The lowest
detection probabilities were located where hydrophones did not
function and thus where the configuration variables were not
favorable. Furthermore, detection probability was lower along
the river banks (Fig. 5A). Comparing Fig. 5A and B showed that
the lowest probabilities of detection were located where the
current velocity exceeded 1.6 m s⁻¹. Scatter plots among envi-
ronmental variables (see an example in Supplementary file S2)
indicated that the influence of environmental variables could be

complicated by a high positive correlation between C-NH and C-
VA and a negative correlation between P-Dp and P-RR, for both
drags and banks data. For drags data, C-NH was also negatively cor-
related with P-CV. For banks data, P-CV and P-Dp were positively
related.

3.3. Environmental effects on positioning error within combinations

For detected tags, environmental variables had weaker relation-
ships with positioning error than with detection probability (Fig. 6
and Fig. 7). Configuration variables were again the main driver of
positioning error, which was reduced when C-NH was high (>12
for drags, >13 for banks, Fig. 6a and Fig. 7a) and C-VA was high
<figure><img src="image_102.png" /><figcaption>Fig. 5. Map of detection probability for the combination C6 associated with minimum positioning errors (A), and map of depth-averaged velocity for a discharge rate of 500 m³ s⁻¹ (B). For readability, detection probabilities were averaged by regular categories of water depth (<1 m: 1–2 m; 2–3 m: 3–4 m; >4 m) and by regular longitudinal sub-reaches (50 m long). Values of velocity were averaged by cells of the hydraulic model. Triangles, squares and circles correspond to hydrophones positions identified in the matrix M2.</figcaption></figure>

(>156° for drags and banks, Fig. 6b and Fig. 7b). However, these effects were not obvious for the two most accurate user-defined combinations for drags (C6 and C5). High roughness (P-RR) notice- ably reduced the error for banks data (Fig. 7f) and high velocities (P-CV) increased the error of drags data for the worse combinations only (Fig. 6c). Finally, indicators of reception quality were consis- tently related to the error, that could be increased (by a factor less than two) when I-VA decreased (Fig. 6i and Fig. 7i) and when I-MD and I-MAT increased (Fig. 6g, h and Fig. 7g, h). I-NR had no obvious relationship with the error (Fig. 6j and Fig. 7j). Detected banks data had errors generally two times larger than drags data (Table 2). This was consistent with their C-VA and I-VA being two times lower and their P-RR being ten times larger (see the Supplementary file S3). Note that I-NR of detected tags were two times lower for banks, i.e. though noise did not explain error variations within drags and banks data, it could partly explain differences between the two data sets.

Maps of positioning errors of detected tags showed similar spa-
tial patterns among combinations (see Fig. 8 for an example) and
reflected that the environment affected positioning errors less than
detection probability. The positioning errors were higher along
river banks, and tended to be higher in areas where hydrophones
did not function properly. Scatter plots among environmental vari-
ables (see an example in Supplementary file S2) indicated that
their influence on positioning errors could be complicated by the
same inter-correlations as those described for the probability of
detection. In addition, they indicated strong correlations among
the indicators of reception conditions (except I-NR) and the config-
uration variables for banks data.

3.4. Differences in detection probability and positioning error
between combinations

The average probability of detection varied among user-defined combinations between 34.6% (combination C3) and 47.2% (C5) for drags data (Table 2) and between 8.3% (C3) and 17.7% (C5) for banks data (Table 2). It was higher when PT was PT-HT1 and M was M2 (for drags and banks). The average positioning error varied more strongly between combinations: between 3.6 m (C6) and 10.7 m (C3) for drags data (Table 2) and between 7.7 m (C7) and 22.3 m (C4) for banks data (Table 2). It was primarily influenced by PT (i.e. it generally reduced two-fold when PT was PT-HT1), and secondarily reduced when M was M2 and SpS was 30 (for drags only). Environmental variables did not differ much between combinations, for both drags and banks (see the Supplementary file S3), and the most accurate combinations (C6 and C5) did not select particular environmental characteristics.

The sensitivity analysis detailing the effects of PT parameters
showed that the probability of detection was very sensitive (i.e.
could be modified by about 40%) to a few software parameters
influencing echo tracking (i.e. minimum and maximum number of
echoes required for a series of echoes from one hydrophone, max-
imum gaps allowed in this series and maximum distance allowed
between two echoes, Supplementary file S1). The average position-
ing error was mostly influenced (with modifications of about 10 m)
by an interpolation parameter (number of echoes used to smooth
positions, Supplementary file S1). Different values of these parame-
ters potentially explained differences observed between the PT-HT1
and PT-JB settings.
<figure><img src="image_103.png" /><figcaption>**Fig. 6.** Positioning error of drags data as a function of six environmental variables and four indicators of reception quality, for the eight combinations of user-defined variables. We fixed the limits of environmental categories so that their sizes were balanced for all combinations, taking into account that some discrete values had a high occurrence. The average class size across combinations and classes was 985 (standard deviation = 504). Note that the x-axis was log-transformed for the P-RR variable.</figcaption></figure>
<figure><img src="image_104.png" /><figcaption>Fig. 7. Positioning error of banks data as a function of six environmental variables and four indicators of reception quality for the eight combinations of user-defined variables. We fixed the limits of environmental categories so that their sizes were balanced for all combinations, taking into account that some discrete values had a high frequency. The average class size across combinations and classes was 28 (standard deviation = 5). Note that the x-axis was log-transformed for the P-RR variable and that outliers (positioning error > 30 m) were indicated using arrows in each panel.</figcaption></figure>
<figure><img src="image_105.png" /><figcaption>Fig. 8. Maps of positioning error for the combination C6. For readability, positioning errors were averaged by regular categories of water depth (<1 m; 1–2 m; 2–3 m; 3–4 m; ≥4 m) and by regular longitudinal sub-reaches (50 m long). Triangles, squares and circles correspond to hydrophones positions identified in the matrix M2.</figcaption></figure>

4. Discussion

Our assessment test of the HTI® system in heterogeneous
environmental conditions showed that the effects of intrin-
sic user-defined variables and environmental variables were
largely independent. Environmental variables were the main vari-
ables affecting detection probability, principally the configuration
of hydrophones around tags and secondarily relative rough-
ness and velocity. For detected tags, user-defined variables (the
post-treatment parameters and secondarily the knowledge of
hydrophone positions) were the main determinants of position-
ing error. Configuration variables had only a secondary influence
on the positioning error, especially along the banks.

In our study, the best user-defined combination provided an
average detection probability of 44% in the channel (16% for banks)
and an average positioning error of 3.6 m in the channel (9.5 m
for banks). Our installation was less accurate than a few others
made in more homogeneous physical conditions and/or with higher
hydrophone densities: e.g., a probability of detection of 75% in

Niezgoda et al. (2002) obtained with a CDMA (Code Division Mul-
tiple Access, Lotek Map_500TM) or positioning errors around 1 m
in Semmens (2008) obtained with the same HTI system used in
the present study. This was largely expected due to the nature of
our experiment, whose originality was to test the system in harsh
conditions for identifying the determinants of tag detection and
positioning error and quantifying their effects.

Our study quantifies how configuration variables can dra-
matically influence detection probability, and to a lesser extent
positioning errors, though the relative role of our two configura-
tion variables was difficult to sort out due to inter-correlation. This
effect of hydrophones configuration can partly explain differences
in detection probability between previous studies. For example, the
study of Niezgoda et al. (2002) obtained a high probability of detec-
tion with four hydrophones installed in a delta estuarine wetland
with a density of one hydrophone for around 375 m². In another
study, Carol et al. (2007) reported a probability of detection of less
than 10% with three hydrophones (Vemco radio-acoustic position-
ing system) installed in a Spanish reservoir with a density of one
hydrophone for around 23,000 m² (one third of the triangle sur-
face formed by the listening trio). In our study, we had on average
one hydrophone for about 7000 m², and correspondingly interme-
diate detection probabilities. Other parameters such as transmitter
power and frequency could also have contributed to differences
among studies. For example, the lower frequency used by Niezgoda
et al. (2002), i.e. 76 kHz, can also explain their higher detection
probability.

The effect of hydrophone configuration on positioning error in
our experiment was secondary compared to its effect on detection
probability. Consistently, Carol et al. (2007) reported positioning
errors of less than 1 m within the hydrophones array despite their
low detection probability. Nevertheless, the influence of configu-
ration (and in particular viewing angles) on positioning errors is
consistent with previous results that reported higher errors outside
the arrays of hydrophones (Juell and Westerberg, 1993; Kell et al.,
1994; Ehrenberg and Steig, 2002; Niezgoda et al., 2002; Espinoza
et al., 2011). In our study, both viewing angles and hydrophone den-
sities were often low, largely explaining that our positioning errors
could be higher than those obtained in some previous studies (e.g.
Carol et al., 2007; Semmens, 2008).

The study of the effect of physical conditions at tag emissions is original to this study, and generally revealed weak effects on the probability of detection and the positioning error. However there was a negative influence of high relative roughness conditions. Higher velocities were associated with lower probabilities of detection for drags data, but this could be due to the negative correlation between velocity and the density of hydrophones (i.e. hydrophones did not work in high velocity areas). Similarly, higher detection probability in fast-flowing and deep conditions for banks data likely reflected a confounding effect: among points of the banks data, those with higher velocity and depth (the two variables being correlated) were closer to the main channel and were better detected. Similar confounding effects along the banks were reported by Melnychuk and Christensen (2009). Concerning the temperature, a weak effect of thermal conditions on detection probability was highlighted but was also likely confounded with spatial patterns. Indeed, the intermediate temperature category associated with high detection probability was different for drags and banks, and was situated in the middle of the reach for both data sets.

Therefore, it is very likely that physical variables affect the prob-
ability of detection and the positioning error only through indirect
mechanisms, as in fast-flowing zones where high velocities may
damage hydrophones or their connections. The clearer effect of
the physical environment is that a lower probability of detection
was observed along banks, as expected and reported in previous
studies. This effect probably results from weak configurations and high levels of sound reflection in these shallow areas with high relative roughness (Cato and Bell, 1992; Juell and Westerberg, 1993; Trevorrow, 1998; Boswell et al., 2007), as also suggested by our indirect indicators of reception quality.

Experience for tuning PT parameter values had, overall, less influence on the probability of detection than environmental factors in our study. However, our sensitivity analysis indicated that a particular attention should be given in future applications to the parameters influencing echo tracking, since these can alter the probability of detection. The lower positioning error obtained with the expert parameter settings was partly due to an interpolation parameter (number of echoes used to smooth tag position tracks) whose tuning also requires attention. These results suggest that a full understanding of the parameter calibration process is necessary to get the best capabilities from the system, especially when the possibilities of parameter values are numerous and closely dependent on the physical characteristics of the study site.

At the core of our study, the knowledge of the positions of hydrophones (matrix M) and speed of sound (SpS) had much weaker influence on the positioning error than PT parameters. This is surprising when considering their predominant role emphasized in the literature (Juell and Westerberg, 1993; Kell et al., 1994; Cote et al., 1998; Wahlberg et al., 2001). Still, M and SpS could affect positioning errors by a meter or more, and they would probably have played a more important role in better configuration conditions. It is also possible that our two M matrices were both inaccurate on the experimental days, but the hydraulic conditions of the site prevented us from checking this aspect. Using the coordinates of the hydrophones measured at the end of the experiment improved our results, and this procedure is recommended for future experiments in fast-flowing rivers. Indeed, it is likely that the hydrophones rapidly moved after deployment and then stabilized on the bed.

The main practical lesson of our methodological study is that fixed acoustic telemetry systems can be used efficiently in fast-flowing rivers with high stresses and velocities, where they provided a reasonable detection probability and positioning error, despite a relatively low density of hydrophones installed within a 234,600 m² area. The probability of detection and the positioning error obtained here can be certainly improved with higher hydrophone densities and/or reduced study areas. However, it would be satisfactory for a number of ecological studies for which a higher quantity of accurate positions is not needed. As an example, for studies on the relationships between the physical habitat and the detailed movement of many fish individuals of several fish species in our reach, a fixed telemetry system is appropriate and a positioning error of 3–5 m is satisfactory considering the uncertainty of the hydraulic model. The main limit of fixed telemetry systems in large rivers seems to be the difficulty of deployment, illustrated by the length of weighted cables that were needed in the Rhone River (16 km) and the number of hydrophones that were temporarily disconnected (up to 11 out of 32), mostly due to scouring around blocks or accidents with trees in fast-flowing areas.

Because our results quantify the probability of detection and the positioning error for a variety of intrinsic and environmental combinations, we hope that they provide useful guidelines for optimizing future installations. Recommendations for improving detection and errors certainly depend on the objectives of the telemetry study (Kell et al., 1994). Studies for which detection probability is important, for example when tracking animals with frequent and large movements, should pay a particular attention to local hydrophone densities and viewing angles. If banks are of particular importance in such studies, our results can be used to quantify how the hydrophone density and angles should be increased near the banks to obtain a satisfactory probability of detection. For studies for which positioning error is more important

(e.g. for some studies on species interaction), our results suggest paying particular attention to system parameter values. Evaluation tests of the system would be particularly useful in such conditions and can serve to optimize intrinsic parameter values. In all cases, high roughness zones should be better covered. Zones with extreme velocities or temperature fluctuations should not be problematic for the probability of detection and the positioning error, but should be avoided if a risk of damaging the hydrophones is identified. We recommend paying particular attention to the hydrophone mounting and installation method in such conditions and to choose an aerial deployment of cables when possible.

## Acknowledgements

The authors thank the Water Agency Rhône Méditerranée & Corse, Electricity of France (EDF), the European Union / FEDER, the General Direction of Irstea and the Aquitaine region for their financial support, the Nuclear Power Plant of Bugey (EDF) for their collaboration, Eric McNeil, Samuel Johnston, Patrick Nealson, Dave Ouellette, Pascal Roger and Raphael Mons for their help during material installation and software calibration. Special thanks to Yann Le Coarer for his help with tachometer measures and to Alizés plongée® for its involvement in the diving operation. Finally, we thank the anonymous reviewers, André St Hilaire and Lise Vaudor for their comments and assistance.

## Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.fishres.2012.02.008.

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# Intérêt d'un modèle hydrodynamique en deux dimensions pour interpréter le comportement des poissons dans les grands cours d'eau

Hervé CAPRA<fnref n="1" />, Eric McNEIL<fnref n="2" />, Marie-Claire BOUILLON<fnref n="3" />, Hervé PELLA<fnref n="1" />, Carola ALFARO<fnref n="1" />

RESUME. – Cet article vise à décrire en quoi un outil performant de caractérisation des conditions abiotiques (modèle hydrodynamique en deux dimensions en l'occurrence) permet d'aborder des questions écologiques originales, comme une étude in situ des réponses comportementales des poissons du Rhône soumis à d'importantes variations des conditions environnementales. Dans un milieu aussi complexe (nombreux paramètres environnementaux qui interagissent) que le Rhône, les résultats présentés ont montré qu'une modélisation en deux dimensions permettait de prendre en compte, pour chaque période d'observation du milieu, la fluctuation des conditions d'écoulement, d'habitat et de température de l'eau. La mise en œuvre d'une telle approche constitue un atout majeur pour la compréhension de la structuration des communautés aquatiques en générale, et de poissons en particulier, trop rarement appliquée en France. Pour la première fois, les conditions environnementales vécues par les poissons au cours de leur déplacement ont été caractérisées. Cette approche peut également être appliquée pour simuler les conditions d'habitat et de température au cours de périodes significativement plus longues, compatibles, par exemple, avec le suivi de la structure de la communauté piscicole du Rhône entamé en 1979. En cela la mise en œuvre d'une modélisation hydrodynamique en 2D répond à l'une des questions majeures formulées par les experts dans la synthèse du projet Rhône Thermie - phase III en 2006. La disponibilité d'une description en deux dimensions, à des échelles spatiales et temporelles adéquates, et ce, pour une large gamme de débits susceptibles d'être observés dans le secteur d'étude, des conditions d'habitat et de température laisse supposer qu'il sera possible de connaître l'influence de ces deux facteurs environnementaux déterminants sur les choix comportementaux des poissons.

Mots clefs : modèle hydrodynamique en 2D, habitat, poissons, Rhône, variabilité, comportement

## Relevance of 2D hydraulic model to address fish behaviour in large rivers

ABSTRACT. – The article presented herein seeks to demonstrate how a two-dimensional hydrodynamic model representing abiotic conditions can be used to study the behaviour of fish affected by large fluctuations of environmental conditions in the Rhône River. Despite the complexity of the Rhône's physical environment, the results presented in this article show that a 2D model is capable of correctly representing the hydraulic conditions, water temperature and habitats for the studied area at each time step. This represents a major advancement in the understanding of aquatic community structures and in particular, for the structure of fish communities that are rarely used in France. For the first time, the environmental conditions affecting fish and their spatial localization will be known simultaneously. The model can also simulate habitat conditions and temperatures over longer periods of time. For example, this methodology is compatible with the monitoring of fish communities of the Rhône initiated in 1979. In this regard, a 2D hydrodynamic model answered a major need of experts working on the Rhône therm - Phase III project in 2006. The interest in modeling precise habitat and temperature conditions in two-dimensions for a large array of flows allows one to speculate that it will be possible to know the role that these two key environmental factors play on the behavioural choices of fish.

Key words : 2D hydrodynamic model, habitat, fish, Rhône River, variability, behaviour

## I. INTRODUCTION

Les ressources hydriques du fleuve Rhône sont exploitées pour différents usages, notamment la production hydroélectrique, le refroidissement des Centres Nucléaires de Production Electrique (CNPE) ainsi que l'approvisionnement en eau. Ces différentes utilisations de l'eau impliquent des modifications plus ou moins significatives du régime hydrologique et donc des conditions abiotiques qui y sont associées.

La production hydroélectrique génère des fluctuations de débit significatives sur de courts laps de temps (infra-journaux) qui ne s'observent pas dans un régime hydrologique

naturel et qui pourraient être assimilées à des éclusées. Ces fluctuations de débit se déclinent, en terme d'habitat du poisson, par des variations importantes de la disponibilité en habitat favorable.

Par ailleurs, l'eau du Rhône est également utilisée pour le refroidissement des réacteurs des CNPE. Il en résulte des rejets d'eau chaude (supérieurs de plusieurs degrés à la température de l'eau pompée) dans le Rhône, ce qui constitue un autre facteur environnemental susceptible d'influencer les communautés de poissons.

L'autorisation de rejet d'eau chaude accordée par les services de l'état oblige les CNPE à organiser des suivis des

<footnote n="1">¹ Cemagref, UR MAEP, Equipe Dynam - e-mail : herve.capra@cemagref.fr</footnote>

<footnote n="2">² HydroQuébec - Production - e-mail : mcneil.eric@hydro.qc.ca</footnote>

<footnote n="3">³ AECOM - e-mail : carola.alfaro@aecom.com</footnote>
communautés aquatiques pour évaluer les conséquences du réchauffement de l'eau sur le fonctionnement écológique de l'écosystème perturbé. Pour le CNPE de Bugey en particulier, après 20 années d'étude, les conclusions et les recommandations du groupe de travail 'Etude Rhône Thermie - Phase III' (EDF, Drire, Agence de l'Eau, Diren, Univ. Lyon I, Aralep, Cemagref Aix et Lyon) illustraient clairement le besoin de prendre en compte conjointement la variabilité spatio-temporelle des conditions de débit, d'habitat et de température pour étudier la variabilité de la structure des communautés vivantes du Rhône (poissons, invertèbres, plantes ; Carrel et al., 2006 [1]). La connaissance des réponses des poissons à des variabilités de débit sous différentes conditions de température constitue une question d'intérêt primordial. L'approche de simulation numérique des conditions abiotiques susmentionnées a été identifiée comme la plus appropriée pour représenter la variabilité spatio-temporelle des conditions d'habitat et de la température. De ces constats est né le projet Bugey2D, initié en 2006, dont les objectifs principaux étaient :

* de caractériser précisément les variations spatio-temporelles des conditions de vie des communautés aquatiques du Rhône (habitat et température);

* d'analyser les réponses comportementales des poissons soumis à des variations simultanées et contrastées d'habitat et de température;

L'objectif global du projet est d'étudier comment la sélection de l'habitat par les poissons peut être influencée par la température de l'eau, notamment dans un contexte perturbé tant d'un point de vue hydrologique que thermique (e.g. le Rhône à la hauteur de la centrale nucléaire de Bugey). L'hétérogénéité spatiale de l'habitat et de la température peuvent jouer un rôle significatif pour certaines espèces de poissons, notamment celles en marge de leurs tolérances. Pour connaître les stratégies d'utilisation de l'habitat par les poissons, une expérimentation a permis de suivre le comportement de 61 poissons (par télémétrie acoustique) pendant 3 mois au cours de l'été 2009 (Bergé et al., soumis [2]) dans le Rhône au droit du CNPE de Bugey.

Le présent article vise à montrer en quoi un outil performant de représentation des conditions abiotiques (modèle hydrodynamique en deux dimensions en l'occurrence) permet d'aborder des questions écologiques originales, comme une étude in situ des réponses comportementales des poissons du Rhône soumis à de fortes variations des conditions environnementales. Les techniques de simulation de l'habitat des poissons en fonction du débit sont des outils fréquemment utilisés dans le cadre d'expertises de gestion (e.g. définition de régime de débit réservé ; Reiser et al., 1989[3]) ; Tharme, 2003[4]) mais beaucoup plus rarement pour étudier les relations entre des caractéristiques du milieu (e.g. disponibilité d'habitat) et le comportement des poissons comme par exemple l'étude de bioénergétique présentée par Hayes et al. (2007) [5].

## II. MATERIEL ET METHODE

### II.1. Présentation du site

Le secteur du Rhône choisi pour l'étude est situé au milieu du plus long tronçon du Rhône français non dérivé par une installation hydroélectrique. Ce tronçon, long de 36 kilomètres, est localisé à l'Est de Lyon, entre les barrages

de Villebois (Sault-Brénaz) à l'amont et de Jons à l'aval (figure 1). Le site étudié s'étend sur 3,5 km entre l'amont (pk47) et l'aval (pk43,5) du CNPE de Bugey (EDF). La largeur du lit mineur sur le secteur est d'environ 140 m. Les débits du Rhône sont essentiellement régulés par les barrages situés en amont du tronçon (notamment Seujet et Verbois, en Suisse, et Génissiat) et sont compris 90 % du temps entre 160 et 742 m³.s⁻¹ pour un module de l'ordre de 473 m³.s⁻¹ (www.rdbrmc.com ; à Lagnieu ; débit médian de 470 m³.s⁻¹). La température journalière moyenne de l'eau du Rhône en amont du site d'étude, évaluée sur la période 1980-2009, est de 12,1°C. Au cours de cette période, la température moyenne journalière de l'eau a fluctué entre 1°C et 25,8°C.

### II.2. Mise en œuvre du modèle hydrodynamique

Une schématisation bidimensionnelle a été retenue pour caractériser les conditions abiotiques qui prévalent au sein du site à l'étude. Ce choix résulte des considérations suivantes : 1) les modèles de préférence d'habitat des poissons sont définis en fonction de vitesses moyennes sur la colonne d'eau ; 2) il n'y a pas de stratification verticale de la température de l'eau ; 3) il convient de disposer d'une description des conditions abiotiques avec une configuration et une réalisation spatiale compatible avec une étude comportementale des poissons (ce que n'aurait pas permis d'atteindre le recours à une schématisation unidimensionnelle ; les poissons étant susceptibles de se déplacer tant latéralement que longitudinalement par rapport au cours d'eau), et 4) une approche de modélisation hydrodynamique en trois dimensions n'aurait pas, dans le contexte du projet, apporté davantage d'information, tout en s'avérant plus complexe à mettre en œuvre et à valider.

La discrétisation géométrique du tronçon à l'étude a été réalisée sous la forme d'un maillage irrégulier d'éléments triangulaires en tenant notamment compte de la configuration bathymétrique du secteur (recours, à titre d'exemple, à un maillage de Delaunay contraint au droit de certaines sections de contrôle de l'écoulement à faibles débits). Le tronçon considéré pour les simulations numériques des conditions abiotiques a été étendu vers l'amont par rapport aux

<figure><img src="image_106.png" /><figcaption>Figure 1 : Site d'étude, localisé sur la Rhône en amont de Lyon, à la hauteur du CNPE de Bugey.</figcaption></figure>
La simulation de la quantité d'habitat favorable potentiel (Surface Pondérée Utile en m²) en fonction du débit semble peu sensible au choix du débit d'étalonnage du modèle hydrodynamique (figure 4). Les courbes établies à partir des cinq débits d'étalonnage différents sont très proches, maîgré un léger décalage à partir de 500 m³·s⁻¹.

Le modèle hydrodynamique mis en œuvre sur le Rhône à Bugey simule les conditions d'habitat utilisées par les poissons pour chacune de leurs localisations (figure 5). La création de chronogrammes permet de visualiser dans le temps et simultanément les profondeurs, les vitesses de courants, les températures ou le substrat utilisés par les poissons et les conditions disponibles (e.g. le débit, dans la figure 5) dans le milieu.

Enfin, le modèle numérique de température de l'eau a également fait l'objet d'un étalonnage et d'une validation. Il en résulte des écarts maximums de l'ordre de 0,5 °C dans le cadre de la phase de validation. Par ailleurs, les résultats de simulation de température de l'eau ont été comparés à une cartographie des rejets d'eau chaude observés par le biais d'une thermographie infrarouge dans le cadre de relevés aéroportés. Cette comparaison démontre que les résultats

<figure><img src="image_107.png" /></figure>

Figure 4 : Évolution de la Surface Pondérée Utile (indice d'habitat favorable potentiel, en m²) du barbeau adulte (Barbus barbus, L. 1758) en fonction du débit pour des modèles hydrodynamiques étalonnés à différents débits (150 à 850 m³·s⁻¹). Les courbes de préférence utilisées pour la profondeur et la vitesse de courant sont présentées à gauche du graphique.

<figure><img src="image_108.png" /></figure>

Figure 5 : Exemple de localization de deux poissons (silure #3870 (Silurus giganis, L. 1758) et barbeau # 3744) pendant 24h le 19 juillet 2009 au sein du site d'étude représenté ici par une cartographie du nombre de Froude (dégradé de gris ; adimensionnel) pour le débit moyen de la journée. Le chronogramme des valeurs du nombre de Froude utilisées par les poissons sur 24h sont confrontés à la variation concomitante du débit.
des simulations numériques sont consistants par rapport à la
configuration du panache thermique observé *in situ*.

IV. DISCUSSION

Les résultats présentés dans cet article ont montré que
l'utilisation d'une modélisation en deux dimensions des
conditions d'écoulement, d'habitat et de la température de
l'eau s'avère un outil performant pour l'analyse des inter-
relations de divers facteurs environnementaux sur le com-
portement du poisson dans des milieux complexes tels
que le Rhône. Cette approche, peu appliquée à ce jour
en France, possède l'avantage marqué de caractériser, de
manière continue, l'interaction de paramètres environne-
mentaux sur l'évolution de la structure des communautés
aquatiques. En revanche, ces outils de simulation sont déjà
largement exploités au Québec, pour le fleuve Saint-Laurent
par exemple (Mingelbier et Morin, 2005 [9]; Morin et al.,
2005 [10]). La connaissance des conditions vécues par les
poissons dans l'approche comportementale envisagée dans le
cadre du projet en cours (projet Bugey 2D) en fait son origi-
nalité et sa force. Pour la première fois, les fluctuations des
conditions environnementales susceptibles d'influencer les
poissons pourront être mises en relation avec leur localisa-
tion spatiale. Mais ces outils peuvent également simuler les
conditions d'habitat et de température sur des périodes signi-
ficativement plus longues, notamment celle correspondant au
suivi des effets des rejets d'eau chaude sur la structure de
la communauté piscicole entamé en 1979. En cela le projet
Bugey 2D, via la mise en œuvre d'une modélisation hydro-
dynamique en 2D, répond à l'une des questions majeures
formulées par les experts dans la synthèse du projet Rhône
Thermie - phase III (Carrel et al., 2006 [1]).

Les courbes de préférences définies par Lamouroux et al. (1999) [8], lesquelles ont été élaborées à partir de campagnes d'échantillonnage effectuées entre autres dans le Rhône, ont été jugées par ces auteurs comme transférables pour les cours d'eau du sud de la France. Il était donc raisonnable d'utiliser ces courbes dans un premier temps afin d'estimer les surfaces d'habitat favorable pour différentes espèces présentes dans le secteur à l'étude. Toutefois, l'analyse des habitats utilisés par les poissons suivis par télemétrie acoustique, dans le cadre du projet en cours Bugey 2D, permettra certainement une analyse assez fine de la pertinence des simulations d'habitat réalisées ici. Par ailleurs, les premières analyses des enregistrements des comportements des poissons, confirment que le comportement des poissons ne sera pas établi en tenant compte de leur position dans la colonne d'eau (dimension verticale). Ce constat justifie encore, a posteriori, le choix d'une schématisation bidimensionnelle des écoulements.

Un des axes forts actuels du projet Bugey 2D est la des-
cription des trajectoires des poissons dans le temps et dans
l'espace. L'une des questions fondamentales associées à
cette description des trajectoires des poissons, incluant les
moments d'immobilisation, est la recherche d'évènements
susceptibles d'expliquer les changements de comportement.
L'intérêt de disposer d'une caractérisation des conditions
d'habitat et de température de l'eau à des échelles tempo-
relles et spatiales adéquates, et ce, pour une large gamme de
débits susceptibles d'être observés dans le secteur d'étude,
laisse supposer qu'il sera possible de connaître l'influence

de ces deux facteurs environnementaux clefs dans le com-
portement des poissons.

V. REMERCIEMENTS

Nous tenons à remercier EDF (CNPE et DTG-Grenoble),
l'Agence de l'Eau RM&C et l'Union Européenne /FEDER)
pour leur collaboration et pour leur soutien financier pour le
projet Bugey 2D. Nous remercions également la Compagnie
Nationale du Rhône pour nous avoir communiqué les enre-
gistrements de débit et de niveau d'eau au pas de temps
horaire à Lagzieu et au droit du CNPE de Bugey. Ces don-
nées sont dites « brutes, visuellement contrôlées mais non
critiquées et non validées ». Enfin, il importe de souligner ici
qu'un tel travail ne peut aboutir que grâce à la collaboration
de beaucoup de personnes dévouées, en particulier, Pascal
Roger, Raphaël Mons, Julien Bergé et Nicolas Lamouroux.

VI. REFERENCES ET CITATIONS

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[2] BERGER J., CAPRA H., OVIDIO M., PELLA H., & LAMOURoux N. (Soumes) — Living with fish in a large river with heterogenous flow and temperature : an acoustic telemetry experiment.

[3] REISER D. W., WESCHE T. A. & ESTES C. (1989) — Status of instream flow legislation and practices in North America. *Fisheries*. 14 22-29

[4] THARMIE R. E. (2003) — A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. *River Research and Applications*. 19 397-441

[5] HAYES J., HUGHES N. & KELLY L. (2007) — Process-based modelling of invert substrate drift transport, net energy intake and reach carrying capacity for drift-feeding salmonids. *Ecological Modelling*. 207 171-188

[6] PELLA H., CAPRA H., & FOULARD S. (2008) — Développement d'un MNT du haut Rhône à partir de relevés bathymétriques réalisés avec un sondeur multi-faisceaux. *Revue Française de Photogrammétrie et de Télédétectron*. 186 81-86

[7] CAPRA H., PELLA H. & ORRIEL E. (2008) — Relais de la tam-
paretura de l'eau du Rhône au cours de l'été 2008 (profils et
anagrupements ponctuels). Rapport Camagruf - EDF. 42 p

[8] LAMOURoux N., CAPRA H., POUILLY M. Et SOUCHON Y. (1999) — Fish habitat preferences in large streams of southern France. *Freshwater Biology*. 42 673-687

[9] MINOELBIER M. & MORIN J. (2005) — Modélisation numérique 2 D de l'habitat des poissons du Saint-Laurent fluvial pour évaluer l'impact des changements climatiques et de la régula-
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[10] MORIN J., CHAMPOUX O., MARTIN S. & TURGON K. (2005) — Modélisation intégrée de la réponse de l'écosystème dans le fleuve Saint-Laurent : Rapport final des activités entre-prises dans le cadre du Plan d'étude sur la régularisation du lac Ontario et du fleuve Saint-Laurent. Rapport scientifique SMC Québec - Section Hydrologie RS-108, Environnement Canada, Sainte-Foy, préparé pour le Groupe de travail tech- nique sur l'environnement du Groupe d'étude International sur le lac Ontario et le fleuve Saint-Laurent (Commission Mixte Internationale). 130 p