1 **Title**

2 Using 3D modeling and remote sensing capabilities for a better understanding of spatio-temporal heterogeneities of phytoplankton abundance in large lakes

3 heterogeneities of phytoplankton abundance in large lakes

4 Author names and affiliations

5 Frédéric Soulignac<sup>a, *, 1</sup>, Pierre-Alain Danis<sup>b</sup>, Damien Bouffard<sup>c</sup>, Vincent Chanudet<sup>d</sup>, Etienne Dambrine<sup>a</sup>,

6 Yann Guénand<sup>a,e</sup>, Tristan Harmel<sup>f,g</sup>, Bastiaan W Ibelings<sup>h</sup>, Dominique Trevisan<sup>a</sup>, Rob Uittenbogaard<sup>i</sup>,

7 Orlane Anneville<sup>a</sup>

8 <sup>a</sup> French National Institute for Agricultural Research (INRA), CARRTEL, Université Savoie Mont Blanc,

9 75bis avenue de Corzent, 74200 Thonon les Bains, France

10 <sup>b</sup> Agence Française pour la Biodiversité, Pôle AFB-Irstea Hydroécologie Plans d'Eau, 3275 route Cézanne, 13182 Aix-en-Provence, France

11 Cézanne, 13182 Aix-en-Provence, France

12 <sup>c</sup> Eawag, Swiss Federal Institute of Aquatic Science and Technology, Surface Waters – Research and Management, Kastanienbaum, Switzerland

13 Management, Kastanienbaum, Switzerland

14 <sup>d</sup> Hydraulic Engineering Center (CIH), Electricité de France (EDF), Savoie Technolac, 73370 Le Bourget-

15 du-Lac, France

16 <sup>e</sup> SEGULA Technologie, 30 allée du lac d'Aiguebelette BP80271, 73375 Le Bourget du Lac, France

17 <sup>f</sup> Sorbonne Université, Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Océanographie de Villefranche, Observatoire Océanologique de Villefranche sur Mer, 181 Chemin du Lazaret, 06230 Villefranche sur Mer, France

18 d'Océanographie de Villefranche, Observatoire Océanologique de Villefranche sur Mer, 181 Chemin du Lazaret, 06230 Villefranche sur Mer, France

19 du Lazaret, 06230 Villefranche sur Mer, France

20 <sup>g</sup> Irstea, UR RECOVER, Pôle AFB-Irstea Hydroécologie des Plans d'eau, 3275 route Cézanne, 13182 Aix-

21 en-Provence, France

<footnote>* Corresponding author
Tel.: +41 21 693 5793, e-mail address: frederic.soulignac@epfl.ch</footnote>

<footnote n="1"><sup>1</sup> Present address: Ecological Engineering Laboratory (ECOL), Federal Polytechnic School of Lausanne (EPFL), Station 2, 1015 Lausanne, Switzerland</footnote>
22 h Department F.-A. Forel for Environmental and Aquatic Sciences (DEFSE), University of Geneva,

23 Boulevard Carl-Vogt 66, 1205 Geneva, Switzerland

24 i Deltares, Boussinesqweg 1, 2629 HV Delft, The Netherlands

25 Telephone and fax numbers and e-mail addresses for each author

<table><thead><tr><td>Author names</td><td>Telephone number</td><td>Fax number</td><td>E-mail address</td></tr></thead><tbody><tr><td>Frédéric Soulignac</td><td>+41 21 693 5793</td><td></td><td>frederic.soulignac@epfl.ch</td></tr><tr><td>Pierre-Alain Danis</td><td>+33 4 42 66 99 18</td><td></td><td>pierre-alain.danis@afbiodiversite.fr</td></tr><tr><td>Damien Bouffard</td><td>+41 58 765 2273</td><td>+41 58 765 2168</td><td>damien.bouffard@eawag.ch</td></tr><tr><td>Vincent Chanudet</td><td>+33 4 79 60 64 60</td><td></td><td>vincent.chanudet@edf.fr</td></tr><tr><td>Etienne Dambrine</td><td>+33 4 79 75 94 54</td><td>+33 4 79 75 94 54</td><td>etienne.dambrine@univ-smb.fr</td></tr><tr><td>Yann Guenand</td><td>+33 4 79 60 11 21</td><td></td><td>yann.guenand@segula.fr</td></tr><tr><td>Tristan Harmel</td><td>+33 7 69 03 69 00</td><td></td><td>tristan.harmel@ntymail.com</td></tr><tr><td>Bastiaan W Ibelings</td><td>+41 22 379 3013</td><td></td><td>bastiaan.ibelings@unige.ch</td></tr><tr><td>Dominique Trevisan</td><td>+33 4 50 26 78 30</td><td>+33 4 50 26 07 60</td><td>dominique.trevisan@inra.fr</td></tr><tr><td>Rob Uittenbogaard</td><td>+31 88 335 8181</td><td></td><td>rob.uittenbogaard@deltares.nl</td></tr><tr><td>Orlane Anneville</td><td>+33 4 50 26 78 04</td><td>+33 4 50 26 07 60</td><td>orlane.anneville@inra.fr</td></tr></tbody></table>

26 Abstract

27 Lake biological parameters show important spatio-temporal heterogeneities. This is why explaining
28 the spatial patchiness of phytoplankton abundance has been a recurrent ecological issue and is an
29 essential prerequisite for objectively assessing, protecting and restoring freshwater ecosystems. The
30 drivers of these heterogeneities can be identified by modeling their dynamics. This approach is useful
31 for theoretical and applied limnology. In this study, a 3D hydrodynamic model of Lake Geneva
32 (France/Switzerland) was created. It is based on the Delft3D suite software and includes the main
33 tributary (Rhône River) and two-dimensional high-resolution meteorological forcing. It provides 3D
34 maps of water temperature and current velocities with a 1 h time step on a 1 km horizontal grid size
35 and with a vertical resolution of 1 m near the surface to 7 m at the bottom of the lake. The dynamics
36 and the drivers of phytoplankton heterogeneities were assessed by combining the outputs of the
37 model and chlorophyll-a concentration (Chl-a) data from MERIS satellite images between 2008 and
38 2012. Results highlight physical mechanisms responsible for the occurrence of seasonal hot-spots in
39 phytoplankton abundance in the lake. At the beginning of spring, Chl-a heterogeneities are usually
40 caused by an earlier onset of phytoplankton growth in the shallowest and more sheltered areas;
41

spatial differences in the timing of phytoplankton growth can be explained by spatial variability in

42

thermal stratification dynamics. In summer, transient and locally higher phytoplankton abundances

43

are observed in relation to the impact of basin-scale upwelling.

44

**Keywords**

45

3D modeling; remote sensing; chlorophyll-a; spatio-temporal heterogeneity; Lake Geneva; Delft3D
46 **Introduction**

In a world where lakes provide multiple services to society while facing strong anthropogenic pressures, the protection and restoration of inland water bodies has become a critical political and scientific issue. For example, the European Water Framework Directive obliges member countries to take steps to reach a good ecological status for all their surface waters (European Commission, 2000).

To reach these challenging goals, the scientific and lake management communities aim to deepen the understanding of lake ecological functioning, using ongoing lake monitoring efforts (Stewart et al., 2016).

Phytoplankton, at the base of the food chain, controls to a fair extent the quality of the ecosystem services and is widely used as an indicator of ecosystem health and sustainability (Xu et al., 2001).

However, evaluating the abundance and composition of this community in routinely obtained water samples raises several issues regarding the representativeness of the collected lake samples since phytoplankton is rarely homogeneously distributed over a water body (Pelechaty and Owsianny, 2003). This is all the more true in large and/or deep lakes (Leoni et al., 2014; Viljanen et al., 2009).

Understanding the mechanisms that drive plankton spatial distribution has been a recurring theme in aquatic ecology and has been studied for several decades (Arhonditsis et al., 2004). Commonly, phytoplankton spatial heterogeneities fall into two categories, vertical and horizontal distributions.

Phytoplankton can only develop in the upper part of the water column where solar energy is high enough to support net photosynthesis (Reynolds, 1997), leading to the concepts of critical depth and turbulence (Huisman et al., 1999). Still, important gradients across the euphotic zone in both the abundance and composition of phytoplankton are frequently observed in deep lakes (Pomati et al., 2011). The vertical distribution of chlorophyll-a concentration (Chl-a) in deep lakes is often characterized by the presence of a deep Chl-a maximum which acts as a local hotspot of primary production. The occurrence and the structure of a deep Chl-a maximum is only now beginning to be properly understood (Leach et al., 2018). Heterogeneities along the horizontal axis – that have been observed using traditional multi-site measurements and more recent techniques such as microwave or optical remote sensing from space – remain equally difficult to explain because of the complexity of the underpinning processes and mechanisms that are the basis of these heterogeneities (Moreno-Ostos et al., 2007). Indeed, the distribution of phytoplankton is strongly affected by wind-driven currents and also by spatial heterogeneity on environmental conditions (Cyr, 2017; Huang et al., 2014).

Despite the demonstration of lake patchiness in satellite observations, horizontal variability in lake water parameters is often neglected. Samples used for lake monitoring and water quality assessment
78 are usually collected at a single station, often the deepest point of the lake, because this area is 79 generally located in the center of the lake where anthropogenic impact and effect are limited. 80 Nevertheless, the recent remote sensing capabilities can facilitate the description and to some extent, 81 depending on the frequency of suitable images, the study of dynamics in lake heterogeneity (Bresciani 82 et al., 2011; Gons et al., 2008; Kauer et al., 2015; Odermatt et al., 2012; Palmer et al., 2015). In large 83 lakes, surface horizontal heterogeneities in Chl-a are a frequent phenomenon and also present 84 structures that could change rapidly (Kiefer et al., 2015). Thus, the existence of such heterogeneities 85 might raise several issues when studying the impact of local and global forcing on phytoplankton that 86 requires a better understanding of the underlying mechanisms responsible for those heterogeneities 87 (Chen et al., 2003). This is not just a matter of academic interest because the distribution of blooms of 88 harmful cyanobacteria across lakes is of prime importance for risk assessment and management and 89 affects the use of lakes for drinking water and recreation (Ibelings et al., 2003).

90 The physical properties of water govern its response to mechanical energy inputs, such as wind, river 91 inflow and heat exchanges, resulting in vertical gradients of physical and chemical characteristics, such 92 as temperature and nutrients, which are major factors controlling phytoplankton development. As 93 such these physical processes may promote spatial heterogeneities in phytoplankton (Reynolds, 1997). 94 Accordingly, we postulate here that a good understanding of physical dynamics is essential to explain 95 the spatial heterogeneity observed in phytoplankton abundance. To test this, we applied a three- 96 dimensional (3D) hydrodynamic model to Lake Geneva (France/Switzerland) and evaluated the ability 97 of this hydrodynamic model to explain horizontal patchiness in phytoplankton abundance. For that 98 purpose, we analyzed the vertical thermal structure predicted by the model at different stations in the 99 lake when Chl-a presented strong surface heterogeneities. The role of hydrodynamic processes was 100 expected to be strong enough that considering hydrodynamics alone would be sufficient to explain 101 some of the phytoplankton heterogeneity patterns observed in Lake Geneva from 2008 to 2012. In 102 other words, the purpose of this study is to show that by considering only hydrodynamics, we are able 103 to explain and predict certain surface phytoplankton heterogeneities that are observed in lakes.

104 **Materials and methods**

105 *Study site*

106 Lake Geneva is a large and deep peri-alpine lake located in the western part of the Alps, on the border 107 between France and Switzerland (Fig. 1 and Table 1). According to the international commission for 108 the protection of Lake Geneva (CIPEL), it is thermally stratified during much of the year, never freezes 109 over and does not undergo complete mixing every year. Its main tributary is the River Rhone which
110

with 184.3 m³/s, accounts for 85% of the total inflow (average from 1965 to 2015). The lake is monitored as part of a long-term in situ monitoring program by CIPEL for water quality (e.g. water temperature, Chl-a, transparency) and biological compartments, including the phytoplankton. This monitoring revealed important changes in phosphorus concentrations. In fact, Lake Geneva was eutrophic for several years in the 1960s and measures to reduce phosphorus in its watersheds were first implemented in the 1970s, leading to a decrease in phosphorus concentrations starting in the early 1980s. Nowadays, Lake Geneva is mesotrophic (Jacquet et al., 2014a).

111

112

113

114

115

116

117

## Data

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

In this study, in situ vertical profiles of Chl-a and water temperature (WT), transparency and remote sensed surface Chl-a maps are used between 2008 and 2012. In situ Chl-a, WT and transparency are available from the CIPEL monitoring program. Sampling takes place at two stations, at the deepest point of the lake in the large basin (monitoring station SHL2: WGS84 6.58872° E, 46.45270° N; depth: 309 m) and in the small basin (monitoring station GE3: WGS84 6.21994° E, 46.29721° N; depth: 72 m) (Fig. 1). At SHL2, sampling is conducted twice a month, except in winter, when it is carried out only once a month. At GE3, sampling frequency is once a month throughout all the year. Samples for Chl-a measurements are collected at 10 depths: 0, 1, 2, 3.5, 5, 7.5, 10, 15, 20 and 30 m. Cells are collected on a Whatman GF/C filter (47 mm) and sonicated. The pigments are extracted with 90 % (v/v) acetone/water and the solution is filtered through a glass fiber filter GF/C (25 mm). Chl-a is measured by spectrophotometry (Strickland, 1968). WT is measured using multiparameter probes, SST-CTD009, SST-CTM214 and RBR-XRX-620. The transparency is measured as Secchi disk depth (SDD) which corresponds to the depth at which the light intensity in the water column is 15% of the intensity of the surface (Lemmin, 1995). Remotely sensed Chl-a is available from Kiefer et al. (2015). It was derived from satellite MERIS observations and the processing was done with the FUB WeW neural network algorithm (Schroeder et al., 2007). The algorithm gives a resolution of 260 × 290 m and can handle Chl-a concentrations between 0.05 and 50 µg/l. Data availability is summarized (Fig. 2).

135

## Delft3D model

136

137

138

139

140

141

The open source Delft3D software used in this study has been widely applied for lakes of different sizes all around the world (Chanudet et al., 2012; Kacikoc and Beyhan, 2014; Li et al., 2015; McCombs et al., 2014; Soulignac et al., 2017; Wahl and Peeters, 2014). A previous modelling effort was performed on Lake Geneva by using Delft3D. It is presented in a series of three articles (Razmi et al., 2013, 2014 and 2017) (the references are given below). We chose the same surface heat flux module, the same bottom and surface roughness formula, the same meteorological inputs and the same Rhone River inputs. We
also simulated Lake Geneva entirely. But whereas Razmi and coauthors divided each year into four periods of three months, our objective was to run one-year simulations to cover the whole thermal stratification period. Also, they used a very fine grid (the lake surface was meshed using 45,000 grid cells) to study the small-scale hydrodynamics in the Vidy bay, a small part of the lake located in front of Lausanne, and they used a non-uniform vertical mesh (sigma-model). However, we wanted a model requiring less computational time, a regular resolution throughout the lake and a uniform vertical mesh to better describe the thermal stratification. For these reasons, we decided to create a new model of Lake Geneva. Delft3D has several modules (e. g.: hydrodynamics, water quality, etc.). In this study, only the hydrodynamic module Delft3D-FLOW was used. It solves the Navier-Stokes equations for an incompressible fluid, under the shallow water and the Boussinesq equations. The system of equations consists of the continuity equation,

$$ \frac{\partial u}{\partial x} + \frac{\partial v}{\partial y} + \frac{\partial w}{\partial z} = 0, $$

the two horizontal equations of motion,

$$ \frac{\partial u}{\partial t} + u \frac{\partial u}{\partial x} + v \frac{\partial u}{\partial y} + w \frac{\partial u}{\partial z} = - \frac{1}{\rho_0} \frac{\partial p}{\partial x} + \frac{\partial}{\partial x} \left( v_H \frac{\partial u}{\partial x} \right) + \frac{\partial}{\partial y} \left( v_H \frac{\partial u}{\partial y} \right) + \frac{\partial}{\partial z} \left( v_V \frac{\partial u}{\partial z} \right) + fV \quad \text{and} $$

$$ \frac{\partial v}{\partial t} + u \frac{\partial v}{\partial x} + v \frac{\partial v}{\partial y} + w \frac{\partial v}{\partial z} = - \frac{1}{\rho_0} \frac{\partial p}{\partial y} + \frac{\partial}{\partial x} \left( v_H \frac{\partial v}{\partial x} \right) + \frac{\partial}{\partial y} \left( v_H \frac{\partial v}{\partial y} \right) + \frac{\partial}{\partial z} \left( v_V \frac{\partial v}{\partial z} \right) - fV, $$

the vertical equation of motion being reduced to the hydrostatic pressure equation, and the transport equation of heat,

$$ \frac{\partial T}{\partial t} + u \frac{\partial T}{\partial x} + v \frac{\partial T}{\partial y} + w \frac{\partial T}{\partial z} = \frac{\partial}{\partial x} \left( D_H \frac{\partial T}{\partial x} \right) + \frac{\partial}{\partial y} \left( D_H \frac{\partial T}{\partial y} \right) + \frac{\partial}{\partial z} \left( D_V \frac{\partial T}{\partial z} \right) + \frac{S}{\rho_0 c_{pw}}, $$

where x, y, and z are Cartesian coordinates (m), t is time (s), u, v and w are the three components of the water velocity (m/s), f is the Coriolis frequency in 1/s, T is the water temperature (K), ρ₀ is the water density (kg/m³), p is the pressure (Pa), v_H and v_V are the horizontal and vertical eddy viscosities (m²/s), D_H and D_V are the horizontal and vertical coefficients of eddy diffusivity of heat (m²/s), S is the source of heat per unit volume (W/m³) and c_pw is the water specific heat (J/K/kg). Model equations are precisely described (Deltares, 2014).
166 *Model set-up*

167 The surface of the numerical domain was created using a curvilinear grid composed of 591 cells of
168 about 1 km² area to fit the lake contour (Fig. 1). In the vertical direction, 100 Z-layers were used to fit
169 the bathymetry. Their thickness varies from 1 m at the surface to about 7 m at the bottom. Going
170 deeper, the thickness of two consecutive layers increases by a factor of 1.02. Based on these length
171 scales, the computational time step was set to 3 min to verify the Courant-Friedrichs-Lewy (CFL)
172 criterion and the background horizontal eddy viscosity and diffusivity was set to 100 m²/s. The k-ε
173 turbulence closure model was chosen to calculate the vertical eddy viscosity and diffusivity because it
174 has proven to perform well in stratified water (Burchard and Baumert, 1995). The explicit multi-
175 directional upwind (MDUE) scheme was selected for the spatial discretization of the horizontal
176 advection terms and the Van Leer-2 scheme was used for the transport equation. The wind drag
177 coefficient was set as a linear function of wind speed between 0.63 10⁻³ at 0 m/s and 7.23 10⁻³ at 100
178 m/s. Stanton and Dalton numbers were set at 1.3 10⁻³. The salinity was set constant at 0.15 ppt,
179 corresponding to the observed specific conductivity of about 300 µS/cm. The sediment transport and
180 resuspension in littoral zones and shallow areas was neglected because no measurements are available
181 to validate this part of the model and also because we did not refine the grid near the lake shore.

182 *Model input data*

183 Seven meteorological variables (air pressure, air temperature, cloud coverage, relative humidity,
184 incident solar radiation, wind speed and wind direction) coming from simulation results of the
185 Consortium for Small-scale Modeling (COSMO) two-dimensional atmospheric model were used in this
186 study (Fig. 3). The time step is 1 h and the spatial resolution is 1.5 km x 1.5 km. Hourly in situ
187 measurements of the discharge and the water temperature in the Rhône River at Porte De Scex, 5 km
188 upstream from the lake were also used (Fig. 3).

189 *Simulations*

190 The simulation starts on March 3, 2008, at midnight. A WT profile measured at SHL2 at this date was
191 used for initialization considering the whole lake water temperature as horizontally homogeneous. The
192 lake was also supposed to be at rest, without water currents. Five years were simulated, from 2008 to
193 2012. The simulation was stopped and restarted on January 1 of each year just to change the
194 transparency value. The average SDD value between May and September, the five months when the
195 solar radiation is the highest, was used (Wahl and Peeters, 2014). Values are presented in Table 2.
196 Simulation results were exported with a time-resolution of 1 h.
197 Model validation

198 The model performance was evaluated by comparing observed and simulated WT at SHL2 and GE3 for
199 the five simulated years. In this study, we focused on the water layer between the surface and 30 m
200 depth because this is where net photoautotrophic phytoplankton growth occurs. Simulated WT
201 profiles were systematically exported when in situ measurements were taken, at approximately
202 midday. The model validation was performed by calculating the mean absolute error (MAE), which
203 quantifies the error between simulation results and observations, defined by

$$
204 \quad MAE = \frac{1}{n} \sum_{i=1}^{n} |T_{sim}(i) - T_{obs}(i)|
$$

205 where Tsim and Tobs are respectively simulated and observed WT vectors whose length is n. One value
206 of MAE was calculated for each observation date by considering all the data between the surface and
207 30 m. One value of MAE was also computed for each depth and each year by considering all the data
208 of one year.

209 Model analysis

210 In this paper, we have chosen to focus on surface Chl-a structures that repeat from time to time. The
211 first structure was a phytoplankton patch with enhanced densities near the north-western shore of
212 the lake in summer. We focused on this particular structure which was observed on a MERIS image on
213 September 7, 2009 (Fig. 4). Accordingly, a first period, from August 31 to September 9, 2009, starting
214 with low and homogenous surface Chl-a was analyzed. The second structure depicts higher
215 phytoplankton abundance in early spring in a specific part of the lake, namely at the entrance of the
216 Rhône River (Kiefer et al., 2015) and it was observed on an image dated March 21, 2011 (Fig. 4).
217 Accordingly, a second period, from March 8 to 28, 2011, was analyzed. A third period of interest, from
218 February 24 to March 29, 2010, was chosen to be used as a counter-example for the 2nd structure.
219 During that period, environmental parameters allowed a homogenous development of phytoplankton
220 as clearly depicted by remote sensing in March 23, 2010. Changes in the lake thermal structure during
221 these three periods were analyzed based on hourly simulated WT profiles for three periods of interest
222 at four stations S1, S2, S3 and S4 (S2 and S4 correspond to SHL2 and GE3, respectively). Starting and
223 ending dates of the three periods corresponded to in situ sampling dates at the station SHL2.

224 Lake Analyzer is a numerical code that allows the calculation of several lake parameters using high
225 frequency data, it was used to calculate the mixed layer depth based on hourly simulation results (Read
et al., 2011). The mixed temperature differential parameter of Lake Analyzer was set to 0.01 °C. For the second and the third period of interest, Lake Analyzer was also used to calculate the buoyancy frequency, again based on hourly simulation results, which represents the local stability of the water column, N², expressed in 1/s² by $N^2 = \frac{g \partial \rho}{\rho \partial z}$

where g is the gravity (9.81 m/s²), ρ is the water mass density (kg/m³) and z is the vertical Cartesian coordinate (m).

## 232 Results

### 233 Model validation

The model was validated by comparing observed and simulated WT between the surface and 30 m depth at SHL2 (Fig. 5a) and at GE3 (Fig. 5b). Simulated WT were interpolated at observed depths and times. The 3D hydrodynamic model accurately reproduced the observations, in particular, the water column warming from the surface and the implementation of the thermal stratification during the summer. The MAE calculated for each observation date varies in a comparable way at SHL2 and GE3 ranging from 0.00 to 3.90 °C at SHL2 and 0.02 to 3.50 °C at GE3 (Fig. 6a). For both sampling stations, MAE values are smaller during winter and spring than during summer and autumn when the lake is stratified. The annual MAE calculated for each depth considering all the observation dates over the course of the year also varies comparably between SHL2 and GE3 with values comprised between 0.00 and 1.43 °C at SHL2 and between 0.22 and 1.89 °C at GE3 (Fig. 6b and Fig. 6c). The MAE is generally minimal at the surface and reaches a maximum at a depth located in the metalimnion near the thermocline, except for 2009 when the maximum value was found at the surface. In fact, the thermocline depth is better reproduced by the model for the year 2009. Below the metalimnion, the MAE decreases. The annual mean of the MAE is systematically lower at SHL2 than at GE3. Based on the MAE value, the best performances are achieved for the year 2012 followed by 2009, 2010, 2011 and 2008 (Table 3).

The comparison between measurements and simulations showed that the model is capable of reproducing the evolution of the lake thermal vertical structure on a smaller time-scale, especially for the three time-periods defined previously from surface Chl-a observations. During the first period from August 31 to September 9, 2009, the model satisfactorily reproduces the cooling of the water surface temperature and the deepening of the surface mixed layer at SHL2 (Fig. 7a). MAE values are presented in Table 4. For the period from February 24 to March 29, 2010, the model also correctly reproduces
256

257

258

259

the homogeneous onset of the thermal stratification (Fig. 7b). Finally, for the period from March 8 to
28, 2011, the model correctly predicted an early start of the stratification on March 28 at SHL2 while
the lake was still not stratified on March 22 at GE3 (Fig. 7c). So, the model presents good results and
can be safely used for the purpose of this study.

260

## Lake thermal structure in summer 2009

261

262

263

264

265

266

267

268

269

270

271

From August 31 to September 2, 2009, simulation results predicted a homogeneous thermal stratification in the lake between the surface and 30 m depth, except at S1 near the main tributary entering the lake (Fig. 8b). In fact, the mixed layer depth was about 15 m at S1 and 10 m at S2, S3 and S4. On September 3, a wind event from west or southwesterly directions induced downwelling at S1 and upwelling at S4. This wind event continued on September 4 and increased, presenting wind speed values greater than 10 m/s (Fig. 8a). The minimum thickness of the surface mixed layer calculated by the model is 5.6 m at S4 and its maximum value is 25.3 m at S1 (Fig. 8c). Simulation results also predicted that water at 18.5 °C was brought up to the surface at the north-western shore (Fig. 9). This temperature value corresponded to a depth of about 15 m where the maximum of Chl-a was observed from in situ measurements at SHL2 on August 31 and September 9 (Fig. 10). Interestingly, the satellite image also shows high Chl-a concentration on September 7 along the north-western shore (Fig. 4).

272

## Lake thermal structure in spring 2010

273

274

275

276

277

278

279

280

281

282

283

From February 24 to March 11, 2010, simulation results showed that thermal stratification did not occur between the surface and 30 m depth because several wind events (>5 m/s) from west or north-west and north or north-easterly directions prevented an increase in water column stability (Fig. 11). Conversely, the wind speed was lower between March 12 and 17 (<5 m/s) and daily stratification was predicted by the model. In fact, based on a stability criterion (thermal stratification was defined as N² > 10⁻⁵ 1/s²), thermal stratification started at the same time at S1, S2, S3 and S4 on March 16 due to the lower wind speed. Until March 23, the thermal stratification developed homogeneously across the four stations, no wind events were recorded that could have broken the stratification at any station. In comparison, the satellite image March 23 showed quite homogeneous Chl-a concentrations at the whole lake scale (Fig. 4) and the maximum of Chl-a was observed from in situ measurements at SHL2 only on March 29 just below the surface (Fig. 10).

284

## Lake thermal structure in spring 2011
From March 8 to 12, 2011, simulation results indicated that the lake was similarly mixed between the surface and 30 m depth at S1, S2, S3 and S4 when the wind speed did not exceed 5 m/s (Fig. 12).
Between March 13 and 16, several strong wind events (>5 m/s) coming from the east or southeast regularly reduced the water column stability at S1 by breaking the small thermal stratification that had begun. On March 13, the buoyancy frequency at S1 passed from 6 $10^{-5}$ to 3 $10^{-6}$ 1/s² and continued to reach values below $10^{-5}$ 1/s² every day until March 16. Meanwhile, stability increased at S2, S3 and S4 where the wind speed was weaker allowing the development of thermal stratification. From March 17, onwards, the stability increased at S1 where the buoyancy frequency returned over $10^{-5}$ 1/s² due to a decrease in wind speed. On March 19, a strong wind event from north or northeasterly directions (10 m/s) mixed the water column at S3 and S4, and significantly decreased the water column stability at S2. At S4, the buoyancy frequency passed from $2 \ 10^{-4}$ to $3 \ 10^{-6}$ 1/s². The wind turned to the west, leaving S1 sheltered and S2 less exposed to the wind compared to S3 and S4. The north or northeast wind event continued after March 19 and prevented the thermal stratification restarting at S3 and S4. As a result, the lake thermal structure was not homogeneous throughout the lake, only S1 and S2 were stratified and S1 was more stratified than S2. In comparison, the satellite image on March 21 showed that algal development took place at S1 (Fig. 4) and the maximum of Chl-a was observed from in situ measurements at SHL2 only on March 28 just below the surface (Fig. 10).

## Discussion

### Model performance

At least five other 3D lake models including modeling of the water temperature were created using the open source Delft3D software: Lake Geneva in France/Switzerland (Razmi et al., 2017, 2014, 2013), Lake Constance in Switzerland/Germany/Austria (Wahl and Peeters, 2014), Lake Egirgir in Turkey (Kacikoc and Beyhan, 2014), Nam Theun 2 reservoir in Laos (Chanudet et al., 2012) and Lake Créteil in France (Soulignac et al., 2017). All these five models along with our proposed model, were validated using in situ measurements and proved to perform satisfactory, capable of accurately simulating the hydrodynamic and thermal structure of these lakes and proved to be ready to be used for research questions, for example climate research (Wahl and Peeters, 2014).

Razmi et al.'s model and ours were validated using vertical profiles of observed water temperature. They used two profiles at a single station in Vidy bay on August 5 and November 7, 2005, while we used monthly/bimonthly profiles at two stations, SHL2 and GE3, in the lake from 2008 to 2012. Model performances are similar during the studied periods. They found a RMSE from 0.90 to 2.00 °C and we obtained a MAE between 0.18 and 1.49 °C during spring and summer.
318 In spring 2010, satellite surface Chl-a data showed that the onset of phytoplankton development was
319 homogeneous in Lake Geneva. This is corroborated by the use of the 3D model which shows that the
320 thermal structure of the lake was homogeneous too. By contrast in spring 2011, satellite images
321 revealed heterogeneous Chl-a with a maximum recorded in the eastern part of the lake indicating that
322 the onset of phytoplankton development occurred earlier at S1. Based on the satellite data, it was
323 shown that this part of the lake regularly exhibits higher Chl-a concentrations in spring (Kiefer et al.,
324 2015).

325 Given the mesotrophic status of the lake, in March, the euphotic layer receives an input of nutrients
326 through winter mixing, bringing up nutrients from the bottom of the lake, which results in reactive
327 phosphorus concentrations higher than 10 µg/l in the euphotic zone. In Lake Geneva, phosphorus is
328 not yet a limiting factor for phytoplankton growth at this time of the year because most species may
329 be limited by phosphorus as concentration becomes less than 10 µg/l (Sas, 1989) and severe reduction
330 in algal growth may occur if soluble reactive phosphorus concentrations fall below 3 µg/l (Grover,
331 1989; Suttle et al., 1988). Moreover, according to the conceptual model of the Plankton Ecology Group
332 (PEG) (Sommer et al., 2012, 1986) and Reynolds (1997) in such deep lakes, light is the critical resource
333 for phytoplankton growth in early spring. Observations confirm that phytoplankton developed in areas
334 of Lake Geneva where our simulations indicated that the thermal stratification sets in earlier in a more
335 sheltered area compared to the rest of the lake. Indeed, our results are coherent with the PEG model
336 which states that thermal stratification triggers the algal development in spring in deep lakes due to
337 enhanced light availability for phytoplankton growth resulting from restrictions on the mixing depth.

338 This concept has long been described as Sverdrup's critical mixing depth, i.e. phytoplankton is able to
339 grow only if the mixing depth is less than a critical depth so that net photoautotrophic growth is
340 possible (Sverdrup, 1953). However, this concept is not always in line with observations since
341 phytoplankton blooms have been observed preceding the onset of stratification, when mixing is still
342 unrestricted (Eilertsen, 1993). Huisman et al. (2009) added the concept of critical turbulence to the
343 concept of critical mixing depth. In deep and relatively clear lakes, phytoplankton can maintain
344 development, irrespective of mixing depth if their growth rate in the upper layer exceeds vertical
345 mixing rates. The sheltered area in the eastern part of Lake Geneva may favor early bloom
346 development following either one of these concepts, either by reducing mixing depth or by reducing
347 turbulent mixing rates to below the respective critical values.
349

Upwelling events were previously observed in some regions of Lake Geneva (Oesch et al., 2008) and more generally in large lakes (Plattner et al., 2006). When characterizing the thermal structure of Lake Geneva using the 3D model, we found that we could link satellite images and simulation results for the upwelling event of September 4, 2009. In situ measurements showed that phytoplankton developed at about 15 m depth at that time. We showed that surface Chl-a heterogeneities observed on September 7, 2009, are related to this upwelling event. The model suggests a displacement of deep phytoplankton to the surface and provides additional evidence of the role of upwelling on phytoplankton abundance heterogeneity in lakes (Cyr, 2017; Huang et al., 2014). At this stage, our model does not include nutrients or biology, so it cannot attest for an effective supply of nutrients to the upper waters that will stimulate algae productivity and phytoplankton growth. So, it remains unresolved whether the upwelling event indeed enhanced primary production and phytoplankton growth as is sometimes the case during upwelling events (Poschke et al., 2015).

350

351

352

353

354

355

356

357

358

359

360

361

However, this effective enhancement of algae production needs to be accurately tested because upwelling events are characterized as a "temporal displacement of near shore water masses which returned at the end of each event" (Haffner et al., 1984). Therefore, depending on the time scale, intensity and duration of the event, phytoplankton in the upper layer should receive little or no "benefit from the upwelled nutrient rich hypolimnetic waters" (Haffner et al., 1984). As a consequence, it is important to understand and assess when upwellings are efficient fertilizers of the euphotic zone as it has been recorded in various ecosystems (Planas and Paquet, 2016; Valipour et al., 2016). In addition, we should consider that special attention needs to be paid to the horizontal mixing occurring during and just after the upwelling event. Coupling our hydrodynamic model to a biological module should most likely allow us to estimate the impact of new nutrient availability in the surface waters. Such fertilization of the euphotic zone by nutrient enriched deep water might have an important role in the functioning of mesotrophic lakes. It is important to better assess if these hydrodynamic events can efficiently sustain primary production and how they impact the outcome of inter-specific competition within the phytoplankton community. Improving our understanding on the impact of these hydrodynamic events on phytoplankton communities, might help to understand usual patterns observed during re-oligotrophication of deep lakes such as changes in taxonomic composition (Jeppesen et al., 2005) or hysteresis in phytoplankton biomass (Tadonleke et al., 2009).

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

Furthermore, several undesirable phytoplankton species are known to develop at depth in alpine lakes. *Mougeotia gracillima* or *Planktothrix rubescens* develop and proliferate at 15-20 m depth near the thermocline in Lake Geneva (Jacquet et al., 2014b; Tapolczai et al., 2015). In the case of *M. gracillima* which is not toxic, an upwelling event should not cause any potential health risks for leisure activities

379

380

381
382

or production of drinking water. In contrast, if upwelling brings potentially toxic species such as *P. rubescens* to the lake surface where the contact with people is more intense than when it remains at greater depth, it may cause serious health issues (Ibelings et al., 2014). For that reason, in lakes subject to harmful algal bloom development in deeper layers, it should be relevant to improve our forecasting of upwelling events (Plattner et al., 2006) and we suggest the addition of additional in situ sampling of phytoplankton in these areas for identification of dominant species. 3D models and remote sensing products could help in identifying the areas where best to perform the sample collection.

383

384

385

386

387

388

389

*Contribution of 3D models in the interpretation of spatial observations of Chl-a concentration from Satellite Data*

390

391

392

393

394

395

396

397

398

Optical satellite remote sensing has been used to retrieve patterns of surface Chl-a concentration in lakes (Bresciani et al., 2011; Kiefer et al., 2015; Odermatt et al., 2012; Palmer et al., 2015). But this technique is not capable of detecting deep Chl-a concentration maxima which are common in deep lakes undergoing re-oligotrophication (Anneville and Leboulanger, 2001; Leach et al., 2018). This is a fundamental limitation of the use of satellite images in characterizing phytoplankton blooms in lakes. In order to improve Chl-a retrieval, 3D hydrodynamic models coupled to ecological models could be used to give information about the Chl-a structure in the vertical dimension which may vary between different portions of the lake.

399

## Conclusion

400

401

402

403

404

405

406

407

408

409

410

411

412

413

A 3D hydrodynamic model of Lake Geneva was created. It is based on the Delft3D suite software and includes the main tributary (Rhône River) and 2D high-resolution meteorological forcing. It provides 3D maps of WT with a 1 h time step on a 1 km horizontal grid size and with a vertical resolution of 1 m near the surface to 7 m at the bottom of the lake. This model was validated using in situ measurements taken at two sampling stations in the lake over 5 years, from 2008 to 2012. Model performances were satisfactory, compared to the literature. Here, we have shown that such a model can be used to detail the lake thermal structure during two periods, one in spring and another in summer, when surface Chl-a heterogeneities were observed by satellite data. Both data comparisons performed well in providing explanations for horizontal heterogeneities in Chl-a abundances. For instance, we highlighted the important role of wind in determining surface phytoplankton abundance. A short and intense wind event in spring is able to create clear surface heterogeneities in phytoplankton distribution which continue through time. Also in summer, the role of the wind is very important. It can generate upwelling events and create surface Chl-a heterogeneities bringing potentially toxic species to the surface. If we look to the future, the validity of this approach and the possibility to export this
414

415

416

417

418

combination of techniques (3D model and remote sensing) to other study areas in the world could be
considered a fruitful approach, as for example Pinardi et al. (20105) who effectively used the
combination of a hydrodynamic model and of remote sensing derived products to assess potential
algal bloom. Also, a coupled biological model to the hydrodynamic model could further help to
understand better the phytoplankton dynamic and heterogeneities observed in Lake Geneva.

419

Acknowledgements

420

421

422

423

424

425

426

427

This study was funded by the French agency for biodiversity (AFB, before French national agency for
water and aquatic environments (ONEMA)) and the European space agency (ESA). We wish to thank
the French alpine lakes observatory (SOERE-OLA) and the International commission for the protection
of Lake Geneva (CIPEL). Data were from © SOERE OLA-IS, AnaEE-France, INRA Thonon-les-Bains, CIPEL
[2016], developed by Eco-Informatics ORE INRA Team. We also wish to thank the Department of
environment, transport and agriculture (DETA) of the Geneva water ecology service for providing
additional in situ data as well as Isabel Kiefer for satellite data. Finally, we would like to thank Tineke
Troost and Hans Los (Deltares) for productive discussions and Pierre Keraudren for English editing.

428

Reference

429

430

431

Anneville, O. and Leboulanger, C., 2001. Long-terms changes in the vertical distribution of phytoplankton biomass and primary production in Lake Geneva: A response to the oligotrophication, Atti Associazione Italiana Oceanologia Limnologia 14, 25-35.

432

433

434

Arhonditsis, G.B., Winder, M., Brett, M.T., Schindler, D.E., 2004. Patterns and mechanisms of phytoplankton variability in Lake Washington (USA). Water Res. 38, 4013-4027.
https://doi.org/10.1016/j.watres.2004.06.030

435

436

437

Bresciani, M., Stroppiana, D., Odermatt, D., Morabito, G., Giardino, C., 2011. Assessing remotely sensed chlorophyll-a for the implementation of the Water Framework Directive in European perialpine lakes. Sci. Total Environ. 409, 3083-3091. https://doi.org/10.1016/j.scitotenv.2011.05.001

438

439

Burchard, H., Baumert, H., 1995. On the performance of a mixed-layer model based on the κ-ε turbulence closure. J. Geophys. Res.-Oceans 100, 8523-8540. https://doi.org/10.1029/94jc03229

440

441

442

Chanudet, V., Fabre, V., van der Kaaij, T., 2012. Application of a three-dimensional hydrodynamic model to the Nam Theun 2 Reservoir (Lao PDR). J. Gt. Lakes Res. 38, 260-269.
https://doi.org/10.1016/j.jglr.2012.01.008
443 Chen, Y.W., Fan, C.X., Teubner, K., Dokulil, M., 2003. Changes of nutrients and phytoplankton
444 chlorophyll-alpha in a large shallow lake, Taihu, China: an 8-year investigation. Hydrobiologia 506, 273-
445 279. https://doi.org/10.1023/b:hydr.0000008604.09751.01

446 Cyr, H., 2017. Winds and the distribution of nearshore phytoplankton in a stratified lake. Water Res.
447 122, 114–127. https://doi.org/10.1016/j.watres.2017.05.066

448 Deltares, 2014. Delft3D-FLOW, User Manual.

449 Eilertsen, H.C., 1993. Spring blooms and stratification. Nature 363, 24–24.
450 https://doi.org/10.1038/363024a0

451 European Commission, 2000. Directive 2000/60/EC of the European Parliament and of the Council of
452 23 October 2000 establishing a framework for community action in the field of water policy. Off. J. Eur.
453 Communities 2000.

454 Gons, H.J., Auer, M.T., Effler, S.W., 2008. MERIS satellite chlorophyll mapping of oligotrophic and
455 eutrophic waters in the Laurentian Great Lakes. Remote Sens. Environ. 112, 4098–4106.
456 https://doi.org/10.1016/j.rse.2007.06.029

457 Grover, J.P., 1989. Phosphorus-dependant growth kinetics of 11 species of freshwater algae. Limnol.
458 Oceanogr. 34, 341–348. https://doi.org/10.4319/lo.1989.34.2.0341

459 Haffner, G.D., Yallop, M.L., Hebert, P.D.N., Griffiths, M., 1984. Ecological significance of upwelling
460 events in Lake Ontario. J. Gt. Lakes Res. 10, 28–37. https://doi.org/10.1016/s0380-1330(84)71804-1

461 Huang, J.C., Gao, J.F., Hormann, G., Fohrer, N., 2014. Modeling the effects of environmental variables
462 on short-term spatial changes in phytoplankton biomass in a large shallow lake, Lake Taihu. Environ.
463 Earth Sci. 72, 3609–3621. https://doi.org/10.1007/s12665-014-3272-z

464 Huisman, J., van Oostveen, P., Weissing, F.J., 1999. Critical depth and critical turbulence: Two different
465 mechanisms for the development of phytoplankton blooms. Limnol. Oceanogr. 44, 1781–1787.
466 https://doi.org/10.4319/lo.1999.44.7.1781

467 Ibelings, B.W., Backer, L.C., Kardinaal, W.E.A., Chorus, I., 2014. Current approaches to cyanotoxin risk
468 assessment and risk management around the globe. Harmful Algae 40, 63–74.
469 https://doi.org/10.1016/j.hal.2014.10.002
470

471

472

Ibelings, B.W., Vonk, M., Los, H.F.J., van der Molen, D.T., Mooij, W.M., 2003. Fuzzy modeling of cyanobacterial surface waterblooms: Validation with NOAA-AVHRR satellite images. Ecol. Appl. 13, 1456–1472. https://doi.org/10.1890/01-5345

473

474

475

Jacquet, S., Domaizon, I., Anneville, O., 2014a. The need for ecological monitoring of freshwaters in a changing world: a case study of Lakes Annecy, Bourget, and Geneva. Environ. Monit. Assess. 186, 3455–3476. https://doi.org/10.1007/s10661-014-3630-z

476

477

478

Jacquet, S., Kerimoglu, O., Rimet, F., Paolini, G., Anneville, O., 2014b. Cyanobacterial bloom termination: the disappearance of Planktothrix rubescens from Lake Bourget (France) after restoration. Freshw. Biol. 59, 2472–2487. https://doi.org/10.1111/fwb.12444

479

480

481

482

483

484

Jeppesen, E., Sondergaard, M., Jensen, J.P., Havens, K.E., Anneville, O., Carvalho, L., Coveney, M.F., Deneke, R., Dokulil, M.T., Foy, B., Gerdeaux, D., Hampton, S.E., Hilt, S., Kangur, K., Kohler, J., Lammens, E., Lauridsen, T.L., Manca, M., Miracle, M.R., Moss, B., Noges, P., Persson, G., Phillips, G., Portielje, R., Schelske, C.L., Straile, D., Tatrai, I., Willen, E., Winder, M., 2005. Lake responses to reduced nutrient loading - an analysis of contemporary long-term data from 35 case studies. Freshw. Biol. 50, 1747–1771. https://doi.org/10.1111/j.1365-2427.2005.01415.x

485

486

Kacikoc, M., Beyhan, M., 2014. Hydrodynamic and Water Quality Modeling of Lake Egirdir. Clean-Soil Air Water 42, 1573–1582. https://doi.org/10.1002/clen.201300455

487

488

489

Kauer, T., Kutser, T., Arst, H., Danckaert, T., Noges, T., 2015. Modelling primary production in shallow well mixed lakes based on MERIS satellite data. Remote Sens. Environ. 163, 253–261. https://doi.org/10.1016/j.rse.2015.03.023

490

491

492

Kiefer, I., Odermatt, D., Anneville, O., Wuest, A., Bouffard, D., 2015. Application of remote sensing for the optimization of in-situ sampling for monitoring of phytoplankton abundance in a large lake. Sci. Total Environ. 527, 493–506. https://doi.org/10.1016/j.scitotenv.2015.05.011

493

494

495

496

497

Leach, T.H., Beisner, B.E., Carey, C.C., Pernica, P., Rose, K.C., Huot, Y., Brentrup, J.A., Domaizon, I., Grossart, H.-P., Ibelings, B.W., Jacquet, S., Kelly, P.T., Rusak, J.A., Stockwell, J.D., Straile, D., Verburg, P., 2018. Patterns and drivers of deep chlorophyll maxima structure in 100 lakes: The relative importance of light and thermal stratification. Limnol. Oceanogr. 63, 628–646. https://doi.org/10.1002/lno.10656
498

Lemmin, U., 1995. Limnologie physique, in: Pourriot, R. and Meybeck, M. (Eds.), Limnologie générale.

499

Masson, Paris, pp. 60–114.

500

Leoni, B., Marti, C.L., Imberger, J., Garibaldi, L., 2014. Summer spatial variations in phytoplankton
composition and biomass in surface waters of a warm-temperate, deep, oligoholomictic lake: Lake
Iseo, Italy. Inland Waters 4, 303–310. https://doi.org/10.5268/iw-4.3.569

501

502

503

Li, Z.J., Chen, Q.W., Xu, Q., 2015. Modeling algae dynamics in Meiliang Bay of Taihu Lake and parameter sensitivity analysis. J. Hydro-Environ. Res. 9, 216–225. https://doi.org/10.1016/j.jher.2014.10.001

504

505

McCombs, M.P., Mulligan, R.P., Boegman, L., 2014. Offshore wind farm impacts on surface waves and circulation in Eastern Lake Ontario. Coast. Eng. 93, 32–39. https://doi.org/10.1016/j.coastaleng.2014.08.001

506

507

508

Moreno-Ostos, E., da Silva, S.L.R., de Vicente, I., Cruz-Pizarro, L., 2007. Interannual and between-site variability in the occurrence of clear water phases in two shallow Mediterranean lakes. Aquat. Ecol. 41, 285–297. https://doi.org/10.1007/s10452-006-9072-0

509

510

511

Odermatt, D., Pomati, F., Pitarch, J., Carpenter, J., Kawka, M., Schaepman, M., Wuest, A., 2012. MERIS observations of phytoplankton blooms in a stratified eutrophic lake. Remote Sens. Environ. 126, 232–239. https://doi.org/10.1016/j.rse.2012.08.031

512

513

514

Oesch, D., Jaquet, J.M., Klaus, R., Schenker, P., 2008. Multi-scale thermal pattern monitoring of a large lake (Lake Geneva) using a multi-sensor approach. Int. J. Remote Sens. 29, 5785–5808. https://doi.org/10.1080/01431160802132786

515

516

517

Palmer, S.C.J., Odermatt, D., Hunter, P.D., Brockmann, C., Presing, M., Balzter, H., Toth, V.R., 2015. Satellite remote sensing of phytoplankton phenology in Lake Balaton using 10 years of MERIS observations. Remote Sens. Environ. 158, 441–452. https://doi.org/10.1016/j.rse.2014.11.021

518

519

520

Pelechaty, M., Owsianny, P.M., 2003. Horizontal distribution of phytoplankton as related to the spatial heterogeneity of a lake - a case study from two lakes of the Wielkopolski National Park (western Poland). Hydrobiologia 510, 195–205. https://doi.org/10.1023/B:HYDR.0000008644.67963.c4

521

522

523

Pinardi, M., Fenocchi, A., Giardino, C., Sibilla, S., Bartoli, M., & Bresciani, M., 2015. Assessing potential algal blooms in a shallow fluvial lake by combining hydrodynamic modelling and remote-sensed images. Water 7(5), 1921–1942. https://doi.org/10.3390/w7051921

524

525
526 Planas, D., Paquet, S., 2016. Importance of climate change-physical forcing on the increase of cyanobacterial blooms in a small, stratified lake. J. Limnol. 75, 201–214.
527
528 https://doi.org/10.4081/jlimnol.2016.1371

529 Plattner, S., Mason, D.M., Leshkevich, G.A., Schwab, D.J., Rutherford, E.S., 2006. Classifying and forecasting coastal upwellings in Lake Michigan using satellite derived temperature images and buoy data. J. Gt. Lakes Res. 32, 63–76. https://doi.org/10.3394/0380-1330(2006)32[63:cafcui]2.0.co;2

532 Pomati, F., Jokela, J., Simona, M., Veronesi, M., Ibelings, B.W., 2011. An Automated Platform for Phytoplankton Ecology and Aquatic Ecosystem Monitoring. Environ. Sci. Technol. 45, 9658–9665.
533
534 https://doi.org/10.1021/es201934n

535 Poschke, F., Lewandowski, J., Engelhardt, C., Preuss, K., Oczipka, M., Ruhtz, T., Kirillin, G., 2015. Upwelling of deep water during thermal stratification onset-A major mechanism of vertical transport in small temperate lakes in spring? Water Resour. Res. 51, 9612–9627.
536
537
538 https://doi.org/10.1002/2015wr017579

539 Razmi, A.M., Barry, D.A., Bakhtyar, R., Le Dantec, N., Dastgheib, A., Lemmin, U., Wuest, A., 2013. Current variability in a wide and open lacustrine embayment in Lake Geneva (Switzerland). J. Gt. Lakes Res. 39, 455–465. https://doi.org/10.1016/j.jglr.2013.06.011

542 Razmi, A.M., Barry, D.A., Lemmin, U., Bonvin, F., Kohn, T., Bakhtyar, R., 2014. Direct effects of dominant winds on residence and travel times in the wide and open lacustrine embayment: Vidy Bay (Lake Geneva, Switzerland). Aquat. Sci. 76, S59–S71. https://doi.org/10.1007/s00027-013-0321-8

545 Razmi, A.M., Lemmin, U., Bouffard, D., Wuest, A., Uittenbogaard, R.E., Barry, D.A., 2017. Gyre formation in open and deep lacustrine embayments: the example of Lake Geneva, Switzerland. Environ. Fluid Mech. 17, 415–428. https://doi.org/10.1007/s10652-016-9494-8

548 Read, J.S., Hamilton, D.P., Jones, I.D., Muraoka, K., Winslow, L.A., Kroiss, R., Wu, C.H., Gaiser, E., 2011. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environ. Model. Softw. 26, 1325–1336. https://doi.org/10.1016/j.envsoft.2011.05.006

551 Reynolds, C.S., 1997. Vegetation process in the pelagic: a model for ecosystem theory. In Kinne, O. (ed.), Excellence in Ecology. ECI, Oldendorf.
553

554

Sas, H., 1989. Lake restoration by reduction of nutrient loading: Expectations, experiences, extrapolations. *Acdemia Verlag Richarz*, St. Augustin.

555

556

557

Schroeder, T., Behnert, I., Schaale, M., Fischer, J., Doerffer, R., 2007. Atmospheric correction algorithm for MERIS above case-2 waters. *Int. J. Remote Sens.* 28, 1469–1486.
https://doi.org/10.1080/01431160600962574

558

559

560

561

Sommer, U., Adrian, R., Domis, L.D., Elser, J.J., Gaedke, U., Ibelings, B., Jeppesen, E., Lurling, M., Molinero, J.C., Mooij, W.M., van Donk, E., Winder, M., 2012. Beyond the Plankton Ecology Group (PEG) Model: Mechanisms Driving Plankton Succession. *Annu. Rev. Ecol. Evol. Syst.* Vol 43 43, 429–448.
https://doi.org/10.1146/annurev-ecolsys-110411-160251

562

563

Sommer, U., Gliwicz, Z.M., Lampert, W., Duncan, A., 1986. The PEG-model of seasonal succession of planktonic events in fresh waters. *Arch. Hydrobiol.* 106, 433–471.

564

565

566

567

Soulignac, F., Vincon-Leite, B., Lemaire, B.J., Martins, J.R.S., Bonhomme, C., Dubois, P., Mezemate, Y., Tchiguirinskaia, I., Schertzer, D., Tassin, B., 2017. Performance Assessment of a 3D Hydrodynamic Model Using High Temporal Resolution Measurements in a Shallow Urban Lake. *Environ. Model. Assess.* 22, 309–322. https://doi.org/10.1007/s10666-017-9548-4

568

569

570

Stewart, T.J., Rudstam, L., Watkins, J., Johnson, T.B., Weidel, B., Koops, M.A., 2016. Research needs to better understand Lake Ontario ecosystem function: A workshop summary. *J. Gt. Lakes Res.* 42, 1–5.
https://doi.org/10.1016/j.jglr.2015.10.017

571

572

573

Suttle, C.A., Stockner, J.G., Shortreed, K.S., Harrison, P.J., 1988. Time-courses of size-fractionated phosphate uptake: are larger cells better competitors for pulse of phosphate than smaller cells. *Oecologia* 74, 571–576. https://doi.org/10.1007/bf00380055

574

575

Sverdrup, H.U., 1953. On conditions for the vernal blooming of phytoplankton. *ICES Journal of Marine Sci.* 18, 287–295. https://doi.org/10.1093/icesjms/18.3.287

576

577

578

Tadonleke, R.D., Lazzarotto, J., Anneville, O., Druart, J.C., 2009. Phytoplankton productivity increased in Lake Geneva despite phosphorus loading reduction. *J. Plankton Res.* 31, 1179–1194.
https://doi.org/10.1093/plankt/fbp063
579 Tapolczai, K., Anneville, O., Padisak, J., Salmaso, N., Morabito, G., Zohary, T., Tadonleke, R.D., Rimet, F., 2015. Occurrence and mass development of Mougeotia spp. (Zygnemataceae) in large, deep lakes. Hydrobiologia 745, 17–29. https://doi.org/10.1007/s10750-014-2086-z

582 Valipour, R., Leon, L.F., Depew, D., Dove, A., Rao, Y.R., 2016. High-resolution modeling for development of nearshore ecosystem objectives in eastern Lake Erie. J. Gt. Lakes Res. 42, 1241–1251. https://doi.org/10.1016/j.jglr.2016.08.011

585 Viljanen, M., Holopainen, A.L., Rahkola-Sorsa, M., Avinsky, V., Ruuska, M., Leppanen, S., Rasmus, K., Voutilainen, A., 2009. Temporal and spatial heterogeneity of pelagic plankton in Lake Pyhaselka, Finland. Boreal Environ. Res. 14, 903–913.

588 Wahl, B., Peeters, F., 2014. Effect of climatic changes on stratification and deep-water renewal in Lake Constance assessed by sensitivity studies with a 3D hydrodynamic model. Limnol. Oceanogr. 59, 1035–1052. https://doi.org/10.4319/lo.2014.59.3.1035

591 Xu, F.L., Tao, S., Dawson, R.W., Li, P.G., Cao, J., 2001. Lake ecosystem health assessment: Indicators and methods. Water Res. 35, 3157–3167. https://doi.org/10.1016/s0043-1354(01)00040-9

593 Tables

594 Table 1: Lake Geneva characteristics

<table><thead><tr><td>Characteristic</td><td>Value</td></tr></thead><tbody><tr><td>Latitude</td><td>N 46° 27'</td></tr><tr><td>Longitude</td><td>E 6° 32'</td></tr><tr><td>Elevation</td><td>372 m</td></tr><tr><td>Mean depth</td><td>153 m</td></tr><tr><td>Max depth</td><td>309 m</td></tr><tr><td>Surface area</td><td>580 km²</td></tr><tr><td>Watershed area</td><td>7475 km²</td></tr></tbody></table>

595 Table 2: Model input data Secchi disk depth (SDD) from 2008 to 2012

<table><thead><tr><td>Year</td><td>SDD (m)</td></tr></thead><tbody><tr><td>2008</td><td>6.1</td></tr><tr><td>2009</td><td>6.4</td></tr></tbody></table>
<table><tr><td>2010</td><td>6.1</td></tr><tr><td>2011</td><td>6.0</td></tr><tr><td>2012</td><td>4.0</td></tr></table>

596 Table 3: Annual mean values of the mean absolute error between the surface and 30 m depth for the two monitoring stations, SHL2 and GE3, from 2008 to 2012
597

<table><thead><tr><th></th><th>SHL2</th><th>GE3</th></tr></thead><tbody><tr><td>2008</td><td>1.07</td><td>1.30</td></tr><tr><td>2009</td><td>0.79</td><td>0.97</td></tr><tr><td>2010</td><td>0.80</td><td>1.29</td></tr><tr><td>2011</td><td>1.07</td><td>1.20</td></tr><tr><td>2012</td><td>0.72</td><td>0.80</td></tr></tbody></table>

598 Table 4: Mean absolute error values between the surface and 30 m depth at the two monitoring stations, SHL2 and GE3, during the three periods analyzed (NA: not available)
599

<table><thead><tr><th></th><th>SHL2</th><th>GE3</th></tr></thead><tbody><tr><td>31/08/2009</td><td>1.27</td><td>NA</td></tr><tr><td>09/09/2009</td><td>1.49</td><td>NA</td></tr><tr><td>24/02/2010</td><td>0.52</td><td>NA</td></tr><tr><td>17/03/2010</td><td>0.57</td><td>NA</td></tr><tr><td>23/03/2010</td><td>NA</td><td>0.53</td></tr><tr><td>29/03/2010</td><td>0.54</td><td>NA</td></tr><tr><td>08/03/2011</td><td>0.57</td><td>NA</td></tr><tr><td>20/03/2011</td><td>NA</td><td>0.18</td></tr><tr><td>28/03/2011</td><td>0.54</td><td>NA</td></tr></tbody></table>

600 **Figure captions**

Fig. 1: Lake Geneva contour, isodepths (100, 200 and 300 m), numerical domain (curvilinear grids), location of the main tributary inflow and outflow (Rhône River) and location of the four sites where simulation outputs are analyzed (S1, S2 (SHL2), S3, S4 (GE3))

Fig. 2: Data availability
Fig. 3: Model input meteorological data at the monitoring station SHL2 (air pressure (AP), air temperature (AT), cloud coverage (CC), relative humidity (RH), incident solar radiation (SW), wind speed (U10) and wind direction α10)) and hourly flow rate (Qin) and water temperature (Tin) of the Rhône River)

Fig. 4: Satellite chlorophyll-a concentration (Chl-a) data

Fig. 5: Comparison between observed and simulated water temperature (WT) at SHL2 (a) and GE3 (b) using contour plots

Fig. 6: a) Times series of mean absolute error (MAE) between the surface and 30 m depth, b) Vertical profiles of annual MAE

Fig. 7: Comparison between observed and simulated vertical profiles of WT at the two monitoring stations, SHL2 and GE3, during the three periods analyzed

Fig. 8: First period of interest analyzed in summer 2009, a) Times series of wind speed (U10) and direction (α10), b) Contour plots of simulated water temperature (WT) at the four stations, S1, S2, S3 and S4, and c) Comparison of mixed layer depths (metaT) across the lake

Fig. 9: Simulated surface water temperature (WT) on September 4, 2009, every two hours during the first period of interest analyzed

Fig. 10: In situ Chl-a measurements at SHL2 monitoring station

Fig. 11: Second period of interest analyzed in spring 2010, a) Times series of wind speed (U10) and direction (α10), b) Contour plots of simulated water temperature (WT) at the four stations, S1, S2, S3 and S4, and c) Comparison of buoyancy frequency (N²) across the lake

Fig. 12: Third period of interest analyzed in spring 2011, a) Times series of wind speed (U10) and direction (α10), b) Contour plots of simulated water temperature (WT) at the four stations, S1, S2, S3 and S4, and c) Comparison of buoyancy frequency (N²) across the lake
<figure><img src="image_1.png" /></figure>

631

632

Fig1

633
634

<figure><img src="image_2.png" /></figure>

635

636

Fig2

637
<figure><img src="image_3.png" /></figure>
642

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

643

644

Fig4

645
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

651

652 Fig6

653
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

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

655

656 Fig7

657

658
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>
<figure><img src="image_15.png" /></figure>
<figure><img src="image_16.png" /></figure>

Fig10
<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>
<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>