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Using 3D modeling and remote sensing capabilities for a better
understanding of spatio-temporal heterogeneities of phytoplankton
abundance in large lakes
Frédéric Soulignac
a,
⁎,Pierre-AlainDanis
b
,DamienBouffard
c
, Vincent Chanudet
d
, Etienne Dambrine
a
,
Yann Guénand
a,e
, Tristan Harmel
f,g
, Bastiaan W. Ibelings
h
,DominiqueTrevisan
a
,
Rob Uittenbogaard
i
, Orlane Anneville
a
a
French National Institute for Agricultural Research (INRA), CARRTEL, Université Savoie Mont Blanc, 75bis avenue de Corzent, 74200 Thonon-les-Bains, France
b
Agence Française pour la Biodiversité, Pôle AFB-Irstea Hydroécologie Plans d'Eau, 3275 route Cézanne, 13182 Aix-en-Provence, France
c
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Surface Waters –Research and Management, Kastanienbaum, Switzerland
d
Hydraulic Engineering Center (CIH), Electricité de France (EDF), Savoie Technolac, 73370 Le Bourget-du-Lac, France
e
SEGULA Technologie, 30 allée du lac d'Aiguebelette BP80271, 73375 Le Bourget du Lac, France
f
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
g
Irstea, UR RECOVER, Pôle AFB-Irstea Hydroécologie des Plans d'eau, 3275 route Cézanne, 13182 Aix-en-Provence, France
h
Department F.-A. Forel for Environmental and Aquatic Sciences (DEFSE), University of Geneva, Boulevard Carl-Vogt 66, 1211 Geneva, Switzerland
i
Deltares, Boussinesqweg 1, 2629, HV, Delft, The Netherlands
abstractarticle info
Article history:
Received 4 October 2017
Accepted 23 April 2018
Available online 18 June 2018
Communicated by Anthony Vodacek
Lake biological parameters show important spatio-temporal heterogeneities. This is why explaining the spatial
patchiness of phytoplankton abundance has been a recurrent ecological issue and is an essential prerequisite
for objectively assessing, protecting and restoring freshwater ecosystems. The drivers of these heterogeneities
can be identified by modeling their dynamics. This approach is useful for theoretical and applied limnology. In
this study, a 3D hydrodynamic modelof Lake Geneva (France/Switzerland) wascreated. It is basedon the Delft3D
suite software and includes the main tributary (Rhône River) and two-dimensional high-resolution meteorolog-
ical forcing. It provides 3D maps of water temperature and current velocities with a 1 h timestep on a 1 km hor-
izontal grid size and with a vertical resolution of 1 m near the surface to 7 m at the bottom of the lake. The
dynamics and the drivers of phytoplankton heterogeneities were assessed by combining the outputs of the
model and chlorophyll-aconcentration (Chl-a)data from MERIS satellite imagesbetween 2008 and 2012. Results
highlightphysical mechanisms responsible for the occurrence ofseasonal hot-spotsin phytoplanktonabundance
in the lake.At the beginning of spring, Chl-aheterogeneities are usually causedby an earlier onset of phytoplank-
ton growthin the shallowest and more sheltered areas;spatial differencesin the timing of phytoplankton growth
can be explained by spatial variability in thermal stratification dynamics. In summer, transient and locally higher
phytoplankton abundances are observed in relation to the impact of basin-scale upwelling.
© 2018 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.
Keywords:
3D modeling
Remote sensing
chlorophyll-a
Spatio-temporal heterogeneity
Lake Geneva
Delft3D
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, eval-
uating the abundance and composition of this community in routinely
obtained water samples raises several issues regarding the representa-
tiveness of the collected lake samples since phytoplankton is rarely ho-
mogeneously 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).
Journal of Great Lakes Research 44 (2018) 756–764
⁎Corresponding author at: Ecolog ical Engineering Laboratory (ECOL), Federal
Polytechnic School of Lausanne (EPFL), Station 2, 1015 Lausanne, Switzerland.
E-mail address: frederic.soulignac@epfl.ch (F. Soulignac).
https://doi.org/10.1016/j.jglr.2018.05.008
0380-1330/© 2018 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.
Contents lists available at ScienceDirect
Journal of Great Lakes Research
journal homepage: www.elsevier.com/locate/jglr
Understanding the mechanismsthat drive plankton spatial distribu-
tion has been a recurring theme in aquatic ecology and has been studied
for several decades (Arhonditsis et al., 2004). Commonly, phytoplank-
ton spatialheterogeneities fall into two categories, vertical and horizon-
tal 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-aconcentration (Chl-a)
in deep lakesis often characterized by the presence of a deep Chl-amax-
imum which acts as a local hotspot of primary production. The occur-
rence and the structure of a deep Chl-amaximum is only now
beginning to be properly understood (Leach et al., 2018). Heteroge-
neities along the horizontal axis –that have been observed using tra-
ditional 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 heterogene-
ities (Moreno-Ostos et al., 2007). Indeed, the distribution of phyto-
plankton 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 observa-
tions, horizontal variabilityin lake water parameters is often neglected.
Samples used for lake monitoring and water quality assessment are
usually collected at a single station, often the deepest point of the lake,
because this area is generally located in the center of the lake where an-
thropogenic impact and effect are limited. Nevertheless, the recent re-
mote sensing capabilities can facilitate the description and to some
extent, depending on the frequency of suitable images, the study of dy-
namics in lake heterogeneity (Bresciani et al., 2011;Gons et al., 2008;
Kauer et al., 2015;Odermatt et al., 2012;Palmer et al., 2015). In large
lakes, surface horizontal heterogeneities in Chl-aare a frequent phe-
nomenon and also present structures that could change rapidly
(Kiefer et al., 2015). Thus, the existence of such heterogeneities might
raise several issues when studying the impact of local and global forcing
on phytoplankton that requires a better understanding of the un-
derlying mechanisms responsible for those heterogeneities (Chen
et al., 2003). This is not just a matter of academic interest because
the distribution of blooms of harmful cyanobacteria across lakes is
of prime importance for risk assessment and management and af-
fects the use of lakes for drinking water and recreation (Ibelings
et al., 2003).
The physical properties of water govern its response to mechanical
energy inputs, such as wind, river inflow and heat exchanges, resulting
in vertical gradients of physical and chemical characteristics, such as
temperature and nutrients, which are major factors controlling phyto-
plankton development. As such these physical processes may promote
spatial heterogeneities in phytoplankton (Reynolds, 1997). Accordingly,
we postulatehere that a good understanding of physical dynamics is es-
sential to explain the spatial heterogeneity observed in phytoplankton
abundance. To test this, we applied a three-dimensional (3D) hydrody-
namic model to Lake Geneva (France/Switzerland) and evaluated the
ability of this hydrodynamic model to explain horizontal patchiness in
phytoplankton abundance. For that purpose, we analyzed the vertical
thermal structure predicted by the model at different stations in the
lake when Chl-apresented strong surface heterogeneities. The role of
hydrodynamic processes was expected tobe strong enough that consid-
ering hydrodynamics alone would be sufficient to explain some of the
phytoplankton heterogeneity patterns observed in Lake Geneva from
2008 to 2012. In other words, the purpose of this study is to show that
by considering only hydrodynamics, we are able to explain and predict
certain surface phytoplankton heterogeneities that are observed in
lakes.
Materials and methods
Study site
Lake Geneva is a large and deep peri-alpine lake located in the west-
ern part of theAlps, on the border between France and Switzerland (Fig.
1and Table 1). According to the international commission for the pro-
tection of Lake Geneva (CIPEL), it is thermally stratified during much
of the year, never freezes over and does not undergo complete mixing
every year. Its main tributary is the River Rhone which with
184.3 m
3
/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 phytoplank-
ton. This monitoring revealed important changes in phosphorus con-
centrations. 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 concen-
trations starting in the early 1980s. Today, Lake Geneva is mesotrophic
(Jacquet et al., 2014a).
Data
In this study, in situ vertical profiles of Chl-aand water temperature
(WT), transparency and remote sensed surface Chl-amaps are used be-
tween 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 fre-
quency is once a month throughout all the year. Samples for Chl-amea-
surements 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 soni-
cated. 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-ais
measured by spectrophotometry (Strickland and Parsons, 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-ais available from Kiefer et al. (2015).Itwasde-
rived 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
Inflow
S1
S2 (SHL2)
S3
S4 (GE3)
Outflow 10 km
10 km
North
Fig. 1. Lake Geneva contours, isodepth s (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)).
757F. Soulignac et al. / Journal of Great Lakes Research 44 (2018) 756–764
concentrations between 0.05 and 50 μg/L. Data availability is summarized
(Fig. 2).
Delft3D model
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 modeling ef-
fort was performed on Lake Geneva by using Delft3D. It is presented in a
series of three articles (Razmi etal., 2013, 2014 and 2017). We chose the
same surface heat flux module, the same bottom and surface roughness
formula, the same meteorological inputs and the same Rhone River in-
puts. We also simulated Lake Geneva entirely. But whereas Razmi and
coauthors divided each year into four periods of three months, our ob-
jective 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 hydrody-
namics in the Vidy bay, a small part of the lake located in front of Lau-
sanne, and they used a non-uniform vertical mesh (sigma-model).
However, we wanted a model requiring less computational time, a reg-
ular resolution throughout the lake and a uniform vertical mesh to bet-
ter 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 hydrody-
namic 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 conti-
nuity equation,
∂u
∂xþ∂v
∂yþ∂w
∂z¼0;
the two horizontal equations of motion,
∂u
∂tþu∂u
∂xþv∂u
∂yþw∂u
∂z¼−
1
ρ0
∂p
∂xþ∂
∂xνH
∂u
∂x
þ∂
∂yνH
∂u
∂y
þ∂
∂zνV
∂u
∂z
þfv and
∂v
∂tþu∂v
∂xþv∂v
∂yþw∂v
∂z¼−
1
ρ0
∂p
∂yþ∂
∂xνH
∂v
∂x
þ∂
∂yνH
∂v
∂y
þ∂
∂zνV
∂v
∂z
−fu;
the vertical equation of motion being reduced to the hydrostatic pres-
sure equation, and the transport equation of heat,
∂T
∂tþu∂T
∂xþv∂T
∂yþw∂T
∂z¼∂
∂xDH
∂T
∂x
þ∂
∂yDH
∂T
∂y
þ∂
∂zDV
∂T
∂z
þS
ρ0cpw
;
where x,y,andzare Cartesian coordinates (m), tis time (s), u,vand w
are the three components of the water velocity (m/s), fis the Coriolis
frequency in 1/s, Tis the water temperature (K), ρ
0
is the water density
(kg/m
3
), pis the pressure (Pa), ν
H
and ν
V
are the horizontal and vertical
eddy viscosities (m
2
/s), D
H
and D
V
are the horizontal and vertical coeffi-
cients of eddy diffusivity of heat (m
2
/s), Sis the source of heat per unit
volume (W/m
3
)andc
pw
is the water specific heat (J/K/kg). Model equa-
tions are precisely described (Deltares, 2014).
Model set-up
The surface of the numerical domain was created using a curvilinear
grid composed of 591 cells of about 1 km
2
area to fit the lake contour
(Fig. 1). In the vertical direction, 100 Z-layers were used to fit the ba-
thymetry. Their thickness varies from 1 m at the surface to about 7 m
at the bottom. Going deeper, the thickness of two consecutive layers in-
creases by a factor of 1.02. Based on these length scales, the computa-
tional time step was set to 3 min to verify the Courant-Friedrichs-
Lewy (CFL) criterion and the background horizontal eddy viscosity
and diffusivity was set to 100 m
2
/s. The k-εturbulence closure model
was chosen to calculate the vertical eddy viscosity and diffusivity be-
cause it has proven to perform well in stratified water (Burchard and
Baumert, 1995). The explicit multi-directional upwind (MDUE) scheme
was selected for the spatial discretization of the horizontal advection
terms and the Van Leer-2 scheme was used for the transport equation.
The wind drag coefficient was set as a linear function of wind speed be-
tween 0.63 10
−3
at 0 m/s and 7.23 10
−3
at 100 m/s. Stanton and Dalton
numbers were set at 1.3 10
−3
. The salinity was set constant at 0.15 ppt,
corresponding to the observed specific conductivity of about 300 μS/cm.
The sediment transport and resuspension in littoral zones and shallow
areas was neglected because no measurements are available to validate
this part of the model and also because we did not refine the grid near
the lake shore.
Model input data
Seven meteorological variables (air pressure, air temperature, cloud
coverage, relative humidity, incident solar radiation, wind speed and
wind direction) coming from simulation results of the Consortium for
Small-scale Modeling (COSMO) two-dimensional atmospheric model
were used in this study (Fig. 3). The time step is 1 h and the spatial res-
olution is1.5 km × 1.5 km. Hourly insitu measurements of the discharge
and the water temperature in the Rhône River at Porte De Scex, 5 km
upstream from the lake were also used (Fig. 3).
Simulations
The simulation starts on March 3, 2008, at midnight. A WT profile
measured at SHL2 at this date was used for initialization considering
the whole lake water temperature as horizontally homogeneous. The
lake was also supposed to be at rest, without water currents. Five
years were simulated, from 2008 to 2012. The simulation was stopped
and restarted on January 1 of each year just to change the transparency
value. The average SDD value between May and September, the five
months when the solar radiation is the highest, was used (Wahl and
Peeters, 2014). Values are presented in Table 2. Simulation results
were exported with a time-resolution of 1 h.
Table 1
Lake Geneva characteristics.
Characteristic Value
Latitude N 46° 27′
Longitude E 6° 32′
Elevation 372 m
Mean depth 153 m
Max depth 309 m
Surface area 580 km
2
Watershed area 7475 km
2
2008 2009 2010 2011 2012
Surface Chla
GE3
SHL2
Time
Fig. 2. Temporal availability of in situ data at lake stations and satellite imagery.
758 F. Soulignac et al. / Journal of Great Lakes Research 44 (2018) 756–764
Model validation
The model performance was evaluated by comparing observed and
simulated WT at SHL2 and GE3 for the five simulated years. In this
study, we focused on the water layer between the surface and 30 m
depth because this is where net photoautotrophic phytoplankton
growth occurs. Simulated WT profiles were systematically exported
when in situ measurements were taken, at approximately midday.
The model validation was performed by calculating the mean absolute
error (MAE), which quantifies the error between simulation results
and observations, defined by
MAE ¼1
nX
n
i¼1
Tsim iðÞ−Tob s iðÞ
jj
where T
sim
and T
obs
are respectively simulated and observed WT vectors
whose length is n. One value of MAE was calculated foreach observation
date by considering all the data between the surface and 30 m. One
value of MAE was also computed for each depth and each year by con-
sidering all the data of one year.
Model analysis
In this paper, we have chosen to focus on surface Chl-astructures
that repeat from time to time. The first structure was a phytoplankton
patch with enhanced densities near the north-western shore of the
lake in summer. We focused on this particular structure which was ob-
served on a MERIS image on September 7, 2009 (Fig. 4). Accordingly, a
first period, from August 31 to September 9, 2009, starting with low
and homogenous surface Chl-awas analyzed. The second structure de-
picts higher phytoplankton abundance in early spring in a specificpart
of the lake, namely at the entrance of the Rhône River (Kiefer et al.,
2015) and it was observed on an image dated March 21, 2011 (Fig. 4).
Accordingly, a second period, from March 8 to 28, 2011, was analyzed.
A third period of interest, from February 24 to March 29, 2010, was cho-
sen to be used as a counter-example for the 2nd structure. During that
period, environmental parameters allowed a homogenous develop-
ment of phytoplankton as clearly depicted by remote sensing in
March 23, 2010. Changes in the lake thermal structure during these
three periods were analyzed based on hourly simulated WT profiles
for three periods of interest at four stations S1, S2, S3 and S4 (S2 and
S4 correspond to SHL2 and GE3, respectively). Starting and ending
dates of the three periods corresponded to in situ sampling dates at
the station SHL2.
Lake Analyzer is a numerical code that allows thecalculation of sev-
eral lake parameters using high 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, N
2
, expressed in 1/s
2
by N2¼g
ρ
∂ρ
∂z.where gis the
gravity (9.81 m/s
2
), ρis the water mass density(kg/m
3
)andzis the ver-
tical Cartesian coordinate (m).
Results
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 sur-
face 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 sum-
mer and autumn when the lake is stratified. The annual MAE calcu-
lated 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 be-
tween 0.22 and 1.89 °C at GE3 (Fig. 6bandFig. 6c). The MAE is gen-
erally 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 ther-
mocline 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
960
1000
1040
AP
(hPa)
0
20
40
AT
(°C)
0
50
100
CC
(%)
0
50
100
RH
(%)
0
500
1000
SW
(W/m2)
0
10
20
U10
(m/s)
0
180
360
α10
(°)
0
500
1000
Qin
(m3/s)
2008 2009 2010 2011 2012
0
10
20
Tin
(°C)
Time
Fig. 3. Model input meteorological data at the monitoring station SHL2 (air pressure(AP),
air temperature (AT),cloud coverage (CC),relative humidity(RH), incident solarradiation
(SW), wind speed(U10) and wind direction(α10)) and hourly flow rate (Qin) and water
temperature (Tin) of the Rhône River).
Table 2
Model input data Secchi disk depth (SDD) from
2008 to 2012.
Year SDD (m)
2008 6.1
2009 6.4
2010 6.1
2011 6.0
2012 4.0
2009
31-Aug 01-Sep 07-Sep
2010
01-Mar 05-Mar 08-Mar 13-Mar 23-Mar
2011
08-Mar 11-Mar 21-Mar
Chl-a (µg/l)
0
2
4
6
8
10
Fig. 4. Satellite chlorophyll-aconcentration (Chl-a)data.
759F. Soulignac et al. / Journal of Great Lakes Research 44 (2018) 756–764
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 ther-
mal vertical structure on a smaller time-scale, especially for the three
time-periods defined previously from surface Chl-aobservations. Dur-
ing the first period from August 31 to September 9, 2009, the model sat-
isfactorily 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 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 strati-
fication 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.
Lake thermal structure in summer 2009
From August 31 to September 2, 2009, simulation results predicted a
homogeneous thermalstratification 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 eventfrom west or south-
westerly directions induced downwelling at S1 and upwelling at S4.
This wind event continued on September 4 and increased, presenting
wind speed values N10 m/s (Fig. 8a). The minimum thickness of the sur-
face mixed layer calculated by themodel 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-awas observed from in situ
measurements at SHL2 on August 31 and September 9 (Fig. 10). Inter-
estingly, the satellite image also shows high Chl-aconcentration on Sep-
tember 7 along the north-western shore (Fig. 4).
Lake thermal structure in spring 2010
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 (N5 m/s) from west or north-west
and north or north-easterly directions prevented an increase in water col-
umn stability (Fig. 11). Conversely, the wind speed was lower between
March 12 and 17 (b5 m/s) and daily stratification was predicted by the
model. In fact, based on a stability criterion (thermal stratification was de-
fined as N
2
N10
−5
1/s
2
), 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
Observation
0
10
20
30
Simulation
)
Time
2009 2010 2011 2012
0
10
20
30
WT (°C)
5
10
15
20
25
Observation
0
10
20
30
Simulation
)
Time
Depth (m
2008
Depth (m
2010 2011 2012
0
10
20
30
WT (°C)
5
10
15
20
25
a)
b)
2008 2009
Fig. 5. Comparison betweenobserved and simulatedwater temperature (WT) at SHL2 (a)
and GE3 (b) using contour plots.
Time
2009 2010 2011 2012
MAE (°C)
0
2
4
SHL2
GE3
2008
MAE (°C )
012
Depth (m)
0
10
20
30
SHL2
012
0
10
20
30
GE3
2008
2009
2010
2011
2012
012
Depth (m)
0
10
20
30
2008
012
0
10
20
30
2009
MAE (°C)
012
0
10
20
30
2010
012
0
10
20
30
2011
012
0
10
20
30
2012
SHL2
GE3
a)
b)
c)
Fig. 6. a) Timesseries of mean absolute error (MAE) between thesurface and 30 m depth,
b) Vertical profiles of annual MAE.
Table 3
Annual meanvalues of the mean absolute error in watertemperature between the surface
and 30 m depth for the two monitoring stations, SHL2 and GE3, from 2008 to 2012.
SHL2 GE3
2008 1.07 1.30
2009 0.79 0.97
2010 0.80 1.29
2011 1.07 1.20
2012 0.72 0.80
WT (°C)
5 10152025
Depth (m)
0
10
20
30
Observation
WT (°C)
5 10152025
0
10
20
30
Simulation
31-Aug-2009 (SHL2)
09-Sep-2009 (SHL2)
WT (°C)
45678
Depth (m)
0
10
20
30
Observation
WT (°C)
45678
0
10
20
30
Simulation 24-Feb-2010 (SHL2)
17-Mar-2010 (SHL2)
23-Mar-2010 (GE3)
29-Mar-2010 (SHL2)
WT (°C)
56789
Depth (m)
0
10
20
30
Observation
WT (°C)
56789
0
10
20
30
Simulation
08-Mar-2011 (SHL2)
22-Mar-2011 (GE3)
28-Mar-2011 (SHL2)
a)
b)
c)
Fig. 7. Comparison betweenobserved and simulated verticalprofiles of watertemperature
(WT) at the two monitoring stations, SHL2 and GE3, during the three periods analyzed.
760 F. Soulignac et al. / Journal of Great Lakes Research 44 (2018) 756–764
stratification at any station. In comparison, the satellite image March 23
showed quite homogenous Chl-aconcentrations at the whole lake scale
(Fig. 4) and the maximum of Chl-awasobservedfrominsitumeasure-
ments at SHL2 only on March 29 just below the surface (Fig. 10).
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 (N5 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/s
2
and con-
tinued to reach values below 10
−5
1/s
2
every day until March 16. Mean-
while, 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/s
2
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 col-
umn stability at S2. At S4, the buoyancy frequency passed from 2 10
−4
to
310
−6
1/s
2
. 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 ho-
mogeneous 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-awas 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 which include modeling of water
temperature were created using the open source Delft3D software: Lake
Table 4
Mean absolute error values in water temperature between the surfaceand 30 m depth at
the two monitoring stations, SHL2 and GE3, during the three periods analyzed (NA: not
available).
SHL2 GE3
31/08/2009 1.27 NA
09/09/2009 1.49 NA
24/02/2010 0.52 NA
17/03/2010 0.57 NA
23/03/2010 NA 0.53
29/03/2010 0.54 NA
08/03/2011 0.57 NA
20/03/2011 NA 0.18
28/03/2011 0.54 NA
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,S3andS4,andc)Comparisonofmixed layer depths (metaT) across the lake.
00:00 02:00 04:00
06:00 08:00 10:00
12:00 14:00 16:00
18:00 20:00 22:00
Simulated surface WT (°C)
18.5 19 19.5 20 20.5 21
Fig. 9. Simulated surface watertemperature (WT) on September 4, 2009, every 2 h during
the first period ofinterest analyzed.
Chl-a (µg/l)
0 5 10 15
Depth (m)
0
10
20
30
2009
31-Aug
09-Sep
Chl-a (µg/l)
0510
0
10
20
30
2010
24-Feb
29-Mar
Chl-a (µg/l)
01020
0
10
20
30
2011
08-Mar
28-Mar
Fig. 10. In situ Chl-ameasurements at SHL2 monitoring station.
761F. Soulignac et al. / Journal of Great Lakes Research 44 (2018) 756–764
Geneva in France/Switzerland (Razmi et al., 2017, 2014, 2013), Lake Con-
stance 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 vali-
dated using in situ measurements and proved to perform satisfactorily,
were 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 usingvertical 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.
Early spring algal development onset in 2010 and 2011
In spring 2010, satellite surface Chl-adata showed that the onset of
phytoplankton development was homogeneous in Lake Geneva. This is
corroborated by the use of the 3D model which shows that the thermal
structure of the lake was homogeneous too. By contrast in spring 2011,
satellite images revealed heterogeneous Chl-awith a maximum re-
corded in the eastern part of the lake indicating that the onset of phyto-
plankton development occurred earlier at S1. Based on the satellite data,
it was shown that this part of the lake regularly exhibits higher Chl-a
concentrations in spring (Kiefer et al., 2015).
Given the mesotrophic status of the lake, in March, the euphotic
layer receives an input of nutrients through winter mixing, bringing
up nutrients from the bottom of the lake, which results in reactive phos-
phorus concentrations higher than 10 μg/L in the euphotic zone. In Lake
Geneva, phosphorus is not yet a limiting factor for phytoplankton
growth at this time of the year because most species may be limited
by phosphorus as concentration becomes b10 μg/L (Sas, 1989) and se-
vere reduction in algal growth may occur if soluble reactive phosphorus
concentrations fall below 3 μg/L (Grover, 1989;Suttle et al., 1988).
Moreover, according to the conceptual model of the Plankton Ecology
Group (PEG) (Sommer et al., 2012, 1986)andReynolds (1997) in
such deep lakes, light is the critical resource for phytoplankton growth
in early spring. Observations confirm that phytoplankton developed in
areas of Lake Geneva where our simulations indicated that the thermal
stratification sets in earlier in a more sheltered area compared to the
rest of the lake. Indeed, our results are coherent with the PEG model
which states that thermal stratification triggers the algal development
in spring in deep lakes due to enhanced light availability for phyto-
plankton growth resulting from restrictions on the mixing depth.
This concept has long been described as Sverdrup's critical mixing
depth, i.e. phytoplankton is able to grow only if the mixing depth is
less than a critical depth so that net photoautotrophic growth is possible
(Sverdrup, 1953). However, this concept is not always in line with ob-
servations since phytoplankton blooms have been observed preceding
the onset of stratification, when mixing is still unrestricted (Eilertsen,
1993). Huisman et al., (1999) added the concept of critical turbulence
to the concept of critical mixing depth. In deep and relatively clear
lakes, phytoplankton can maintain development, irrespective of mixing
depth if their growth rate in the upper layer exceeds vertical mixing
rates. The sheltered area in the eastern part of Lake Geneva may favor
early bloom development following either one of these concepts, either
by reducing mixing depth or by reducing turbulent mixing rates to
below the respective critical values.
Upwelling event in summer 2009
Upwelling events were previously observed in some regions of Lake
Geneva (Oesch et al., 2008) and more generally in large lakes (Plattner
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
fourstations,S1,S2,S3andS4,andc)Comparisonofbuoyancyfrequency(N
2
)acrossthelake.
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 (N
2
)acrossthelake.
762 F. Soulignac et al. / Journal of Great Lakes Research 44 (2018) 756–764
et al., 2006). When characterizingthe 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-aheterogeneities
observed on September 7, 2009, were related to this upwelling event.
The model suggests a displacement of deep phytoplankton to the sur-
face and provides additional evidence of therole of upwelling on phyto-
plankton 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 wa-
ters that will stimulate algae productivity and phytoplankton growth.
So, it remains unresolved whether the upwelling event indeed en-
hanced primary production and phytoplankton growth as is sometimes
the case during upwelling events (Poschke et al., 2015).
However, this effective enhancement of algal production needs to be
accurately tested because upwelling events are characterized as a “tem-
poral 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 nutri-
ent rich hypolimnetic waters”(Haffner et al., 1984). As a consequence, it
is important to understand and assess when upwellings are efficient fer-
tilizers of the euphotic zone as it has been recorded in various ecosys-
tems (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 sur-
face waters. Such fertilization of the euphotic zone by nutrient enriched
deep water might have an important role in the functioning of mesotro-
phic lakes. It is important to better assess if these hydrodynamic events
can efficiently sustain primary production and how they impact the out-
come 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 exem-
plary patterns observed during re-oligotrophication of deep lakes such as
changes in taxonomic composition (Jeppesen et al., 2005)orhysteresisin
phytoplankton biomass (Tadonleke et al., 2009).
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 thermo-
cline 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 or production
of drinking water. In contrast, if upwelling brings potentially toxic spe-
cies such as P. rubescens to thelake surfacewhere the contact with peo-
ple 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 sub-
ject 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 more in situ sampling of phy-
toplankton 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.
Contribution of 3D models in the interpretation of spatial observations of
Chl-a concentration from Satellite Data
Optical satellite remote sensing has been used to retrieve patterns of
surface Chl-aconcentration 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-aconcentration 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-aretrieval, 3D hydrodynamic models
coupled to ecological models could be used to give information about
the Chl-astructure in the vertical dimension which may vary between
different portions of the lake.
Conclusion
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 res-
olution 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. Our model performed
well in providng explanations for these two horizontal heterogeneities
in Chl-aabundances. For instance, we highlighted the important role of
wind in determining surface phytoplankton abundance. A short and in-
tense wind event in spring is able to create clear surface heterogeneities
in phytoplankton distribution which continue through time. Also in sum-
mer, the role of the wind is very important. It can generate upwelling
events and create surface Chl-aheterogeneities bringing potentially
toxic species to the surface. If we look to the future, the validity of this ap-
proach and the possibility to export this 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. (2015) 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 under-
stand better the phytoplankton dynamic and heterogeneities observed in
Lake Geneva.
Acknowledgements
This work was supported the French Agency for Biodiversity (AFB,
before French National Agency for Water and Aquatic Environments
(ONEMA)), ModeL contract N° 15000239, and by the European Space
Agency (ESA) Scientific Exploitation of Operational Missions element
(SEOM), CORESIM contract N° A0/1-8216/15/I-SBo. We wish to thank
the French Alpine Lakes Observatory (SOERE-OLA) and the Interna-
tional 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 dis-
cussions and Pierre Keraudren for English editing.
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