The document describes a study using the Delft3D hydrodynamic and water quality model to assess the representativeness of water quality data from a single monitoring location in Lake Geneva. The model was able to reproduce observed water temperatures and some spatial patterns in chlorophyll-a concentrations seen by satellite. Going forward, the model will be used to better understand long-term phytoplankton dynamics and impacts of climate events, and to evaluate how well the single monitoring station represents conditions across the large lake.

1. 1: The French National Institute for Agricultural Research - Inra
Lake Hydrobiology Unit, Thonon-les-Bains, France
Application of Delft3D to Lake Geneva: towards the
assessment of the representativeness of one
sampling location in large lakes
Delft3D User Meeting - 04 November 2015
Frédéric Soulignac1, P.-A. Danis2,10, D. Bouffard3,
V. Chanudet4, E. Dambrine5, Y. Guenand6, B. Guillermin1,
T. Harmel7,10,2, B. Ibelings8, I. Kiefer3, D. Trevisan1,
R. Uittenbogaard9 and O. Anneville1
3 4 5 6 7 8 9 10
2: The French National Agency for Water and Aquatic Environments

2. Context
• In 2000, the European Parliament set out the European Water Framework
Directive for managing and protecting water bodies in Europe.
• Classification of water bodies into ecological status
• For lakes, the assessment of this status is based on:
– Biological indicators
– Physical-chemical indicators
– Hydro-morphological indicators
• Sampling method
– Integrated samples in the euphotic zone over the deepest point of the lake
– At least 4 observations per year over the growing season
• Spatial heterogeneities in large lakes
• Representativeness of those data versus the whole lake needs to be
verified (Kiefer et al. , 2015)
Introduction Material and methods Results Conclusion
2

3. Objective and method
• Does spatial heterogeneities in large lakes bias our estimation of their
ecological status ?
• Chlorophyll-a
• To combine
– Data analysis of in-situ and remote satellite data
– 3D modeling
Introduction Material and methods Results Conclusion
3

4. Lake Geneva
Introduction Material and methods Results Conclusion
Image adapted from http://www.cipel.org
Monitoring station SHL2
Lake Geneva catchment (7 419 km2)
Catchment of the Rhône river
after Lake Geneva
Rivers
Border France-Switzerland
Lake GenevaAltitude = 372 m
Surface area = 580 km2
Mean depth = 153 m
Max depth = 309 m
Rhône inflow = 182 m3/s
Rhône outflow = 250 m3/s
Residence time = 11 years
Volume = 89 km3
4
Lake Geneva provides numerous ecosystem services.

5. Data
• In-situ lake monitoring
– 1 station: SHL2 (deepest point, 309 m)
– 1 or 2 samples per month since 1963
– CTD and dissolved oxygen vertical profiles (1 m resolution)
– Physical-chemical parameters at 20 depths between surface and bottom
– Chlorophyll-a concentration at 10 depths between surface and 30 m depth
– Phytoplankton abundance and composition (0-18 m depth-integrated)
– Zooplankton abundance and composition (0-50 m depth-integrated)
• Satellite: MERIS sensor (https://earth.esa.int/)
– Processing done with FUB WeW algorithm (Schroeder et al., 2007)
– 624 images of surface chlorophyll-a concentration from 2002 to 2012 (300 m
resolution)
Introduction Material and methods Results Conclusion
Monitoring point SHL2
5
Satellite images processed by Isabel Kiefer, EPFL, Lausanne, Switzerland

6. Chlorophyll-a measurements
In-situ at SHL2 Satellite
Introduction Material and methods Results Conclusion
11/04/2010
SHL2
6

7. Satellite images calibration
2002-2012 (37 dates) 2011
Introduction Material and methods Results Conclusion
SHL2
2.29Chla8.1Chla atednot_calibrcalibrated 
7
Depth averaged
0 m-Secchi depth

8. Satellite correction
Not calibrated Calibrated
Introduction Material and methods Results Conclusion
2.29Chla8.1Chla atednot_calibrcalibrated 
8
But what does really see the satellite ?

9. What does see the satellite ?
In-situ at SHL2 Satellite (calibrated)
Introduction Material and methods Results Conclusion
Satellite does not see algal development below Sd.
SHL2
9

10. Processes:
• Ocean heat flux model
• k-ε turbulence closure model
• BLOOM phytoplankton module
• Grazing of phytoplankton by zooplankton
• Nitrification and denitrification
• Mineralization and settling of detritus
• Light extinction by phytoplankton
• Reaeration
State variables:
• Water temperature
• Nutrients: NH4, NO3, PO4, SiO2
• Phytoplankton: Diatoms and Green algae
• Dissolved oxygen
• Detritus (POC, PON, POP)
Delft3D set-up
Introduction Material and methods Results Conclusion
Hydrodynamics
Delft3D-Flow
Initial
conditions
Simulation
results
Forcing:
• Meteorology
• Rivers (flow)
• Transparency
Ecology
Delft3D-ECO
Forcing:
• Photosynthetically active radiation
• Rivers (composition)
• Grazing by zooplankton
Simulation
results
10
Initial
conditions
Hydrodynamic grid: curvilinear grids, 400 m resolution, Z-layers
Ecological grid: 3 x 3 horizontal aggregated
Hydrodynamics set-up by
Damien Bouffard, EPFL, Lausanne, Switzerland

11. Model parameters
11
Introduction Material and methods Results Conclusion
Parameter Value
Delft3D-Flow
Secchi depth 5 m
Wind drag coefficient
For young waves and limited fetch (Smith, 1991)
Cd = 0.001 if U ≤ 3 m/s
10*Cd = 0.63 + 0.12*U if U > 3 m/s
Stanton number 0.00145 (default)
Dalton number 0.0015 (default)
Coefficient for the free convection 0.14 (default)
Background horizontal viscosity and diffusivity of heat 10 m2 s-1
Background vertical viscosity and diffusivity of heat 10-6 m2 s-1
Ozmidov length 0 m (default)
Delft3D-ECO
Background extinction of visible light 0.12 m-1
Mineralisation rate 0.04 d-1

12. Water temperature
Observation Simulation
12
Introduction Material and methods Results Conclusion
SHL2
Start end
Interpolated
model outputs
Depth (m) 5 15 25 35 45
RMSE (°C) 0.6 1.3 1.2 0.8 0.5

13. Water temperature
13
Introduction Material and methods Results Conclusion
SHL2
Model reproduces well the water temperature in 2010, 2011 and 2012.
Hourly
model outputs
In 2009, problem with data used to force the model.

14. Chlorophyll-a
14
Introduction Material and methods Results Conclusion
SHL2
The model overestimates the algal development in spring.
The model reproduces well the chlorophyll-a concentration from May to September.
Depth averaged
0-30 m

15. Chlorophyll-a
Observation Simulation
Introduction Material and methods Results Conclusion
15
21/04/2011
Observation at 10 o’clock in the morning
Simulation: update of the BLOOM module at midnight
The model succeeded in reproducing several observed spatial heterogeneities.
Depth averaged
0 m-Secchi depth

16. Chlorophyll-a
Observation Simulation
Introduction Material and methods Results Conclusion
16
05/05/2011
Observation at 10 o’clock in the morning
Simulation: update of the BLOOM module at midnight
Depth averaged
0 m-Secchi depth
The model succeeded in reproducing several observed spatial heterogeneities.

17. Chlorophyll-a
Observation Simulation
Introduction Material and methods Results Conclusion
17
10/08/2011
Observation at 10 o’clock in the morning
Simulation: update of the BLOOM module at midnight
Depth averaged
0 m-Secchi depth
The model succeeded in reproducing several observed spatial heterogeneities.

18. Towards the assessment of representativeness of
one monitoring station
Introduction Material and methods Results Conclusion
18
Depth averaged
0-30 m

19. Conclusions and perspectives
• The model reproduced well the water temperature.
• The model succeeded in catching several observed spatial heterogeneities
of Chlorophyll-a by the satellite.
• To improve model performances (until the end of 2015)
– Algal development onset in spring is very sensitive to the hydrodynamics
• To use the model (2016)
– To assess the representativeness of one lake monitoring station in large lakes
– To better understand the reoligotrophication process in Lake Geneva: long-
term phytoplankton dynamics with phosphorus concentration decreasing
– To analyse impacts of extreme climate events (storms, heat waves)
– Temporal variability of spatial heterogeneities
Introduction Material and methods Results Conclusion
19

20. 1: The French National Institute for Agricultural Research - Inra
Lake Hydrobiology Unit, Thonon-les-Bains, France
Thank You !
Delft3D User Meeting - 04 November 2015
Frédéric Soulignac1, P.-A. Danis2,10, D. Bouffard3,
V. Chanudet4, E. Dambrine5, Y. Guenand6, B. Guillermin1,
T. Harmel7,10,2, B. Ibelings8, I. Kiefer3, D. Trevisan1,
R. Uittenbogaard9 and O. Anneville1
3 4 5 6 7 8 9 10
2: The French National Agency for Water and Aquatic Environments