Numerical modeling in lakes, tools and
application
Marie-Paule Bonnet, Frédéric Guérin
UMR 5563 GET IRD, CNRS, OMP, Toulouse III
Outlook
• DYLEM1D : controlling factors of
Microcystis blooms and restoration
process evaluation of the Villerest
Reservoir (France)
•
SYMPHONIE 2D: Controlling factors
of CH4 emissions in Petit Saut
Reservoir (French Guiana)
13/04/2015
DYLEM1D
1D vertical model for lakes and reservoirs
13/04/2015
Application to the Reservoir Villerest (Loire, France)
Impounding : 1984
Mean volume: 62 Mm3
Maximum depth : 45 m
Mean depth : 18 m
Annual water level variation : ±15 m
Biogeochemical conceptual scheme
Controlling factors of Microcystis aeruginosa blooms in a highly
eutrophic reservoir
Evaluate the restoration processes comparing two periods of study
90-92 and 97-2000
13/04/2015
A large dataset available for modeling
Temperature :
Nutrients (NO3, NH4, PO4, SiO2) :
Every 3 hours, 11 levels in the lake
Every hour in the inflow
Meteo data (every 20 mn):
Every day in the inflow
Every two weeks during blooms
Every month otherwise
Phytoplankton (algae species) :
Solar radiation
Wind speed/direction
Specific relative humidity
Species identification and biomasse
estimation every two weeks during blooms
Every month otherwise
Air temperature
Inflow/outflow (every 3 hours)
Between the two periods of study P and N inputs are
about 40 % less
Radiative net balance
Inflow
The physics model
wind
Outflow
Mixing processes included:
- Dispersion induced by wind and internal seiche
- advection induced by inflow/outflow
- free convection
- mixing induced by surface waves
Simple but requires calibration
13/04/2015
The biogeochemical model
O2
H2CO3*
photosynthesis
respiration
nitrification
NH4
CO32-
grazing
PO4
HCO3-
CaCO3
COP
H2CO3*
hydrolysis
NO 3
O2, NO3
COD
NH4
mineralisation
A complex conceptual scheme developed step by step
The phytoplankton module was developed first without considering
nutrients limitation
Phytoplankton module
5 species
Parameters for growth
optimum conditions
estimated from lab
Buoyancy regulation for Microcystis only
13/04/2015
Temperature simulation
Calibration year
Validation
Important differences when :
the 1D assumption is wrong (winter)
The vertical stratification is very strong
13/04/2015
Phytoplankton simulation
Calibration : sensitivity analysis and monte-carlo analysis
Microcystis aeruginosae
mg.l-1
The model is able to reproduce the
phytoplankton biomass at the species
level
Calibration was required mainly because :
Not all the parameters were estimated
species interactions (self-shading, grazing)
mg.l-1
Cyclotella sp.
13/04/2015
Some controlling factors of Microcystis blooms
buoyancy regulation
Reference
Beside optimum conditions in terms
of temperature, buoyancy regulation
ability combined with a strong
vertical stratification is an important
feature for explaining Microcystis
dominance in the reservoir
Vertical stratification
13/04/2015
Evaluation of the Restoration process
Despite significant P-PO4 load reduction, Microcystis remains dominant
13/04/2015
Evaluation of the Restoration process
13/04/2015
Conclusions
Model strength :
•Working at the planktonic species level which enables to tackle some of the
controlling factors of the planktonic succession and of Microcystis dominance
•Relatively good “predictive capacities” which enable following the reservoir
evolution in response to nutrients inputs reduction
Model weakness :
• 1D assumption is not always valid and influences biogeochemical results
• Large calibration effort was required to work at the species level despite
laboratory estimation of main parameters
13/04/2015
SYMPHONIE 2D applied to reservoir
Modeling CH4 and CO2
emissions from a tropical freshwater reservoir: The Petit Saut
Reservoir
F. Guérin, MP Bonnet, G. Abril, R. Delmas
13/04/2015
Methodology
Site: Petit Saut Reservoir in French Guiana, filled in 1994
The most documented tropical reservoir
(10 years of monitoring)
Identification of the main processes
controlling emissions
Determination of the kinetics in the lab/field
Process-based model
Physical model SYMPHONIE 2D
1 mesh
Mean daily atmospheric forcing
Wind speed
Air temperature
Relative humidity
Air pressure
Solar radiation
IR Radiation
Daily water inflow (including
rainfall) and outflow
Constant temperature for water
entering the Reservoir
View from
above
1 mesh
Dam
148 meshes in the Ox direction
≈ 100 km
Longitudinal
view in the
main channel
≈ 3.5 km3
Submerged wall
No model for the river downstream
Run must be started with the reservoir at full operating level
Biogeochemical model
Source and sink terms of the
biogeochemical model
C
uC ( w  ws )C
 
C 



K
S &S
t
x
z
z 
z 
vertical turbulent diffusion
Advection
Diffusive fluxes
No model for bubbling
No module for OM cycling in the water column
CH4 and CO2 production
Production by flooded soil and biomass
Incubation in anaerobic condition during one year of
 ≠ Soils &
 ≠ Plant material from the forest surrounding the reservoir
CH4
2000
2000
1500
1500
Prod (nmol g-1 h-1)
Prod (nmol g-1 h-1)
CO2
1000
500
100
75
50
25
0
1000
500
100
75
50
25
0
SOILS
PLANT
SOILS
Guérin et al., submitted
Production CH4 and CO2 -> PLANT > SOIL
PLANTS ≈ 40-50% CH4
SOILS < 30% CH4
PLANT
CH4 and CO2 production
Production by flooded soil and biomass
350
CH4 emission
GgC y-1
300
CO2 emission
250
CH4 production
200
CO2 production
150
100
50
0
1
2
3
4
5
6
7
8
9
10
Year
Guérin et al., 2008
Emissions from Abril et al., 2005
Oxidation = Production - Emission
Year 2003: CH4 Oxidation = 85% of CH4 production ( ≈ 50GgC y-1)
CH4 oxidation
Incubation of water
In aerobic conditions
In the dark
At different CH4 concentrations
Water from
different stations in the lake
Different depths
In the epilimnion
At the oxycline
In the river below the dam
Guérin and Abril, 2007
Specific oxidation rate
VCH4= 0.11±0.01 h-1
Diffusive fluxes
Fdiff = kGHG, T (Pwater – Patm)
Rain effect
Wind effect
12
This study
This study, exp model
CW03
UG91
FU-G02
W85
k600 (cm.h-1)
10
8
6
4
2
0
0
1
2
3
4
5
6
7
U10 (m.s-1)
Guérin et al., 2007
k at low wind speed ≈ 50% higher than in temperate/cold environment
Rainfall contributes to 25% of diffusive fluxes
Biogeochemical modeling
In contrast, very simple scheme for other processes
Respiration and Photosynthesis
Photosynthesis
Phot  PhotmaxChloamoy
(After Vaquer et al., 1997 & Collos et al., 2001)
Autotrophic respiration
 1  PAR
PARz
z
exp
T Tref
 PAR 
PARopt
opt

RA  RA Chloamoy
T Tr e f
ma x

NH 4

 NH 4  K NH
4

O2
O2  K O
2
Heterotrophic respiration
RH  BODMAX 
T Tref
O2
O2  KO2
(BOD determined after Dumestre (1998) and HYDRECO unpublished data)
Results
26
Depth (m)
January
µmol(O 2).L-1
T(°C)
28
30
32
0
0
-5
1000
1500
250 500 750 1000
-5
-5
-5
-10
-10
-10
-10
-15
-15
-15
-15
-20
-20
-20
-20
-25
-25
-25
-25
Temp
-30
-35
26
28
30
32
O2
CO2
-30
-35
50 100 150 200 250
-30
1000
1500
250 500 750 1000
0
0
-5
-5
-5
-5
-10
-10
-10
-10
-15
-15
-15
-15
-20
-20
-20
-20
-25
-25
-25
-25
-30
-30
-30
-30
Depth (m)
0
-35
26
28
30
32
0
-35
50 100 150 200 250
0
-35
500
1000
1500
0
250 500 750 1000
0
-5
-5
-5
-10
-10
-10
-15
-15
-15
-15
-20
-20
-20
-20
-25
-25
-25
-25
-30
-30
-30
-30
Depth (m)
-5
-10
-35
-35
26
28
30
32
-35
50 100 150 200 250
-35
500
1000
1500
250 500 750 1000
0
0
0
0
-5
-5
-5
-5
-10
-10
-10
-10
-15
-15
-15
-15
-20
-20
-20
-20
-25
-25
-25
-25
-30
-30
-30
-30
-35
-35
-35
-35
Depth (m)
CH4
-35
500
0
-35
December
500
0
-35
July
50 100 150 200 250
0
-30
June
µmol(CH 4).L-1
µmol(CO 2).L-1
Results
Dry Season
µmol(O2).L-1
T(°C)
26
28
30
32
µmol(CO2).L-1
50 100 150 200 250
500
1000
µmol(CH4).L-1
1500
250 500 750 1000
0
0
0
-5
-5
-5
-5
-10
-10
-10
-10
-15
-15
-15
-15
-20
-20
-20
-20
-25
-25
-25
-25
-30
-30
-30
-30
-35
-35
-35
-35
Depth (m)
0
OM cycling in the reservoir has a significant impact on Conc.
Results
Atmospheric fluxes
Degassing
Diffusive fluxes
12000
CO2
300
200
100
0
1994
1996
1998
2000
2002
CO2
10000
tC-CO2 month-1
F(CO2) (mmol.m -2.d-1)
400
8000
6000
4000
2000
0
1994
2004
1996
2002
2004
6000
150
CH4
50
tC-CH4 month-1
F(CH4) (mmol.m -2.d-1)
2000
Year
Year
250
20
10
0
1994
1998
1996
1998
2000
Year
2002
2004
CH4
4000
2000
0
1994
1996
1998
2000
2002
2004
Year
Good reproduction of vertical profiles of conc. is crucial for degassing
Conclusion
Strength of model
Simple formulation
Kinetics determined on site -> No calibration required
Models are efficient tools for the computation of mass
balance since it integrates:
Biogeochemical processes
Hydrodynamics
The approach enables to identify lack in the scheme
A module for OM (Allochthonous and Autochthonous)
cycling in the water column of reservoirs must be
included