numerical modeling in lakes, tools and application marie-paule bonnet, frédéric guérin umr 5563...
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Numerical modeling in lakes, tools and application
Marie-Paule Bonnet, Frédéric GuérinMarie-Paule Bonnet, Frédéric GuérinUMR 5563 GET IRD, CNRS, OMP, Toulouse UMR 5563 GET IRD, CNRS, OMP, Toulouse
IIIIII
OutlookOutlook
•DYLEM1D : controlling factors DYLEM1D : controlling factors of of MicrocystisMicrocystis blooms and blooms and
restoration process evaluation restoration process evaluation of the Villerest Reservoir of the Villerest Reservoir
(France)(France)
• SYMPHONIE 2D: Controlling SYMPHONIE 2D: Controlling factors of CHfactors of CH44 emissions in emissions in
Petit Saut Reservoir (French Petit Saut Reservoir (French Guiana)Guiana)
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DYLEM1DDYLEM1D1D vertical model for lakes and reservoirs1D vertical model for lakes and reservoirs
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Application to the Reservoir Villerest (Loire, France)
Impounding : 1984Mean volume: 62 Mm3
Maximum depth : 45 m Mean depth : 18 mAnnual water level variation : ±15 m
Biogeochemical conceptual Biogeochemical conceptual schemescheme
Controlling factors of Controlling factors of MicrocystisMicrocystis aeruginosaaeruginosa blooms in a highly eutrophic reservoirblooms in a highly eutrophic reservoir
Evaluate the restoration processes comparing two Evaluate the restoration processes comparing two periods of study 90-92 and 97-2000periods of study 90-92 and 97-2000
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A large dataset available for modeling
Meteo data (every 20 mn):
Solar radiationWind speed/directionSpecific relative humidityAir temperature
Temperature :
Every 3 hours, 11 levels in the lakeEvery hour in the inflow
Inflow/outflow (every 3 hours)
Nutrients (NO3, NH4, PO4, SiO2) :
Every day in the inflowEvery two weeks during bloomsEvery month otherwise
Phytoplankton (algae species) :
Species identification and biomasse estimation every two weeks during bloomsEvery month otherwise
Between the two periods of study P and N inputs are about 40 % less
The physics modelThe physics model
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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
The biogeochemical modelThe biogeochemical model
A complex conceptual scheme developed step by step
The phytoplankton module was developed first without considering nutrients limitation
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Phytoplankton modulePhytoplankton module
5 species Parameters for growth optimum conditions estimated from lab
Buoyancy regulation for Microcystis only
Temperature simulationTemperature simulation
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Calibration year Validation
Important differences when : the 1D assumption is wrong (winter)The vertical stratification is very strong
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Phytoplankton simulationPhytoplankton simulationCalibration : sensitivity analysis and monte-carlo analysis
mg.l
-1
Cyclotella sp.
mg.l
-1
Microcystis aeruginosae
The model is able to reproduce the phytoplankton biomass at the species level
Calibration was required mainly because :Not all the parameters were estimatedspecies interactions (self-shading, grazing)
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Some controlling factors of Some controlling factors of MicrocystisMicrocystis bloomsblooms
buoyancy regulation
Vertical stratification
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
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Evaluation of the Evaluation of the Restoration processRestoration process
Despite significant P-PO4 load reduction, Microcystis remains dominant
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Evaluation of the Evaluation of the Restoration processRestoration process
ConclusionsConclusions
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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
SYMPHONIE 2D applied to SYMPHONIE 2D applied to reservoirreservoir
Modeling CHModeling CH44 and CO and CO22
emissions from a tropical freshwater reservoir: emissions from a tropical freshwater reservoir: The Petit Saut ReservoirThe Petit Saut Reservoir
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F. Guérin, MP Bonnet, G. Abril, R. Delmas
Methodology
Site: Petit Saut Reservoir in French Guiana, filled in 1994
The most documented tropical reservoir (10 years of monitoring)
Process-based modelProcess-based model
Identification of the main processescontrolling emissions
Determination of the kinetics in the lab/field
Physical model
View from above
Longitudinal view in the main channel
1 mesh
1 mesh
Dam
Submerged wall
148 meshes in the Ox direction
View from above
Longitudinal view in the main channel
1 mesh
1 mesh
Dam
Submerged wall
148 meshes in the Ox direction
Mean daily atmospheric forcing
Wind speedAir temperatureRelative humidityAir pressureSolar radiationIR Radiation
Daily water inflow (including rainfall) and outflowConstant temperature for water entering the Reservoir
SYMPHONIE 2D
No model for the river downstream Run must be started with the reservoir at full operating level
≈ 100 km
≈ 3.5 km3
Biogeochemical model
SSz
CK
zz
Cwsw
x
uC
t
C&
)(
vertical turbulent diffusion
Source and sink terms of the biogeochemical model
AdvectionDiffusive fluxes
No model for bubblingNo 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
Production CH4 and CO2 -> PLANT > SOILPLANTS ≈ 40-50% CH4
SOILS < 30% CH4
CO2
SOILS PLANT0
25
50
75
100
500
1000
1500
2000
Pro
d (
nm
ol
g-1
h-1
)
CH4
SOILS PLANT0
25
50
75
100
500
1000
1500
2000
Pro
d (
nm
ol
g-1
h-1
)
Guérin et al., submitted
1 2 3 4 5 6 7 8 9 100
50
100
150
200
250
300
350CH4 emission
CO2 emission
CH4 production
CO2 production
Year
Gg
C y
-1
Year 2003: CH4 Oxidation = 85% of CH4 production ( ≈ 50GgC y-
1)
CH4 and CO2 production Production by flooded soil and biomass
Guérin et al., 2008Emissions from Abril et al., 2005
Oxidation = Production - Emission
CH4 oxidation
Incubation of waterIn aerobic conditionsIn the darkAt different CH4 concentrations
Water fromdifferent stations in the lakeDifferent depths
In the epilimnionAt the oxycline
In the river below the dam
Specific oxidation rateVCH4= 0.11±0.01 h-1
Guérin and Abril, 2007
Diffusive fluxes
Fdiff = kGHG, T (Pwater – Patm)
k at low wind speed ≈ 50% higher than in temperate/cold environment
Rainfall contributes to 25% of diffusive fluxes
Wind effect Rain effect
Guérin et al., 2007
0 1 2 3 4 5 6 70
2
4
6
8
10
12
CW03
This study
UG91FU-G02
This study, exp model
W85
U10 (m.s-1)
k 60
0 (
cm.h
-1)
Respiration and Photosynthesis
Photosynthesis
(After Vaquer et al., 1997 & Collos et al., 2001)
Autotrophic respiration
Heterotrophic respiration
(BOD determined after Dumestre (1998) and HYDRECO unpublished data)
44
4max
1exp
NHTT
opt
z
opt
zmoy KNH
NH
PAR
PAR
PAR
PARChloaPhotPhot
ref
22
2
O
TTMAXH KO
OBODR ref
2
max
2
2
O
TT
moyAA KO
OChloaRR ref
Biogeochemical modeling
In contrast, very simple scheme for other processes
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T(°C)
Dep
th (
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December
July
June
JanuaryCO2 CH4O2Temp
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T(°C)
Dep
th (
m)
50 100 150 200 250
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050 100 150 200 250
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µmol(O2).L-1
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µmol(CH4).L-1
Dry Season
Results
OM cycling in the reservoir has a significant impact on Conc.
1994 1996 1998 2000 2002 2004
0
100
200
300
400
Year
F(C
O2)
(m
mo
l.m
-2.d
-1)
1994 1996 1998 2000 2002 20040
10
20
50
150
250
Year
F(C
H4)
(m
mo
l.m
-2.d
-1)
1994 1996 1998 2000 2002 20040
2000
4000
6000
8000
10000
12000
Year
tC-C
O2 m
on
th-1
1994 1996 1998 2000 2002 20040
2000
4000
6000
Year
tC-C
H4 m
on
th-1
Diffusive fluxes Degassing
CO2
CH4
CO2
CH4
Results
Good reproduction of vertical profiles of conc. is crucial for degassing
Atmospheric fluxes
Conclusion
Strength of modelSimple formulationKinetics determined on site -> No calibration required
Models are efficient tools for the computation of mass balance since it integrates:
Biogeochemical processesHydrodynamics
The approach enables to identify lack in the schemeA module for OM (Allochthonous and Autochthonous) cycling in the water column of reservoirs must be included