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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December

July

June

JanuaryCO2 CH4O2Temp

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th (

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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