LAKEMonitoring & Modelisation

S Jacquet, B Vinçon-Leite & B Tassin

Monitoring of « our lakes »

Léman

Dranse

Lake

Equipments

Rivers

Month (winter)Bi-weekly

Frequency of sampling

Weekly3 per day

Boats, Nets, SeabirdSecchi disk, …

Measurements

Macro- & micronutrients, TOCChl a, PP,Transp, zoopk, microphytopk,nanophytopk, O2, pH, temp, cond, fluo

Sampling

18 – 20 depths

Automatic watersampler

Rhône

Aubonne

Venoge

Macro- & micronutrients,cond, DOC

50 cm from bottom

SHL2

Long-term time series for many parameters

Equipments

Bi-weekly

Frequency of sampling

Boats, Nets, SeabirdSecchi disk, …

Measurements

Macro- & micronutrients, TOCtransp, zoopk, microphytopk, O2, pH, temp, cond, fluo, light…

Sampling

7 – 11 depths

Lake Bourget

B

EffortEvery 6-7 years

Next time : 2003

Lake + rivers

Long-term time series for a few parameters

Monitoring of P. rubescens

C*

T*

M*

B B1B2

M1M2

A

BL

** *

**

*

*

P*

An improved approach since April 2002

Sampling B+

Spatial variability of algal distribution

Modelisation

The lake Bourget water quality modelVinçon-Leite and co-authors (1991, 95, 98)

initially developed for Lake Lémanadapted and completed for lake Bourget

(Tassin and co-authors)

AIM

deep lake functioning

to predict the future changesof Lake Bourget water quality

to assess the efficiency eutrophication control programs

1-D vertical model, dispersive type

Coupling of thermal & biogeochemical models

Empirical formulation for dispersion coefficients

Incrementing time = 3 h

Modelisation

• P phosphorus• N nitrogen• DIC dissolved inorganic

carbon• Si silicium• Zoo zooplankton• Bac bacteria• PB bacterial production• PP primary production• R respiration• Ex excretion• M mortality• Pred predation• Relar release from sediment

Conceptual representation of lake Bourget model

Cereve25/05/02

3

RPP

R (minér)

R

PP

PB

R (minér)

M

M

Cyano

Diatomées

Autres

Detritus

C.I.D.

Bac

Zoo

Sédiments

N, P, Si Ex, M

Ex

Préd

Préd

Le modèle biologique

Modelisation

Good agreement between modeling and physical data

= First necessary step

Modelisation

Winter mixing1981 – 1998

Evolution of dissolved P stock (in tons)

Données

Modèle

Régression linéaire

Model vs. data

better understanding of biogeochemical processes

intervening in P evolution

co-sedimentation of P and calcite

role of calcite in sedimentation process of algae

role of iron hydroxides in Bourget hypolimnion

Modelisation

such predictive 1-D model on the management sideare still scarce; however

They can help in the planning of the field survey &in the understanding of the meteo impacts on the lake

Exemple of application

Algal successions

Algal successions ?

Temperature Water column stability

Mixing time scale

meteorology

1D physical model

Light climate

Modelisationof P. rubescens behavior in lake Bourget

Towards a predictive model

1-D biogeochemical model(2002 : cyanobacterial growth)

(2003 : integration of meteo data)TEST & EXPLOITATION

& 3-D hydrodynamical model

(2002 : construction)(2003 : scenario definition)

TEST & EXPLOITATION

COUPLING3-D map of this cyanobacterial bloom

Alert system