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LAKE Monitoring & Modelisation S Jacquet, B Vinçon-Leite & B Tassin. Monitoring of « our lakes ». Venoge. Aubonne. Léman. SHL2. Rhône. Dranse. Lake. Rivers. Frequency of sampling. Month (winter) Bi-weekly. Weekly 3 per day. Equipment s. Boats, Nets, Seabird Secchi disk, …. - PowerPoint PPT Presentation
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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