LAKE Monitoring & Modelisation S Jacquet, B Vinçon-Leite & B Tassin

1. LAKE Monitoring & Modelisation S Jacquet, B Vinçon-Leite & B Tassin

2. Monitoring of « our lakes » Venoge Aubonne Léman SHL2 Rhône Dranse Lake Rivers Frequency of sampling Month (winter) Bi-weekly Weekly 3 per day Equipments Boats, Nets, Seabird Secchi disk, … Automatic water sampler Measurements Macro- & micronutrients, TOC Chl a, PP,Transp, zoopk, microphytopk,nanophytopk, O2, pH, temp, cond, fluo Macro- & micronutrients, cond, DOC Sampling 18 – 20 depths 50 cm from bottom Long-term time series for many parameters

3. Lake Bourget B Frequency of sampling Bi-weekly Effort Every 6-7 years Next time : 2003 Lake + rivers Equipments Boats, Nets, Seabird Secchi disk, … Measurements Macro- & micronutrients, TOC transp, zoopk, microphytopk, O2, pH, temp, cond, fluo, light… Sampling 7 – 11 depths Long-term time series for a few parameters

4. * C * * * B B2 B1 * * * M M2 M1 * A * * T BL * P Monitoring of P. rubescens An improved approach since April 2002 Sampling B + Spatial variability of algal distribution

5. Modelisation The lake Bourget water quality model Vinçon-Leite and co-authors (1991, 95, 98) initially developed for Lake Léman adapted and completed for lake Bourget (Tassin and co-authors) AIM deep lake functioning to predict the future changes of 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

6. Modelisation Conceptual representation of lake Bourget model • 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

7. Modelisation Good agreement between modeling and physical data = First necessary step

8. Données Régression linéaire Modèle Modelisation Winter mixing 1981 – 1998 Evolution of dissolved P stock (in tons) 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

9. Modelisation such predictive 1-D model on the management side are 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 1D physical model Water column stability Temperature Mixing time scale Algal successions ? Light climate meteorology

10. Modelisation of 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  COUPLING 3-D map of this cyanobacterial bloom  Alert system