1. Numerical modeling in lakes, tools and application Marie-Paule Bonnet, Frédéric Guérin UMR 5563 GET IRD, CNRS, OMP, Toulouse III

2. Outlook DYLEM1D : controlling factors of Microcystis blooms and restoration process evaluation of the Villerest Reservoir (France) DYLEM1D : controlling factors of Microcystis blooms and restoration process evaluation of the Villerest Reservoir (France) SYMPHONIE 2D: Controlling factors of CH 4 emissions in Petit Saut Reservoir (French Guiana) SYMPHONIE 2D: Controlling factors of CH 4 emissions in Petit Saut Reservoir (French Guiana) 04/10/2015

3. DYLEM1D 1D vertical model for lakes and reservoirs 04/10/2015

4. Application to the Reservoir Villerest (Loire, France) Impounding : 1984 Mean volume: 62 Mm 3 Maximum depth : 45 m Mean depth : 18 m Annual water level variation : ±15 m

5. Biogeochemical conceptual scheme Controlling factors of Microcystis aeruginosa blooms in a highly eutrophic reservoir Evaluate the restoration processes comparing two periods of study 90-92 and 97-2000 04/10/2015

6. A large dataset available for modeling Meteo data (every 20 mn): Solar radiation Wind speed/direction Specific relative humidity Air temperature Temperature : Every 3 hours, 11 levels in the lake Every hour in the inflow Inflow/outflow (every 3 hours) Nutrients (NO 3, NH 4, PO 4, SiO2) : Every day in the inflow Every two weeks during blooms Every month otherwise Phytoplankton (algae species) : Species identification and biomasse estimation every two weeks during blooms Every month otherwise Between the two periods of study P and N inputs are about 40 % less

7. The physics model 04/10/2015 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

8. The biogeochemical model A complex conceptual scheme developed step by step The phytoplankton module was developed first without considering nutrients limitation

9. 04/10/2015 Phytoplankton module 5 species Parameters for growth optimum conditions estimated from lab Buoyancy regulation for Microcystis only

10. Temperature simulation 04/10/2015 Calibration year Validation Important differences when : the 1D assumption is wrong (winter) The vertical stratification is very strong

11. 04/10/2015 Phytoplankton simulation Calibration : 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 estimated species interactions (self-shading, grazing)

12. 04/10/2015 Some controlling factors of Microcystis blooms 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

13. 04/10/2015 Evaluation of the Restoration process Despite significant P-PO4 load reduction, Microcystis remains dominant

14. 04/10/2015 Evaluation of the Restoration process

15. Conclusions 04/10/2015 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

16. SYMPHONIE 2D applied to reservoir Modeling CH 4 and CO 2 emissions from a tropical freshwater reservoir: The Petit Saut Reservoir 04/10/2015 F. Guérin, MP Bonnet, G. Abril, R. Delmas

17. Methodology Site: Petit Saut Reservoir in French Guiana, filled in 1994 The most documented tropical reservoir (10 years of monitoring) Process-based model Identification of the main processes controlling emissions Determination of the kinetics in the lab/field

18. Physical model  Mean daily atmospheric forcing Wind speed Air temperature Relative humidity Air pressure Solar radiation IR Radiation  Daily water inflow (including rainfall) and outflow  Constant 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 km 3

19. Biogeochemical model vertical turbulent diffusion Source and sink terms of the biogeochemical model Advection Diffusive fluxes No model for bubbling No module for OM cycling in the water column

20. CH 4 and CO 2 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 CH 4 and CO 2 -> PLANT > SOIL PLANTS ≈ 40-50% CH 4 SOILS < 30% CH 4 Guérin et al., submitted

21. Year 2003: CH 4 Oxidation = 85% of CH 4 production ( ≈ 50GgC y -1 ) CH 4 and CO 2 production Production by flooded soil and biomass Guérin et al., 2008 Emissions from Abril et al., 2005 Oxidation = Production - Emission

22. CH 4 oxidation Incubation of water  In aerobic conditions  In the dark  At different CH 4 concentrations Water from  different stations in the lake  Different depths In the epilimnion At the oxycline  In the river below the dam Specific oxidation rate V CH4 = 0.11±0.01 h -1 Guérin and Abril, 2007

23. Diffusive fluxes F diff = k GHG, T (P water – P atm ) 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

24. 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) Biogeochemical modeling In contrast, very simple scheme for other processes

25. Results December July June January CO 2 CH 4 O2O2 Temp

26. Dry Season Results OM cycling in the reservoir has a significant impact on Conc.

27. Diffusive fluxes Degassing CO 2 CH 4 CO 2 CH 4 Results Good reproduction of vertical profiles of conc. is crucial for degassing Atmospheric fluxes

28. Conclusion Strength of model  Simple formulation  Kinetics determined on site -> No calibration required Models are efficient tools for the computation of mass balance since it integrates:  Biogeochemical processes  Hydrodynamics The approach enables to identify lack in the scheme  A module for OM (Allochthonous and Autochthonous) cycling in the water column of reservoirs must be included