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DSD-INT 2017 Coupling 3D models and earth observation to develop algae forecasting services - Dionisio Pires
The document discusses the integration of 3D models with Earth observation techniques to enhance algae forecasting services, specifically targeting cyanobacteria blooms. It details the CYMONS and EOMORES projects, funded by ESA and H2020, which aim to develop monitoring and reporting tools for ecological status and provide tailored products to users in various EU countries. The work involves complex hydrodynamic and water quality models to predict scum presence and improve forecasting accuracy through high-frequency monitoring and validation methods.
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DSD-INT 2017 Coupling 3D models and earth observation to develop algae forecasting services - Dionisio Pires
- 1. Coupling 3D modelsand earth observation to develop algae forecasting services Miguel Dionisio Pires (Deltares) & Yi Hong, Lucas Jardim Porto, D. Fürstenau Plec, B. J. Lemaire, B. Vinçon-Leite (LEESU, Ecole des Ponts ParisTech), Lilith Kramer, Tineke Troost (Deltares) User days 1/11/2017
- 2. CyMonS and EOMORES •CyMonS • Funded by ESA • Coupling EO, IS and model for cyanobacteria scum forecasting • https://business.esa.int/project s/cymons-fs • https://www.youtube.com/watc h?v=RmVFPL4k5x0 • EOMORES • H2020 • Coupling EO, IS and model for ecological status assessment • http://eomores-h2020.eu/ 2 This project is co-funded by the European Union
- 3. Cyanobacteria Monitoring Services (CyMonS) MiguelDionisio Pires (miguel.dionisio@deltares.nl)
- 4. Wind speed Wind direction Cloudcover Relative humidity Air temperature Delft 3D Flow Transport Water Temp Delwaq-Bloom- EcoFuzz Delwaq Species biomass Buoyancy rate Process parameters Vertical transport Horizontal transport Scum potential (Low, med, high, v. high)) Scum potential & location Validation (Cyano biomass, presence/absence) EcoFuzz Wind speed Solar radiation Time of day Expert rules Wind speed Wind direction Cloud cover Relative humidity Air temperature Delft 3D Flow Transport Water Temp Delwaq-Bloom- EcoFuzz Delwaq Species biomass Buoyancy rate Process parameters Vertical transport Horizontal transport Scum potential (Low, med, high, v. high)) Scum potential & location Validation (Cyano biomass, presence/absence) EcoFuzz Wind speed Solar radiation Time of day Expert rules Algae forecasting model Meteorological data Sentinel-2 WISP-3/EcoSpot & EcoWatch System and Service Architecture
- 5. Earth Observation-based servicesfor Monitoring and Reporting of Ecological Status EOMORES EOMORES - project overview
- 6. Details EOMORES isa H2020 (EC) research project Project time: 3 year, starting 1 December, kick off 9 & 10 January There are 9 partners from 6 EU countries Almost all (8) partners have one or several users in their country 13 users 6
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- 8. User relevant • Tailorproducts and services to users • Generate higher-level products that suit their needs • Integrate products into users’ systems 8
- 9. 1. Earth observationexample Suspended matter maps for monitoring turbidity EOMORES
- 10. 2. In situcomponent Optical in situ instruments Quick-scans (direct result) Continue, automatic monitoring Also used for validation and calibration of atmospheric correction EOMORES Above: WISP-3 Below: fixed position instrument
- 11. 2. Validation opticalin-situ observations EOMORES
- 12. 12 Models in EOMORES EWACSAlgae P N C N NH4-N NO3-N P PO4-P Detritus P N C settlingsettling respiration photosynthesis Nutrient mineralisation mineralisation metabolism mortality DO production consumption reaeration Detritusin Sediment C N P Si Si Si N2 denitrification mineralisation & nitrification autolysis Si consumption nitrification Grazers grazing grazing oxygen consumption biodeposition AIP adsorption Microphytobenthos C N P Si AIP in sediment settling mortality photosynthesis BLOOM Delft3D-WAQ Algae Radar
- 13. Monitoring and forecastingthe cyanobacteria blooms in lakes 13
- 14. 14 Lake Champs-sur-Marne -France (Geoportail, IGN, 2017) (Photos D. Plec, 2016) Surface area 0.12 km2 Average depth 2.3 m Maximum depth 4 m
- 15. Cyanobacteria in Lake Champs-sur-Marne 15 Since2006 Survey according to the French bathing regulation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Monthly Bi-weekly Weekly Bi-weekly|Monthly 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Phytoplankton counting and Chlorophyll Relative abundance
- 16. 16 Main phytoplankton species AnabaenaAphanizomenon Microcystis Ceratium Peridinium Nostocales DinoflagellatesCyanobacteria (Peiffer, 2016) Chroococcales
- 17. High frequency monitoring 20152016 2017 OSS-Cyano 17 0.5 m 1.5 m 2.5 m Temperature Dissolved Oxygen Chlorophyll-a Conductivity A B C Since 2015 5-minute time step
- 18. 18 A B C 2015 2016 2017 OSS-Cyano Since2015 Vertical profile measurements Surface Bottom Total Chl-a Blue Green Green Diatoms Cryptophyta Temperature Dissolved Oxygen Conductivity pH BBE Fluoroprobe Seabird CTD Licor photometer
- 19. 19 Phytoplankton concentration andtemperature at point A in May – Dec. 2015
- 20. Simulation period -July 2015 20
- 21. Model configuration • Mesh –Measured bathymetry – 10 x 10 x 0.33 m cells, 10 layers • Meteorological data – Meteo-France Orly airport station • Parameters – Hydrodynamic model: drag coef. 0.0013; Dalton coef. 0.0015; Stanton coeff. 0.00145; horizontal viscosity and diffusivity 0.0025 m²/s; – Calibration parameters: wind reduction factor; albedo – Biological model: Settling velocities; default parameters • Biological variables – 4 phytoplankton groups • Initial conditions – Hydrodynamic model – Biological model 21
- 22. Initial condition forphytoplankton groups 22 4 phytoplankton variables: • Green algae; Cyanobacteria; Diatoms and Dinoflagellates. The initial concentrations of the 4 phytoplankton groups: • Total chlorophyll measured continuously • Spectrofluorometer vertical profile; 2015 5 mg/l
- 23. Hydrodynamics results R2 =0.63 - 0.89 MAE= 0.30 - 0.62°C MRE < 3 % 23 The performance indicators were calculated using the hourly mean of the measured data data model
- 24. • n=342 • MAE=5.8mg/L • R2=0.46 • RE= 42% 24 Total phytoplankton biomass at 1.5 m depth 13 - 27 July 2015
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- 26. Two components: 1. HydrodynamicsModel: Delft3D-FLOW 2a. Water quality/Ecology Model: Delft3D-WAQ (DELWAQ engine) 2b. Fuzzy logic model coupled to WQ model Model output: • Map of cyanobacteria biomass in surface waters for each model time-step • Used to generate weekly scum bulletin Flow Model Transport Water Temp. Water Quality model Vertical transport Horizontal transport Scum potential (app/disappearance) Scum presence & location Fuzzy Logic model Set up EWACS model HHNK 30 maart 2016
- 27. Delft3D - EWACSModel 27 EWACS Early Warning Against sCumS Logical inference used to predict scum appearance and disappearance in EcoFuzz (taken from Burger et al., 2008)
- 28. Delft3D - EWACSModel 28
- 29. Delft3D - EWACSModel 29
- 30. Delft3D - EWACSModel 30
- 31. Delft3D-BLOOM - EWACSModel 31
- 32. Next steps 32 • Validationon other periods – September 2017
- 33. Conclusion and Perspectives 33 •High-frequency monitoring system is a promissing approach to improve model performance • Firstly applied Ewacs – BLOOM coupling on small urban lake; • The only scum forecasting model in the world • Next steps … • Collection of nutrient data • Implementation and continue to improve over other lakes; • Validate the outcomes of the model with satellite images; • Real Time forecasting system
- 34.