Presentation by Miguel Dionisio Pires, Deltares, The Netherlands, and Yi Hong, École des Ponts ParisTech, France, at the Delft3D - User Days (Day 3: Water quality and ecology), during Delft Software Days - Edition 2017. Wednesday, 1 November 2017, Delft.

1. Coupling 3D models and 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)
Miguel Dionisio Pires
(miguel.dionisio@deltares.nl)

4. 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
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 services for
Monitoring and Reporting of Ecological Status
EOMORES
EOMORES - project overview

6. Details
 EOMORES is a 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

7. EOMORES overview
7

8. User relevant
• Tailor products and services to users
• Generate higher-level products that suit their
needs
• Integrate products into users’ systems
8

9. 1. Earth observation example
Suspended matter maps for monitoring
turbidity
EOMORES

10. 2. In situ component
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 optical in-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
Detritus in 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 forecasting the 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
Since 2006 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
Anabaena Aphanizomenon Microcystis Ceratium Peridinium
Nostocales
DinoflagellatesCyanobacteria
(Peiffer, 2016)
Chroococcales

17. High frequency monitoring
2015 2016 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
Since 2015
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 and temperature
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 for phytoplankton 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=5.8 mg/L
• R2=0.46
• RE= 42%
24
Total phytoplankton biomass at 1.5 m depth
13 - 27 July 2015

25. Phytoplankton groups
25

26. Two components:
1. Hydrodynamics Model: 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 - EWACS Model
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 - EWACS Model
28

29. Delft3D - EWACS Model
29

30. Delft3D - EWACS Model
30

31. Delft3D-BLOOM - EWACS Model
31

32. Next steps
32
• Validation on 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. Thank you!!!