Water quality forecasting with FEWS

1. 30 oktober 2015
Water quality forecasting with FEWS
Case studies: Guanabara Limpa,
Bathing water quality in the UK
Lora Buckman

2. Outline
• General overview of water quality applications
• WQ issues
• modeling strategies
• handling samples in FEWS -> breakout session
• data assimilation -> breakout session
• Case studies (in brief):
• Guanabara Limpa – preparation for 2016 Rio de Janeiro
Olympic Games
• Bathing water quality in UK – daily risk assessment and
warning
30 oktober 2015

3. What type of issues can we forecast?
Algae blooms
Oil spills
Floating trash
Habitat suitability/aquaculture
Bacterial contamination
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4. How does it work?
Numerical models
• Start with hydrodynamic model in FEWS system.
• Add ecological data and water quality models using a similar
approach.
Different types:
• Particle tracking & backtracking: litter, spills, transport of
observed algal bloom;
• Particle tracking with limited processes: oil, larvae, jellyfish,
bacteria;
• Ecological model (Delwaq, others) simulating concentrations of
nutrients, phytoplankton, oxygen etc.
Data-driven models
• based on historic correlations between variables
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5. Case Study: Guanabara Limpa
30 oktober 2015
Olympic games in Rio de Janeiro in summer 2016
Floating litter in the Bay poses a risk to sailing and
windsurfing events

6. Guanabara Limpa : Project background
• 380 km2
• 15 municipalities
• 8.6 million people
30 oktober 2015

7. Guanabara Limpa: Project background
• 380 km2
• 15 municipalities
• 8.6 million people
30 oktober 2015

8. Guanabara Limpa: Project background
• 380 km2
• 15 municipalities
• 8.6 million people
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9. Guanabara Limpa: Clean-up
• Litter coming from rivers partially collected by ‘Ecobarriers’ at the river
mouth before entering the bay
• Simply not enough
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10. 30 oktober 2015
Guanabara Limpa: Clean-up
http://www.bbc.com/news/world-latin-
america-25635140

11. Guanabara Limpa: Forecasting system
Rainfall-runoff model
(W-FLOW)
3D Hydrodynamic
Model (Delft3D-FLOW)
Data Collection (with PSAM, INEA, CHM)
Particle-tracking
Module (PART)
Online Visualization
(DSC)
Operational framework (Delft-FEWS)
System Hosting
(ICT)
+ code development
+ tailored online visualization
+ app. with input from eco-boats
Forecasts imported
Water Quality Model
(DELWAQ)
Consulate-
General,
Rio de Janeiro
System Owner / End-User: Sponsorship:
Other contributions by:
Ministries:
Infrast. & Environ.,
Economic Affairs
Major project components
System development,
implementation, coordination:
01 January 2015

12. Website: http://guanabaralimpa.deltares.nl/

13. Guanabara Limpa: Challenges
Addressing the underlying issue
• Floating litter is only a part of the equation
• Issues not addressed:
• lack of waste management services
• limited operation/effectiveness of Eco-barriers
• limited sanitation – raw sewage discharges
Political environment
• What happens after 2016 is unclear
Lack of validation data
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14. Case study: Bathing water quality UK
30 oktober 2015
Heavy rain may cause short term
bacterial pollution of bathing waters
due to:
• Sewer overflows
• Run-off from wildlife and livestock
• Urban drainage
Swimmers are warned about risk of
reduced water quality through
internet and signposts.

15. Bathing water quality UK: Project background
30 oktober 2015
European Directive concerning management of bathing water quality requires:
• regular monitoring of bathing waters
• determination of bathing water quality on basis of microbiological: poor,
sufficient, good, excellent
• all bathing waters in EU must be sufficient in 2015
• additional improvement to good or excellent required
• mandate to inform bathers of poor water quality

16. Case study: Bathing water quality UK
30 oktober 2015
http://environment.data.gov.uk/bwq/profiles/

17. Rain gauge
H19 Radar Actual 15 min
Accumulated
H13 Radar Nowcast 15 min
Accumulated
24 hrs. and 48 hrs. trigger
based on accumulated actual
measured rainfall OR
Bathing Water Quality
affected by External Factors
Dissemination of Result via
Bathing Water Explorer
and put up Signs at
Bathing Sites.
Pilot Study – Bathing Water Quality STP System
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18. How to estimate bacterial pollution risk from rainfall data?
Approach: data-driven modeling (courtesy of UK Environment Agency)
Bathing water quality UK: Modeling approach
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19. Bathing water quality UK: Modeling approach
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Not based on water quality samples
Not predicting Actual Water Quality
But predicting an increase in RISK due to
possible reduction in bathing water quality
Heavy Rainfall in Catchment washing
down pollutant to bathing sites
Other External factor contributing to
increase in RISK to bathing water quality

20. Bathing water quality UK: Initial findings
Most days with pollution risk are missed by sampling scheme
30 oktober 2015
Samples and Pollution Risk Warnings
More information: ian.dunhill@environment-agency.gov.uk

21. Problems with verification
• Can you verify pass/fail by looking at a few samples? –low
statistical confidence
• The distribution of samples will always favour ‘good’
conditions
• Easy to conclude that a clean sample with no warning is a
success
• Or a warning with a clean sample is protective
• A system that always predicts good will be correct most of
the time
• A system that predicts good most of the time with
occasional warnings will usually be correct

22. Bathing water quality UK: Challenges
Challenges:
• lack of sample data
• high uncertainty in results
• many processes unaccounted
• lack of validation data
• biological contamination difficult to assess
• public awareness, education
• beach operators especially resitsant
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23. Future challenges in WAQ forecasting
Improving accuracy and reliability by:
• More real-time measurements!!!
• Explore use of citizen monitoring data
• Increasing use of satellite data
• Improvement of data-driven models
• Improve understanding of biological processes
• Especially algal blooms and jellyfish
• Data model integration
• Preferably with high resolution sensor data from
satellites and buoys.
30 oktober 2015

24. Breakout Session: FEWS for WQ and Ecology
This Afternoon in Innovation:
• 2 Hands-on activities:
• Floating litter forecasting
• FEWS-ECO
• In-depth discussion:
• Data assimilation
• Sample handling in FEWS (new functionality)
• Other water quality topics
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25. Thank you for you attention
30 oktober 2015