1. Information content of weather predictions for flood-control in a Dutch lowland water system
Steven Weijs
December 9, 2018
Section Water Resources Management

2. Outline Introduction water system Delfland
Water level Model Predictive Control system
Forecast uncertainty
Influence forecast uncertainty
Conclusions
December 9, 2018

3. Delfland water board
Water board of delfland: most densely populated, fast runoff due to paved areas.
My university is located here.
Flood control is important because I live there
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4. The Delfland water system
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5. Structural measures and control
BOEZEM LAND
POLDER LAND
BOEZEM P Q Q e P
Bigger pumps (but limited channel capacity), larger storage
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6. Model predictive control
MPC
-objective
-constraints
-optimization
-MODEL:
Inflow can exceed max outflow, water level margins are low
anticipation required
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7. Objectives of control Main: Keep water levels within bounds Derived:
Keep average water level within bounds
Avoid high pump flows (high gradients)
Secondary:
Minimize pumping costs
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8. The Delfland DSS Previous work, NS Hydroinformatics, develop system
Vertical line present time
Left = past measurements
Right = future predictions, but not always accurate!
Look what happened 3 weeks ago
What did we see?
We saw operators maintain a lower level than advised, because they implicitly account for the risk of underestimated rainfall
Problems would have been larger in automatic mode
December 9, 2018

9. December 9, 2018

10. Information and uncertainty
Rainfall in next hours is uncertain
Uncertainty is lack of information
Adding information reduces uncertainty
Mutual information measures the amount of information that is added by forecast
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11. Information and uncertainty
Rainfall
Forecast
H(R)
I(R;F)
H(F)
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12. Information and uncertainty
Rainfall
Forecast
H(R)
I(R;F)
H(F)
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13. Information and uncertainty
Rainfall
Forecast
H(R|F)
I(R;F)
H(F)
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14. Info fades away with lead time
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15. Information and relevant information
We now know how much info is in forecast.
Generally, info improves decisions.
But how does this info contribute to our decision?
Does it improve control?
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16. Test setup Model Predictive Control
Rainfall
forecast
Rainfall runoff model
Optimization Horizon
INTERNAL
MODEL CONTROLLER
Predicted
state
system
MPC controller
Inputs
Actual system state in model
Optimization loop
Objective function:
PERFORMANCE
Actions
Current action
Measured Rainfall
Rainfall runoff model
SIMULATION MODEL
“Measured” state variables
Objective function:
Simulation model
December 9, 2018

17. Test setup Model Predictive Control
Rainfall
forecast
Rainfall runoff model
INTERNAL
MODEL CONTROLLER
Predicted
state
system
Actual system state in model
Optimization loop
Objective function:
PERFORMANCE
Actions
Measured Rainfall
Rainfall runoff model
SIMULATION MODEL
“Measured” state variables
Objective function:
December 9, 2018

18. Model for rainfall runoff process
BOEZEM LAND
POLDER LAND
BOEZEM P Q Q e P
Inflow can exceed max outflow, water level margins are low
anticipation required
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19. Derived rainfall runoff relation from balance
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20. Test conditions and assumptions
Rainfall
forecast
Rainfall runoff model
INTERNAL
MODEL CONTROLLER
Predicted
state
system
Actual system state in model
Optimization loop
Objective function:
PERFORMANCE
Actions
Measured Rainfall
Rainfall runoff model
SIMULATION MODEL
“Measured” state variables
Objective function:
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21. Behavior: feed forward control
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22. perfect predictions 3 hours
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23. perfect predictions 5 hours
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24. perfect predictions 12 hours
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25. True, imperfect predictions 12 hours
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26. Dependency performance on optimization horizon
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27. Results Rainfall forecasts contain information up to at least 20 hrs
The controller is sensitive to inputs up to 8 hrs
Information about the current inflow improves performance by 4 times compared to knowledge of just water levels
Anticipation by using information in rainfall forecasts could improve performance another factor 3.
However, not with current forecast uncertainties
Focus should be on increasing info about first 8 hours.
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28. Conclusions Real time control improves water system performance.
Anticipation helps
Good information is essential
Improve forecasts in first 8 hours
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29. s.v.weijs@tudelft.nl Conclusions
Real time control improves water system performance.
Anticipation helps
Good information is essential
Improve forecasts in first 8 hours
December 9, 2018