Presentation by Koen Berends, University of Twente, The Netherlands, at the Delft3D - User Days (Day 1: Hydrology and hydrodynamics), during Delft Software Days - Edition 2018. Monday, 12 November 2018, Delft.

1. Using Delft3D FM for river engineering:
efficiently taking into account parameter
uncertainty
Koen Berends
12 November 2018
Delft Software Days 2018

2. 2
River Rhine
River Waal

3. 1. Nijmegen Lent: max. -27 cm* @ ~350 million €
2. Groyne lowering: max. -12 cm* @ ~290 million €
3. Millingerwaard: max. - 9 cm* @ ~125 million €
Top 3 “Room for the River” projects on River Waal:
Source: Eindevaluatie Ruimte voor de Rivier & infrasite.nl
Models are used to predict effects of river engineering
*Predicted decrease of water level during design discharge after intervention
Introduction – method – results – take home message

4. 68 cm
Water level on River Waal during design discharge
Introduction – method – results – take home message

5. Is there historical evidence?
1. Describe uncertainty in model assumptions
yes • Probabilistic (GLUE, DREAM)
• Deterministic (Calibration)no
Forwarduncertaintyanalysis
“Inverse problem”
2. Propagate uncertainty forward (input  output)
3. Summarise uncertainty of model output
Introduction – method – results – take home message

6. Natural grassland
Production meadow
Pioneer vegetation
Dry herbaceous vegetation
Softwood forest
Roughness mapping: trachytopes

7. Describe uncertainty in model assumptions
+ vegetation parameters (37) + classification error (1)
bed roughness (1)
Trachytope definition file (*.tdf) Area file (*.arl)
Introduction – method – results – take home message

8. Monte Carlo simulation
reference case
Monte Carlo simulation
intervention case
- =
Monte Carlo simulation
intervention effect
Common approach:
1000* simulations 12 x 1000 simulations 13,000 simulations
@ ~ 3hr/120MB per simulation
Introduction – method – results – take home message

9. Monte Carlo simulation
reference case
Monte Carlo simulation
intervention case
- =
Monte Carlo simulation
intervention effect
New approach:
1000* simulations 12 x 20 simulations 1240 simulations
@ ~ 3hr/120MB per simulation
Introduction – method – results – take home message

10. Δ𝐻 = 𝑌 − 𝑋
Water level before intervention (X)
Water level after
intervention (Y)
The effect of the intervention
Introduction – method – results – take home message

11. Water level before intervention (X)
Water level after
intervention (Y)
Identity line (Δ𝐻=0)
Δ𝐻i
Introduction – method – results – take home message

12. Water level before intervention (X)
Water level after
intervention (Y)
𝑌 = 𝑓 𝑋 + 𝜖
Introduction – method – results – take home message

13. Introduction – method – results – take home message

14. Introduction – method – results – take home message

15. Cumulativeprobability
Water level
Cumulative probability density functions
X (Pre-intervention) known
1
0
Y (Post-intervention) unknown
Introduction – method – results – take home message

16. Introduction – method – results – take home message

17. Introduction – method – results – take home message

18. We found relative uncertainty* between 15% and 80%
* = 90% confidence band / expected effect
Introduction – method – results – take home message

19. Take home message:
• Novel method to estimate model uncertainty
• Reduced number of model evaluations
• Especially helpful for large-scale analysis & iterative design
To learn more:
• Contact: k.d.berends@utwente.nl or koen.berends@deltares.nl
• Methodological background: Berends et al. (2018), https://doi.org/10.1016/j.envsoft.2018.05.021
• Large-scale application: NHESS discussion paper
• Source code for CORAL: https://github.com/kdberends/coral
Introduction – method – results – take homemessage