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DSD-INT 2018 Using Delft3D FM for river engineering: efficiently taking into account parameter uncertainty - Berends
The document discusses the use of Delft3D FM in river engineering, emphasizing the importance of accounting for parameter uncertainty in modeling interventions on the River Waal. It introduces a novel approach using Monte Carlo simulations to estimate model uncertainty, showing that relative uncertainty can range between 15% and 80%. The findings highlight the method's utility for large-scale analyses and iterative designs in river management projects.
DSD-INT 2018 Using Delft3D FM for river engineering: efficiently taking into account parameter uncertainty - Berends
- 1. Using Delft3D FMfor river engineering: efficiently taking into account parameter uncertainty Koen Berends 12 November 2018 Delft Software Days 2018
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- 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 levelon River Waal during design discharge Introduction – method – results – take home message
- 5. Is there historicalevidence? 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 Pioneervegetation Dry herbaceous vegetation Softwood forest Roughness mapping: trachytopes
- 7. Describe uncertainty inmodel 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 referencecase 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 referencecase 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 beforeintervention (X) Water level after intervention (Y) Identity line (Δ𝐻=0) Δ𝐻i Introduction – method – results – take home message
- 12. Water level beforeintervention (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 probabilitydensity 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 relativeuncertainty* 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