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DSD-INT 2018 Using Delft3D FM for river engineering: efficiently taking into account parameter uncertainty - Berends

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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.

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DSD-INT 2018 Using Delft3D FM for river engineering: efficiently taking into account parameter uncertainty - Berends

  1. 1. Using Delft3D FM for river engineering: efficiently taking into account parameter uncertainty Koen Berends 12 November 2018 Delft Software Days 2018
  2. 2. 2 River Rhine River Waal
  3. 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. 4. 68 cm Water level on River Waal during design discharge Introduction – method – results – take home message
  5. 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. 6. Natural grassland Production meadow Pioneer vegetation Dry herbaceous vegetation Softwood forest Roughness mapping: trachytopes
  7. 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. 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. 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. 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. 11. Water level before intervention (X) Water level after intervention (Y) Identity line (Δ𝐻=0) Δ𝐻i Introduction – method – results – take home message
  12. 12. Water level before intervention (X) Water level after intervention (Y) 𝑌 = 𝑓 𝑋 + 𝜖 Introduction – method – results – take home message
  13. 13. Introduction – method – results – take home message
  14. 14. Introduction – method – results – take home message
  15. 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. 16. Introduction – method – results – take home message
  17. 17. Introduction – method – results – take home message
  18. 18. We found relative uncertainty* between 15% and 80% * = 90% confidence band / expected effect Introduction – method – results – take home message
  19. 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

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