2D flood modelling: coping with real world applications

Transcription

1 2D flood modelling: coping with real world applications Dr Vasilis Bellos CH2M, Experienced Researcher Marie Curie fellow

2 Introduction Flooding is a natural hazard of great importance Improving the accuracy of simulation 1D approach dominant choice in practice weakness in complexities of real world 2D approach feasible option the last two decades computational burden (hours/days)

3 Aspects of modelling Friction modelling Representation of buildings Boundary conditions Source and sink terms Calibration Uncertainties

4 FLOW-R2D model Fortran 90/95 language 2D Shallow Water Equations (2D-SWE) Finite Difference Method Modification of McCormack numerical scheme Non-staggered, cell-centred grid Wet/dry modelling Urban environments Catchment scale

5 Friction modelling Manning Darcy-Weisbach Chézy

6 Friction modelling

7 Friction modelling homogeneous computational domains discharge in steady state Zone Roughness (mm) Parameters A B C Silt Concrete Untreated shot-concrete Rubble masonry Asphalt Fine sand Coarse sand Sand Fine gravel Medium coarse gravel Very coarse gravel Coarse gravel Gravel Cobble

8 Friction modelling A=2.596 B=10.0 C=0.1 heterogeneous computational domains Tous dam break

9 Representation of buildings Solid boundaries (free-slip or no-slip) Local elevation rise Local increase of friction Solid boundaries better option Model performance Computational time Added uncertainty

10 Solid boundaries

11 Comparison of the 3 methods Toce river physical model

12 Comparison of the 3 methods Toce river physical model

13 Upstream boundaries steady state flow elevation depth velocity

14 Upstream boundaries hydrograph

15 Upstream boundaries hydrograph Tous dam break

16 Downstream boundaries open kinematic wave

17 Source/sink terms Rainfall Infiltration Kostiakov equation Green-Ampt model Drainage Subway network

18 Catchment scale modelling Halandri catchment

19 Catchment scale modelling Halandri catchment

20 Calibration Computational burden Trial and error method Surrogate models Black-box or physically-based parameters? Friction coefficients Infiltration model parameters Building representation Grid size DTM Diffusion factor Effective slope (upstream boundaries) Courant number Wet/dry threshold

21 Surrogate models data driven Multistart Local Metric Stochastic Radial Basis Function Computational budget 100 runs Parameters calibrated: Manning coefficient n=0.194 s/m 1/3 Effective slope S eff =0.019 Better than trial and error method Sufficient space exploration Tous dam break

22 Surrogate models simplification Catchment scale modelling Hybrid method combining hydrodynamic and hydrological techniques Halandri catchment Unit Hydrograph derivation Effective rainfall determination Flood hydrograph simulation

23 Uncertainties Input data DTM Model structure 1D vs 2D FDM vs FEM Model parameters Friction coefficients Building representation parameters Grid size Monte-Carlo technique cannot be implemented Surrogate models Interval analysis

24 Uncertainty DTM Tous dam break

25 Uncertainty model structure Acheloos river 1D vs 2D

26 Uncertainty model structure Sperhios river FDM vs FEM

27 Uncertainty - friction Tous dam break Halandri catchment Scenario MSEMIN MSEAVG MSEMAX

28 Uncertainty building representation Toce river physical model elevation increase friction increase

29 Uncertainty grid size Experiment

30 Conclusion Decrease of computational burden for Decision Making Parallel programming Upscaling techniques Surrogate models Physically-based or black-box parameters?

31 Partners and Acknowledgements This project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration under grant agreement no