2D flood modelling: coping with real world applications

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

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)

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

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

Friction modelling Manning Darcy-Weisbach Chézy

Friction modelling

Friction modelling homogeneous computational domains discharge in steady state Zone Roughness (mm) Parameters A B C Silt 0.0039-0.0625 3.355 58328.9 0.169 Concrete 0.3-3 2.001 475.9 0.220 Untreated shot-concrete 3-10 1.995 318.8 0.264 Rubble masonry 5-10 1.994 304.3 0.269 Asphalt 1-1.5 2.007 496.6 0.211 Fine sand 0.0625-0.5 2.155 1004.9 0.184 Coarse sand 0.5-2 2.016 507.4 0.214 Sand 0.0625-2 2.041 579.7 0.206 Fine gravel 2-16 2.000 292.7 0.286 Medium coarse gravel 16-32 2.007 209.5 0.347 Very coarse gravel 32-64 1.997 152.4 0.431 Coarse gravel 16-64 2.057 187.9 0.410 Gravel 2-64 1.980 180.2 0.377 Cobble 64-256 1.834 17580.2 2.655

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

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

Solid boundaries

Comparison of the 3 methods Toce river physical model

Comparison of the 3 methods Toce river physical model

Upstream boundaries steady state flow elevation depth velocity

Upstream boundaries hydrograph

Upstream boundaries hydrograph Tous dam break

Downstream boundaries open kinematic wave

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

Catchment scale modelling Halandri catchment

Catchment scale modelling Halandri catchment

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

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

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

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

Uncertainty DTM Tous dam break

Uncertainty model structure Acheloos river 1D vs 2D

Uncertainty model structure Sperhios river FDM vs FEM

Uncertainty - friction Tous dam break Halandri catchment Scenario MSEMIN MSEAVG MSEMAX 1.3.8 48.232 45.703 43.376 1.3.10 58.145 55.346 52.749 1.3.12 66.120 63.122 60.325 1.4.8 63.062 60.134 57.407 1.4.10 77.591 74.322 71.254 1.4.12 89.866 86.334 83.005 1.5.8 75.266 72.045 69.026 1.5.10 94.261 90.635 87.212 1.5.12 110.610 106.670 102.932 2.3.8 4.074 3.731 3.591 2.3.10 4.900 4.429 4.160 2.3.12 5.681 5.110 4.741 2.4.8 5.150 4.654 4.360 2.4.10 6.595 5.933 5.474 2.4.12 7.971 7.178 6.589 2.5.8 6.230 5.613 5.198 2.5.10 8.276 7.464 6.855 2.5.12 10.261 9.292 8.526 3.3.8 2.530 2.672 3.017 3.3.10 2.480 2.542 2.806 3.3.12 2.495 2.495 2.697 3.4.8 2.361 2.599 3.038 3.4.10 2.274 2.433 2.795 3.4.12 2.251 2.347 2.645 3.5.8 2.275 2.571 3.068 3.5.10 2.161 2.376 2.794 3.5.12 2.116 2.265 2.616 4.3.8 2.819 2.867 3.116 4.3.10 2.775 2.734 2.896 4.3.12 2.804 2.697 2.793 4.4.8 2.396 2.596 2.998 4.4.10 2.307 2.429 2.754 4.4.12 2.293 2.352 2.614 4.5.8 2.315 2.611 3.109 4.5.10 2.179 2.402 2.827 4.5.12 2.116 2.277 2.640 5.3.8 3.423 3.442 3.662 5.3.10 3.695 3.537 3.582 5.3.12 3.871 3.599 3.529 5.4.8 2.765 2.810 3.056 5.4.10 2.734 2.684 2.837 5.4.12 2.777 2.658 2.741 5.5.8 2.354 2.535 2.918 5.5.10 2.265 2.369 2.676 5.5.12 2.259 2.300 2.544

Uncertainty building representation Toce river physical model elevation increase friction increase

Uncertainty grid size Experiment

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

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 607000. www.quics.eu