Ten Years of Coupled Hydrology and Hydraulic Modelling Supporting Storm Water Management

1. Ten Years of Coupled Hydrology Hydraulic Modelling supporting Storm Water Management – some examples, Lessons Learnt and a look forward Ole Larsen, APAC Research Director, DHI Singapore Stephen J Flood, Senior Engineer, DHI UK Nick Elderfield, MD DHI UK © DHI

2. Urban Hydrology Describes the complex hydrology as a series of individual processes in the urban environment

3. But is a reductionist approach valid?

4. Early steps of urban overland flow models

5. The workhorse of the 90’s © DHI • Interpreta-tion of results!

6. Creating flood maps using GIS • Lumped conceptual rainfall-runoff models translate rainfall to pipe nodes • Surcharge would be storred in ”artificial” basins that represented flood areas • Interpolation of waterlevels in GIS was used to map flood events

7. Soon smarter solutions were made – 2D models • Faster 2D solvers • Availability of Lidar (an other) data • Rainfall stations • Data driven models

8. Structures Structures (weirs, pumps gates etc) cannot be simulated in 2D. Structures are added as 1 D elements in the 2D models Depending on structure also transfer of momentum

9. Example of 1D pipe and 2D flood model coupling © DHI

10. Coupling of river and pipeflow models © DHI

11. First coupling interface from early 2000’s (Mouse – M21) © DHI

12. Current interface – Pipe flow, River, and 2D - HD and AD © DHI

13. Realistic view of urban flooding

14. Detailed models with long run time © DHI Slight change in work – are the data correct? In this case missing inlets lead to misleading results

15. MIKE 21 FM Christchurch Supermodel © DHI Catchment area approx. 420 km2 including three river systems in the model domain:  Avon River  Styx River  Heathcote River 2D model domain:  4.2 million elements  10 m x 10 m resolution flexible mesh (rectangular elements)  Distributed rainfall-runoff, with no losses (rain-on-grid) Design rainfall event:  100 year ARI rainfall  single peak storm  21 hour duration

16. MIKE 21 FM Christchurch Supermodel © DHI Run time on desktop PC with GPU is 3.5 hours approx. compared to weeks on desktop CPU only hardware 16 core Dell workstation with:  2 x Intel® Xeon® CPU ES- 2687W v2 (8 core, 3.40 GHZ)  32 GB of RAM  ONE GeForce GTX TITAN GPU card  Windows 7 operating system Tests with TWO GPU cards show a run time of 1.75 hours approx.

17. Desktop CPU user Desktop CPU+GPU user

18. Can we now handle the uncertainty? © DHI

19. • Precipitation and snowmelt • Vegetation based evapotrans-piration and infiltration • Un- and saturated groundwater flow • Channel flow in rivers and lakes • Overland surface flow and flooding • Demand driven irrigation • Solute Transport Distributed hydrology

20. SHE • Promotion from the 80’s

21. © DHI drainflow leakage rain infiltration runoff evaporation MOUSE infiltration MOUSE MIKE SHE MIKE SHE

22. Groundwater and sewer © DHI

23. Detailed hydrological modelling © DHI

24. © DHI

25. New standards for sewer rehabilization © DHI

26. Distributed physically based hydrology vs RR MIKE SHE was set up using global coverage spatial data sets… Nash-Sutcliffe (R2) 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 Osobloga Olza Klodnica Mala panew Olawa Nysa Klodska Kaczawa Sleza Bystrzyca Barycz Czerna Bobr Average SHE R2 NAM R2 Topography Land use Soil map GW zones ..and was found to perform better than traditional RR models

27. Local Area Weather Radar aka Hydrology Radar 18. August 2010 - Billund airport closed 45 minutes due to heavy rain 30 Circle diameter: 120 km Pixel size: 500x500 Image frequency: 5 minute Data from Vejle LAWR, DK

28. 31 LAWR – brief history • Developed as part of EU ESPRIT project in 1997 • First installation in 1998 – now 40+ worldwide − One nationwide network in El Salvador • Designed for high resolution precipitation measurement over small areas

29. • Range − 60 km for forecast − 20 km for Quantitative Precipitation Est. • Spatial resolution (Cartesian) − 500x500 − 250x250v − 100x100 • Image frequency − 1 or 5 minute • Single layer 32

31. Example of forecasts © DHI Best model results for hindcast and forecast are achieved with distributed rainfall and hydrology Now possible to significantly expand urban flood forecast lead time

32. Integrated Real Time Control System © DHI SCADA Models Rainfall forecast • Automated operation of: − Data collection − Data processing − Model execution − Finding the optimal solution − Control of structures − Issue warnings #35

34. Trends • Sensor technology changes from analogue to © DHI digital • Crowd sourcing of data • Apps for direct communication • Availability of global data • Easy distribution of results (databases, portals, web...) • Enough challenges for the future – but different from the past

35. Take home messages • Urban drainage storm water models typically need high degree of detail to resolve the flooding – it’s important, but data are available today • Detailed models are slow, too slow – GPU and HPC technology is a game changer • With all this speed provided by GPU and HPC, the uncertainty in model set-up and parameters can be assessed => use speed advantages from GPU for uncertainty assessment • Physically based, distributed hydrological modelling and distributed rainfall can be used in coupled modelling to great effect

36. Thank you for your attention (please visit our booth to see more!) Ole Larsen; Stephen J Flood; and Nick Elderfield © DHI

37. The expert in WATER ENVIRONMENTS