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Slow Response Runoff Modelling and ‘Real Time’ simulations

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Slow Response Runoff Modelling and ‘Real Time’ simulations: A Swedish perspective - Andy Wilson, DHI Sweden

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Slow Response Runoff Modelling and ‘Real Time’ simulations

  1. 1. Slow Response Runoff Modelling and ‘Real Time’ simulations – a Swedish perspective CIWEM UDG Conference 12th November 2014 Andy Wilson (awi@dhigroup.com)
  2. 2. Aim of presentation To provide an overview on the Swedish processes of slow response runoff modelling, and the its functioning within real time, on-line simulations. © DHI
  3. 3. Objectives © DHI 1. Overview of runoff componentry and the seasonal variations. 2. Land phase of the hydrological cycle and its influence on the Rainfall Dependent Infiltration (RDI) modelling componentry. 3. Example of the RDI calibration process and parameters. 4. Rya Avloppsreningsverk Område Modell (RAOM) 5. Real Time modelling processes and functions. 6. Applications within UK.
  4. 4. Runoff Componentry and Seasonal Variation • Runoff – Dry Season Rainfall Event Fast response to rainfall • Runoff – Wet Season © DHI RDI Element Slow Response to Rainfall
  5. 5. Sources of Rainfall Dependent Infiltration (RDI) © DHI
  6. 6. RDI Calibration – Storage Zones © DHI Snow* Rain Snow Storage Surface Storage Evaporation Overland Flow Inter Flow Root Zone Storage Ground Water Storage Base Flow Root Suction Capiliary flux Rainfall Dependent Infiltration Input Precipitation Outputs * RDI component requires time-series input for rainfall, temperature and evaporation.
  7. 7. RDI Calibration © DHI 1. Typical RDI calibration process requires 3 years flow meter data. 2. Sourced from WwTW inlet FM and Met Office – daily values. 3. RDI is significantly affected by metreological conditions. The ’present’ conditions are affected by events 6 months prior. 4. RDI calibration involves the dimensioning of the storage zones, and balancing the inter-relationships between the zones. 5. No requirement for empirical geological / metreological data during the calibration process.
  8. 8. RDI Componentry – example (Separate System) © DHI WwTW RDI Catchments
  9. 9. 2011 – 2012 Rainfall (2011 used as ’hydrological memory’) © DHI 2011 Rainfall Used as antedcedent hydrological conditions 2012 Rainfall
  10. 10. 2011 – 2012 Rainfall (2011 used as ’hydrological memory’) © DHI 2011 metered inflow (m³/s) 2012 metered inflow (m³/s)
  11. 11. 2011 – 2012 Rainfall (2011 used as ’hydrological memory’) © DHI 2011 modelled Inflow (m³/s) (Used as RDI Hotstart file) 2012 metered Daily Inflow (m³/s)
  12. 12. 2012 Inlet flows – metered v modelled © DHI
  13. 13. Annual Calibration Parameters © DHI Result Value Annual Volume observed 992,000m³ Annual Volume modelled 1,032,000m³ Volume error +4% Average ’Dry Day’ inlet volume 1,750m³ Peak ’Wet Day’ inlet volume 8,650m³ Variance between wet / dry day ≈500%
  14. 14. RAOM Model © DHI
  15. 15. RAOM Model Extent © DHI Rain Meters Subcatchments My house Tunnels 35km
  16. 16. Rya WwTW Catchment Parameters © DHI Parameter Value Population Equivalent (Pe)* 765,000 Percentage Combined Network 40% Tunnel Network 132km Typical loading 173,000 – 1,425,000 m³/day Contributing Area 240km² Impermeable Area 20km² * Largest WwTW in Sweden
  17. 17. Forecast Data Management Principle – On-Line RAOM Model © DHI ’Now’ ’Tomorrow’ Historical rain data (Used to develop ’Hotstart’ File) 24hr forecast ’4 days ahead’
  18. 18. Routine simulation data outputs © DHI Measured Flow Modelled Flow Forecast Flow Rainfall (Observed & Forecast)
  19. 19. Forecast and Calibration Process • Utilisation of historical metreological data as ’wetness memory’ as a © DHI ’Hotstart’ model parameter. • Daily simulation of preceding 24 hours rainfall condition: − Confidence check on model calibration parameters. − Quality of flow meter data. • MIKE CUSTOMISED platform has realised ’real time’ functioning capability and ’on-line’ modelling. • Forecast on Demand is used by WwTW operators to develop operational strategies at the work inlet to optimise treatment efficiency and minimise overflow discharges.
  20. 20. Forecast On Demand - Operation Strategy © DHI Predicted Flow Operators Strategy
  21. 21. Forecast On Demand - Operation Strategy © DHI Forecast flow ’On Demand’ flow Forecast level ’On demand’ level
  22. 22. On-Line model • http://raom.dhigroup.com/ © DHI
  23. 23. Applications within UK • Joint research project in conjunction with Scottish Water and the © DHI University of Edinburgh. • ’Scone’ catchment - develop an RDI model (based on 3 years flow data).
  24. 24. Scone Catchment – Preliminary ’Draft’ Results © DHI Assumptions Impervious Area 5% Wastewater consumption 400 l / h / day Temperature Profile Derived from Met Office historical data Comment WwTW inlet inflow ’time series’ for both observed and modelled data pertain to Daily values. There is no detailed fast response inputs.
  25. 25. Scone Catchment – Subsurface Flows (Baseflow + Interflow) © DHI
  26. 26. Scone Catchment – Observed v Modelled © DHI
  27. 27. Scone Catchment – Observed v Modelled © DHI
  28. 28. Scone Catchment – Observed v Modelled © DHI
  29. 29. Scone Catchment © DHI Result Value (without RDI) Value (with RDI) Annual Volume observed 1,180,600 m³ 1,180,600 m³ Annual Volume modelled 780 500 m³ 1,154,900 m³ Volume error -34% -2%
  30. 30. Applications within UK • Joint research project in conjunction with Scottish Water and the © DHI University of Edinburgh. • ’Scone’ catchment - develop an RDI model (based on 3 years flow data). • Couple the outputs of the RDI modelled elements with existing InfoWorks DAS model. • Goal – development of a ’real time’ catchment model – Environmental warning system. • Long term goal – development of a ’Bolt On’ RDI model element to existing UK models using a MIKE CUSTOMISED platform. − Flood warning, strategic operational management, rain statistics
  31. 31. Conclusions © DHI 1. Reviewed runoff componentry and the impact of seasonal variations. 2. Looked at the hydrological cycle, and its influence on the Rainfall Dependent Infiltration (RDI) modelling componentry. 3. Demonstrated the RDI calibration process, parameters and example. 4. Overview of the RAOM model 5. Real Time modelling processes, Forecast on Demand (FOD) functions and on-line modelling. 6. Potential UK applications.
  32. 32. Questions? © DHI awi@dhigroup.com

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