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

1. Slow Response Runoff Modelling and ‘Real Time’
simulations – a Swedish perspective
CIWEM UDG Conference
12th November 2014
Andy Wilson (awi@dhigroup.com)

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. 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. 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. Sources of Rainfall Dependent Infiltration (RDI)
© DHI

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. 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. RDI Componentry – example (Separate System)
© DHI
WwTW
RDI
Catchments

9. 2011 – 2012 Rainfall (2011 used as ’hydrological memory’)
© DHI
2011 Rainfall
Used as antedcedent
hydrological
conditions
2012 Rainfall

10. 2011 – 2012 Rainfall (2011 used as ’hydrological memory’)
© DHI
2011 metered inflow
(m³/s)
2012 metered inflow
(m³/s)

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. 2012 Inlet flows – metered v modelled
© DHI

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. RAOM Model
© DHI

15. RAOM Model Extent
© DHI
Rain Meters
Subcatchments
My house
Tunnels
35km

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. 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. Routine simulation data outputs
© DHI
Measured Flow
Modelled Flow
Forecast Flow
Rainfall
(Observed &
Forecast)

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. Forecast On Demand - Operation Strategy
© DHI
Predicted Flow
Operators Strategy

21. Forecast On Demand - Operation Strategy
© DHI
Forecast flow
’On Demand’
flow
Forecast level
’On demand’
level

22. On-Line model
• http://raom.dhigroup.com/
© DHI

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. 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. Scone Catchment – Subsurface Flows (Baseflow + Interflow)
© DHI

26. Scone Catchment – Observed v Modelled
© DHI

27. Scone Catchment – Observed v Modelled
© DHI

28. Scone Catchment – Observed v Modelled
© DHI

29. Scone Catchment
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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. 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. 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. Questions?
© DHI
awi@dhigroup.com