This document discusses how local X-Band radar systems can improve flood forecasting and resilience. It provides two case studies: 1) In Aarhus, Denmark, an integrated real-time control system using a LAWR radar and hydrodynamic models optimizes the operation of retention basins and wastewater treatment plants to reduce flood risks. 2) In El Salvador, an X-band radar network provides rainfall data to forecast floods from hurricanes and tropical storms, allowing evacuation of over 7,000 people during one event. The network provides critical information to emergency managers.

1. Improving flood resilience: Application of local
X-Band Radar Systems in flood forecasting
Nick Elderfield
Managing Director, DHI UK

2. © DHI
• Introduction: Beyond flood risk modelling
• Flooding and infrastructure – what’s the problem?
• Improving resilience: Real-time catchments
• Case study – Aarhus
• Case study – El Salvador
Outline

3. Beyond flood risk modelling
…truly models the world of water –
from mountain streams to the ocean and
from drinking water to sewage

4. The evolution of flood modelling

5. The evolution of flood modelling
The mists
of time…
Early
1900’s
Mid
1900’s
Late
1900’s
Etapa 0003 - Karlín - Libeň
ETAPA 0004 - Holeš ov ic e
ETAPA 0007 - Troja
ETAPA 0001 Staré M ěs to J os efov
ETAPA 0002
M alá s trana Kam pa
Etapa 0006 Zbras lav Radotín
1D
Early
2000’s
2D

6. The evolution of flood modelling

7. Beyond flood risk modelling

8. Flooding & Infrastructure -
what’s the problem?

9. © DHI
Why do we get flooded?
Storm systems development slower than the urban growth
Flood plain urbanization
Uncertain climate change conditions
…

10. The problem

11. Extreme Rainfall

12. Extreme Rainfall

13. Channel flow in
rivers and lakes
Overland surface
flow and flooding
Saturated
groundwater flow
Unsaturated
groundwater flow
Precipitation and
snowmelt
Vegetation based
evapotranspiration and
infiltration
Physics based hydrology - challenges
Precipitation

14. Mean Areal Rainfall - MAR
© DHI
• Estimation of areal rainfall on basis of point measurements
• Assumptions:
• Homogenity of rainfall in space (allow in- / extrapolation)
• Linear variation in time
Tipping bucket gauges without corrections
The majority of the tipping-bucket rain gauges analyzed (seven out of twelve) do not apply
any correction based on dynamic calibration. Single point calibration is applied in some cases at a single rain intensity
around 30-50 mm/h. This is used to obtain a relative error very close to zero at a given rainfall intensity that is of practical
interest for operational purposes. Outside of this range the errors are not generally within the limits of ± 5 % defined by WMO
for the required uncertainty of rainfall intensity measurements.
The most relevant characteristic of this group of instruments is the large errors associated
with the highest rain rates (rising up to 15-20 % at intensities of around 300 mm/h). The relative error increases with rainfall
intensity and is well fitted by a second order polynomial.
(WMO LABORATORY INTERCOMPARISON OF RAINFALL INTENSITY GAUGES De Bilt (The Netherlands)
Genoa (Italy) Trappes (France) September 2004 . September 2005)
5 to 20 %
uncertainty

15. © DHI

16. Improving Resilience - Real-time
Catchments

17. © DHI

19. 19
DHI LAWR Specifications
• Spatial resolution
• 500x500
• 250x250
• 100x100
• Image frequency
• 1 or 5 minute
• Beam width
• H: 0.95º
• V: ±10º
• Continuous scanning
• Range
• 60 km for forecast
• 20 km for QPE
LAWR at Klein Matterhorn, CH
LAWR with clutter fence, Hvidovre Denmark

20. 20
What’s that like in London

21. 21
Beam Width Comparison
Horizontal and vertical beam width as function of opning angles
0
2
4
6
8
10
12
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250
Range from radar [km]
Beamwidth[km]
1° Horz./vert. opening angle C/S-band
0.96° horz. opening angle - LAWR
10° vert. opening angle LAWR

22. 22
C – bånd
bølgelængde 5 cm
X – band
Wavelength 3.2 cm
Power: 25 kW
Beam: 1° (Horz) 10° (vert)
C – band
Wavelength 5 cm
Power: 250 kW
Beam: 1°
C-band vs. X-band
Courtesy of DMI

23. Comparing McGill S-Band with City LAWR

24. 24
What does the modeller want/need?
• Rainfall at the surface
• Data for individual catchments
• Data in run-off time-frame
• Real-time (online) access to data
• Forecast for Real Time Control and Decision Support Systems
Planning
Real-time

25. 252 km
2
km
•Number of time series
Gauge = 0/1
C-band = 1
LAWR-500 = 16
LAWR-100 = 400

26. Case Study:
Aarhus

27. Integrated real-time control and warning
for urban areas and receiving waters
© DHI #27

28. © DHI
Aarhus – Denmark
…Located by the
Sea, second
largest city in
Denmark
#28
…Recreational
and active use of
the environment
… Business and
industries

29. © DHI
3 projects - 1 solution
…Implementation of
infrastructure
2007-2012
…Integrated modelbased
control and warning
PREPARED
2009-2013
…Analysis and design
2006-2007
#29

30. Solution ~50 mill. EUR project 2009-2013
• Infrastructure investment
• 9 retention basins
• Disinfection at WWTPs and basin
• Increased hydraulic capacity at WWTPs
• Optimized control System
• Integrated real time modelling/control (sewer system/WWTP)
• Early Warning System
© DHI #30
Ordinary and larger retention basins 79 million EUR
Controllable and smaller retention basins 45,6 million EUR
Automation and control system 1,7 million EUR
Total 47,3 million EUR
Saving 32 million EUR
40%

31. Integrated Real Time Control System
© DHI
SCADA
Models
Rainfall
forecast
• Automated operation of:
#31

32. Integrated real-time control of urban waters
© DHI
Sewer
#32

33. DHI Local Area Weather Radar (LAWR)
LAWR installation in Aarhus Denmark.
© DHI #33

34. How the Radar system works
© DHI
Sewer
#34

35. How the Radar system works
© DHI
Sewer
#35
MAR mm/hr
Reflectivity dBz
Radar calibrated by local rain gauges

36. © DHI
MIKE 11
MIKE URBAN
MIKE 3MIKE SHE
Data flow
Model preparation
Model execution
Real time control
Issues of warning
DIMS.CORE
Distributed
rainfall
from DHI
Radar
Automated Integrated Modelling

37. Case Study:
El Salvador

38. Background
• Hurricane Mitch, 1998: 240 dead, 861 mm of rainfall, 10.000 affected
• Tropical Storm Ida, November 7-8, 2009: 198 dead, 355 mm of rainfall,
7.428 evacuated
• Tropical storm Agatha 2010, May 24, 12 dead, 400 mm rainfall, 140
landslides
38

39. The radar network
39
22 active or extinct volcanoes
of which 14 are over 1000 m
elevation

40. The X-band radar network
40

41. The event 10-18 of October 2011
41

42. Using the radar network
42
Web traffic on the days of
emergency: 115,688 visits and
362,153 web pages accessed

43. So while waiting for the next flood….

44. So while waiting for the next flood...
• What will be flooded?
• What will it cost?
• How damages can be reduced?
• What else can we do to improve
resilience?

45. 45
And for London?

46. Thank you for your attention …
nje@dhigroup.com
© DHI #46