1. Forecasting Technologies Focus Group
or Beyond Modelling

2. © DHI
Contents
Introduction
Development of a Storm Water Forecast system for
Singapore
Water Data Portal and online services
Probabilistic Flood Risk Modelling

3. Forecasting Technologies focus group, or
– beyond modelling
Introduction
Torben, S. Jensen, Andy Wilson, Ole Larsen

4. © DHI
Agenda
Introduction to customizable products by DHI
Slow response runoff modellling and ”real time”
simulations
Urban flood forecasting using MIKE SHE and rainfall
radar
Water Data Portal and online services
Demonstration of online systems
Demonstration of online systems (II)
Making your model run fast – GPU, HPC etc
Probabilistic flood modelling
Q and A

5. Help solving the world’s toughest challenges in all water environments
Water
Modeling
Data
Management
Planning &
DSS
Operational
Forecasting

6. Help solving the world’s toughest challenges in all water environments
Water
Modeling
Data
Management
Planning &
DSS
Operational
Forecasting

7. More than 10000 licenses
More than 200 client solution
More than 150 countries on all continents
In all water environments
MIKE
Models
Scenario
analysis
Forecasting
And
Early Warning
Real Time
operations
GIS
SCADA
link
Optimisation
And control
Time-series
Analysis
Reporting
Publication
and
sharing
Information Management
…manage, analyse and share
data and information
DHI Water Data
…your one-stop data
shop
Costs
and
Benefits
Risks and
Uncertainties
Key
Performance
Indicators
Master Planning and Decision Support
…make wise investments and robust
management decisions
System Optimisation
…optimise operations
and planning
Disasters and Risks
…Protecting people
and properties

8. Lake Victoria IMS
…manage, analyse and
share data and information
DHI Water Data
…your one-stop data shop
- Climate, DEM….
Nile Basin DSS
…make wise investments
and robust management
decisions
CARM, Australia
…optimise operations
and planning
Disasters and Risks
…Protecting Thailand from floods

9. © DHI
Information Management, planning and operations
Tailor made
MIKE Products

10. Different Users – Different needs – Different Solutions
Models, scenarios, timeseries, GIS, Scripting, Jobs, Reports, Spreadsheets, Indicators, MCA, CBA
Specialists
Import Data
Analyse Data
Prepare data
Build models
Prepare workflows
Create Report Template
Configure Operation level systems
Modeling and Data Management Framework
Customisable Products
System Operators, Planners
Respond to Incidents
Publish information
Create notification
Adjust operations
Mobilise staff
Run scenario
Create Report
Web and Mobile
Managers, Decision Makers,
The public
View on mobile and tablet
Receive notification
Make decision
Run Scenario

11. Creating the basis for improved
water management
© DHI
Kenya Uganda Tanzania Burundi Rwanda
WRIS – a Water Resources Information
System for Monitoring Surface- and
Groundwater and Water Quality conditions
in Lake Victoria Basin

12. Creating the basis for improved
water management
© DHI
Making information available
Data base

13. Sharing water resources fairly
© DHI
One DSS allowing nine countries to
cooperatively manage their scarce
water resources, with accepted
processes and tools as well as
sharing of data and knowledge
You can too – with our software platform
The Nile Basin Decision Support System
will provide the basis for agreement on
and development of sustainable water
resources projects in the Nile Basin”
Dr. Abdulkarim H. Seid, Head, Water Resources
Management, Nile-Sec
Nile river basin

14. © DHI
Sharing water resources fairly
Nile river basin, AfricaNile Basin DSS
Time
series
GIS
Scenarios
Scripting Indicators
Spread-
sheets
Work
spaces
Meta data
Dashboard
(web
publishing)
Optimisation
Ensembles
MCA/CBA
Data base
Multi Criteria Analysis

15. Early warning and forecast system
Aarhus Denmark
© DHI
An user-friendly online forecasting
system providing decision makers
and the public with an overview,
every hour 365 days/year.
You can too – with our software platform
”The public get easy access to relevant
information on the web and we have the
detailed overview over the entire water
system”
Ole Helgren – Engineer, Water and Environment-
Municipality of Aarhus

16. Public web site
© DHI
http://aarhus.dhigroup.com
#16
Aarhus, Denmark

17. Early warning and forecast systems
© DHI
An user-friendly online forecasting
system providing decision makers
with an overview of the entire river
network, every hour 365 days/year.
You can too – with our software platform
With the new system we expect to reduce
costs caused by flooding by 20-30%.”
Janez Polajnar, Head, Flood Forecasting Department,
ARSO
Slovenia

18. Early warning and forecast systems
© DHI
Trained staff in the operation room
Status map in MIKE CUSTOMISED RealTime
The system is running automatically, providing new 6-day-
forecasts at 74 locations in the network every hour 365 days/year.
Slovenia

19. Early warning and forecast systems
© DHI
The Chao Phraya River Basin.
160,000km2. One Decision
Support System to protect
against devastating flooding.
You can too – with our software platform
HAII highly appreciates DHI for their
excellent job, especially on the close
collaboration and hands on experience
that made us become a good partner.”
Dr. Piyamarn Sisomphon, Project Leader, Hydro and Agro
Informatics Institute
Chao Phraya, Thailand

20. Early warning and forecast systems
© DHI
Chao Phraya, Thailand

21. River operations
© DHI
CARM is a world class development
designed to maximise the efficiency of the
Murrumbidgee River system.”
Brett Tucker, Chief Executive Officer, State Water
Corporation, New South Wales, Australia
Over 1,600km of river with two
dams and thousands of water
users. One river management
system.
You can too – with our Computer Aided River
Management (CARM) system
New South Wales, Australia

22. River operations
© DHI
“The CARM project will
make control of water
flows more responsive
and more precise.”
State Water Corporation
New South Wales, Australia

23. © DHI
Open and Extendible

24. Thank You
Ole Larsen
© DHI

25. Development of a Storm Water Forecast system
for Singapore
Dr. Ole Larsen
22 April 2014

26. Another rainy day in Singapore
© DHI

27. Singapore
© DHI
• 720 km2
• 5.4 mill inhabitants
• 2550 mm (100 inch) annual rainfall
• 90 Rainy days a year
• One of the most water scarse countries in the world (precipitation per
capita)
• Attempts to close the water loop
• Collection of stormwater without compromising flood risk is a major
challenge
• Operation and control is required for efficient water management

28. Singapore catchments
© DHI

29. Starting point
© DHI
• MIKE 11 models for simulation of drains and channels
• UHM models to describe run-off – catchment concentration time
about 10 min
• 50 Rain gauges
• 50 discharge measurement stations
• No lead time and inaccurate reservoir inflow and flood forecasts
• Reason: Spatial and temporal variation in run-off not adequately
resolved – neither in data, nor in model

30. Accuracy of data
© DHI

31. Solution
© DHI
• Measurement of rainfall at appropriate spatial and temporal scale =
DHIs LAWR radar system
• Distributed hydrological modelling instead = MIKE SHE
• Efficient forecast and evaluation system = MIKE Customizable
• Test, training and delivery of system to PUB

32. © DHI #8
Deliverables

33. LAWR installation
© DHI #9

34. MIKE SHE Modelling (distributed rainfall-runoff modelling)
© DHI

35. Calibration and Validation
© DHI
Calibration
Validation

36. Rainfall comparison
© DHI
Radar
Rain
Gauge
• Rain gauge data can hardly be compared to radar data

37. Validation events
© DHI
• Selected from May
2013 – Nov 2014
• Total of 46 events
observed
• Total of 11 events
selected finally
(ranging from light to
moderate to heavy)
May-Dec 2013
Jan – June 2014
PUB’s selected
list from July-
Nov 2014

38. 08 Sep 2013 (Heavy (<2yr), No attenuation, 100% coverage)
© DHI
Characteristics Analysis
Rainfall Starts From West (W)
Rainfall Move towards East (E)
Speed of movement
29 km/hr (from visual observation, time and length crossed);
steady speed
Rainfall coverage
Linear strip of rain spread across North (N) to South (S) of
Marina; Whole Marina
At Radar location
Moves away from radar towards East; Initially crossed the
radar location when it starts from West
Attenuation effect
No Attenuation;
No heavy rain observed behind the linear strip of rain
spreading N-S
Equivalent design storm
(from observation of NEA
rain gauges, peak value)
132 mm/hr; < 1-in-2 year design storm

39. Water level forecasts – CWS049 (ST, MS) – 8 Sep 2013
© DHI
• RG model tend to over
estimate consistently

40. Water level forecast evaluation summary – 08 Sep 2013
© DHI

41. 3 Sep 2014 (Light to Moderate (>15yr), Attenuation, 67% coverage)
© DHI

42. Water level forecasts – CWS047 (BT, DS) – 3 Sep 2014
© DHI

43. Conclusions and Recommendations
#19© DHI

44. Conclusions
© DHI
1. Rainfall Comparison: Significant differences observed in Mean Areal Rainfall (MAR) calculated
from rain gauge (NEA) and radar (DHI’s LAWR)
2. Change of Focus: Focus changed from rainfall to runoff/Water Level comparison to address the
differences between point (rain gauge) and spatially-distributed (Radar) measurements, so that it
will be an “apple-to-apple” comparison
3. Rain gauge rainfall runoff model: Thiessen rain gauge distribution rainfall runoff model (MIKE 11
RR/HD) shows consistently overestimated water levels and runoff hydrographs, due to its
simplified description of the spatial rainfall distribution
4. Distributed radar rainfall-runoff model: Distributed radar rainfall runoff model (MIKE SHE)
produces better results compared to rain gauge data and lumped hydrological models
5. Forecast time: Radar based forecasts lead time is 10-70 min – average enable up to 30 min lead
time on flooding

45. Conclusions
© DHI
6. Best water level forecasting performance:
 Heavy to moderate rain events (>120 mm/hr; >1-in-2 year design storm)
 Wide spread coverage
 Away from radar location
 No attenuation effect
 Steady speed of storm movement
 Convection outside Singapore island
These events are likely to cause flooding in the catchment

46. Conclusions
© DHI
7. Poor water level forecasting performance:
 Very light to light rain events
 Partial catchment coverage
 Close to and over the radar location
 Fast movement of storm
Not so important as these events are less likely to cause flooding in the
catchment

47. Recommendations
© DHI
• For attenuation effect: Two additional radar
20km range
CTO - 3600
Woodlands Booster Station - 3600
Changi WRP Admin Building - 3600

48. Thank You
#24© DHI

49. Water Data Portal and online services

50. 22 June, 2015© DHI #2
We got used to taking it for granted when we managed the core of our consultancy …
data
 Gone are the mainframes
 Gone is leaving data on the number crunchers
 Project Data lives on USB hard-disks that may or may not boot up
 No clear business strategy on Choosing, Handling, Storing the right data
 More data options and “technical” products emerge every day
 Maintenance of IT is costly and require high level of expertise
Experience from DHI Projects:

51. 22 June, 2015© DHI #3
DATA
Portal
Data
Revenue
Internal
Efficiency
New Entry
point to
DHI
Global
intelligence
The
Academy
& RD
Tools and
Engines
Domain Knowledge + Tools + Presentation + Data

52. Water Data Portal and Online Services
Consumers of water data and information want real-time information – the water world is going
online. We attempt to make the wealth of digital data available to MIKE Users.
22 June, 2015© DHI #4
Water Data
Portal
• Precipitation
• Bathymetry
• Boundary
conditions
Modelling
• MIKE
software
Presentation
• Reports
• Data base
• Web clients
Tools supporting online access

53. Technology
Challenges:
• Volume: Sheer amount of data
• Velocity: How quickly is the data generated/collected
• Variety: Formats – structured to free text
• Value: How to capitalize the value of the data
Solutions:
• Water Data portal
• Remote HPC model runs
• Data and model warehouse
• Ready made MetOcean design base
Needs:
• Speed in accessing data (server systems)
• Partial access (license system)
• Speed of streaming/transfer (connection of multiple users)
• Visualization of large data sets (new tools to view partial data sets)
WaterData

54. 22 June, 2015© DHI #6
GLOBAL
Processed
FREE
SATELITE
DATA
Images &
Processed
IN-SITU
BY
DHI
FORECAST
DATA
DESIGN
DATA
NOAA/ESA
to
GRAS
DATA
CENTRE
(CED)
OFFICEs
Waterforecast
Info@Sea
FloodWatch
PERGOS
Partners
MIKE
Customised
New Portal
Service

55. Data portal Data Viewer
22 June, 2015© DHI #7
Search and download data:
• Provide access to many (30+) different data
• Order data for download
• Access to specialized viewers
• Visualize one data entry
Search and download data:
• Provide access to selected data groups
• Download data for relevant area and time
• Derived data (statistics etc)
http://waterdata.dhigroup.com/DEM/data
http://waterdata.dhigroup.com/trmm/trmm-data
How does it look?

56. Thank you
Ole Larsen
22 June, 2015© DHI #8

57. Probabilistic Flood Risk Modelling
Novel methods and models for South-East Asia
22/06/2015© DHI #1

58. Economic exposure
to flood hazards in
South East Asia
22 June, 2015© DHI #3
Source: Global Assessment
Report on Disaster Risk
Reduction

59. 22 June, 2015© DHI #4
PastPresent

60. 22/06/2015© DHI #5
Modelling challenges

61. 22/06/2015© DHI #9
Major hydrological units Preliminary Flood hazard analysis Preliminary Exposure/vulnerability
analysis
Analysis and segregation of priority areas

62. 22/06/2015© DHI #10
Fully Dynamic 2D
Modelling approaches
Coupled RR-1D Coupled RR- Quasi 2D

63. © DHI
Tools/Products
• Stochastic rainfall generator (R - DHI) –
TRMM based
• Precipitation-Area-Duration-Frequency
analyser (R - DHI)
• Stochastic rainfall generator (PADF based –
VB – CAPRA)
• Stochastic typhoon track generator (VB -
CAPRA)
• Typhoon gridded rainfall generator (R - DHI)

64. 22/06/2015© DHI #12
Stochastic weather approaches
Rainfall generator -
medium/large scale studies
Precipitation Area Duration Frequency -
small scale studies
Stochastic typhoons

65. 22/06/2015© DHI #13
Stochastic weather generation – Synthetic discharge
Simulated discharge with historical
and synthetic rainfall (Full Chao
Phraya - 250 TRMM grids)
Discharge from stochastic rainfall [m^3/s]
Discharge from historical rainfall [m^3/s]
2000 - 09 2010 - 19 2020 - 29 2030 - 39 2040 - 49 2050 - 59 2060 - 69 2070 - 79 2080 - 89 2090 - 99
2000
4000
6000
8000
10000
12000
14000
16000
18000
Discharge from stochastic rainfall [m^3/s]
Discharge from historical rainfall [m^3/s]
May
2013
June
2013
July
2013
August
2013
September
2013
October
2013
November
2013
December
2013
2000
4000
6000
8000
10000
12000
14000

66. 22/06/2015© DHI #14
Stochastic weather generation – Synthetic discharge
Simulated discharge with historical
and synthetic rainfall (Nan Basin - 28
TRMM grids)
Discharge from stochastic rainfall [m]
Discharge from historical rainfall [m]
2000 - 09 2010 - 19 2020 - 29 2030 - 39 2040 - 49 2050 - 59 2060 - 69 2070 - 79 2080 - 89 2090 - 99
1000
2000
3000
4000
5000
6000
7000
Discharge from stochastic rainfall [m^3/s]
Discharge from historical rainfall [m^3/s]
April
2000
May
2000
June
2000
July
2000
August
2000
September
2000
October
2000
November
2000
December
2000
January
2001
0
500
1000
1500
2000
2500
3000

67. 22 June, 2015© DHI #15
Annual runoff
volumes – Impact of
time shifting
Comparison of yearly accumulated
runoff volumes simulated with 16Y
historical TRMM data and 500Y
synthetic rainfall

68. © DHI
Tools/Products
• Catchments delineation (country scale)
• Nam parameters transfer based on
slope/land-use/size classification
• Automatic creation of MIKE11 model from raw
DEM datasets (incl. full network branches and
connections, in-bank and floodplain transects
Xsec, RR and RR links, boundaries)
(ARCGIS – Python)

69. 22/06/2015© DHI #17
River network and catchments
Coupled hydrological and 1D hydraulic model – Model development
(all within MIKE 11)
River network and cross sections

70. © DHI
Tools/Products
• Wetness mapping
• Open Street Map roads generalization
• Statistical correction of SRTM elevation
• Auto creation of flood cell model (MIKE1D)

71. Conceptual
floodplain model
22/06/2015© DHI #19

72. Conceptual
floodplain model
22/06/2015© DHI #20
SRTM not able to
describe main flood
plain features

73. Enhanced floodplain
elevation information
22/06/2015© DHI #21
Field surveys Population
Density/Land use

74. © DHI SRTM Landsat Wetness

75. OpenStreet
Map
22/06/2015© DHI #23

76. Site surveys
Chao Phraya Dec 2013
22/06/2015© DHI #24

77. DEM Improvements
example: lowest elevation
22/06/2015© DHI #25
Before After

78. Hydrodynamic
model
schematization
22/06/2015© DHI #26
• Automated: easy to replicate and update
• Fast simulation time
• Capture structures and simulate structure behavior

79. © DHI
Tools/Products
• Stochastic failure (MIKE1D)
• Stochastic failure parametrization (RELIABLE
– HR Wallingford/DEFRA)

80. The MIKE SDK is:
© DHI
• A small installer program.
• A set of API’s for .NET
− DFS, PFS
• Documentation
• Examples
API = Application Programmer Interface

81. Included in MIKE SDK installer
© DHI
• Binaries for execution
• Binaries for development
• Documentation and examples

82. Included in MIKE SDK: Documentation
© DHI
• MIKE SDK Documentation Index (start menu – 2014)
• DFS User Guide (PDF)
• DFS API documentation, DHI.Generic.MikeZero.DFS.chm
• FM file specification (PDF)
• PFS API documentation, DHI.PFS.chm (not in 2012)
• SDK Examples (zip with examples and test data)

83. Probabilistic structure failure
22/06/2015© DHI #31

84. Fragility curves
22/06/2015© DHI #32
Protection
Designation
Relative
elevation
Type
M1D
RASP
Class
Condi
tion
Grade
Type RASP Type Dominant Failure mode
Front
face
Crest
Rear
face
IE Navanakorn 4.5 100 7 1 Cantilever sheet pile wall vertical wall
Rotational failure due to toe
scour and erosion of earth bank
x
IE Bang Pa-in 4 101 27 1 Cantilever sheet pile wall vertical wall
Overturning due to toe scour and
erosion of earth bank
x x x
IE Bang padi 3.5 102 7 1 Cantilever sheet pile wall vertical wall
Rotational failure due to toe
scour and erosion of earth bank
x
IE Hi Tech 3.4 103 1 3
Gravity based wall
(gabions)
vertical wall
Overturning failure due to toe
scour and erosion of earth bank
x
IE Rojana 5 104 7 1 Cantilever sheet pile wall vertical wall
Rotational failure due to toe
scour and erosion of earth bank
x
IE Bang Poo 0.3 105 45 4 Turf embankment - wide Embankment Piping x
dyke 4 110 44 1 Anchored sheet pile wall vertical wall
Rotational failure due to toe
scour and erosion of earth bank
x x
Railway - no dyke 1.2 120 45 3
Turf embankment -
narrow
Embankment Piping x
Railway - dyke 3.1 121 13 3
Turf embankment -
Narrow
Embankment Piping x

85. © DHI
-0.70
-0.50
-0.30
-0.10
0.10
0.30
0.50
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ratiowaterleveltodykecrest
Probabilityof failure
RASP1 - CG3
RASP7 - CG1
RASP10 - CG4
RASP13 - CG4
RASP44 - CG1
RASP45 - CG4
Fragility
curves

86. 22/06/2015© DHI #34
Modelling challenges

87. 22/06/2015© DHI #35
Chao Phraya 2011 - No Failure

88. 22/06/2015© DHI #36
Failure 1 – Simulation 1
Difference water level
Failure vs No failure

89. 22/06/2015© DHI #37
Failure 1 – Simulation 2
Difference water level
Failure vs No failure

90. 22/06/2015© DHI #38
Validation 2011

91. © DHI
Exposure models
Residential

92. © DHI
Exposure models
Industrial
Input Data
Weight
Industrial Commercial
Population Density 0.10 0.30
Industrial Estate 0.60 0.10
OSM Industrial 0.30 0
OSM Commercial 0 0.6
Example of potential weighting

93. 22/06/2015© DHI #42
Impact on
industrial losses
Relative losses for different
exposure and vulnerability scenarios
and parametrization of the structure
failure models (14 classes, with low
failure to high failure probability)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
F2 (low) F1 (middle) F4 (high) F3 (very high) F5 (no Failure) F6 (no structure)
Exposure model 1
V1 V2 V3
RelativeIndustrialloss
Increasing concentration in Industrial Estates
Exposure model 2
V1 V2 V3
Exposure model 3
V1 V2 V3

94. 22/06/2015© DHI #43
Impact on
residential losses
Relative losses for different
exposure and vulnerability scenarios
and parametrization of the structure
failure models (14 classes, with low
failure to high failure probability)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
F2 (low) F1 (middle) F4 (high) F3 (very high) F5 (no Failure) F6 (no structure)
Exposure model 1
V1 V2 V3
Exposure model 2
V1 V2 V3
Exposure model 3
V1 V2 V3
Relativeresidentialloss

95. Summary
and way forward
22/06/2015© DHI #44
Outcome:
• Flexible modelling architecture based on semi-
automated procedures
• Easy update
• Easy replication
• MIKE11 models covering Thailand, Malaysia,
Indonesia, Vietnam, Singapore
Way forward and applications:
• Support the development of insurance in the
region (NATHAN, DHI Water Data)
• Regional flood forecast using near-real time
precipitation products
• Parametric (trigger) flood insurance products
• Projects (WB, ADB)
• Software components
• Probabilistic tools integrated MIKE

96. Thanks for your attention
22/06/2015© DHI #45