The document discusses the development of a proactive water management system for Bangkok using 1D2D urban models in Delft3D. It outlines challenges including the large size and complexity of Bangkok's drainage system. An approach was taken to develop Delft-FEWS applications for flood forecasting using a Delft3D FM model of the metropolitan area. The modelling approach involved automated generation of nested 1D2D models at different scales. The resulting proactive water management system integrates observed and forecast data within a Delft-FEWS database to run forecast simulations and support decision making. Next steps include further improving input data and implementing real-time control of important gates.

1. Rinske Hutten (Deltares),
On behalf of PANYA consultants and Deltares
Via scripting generated 1D2D urban (sewer and surface water)
models
15 November 2022
Development of a proactive
water management system
for Bangkok

2. Outline
2
• Introduction to the project
• Approach
• Challenges
• Modelling approach
• Modelling cycle
• Proactive Water Management System
• Conclusions
• Next steps
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3. About Bangkok
Some facts & figures about Bangkok:
• Capital of Thailand
• Inhabitants: 8 – 15 million people
• Rainfall: ~1500 mm/year
• Situated at the:
− Downstream stretch of the Chao Phraya river
− Coast of Gulf of Thailand
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4. About the drainage system
Some facts & figures about the drainage system:
• > 2000 km of main pipes
• > 1500 main drainage canals (length: > 2500
km)
• > 190 pumping stations
• ~ 250 regular gates
• 4 large scale drainage tunnels (+ 6 planned)
• Divided over 38 sub-polders
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5. About the floods

6. About the project
6
Initiated and owned by the Bangkok Metropolitan Administration,
The goal of the project is
“Setting up a functioning Proactive Water Management System for the Bangkok Metropolitan Authority
(BMA)”
Executed by,
&
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7. Approach
7
• Develop a Delft-FEWS application to be used
for flood forecasting in Bangkok
• Develop a flood model based on Delft3D FM
for the metropolitan area in Bangkok
• Incorporate the flood model in Delft-FEWS
• Build-up local capacity
Delft-FEWS
Delft3D FM 1D2D
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8. Challenges in
the project (I)
8
Complex
Size of the area
m2
Computational speed
GIS database
Data availability
Model calibration
Working on distance
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9. Challenges in
the project (II)
9
GIS database
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10. Challenges in
the project (II)
10
How did we cope with these challenges?
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11. Modelling approach (I)
11
m2
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12. Modelling approach (II)
12
Detail models
RR
D-RTC
1d (canals & drainage tunnels)
Bangkok model – main model
m2
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13. Bangkok model
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m2
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14. Drainage tunnels in Bangkok model
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m2
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15. Bangkok model versus detail model
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m2
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16. 2D grid only for big streets in detail model
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17. Modelling cycle
17
Build and
run the
model
Analyse
results
Identify
missing or
incorrect
data
Improve
data
m2
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18. Automated model generation via scripts (I)
18
• GIS data is collected and validated by PANYA.
• The model generation is automized via Python scripts.
• The scripts use some functionalities of D-HyDAMO: a Dutch model converter created
to convert for the HyDAMO database of waterboards
• The automatic model generation is structured according to the HydroMT model API
GIS data storage
Delft3D FM 1D2D models
Script
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19. Automated model generation via scripts (II)
19
• Includes.:
− 1D sewers coupled with surface water (channels and rivers), and a 2D grid
− Rainfall Runoff NWRW concept,
− Automatic generation of 1D boundaries,
− Structures and storage nodes,
− Determination of sub-catchments via Thiessen-polygon (within the catchments of the real
drainage system area),
− Logging of the model building process
− Validation plots
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20. Analysis of model results
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21. Identification of missing and incorrect data
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• The validation plots generated during the model building help with the identification of
e.g. non-connected network elements.
&
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22. Further automatization in the project
22
In addition to the automatization of model generation, the following
steps were automatized:
• Integration of Delft3D FM 1D2D models in Delft-FEWS
• Semi-automatic model calibration
&
Python Snakemake
=
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23. Proactive Water Management System: Database
23
• Based on Delft-FEWS software
• Installed as client-server system at PANYA and
BMA
• Database consist of all time series received from
Bangkok:
− Observed telemetry data (from 2012)
− Observed radar data (from August 2021)
− Precipitation radar now cast (from August 2021)
− NWP prediction (from August 2021)
• Time series are used for:
− Analysis data quality
− Historical flood scenarios(model calibration)
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24. Proactive Water Management System: Bangkok model
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1D water levels
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25. Proactive Water Management System: detail models
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2D water depths
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26. Proactive Water Management System: planning
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Models runs every 4
hours with a
prediction horizon of 8
hours
Triggered by crossing
rainfall thresholds
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27. Proactive Water Management System: End Users
27
• PANYA retrieves measurements and model simulations from the FEWS database
via Delft-FEWS API.
• For example, maps with observed water levels and rainfall predictions are exported.
• These are used in the following applications:
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28. Conclusions
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• Automatic model generation was challenging but needed to achieve full coverage over such a large
area and to cope with the growing database.
• Urban flood modelling remains very complex and good local data and information remains crucial.
• We applied Delft3D FM Suite 1D2D for the first time on this scale in an operational setting, with
success!
• By training our local partner, we contribute to the capacity building needed to improve flood
forecasting in Thailand.
• The project taught us many insights on how to best model a large urban water management system
in Delft3D FM Suite 1D2D.
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29. Next steps
29
• Model quality and –speed are dependent on
the GIS database. Further improvement of
GIS database is therefore needed.
• Implementation of Real-Time Control at the
important gates in the Bangkok model.
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30. Contact
rinske.hutten@deltares.nl