Symposium Next Generation Hydro Software (NGHS)

1. 2D hydrodynamics of Pearl River Estuary using Delft3D Flexible Mesh Li Li University of Hamburg Qinghua Ye Deltares Arthur van Dam Deltares
Delft Software Days 2014

2. Content of Presentation
•Significance
•Objectives
•Approach
•Model setup
•Future work

3. Significance
Threaten from the sea, e.g. storm
surge and typhoon
Threaten from the river, extreme
rainfall flooding risk caused by
storm surge becomes the major risk
along the coastal area in the world.
These two threatens happen
nearly every year in Pearl River
Delta after 2000.
Huge loss every year(half billion
dollars last year)
Fig. 1: Location of research area, Pearl
River Delta

4. Objectives
•To understand the interaction between marine system and river system during the typhoon period.
•
•To model the flooding inundation caused by typhoons
•To use the economic losses as quantified index .
•Uncertainty analysis (typhoons from different direction and intensity, etc)
•Inundation simulation along coastline area is highly depend on the accurate coastline definition, Delft3D Flexible Mesh model can properly resolve the complicated coastline area with various topography and forcing using unstructured mesh.

5. Approach
Risk Assessment & Climate Adaptation and Mitigation Strategies
Delft3D Flexible Mesh including D-Flow Flexible Mesh + WES
GIS-based topological analyses
Flooding Inundation
Inland flooding (heavy rains and peak flows)
Coastal flooding (storm surges and tide)
Topographic Exposition
Main Threats

6. Mesh Generation
(HK model ,Detalres,1980-2014)
(Geerstmaar, 2013)

7. Final Mesh

8. Bathymetry
Fig. 3 Combine bathymetry with patches
(HK model ,Detalres,1980-2014)
(Geerstmaar, 2013)

9. Boundary Conditions
•Sea side , 10 astronomic tidal components are used.
•River side, pearl river average discharge every year in wet and dry seasons.

10. Input Parameters
•Timestep: 60 s
•Roughness: Manning 0.023 (constant), future using spatial varying value
•Running time: one month in wet season and dry season separately
•Cell size: from ~100 m to ~1000 m
•Domain area: 33,000 km2
•Number of cells: 48640 cells, 56092 nodes

11. Calibration
4 points for the calibration is selected for water level and discharge in wet and dry seasons. They are,
Gaoyao(river side)
Shijiao (river side)
Makou(river side)
Kat o(sea side)
Gaoyao
Makou
Shijiao
Kat O

12. Makou Wet Season Measured Discharge (1999.07)
0
0,5
1
1,5
2
2,5
3
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
Discharge()m3/s
Makou Wet Season Daily
Discharge(m3/s)
10 4

13. Makou Dry Season Measured Daily Discharge (1999.12)
0
500
1000
1500
2000
2500
3000
3500
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
Discharge (m3/s)
Makou Dry Season
Daily
Discharge(m3/s)

14. Kat O Station Tidal Level (2002.10.18-2002.10.23, hourly)
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
15:00
21:00
03:00
09:00
15:00
21:00
Water level (mC.D.)
Kat O tidal level
Measurement
Modelingl

15. Future Work
•Add the land mesh part for the inundation simulation
•3 typical typhoon cases in different route and intensity
•Validate the inundation area using satellite image
•Applications

16. The Digital Elevation Model -> mesh on land

17. Future Work
•Add the land mesh part for the inundation simulation
•3 typhoon cases in different route and intensity
•Validate the inundation area using satellite image
•Applications

18. Typhoon Dujuan in 2003(Middle route)
Track of Dujuan:29 August – 3 September 2003

19. Typhoon Utor in 2001(East route)
Track of Utor : 30 June – 1 July 2001

20. Typhoon Koppu in 2009(west route)
Track of Koppu:11 September – 16 September 2009

21. Future Work
•Add the land mesh part for the inundation simulation
•3 typhoon cases in different route and intensity
•Validate the inundation area using satellite image
•Applications

22. Inundation Area from Satellite Image

23. Applications
•Operational appliaction
•Hindcast application
•Forecast application
Requirement
•Meteorology data
•Typhoon data
•Satellite image
•Bathymetry
Model system
•Inundation area
•Water level
Application
•Urban planning
•Emergency management
•Economic lost evaluation

24. Reference
•Ying LI, Lianshou Chen, Shengjun Zhang. STATISTICAL CHARACTERISTICS OF TROPICAL CYCLONE MAKING LANDFALLS ON CHINA. JOURNAL OF TROPICAL METEOROLOGY,2009, 20(1): 14-23.
•YUAN Jin nan, ZHEN Bin.Yearly variation features of tropical cyclone and its precipitation in Guangdong Province of China. JOURNAL OF NATURAL DISASTERS, 2008,17(3):140-147.
•http://typhoon.weather.com.cn/hist/2013.shtml
•TROPICAL CYCLONES from 2000 to 2011. Hong Kong Observatory.
•http://gdem.ersdac.jspacesystems.or.jp/ gdem.ersdac.jspacesystems.or.jp
•http://www.noaa.gov/

25. Thank you for your attention!
Welcome suggestions!
Delft Software days 2014
Li Li University of Hamburg li.li@uni-hamburg.de