dsd-int 2014 - symposium next generation hydro software (nghs) - 2d hydrodynamics of pearl river...

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

Content of Presentation

•Significance

•Objectives

•Approach

•Model setup

• Future work

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

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.

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

Mesh Generation

(HK model ,Detalres,1980-2014)

(Geerstmaar, 2013)

Final Mesh

Bathymetry

Fig. 3 Combine bathymetry with patches

(HK model ,Detalres,1980-2014)

(Geerstmaar, 2013)

Boundary Conditions

• Sea side , 10 astronomic

tidal components are

used.

• River side, pearl river

average discharge every

year in wet and dry

seasons.

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

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

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

Dis

ch

arg

e()

m3/s

Makou Wet Season DailyDischarge(m3/s)

10 4

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

Dis

ch

arg

e (

m3/s

)

Makou Dry SeasonDailyDischarge(m3/s)

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

Wate

r le

vel (m

C.D

.)

Kat O tidal level Measurement Modelingl

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

The Digital Elevation Model -> mesh on land

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

Typhoon Dujuan in 2003(Middle route)

Track of Dujuan:29 August – 3 September 2003

Typhoon Utor in 2001(East route)

Track of Utor : 30 June – 1 July 2001

Typhoon Koppu in 2009(west route)

Track of Koppu:11 September – 16 September 2009

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

Inundation Area from Satellite Image

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

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/

Thank you for your attention!

Welcome suggestions!

Delft Software days 2014

Li Li

University of Hamburg

li.li@uni-hamburg.de