Extreme value analysis in typhoon

prone areas

Case study for the Pearl River Estuary

IAHR 2015

Emiel Moerman, Reimer de Graaff, Deepak Vatvani, Joao de Lima Rego

Deltares, emiel.moerman@deltares.nl

8 July, 2015

Introduction

• Extreme events such as tropical storms and typhoons often are the

determining factor in the extreme values of wind, wave and water

level conditions

• Strong stochastic behavior leading to uncertainty in standard EVA

• Comparison of classic scenario based modeling versus typhoon

modeling (historic and artificial)

• Approach to better quantify the uncertainty in the extreme values

analysis in typhoon prone areas

• Case study Pearl River estuary China

8 July, 2015

Content

• Introduction

• Pearl River estuary

• Scenario based modeling (standard EVA)

• Typhoon modeling (historic + artificial)

• Comparison

• Conclusions

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Pearl River estuary

• 5 to 10 tropical storms or typhoons observed every year

• Once every few years and extreme typhoon

Wanda, 1962

Ida, 1964

Ruby, 1964

Rose, 1971

Elsie, 1975

Hope, 1979

Ellen, 1983

York, 1999

Dujuan, 2003

Hagupit, 2008

Vicente, 2012

8 July, 2015

Pearl River estuary

• 5 to 10 tropical storms or typhoons observed every year

• Once every few years and extreme typhoon

Wanda, 1962

Ida, 1964

Ruby, 1964

Rose, 1971

Elsie, 1975

Hope, 1979

Ellen, 1983

York, 1999

Dujuan, 2003

Hagupit, 2008

Vicente, 2012

8 July, 2015

Extreme wave modeling

• extreme wave climate governed by local wind (limited swell)

• scenario based wave modeling (Delft3D)

• stationary modeling

• uniform extreme wind conditions + assoc. water levels

• extreme wind + waves boundary conditions from offshore

ERA-Interim hind cast data (ECMWF)

• waves associated to wind

• extreme water levels derived from long-term measurements

10-yr wind speed

with 100-yr water level

12 directional sectors with 30 degree interval

50-yr wind speed

with 50-yr water level

12 directional sectors with 30 degree interval

100-yr wind speed

with 10-yr water level

12 directional sectors with 30 degree interval

100-yr wind speed

with MLLW

12 directional sectors with 30 degree interval

conservative

8 July, 2015

Extreme wave modeling

• extreme wave climate governed by local wind (limited swell)

• scenario based wave modeling (Delft3D)

• stationary modeling

• uniform extreme wind conditions + assoc. water levels

• extreme wind + waves boundary conditions from offshore

ERA-Interim hind cast data (ECMWF)

• waves associated to wind

• extreme water levels derived from long-term measurements

10-yr wind speed

with 100-yr water level

12 directional sectors with 30 degree interval

50-yr wind speed

with 50-yr water level

12 directional sectors with 30 degree interval

100-yr wind speed

with 10-yr water level

12 directional sectors with 30 degree interval

100-yr wind speed

with MLLW

12 directional sectors with 30 degree interval

conservative

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Extreme wave modeling

Classic Extreme Value Analysis:

• sample the extremes by a Peak Over Threshold method

• fit an trend line (distribution) to these extreme values (exponential)

• determine the 10, 50 and 100 year return values, including the 95%

confidence intervals (total: 4x12x3 scenarios)

8 July, 2015

Extreme water levels + currents

Extreme water levels are derived from local measurements

Extreme currents in the area are related to:

• extreme hydrodynamic processes (e.g. extreme tidal water level

variations and/or extreme river discharges) or

• extreme wind conditions (e.g. storms and typhoons)

No coincidence: wind effect relatively short (few hours) and direct: →

allows for separation in the analysis

3 scenarios applied to determine the extreme currents

Delft3D-FLOW

8 July, 2015

Extreme water levels + currents

Extreme water levels are derived from local measurements

Extreme currents in the area are related to:

• extreme hydrodynamic processes (e.g. extreme tidal water level

variations and/or extreme river discharges) or

• extreme wind conditions (e.g. storms and typhoons)

No coincidence: wind effect relatively short (few hours) and direct: →

allows for separation in the analysis

3 scenarios applied to determine the extreme currents

Delft3D-FLOW

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Extreme currents

Scenario 1: Highest or Lowest Astronomical Tide (HAT or LAT)

conditions

tide related gradient between high and low water is at its maximum.

Scenario 2: Extreme river discharge conditions

dry season average: 4,100 m3/s, wet season average: 19,400 m3/s

extreme river discharge estimated at 3 or 4 times the wet season

discharge

strong relation between water level

differences and current magnitude

8 July, 2015

Extreme currents

Scenario 3: Extreme wind conditions

a combination of the tide and wind drive currents determines the total

current in the area.

wind-only simulations to determine the ‘surge current’

surge current is combined with a representative tidal current

(average wet season tidal current)

since extreme wind conditions are related to typhoons in the area,

they are not limited to a specific wind direction

omni-directional extreme wind speeds applied for each wind direction

(1, 10, 50 and 100 year extreme winds)

extreme currents from maximum of the 3 scenarios

8 July, 2015

Extreme currents

Scenario 3: Extreme wind conditions

a combination of the tide and wind drive currents determines the total

current in the area.

wind-only simulations to determine the ‘surge current’

surge current is combined with a representative tidal current

(average wet season tidal current)

since extreme wind conditions are related to typhoons in the area,

they are not limited to a specific wind direction

omni-directional extreme wind speeds applied for each wind direction

(1, 10, 50 and 100 year extreme winds)

extreme currents from maximum of the 3 scenarios

8 July, 2015

Typhoon modeling

• Extreme events such as tropical storms and typhoons often are the

determining factor in the extreme values of wind, wave and water level

conditions

• No typhoon events in ERA-interim data: need for typhoon modeling

• Stochastic behavior of typhoons: a slight variation of the typhoon track,

propagation speed or wind speed intensity can have a significant impact

on the local extreme values: need for (artificial) typhoon modeling

• Extreme values that follow from the typhoon modeling assessment are

compared against the scenario based modeling results to better

quantify the uncertainty in the extreme values analysis in typhoon prone

areas when using a scenario-based approach

8 July, 2015

Typhoon modeling

• Coupled Delft3D FLOW + WAVE model (domain decomposition)

• Offshore boundaries: astronomical components

• Time and space varying (spiderweb) wind and pressure fields

(making use of Deltares’ Wind Enhancement Scheme: WES)

• Input WES: typhoon track data:

• location of the typhoon center, maximum wind speed, distance of

the typhoon center to the maximum wind speed and the minimum

central pressure

• typhoon track data from The Hong Kong Observatory

www.hko.gov.hk/

• Hindcast of historic typhoons validated against measured wind and

water level data

• Spiderweb wind fields (WES)

8 July, 2015

Typhoon modeling

• Coupled Delft3D FLOW + WAVE model (domain decomposition)

• Offshore boundaries: astronomical components

• Time and space varying (spiderweb) wind and pressure fields

(making use of Deltares’ Wind Enhancement Scheme: WES)

• Input WES: typhoon track data:

• location of the typhoon center, maximum wind speed, distance of

the typhoon center to the maximum wind speed and the minimum

central pressure

• typhoon track data from The Hong Kong Observatory

www.hko.gov.hk/

• Hindcast of historic typhoons validated against measured wind and

water level data

• Spiderweb wind fields (WES)

8 July, 2015

Typhoon modeling

• Coupled Delft3D FLOW + WAVE model (domain decomposition)

• Offshore boundaries: astronomical components

• Time and space varying (spiderweb) wind and pressure fields

(making use of Deltares’ Wind Enhancement Scheme: WES)

• Input WES: typhoon track data:

• location of the typhoon center, maximum wind speed, distance of

the typhoon center to the maximum wind speed and the minimum

central pressure

• typhoon track data from The Hong Kong Observatory

www.hko.gov.hk/

• Hindcast of historic typhoons validated against measured wind and

water level data

• Spiderweb wind fields (WES)

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Typhoon modeling- wind

wind speed wind direction

YORK, 1999

HAGUPIT, 2008

VICENTE, 2012

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Typhoon modeling – water level

water level surge

YORK, 1999

HAGUPIT, 2008

VICENTE, 2012

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Typhoon modeling - waves

Reasonable at KYC station

Overestimation at (more offshore) WLC station (coarse track data)

YORK, 1999

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Typhoon modeling - waves

DUJUAN, 2003

Reasonable at (more offshore) WLC station (coarse track data)

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Typhoon modeling - waves

HAGUPIT, 2008

Overestimation at (more offshore) WLC station (coarse track data)

Overall it is concluded that the historical typhoon model simulations are

in reasonable agreement with the (limited amount of) measurements.

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Typhoon modeling - artificial

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wave height - historic typhoons

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wave height - artificial typhoons

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water level

HA Shift track north by 0.25

HB Reduce propagation speed by 80 %

HC Increase of radius to max wind speed: 40 km

+10%

+10%

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Typhoon modeling - historic

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Typhoon modeling - historic

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Conclusions and recommendations

• Historic typhoon simulations are generally in line with the scenario

based modeling: waves, water levels and currents

• Artificial typhoon simulations however can lead to higher extreme

values: for wave, water levels and currents

• 95% confidence intervals in the scenario-based modeling approach

generally ‘save’ the scenario-based approach

• With extreme events becoming more and more dominant in the

changing climate, the modeling of artificial typhoons is however

considered to be a valuable addition

• Model improvements and more data for validation is needed to further

assess and quantify the uncertainties in the extreme values in typhoon

prone areas