Coastal Inundation Risk Assessment 

Jen Irish 

January 31, 2012 

 Rockville, MD 

Sponsored by U.S. Department of Energy 

U.S. Army Corps of Engineers 

and NOAA Sea Grant 

Quogue (vicinity), NY 

Hurricane Sandy 

Mantoloking, NJ 

Hurricane Sandy 

NOAA/GoogleEarth
Coastal Inundation Risk Assessment 

Why is Coastal Flooding Unique? 

• Coasts are highly dynamic, landscape constantly changing 

• Storm surge depends on meteorology + generation capacity 

• Physics extremely important for accurate surge estimation 

• A single event spans a wide range of return periods 

• More difficult to mitigate because of the short timeframe 

• Almost always have multiple hazards at same time 

• ~50% of weather-related damages due to coastal storms (since 1980) 
!"#$%&'%$(")$*"+,-."

!"/0*12"*234$(32")0(5%$(3"6/7,8"

!"9:2$*2%5';"<'3=3"

!"+'*'>2?2*=@'%$("

!"A';=B'%$("

!"CD?*2>2E&';02"'(';F3=3G"H+#EI/"

!"J(52*?'=(?F"

!"CD'>4;23"

!"/:$*?5$>=(13K;'5L"$)"L($M;2B12"

Coastal Inundation Risk Assessment 

Outline 

www.greatdreams.com

Galveston, TX 

Hurricane Ike 
Coastal Inundation Risk Assessment 

Motivation for PFHA 

A robust and accurate method for determining hurricane surge extreme 

value probabilities is required. 

10 100 1000

Return period

Katrina level at Point 1

Direct Hit

Asymptote

Non-event

Asymptote

10 100 1000

Return period

Katrina level at Point 1

Direct Hit

Asymptote

Non-event

Asymptote

Historical surge population approach Joint probability method 
Coastal Inundation Risk Assessment 

Surge Response Functions 

General form for maximum surge response: 

From Resio, Irish, and Cialone, 2009, Nat. Hazards 

! max (x) = !(x, po ,R

p ,v f ,!, xo )+! z

! z

2 = ! tide2 + !

surge simulation

2 + !

waves

2 + !

winds

2 + ...

where:

! is a continuous flood response function,

x is location of interest,

po is central pressure,

xo is landfall location,

Rp is hurricane pressure radius near landfall (Thompson and Cardone 1996),

! is hurricane track angle with respect to the shoreline, 

vf is hurricane forward speed near landfall, and

"z is epistemic uncertainty in the flood response (Resio et al. 2012)
Coastal Inundation Risk Assessment 

Surge Response Functions 

!"/:';;$MEM'?2*">$>2(?0>"N';'(52G"

!".330>2G"

!"/?2'BF"M=(B3"$&2*";$(1"42*=$B"

!"O=(B"3?*233"=("P0'B*'%5")$*>"

!".330>2"5$(%(2(?';"3:2;)"$)"5$(3?'(?"B24?:"

""M=?:"M=B?:Q"RG"

V2 ! "p

where "p = Pfar– po

40 60 80 100 120

0

5

10

15

6p (mb)

c (m)

observed

simulated 1:1000

simulated 1:10000

1000 2000 3000 4000 5000

0

5

10

15

V

max

2 (m2

s

ï2

)

c (m)

observed

simulated 1:1000

simulated 1:10000

5

10

15

c (m)

observed

simulated 1:1000

simulated 1:10000

From Irish and Resio, 2010, Ocean Eng. 

( )

a

w

u

g t x hx

" !

#

$ $

+ =

$ $

2

( )

a d

w

d c V

dx gh x

" !

!

# $ = % &

' (

2

a d

w

c V L

g h

! "

!

# $ = % &

' ( 1

L

p h ! " = #
Coastal Inundation Risk Assessment 

Surge Response Functions 

!"S(T02(52"$)"B24?:"4*$U;2G"

!"S(T02(52"$)":0**=5'(2"3=@2G" Coastline 

CD?2(?"$)"

M=(B3"

CD?2(?"$)"

M=(B3"

R 

R 

!* = dimensionless depth function 

L* 

From Irish and Resio, 2010, Ocean Eng. 

*

1

* *

L

p

h ! " # = $

*

1

** *

x

L R

p

h L ! " #

$ % = & ' ( )

* +
Coastal Inundation Risk Assessment 

Surge Response Functions 

!"S(T02(52"$)")$*M'*B"3422BG"

!"S(T02(52"$)"'44*$'5:"'(1;2G"

Coastline 

L* 

! = 0o ! = 30o

t* 

#

t"

#steady-state

From Irish and Resio, 2010, Ocean Eng. 

* *

1

** *

x t

L t R

p

h Lt !" # # $ %

& '&' = ( ) *)*

+ ,+,

( ) * *

1

** *

x t

L t R

p

h Lt ! " # $ $ $! % &

' ('( = ) * +*+

, -,-
Coastal Inundation Risk Assessment 

Surge Response Functions 

Storm surge and tides – ADCIRC (Luettich et al. 1992) 

• Hydrodynamic model: 

• Finite element, variable resolution 

• Model forcing: 

• Wind stress & Barometric pressure 

 (Thompson & Cardone 1996): 

• Vf, !, cp, Rp, track position, #

• Simulation time: 1000+ hours (on 1 CPU) 

104:575–597 585

er hydrodynamic model ADCIRC (ADvanced CIRCulation;

rink 2008) was used to simulate hurricane flood elevations for

dition in Table 2. ADCIRC is a finite-element hydrodynamic

simulating currents and water levels resulting from tidal, wind,

ing. The two-dimensional depth-integrated version of ADCIRC

water equations for conservation of mass (Eq. 5) and momentum

∂ H

∂t

+ ∇H

!−→

U H"= 0 (5)

−→

U = −g∇H

#

ζ2 + p (x, y)

gρw

− αη$+ f

−→

k × −→U +

−→

τs

Hρw

−

−→

τb

Hρw

(6)

ntaneous free surface elevation,

l depth (h(x, y) + ζ2, where h(x, y) is the spatially variable still

h),

h-integrated horizontal velocity,

ally variable atmospheric pressure,

ity of water,

olis parameter,

zontal unit vector, and

om shear stress.

odel grid spans the Gulf of Mexico and the northeastern Atlantic

o 98◦ W in longitude and between 8◦ N and 46◦ N in latitude in

simulate storm surge by large weather events like hurricanes.

within the study area (nearshore and Corpus Christi Bay) is

Climatic Change (2011) 104:575597 585

The shallow-water hydrodynamic model ADCIRC (ADvanced CIRCulation;

Luettich and Westerink 2008) was used to simulate hurricane flood elevations for

each hurricane condition in Table 2. ADCIRC is a finite-element hydrodynamic

model designed for simulating currents and water levels resulting from tidal, wind,

and ocean wave forcing. The two-dimensional depth-integrated version of ADCIRC

solves the shallow-water equations for conservation of mass (Eq. 5) and momentum

(Eq. 6):

∂ H

∂t

+ ∇H

!−→

U H"= 0 (5)

∂

−→

U

∂t

+

!−→

U · ∇H

" −→

U = −g∇H

#

ζ2 + p (x, y)

gρw

− αη$+ f

−→

k × −→U +

−→

τs

Hρw

−

−→

τb

Hρw

(6)

where:

ζ2 is the instantaneous free surface elevation,

H is the total depth (h(x, y) + ζ2, where h(x, y) is the spatially variable still

water depth),

U is the depth-integrated horizontal velocity,

p(x, y) is the spatially variable atmospheric pressure,

ρw is the density of water,

f is the Coriolis parameter,

k is the horizontal unit vector, and

τb is the bottom shear stress.

The ADCIRC model grid spans the Gulf of Mexico and the northeastern Atlantic

Ocean from 60◦ W to 98◦ W in longitude and between 8◦ N and 46◦ N in latitude in

order to adequately simulate storm surge by large weather events like hurricanes.

The grid resolution within the study area (nearshore and Corpus Christi Bay) is

sufficiently refined (as fine as 70 m within channels) to simulate the surge response

tll hid bthtifthid bthtbd
Coastal Inundation Risk Assessment 

Surge Response Functions 

10

shows simulated highest alongshore maximum surge versus Rp/L30, showing that

surge decreases with increasing Rp/L30 in an almost linear fashion.

To account for the relative roles of storm size and continental shelf width 

in the dimensionless surge magnitude variable, the ratio Rp/L30 is introduced into 

Eq. 2a as follows:

!! = !"

!p

+ m2 (x, x!) Pfar ! c

p

Pfar ! c

p!max

!

"

#

#

$

%

&

&

!(x,x!) R

p

L30 x( o )

R

p

L30 !

" #$ref

!

"

#

#

$

%

&

&

!(x,x!)

(6)

where:

m2, ! , and ! are dimensionless scaling coefficients varying by location 

and x’ as follows:

m2 (x, x!),! (x, x!),! (x, x!) "

# $%= m2L (x),!L (x),! L (x) "

# $% for x! < 0

m2R (x),!R (x),! R (x) "

# $% for x! & 0

'

(

)

*

)

, and

[Rp/L30]ref is a regional constant representing the maximum value of Rp/L30.

as the storm passes over the continental shelf. We introduce the following 

correction into the dimensionless alongshore variable in order to account for this 

asymmetry:

x! = x " x ( o )

R

p

" !(xo )+ cH

x " x ( o )

R

p

" ! x( o ) "1

#

$

%

%

&

'

(

(

R

p

L30

#

$

%

%

&

'

(

(" F 1" R

p

Rthres

#

$

%

%

&

'

(

(H 1" R

p

Rthres

#

$

%

%

&

'

(

(

(5)

where c is a dimensionless regional scaling constant. For the Texas coast, the 

constant c = 0.75 and was determined by inspection. Fig. 5 (bottom pane) shows 

the resulting shift and general convergence of the data in the preliminary 

dimensionless space.

Irish and Resio (2010) argued that both storm size and the size of the 

shallow region, specifically the continental shelf region, can limit surge 

magnitude. In the case where storm size is smaller than the continental shelf

region over which the storm passes, the wind field size effectively limits surge 

generation. On the other hand, in the case where the storm size is larger the 

continental shelf region, the size of the continental shelf effectively limits surge 

generation. Thus, surge magnitude can be expected to scale with Rp/L30. Fig. 6

shows simulated highest alongshore maximum surge versus Rp/L30, showing that

surge decreases with increasing Rp/L30 in an almost linear fashion.

To account for the relative roles of storm size and continental shelf width 

in the dimensionless surge magnitude variable, the ratio Rp/L30 is introduced into 

From Song et al., 2012 Nat. Hazards 

mean error = -3 to +1 cm ! NO BIAS!

RMS error = 11 to 22 cm 
Coastal Inundation Risk Assessment 

Surge Response Functions 

0 100 200 300 400 500

0

2

4

6

Hurricane Carla (1961)

x (km)

water level (m, NAVD88)

0 100 200 300 400 500

0

2

4

6

Hurricane Alicia (1983)

x (km)

water level (m, NAVD88)

0 100 200 300 400 500

0

2

4

6

Hurricane Bret (1999)

x (km)

water level (m, NAVD88)

0 100 200 300 400 500

0

2

4

6

Hurricane Rita (2005)

x (km)

water level (m, NAVD88)

0 100 200 300 400 500

0

2

4

6

Hurricane Ike (2008)

x (km)

water level (m, NAVD88)

Gauge

HWM (stillwater)

HWM (with wave runup)

Visual observation

SRF

SRF plus wave setup range

From Irish et al., 2011, Geophys. Res. Lett. 

ï98 ï97 ï96 ï95 ï94 ï93 ï92

25

25.5

26

26.5

27

27.5

28

28.5

29

29.5

30

Galveston Bay

Matagorda Bay

@ US/Mexico border

Shoreline

Alongshore xïaxis

0 

0

0.05

0.1

p(Vmax)

9

0

0.05

0.1

p(cp

)
Coastal Inundation Risk Assessment 

Joint Probability Method with Optimal Sampling (JPM-OS) 

f ( po ,R

p ,v f ,!, xo ) = !1

!2!3!4!5 (5a)

!1 = f ( po | xo ) = 1

a1(xo )

exp ! "p ! ao (xo )

a1(xo )

!

"

# $

%

&exp 'exp ' (p ! ao (xo )

a1(xo )

!

"

# $

%

&

'

(

)

*)

+

,

)

-)

(Gumbel Distribution) (5b)

!2 = f (R

p | po ) = 1

! ("p) 2!

exp #

R

p ("p) # R ( p )

2

2!2

("p)

$

%

&

'

&

(

)

&

*

&

(Normal Distribution) (5c)

!3 = f (v f |!) = 1

! 2!

exp "

v f (!) " v ( f)

2

2!2

#

$

%

&

%

'

(

%

)

%

(Normal Distribution) (5d)

!4 = f (! | xo ) = 1

! (xo ) 2!

exp !

!(xo ) !! ( )

2

2!2(xo )

!

"

#

$

#

%

&

#

'

#

(Normal Distribution) (5e)

!5 = g !, x ( o )

" Extreme-value 

 distribution

TR z( max ) = 1! f ( po ,R

p ,v f ,!, xo ) H zmax ! !(x, po ,Rp ,v f ,!, xo ,MSL)+! z

"

# $ ( %) "

# $%

dxo d! dv f dR

p

dpo

xo

&

!

& v f&

Rp

&

po

& '

(

)

*)

+

,

)

-)

!1

From Resio et al., 2009 Natural Hazards 

a1(xo )a1(xo )

"%

a1(xo )

"%

*)-)

!2 = f (R

p | po ) = 1

! ("p) 2!

exp #

R

p ("p) # R ( p )

2

2!2

("p)

$

%

&

'

&

(

)

&

*

&

(Normal Distribution) (5c)

!3 = f (v f |!) = 1

! 2!

exp "

v f (!) " v ( f)

2

2!2

#

$

%

&

%

'

(

%

)

%

(Normal Distribution) (5d)

!4 = f (! | xo ) = 1

! (xo ) 2!

exp !

!(xo ) !! ( )

2

2!2(xo )

!

"

#

$

#

%

&

#

'

#

(Normal Distribution) (5e)

!5 = g !, x ( o )
Coastal Inundation Risk Assessment 

Joint Probability Method with Optimal Sampling (JPM-OS) 

!5 = g !(t), x ( o )

f (SST, po ,R

p ,v f ,!, xo ) = !SST!1

!2!3!4!5

!1 = f ( po | xo ) = 1

a1(xo ,t)

exp " #p " ao (xo ,t)

a1(xo ,t)

$

%

& '

(

)exp "exp " #p " ao (xo ,t)

a1(xo ,t)

$

%

& '

(

)

*

+

,

-,

.

/

,

0,

(Gumbel Distribution)

!SST = constant

TR z( max ) = 1! f ( po ,R

p ,v f ,!, xo ) H zmax ! !(x, po ,Rp ,v f ,!, xo ,MSL)+! z

"

# $ ( %) "

# $%

dxo d! dv f dR

p

dpo dSST

xo

&

!

& v f&

Rp

&

po

&

SST

& '

(

)

*)

+

,

)

-)

!1

V"

From Irish and Resio, 2013 ASCE Waterw. 
Coastal Inundation Risk Assessment 

Uncertainty 

From Irish and Resio 2013 ASCE Waterw. 

!"#$%&'()*)+,-.)/0)12)*)3+)('

))))+,33)/0)12)*)3++)('

!

z

2

= ! tide

2 + !

surge simulation

2 + !

waves

2 + !

winds

2 + ...

4567%#/68)*)+,9+)/)

!"#$%&'( W"4567%#/68"X"YZ[\">Q"97"X"]Y"F*

"""""""""""YZ^_">Q"97"X"]YY"F*

!Climate

2

= ! !SST

2 +! SLR2 +! p2 +! "2 +...

:;8#'%$6;%()*)!"#$%&'( <)4567%#/68)
Coastal Inundation Risk Assessment 

Uncertainty - Aleatory 

,$*"23%>'%(1"30*123"=(";'*12"5$'3?';"'44;=5'%$(3"?$"B'?2Q"?:2"3?'(B'*B":'3"N22("?$"

=(5;0B2"24=3?2>=5"0(52*?'=(?F"N0?"?$"(21;25?"';2'?$*F"0(52*?'=(?FQ"'330>=(1"=?"M'3"

3>';;")$*"?:2"*'(12"$)"'((0';"2D522B'(52"4*$N'N=;=%23"*2P0=*2B"=("?:$32"3?0B=23Z"

-$M2&2*Q"';2'?$*F""0(52*?'=(?F"5'("N2"3:$M("?$":'&2"'")$*>"305:"?:'?"

1

2

( ) ()

, ( )

where

( ), ( ) are the CDFs of the results with and without aleatory uncertainty included

 is highly nonlinear function of its 2 arguments

( ) is the return period as

Fz Tz

Fz N

Fz Fz

T z

!

!

" # $ " # & ' = % & ' ( ) ( )

$

%

1 2

sociated with surge level z

 is the number of years in the sample

, are the measures of dispersion for the extremal distribution and the uncertainty

 distribution (which varies with ( ))

N

T z

! !
Estimated return periods with and without uncertainty: 

• Case 1&2: deterministic and delta function approximation 

• Case 3: using estimated standard deviations divided by 2 

• Case 4: using actual estimated standard deviations 

`0**2(?;F"

C3%>'?2B"

#+S")$*""

a0;)"$)"#2D=5$"

="&'6>$)4?$/5"#0)@AB)

Coastal Inundation Risk Assessment 

Uncertainty - Aleatory

From Resio et al, 2013 Natural Hazards 
Coastal Inundation Risk Assessment 

Examples – Strength of JPM-OS 

!"I4%>';"3'>4;=(1"*2B0523"5$>40?'%$(';"*2P0=*2>2(?3"NF"[]e"

!"#2'(3"?$"N$0(B"0442*"?'=;"$)"B=3?*=N0%$("03=(1"#'D=>0>"+$33=N;2"S(?2(3=?F"

!"/?'N;2"M=?:"*23425?"?$"3>';;"5:'(123"=("3'>4;2"

!"`$(3=B2*3"';;"3?$*>3"=("4'*'>2?2*"34'52"5$(?*=N0%(1"?$"30*12";2&2;"

From Irish et al., 2011, J. Geophys. Res. 
Coastal Inundation Risk Assessment 

Examples – Strength of JPM-OS 

!"I4%>';"3'>4;=(1"*2B0523"5$>40?'%$(';"*2P0=*2>2(?3"NF"[]e"

!"#2'(3"?$"N$0(B"0442*"?'=;"$)"B=3?*=N0%$("03=(1"#'D=>0>"+$33=N;2"S(?2(3=?F"

!"/?'N;2"M=?:"*23425?"?$"3>';;"5:'(123"=("3'>4;2"

!"`$(3=B2*3"';;"3?$*>3"=("4'*'>2?2*"34'52"5$(?*=N0%(1"?$"30*12";2&2;"

From Irish et al., 2011, J. Geophys. Res. 
Coastal Inundation Risk Assessment 

Examples – Upper Limits 

-200

-100

0

100

200

0

50

100

0

1

2

3

4

0

1

2

3

-200

-100

0

100

200

0

50

100

0

1

2

3

4

5

6

7

8

0

1

2

3

4

5

6

30*12"6>8"

30*12"6>8"

74"6L>8" DED$"6L>8"

CB$?6/D/)5&776E"#)6;%#;76%(FG))

) )5&)*)H9+)/)I1&;J6;)-+++K)

f#'D=>0>"4$33=N;2"30*12g"
Coastal Inundation Risk Assessment 

Shortcomings: Multi/Combined-Hazards 

• Within singe events: 

• Surge + tides + waves + winds + precipitation 

• Tsunami + earthquake 

• Consecutive events: 

• Storm then storm 

• Storm then tsunami, vice versa 

• Concurrent events: 

•“Superstorm” Sandy = hurricane + Nor’easter 

• 2011 MS River flood = inland rainfall event + coastal storm 

• Tsunami + hurricane (??) 
Coastal Inundation Risk Assessment 

Shortcomings: Future Conditions (UNCERTAINTY!) 

• Sea-level rise 

• Climate variability/climate change 

• Landform changes: 

• Human impacts: 

 

n to provide some level of

d wave action (e.g., [44]).

tural surge barriers, their

lt in higher flood elevations

zares and Irish [45] showed

d, New York, surge waters

hurricanes can raise flood

stal bays. In this paper, we

adation on future hurricane

Texas, USA Gulf of Mexico

ing the potential impacts of

adation on hurricane inun

n affected and economic

s regularly subjected to high

Corpus Christi is vulnerable

ensive coastal infrastructure

More than 275,000 people

barrier islands, this urban

ndustry, multiple oil refin

n, the Port of Corpus Christi,

risti.

SLR in this region include

ustatic rise and land subsi

ns reported by the National

tion (NOAA) near Corpus

egion is 0.46 cm/year [46].

ustatic SLR rates, observed

wal and soil consolidation

29 cm/year to the historical

e in Texas [48e50] indicate

have slowed somewhat in

e to reduced groundwater

ce is projected to accelerate

demands on groundwater

Due to the uncertainty in

ter extraction, it is assumed

hristi will continue into the

9 cm/year. However, if fluid

due to mitigation measures,

ndingly. Conversely, if fluid

population growth, land

ngly; the United Nations

18% by the 2030s [53].

higher) have made landfall

4]. Of these storms, Hurri

Bret (1999) generated high

ed maximum flood levels

rricanes were between 1 m

while Hurricane Ike (2008),

he north of Corpus Christi,

3.3. Role of barrier islands for surge protection

The City of Corpus Christi is divided into two geographic

sections: a mainland portion, situated on Corpus Christi Bay, and

a barrier island portion, Mustang and Padre Islands, which separates Corpus Christi Bay from the Gulf of Mexico. The natural barrier

island system fronting Corpus Christi Bay acts as a natural surge

barrier to mainland Corpus Christi and other bayside communities.

If surge overtopping of the barrier islands is limited, flood levels

along mainland Corpus Christi are generated predominantly by

locally generated wind surge within Corpus Christi Bay and by

ocean flood waters passing through Aransas Pass at the northeastern end of the Bay. Much of this barrier island system is lowlying (Fig. 2), however, with long stretches of elevations as low as

1.25 m above present-day (2000s) mean sea level (MSL2000s) and

some areas with elevations between 0 and 0.5 m above MSL2000s.

During major hurricane flooding events, ocean flood waters flow

over these low-lying portions of this barrier island system,

Fig. 2. Padre, Mustang, and San Jose Island topography in the vicinity of Corpus Christi.

Left pane shows present-day (2000s) topography while right pane shows a possible

future degraded condition, where the entire barrier island system has an elevation no

higher than 1 m with respect to MSL during the time period of interest. Areas 1, 2, and

3 indicate morphological reach designation for idealized XBEACH simulations.

as, USA Gulf of Mexico

the potential impacts of

ion on hurricane inun

ffected and economic

ularly subjected to high

pus Christi is vulnerable

ve coastal infrastructure

re than 275,000 people

rier islands, this urban

stry, multiple oil refin

e Port of Corpus Christi,

in this region include

ic rise and land subsi

eported by the National

(NOAA) near Corpus

n is 0.46 cm/year [46].

tic SLR rates, observed

and soil consolidation

m/year to the historical

Texas [48e50] indicate

e slowed somewhat in

reduced groundwater

projected to accelerate

mands on groundwater

e to the uncertainty in

xtraction, it is assumed

i will continue into the

m/year. However, if fluid

to mitigation measures,

ngly. Conversely, if fluid

pulation growth, land

y; the United Nations

by the 2030s [53].

her) have made landfall

Of these storms, Hurri

(1999) generated high

maximum flood levels

anes were between 1 m

le Hurricane Ike (2008),

north of Corpus Christi,

eastern end of the Bay. Much of this barrier island system is low

lying (Fig. 2), however, with long stretches of elevations as low as

1.25 m above present-day (2000s) mean sea level (MSL2000s) and

some areas with elevations between 0 and 0.5 m above MSL2000s.

During major hurricane flooding events, ocean flood waters flow

over these low-lying portions of this barrier island system,

Fig. 2. Padre, Mustang, and San Jose Island topography in the vicinity of Corpus Christi.

Left pane shows present-day (2000s) topography while right pane shows a possible

future degraded condition, where the entire barrier island system has an elevation no

higher than 1 m with respect to MSL during the time period of interest. Areas 1, 2, and

3 indicate morphological reach designation for idealized XBEACH simulations.

and flood depth were intersected with the population census data

to determine the number of people impacted during each hurricane-climate scenario. Here, the population within each census

tract was assumed to be evenly distributed throughout the parcel

areas of the tracts. In this analysis, population affected reflects only

to people living in the flooded areas according to census data but

does not include all others affected by loss of jobs, overall slowdown of the economy in the broader area and other social impacts.

The above method was executed for the mainland portion of

Corpus Christi; as mentioned previously, here we assume that the

barrier island would be uninhabited under the future degraded

condition considered. Finally, the points of comparison used in the

results and discussion are inundated area, property damage, and

population impacted on the mainland evaluated for the case in

which the barrier island was assumed to retain its present-day

(2000s) condition (i.e., no-degradation assumed), where barrier

island elevations were assumed to rise correspondingly with future

SLR [38,39].

5. Results

5.1. Storm-induced overwash and breaching of Mustang and Padre

islands

Under present-day (2000s) conditions, simulated flood elevation on the ocean side of Mustang Island (station 9 on Fig. 1) is

1.0 m, MSL2000s. For the case of no barrier island degradation, poststorm simulation results for the present-day (2000s) hurricane

scenario indicate some overwash of Mustang and Padre Islands.

Specifically, near Packery Channel, elevations were generally lowered to about 1 m above MSL2000s due to wave-induced erosion,

and some narrow channels of slightly lower elevation, about 0.8 m

above MSL2000s were predicted (Fig. 5, left pane). For discussion

purposes, Fig. 5 (left pane) also shows the predicted post-storm

topography under present-day (2000s) conditions when the

degraded barrier island condition is specified. Here, predicted

storm-induced erosion of the degraded barrier island is negligible.

It is worth noting that with this degraded condition, no alongshore variability is predicted over the large region specified at

652 J.L. Irish et al. / Ocean & Coastal Management 53 (2010) 645e657

From Irish et al. 2011, Ocean Coastal Mgt. 

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Coastal Inundation Risk Assessment 

Shortcomings: Complex/Coupled Processes 

• Surge response coupled with sediment transport, vice versa 

• Example: Barrier island overwash and breaching

 

• Spatiotemporal variation in surges, waves, winds, etc. 

• Natural limits 

• Methods validation 

• What else have we yet to learn? 

 

or top) versus observed (right or bottom) barrier island breaching and overwash during the 1938 Hurricane in the vicinity of present-day Shinnecock Inlet (top

hes and present-day Shinnecock Inlet (center pane), and in the vicinity of Moriches Inlet (bottom pane).

Fig. 9. Simulated water levels with (dashed) and without (solid) barrier island overwash and breaching for the Hurricane of 1938. Locations A, B, and C are shown in Fig

From Cañizares and Irish 2008, Coastal Eng. 

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Coastal Inundation Risk Assessment 

Shortcomings: Probability vs. Risk 

Natural/Human 

• Policy/Decisions 

• Procedures 

• Maintenance 

• Adaptation 

Single flood probability = multitude of outcomes 

Natural: Flood level + 

• Velocity 

• Forces 

• Erosion 

. 

. 

.