NISTIR 7689

 

INTRODUCTION OF WAVE SET‐UP EFFECTS AND MASS

FLUX TO THE SEA, LAKE, AND OVERLAND SURGES FROM

HURRICANES (SLOSH) MODEL

 

 

 

 

 

 

 

 

 

 

 

 
 

 
NISTIR 7689

 

INTRODUCTION OF WAVE SET‐UP EFFECTS AND MASS

FLUX TO THE SEA, LAKE, AND OVERLAND SURGES FROM

HURRICANES (SLOSH) MODEL

Long T. Phan

National Institute of Standards and Technology

Gaithersburg, MD 28099

 

Donald N. Slinn

Associate Professor

 

and

Shaun W. Kline

Graduate Research Fellow

University of Florida

Department of Civil and Coastal Engineering

548 Weil Hall

Gainesville, Florida 32611

 

Grant 60NANB8D8103

 

May 2010

 

U.S. Department of Commerce

Gary Locke, Secretary

National Institute of Standards and Technology

Pat Gallagher, Director 
© 2010 
ACKNOWLEDGEMENTS

The authors would like to thank the following units and individuals from the U.S.

Department of Commerce for the contributions and technical guidance they provided throughout

this study: Dr. Long T. Phan of the Building and Fire Research Laboratory (BFRL), National

Institute of Standards and Technology (NIST); Drs. Bob Glahn, Mark A. McInerney, Arthur A.

Taylor, and Wilson Shaffer of the Meteorological Development Laboratory (MDL), National

Oceanic and Atmospheric Administration (NOAA); and Dr. Stephen Baig (retired) of the

National Hurricane Center (NHC), NOAA.

i
ii
TABLE OF CONTENTS

ACKNOWLEDGEMENTS.........................................................................................................i

LIST OF TABLES.......................................................................................................................v

LIST OF FIGURES..................................................................................................................vii

EXECUTIVE SUMMARY........................................................................................................ix

CHAPTER 1

INTRODUCTION.......................................................................................................................1

1.1 General Introduction.........................................................................................................1

1.1.1 Air-Sea Interface.......................................................................................................2

1.1.2 Wave Set-up and Mass Flux.....................................................................................3

1.1.3 Bathymetry and Other Effects..................................................................................4

1.2 Storm Surge Model...........................................................................................................5

1.3 Two-way Coupling...........................................................................................................6

1.4 Hurricane Simulation........................................................................................................6

CHAPTER 2

METHODOLOGY......................................................................................................................8

2.1 Storm Surge Model...........................................................................................................8

2.1.1 SLOSH Computational Grid...................................................................................12

2.1.2 SLOSH Input Parameters.......................................................................................13

2.1.3 SLOSH Implementation.........................................................................................14

2.2 Wave Set-up and Mass Transport...................................................................................15

2.2.1 SWAN Computational Grid....................................................................................15

2.2.2 SWAN Input Parameters.........................................................................................16

2.2.3 SWAN Implementation...........................................................................................17

2.3 Two-way Coupling.........................................................................................................17

CHAPTER 3

RESULTS..................................................................................................................................20

3.1 Wave Set-up and Mass Flux Forcing..............................................................................20

3.2 Spatial Results................................................................................................................22

iii
3.2.1 Maximum Storm Tide Levels.................................................................................23

3.2.2 Selected Points Analysis.........................................................................................28

3.2.3 Overland Inundation...............................................................................................30

3.3 Temporal Results............................................................................................................32

3.3.1 Maximum Storm Tide Levels.................................................................................32

3.3.2 Selected Points Analysis.........................................................................................34

3.3.3 Overland Inundation...............................................................................................39

3.4 Hurricane Andrew Hindcast...........................................................................................42

CHAPTER 4

CONCLUSIONS.......................................................................................................................45

4.1 Bathymetry ....................................................................................................................45

4.2 Storm Strength .............................................................................................................46

4.3. Significance of Wave Set-up and Mass Flux ..............................................................46

4.4 Historical Comparison....................................................................................................47

4.5 Future Study...................................................................................................................47

APPENDIX A...........................................................................................................................49

APPENDIX B...........................................................................................................................56

REFERENCES..........................................................................................................................58

iv
LIST OF TABLES

Tb. Pg.

3-1 Domain-wide maximum storm tide levels.........................................................................22

3-2 Storm surge levels outside SLOSH error range.................................................................25

3-3 Coordinates and descriptions of selected analyzed points.................................................27

3-4 Overland average maximum surges...................................................................................29

v
vi
LIST OF FIGURES

Fig. Pg.

2-1 SLOSH ETPA and HMIA computational grids on latitude-longitude axes.......................12

2-2 SWAN computational grids with SLOSH grid overlays....................................................15

3-1 Maximum significant wave heights and mass flux velocities for Category 1 storm.........19

3-2 Maximum wave-induced stresses for Category 1 storm....................................................21

3-3 Zoomed maximum storm tide levels over Tampa Bay for Category 1 storm....................23

3-4 Maximum surge comparison of wave and wind stress and wind stress only....................24

3-5 Maximum surge comparison of both wave components and wind stress only..................24

3-6 Percent difference histogram for Category 1 storm...........................................................26

3-7 Maximum surges at selected points...................................................................................28

3-8 Spatial distribution of overland storm surge levels............................................................30

3-9 Maximum surges at each time step for all storm simulations............................................32

3-10 Hourly storm surge levels at ETPA point 2984 for Category 1 hurricane.........................33

3-11 Hourly storm surge levels at ETPA point 2984 for Category 4 hurricane.........................34

3-12 Hourly storm surge levels at ETPA point 5143 for Category 1 hurricane.........................35

3-13 Hourly storm surge levels at ETPA point 5143 for Category 4 hurricane.........................36

3-14 Storm surge levels at each time step at HMIA point 8551 for Hurricane Andrew............37

3-15 Temporal progression of overland surge levels for Category 1 storm...............................38

3-16 Temporal progression of overland surge levels for Category 4 storm...............................39

3-17 Temporal progression of overland surge levels for Hurricane Andrew.............................40

3-18 Storm surge levels at each time step at HMIA point 6042 for Hurricane Andrew............41

\

vii
viii
EXECUTIVE SUMMARY

Hurricanes wreak havoc on the lives and infrastructure of coastal communities. Storm

surge, a local rise in sea elevations, is perhaps the most devastating element of these tropical

cyclones. Storm surge depends on the tidal stage, barometric pressure, Coriolis effect, wind

stress, and wave forcing, as well as the local bathymetry. In the past, many storm surge

numerical models, such as Sea, Lake, and Overland Surges from Hurricanes (SLOSH)

(JELESNIANSKI et al, 1992), neglect wave forcing components to conserve computational

efficiency. However, numerous situations necessitate the inclusion of waves' effects to more

correctly model the surge both spatially and temporally.

In its effort to characterize the combined effects of hurricane hazards (hurricane wind,

storm surge, and waves) for use in developing structural design criteria for coastal structures, the

Building and Fire Research Laboratory (BFRL) of the National Institute of Standards and

Technology (NIST) – in collaboration with the Meteorological Development Laboratory (MDL)

and the Oceanic and Atmospheric Research (OAR) of the National Oceanic and Atmospheric

Administration (NOAA) – have developed a methodology that incorporates hurricane science,

hydrology, probabilistic methods, and structural engineering needs for use in developing site

specific, risk-based design criteria for coastal structures subjected to the above hurricane hazards

(PHAN et al, 2007). This early effort utilizes program SLOSH for hydrodynamic simulations

without consideration of wave effects. Recognizing that wave set-up and mass flux might have a

significant influence on total storm surge levels, the BFRL/NIST then collaborated with the

National Hurricane Center (NHC) of NOAA to provide funding (NIST Grant 60NANB8D8103)

and technical guidance to the University of Florida/Department of Civil and Coastal

ix
Engineering) for the incorporation of a wave model into the SLOSH model to extend its

capability. The result of this effort is described in this report.

We chose two wave forcing components, that is, set-up from wave stresses and mass flux

transport, to incorporate into the SLOSH storm surge model through a two-way coupling

methodology. Our aim was to better understand the relative contribution of each effect and their

relationship to both storm strength and bathymetry.

To this end, we conducted numerous tests of different forcing variations: wind-stress

only, wind and wave stresses, and wind and wave stresses with mass flux transport. These

options were simulated on three hurricanes and two SLOSH basins. The storms range in

intensity from a Category 1 (wind speed of 34 m/s) to Hurricane Andrew, a Category 5 storm

(wind speed of 74 m/s). Our two basins were chosen for bathymetric contrast: Tampa Bay, a

shallow and gentle shelf, and Miami, which has a steeper and deeper shelf. Wave stresses and

mass transports were obtained using the Simulating Waves Nearshore (SWAN) third-generation

wave model with time dependent water level and wind inputs from the SLOSH wind-stress-only

test. 

We determined that the impact of wave set-up and mass flux to storm surge levels varies

between locations – even for the same storm in the same basin – proving that the interaction

between the wind and wave forcing components is indeed complex. On average, however, the

addition of the wave set-up and mass flux raised the maximum storm surge levels 10 to 30

percent, although isolated positions experience increased well above 100 percent. We also

looked at the effects of the wave forcing on specific points and overland inundation. We found

that the largest relative contributions to the wave stress occurs in the Tampa Basin, which can be 

x
attributed to the differences in the storm diameter, intensity, and the complex interactions

between the storm and the local topography and bathymetry of the bays themselves. Further, we

found wave forcing components contributed the most increase by percentage for the weaker

Category 1 storm than during the stronger hurricane simulations. This result does not imply that

wave forcing is not of importance for Category 4 and 5 storms, only that wind stresses are

responsible for a larger percentage of the overall storm surge.

xi
xii
1 CHAPTER 1

CHAPTER 1

INTRODUCTION

1.1 GENERAL INTRODUCTION

Tropical cyclones form in tropical or subtropical waters as convection occurs around lowpressure systems. In turn, winds circulate around the center of the storm (HOLLAND, 1993). In the

northern Atlantic Ocean, these disturbances are referred to as hurricanes when their maximum

sustained winds exceeds 74 mph (33 m/s). Hurricanes devastate the Atlantic and Gulf coasts of

the southeastern United States, destroying infrastructure and dwellings, while placing countless

lives in peril. Perhaps the most dangerous element of hurricanes is the associated storm tide.

Commonly referred to as a storm surge, it is a local rise in sea elevations due to the presence of

low-pressure storms. Hurricane Katrina made landfall on the Gulf of Mexico coast in August,

2005, resulting in a storm tide of 28 feet (8.5 m), inundating areas of Mississippi and Louisiana

for weeks (KNABB, 2006). Less intense hurricanes can still cause several feet of coastal flooding.

The storm tide has four main components. The first is the tidal stage. Coastal locations

experience either diurnal (one high tide, one low tide per day), semidiurnal (two high, two low),

or mixed (like semidiurnal, but with unequal high and low) tides. In the United States, the Gulf

Coast sees mixed or semidiurnal microtidal (< 2 m magnitude) events, while the Atlantic coast

has diurnal mesotidal (2–4 m magnitude) events (PINET, 1998). The tidal stage during a

hurricane's landfall can profoundly effect the resulting storm tide, since the mean sea elevation

might fluctuate as much as the other three surge components combined. Ignoring the tidal stage

does not bias storm surge results positively or negatively; high and low tides occur with the same

frequency and magnitude. Instead, they merely present an envelope of best/worst case scenarios.

1
The second component is the barometric tide, which is the sea surface's response to the

low pressure at the center of the storm. This rise is essentially due to water being drawn up into

the eye of the hurricane. Ambient atmospheric pressure fluctuates around 1012 millibars (mb).

Every millibar drop in pressure causes a centimeter rise in the local sea level in deep water

(ANTHES, 1982). To date, the lowest pressure measured in a hurricane is 882 mb in Hurricane

Wilma, as it moved through the eastern Caribbean in 2005 (PASCH, 2006). This pressure resulted

in an approximate 1.38 m sea level rise at the storm's center. Although hurricanes typically

weaken as they approach the shore, horizontal convergence can increase the associated

barometric surge tide. Ignoring the barometric tide would under-predict actual surge levels. 

1.1.1 Air-Sea Interface

The wind stress tide, the third component, is caused by wind forces acting on the water

surface. The storm's winds “push” water to the shore as it makes landfall, causing a build-up

near the shoreline. Since hurricane winds can often reach upwards of 125 mph (200 km/hr), this

contribution is usually dominant compared with the other components. 

Interaction between the sea and atmosphere boundary causes energy to be transferred

from a hurricane's winds to the water column; this interface has been broadly studied

(GEERNAERT, 1990; DONELAN et al, 1993; DONELAN, 1998; GRACHEV et al, 2003). Most studies

approximate wind stress per unit mass using Eq. 1-1. Different approaches to the drag coefficient

result in up to a 0.5 m difference in storm surge predictions (WEAVER, 2004). However, those

effects were not explored in this study. The storm surge model uses an empirically derived

constant drag coefficient, whereas the third-generation wave model defines the drag coefficient

as a function of the wind speed.

2
=cd

air

sw

∣W∣W (1-1)

where

τ – surface wind stress per unit mass 

cd – dimensionless drag coefficient 

ρair – density of air

ρsw – density of sea water 

W – wind velocity vector at 10 meters above sea surface

1.1.2 Wave Set-up and Mass Flux

The fourth constituent of storm surge, wave set-up, is the transfer of momentum from

breaking waves to the water column (DEAN and DALRYMPLE, 2002). The momentum flux due to

waves is called radiation stress (LONGUETT-HIGGINS and STEWART, 1964). To conserve energy,

waves exchange their momentum with the water column. Far offshore, before they break, waves

gain momentum, causing the mean sea level to decrease. When waves shoal and eventually

break near the shore, their radiation stresses are imparted into the water column. This transfer

occurs rapidly, forcing the mean sea level to rise. Wave set-up is primarily limited to the surf

zone where wave breaking typically occurs. Theoretical computations of the wave set-up

contribution on a steady-state, monotonically decreasing beach profile have found that the

maximum wave set-up at the coast should be approximately 20% of the breaking wave height

(DEAN and DALRYMPLE, 2002). For a hurricane producing five meter waves, an additional set-up

of one meter can be expected. The Gulf of Mexico is a close natural representation of this

idealized case.

Waves also contribute a non-linear effect to the storm tide—mass transport or mass flux.

Since wind waves are actually non-linear (narrow, steep crests and broad, shallow troughs),

every wave period results in a small net transport of mass in the direction of the wave train. For

stronger hurricanes, this mass flux can significantly contribute to the storm tide. STARR (1947)

3
determined that Eulerian mass transport can be averaged over the water column and through time

(Eq. 1-2). This result demonstrates how larger hurricane-induced waves can lead to considerable

shoreward currents; mass flux is dependent only on the water depth, h+ζ, and wave height, H.

For a straight, steady-state shoreline, the Eulerian transport is equivalent and opposite to the

wave-induced mass transport (SMITH, 2006).

wmass flux=

M

W

sw h

=

∫

−h



sw w x ,dz

swh

=

E

C sw h

=

1

8

 g H

2

h

3/ 2

(1-2)

where

wmass flux – depth-averaged time-mean horizontal velocity

MW– wave-induced mass transport

ρsw – reference density of sea water (1.979 slugs/ft3 or 1020 kg/m3)

h – mean water depth

ζ – free surface elevation above the mean sea level

w(x,ζ) – horizontal velocity

z – vertical direction

E=

1

8

sw g H

2

 – total energy per unit surface area

C=gh – shallow water phase speed

g – acceleration due to Earth's gravity

H – wave height

1.1.3 Bathymetry and Other Effects

Bathymetry also plays a vital role in determining storm tide levels. Shallow, gentlesloping areas, such as the Gulf of Mexico, often see higher storm tides than steeper shelves like

the Atlantic coast of Florida from equivalent storms. This phenomenon occurs because a storm

tide can be diffused away from the hurricane in deep water, whereas in shallow water it remains

focused to the coast. For a one-dimensional case, the steady state solution for storm surge from

wind-only forcing is shown in Eq. 1-3 (DEAN and DALRYMPLE, 1991).

∂

∂ x

=

nzx 

sw gh

(1-3)

where

x – cross-shore direction

4
n=1−[

zx −h

zx  ]

 (must be greater than 1)

τzx(ζ) – surface shear stress

τzx(-h) – bottom shear stress

Other factors that influence storm surge levels include the Coriolis effect, rainfall, and

river discharges. The Coriolis effect occurs because the Earth's rotation bends currents counterclockwise in the northern hemisphere. During a hurricane, the effect can be especially relevant if

currents are bent perpendicular to the coast. The Rossby number, a ratio of inertial to Coriolis

forces, can be used to evaluate the relative importance of this component. Large Rossby

numbers indicate the Coriolis tide will not contribute significantly to the overall storm tide

(KANTHA, 2000). Rainfall and river discharges can locally influence storm tide levels, but are

generally ignored in storm surge modeling. 

1.2 STORM SURGE MODEL

The numerical model we used to compute sea surface level is Sea, Lake, and Overland

Surges from Hurricanes (SLOSH). The model solves the conservation of mass and momentum

equations integrated over the water column spatially and temporally, while treating the coastline

as a physical boundary. In historical comparisons, the SLOSH model has been found to be

accurate to ± 20 percent (JELESNIANSKI et al, 1992). We predict the water elevations over the test

basin with three test cases:

• Wind stress only (SLOSH model unedited)

• Wind stress and wave radiation stress 

• Wind stress, wave radiation stress, and mass flux

We can then analyze the results to determine the relative significance of each wave

contribution to storm surge. 

The SLOSH model incorporates approximately forty gridded basins that cover the eastern

5
and southern coasts of the United States. Two basins were chosen for comparison: Elliptical

Tampa Bay Basin (ETPA) and Hyperbolic Miami Basin (HMIA). These two basins were

selected to compare bathymetry's impact on wave forcing. The Tampa Bay basin exemplifies a

shallow, gradually sloping shelf, whereas the Miami basin is both steeper and deeper.

1.3 TWO-WAY COUPLING

To study the effect of wave set-up and mass flux effects on the storm surge model we

used a two-way coupling methodology. By this, we mean water levels and wind velocities from

the time dependent storm surge simulation are passed to a wave model. For this purpose, we

used Simulating Waves Nearshore (SWAN), a third-generation wave numerical model developed

by Delft University (BOOIJ et al, 2004). The model simulates wave fields over a Cartesian grid

system from its bathymetry and optional inputs at each time step: currents (assumed to be zero),

wind, and water levels. This aspect of the two-way coupling changes the wave height

distributions in coastal waters. The second component of the two-way coupling passes the forces

from breaking waves and mass flux transport back to the storm surge model. The results from

each of the three cases from Section 1.2 are then analyzed for spatial and temporal differences. 

 

1.4 HURRICANE SIMULATION

For our experiment, we chose a representative test storm (etpa_SLOSHtrk998) provided

by the NWS (a significant historical hurricane would have been preferred but no such accurate

records exist for the ETPA basin). This hurricane has maximum wind strength of 34 m/s (75

mph) and would be classified as a Category 1 hurricane on the Saffir-Simpson scale. It was

selected because of its northeastward trajectory and landfall location just north of Tampa Bay.

Preliminary tests with this track and other tests storms (namely etpa_SLOSHtrk99 and

6
etpa_SLOSHtrk2721) showed that this trajectory led to maximum storm surge levels in and

around Tampa Bay. Each test storm file has hourly track points, a radius of maximum winds,

and maximum central winds. However, by manipulating these values, we can alter the test

hurricane into a stronger storm. To test the impact of a storm's strength on storm surge values,

we altered this test storm into a Category 4 hurricane; by scaling the maximum winds by a factor

of 1.75, we created a maximum wind strength of 60 m/s (130 mph). 

We also performed a hindcast analysis on Hurricane Andrew in the HMIA basin. On

early morning of August 24, 1992, Hurricane Andrew made landfall at Elliot Key, Florida, with

maximum sustained winds of 165 mph, followed shortly by a second landfall at Homestead,

Florida (LANDSEA et al, 2004). The last Category 5 Hurricane to make landfall in the United

States, Andrew caused approximately $25 billion in damages in Florida and was responsible for

65 deaths (HERBERT et al, 1992; RAPPAPORT, 1993). We modeled the storm while it remained

within the HMIA boundaries, a total time of about sixteen hours. The NOAA National Ocean

Service Center for Operation Oceanographic Products and Services (NOAA NOS CO-OPS)

website contains a plethora of historical buoy data, but, unfortunately, no time dependent sea

level recorders were deployed in the region during the time of Andrew's strike. Hence, the

historical comparison can only be made for the maximum storm tide levels recorded at the

Atlantic coast.

Hurricane Andrew does provide another opportunity for comparison; it can be compared

to the Category 4 results from Tampa Bay to gain an assessment of bathymetry's impact on wave

set-up and mass transport. The Category 1 and 4 hurricane simulations over Tampa Bay

additionally allow for an examination of two storms with identical characteristics, except for

scaled maximum winds, over the same bathymetry. Hence, we can answer how much the

strength of the storm impacts the relative contributions of the two forcing options. 

7
2 CHAPTER 2

CHAPTER 2

METHODOLOGY

2.1 STORM SURGE MODEL

Storm tide levels were predicted using SLOSH numerical model created by the National

Weather Service's (NWS) Meteorological Development Laboratory (MDL). SLOSH's primary

developers were Chester Jelesnianski, Jye Chen, and Wilson Shaffer (JELESNIANSKI et al, 1992).

Horizontal transports are solved through application of the Navier-Stokes momentum equations

for an incompressible and turbulent flow. For a generic real plane, the momentum equations can

be written so that the time rate of change of the velocity vector is equivalent to four forces:

pressure, Coriolis effect, gravity, and friction. Eqs. 2-1, 2-2 and 2-3 show the expanded

equations of motion for the horizontal and vertical velocities after Boussinesq's approximation

for incompressible flow (constant density) and hydrostatic pressure are applied. Since the

gravitational force only acts in the vertical direction, it does not appear in the horizontal

momentum equations. Likewise, Coriolis forcing does not appear in the vertical momentum

equation.

∂u

∂ t

u

∂ u

∂ x

v

∂u

∂ y

w

∂u

∂ z

− fv=−

1

sw

∂ p

∂ x

Ah[

∂

2

u

∂ x

2

∂

2

u

∂ y

2 ]

Av

∂

2

u

∂ z

2

(2-1)

∂v

∂t

u

∂ v

∂ x

v

∂ v

∂ y

w

∂ v

∂ z

 fu=−

1

sw

∂ p

∂ y

Ah[

∂

2

v

∂ x

2

∂

2

v

∂ y

2 ]

Av

∂

2

v

∂ z

2

(2-2)

∂w

∂t

u

∂w

∂ x

v

∂w

∂ y

w

∂w

∂ z

=−

1

sw

∂ p

∂ z

−gAh [

∂

2

w

∂ x

2 

∂

2

w

∂ y

2 ]

Av

∂

2

w

∂ z

2

(2-3)

where

u(x,y,z,t), v(x,y,z,t) – time-averaged horizontal velocities

8
w(x,y,z,t) – time-averaged vertical velocity

t – time

x,y – horizontal coordinates

z – vertical coordinate

f = 2Ωsinφ – Coriolis parameter

ρsw – reference density of sea water (1.979 slugs/ft3 or 1020 kg/m3)

p – time-averaged pressure

Ah – horizontal eddy viscosity coefficient 

Av – vertical eddy viscosity coefficients

Ω – angular speed of the Earth (7.29x10-5 rad/s)

φ – latitude (degrees)

SLOSH first negates vertical velocity, w, and horizontal eddy viscosity, Ah , since both are

several orders of magnitude smaller than their perpendicular counterparts. Further, the model

does not account for the advective terms (the second, third, and fourth terms on the left hand side

of Eqs. 2-1, 2-2 and 2-3) because the storm tide only shows a sensitivity to them in localized

areas with strong current gradients (WHITAKER et al, 1975; JELESNIANSKI et al, 1992). SLOSH does

incorporate small amplitude effects to compensate for this omission. These assumptions, along

with the application of the definition of shear stresses (see below), yield Eqs. 2-4 and 2-5, which

demonstrate that the horizontal velocities do not vary vertically through the water column. Eq.

2-3 is reduced to a relationship between the vertical pressure gradient and the product of gravity

and sea water density (Eq. 2-6). Thus, pressure is no longer a dependent variable in the

horizontal momentum equations. 

∂u

∂ t

− fv=−

1

sw

∂ p

∂ x



1

sw [

∂ zx

∂ z ]

(2-4)

∂v

∂t

 fu=−

1

sw

∂ p

∂ x



1

sw [

∂ zy

∂ z ]

(2-5)

∂ p

∂ z

=−sw g (2-6)

9
where

zx=[

∂ u

∂ z ]

 – viscous shear stress 

zy=[

∂ v

∂ z]

– viscous shear stress 

μ = νρsw – molecular viscosity

These equations are then translated to a complex plane (z=x+iy) and applied to the

horizontal momentum, Eq. 2-7, which defines the real and imaginary components of transport by

vertically integrating the complex horizontal velocity over the entire water column. 

M =UiV =∫

−h



q dz=h∫

0

1

q dz' (2-7)

where

M – horizontal transport

U – real component of horizontal transport

V – imaginary component of horizontal transport

z – vertical coordinate

z'=

hz

h

 – vertical coordinate transformation 

q = u + iv – complex horizontal velocity (real and imaginary components)

At this point, the complex horizontal transport equations can be solved by specifying

boundary conditions (Eqs. 2-8 and 2-9). Two noteworthy conditions are the treatment of the

viscous shear stresses. At the bed, it is assumed to be the complex horizontal velocity qbed,

multiplied by a slip coefficient, s (PLATZMAN, 1963; JELESNIANSKI, 1967). The surface shear stress

per unit mass takes the form of Eq. 1-1. The bottom stress is “absorbed” into six terms that are a

function of the water depth, h+ζ, and modify each right-hand-side term of Eqs. 2-8 and 2-9.

Undetermined coefficients hidden in these bottom stress terms, along with the two surface stress

terms, were all set empirically through historical hindcasts of 43 landfall hurricanes on the Gulf

and Atlantic coasts of the United States (JELESNIANSKI et al, 1992). 

10
∂U

∂t

=−gh[Br

∂−0

∂ x

Bi

∂−0

∂ y ] f  ArVAiU Crx−Ciy

(2-8)

∂V

∂t

=−g  h[Br

∂−0

∂ y

Bi

∂−0

∂ x ] f  Ar UAiV Cr yCix

(2-9)

where

Ai, Ar, Bi, Br, Ci, Cr – bottom stress terms

ζ0 – mean sea level elevation

τx, – real surface stress per unit mass component

τy – imaginary surface stress per unit mass component

The model applies the conservation law for mass to relate these horizontal transports to a

rise in sea level. Eq. 2-10 shows the continuity equation with Boussinesq's approximation for

incompressible flow. Vertical velocity is again assumed to be negligible. Following the same

derivation procedure as the momentum equations, the coordinates are translated to the complex

plane and the definitions of transport from Eq. 2-7 are applied. SLOSH's form of the continuity

equation (Eq. 2-11) results, noting that x and y in this equation are the real and imaginary

components of the complex plane, respectively. 

∂u

∂ x



∂v

∂ y



∂w

∂ z

=0 (2-10)

∂

∂t

=−

∂ U

∂ x

−

∂V

∂ y

(2-11)

Eqs. 2-8, 2-9, and 2-11 are the essence of the SLOSH model. At every time step, the

horizontal transports are solved from the pressure, Coriolis, and frictional (shear stress) forces.

In turn, these horizontal transports lead to a new surge level, ζ, at every grid location.

A complete derivation of the SLOSH equations of motion can be found in Numerical

Computations of Storm Surges with Bottom Stress (JELESNIANSKI, 1967) and Appendix A of the

SLOSH Technical Report (JELESNIANSKI et al, 1992).

11
2.1.1 SLOSH Computational Grid

SLOSH operates on over forty basins covering nearly the entire eastern and southern

seaboards of the United States. Originally, all of these basins were polar grids to allow for both

greater resolution near the coast, the focus of storm surge prediction, and also the

implementation of simple boundary conditions (JELESNIANSKI et al, 1992). However, many of

these original basins have been phased out in favor of elliptical and hyperbolic grids that provide

the same advantages offered by polar grid with greater coverage and even finer resolution in

areas of interest. The Elliptical Tampa Bay (ETPA) Basin is an elliptical grid with 9090 points

that covers Tampa Bay and the adjacent coastline approximately 50 miles north and south

(Figure 2-1A). The grid centers around the bay and radiates outward into the Gulf of Mexico.

Figure 2-1B shows the Hyperbolic Miami (HMIA) Basin, a hyperbolic grid of 23,750 points that

covers the area between Lake Okeechobee and the Bahamas. The ETPA and HMIA basins both

have fine resolution (two kilometer spacing) near the coast and much coarser resolution (four

12

Figure 2-1: SLOSH computational grids on a longitude-latitude grid. Figure A is the Elliptical

Tampa Bay Basin (ETPA) and Figure B is the Hyperbolic Miami Basin (HMIA). The grid

spacing is approximately 2000 m near the coasts and 4000 m in deep water. 

A B
kilometer spacing) in deeper waters of the Gulf and Atlantic, respectively. The bathymetry data

for these grids have minimum and maximum cutoff values and range between -290 feet in the

Gulf and Atlantic and 40 feet on land. 

It should be noted that although all of the inputs and output values of SLOSH are based

on real space (longitudinal and latitudinal coordinates for the grid points), a grid transformation

to an image plane is used for all computations. SLOSH implements four boundary conditions for

this computational grid: 

• Zero transport over dry land.

• Surface gradients are replaced by the nearest interior, contiguous point in shallow water.

• Surface gradients are replaced by the storm's hydrostatic gradient in intermediate depths.

• Hydrostatic height of the storm is set at height points of boundary squares in deep water.

2.1.2 SLOSH Input Parameters

The SLOSH model's input parameters for a hurricane are its position, size, and intensity.

These values are saved in a SLOSH TRK file, which contains up to 100 track points of data.

Each point gives the longitudinal and latitudinal coordinates of the storm's center, central

pressure, maximum wind speed, radius of maximum winds (distance from center the maximum

wind speed is found), and direction . Each point is an hourly progression of the hurricane. An

example of a TRK file used in our study can be found in Appendix A.

Wind speeds are calculated at every grid point using the wind speed profile depicted in

Eq. 2-12 for a stationary storm (MYERS and MALKIN, 1961). At the storm's center and far away

from the hurricane, the wind speed tapers off to zero. A vector correction for the storm's motion

is added to the stationary wind speed to arrive at the wind vector used in surface shear stress

computations (JELESNIANSKI and TAYLOR, 1973).

W s=V R

2 Rr

R

2r2

(2-12)

13
where

Ws(r) – wind speed for stationary storm

VR – maximum wind speed

R – radius of maximum winds 

r – distance from storm's center to grid point

The other input parameters SLOSH requires are the selected basin's grid locations and

their associated bathymetry. Subgrid features, such as barriers, can be incorporated, but are not

required. Every created basin has its own data file.

2.1.3 SLOSH Implementation

Once the above input parameters have been specified, SLOSH can operate. SLOSH was

designed to output a REX and ENV file, both of which are viewed using the SLOSH display

program. The REX file contains water levels at every grid cell at a specific interval of time and

are displayed as time-lapsed animated movies. The ENV file contains the maximum water level

at any point over the duration of the storm(s) (MCINERNEY, 2006). Essentially, we wrote the REX

file data to a separate water level file at set time steps. Additionally, we wrote out the wind fields

across the basin at the same time intervals. These time steps were matched to default SLOSH

time steps, which are dependent on the basin and the storm's distance from the coast. These time

steps were determined by stability testing on SLOSH's finite difference scheme. The critical

time value was set for a Courant number equal or less than 1.0 on the polar grid. For the ETPA

grid, a perpetual time step of one hour is used, whereas on the HMIA grid, the time step

decreases from fifteen to ten and finally to five minutes as the storm's center nears land.

Computations are made sixty times per time step. The output data (wave levels and wind

velocities) would be used in the two-way coupling with the wave model. 

14
2.2 WAVE SET-UP AND MASS TRANSPORT

To study wave set-up and mass flux effects on the storm surge, we needed a way to

accurately model the wave field, and then interpolate those results to the SLOSH computational

grid. To this end, we used the Simulating Waves Nearshore (SWAN) model, created by Delft

University. One of the most important outputs from SWAN is time dependent wave-induced

stress, or gradient of the radiation stresses, as shown in Eqs. 2-13 and 2-14. These gradients are

calculated from a 2nd order central difference scheme everywhere except at the boundaries, which

employ a first order forward difference strategy (BOOIJ et al, 2004). Other noteworthy time

dependent variables SWAN can provide are significant wave heights, peak periods, wave

direction, and water depths. 

Fx=−

∂ Sxx

∂ x

−

∂ Sxy

∂ y

(2-13)

F y=−

∂ S yx

∂ x

−

∂ Syy

∂ y

(2-14)

where

S xx= g∫n cos

2

n−

1

2 

E d  d 

S xy=Syx= g∫ncossin  E d  d 

S yy= g∫n sin2n−

1

2 

E d  d 

n=Cg/C

Cg – group velocity

2.2.1 SWAN Computational Grid

The SWAN computational grids were created from NOAA's National Geophysical Data

Center's (NGDC) GEODAS system. Figure 2-2A shows the grid used for the Tampa Bay testing.

This Cartesian grid covers three longitudinal and latitudinal degrees (83°W to 80°W and 26°N to

29°N, respectively), completely covering SLOSH's ETPA basin grid. The SWAN grid has a

15
uniform spacing of just under two kilometers, chosen to match the finest resolution of the

SLOSH grid and allow for meaningful interpolations between the SLOSH and SWAN especially

nearshore where waves break and impart their momentum on the water column. The SWAN grid

for Miami (Figure 2-2B) was created in the same manner and has the same grid spacing. It

covers three longitudinal degrees (78.5°W to 81.5°W) and almost four latitudinal degrees

(27.125°N to 28.875°N). In the interest of consistency, we interpolated the SLOSH bathymetric

values to the SWAN grid for any points inside the SLOSH grid boundaries. The remaining

points outside the SLOSH grid boundaries were not altered, except in the case where bathymetric

values exceeded the SLOSH bathymetric range (see Section 2.1.1). In these instances, the

bathymetry values were rounded to the appropriate cutoff value.

2.2.2 SWAN Input Parameters

The SWAN model allows for multiple input grids: depth, water level, currents, wind

velocity, and friction. The latter four inputs can be either time dependent or constant. For the

16

Figure 2-2: SWAN computational grids with SLOSH computational grid overlays. Figure A is

the Tampa Bay basin with ETPA overlay and Figure B is the Miami basin with HMIA overlay.

The contours on both figures show the bathymetry, which range from 88 m to -12 m. 

B

A
purposes of this study, currents were assumed to be zero and no time dependent bottom friction

terms were used. Instead, the JONSWAP expression for bottom friction dissipation was turned

on. The hourly water level and wind velocities from the initial wind-stress-only SLOSH run

were interpolated onto the SWAN Cartesian grid. 

2.2.3 SWAN Implementation

SWAN can produce numerous selected outputs. For our study, we focused on the hourly

wave stresses (Eqs. 2-13 and 2-14), significant wave height, wave direction, peak period, and

water depth at every grid point. An example of the SWAN input file for the Category 1 hurricane

simulation over Tampa Bay can be found in Appendix B of this report.

2.3 TWO-WAY COUPLING

As discussed in Section 1.3, SLOSH and SWAN were joined via a two-way coupling

method. The first aspect of this task, already mentioned in Section 2.2.2 involved the

interpolation of hourly water levels and wind velocities from the unedited SLOSH model

computations on either the elliptical or hyperbolic grid onto the SWAN Cartesian grid. For those

points outside the SLOSH grid boundaries, wind velocities were assumed to be zero and water

levels were made equivalent to the SLOSH datum elevation (0.00 m for the Tampa Bay

simulations and 0.61 m for the Hurricane Andrew hindcast). Although these assumptions might

cause discrepancies at the SLOSH boundaries where water levels and wind velocities possibly

shift dramatically, it should be noted that nearly all of these points are overland or in deep water,

far away from the zone of interest nearshore. However, we analyzed the results to ensure that the

wave set-up effects and mass flux transport acted away from the SLOSH boundaries. As an extra

precaution, we removed these SLOSH boundary points from any analysis. 

 With these inputs, SWAN then modeled the hourly wave fields, providing time

17
dependent wave stresses (Eqs. 2-13 and 2-14), wave heights, peak period, wave direction, and

water depth as detailed in Section 2.2.3. Eq. 2-15 along with the definition of depth-averaged

time-mean mass flux velocity from Eq. 1-2, was used to determine the horizontal mass flux

transport given the time dependent significant wave height, water depth, and wave direction at

each grid location. 

M mass flux=U mass fluxiV mass flux=hwmass flux (2-15)

where

Mmass flux – horizontal transport due to mass transport

Umass flux – real component of horizontal transport due to mass transport

Vmass flux – imaginary component of horizontal transport due to mass transport

We then updated the governing equations for continuity and transport in SLOSH to

include wave stresses and our calculated horizontal transport due to mass flux. First, the new

continuity equation (Eq. 2-16) becomes a relationship between the time rate of change of the sea

level and the divergence of both the mean current transport (the first and second terms on the

right-hand-side of Eq. 2-16) and mass flux transport (the remaining terms on the right-hand-side

of Eq. 2-16) (SMITH, 2006). The new versions of the the transport equations (Eqs. 2-8 and 2-9)

appear below as Eqs. 2-17 and 2-18, respectively. Wave stresses were essentially added as

additional wind shear stresses. The mass flux transport was then introduced using the

methodology from SMITH (2006) to account for the fact that mass flux lost from the waves

appears at the mean flow speed, not with zero velocity. The wave stress and mass flux transport

inputs were interpolated to the image plane on the SLOSH computational grid.

18
∂

∂t

=−

∂ U

∂ x

−

∂ V

∂ y

−

∂U mass flux

∂ x

−

∂V mass flux

∂ y

(2-16)

∂U

∂t

=−gh[Br

∂−0

∂ x

Bi

∂−0

∂ y ] f  ArVAiU 

Crxwx −Ciywy −

U

h[

∂U mass flux

∂ x



∂ V mass flux

∂ y ]

(2-17)

∂V

∂t

=−g h[Br

∂−0

∂ y

Bi

∂−0

∂ x ] f Ar UAiV 

Crywy Cixwx−

V

h[

∂V mass flux

∂ x



∂ U mass flux

∂ y ]

(2-18)

Where: τwx, – real wave radiation stress per unit mass component

 τwy – imaginary wave radiation stress per unit mass component

19
3 CHAPTER 3

CHAPTER 3

RESULTS

3.1 WAVE SET-UP AND MASS FLUX FORCING

We completed testing of three different hurricane simulations, each with three different

forcing options as follows: 1. wind stress only (SLOSH unchanged); 2. wind stress with wave

set-up effects; and 3. wind stress with both wave set-up and mass flux effects. Two of the

hurricanes (a Category 1 and 4) were tested on the SLOSH model's ETPA basin; a simulation of

Hurricane Andrew was run on the HMIA basin. The storm tide for the latter two forcing options

are dependent on the wave fields determined from running the SWAN third-generation wave

model on the wind-stress-only results from SLOSH. The wave characteristics detailed in Section

2.3 were of greatest importance. Figure 3-1 shows the contours of the maximum significant

20

Figure 3-1: Maximum significant wave height contours with mass flux velocity vectors from

Category 1 hurricane simulation over Tampa Bay on the SWAN Cartesian grid. The contours

shown vary from 0 m (on land) to 5.66 m (in the Gulf). The mass flux velocities vary from 0

cm/s to 61 cm/s. The reference vector shown represents a velocity of 1 m/s. 
wave heights for the Category 1 storm with vectors for the maximum mass flux velocities

superimposed. The maximum significant wave heights occur in the open Gulf, topping out at

5.66 m. As the waves travel toward the shore, larger waves loose energy from interactions with

the sea bed and break farther from the shoreline. This pattern of successive breaking by wave

height is clearly evident in Figure 3-1. In both hurricane simulations over Tampa Bay, as well as

Andrew in the Atlantic, the maximum wave heights have the same spatial pattern. The Category

4 hurricane caused larger maximum significant wave heights (up to 11.48 m), although these

maximums were closer to the ETPA basin deep water boundary. Hence, a 75 percent increase in

the maximum wind parameter led to over a doubling of the maximum wave heights. Hurricane

Andrew caused maximum significant waves of 7.67 m beyond the continental shelf off of

Miami. The maximum flux velocity vectors shown on Figure 3-1 are intrinsically linked to the

maximum significant wave heights, since wave flux velocities vary as a square of the wave

height over the same depth contours (Eq. 1-2). This relationship explains why the maximum

velocities from the Category 4 simulation were 93% greater than the Category 1 storm (1.18 m/s

versus 0.61 m/s). Both tests showed very similar spatial distributions of mass flux velocities; the

greatest values were found between the coast and 100 km offshore. Hurricane Andrew yielded a

maximum mass flux velocity of 0.86 m/s. In all three cases, the mass flux velocity vectors are

nearly perpendicular to the shoreline in the surf zone, leading to the maximum transfer of

momentum to the water column. Wave stresses (Eqs. 2-13 and 2-14) were also obtained from

SWAN in each simulation. Figure 3-2 shows the maximum wave stress values for the Category

1 simulation over Tampa Bay. For reasons explained in Section 1.1.2 , the maximum forces

occur near the shoreline where hurricane-induced waves shoal and break, imparting their energy

to the water column. The maximum wave stress was 1.14 N/m2, but most nearshore stresses

range between 0.5 and 0.7 N/m2 . Like wave height and mass flux velocities, the maximum

21
wave stresses from the Category 4 storm share a similar spatial distribution but differ in

magnitude. The Category 4 storm sees maximum stress values of 1.57 N/m2 ; Hurricane

Andrew's maximum wave stresses also occur near the coast and top 5.45 N/m2.

3.2 SPATIAL RESULTS

We analyzed the spatial variability of the storm tide due to different forcing by looking at

the results in three ways. First we examined the maximum storm tide levels over the entire

domain. This method is perhaps the most apparent since the SLOSH model was designed to

provide ENV files with the maximum water levels computed at each grid point. The second

analysis looks at maximum storm surges incurred at chosen points. Finally we compared both the

maximum and average overland surge values and number of inundated points in each forcing

22

Figure 3-2: Maximum wave-induced stress contours from Category 1 hurricane simulation over

Tampa Bay. The maximum stresses range between 0.0 and 1.14 N/m2. The highest maximum

stresses occur nearshore where hurricane-induced waves shoal and break, imparting their

momentum into the water column.
case. This final analysis targets the most practically relevant question: to what extent does each

forcing component impact storm surge levels overland? 

3.2.1 Maximum Storm Tide Levels

We determined domain-wide maximum storm tide levels for each storm simulation and

for three forcing options: A) wind stress only; B) wind and wave stresses; and C) wind and wave

stresses with mass flux transport. Table 3-1 shows the maximum surge level in each simulation.

The greatest surges occur in the Category 4 hurricane, followed by Hurricane Andrew and finally

the Category 1 storm. Mass flux transport nearly doubles the effect of wave stresses in each

storm. A domain-wide snapshot (not pictured) shows that the maximum storm tide levels are

found in and around Tampa Bay for the Category 1 and 4 hurricanes and Biscayne Bay for

Hurricane Andrew. Focusing on the zone of greatest surges, Figure 3-3 shows a zoomed view of

the Bay and surrounding coastline for the Category 1 simulation. We see a clear increase in

maximum storm surge levels with the addition of each forcing component. Wind-only forcing

levels range from about 1.0 m - 2.0 m, wind and wave forcing yield a range of 1.25 m - 2.25 m,

and the addition of mass flux raises the range from 1.75 m – 2.35 m. Similar spatial patterns are

observed with the other two storm simulations.

23

Table 3-1: Domain-wide maximum storm tide levels (m) calculated for each simulation.

Percent difference from wind stress only case appears in parentheses.

Storm

Simulation

Wind-Stress Only Wind and Wave Stress Wind and Wave Stresses

with Mass Flux

Cat1-Tampa 2.14 2.23 (4%) 2.35 (10%)

Cat4-Tampa 5.33 5.83 (9%) 6.19 (16%)

Andrew 4.00 4.40 (10%) 4.59 (15%)
We can take this analysis a step further by first plotting the differences between the

forcing options. It would be useful to look at these results both as a positive or negative change

in height and also as a percent difference. Figure 3-4 contrasts the maximum surge levels with

and without wave set-up effects for the Category 1 storm over Tampa Bay. Figure 3-4A shows

the difference in meters, whereas Figure 3-4B shows the absolute difference in percent. Figure

3-5 shows the differences when mass flux transport is also included in the forcing. Again, Figure

3-5A shows the difference in meters and Figure 3-5B shows the absolute percent difference.

Both of these figures show that wave forcing has two main impacts on storm surge levels. A

24

Figure 3-3: Zoomed view of maximum storm tide levels from three Category 1 hurricane

simulations over Tampa Bay: A) Wind-stress only; B) Wind-stress and wave set-up; and C)

Wind-stress, wave set-up, and mass flux effects. The contours show storm tide levels in meters,

as shown in the legend.

A B

C
decrease in surge is evident in the open Gulf, whereas the surge within the Bay clearly rises.

Both of these results are completely consistent with the theory of wave set-up presented in

Section 1.1.2. As the waves form and propagate, they gain momentum, lowering the mean sea

elevation. As the waves approach the shallows, they break and impart their momentum to the

water column. This transfer of momentum is responsible for the rise in surge levels. We can see

that the greatest percent differences occur at the Bay's shoreline, where numerous points

experience over a 100 percent increase in surge levels. The Bay itself generally experiences

between a 10 and 20 percent increase for both wave forcing cases. 

From historical comparisons, SLOSH has an accepted error of ± 20 percent for its

maximum storm surge calculations (JELESNIANSKI et al, 1992). We examined our data to

determine how many points fell outside of this error range when wave effects were included. If a

majority of points were inside this envelope, then the net effect of neglecting waves would be on

the same magnitude as the other model assumptions, and, thus, inconsequential. However,

25

 A B

Figure 3-4: Comparison of maximum storm tide levels for Category 1 hurricane simulations

using wind stress only and wave set-up with mass flux and wind stress. Figure A shows the

contours of the height differential in meters. Note that the darkest blue values are actually dry

points for the wind stress only case. Figure B shows the contours of the height differential as

percent difference. 
numerous “extreme” points emerged (Table 3-2). Only one case (Hurricane Andrew with only

wind and wave stresses) resulted in less than ten percent of the inundated points experiencing

changes outside the ± 20 percent range. For the Tampa Bay storms, at least 23 percent of the

points were outside the error for the four wave forcing simulations. Yet, these results do not infer

universal storm surge rise. In fact, less than half of the “extreme” points experienced increased

surge. Figure 3-6 is a histogram of the percent difference between A) wave and wind stress and 

26

 A B

Figure 3-5: Comparison of maximum storm tide levels for Category 1 hurricane simulations

using wind stress only and wave set-up with wind stress. Figure A shows the contours of the

height differential in meters. Note that the darkest blue values are actually dry points for the

wind stress only case. Figure B shows the contours of the height differential as percent

difference. 

Table 3-2: Storm surge levels outside the SLOSH error envelope for each storm simulation.

Storm

Simulation

Total Inundated

Points (Wind-Stress

Only)

Number of Points Recording

Higher Maximum Surges

Points Outside ± 20%

Range (+20%)

Wave Set-up Mass Flux Wave Set-up Mass Flux

Cat1-Tampa 6903 3066 3403 1678 (266) 1592 (640)

Cat4-Tampa 7267 3497 3754 2848 (673) 3131 (1106)

Andrew 13397 11897 12827 841 (792) 2062 (2015)
wind stress only and B) wave and wind stresses with mass flux and wind stress only for the

Category 1 hurricane. For the former case, most points show a decrease in storm surge levels,

whereas the latter case is more balanced. Although this result seemingly contradicts the

maximum storm surge plots, two key points must be mentioned. First, almost all of the points

that experience smaller surge values are found in the open Gulf where wave set-down occurs.

Second, this analysis does not account for points that become inundated only during wave

forcing simulations (see Section 3.3.3).

27

Figure 3-6: Percent difference histogram between A) wave set-up and wind stress only

and B) wave set-up with mass flux and wind stress only for a Category 1 hurricane

simulation over Tampa Bay. Each bin represents five percent. Note than in both cases

there are a small number of points with greater than +50 percent difference.
3.2.2 Selected Points Analysis

We selected four points in each basin to perform individual spatial and temporal analysis.

These points and their coordinates are listed in Table 3-3. We chose an additional point in the

HMIA basin (6042) because it corresponds to the location of Burger King Headquarters on SW

184th Street in Miami, Florida, site of the maximum recorded storm surge level during Hurricane

Andrew (16.9 feet or 5.2 m). The other points were selected as sites of interests because they

were located near the coast and/or showed a significant change in the domain-wide comparisons.

First, we can analyze the maximum storm surge levels computed at each of these points

for all the simulations run on their respective grids, as depicted in Figure 3-7. From these results

we can see a general trend emerge: storm surge levels increase with each additional wave forcing

component. Neglecting the points 5143 and 2700, which were chosen specifically for their

28

Table 3-3: Coordinates and location description for grid points selected for spatial and temporal

analysis. 

Basin Point ID Longitude (oW) Latitude (oN) Description

ETPA 2452 82.4216 27.6746 Middle of Tampa Bay

ETPA 2984 82.5335 27.7772 Tampa Bay eastern shoreline

ETPA 4253 82.6429 27.4916 South side of Tampa Bay

ETPA 5143 82.4979 27.2597 Gulf coast between Venice and Sarasota

HMIA 2700 80.5400 25.2999 Atlantic coast south of Biscayne Bay

HMIA 6042 80.3118 25.6062 Burger King Headquarters in Miami

HMIA 6788 80.2885 25.6440 Biscayne Bay western shoreline

HMIA 8551 80.1477 25.6344 North side of Biscayne Bay

HMIA 11408 80.1695 25.8240 Atlantic coast north of Biscayne Bay
negative-trend irregularity, we arrive at an average increase in storm surge of 25 percent from

wave stresses and 29 percent from mass flux. However, this trend is by no means universal. In

the HMIA basin, point 6788 records its highest surge level with wave and wind stresses only and

point 2700 sees its greatest surge when only wind forcing is active. These results by location

show that the interaction between the wind and wave forcing is complex; indeed, wind and wave

forcing at times work in tandem, but also against one another based on wind and bathymetric

characteristics. Moreover, the individual wave components also share a similar nonlinear

relationship. Like the domain-wide analysis of maximum surge levels, however, we sill neglect

those overland points that become inundated with each successive addition of wave forcing.

29

Figure 3-7: Maximum storm tide levels (m) at selected points for A) Category 1 simulation over

Tampa Bay, B) Category 4 simulation over Tampa Bay, and C) Hurricane Andrew simulation

over Miami. 
3.2.3 Overland Inundation

An assessment of overland inundation data would perhaps be more meaningful than

assessment of the total inundation shown over the entire domain or maximum surge levels at

selected points since it focuses on the essence of storm surge modeling – predicting surge

incurred by coastal inhabitants and infrastructure. Here we examine only the land grid points in

either SLOSH basin (those with an elevation greater than the mean sea level, 0 m). This

approach allows us to see how much land becomes inundated in each case, and to what degree.

Since the minimum spacing of the ETPA and HMIA grids is about two kilometers, we can

approximate every inundated point as an additional four square kilometers of coastal flooding.

The results from the nine simulations appear in Table 3-4. For the Category 1 storm, the addition

of wave stresses led to a 24 percent increase in surge levels over nearly twice as many grid

locations. The extra addition of mass flux only increased the number of flooded points by 15

percent, while the average surge level rose to 35 percent greater than the wind-only case. Figure

3-8 shows the spatial distribution of the overland surge due to the Category 1 hurricane. Figure

3-8B reveals that most of the new inundation from wave stresses occurs around the Bay, while

Figure 3-8C shows only a rise in surge levels when mass flux is considered. Topography is sure

to play a vital role in determining what proportion of the additional surge from wave forcing

inundates further onto land or piles higher at the same locations.

The overland surge results for the Category 4 hurricane and Hurricane Andrew bare a

striking similarity – almost identical percent increases in average surge levels for both wave

forcing cases. This suggests that overland inundation is more closely linked the the storm's

strength than coastal bathymetry.

30
31

Table 3-4: Overland average surge results for all test storms and forcing options.

Storm

Simulation

Forcing Average

Maximum

Overland

Surge (m)

Number of

Inundated

“Dry” Points

Difference from

Wind Stress Only

Case (m)

Difference from

Wind Stress

Only Case (%)

Cat1-Tampa Wind Stress 1.22 52 -- --

Cat1-Tampa Wind & Wave

Stress

1.52 96 0.30 25

Cat1-Tampa Wind & Wave

Stress with

Mass Flux

1.65 110 0.43 35

Cat4-Tampa Wind Stress 4.27 417 -- --

Cat4-Tampa Wind & Wave

Stress

4.75 486 0.48 11

Cat4-Tampa Wind & Wave

Stress with

Mass Flux

5.04 535 0.77 18

Andrew Wind Stress 1.89 639 -- --

Andrew Wind & Wave

Stress

2.07 721 0.18 10

Andrew Wind & Wave

Stress with

Mass Flux

2.22 1076 0.33 17
3.3 TEMPORAL RESULTS

We have so far explored the spatial variations in storm surge for the nine simulations. We

will now examine the temporal variations in storm tide levels the same three ways: domain-wide

maximum surges, analysis of selected points, and overland inundation. 

3.3.1 Maximum Storm Tide Levels

The first temporal analysis is a comparison of the maximum surge level due to each

forcing option at given time increments during the three storms. The results are shown in Figure

3-9. Looking first at the Tampa Bay simulations (Figure 3-9A and 3-9B), we can see that the

three forcing options record similar maximum surges during the first 6 hours of the storm

duration, with slightly lower levels for the wave and wind stress case around the 4th hour. This

32

A B C

Figure 3-8: Contours of overland storm tide levels (m) for three simulations of a Category 1

hurricane over Tampa Bay: A) Wind stress only; B) Wave and wind stress; and C) Wave and

wind stress with mass flux.
result coincides with theory; as the hurricane is located far offshore, waves have not yet arrived

at the coast, and those that have formed cause set-down in deep water. Hence, wave set-up

(through breaking) and mass flux transport have minimal to no influence. As the storm nears the

coast and makes landfall north of Tampa Bay (between 6 hours and 12 hours), the three series

diverge. Maximum surge levels increase to their apexes during this period, with each additional

wave forcing component yielding higher maximum surges (except for another slight decrease in

levels from wave stresses again due to set-down). Surge waters then recede for the rest of the

duration, lowering surge levels in each case.

33

Figure 3-9: Maximum storm tide levels recorded for three forcing options through the storm's

duration during the following simulations: A) Category 1 on ETPA basin; B) Category 4 on

ETPA basin; and C) Hurricane Andrew on HMIA basin. 

A B

C
For the Hurricane Andrew simulation (Figure 3-9C), a nearly identical pattern emerges.

Maximum surge levels for all three forcing cases occur around landfall. Interestingly, after the

storm has moved onto land, the wave stresses lower maximum surge below wind-stress-only

levels. Most likely, this depreciation can be attributed to offshore-directed winds forming waves

that produce a set-down in the water column.

3.3.2 Selected Points Analysis

Using the same points listed in Table 3-3, we can analyze the time series progression of

surge levels at independent positions throughout each domain and determine if each site sees the

same general trend that the domain-wide maximum levels follow. The first position of interest is

point 2984 in the ETPA basin. This point corresponds to a location just off the eastern shoreline

of Tampa Bay. We will look at the hourly surge levels from both the Category 1 and 4 hurricanes

at this site. Figure 3-10 shows the results for each forcing case for the Category 1 storm. Like

the domain-wide results, the profiles show remarkable agreement for the first 10 hours of the

storm. Surge levels decrease as wind is blowing down the area; wave effects have yet to extend

into the Bay. Surge levels then steeply increase in each case, reaching their maximums in the

15th to 16th hour of the storm. Interestingly, these maximum levels occur nearly four hours after

the storm's landfall, demonstrating that surge generated from both wind and waves does not

reach the shore instantaneously. Wave set-up effects do reach the location slightly faster than the

wind stress effects, a finding supported by other studies (WEAVER, 2004). The mass flux profile

appears to be a composite of the wind-stress-only and wave-and-wind-stress profiles; it has the

same general shape as the wind stress case, but has levels slightly higher than the wave-andwind-stress scenario.

34
We can see that the Category 4 simulation yielded very similar results (Figure 3-11).

Again, the profiles considerably agree over the first 10 hours of the storm before diverging.

Maximum surges are reached two hours earlier than in the Category 1 simulation, however, since

the stronger storm produces higher period waves that travel faster. The overall accord between

the Category 1 and 4 time series implies that storm surge evolution is not strongly dependent on

the strength of the hurricane. Conversely, maximum surge levels are indeed heavily influenced

by a storm's magnitude, a result clearly evident in all of the results presented. 

35

Figure 3-10: Hourly surge levels at point 2984 in the ETPA basin for Category 1 hurricane

simulations of each forcing option. This point is located just off the eastern shoreline of Tampa

Bay. 
At another site in the ETPA basin, point 5143 located on the Gulf coast between Sarasota

and Venice, wave forcing has an opposite effect on surge levels. Figure 3-12 shows the time

series profiles for the three Category 1 tests. For the first six hours of the storm, the profiles are

nearly identical. From this time to end of the simulation, wave set-up begins to counteract wind

stresses and yield lower surge levels. When mass transport is included, surge levels reach the

same maximum as wind-only forcing but then also drop below the unedited SLOSH levels.

These results prove that wave effects do not automatically lead to higher surge levels at every

location, even along the coast. Onshore directed winds blowing as the hurricane passes the point

induce wave development, and, thus, wave set-down in the nearshore region, driving water

36

Figure 3-11: Hourly surge levels at point 2984 in the ETPA basin for Category 4 hurricane

simulations of each forcing option. This point is located just off the eastern shoreline of Tampa

Bay. 
elevations lower. Another noteworthy finding at this location is the role of mass flux. Within the

Bay, mass flux almost always adds additional surge, especially in the interval of peak surge.

Here, mass flux only alters the surge levels in a narrow two-hour window. Outside of this range,

mass transport contributes no positive or negative effects to surge results, showing the location

dependence of its effect. 

Figure 3-13 shows the surge results at the same point for the Category 4 hurricane tests.

Maximum levels are reached approximately an hour earlier, but the same overall trends persist

for the wind-only and wave set-up cases. The mass flux simulation veers from the Category 1

trend after the maximum surge is reached. Instead of dropping to meet the wave set-up series,

37

Figure 3-12: Hourly surge levels at point 5143 in the ETPA basin for Category 1 hurricane

simulations of each forcing option. This point is located just off the coast between Sarasota and

Venice, south of Tampa Bay. 
the mass flux case remains inundated. Near the end of the storm, the mass flux component raises

the sea elevation by around 2 m. A plausible explanation for this discrepancy is that the addition

of mass flux caused excessive inland inundation. Hence, as the storm passed, water begins to

recede from farther onshore. This process would keep surge levels higher for a longer duration

for points along the coastline, such as 5143. 

For the Hurricane Andrew simulations, we focus on point 8551, located near Biscayne

Bay (Figure 3-14). Like the previous four simulations, the three cases begin with nearly

identical surges while the hurricane is far from shore. The wave set-up and mass transport levels

then drastically rise and reach maximum surges 60 percent higher than the wind-forcing only

test. The three series then converge at the end of the simulation. At this particular location, the

38

Figure 3-13: Hourly surge levels at point 5143 in the ETPA basin for Category 4 hurricane

simulations of each forcing option. This point is located just off the coast between Sarasota

and Venice, south of Tampa Bay. 
test with mass flux forcing does not deviate much from the wave and wind stress forcing results.

Like the tests on the Tampa Bay basin, wave effects neutralize wind-forced surge at

certain positions. At point 11408, wave forcing tests yield surge levels smaller than wind-only

forcing throughout the storm. In fact, the maximum levels deviate by nearly 80 percent (0.4 m

versus 0.05 m). Clearly, localized effects can effect the impact of wave forcing as much or more

than the basin's overall bathymetry.

3.3.3 Overland Inundation

The last method of temporal analysis is the comparison of the average overland surges

and inundation extent for each of the forcing options. Figure 3-15A shows the hourly average

39

Figure 3-14: Surge levels at each time step at point 8551 in the HMIA basin for Hurricane

Andrew simulations of each forcing option. This point is located near Biscayne Bay, south of

Miami. 
surge levels and Figure 3-15B displays the number of inundated land points each hour for the

Category 1 tests. For the first eight hours of the storm, all land points remain “dry,” with no

storm surge present. All three profiles then drastically increase, as surge from both wind and

waves reaches the coast. At the 13th hour, one hour after landfall, maximum average overland

surge occurs, with highest levels from the mass flux case, then wave set-up case, and finally the

wind-only case. However, the maximum number of inundated points is not reached until the 15th

hour, showing that maximum spatial coverage will not necessarily occur at either the times of

landfall or of highest overland average surge.

For the Category 4 testing, the time series graphs (Figure 3-16) reveal an analogous result

to the Category 1 simulations. No overland inundation occurs until the seventh hour, at which

40

Figure 3-15: Time series hourly results for A) average overland storm tide levels and B) number

of inundated overland points for the Category 1 tests on ETPA basin. 
time all three forcing averages dramatically increase. All cases do see a brief decrease during the

ninth hour due to an approximate 25 percent weakening in the storm's maximum winds in the

eigth hour. This same depreciation occurs in the Category 1 storm, but it is more pronounced in

the Category 4 results because of the 1.75 scaling of the wind velocities. Like the Category 1

tests, overland averages reach maximum levels at the 13th hour in each case. The number of

inundated points follows a similar pattern as well, but the gap between the mass flux and windonly forcing tests is much less pronounced due to land topography. 

The overland surge results for Hurricane Andrew are shown in Figure 3-17. Unlike the

Tampa Bay simulations, the overland surge begins above zero because of the sea level datum (2

feet) used by SLOSH. The overall pattern of coastal inundation does closely follow the previous

41

Figure 3-16: Time series hourly results for A) average overland storm tide levels and B) number

of inundated overland points for the Category 4 tests on ETPA basin. 
testing, with a dramatic rise as the storm nears landfall followed by a gentle receding of flood

waters. The mass-flux-included case does show a dramatic increase in number of inundated

points, flooding greater than 60 percent more points than the unedited SLOSH test.

3.4 HURRICANE ANDREW HINDCAST

As mentioned in Section 1.4, the hindcast tests of Hurricane Andrew could not be

compared temporally to any real-time records since no NOAA buoys were active in the region at

the time. Other than qualitative looks at the derived storm surge maps, the only meaningful

quantitative comparison to be performed is of the storm surge levels recorded at Burger King

Headquarters at SW 184th St. in Miami, Florida. Andrew's maximum surge level of 16.9 feet

42

Figure 3-17: Time series hourly results for A) average overland storm tide levels and B)

number of inundated overland points for Hurricane Andrew on HMIA basin.
(5.15 m) was recorded at this location. HMIA point 6042 was the closest grid position to this

place. Figure 3-18 is a time series of the surges computed with each forcing option at this

position. The maximum surge level for the wind-stress-only test is 4.00 m, whereas the levels

for the wave set-up and mass flux tests are 4.40 m and 4.53 m, respectively. The inclusion of just

wave set-up effects improved the prediction by 12 percent; inclusion both wave forcing

components improved the prediction by another 3 percent.

In summary, the results show that the effect of wave forcing in the storm surge model is

highly location dependent. Waves can have large effects, such as at point 9569 in the Miami

basin. At other positions, however, wave forces yield no significant change to wind-only forcing

43

Figure 3-18: Surge levels at each time step at point 6042 in the HMIA basin for Hurricane

Andrew simulations of each forcing option. This point is the closest grid location to the Burger

King Headquarters on SW 184th Street in Miami, where maximum surge levels of 5.15 m were

recorded. 
predictions. Still at other locations, wave interactions reduce the surge levels. On a domainwide level, however, the inclusion of waves does result in larger maximum levels and more

inland inundation. Temporally, waves cause maximum levels to be reached slightly earlier, on

the order of one hour, for the storms tested. Also, for coastal locations, we typically see higher

surge levels after the storm has passed caused by greater overland inundation.

44
CHAPTER 4

CHAPTER 4

CONCLUSIONS

The two-way coupling of the SLOSH storm surge and the SWAN third-generation wave

models demonstrated that the addition of wave forcing can produce surges that are 10 to 30

percent higher than observed in the absence of this coupling. Moreover, some coastal locations

could expect a 50 to 100 percent increase in surge levels. Each wave component, wave set-up

effects and mass flux transport, yielded similar increases. However, these results are highly

dependent on coastal characteristics such as local bathymetry and geometry, as well as the

influence of bays and barrier islands. 

4.1 BATHYMETRY

Previous studies of wave effects on storm surge have found that waves influence surge

levels more on steep shelves than mild, gentle slopes (WEAVER, 2004). The opposite trend was

found in our study. By every measure, the wave forcing in similar strength hurricanes produced

nearly identical or higher surges on the gentle slope of the Gulf. The wave stress outputs from

SWAN had maximum values that are five times greater than those observed for Hurricane

Andrew than the Category 4 storm at Tampa Bay on their respective SWAN Cartesian grids.

This is reasonable because the wave breaking zone is more narrow on the steep shelf leading to a

more intense localized zone. The differences between the two bay geometries, as well as the

diameter and forward speed of the storm, can explain the differences in the overall response of

the system. A balance between computational efficiency and adequate grid spacing is

sufficiently achieved as long as the surge and wave grids are of comparable resolution so that no

momentum transfer from the wave to the surge is lost due to aliasing of the signal in the

45
interpolation process. Momentum is conserved on any size of control volumes, though second

order effects can generally be captured better on higher resolution grids. As the SLOSH surge

grids increase in resolution in the future, corresponding increases in the SWAN wave grids will

also be necessary. The optimal grid calibration for accurate prediction differs based on

individual basin and storm characteristics. Adequate resolution between the shoreline out to a

distance where the largest generated waves from the strongest hurricane simulation break would

be optimal for accurate inclusion of wave forces.

4.2 STORM STRENGTH 

The wave forces caused a larger percent increase in surge levels for the weaker storm.

Most of this difference occurs because of the relative importance of wind-stress in each case;

over mild slopes, wind stress contributes much more to surge levels. For instance, a 75 percent

growth of the maximum winds leads to a 250 percent increase in maximum surge levels. On the

other hand, stronger hurricanes produced bigger waves that break farther from the coast. Thus,

although the wave stress and mass flux transports are higher, they are imparted into the water

column sooner and, thus, dissipated at a higher rate. Hence, one could expect an optimal

condition of wave effects based on the storm strength over the same bathymetry. Wave effects

will grow as a depression turns into a hurricane but ultimately decline as the hurricane becomes a

behemoth storm. 

4.3. SIGNIFICANCE OF WAVE SET-UP AND MASS FLUX 

Wave set-up and mass flux are important contributers to the physics of storm surge

modeling, although they are often neglected in favor of computational efficiency. Wave stresses

46
caused an increase of 6 percent to the maximum surge levels; a combination of mass flux and

wave stresses yielded a 17 percent gain. This result is very near to the extent of SLOSH's

accepted error range. Individual storms and basins are likely to experience maximum level

increments much higher than these averages. 

We have shown that the impact of these forces can vary from location to location. Some

locations will experience a significant rise in surge levels, others will remain relatively

unchanged, while still other locations will see a decrease in storm surge. In the absence of

coupling the SWAN and SLOSH models, it would be an impossible task to identify which of the

above categories a specific location would be, a fact that testifies to the necessity of wave

inclusion. A blanket coefficient or linear combination could closely predict maximum surges

with wave forces, but neither option could adequately capture the complex interaction between

each forcing component and the basin's and storm's characteristics. 

Overland surge predictions are extremely valuable to evacuation planning, risk

assessment, and construction design for coastal infrastructure. In our tests, wave forcing raised

the average inundation levels between 10 and 35 percent. As many as twice as many grid points

became flooded when wave forcing was considered. For the Tampa Bay simulation, this change

was equivalent to approximately 90 square miles of additional inundation around the Bay.

Additionally, this extra inundation would cause a larger area to remain inundated longer as the

surge recedes, an important result for emergency relief plans.

4.4 HISTORICAL COMPARISON

Real-time records of sea level elevations from buoy data were not available for the

hindcast of Hurricane Andrew. However, we could compare the results from the three forcing

cases at the maximum surge location. Here, inclusion of mass flux and wave set-up effects

47
improved the prediction by 15 percent. A further improvement would be seen with more refined

grids in the storm surge model.

4.5 FUTURE STUDY

In addition to improving SLOSH's nearshore grid resolution, we plan to focus on other

basins and test storms to arrive at a better comprehension of the complex relationships between

wind and wave forcing. Additional testing and improvement of the bottom slip and drag

coefficients are needed to better represent the physics of storm surge. We also plan to install a

nested grid system in SWAN. A coarse resolution grid (at least 4 kilometer grid spacing) would

cover the entire SLOSH grid, and a fine resolution grid (2 kilometer spacing) would be fitted

nearshore to capture the momentum transfer from breaking waves. Such a system would

improve computational efficiency while preserving the general physics of hurricane-generated

waves.

48
APPENDIX A

The following files are the TRK files used during our simulations. The first file is the one

used for the Tampa Bay storms. The same TRK file (etpa_SLOSHtrk998) was used for the

Category 1 and 4 scenarios, only the maximum winds were scaled by a factor of 1.75 for the

stronger hurricane. The second TRK file represents a hindcast of Hurricane Andrew. The

columns for each file correspond to the following variables: track point, latitude, longitude, wind

speed (mph), storm direction and speed (knots), pressure change (from ambient) (mb), radius of

maximum winds (statute miles), actual STM points (MCINERNEY, 2006).

SLOSH Track ID: etpa_SLOSHtrk998

Derived from HRD Simulated Track etpa_storm0004388 (36 pts)

NAP----- 1 25.9500 84.350 13.00 25.42 31.11 20.24 1 ---NAP

 2 26.1200 84.260 12.67 29.26 31.37 20.40 2

 3 26.2800 84.160 16.99 35.59 30.62 20.43 3

 4 26.4800 84.000 16.98 35.55 29.87 20.49 4

 5 26.6800 83.840 17.54 34.19 29.12 20.54 5

 6 26.8900 83.680 16.96 35.45 28.37 20.59 6

 7 27.0900 83.520 16.95 35.40 27.62 20.64 7

 8 27.2900 83.360 17.51 34.04 26.87 20.69 8

 9 27.5000 83.200 12.14 37.20 26.83 20.85 9

 10 27.6400 83.080 13.06 37.47 26.79 20.97 10

 11 27.7900 82.950 12.69 35.24 26.76 21.10 11

 12 27.9400 82.830 12.68 35.20 26.76 21.22 12

 13 28.0900 82.710 12.68 35.17 26.35 21.28 13

 14 28.2400 82.590 12.67 35.13 25.96 21.34 14

 15 28.3900 82.470 11.95 41.29 25.56 21.41 15

 16 28.5200 82.340 12.36 43.37 25.18 21.45 16

 17 28.6500 82.200 12.35 43.33 24.80 21.50 17

 18 28.7800 82.060 11.93 41.18 24.42 21.54 18

49
 19 28.9100 81.930 12.33 43.26 24.05 21.59 19

 20 29.0400 81.790 12.45 39.02 23.69 21.64 20

 21 29.1800 81.660 15.89 49.27 23.33 21.69 21

 22 29.3300 81.460 15.88 49.23 22.98 21.77 0

 23 29.4800 81.260 16.32 47.35 22.63 21.84 1

 24 29.6400 81.060 16.31 50.51 22.94 22.05 2

 25 29.7900 80.850 16.30 47.26 23.24 22.26 3

 26 29.9500 80.650 15.82 49.05 23.55 22.47 4

 27 30.1000 80.450 13.11 46.74 23.52 22.62 5

 28 30.2300 80.290 13.09 50.69 23.49 22.74 6

 29 30.3500 80.120 13.09 46.67 23.46 22.86 7

 30 30.4800 79.960 13.07 50.62 23.43 22.98 8

 31 30.6000 79.790 13.07 46.59 23.40 23.11 9

 32 30.7300 79.630 13.05 50.55 23.37 23.23 10

 33 30.8500 79.460 15.02 71.17 23.13 23.31 11

 34 30.9200 79.220 14.81 73.69 22.88 23.33 12

 35 30.9800 78.980 14.80 73.68 22.64 23.34 13

 36 31.0400 78.740 0.00 0.00 22.40 23.35 14

 37 31.0400 78.740 0.00 0.00 22.40 23.35 15

 38 31.0400 78.740 0.00 0.00 22.40 23.35 16

 39 31.0400 78.740 0.00 0.00 22.40 23.35 17

 40 31.0400 78.740 0.00 0.00 22.40 23.35 18

 41 31.0400 78.740 0.00 0.00 22.40 23.35 19

 42 31.0400 78.740 0.00 0.00 22.40 23.35 20

 43 31.0400 78.740 0.00 0.00 22.40 23.35 21

 44 31.0400 78.740 0.00 0.00 22.40 23.35 22

 45 31.0400 78.740 0.00 0.00 22.40 23.35 23

 46 31.0400 78.740 0.00 0.00 22.40 23.35 24

 47 31.0400 78.740 0.00 0.00 22.40 23.35 25

 48 31.0400 78.740 0.00 0.00 22.40 23.35 26

 49 31.0400 78.740 0.00 0.00 22.40 23.35 27

 50 31.0400 78.740 0.00 0.00 22.40 23.35 28

50
 51 31.0400 78.740 0.00 0.00 22.40 23.35 29

 52 31.0400 78.740 0.00 0.00 22.40 23.35 30

 53 31.0400 78.740 0.00 0.00 22.40 23.35 31

 54 31.0400 78.740 0.00 0.00 22.40 23.35 32

 55 31.0400 78.740 0.00 0.00 22.40 23.35 33

 56 31.0400 78.740 0.00 0.00 22.40 23.35 34

 57 31.0400 78.740 0.00 0.00 22.40 23.35 35

 58 31.0400 78.740 0.00 0.00 22.40 23.35 36

 59 31.0400 78.740 0.00 0.00 22.40 23.35 37

 60 31.0400 78.740 0.00 0.00 22.40 23.35 38

 61 31.0400 78.740 0.00 0.00 22.40 23.35 39

 62 31.0400 78.740 0.00 0.00 22.40 23.35 40

 63 31.0400 78.740 0.00 0.00 22.40 23.35 41

 64 31.0400 78.740 0.00 0.00 22.40 23.35 42

 65 31.0400 78.740 0.00 0.00 22.40 23.35 43

 66 31.0400 78.740 0.00 0.00 22.40 23.35 44

 67 31.0400 78.740 0.00 0.00 22.40 23.35 45

 68 31.0400 78.740 0.00 0.00 22.40 23.35 46

 69 31.0400 78.740 0.00 0.00 22.40 23.35 47

 70 31.0400 78.740 0.00 0.00 22.40 23.35 48

 71 31.0400 78.740 0.00 0.00 22.40 23.35 49

 72 31.0400 78.740 0.00 0.00 22.40 23.35 50

 73 31.0400 78.740 0.00 0.00 22.40 23.35 51

 74 31.0400 78.740 0.00 0.00 22.40 23.35 52

 75 31.0400 78.740 0.00 0.00 22.40 23.35 53

 76 31.0400 78.740 0.00 0.00 22.40 23.35 54

 77 31.0400 78.740 0.00 0.00 22.40 23.35 55

 78 31.0400 78.740 0.00 0.00 22.40 23.35 56

 79 31.0400 78.740 0.00 0.00 22.40 23.35 57

 80 31.0400 78.740 0.00 0.00 22.40 23.35 58

 81 31.0400 78.740 0.00 0.00 22.40 23.35 59

 82 31.0400 78.740 0.00 0.00 22.40 23.35 60

51
 83 31.0400 78.740 0.00 0.00 22.40 23.35 61

 84 31.0400 78.740 0.00 0.00 22.40 23.35 62

 85 31.0400 78.740 0.00 0.00 22.40 23.35 63

 86 31.0400 78.740 0.00 0.00 22.40 23.35 64

 87 31.0400 78.740 0.00 0.00 22.40 23.35 65

 88 31.0400 78.740 0.00 0.00 22.40 23.35 66

 89 31.0400 78.740 0.00 0.00 22.40 23.35 67

 90 31.0400 78.740 0.00 0.00 22.40 23.35 68

 91 31.0400 78.740 0.00 0.00 22.40 23.35 69

 92 31.0400 78.740 0.00 0.00 22.40 23.35 70

 93 31.0400 78.740 0.00 0.00 22.40 23.35 71

 94 31.0400 78.740 0.00 0.00 22.40 23.35 72

 95 31.0400 78.740 0.00 0.00 22.40 23.35 1

 96 31.0400 78.740 0.00 0.00 22.40 23.35 2

 97 31.0400 78.740 0.00 0.00 22.40 23.35 3

 98 31.0400 78.740 0.00 0.00 22.40 23.35 4

 99 31.0400 78.740 0.00 0.00 22.40 23.35 5

 100 31.0400 78.740 0.00 0.00 22.40 23.35 6

 1 23 1 IBGNT ITEND JHR

HR1200 20 SEP 2333 NEAREST APPROACH, OR LANDFALL, TIME

 0.0 0.0 SEA AND LAKE DATUM

 HURRICANE "ANDREW": BRJ "BEST TRACK #4" DATUMS = 2.0 FT.

 DELTA-P = 87MB:(1013-926 MB); RMW= 09. ST MI.; WEST 18 MPH.

 1 25.3914 62.252 15.59 270.05 71.00 9.00 1

 2 25.3918 62.502 15.59 270.05 71.00 9.00 2

 3 25.3922 62.752 15.59 270.05 71.00 9.00 3

 4 25.3927 63.002 15.59 270.05 71.00 9.00 4

 5 25.3931 63.252 15.59 270.05 71.00 9.00 5

 6 25.3935 63.502 15.59 270.05 71.00 9.00 6

 7 25.3939 63.752 15.59 270.05 71.00 9.00 7

 8 25.3943 64.001 15.59 270.05 71.00 9.00 8

52
 9 25.3947 64.251 15.59 270.05 71.00 9.00 9

 10 25.3951 64.501 15.59 270.05 71.00 9.00 10

 11 25.3955 64.751 15.59 270.05 71.00 9.00 11

 12 25.3959 65.001 15.59 270.05 71.00 9.00 12

 13 25.3963 65.251 15.59 270.05 71.00 9.00 13

 14 25.3967 65.501 15.59 270.05 71.00 9.00 14

 15 25.3971 65.751 15.59 270.05 71.00 9.00 15

 16 25.3976 66.001 15.59 270.05 71.00 9.00 16

 17 25.3980 66.251 15.59 270.05 71.00 9.00 17

 18 25.3984 66.500 15.59 270.05 71.00 9.00 18

 19 25.3988 66.750 15.59 270.05 71.00 9.00 19

 20 25.3992 67.000 15.59 270.05 71.00 9.00 20

 21 25.3996 67.250 15.59 270.05 71.00 9.00 21

 22 25.4000 67.500 15.59 270.05 71.00 9.00 0

 23 25.4000 67.750 15.59 270.05 71.00 9.00 1

 24 25.4000 68.000 15.60 270.05 71.00 9.00 2

 25 25.4000 68.250 15.60 270.05 71.00 9.00 3

 26 25.4000 68.500 15.60 270.05 71.00 9.00 4

 27 25.4000 68.750 15.60 270.05 71.00 9.00 5

 28 25.4000 69.000 15.60 270.05 71.00 9.00 6

 29 25.4000 69.250 15.60 270.05 71.00 9.00 7

 30 25.4000 69.500 15.60 270.05 71.00 9.00 8

 31 25.4000 69.750 15.60 270.05 71.00 9.00 9

 32 25.4000 70.000 15.59 270.05 71.00 9.00 10

 33 25.4000 70.250 15.59 270.05 71.00 9.00 11

 34 25.4000 70.500 15.58 270.06 71.00 9.00 12

 35 25.4000 70.750 15.58 270.06 71.00 9.00 13

 36 25.4000 71.000 15.58 270.06 71.00 9.00 14

 37 25.4000 71.249 15.59 270.05 71.00 9.00 15

 38 25.4000 71.499 15.61 270.05 71.00 9.00 16

 39 25.4000 71.749 15.63 270.05 71.00 9.00 17

 40 25.4000 72.000 15.65 270.05 71.00 9.00 18

 41 25.4000 72.251 15.66 270.05 71.00 9.00 19

53
 42 25.3999 72.502 15.64 270.05 71.00 9.00 20

 43 25.3999 72.753 15.61 270.05 71.00 9.00 21

 44 25.3999 73.003 15.55 270.06 71.00 9.00 22

 45 25.3999 73.252 15.47 270.07 71.00 9.00 23

 46 25.4000 73.500 15.39 270.08 71.00 9.00 24

 47 25.4001 73.747 15.37 270.08 71.00 9.00 25

 48 25.4002 73.993 15.43 270.08 71.00 9.00 26

 49 25.4003 74.241 15.56 270.06 71.00 9.00 27

 50 25.4003 74.490 15.77 270.03 71.00 9.00 28

 51 25.4002 74.743 16.05 270.00 71.00 9.00 29

 52 25.4000 75.000 16.40 269.96 71.00 9.00 30

 53 25.3996 75.263 16.77 269.96 71.00 9.00 31

 54 25.3992 75.532 17.15 269.98 71.00 9.00 32

 55 25.3988 75.807 17.55 270.04 71.00 9.00 33

 56 25.3988 76.088 17.95 270.14 71.00 9.00 34

 57 25.3991 76.376 18.36 270.25 71.00 9.00 35

 58 25.4000 76.670 18.77 270.39 71.00 9.00 36

 59 25.4016 76.971 19.09 270.54 71.00 9.00 37

 60 25.4038 77.277 19.31 270.68 71.00 9.00 38

 61 25.4068 77.586 19.43 270.82 71.00 9.00 39

 62 25.4105 77.898 19.45 270.96 71.00 9.00 40

 63 25.4149 78.210 19.36 271.11 71.00 9.00 41

 64 25.4200 78.520 19.18 271.27 71.00 9.00 42

 65 25.4258 78.827 18.96 271.46 73.67 9.00 43

 66 25.4325 79.131 18.70 271.68 76.33 9.00 44

 67 25.4402 79.431 18.41 271.94 79.00 9.00 45

 68 25.4489 79.726 18.08 272.23 81.67 9.00 46

 69 25.4588 80.016 17.73 272.56 84.33 9.00 47

NAP----- 70 25.4700 80.300 17.38 272.97 87.00 9.00 48 ---NAP

 71 25.4828 80.578 17.21 273.53 82.83 9.50 49

 72 25.4979 80.854 17.27 274.27 78.67 10.00 50

 73 25.5162 81.130 17.57 275.14 74.50 10.50 51

 74 25.5388 81.411 18.09 276.12 70.33 11.00 52

54
 75 25.5664 81.700 18.84 277.14 66.17 11.50 53

 76 25.6000 82.000 19.76 278.11 62.00 12.00 54

 77 25.6400 82.314 20.53 278.75 61.67 12.00 55

 78 25.6849 82.640 21.09 279.03 61.33 12.00 56

 79 25.7324 82.975 21.43 278.99 61.00 12.00 57

 80 25.7805 83.315 21.55 278.66 60.67 12.00 58

 81 25.8271 83.658 21.46 278.01 60.33 12.00 59

 82 25.8700 84.000 21.20 277.13 60.00 12.00 60

 83 25.9077 84.339 20.96 276.33 60.00 12.00 61

 84 25.9408 84.674 20.78 275.72 60.00 12.00 62

 85 25.9704 85.007 20.66 275.31 60.00 12.00 63

 86 25.9977 85.339 20.60 275.10 60.00 12.00 64

 87 26.0238 85.669 20.60 275.10 60.00 12.00 65

 88 26.0500 86.000 20.64 275.28 60.00 12.00 66

 89 26.0771 86.331 20.69 275.49 60.00 12.00 67

 90 26.1054 86.663 20.74 275.69 60.00 12.00 68

 91 26.1348 86.996 20.80 275.90 60.00 12.00 69

 92 26.1654 87.330 20.85 276.11 60.00 12.00 70

 93 26.1971 87.665 20.90 276.31 60.00 12.00 71

 94 26.2300 88.000 20.90 276.31 60.00 12.00 72

 95 26.2629 88.335 20.90 276.31 60.00 12.00 1

 96 26.2957 88.671 20.90 276.31 60.00 12.00 2

 97 26.3286 89.007 20.90 276.31 60.00 12.00 3

 98 26.3615 89.342 20.90 276.31 60.00 12.00 4

 99 26.3944 89.678 20.90 276.31 60.00 12.00 5

 100 26.4272 90.014 20.90 276.31 60.00 12.00 6

 61 77 70 IBGNT ITEND JHR

HR0500 24 AUG 1992 NEAREST APPROACH, OR LANDFALL, TIME

 2.0 2.0 SEA AND LAKE DATUM

55
APPENDIX B

The following is an example of the input file used to implement SWAN for the Tampa

Bay tests. From this input file, SWAN reads the appropriate water level, wind, and bathymetric

data and then calculates the wave fields at the prescribed time intervals.

$*************************START-UP************************

$

PROJ 'tampa.cat1' 'HJ'

$

SET 0.0 90 DEPMIN=0.05 200 2 GRAV =9.81 RHO=1025 CARTESIAN

$

MODE NONSTATIONARY TWODIMENSIONAL

$

COORDINATE CARTESIAN

$

$**********************MODEL DESCRIPTION******************

$

$-----------COMPUTATIONAL GRID--

CGRID REGULAR 0.0 0.0 0.0 333902.2 332838.7 180 180 CIRCLE 12 0.03138 0.4177248 

READGRID COORDINATES 1.0 'slosh.loc' 3 0 0 FREE

$-----------INPUT GRID--

INPGRID BOTTOM REGULAR 0.0 0.0 0.0 180 180 1855.012 1849.104

READINP BOTTOM 1.0 'slosh.bot' 3 0 FREE

$

INPGRID WIND REGULAR 0.0 0.0 0.0 180 180 1855.012 1849.104 NONSTATIONARY

20080820.1200 1 HR 20080821.0900

$

READINP WIND 1.0 SERIES 'slosh.wind' 3 0 FREE

$

INPGRID WLEVEL REGULAR 0.0 0.0 0.0 180 180 1855.012 1849.104 NONSTATIONARY

20080820.1200 1 HR 20080821.0900

$

READINP WLEVEL 1.0 SERIES 'slosh.wlevel' 3 0 FREE

$-----------BOUNDARIES-----

$

$ physics:

$

56
SETUP

GEN3

FRICTION

TRIAD 0.1 2.0 0.2 0.01

QUAD

PROP 

BSBT

$

NUMERIC ACCUR DREL = 0.02 DHOVAL= 0.02 DTOVAL= 0.02 NPNTS = 90.0 NONSTAT

100 0.1

$

$

$****************************OUTPUT***********************

$

OUTPUT OPTIONS ' ' TABLE 16 BLOCK 6 121

FRAME 'OUT' 0.0 0.0 0.0 333893.3 332838.7 180 180

TABLE 'OUT' NOHEAD 'out' XP YP WIND FORCE DEPTH HSIGN DIR PER RTP

OUTPUT 20080820.1200 1 HR

$

COMPUTE STAT 20080820.1200

COMPUTE NONSTAT 20080820.1200 1 HR 20080821.0900

$

STOP

57
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