Modeling System for 

Applications to Very-Low 

Probability Events and Flood 

Response

Ty Wamsley, PhD

Chief, Flood and Storm Risk Reduction Division

U.S. Army Engineer Coastal and Hydraulics Laboratory

January 31, 2013

Workshop on Probabilistic Flood Hazard 

Assessment

Panel 7: Extreme Storm Surge for 

Coastal Areas
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Storm Surge

 Function of 

► Storm Characteristics (e.g. 

central pressure, size, 

forward speed, angle of 

storm heading, Holland B 

parameter, rainfall)

► Tides and river flows at 

time the storm approaches 

the coast

► Shelf and coastal 

landscape over which the 

storm surge propagates. 
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Simulation Requirements*

 Basin to shelf to floodplain domains to simplify 

boundary conditions and capture shelf waves

 Sufficient resolution of the physical system 

(topography/bathymetry and land cover)

 Consideration of all terms contributing to the 

surge:

► Coupled multi-process system including winds, wind 

waves, tides, rivers, rainfall-runoff

 Validated with historical data in the area of 

interest

*Needed for objective estimates of flood magnitude 

and to properly reflect flood duration
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Physical System

Mesh resolution From 28 km down to 23 m

Approximately 

98% of all 

elements are 

in the study 

area.

 Appropriate definition of physical 

system while maintaining reasonable 

computational efficiency is facilitated 

through the application of unstructured 

surge meshes.
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Resolution

Sub-grid features
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Parameterization of Land Cover

Manning’s n

values
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Coastal Storm-Modeling System

(CSTORM)

Application of multi-scale, highly skilled numerical models in a tightly 

integrated modular modeling system with user friendly interfaces

The CSTORM-MS was “born” during IPET and provides 

for a robust, standardized approach to characterizing 

storm hazards.

Sample CSTORM-MS Workflow

CSTORM-Database with 

StormSim tools
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Without Project With Project

Sea Level Rise 0.35 m Existing Water LevelSea Level Rise 0.35 m Existing Water Level

8

1 ft SLR => 3 ft WL 

increase

1 ft SLR => 2 ft WL 

increase
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The Earth System Modeling Framework

 ESMF are open source tools with multi-agency buy in.

 Having models ESMF compliant makes them readily available to be 

linked with each other and with other agencies’ ESMF compliant models.

 This leads to expanded collaborations and community development.

Sponsored 

By

9

Code is organized into three 

distinct phases: initialization, 

run, and finalization. 
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Advantages of Framework

•Reduced required human operator time by 

75%, cut required computer simulation 

time by 50%. (Ex: Coastal Louisiana Storm 

with half-plane waves: Old System – 3 

days, New System – 4 hrs)

•Allows for models to be at appropriate 

resolution and on appropriate domains for 

the process being modeled.

•Models can be improved (by the 

development community) and easily pulled 

back into the framework.

•New models can be implemented allowing 

for multiple model options.

•Easily expandable to include 

rainfall/runoff/urban flooding models (e.g. 

GSSHA)

Rainfall 

/Runoff

/Urban
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Synthetic Storms

 Joint Probability Method with 

Optimal Sampling (JPM-OS).

► Paucity of storm data necessitates 

using numerical simulations of 

synthetic storms and joint probability 

methods to estimate the expected 

probability density functions and 

cumulative density functions

► Optimal sampling minimizes the 

number of required storm 

simulations by limiting the parameter 

space based on known physical 

constraints and improved 

interpolations.

► For very low probability storms, we 

look for asymptotic upper limits of 

parameters affecting storm surge
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Hurricane Parameters with Asymptotic Limits

Storm Size (Φ2): As noted by Irish and Resio (2010), when the storm 

size becomes as large as the region of primary surge generation, 

additional increases in storm size do not produce substantial 

increases in storm surge. 

max 1 2 max 3 0 4 5 ( ) ( ) ( ) () () η θ =Φ ∆ Φ Φ − Φ Φ p R xx vf f
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Hurricane Parameters with Asymptotic Limits

Landfall Location (Φ3): Maximum surge occurs near the location 

where the maximum winds come ashore with surge levels 

. 

max 1 2 max 3 0 4 5 ( ) ( ) ( ) () () η θ =Φ ∆ Φ Φ − Φ Φ p R xx vf f

dimensionless 

alongshore surge

deviation between a site and the landfall location divided by 

the radius to maximum winds

From Irish et al. 2008
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Hurricane Parameters with Asymptotic Limits

Forward Speed (Φ4): Two physical mechanisms which tend to have 

opposite effects on surge levels. On one hand, as the storm 

speeds up, wind speeds inside a hurricane increase due to the 

contribution of these background winds. On the other hand, as 

the storm speeds up the time winds blow over the surge 

generation area is decreased. As might be expected in such a 

situation, the result of combining these two effects is that the 

surges typically increase to a maximum value at some 

intermediate forward speed and decrease monotonically to either 

side of this maximum value. 

max 1 2 max 3 0 4 5 ( ) ( ) ( ) () () η θ =Φ ∆ Φ Φ − Φ Φ p R xx vf f
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Hurricane Parameters with Asymptotic Limits

Angle of Approach (Φ5): Changes in track angle increase peak surge 

by no more than 10% with respect to a shore normal approach. 

. 

max 1 2 max 3 0 4 5 ( ) ( ) ( ) () () η θ =Φ ∆ Φ Φ − Φ Φ p R xx vf f

From Irish et al. 2008
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Very Low Probability Events

max 1 2 max 3 0 4 5 ( ) ( ) ( ) () () η θ =Φ ∆ Φ Φ − Φ Φ p R xx vf f

Pressure Differential (Φ1): Does not appear to have a clear upper 

limit. 
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Very Low Probability Events

max 1 2 max 3 0 4 5 ( ) ( ) ( ) () () η θ =Φ ∆ Φ Φ − Φ Φ p R xx vf f

MPI Concept: The lowest North Atlantic Values appear to support a 

value around 880 mb as the lowest central pressure for the range 

of water temperatures in the Gulf of Mexico 

Schade 2000 Tonkin et al. 2000
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Very Low Probability Events

max 1 2 max 3 0 4 5 ( ) ( ) ( ) () () η θ =Φ ∆ Φ Φ − Φ Φ p R xx vf f

Pressure Differential (Φ1): Another factor suggesting a natural limit is 

that wind speed profile data recently collected in real hurricanes 

(e.g. Powell et al. 2003, and Powell 2006) indicate that wind drag 

coefficients are not constant but rather increase until wind speeds 

of approximately 30-40 m/sec are reached and then the drag 

coefficient is reduced for increasing wind speeds above this 

range of values. Capping or reducing the wind drag coefficient 

results in a nonlinear surge response to further increase in 

pressure differential. 

Schade 2000 Tonkin et al. 2000
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Synopsis of Simulation Approach 

for Very Low Probability Storms

 Use near MPI values for central pressure and 

large Rmax values (e.g. 880 and 870 mb and 

30,45 nm, respectively for the Gulf of Mexico)

 Set track to correspond to expected position of 

maximum surge

 Allow speed and track angle vary consistent with 

expected large intense storms

 Use state of the art modeling system with good 

resolution and physics to simulate the storm set 

including inland propagation of surge
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CSTORM-DB

20

System: Archive, database and Google Map 

based web application

Authoritative measurements

Authoritative high-fidelity model results

Query/sort capability

Plotting

StormSim: Extremal statistical analysis Maximum WL (NAVD88) Hurricane Isaac:

Measured ~14 ft

CSTORM-DB 13 ft

LSU - Coastal Emergency

Risk Assessment 13ft 

NHC-NOAA 7 ft

Surge Forecasting Capability

Sandy
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 CSTORM-DB stores a large suite of

basis hurricane scenarios that 

cover statistical range of events

► Tracks and landfall locations

► Hurricane characteristics (min central 

pressure, forward speed, radius of 

max winds, etc.)

► Regional high-fidelity wave and surge 

response (e.g. CSTORM-MS)

 Use basis scenarios as support for 

surrogate model to rapidly and 

accurately predict inundation for 

any new hurricane scenario

► Radial basis function weighted least 

squares moving average

 160°

 W 158°

 W 156°

 W 154°

 W 

 18°

 N 

 20°

 N 

 22°

 N 

Basis hurricane scenarios 

New hurricane scenario 

Expedient Hurricane Inundation 

Tool to facilitate 

flood fighting to 

mitigate hazard
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 Formalized and Generalized Coastal Storm Statistics 

Tools for PFHA

► JPM-OS: Joint Probability Method-Optimal Sampling for Tropical 

Storms

► StormSim-ES: Traditional parametric marginal and joint 

probability method

► Empirical Simulation Technique (EST): Nonparametric 

bootstrap with re-sampling techniques

► Wave/water level Empirical Lifecycle Simulation (WELS): 

Empirical joint probability to generate continuous long-term time 

series of any number of storm parameters 

StormSim
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Questions?
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Parameterization of 

Frictional Resistance

 Wind Reduction

► Winds in ADCIRC and STWAVE are reduced to 

account for higher surface roughness through a 

directional land masking procedure

0.0706

0

0

















=

land

marine

z

z

fr

Roughness length scales

g

C W

z c d

marine

2

10

0

α

=

αc=0.18 (Charnock parameter)

30 0

'

0

d

z z = land − As inundation takes place, 

roughness is reduced for '

0 0marine

z z ≥

Varies w/land cover and 

quantified by FEMAHAZUS study (NLCD)
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Parameterization of 

Frictional Resistance

 Wind Reduction

► Winds in ADCIRC and STWAVE are reduced to 

account for higher surface roughness through a 

directional land masking procedure
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Parameterizations of 

Frictional Resistance

 Wind Reduction

► A canopy is applied to areas classified as NLCD/GAP 

forest precluding momentum transfer from the wind 

fields to the water column
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ADCIRC

Natural feature impacts on surge:

► Depth (surge gradient is inversely proportional to 

depth)

► Wind 

► Bottom Friction (through Manning formulation) slows 

surge propagation

• Momentum equations use standard quadratic 

parameterization for bottom stress and applies the following 

bottom friction coefficient:

3

1

2

d

n Cf = g
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Evolution of the Modeling System

 New Workflow / Coupler: Reduced required human 

operator time by 75%, cut required computer 

simulation time by 50%. (Ex: Coastal Louisiana 

Storm with half-plane waves: Old System – 3 days, 

New System – 4 hrs)

 Improved Coupling: STWAVE gets feedback 

response.

 Upgraded Full-Plane STWAVE: Computations 

parallel in space (reduced STWAVE full-plane 

simulation times by 97%). 

 New and/or Improved GUIs for Coupler, PBL, WAM, 

STWAVE. Models can be set up on PCs and 

executed either on a PC or an HPC.

 Morphology Evolution has been developed and is 

being tested.