Port of Houston Challenges and Issues - University of Houstonhurricane.egr.uh.edu/sites/hurricane.egr.uh.edu/... · well as everything in-between. With advances in geospatial technology,

Proceedings THC-IT-2013 Conference & Exhibition

I-1

Port of Houston Challenges and Issues

Colonel Len Waterworth,

Executive Director, Port Authority of Houston, Houston, Texas

Introduction (Connection with UHSBDC)

During the past eight years, the Port of Houston Authority has worked with the UHSBDC

center to offer small businesses workshops, consulting and research to encourage growth

and employee training for small businesses.

In April, PHA participated in a highly successful import/export workshop. The UHSBDC

provided one of the premier instructors – a small business owner and former Exporter of

the Year - for this program, as well as a manual that attendees took away.

We have used UHSBDC’s up-to-date facilities for Port University and have always

received good reviews from our small businesses and a warm welcome from the staff.

Over 11 years, 364 million dollars (or 42 percent) has been awarded to eligible small

businesses through PHA’s Small Business Development Program.

More than 1,060 registered small businesses working with our program.

Since 2002, Small Business has conducted nearly 100 forums, reaching out to more than

5,000 firms.

National Export Initiative

In 2010 State of the Union Address, President Barack Obama unveiled the National

Export Initiative in concert with his announcement of an ambitious goal of doubling U.S.

exports within five years.

Long before that challenge was put before America, Texas ports, particularly the Port of

Houston, had strong export programs.

Texas has led the nation in exports each year for the past 11 years, with more than $265

billion in goods reaching consumers worldwide.

Houston is home to the state’s largest port and last year’s No. 1 exporting port in the

nation.

Trends expected to continue as shale plays in the oil & gas industry will have spinoff

effect on plastics and resin production, increasing plastic resin containerized exports.

Resins are No. 1 export commodity through Port Authority container terminals.

Port Authority & Global Trade

Port of Houston Authority is a key player in the global trade arena.

Three primary trade lanes connect U.S. with the rest of the world – Transpacific (bilateral

trade lanes connecting West Coast with Asia); Transatlantic (connecting the densely

populated East Coast with Europe, the Middle East and Africa) and the north-south


I-2

Transamerica lanes that transit the Gulf of Mexico, connecting the U.S. with Central and

South America.

Because of its central location, huge consumer base and its housing of the world’s second

largest petrochemical complex, Houston captures most of the cargo using those north-

south trade lanes.

Houston dominates container cargo segment, holding nearly 70% of market share along

U.S. Gulf Coast. Houston is attractive to shippers because of relatively low labor costs,

favorable business climate and its position as a leading exporter contributes to larger

profits per inbound-outbound containers. (Note: That’s because most containers handled

by East Coast and West Coast ports are full coming inbound and shipped empty on the

outbound side.)

The average income from a container leaving Shanghai going to the West Coast

generates 10 times more than an outbound container returning to Shanghai. In Houston,

that ratio is closer to 4-to-1.

The Port of Houston Authority’s heavy-cargo handling capabilities have helped it attain

the position of the nation’s No. 1 port in break bulk cargo.

The key to further growth is our port’s continued development of diverse cargo services.

Panama Canal

Completion of $5.25 billion expansion project expected in 2015. Project involves

construction of third set of locks designed to handle world’s largest vessels.

No one knows what effect this expansion will have on the amount of cargo coming

through Houston, but the outlook is promising because:

Asia is the fastest growing segment of Houston’s trade.

Although the third set of locks not yet completed, Panamanian officials already studying

the feasibility of constructing a fourth set.

Houston’s continued population/consumer growth makes it a natural port of call.

Expansive population base is good for the country because Gross Domestic Product is

based on consumption.

Challenge: Convincing owners to build warehouses in Houston to create capacity in the

supply chain -- then selling shippers on Houston as the smart choice to call.


I-3

Framework for Developing a Real-Time Broad-Area

Disaster Management System

Olufemi A. Omitaomu1,2

1Computational Sciences and Engineering Division

Oak Ridge National Laboratory, Oak Ridge, TN 37831. 2Department of Industrial and Systems Engineering

University of Tennessee, Knoxville, TN 37996.

Phone: (865) 241-4310, E-mail: [email protected]

ABSTRACT

When disaster strikes, effective incident management and response coordination is essential to

ensuring the resilience of critical infrastructure. This, in turn, depends on the availability of

critical infrastructure data, as well as geospatial modeling and simulation capabilities, that can

complement the decision making process at various stages of the disaster. Hence, disaster

consequence management organizations should have access to the best available geospatial

technical expertise, global and regional datasets, and modeling and analytical tools. However, an

optimal combination of data assets and modeling expertise are often beyond the resources

available internally within a single agency/county/state, but can be accessed by leveraging

existing investments by the federal, state, and county governments as well as community-based

NGOs. This collaboration provides an opportunity to develop a unified framework for

emergency response. Such a framework would become a platform for sharing data and model

outputs. For such a framework to be successful, though, there are at least three necessary

conditions that must be fulfilled:

1. Allow the sharing of data and information among subscribers without any proprietary

restrictions. This condition may necessitate the re-definition of partners to include NGOs,

utility companies, and other organizations. With the promise of better informatics for

disaster management, these new partners can justify their involvement and be more

willing to share data and knowledge. Such a community-based network can change the

calculus of partners’ engagement.

2. Support the need for real-time updates of data and models as the disaster evolves. The

unified framework will focus on all the stages (before, during, and after) of a disaster as

well as everything in-between. With advances in geospatial technology, the barrier to

disaster management is not the technology or lack of data or models, but connecting the

dots between data, models, and decision making in real-time.

3. Promote the development of a new kind of expertise for a truly all-hazards framework.

The ability to have all data and models within a single unified framework would promote

the integration of new expertise in order to understand correlations between events; and

thus, create an all-hazards framework.

If successful, the framework will allow the delivery of real-time solutions and in-depth analysis

of disasters to help emergency workers and government decision makers better understand and

manage their responses at different stages of major disasters. To achieve such a framework in the

State of Texas, the design, implementation, and application of a real-time all-hazards situational

awareness decision support system called the Energy Awareness and Resiliency Standardized

Services (EARSS) is presented. The EARSS system is developed at the Oak Ridge National

Laboratory as a global real-time disaster informatics for federal emergency workers and decision

mailto:[email protected]


I-4

makers. Even though the EARSS system is a federal emergency management product, its

application to disasters in the State of Texas will also be presented.

Copyright

This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-

AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States

Government retains and the publisher, by accepting the article for publication, acknowledges that

the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license

to publish or reproduce the published form of this manuscript, or allow others to do so, for

United States Government purposes.


I-5

Emergency Management Challenges and Planning Collaboration at

Texas Medical Center

Angela N. Smith

Emergency Management Planner

Texas Medical Center

John P. McGovern Campus

2450 Holcombe Boulevard, Suite 1

Houston, Texas 77021-2040

ABSTRACT

Chartered in 1945, the renowned Texas Medical Center has grown to the largest concentration of

medical assets in the world. Its campuses include 290 buildings on 1,345 acres where it ranks as the

eighth largest business district in the United States. In 2012, Texas Medical Center reported 7.2

million patient visits, 106,000 employees and 49,000 students. Some of Texas Medical Center’s

critical assets include 7,000 patient beds, 19 hospitals, and all five Level One Trauma Centers in the

region. The inability of even one trauma center to provide services, due to a man-made or natural

disaster, would have devastating effects on the delivery of emergency healthcare for the Southeast

Texas region. The Texas Medical Center campuses are vulnerable to many natural and man-made

hazards, and suffered $2 billion in damages from Tropical Storm Allison in 2001. This presentation

will share Texas Medical Center’s history, unique emergency management challenges, and the

collaboration of its 54 member institutions and emergency management partners to plan for effective

emergency response to these challenges.

INTRODUCTION AND HISTORY

On October 20, 1945, the Texas Medical Center started as a dream to create a medical center where

people from all walks of life could access the best healthcare anywhere. The largest employer in

Houston, its 54 member institutions are dedicated to the highest standards of patient care, research,

and education. Either not-for-profit or government, these institutions include 19 renowned hospitals;

three public health organizations; two universities; three medical schools; six nursing programs; two

pharmacy schools; a dental school; eight academic and research institutions; and 13 support

organizations. The Texas Medical Center Corporation was formed and exists today exclusively for

benevolent, charitable, and educational purposes to form the foundation and continuing support for a

"City of Medicine." Since its inception, the Texas Medical Center Corporation has sought to attract

academically oriented institutions dedicated to medical care, education, innovation, and research.

The Texas Medical Center Corporation acts as a “municipal government,” enforcing covenants and

restrictions, and coordinating activities among member institutions. It serves as a moderator of

campus-wide issues and concerns through 21 advisory councils comprised of representatives from

member institutions; oversees emergency preparedness planning; and is responsible for land

management, real estate, and master planning for long-term growth and development.

UNIQUE EMERGENCY MANAGEMENT CHALLENGES

With the growth of Texas Medical Center, emergency management challenges have multiplied. Over

half of the Southeast Texas region’s medical assets are owned and operated by Texas Medical Center

member institutions, including all five of the region’s Level One Trauma Centers and nearly half of

the patient beds. The citizens of the entire region depend on the highly specialized medical care that

Texas Medical Center member institutions provide. These highly specialized beds and services are


I-6

not duplicated in the near vicinity and are even more vital during and following disasters.

Consequently, the assets of Texas Medical Center are essential to the region and must be protected

and hardened to make sure they can be utilized during and after a disaster. The Texas Medical

Center campus is vulnerable to numerous natural and man-made hazards. Most buildings have open

access with frequent international and high-profile patients and visitors. The campus was built on

top of the Harris Gully Watershed and is prone to flooding. During Tropical Storm Allison in 2001,

the campus suffered $2 billion in damages, several hundred patients had to be relocated and recovery

took many months. With these devastating lessons learned, intense hazard mitigation planning

followed and Texas Medical Center’s first hazard mitigation plan, including 112 projects, was

approved by the Federal Emergency Management Agency in 2005. Over half of these projects have

since been completed and Texas Medical Center’s current list of 51 projects is included in the Harris

County and City of Houston’s Hazard Mitigation Plans.

EMERGENCY MANAGEMENT PLANNING COLLABORATION

To mitigate Texas Medical Center’s unique vulnerabilities, emergency management planning is

continual through collaborative institutional councils and regional planning efforts. Texas Medical

Center’s Security Directors, Emergency Directors, Flood Management Group and Flu Advisory

Councils meet regularly to identify concerns and share best practices. Texas Medical Center staff’s

participation in over a dozen regional emergency planning groups such as the Regional Healthcare

Preparedness Coalition, the Regional Catastrophic Preparedness Initiative, the Department of

Homeland Security Regional Resiliency Assessment Program, and the Urban Area Security Initiative

Critical Infrastructure and Key Resources Committee improve readiness to respond to mass

casualties, pandemic flu outbreaks, terrorism acts, hurricanes and other disasters. An annual Hazard

Mitigation Advisory Group meeting is held with Texas Medical Center’s member institutions and

other governmental and private vulnerability partners to identify new hazards and solutions. Annual

trainings are also hosted by Texas Medical Center including the Healthcare Hurricane Preparedness

Workshop for regional healthcare providers and the Flood Alert System training for the Texas

Medical Center Flood Management Group.

CONCLUSION

Texas Medical Center is the world’s largest medical center which is vulnerable to numerous natural

and man-made hazards. Over half of the Southeast Texas region’s medical assets are owned and

operated by Texas Medical Center member institutions, including all five of the region’s Level One

Trauma Centers and nearly half of the patient beds. The citizens of the entire region depend on the

highly specialized medical care that Texas Medical Center member institutions provide. These assets

are essential to the region and must be protected and hardened to make sure they can be utilized

during and after a disaster. Although the vulnerabilities of Texas Medical Center present unique

emergency management challenges, collaborative emergency management planning with the Texas

Medical Center member institutions and regional partners is phenomenal. Collaboration is the key to

effective emergency management planning success!


I-7

Real-Time Geospatial Infrastructure Modeling for Disaster Response and

Rapid Recovery:

NSF - Science, Engineering and Education for Sustainability (SEES) Research

Study

Craig Glennie1, Ioannis Kakadiaris

2, Shishir Shah

2, Cumaraswamy Vipulanandan

1

1Texas Hurricane Center for Innovative Technology (THC-IT)

2Department of Computer Science Department

University of Houston

Hurricanes and earthquakes are two of the most destructive natural disasters that impact

communities in the United States. Emergency response in the aftermath of a major event requires

the immediate assembly and dissemination of information on the size, shape, scale and nature of

the devastation caused by the natural disaster. Increasingly, accurate mapping data and GIS

software are being used to plan, coordinate and respond to a disaster using airborne or satellite

reconnaissance as the primary geospatial data source. Currently, however, the primary difficulty

with geospatial response is that the mapping and situational data being collected are not

immediately available, and there is often no quantitative change assessment of the geospatial

data with respect to reference models (i.e., pre-event information) to efficiently determine

significant areas of disaster event impact. Through a multi-year grant from the National Science

Foundation Hazard SEES (Science, Engineering, and Education for Sustainability) program, we

are developing of real-time geospatial infrastructure model for disaster response and rapid

recovery. Our specific objectives are to develop methods for: (1) real-time georeferencing of

geospatial data (along with an analysis of the obtainable accuracy): (2) rapidly (near real-time)

quantitative determination of change post-event using pre-event geospatial data as a benchmark,

and (3) dissemination of the detected change into actionable intelligence for emergency

responders using infrastructure models and disaster response tools. The proposal will focus on

the analysis of LiDAR (Light Detection and Ranging) data due to large amount of pre-event

(benchmark) LiDAR data available for urban areas and earthquake hazard zones and because of

the unique ability of LiDAR to directly measure high-resolution 3D change.


I-8

Addressing Forecast Uncertainty in Hurricane Response Plans

Dante Diaz, Impact Weather Inc.

Address: 12650 N Featherwood Dr, Houston, TX 77034

Phone: (281) 652-1000

Every type of business has its own hurricane response plan, and each of these plans has specific

actions that must be taken at certain times before impact. The problem is that it is often difficult

to determine when to escalate (or de-escalate) a hurricane response plan. This is particularly true

in the case of hurricanes when the track may shift a little left or right with each advisory update.

We know from past hurricane seasons what the forecast track and intensity errors with time are.

These data, along with data of the cyclone’s structure (wind radii), can help us create useful

information in developing objective guidance for a hurricane response plan.

The first step in any hurricane response plan is to identify the risk. If one waits until a

disturbance becomes a tropical depression to begin the response plan, it could be too late to

complete preparations. Consider that once every 2.3 years (1982-2011) a tropical depression

forms in the Gulf of Mexico and goes on to become a hurricane. Fortunately, hurricanes do not

form out of thin air; they require a pre-existence weather system for initiation. The initial

disturbances may form thousands of miles away only to develop later. Alternatively, formation

could occur close-in along an old frontal boundary pushing just offshore. We can identify those

disturbances that have the potential to develop and estimate when development may occur by

close examination of the model guidance. By identifying the potential for development early on

we can give our clients “a tap on the shoulder,” which may warrant some early actions to be

made in the hurricane response plan.

Another tool that can be used is what will be referred to as a Worst Case Scenario (WCS). The

WCS assumes a more direct path toward a client’s location and at a slightly faster forward speed.

The speed and intensity increases for the WCS are based on running averages from previous

hurricane seasons. This scenario suggests an earliest possible arrival time of the storm center or

any critical wind radii, such as the 39-mph and 58-mph radii. The main drawback to the WCS is

that, under certain circumstances, it can be unfeasible. For example, it would not be practical to

use WCS for a location in North Carolina when the hurricane is travelling westward in the Gulf

of Mexico. The WCS is most useful for locations that have a tropical cyclone heading in their

general direction.

Another tool used in creating a hurricane response plan is the Probability of Wind Impact (PWI).

This feature gives a probabilistic occurrence of sustained winds meeting or exceeding a specific

threshold. Most often, we employ the PWI of the 58-mph wind when creating a hurricane

response plan. That threshold is used because it roughly coincides with the start of significant

wind damage. The PWI of the 58-mph wind can be used as objective guidance in gauging the

risk of potentially damaging winds. It avoids the issues that arise when too much focus is placed

on the forecast track line alone. The PWI is sensitive to the size of the hurricane wind field,

whereas the forecast track and cone are not.


I-9

On its own the PWI can be used as objective guidance when escalating or de-escalating a

hurricane response plan. The combination of the PWI of the 58-mph wind and the WCS arrival

time of the 39-mph wind can be used to create an even more useful set of objective guidance for

a hurricane response plan. By plotting the hours until the WCS arrival of the 39-mph wind vs.

the PWI of the 58-mph wind for many locations and many hurricanes some patterns are revealed.

There is a distinct pattern for locations where the hurricane moves toward the location and

strikes. Another pattern exists in the situation where the hurricane moves toward the location and

then moves away. By comparing these two scenarios, we can discover a range of values for the

WCS arrival of the 39-mph wind and the PWI of the 58-mph that can be used to develop

objective guidance to escalate or de-escalate a hurricane response plan Incorporation of

objective guidance into a hurricane response plan can help to assure that a business will take the

proper actions at the proper times when a hurricane threatens.


I-10

Federal and State Disaster Coordination under ESF-10 and ESF 3: Lessons

Learned from Hurricane Ike

Williams Grimes

Texas General Land Office

Address: 1700 Congress Ave, Austin, TX 78701

Phone: (512) 463-5256

Following Hurricane Ike (September 13, 2008), a hot wash identified several areas that needed

improvement and better coordination relative to Emergency Support Function (ESF) 3

(Public Works and Engineering) and ESF-10 (Oil and Hazardous Materials Response). A work

group of operational personnel was initially formed from two state agencies, and two federal

agencies. They included the Texas Commission on Environmental Quality (TCEQ), Texas

General Land Office (GLO), US Environmental Protection Agency (EPA) Region 6 and the US

Coast Guard (USCG) District Eight Strike Team and District Response Advisory Team (DRAT).

The group held their first meeting on 27 April 2009, in Austin, and came to be called the

Natural Disaster Operational Workgroup, or NDOW. The Texas Parks and Wildlife

Department (TPWD), and the National Oceanographic and Atmospheric Administration

(NOAA) , have since joined the workgroup.

Over the next two years, the NDOW worked to address the needs identified in the how wash,

specifically…

1) One centralized data management system with agreed upon Data Quality Objectives.

EPA’s Response Manager is the centralized data management system to be utilized.

Data Quality Objectives have been created by all agencies to utilize during a natural

disaster event to fit all operational and reporting requirements. Standardized field data

sheets have been created to utilize in the field during the assessment, response and

closure process.

2) Standard Operating Procedures (SOP) and forms (Field Evaluation and Recovery

Procedures), and an ICS form (214B) have been provided to all agencies and are

available in hardcopy and in electronic format for laptops or PDA’s.

3) Co-location and coordination of agencies pre-landfall at pre-selected locations.

4) Formalized data management training and software delivery for field personnel.

5) Pre-identified staging areas and waste collection pad sites.

Currently, seven SOPs have been finalized by the workgroup, and include Rapid Needs

Assessment, Orphan Container Evaluation and Recovery, Oil Spill Assessment and Removal,

and Drinking Water and Waste Water Evaluation. Four field data sheets have been finalized for

field use. Since May 2010, four multi-day training sessions have been held in Corpus Christi,

Houston, Port Arthur and Harlingen/Brownsville. Personnel from TCEQ, GLO and the USCG

have been trained in EPAs Response Manager software, the SOPs and field data sheets.


I-11

The Eighth District United States Coast Guard directed that all USCG Sectors in Texas adopt

and utilize this protocol for all future federally-declared disasters. EPA Region 6 Federal On-

Scene Coordinator Nicolas Brescia led this effort from the beginning with support from START

contractor Weston Solutions, Inc. EPA also contributed the use of their “Response Manager”

software which is the foundation of the data collection and management system.

While this endeavor is relatively new, various aspects of it have been used in field-scale

responses over the last three years, (Deep water Horizon, Hurricane Irene and Hurricane Sandy)

and emergency responders in other regions are realizing its utility.

The NDOW has conducted regional training along the Texas coast the last three years prior to

the start of Hurricane season. A full scale deployment drill was conducted on July 17-19, 2012

in Corpus Christi, Texas. The drill was a multi-agency Hurricane Field Exercise utilizing

NDOW products. State and Federal Agencies participating included EPA, USCG, GLO, TCEQ

and TPWD. An Incident Management Team was formed with three operational branches

utilizing NDOW products and pre-designated staging areas.

Finally, if you would like to know more about this project, you can find it at

http://NDOW.net

Also, all of the documents have been added to our 2012 Texas Coastal Oil Spill Planning and

Response Toolkit.

http://ndow.net/


I-12

Mobile Apps for Emergency Situation Awareness

During Hurricane and Disaster

Thomas C. Chen, PE, Ph.D.

Director, RFID and Sensor Research Lab

Department of Industrial Engineering,

University of Houston

Abstract

Nowadays, people are surrounded by mobile devices, such as smart phones, tablet, and others

that allow them to get access to Internet and link to social networks, such as Facebook and

Twitter. In viewing of those emerging technologies, a need existed to develop a system for real-

time emergency situations awareness and sharing among all interest parties, at anytime from

anywhere. This paper presents the design of an application that creates a situation awareness

system using mobile devices such as android cellphone or iPhone. The design was implemented,

in-part, by the senior Capstone project team at the Industrial Engineering Department, University

of Houston.

Development of the application consists of four stages: 1) to develop an android application

using “Android SDK”, 2) to modify the application of 1) into a Facebook application using

“Facebook SDK for Android”, and 3) to enhance the application of 2) with real-time images,

streaming video and voice capturing capability and wireless upload functionality for storing

situation contents at the Facebook server, and 4) to test the application in a real-world emergency

situation, such as hurricane or flash flood . The main purpose of the application is to inform users

of any emergency situations during hurricane and/or other disasters, using social network links,

and keep them updated of any dangerous circumstances that will help them when it comes to

make personal decision.


I-13

The Ike Dike: A Coastal Barrier Protecting the Houston/Galveston Region

from Hurricane Storm Surge

William Merrell, Ph.D.

George P. Mitchell Chair in Marine Sciences

Texas A&M University at Galveston

And

Robert W. Whalin, Ph.D., PE

Director, Coastal Hazards Center of Excellence-Education

Jackson State University

On September 13, 2008, Hurricane Ike came ashore near the east end of Galveston Island in

Texas. Ike’s strong Category 2 winds and near Category 5 equivalent storm surge, created

sufficient devastation to be ranked as the third costliest hurricane to make landfall in the United

States. Two storm surge barriers that mitigated damage and loss of life in the region were the

existing Galveston Seawall constructed after the 1900 Galveston hurricane and the Texas City

Barrier constructed after Hurricane Carla. Unlike Hurricane Katrina, when the media and the

power of political process focused on the difficulties in New Orleans, the impact of Ike on the

Houston/Galveston region was quickly forgotten as national attention turned to the US

presidential race and the worldwide financial meltdown.

Despite the initial lack of attention, Hurricane Ike may well be a watershed storm. It has already

changed how NOAA will classify hurricanes by giving more credence to surge potential.

Moreover, the devastation caused by Ike clearly reinforced the century old demonstrated

vulnerability of the Houston/Galveston area to hurricane storm surge and triggered ideas on

regional approaches to suppressing surge for this urbanized region.

One such approach is the “Ike Dike” concept, a coastal barrier that would protect the Houston-

Galveston region, including Galveston Bay, from hurricane storm surge. The project would

extend the protection afforded by the existing Galveston Seawall along the rest of Galveston

Island and along the Bolivar Peninsula, with a 17ft high revetment near the beach or by raising

the coastal highways. The addition of flood gates at Bolivar Roads, the entrance to the Houston,

Texas City, and Galveston ship channels, and at San Luis pass on the west end of Galveston

Island would complete a coastal spine. At 17ft heights, the Ike Dike approach could conceivably

provide a barrier against hurricane surges into the Bay for a storm expected to occur every

10,000 years. The coastal spine could be built using existing, proven technology such as the

gates and barriers now in use in the Netherlands and in the recently completed New Orleans

Hurricane Barrier. Greater New Orleans is now protected by a 133 mile perimeter of levees,

flood walls and gated barriers. The total cost of Greater New Orleans Hurricane and Storm Risk

Reduction System is about a billion dollars. The strategy is to keep massive surges from

entering the system by shortening the outer protection needed by using 4 gated passages. The

System was started in 2008 and achieved 100-yr surge event protection in June 2011. When

compared to New Orleans Barrier, the Ike Dike is a simpler and less costly project, yet would

afford protection for a much larger industrial base and population. The Dutch Deltaworks, the

New Orleans Barrier and the Ike Dike share the proven Dutch strategy in that they shorten the

perimeter as much as possible, keep the surge out of internal waters, and use gates to accomplish

http://en.wikipedia.org/wiki/Galveston_Bay

http://en.wikipedia.org/wiki/Galveston_Seawall

http://en.wikipedia.org/wiki/Galveston

http://en.wikipedia.org/wiki/Bolivar_Peninsula


I-14

this. However, because the areas needing protection in the Houston- Galveston region are above

Sea Level, less robust technologies relative to the New Orleans situation will be needed to

achieve the desired protection.

Important research needs to be completed so details of the coastal barrier Ike Dike design and its

costs and benefits can be better understood. This work is necessary to convince decision-makers

at the regional, state and national level of the merits of the project.

Ultimately federal government support for the construction and maintenance of the barrier will

depend on detailed quantifiable analyses of its costs and benefits. Although the antidotal

evidence of the national value and strategic importance of the Galveston Bay petrochemical,

maritime and related economies is strong, it must be fully detailed and documented. Potential

losses to the local, state, and national economies with and without surge suppression must be

understood and quantified. Moreover, in order to truly define the benefits, this understanding

must be carefully based on the probabilities of tropical hurricanes impacting the region, rather

than speculative hurricane possibilities.

A necessary component to the economic and barrier design studies is probabilistic storm surge

inundation information for the region, with and without an Ike Dike in place. Engineers from

the Homeland Security Center of Excellence at Jackson State University led by Dr. Robert

Whalin (Center Director) and Mr. Thomas Richardson (Center Deputy Director) working with

engineers at the United States Army Corps of Engineers Engineer Research and Development

Center will conduct the necessary storm surge modeling studies. This modeling will comprise

integral input information to our economic and barrier research strategies and better quantify

Galveston Bay’s important role as a retention basin for surge waters.


I-15

Modeling of Shutter Coastal Protection against Storm Surge

for Galveston Bay C. Vipulanandan, Ph.D., P.E., Y. Jeannot Ahossin Guezo and and B. Basirat

Texas Hurricane Center for Innovative Technology (THC-IT) Department of Civil and Environmental Engineering University of Houston, Houston, Texas 77204-4003

Phone: (713) 743-4278 Email: [email protected], [email protected] and [email protected]

Abstract

It is important using numerical models to evaluate the effectiveness of man made barriers to

protect coastline from storm surges. Coastal protection against storm surge has become a

national issue in the U.S. especially after the hurricanes Sandy, Ike and Katrina. Galveston bay

with the Houston Ship channel could become potential source of concern to the region because

of the industrial activities and the potential exposure to residential neighbor hoods. In the past

100 years, Galveston has had the highest number of hurricanes in Texas. In this study, Galveston

Island and Houston ship channel was numerically modeled using the advanced circulation

ADCIRC model for storm surge estimation and protecting using the innovative shutter concept.

The modified ADCIRC can be used to numerically model and determine the effectiveness of the

various heights of the shutter barrier in the Galveston Bay.

Introduction

The recent hurricanes Sandy (2012), Ike (2008) and Katrina (2005) respectively have raised the

need for coastal protection for the populated coastal regions in the U.S. Hurricane Ike, in Texas

Gulf Coast caused approximately $30 billion in damage and killed nearly 200 people. Galveston,

Houston ship channel with the port of Houston are vital for the state of Texas and for the United

State government. In fact, based on the Bay Area Houston Economic Partnership report, about

46 percent of the U.S. aviation fuel, 20 percent of the nation’s gasoline supply and 40 percent of

chemical-feed stocks are made in the Galveston Coast area. To prevent Galveston coast against a

potential more devastating storm surge, different types of barriers are being proposed.

At present, there are only a handful of European countries that manage or have constructed large

sea-resistant storm flood surge barriers. These countries include United Kingdom, Netherlands,

Italy and Russia. Specially now when climate change and sea level rise are recognized facts that

should be taken into account (Coastal Portal, 2010).

Through the years, numerical models have been developed to estimate the storm surges

generated by hurricanes. This is done using the landing point topography, bathymetry and the

hurricanes parameters including pressure, radius of max winds, location, direction and forward

speeds.

The Sea, Lake, and Overland Surge from Hurricanes (SLOSH) is a computerized model

developed by the National Weather Service (NWS) to estimate storm surge heights and winds

resulting from historical, hypothetical, or predicted hurricanes. SLOSH is used by the National

Hurricane Center (NHC) for the exclusive benefit of NWS, US Army Corps of Engineers

(USACE), and Emergency Management personnel (FEMA et al, 2003). It is the primary

computerized model used by US official to assess a foregoing hurricanes effect on the predicted

landing point to issue emergency evacuation if required.




I-16

A more research oriented numerical model named Advanced Circulation (ADCIRC) for storm

surge was developed (Luettich and Westerink, 2004) for better estimation of hurricane storm

surge. The advantage of utilizing ADCIRC is its ability to map intricate shoreline and the

corresponding topography needed to resolve complex fluid dynamics (Desback et al, 2010).

ADCIRC unstructured grid allows modeling complex coastal regions at fine spatial scale (Chu et

al, 2010)

ADCIRC Model

Coastal areas are characterized by geometrically complex features which include bathymetry,

rivers, channels, bays, wetlands and man made structures (dunes, levees, harbors and transport

systems). Accurate modeling of hurricane or tsunami induced coastal flooding has been limited

by the use of fixed size computational domains and the lack of sufficient clarity in the grid

resolution. The fixed size computational domain limits the volume of water involved in the

event. Grid resolution is important to capture the varying natural features such as bathymetry and

coastal profile with man made structures and barriers. ADCIRC has a large domain-unstructured

grid approach to compute hurricane and storm surge. The large domain allows the storm surge to

naturally and accurately propagate from deep waters on to continental shelf and adjacent coastal

region. The use of unstructured grid resolves important flow features on a localized basis,

accurately solving the flow features on a localized basis. In order to develop proper and adequate

coastal protection, it is critical to capture the flow features and transport of sediments as the

storm surge propagates and recede thorough the Galveston bay and Houston Ship channel.

Local Modeling

The use of basin size domains with highly localized grid resolution significantly improves the

predictive ability of computational models of hurricane storm serge in very complex flood plains.

ADCIRC-SMS Model Controls

SMS is used to input the important parameters to the ADCIRC model. There are six different

tabs that are used to input the data. The tabs include the following: (1) General, (2) Timing, (3)

Files, (4) Tidal/Harmonics, (5) Wind and (6) Sediment options.

(1) General Tab: it includes the model, initial condition, Carioles option (forces due to the

latitude), solver type, number of iterations per time step, generalized properties (lateral viscosity)

and bottom friction(for greater than 10 m use a value of 0.005 and for shallow water use a value

of 0.02).

Modeling approach

For this study of hurricane storm surge in the Gulf of Mexico around Galveston the model

encompassed the domain between longitudes 93.0 W to 96.3 W and latitude 27.6 N and 30.0 N

as shown Fig. 1.


I-17

Figure 1. Shoreline and ocean boundary.

In this study, the global effect of the hurricane around Galveston with and without shutter was

investigated. ADCIRC two-dimensional depth integrated (2DDI) model was used and Surface

Water Modeling System (SMS) was used for preprocessing and post-processing the datas.

Coastline

The coastline data were imported from National Geographical Data Center (NGDC). Model 1

shoreline has a resolution of 1:250,000.

Bathymetry

The bathymetry data were also imported from National Geographical Data Center (NGDC). The

resolution of the bathymetry data extracted can also be variable with a limitation on the

maximum matrix of data that can be extracted at once. The bathymetry of model 1 has a

resolution of 1 minute, (Fig. 2).

Figure 2. Shoreline, ocean and bathymetry


I-18

Protection Systems

Different methods are proposed to protect the coastal area from storm surge and coastal flooding.

The immediate focus is on evaluating the potential of Ike Dike and the new shutter system

(Vipulanandan et al. 2010) separately and together. In order to see the effects of shutter the

model was analyzed with and without the shutter with different elevations. The objective was to

determine the difference in elevation and velocity of the waves behind the shutter.

Analysis

In the analysis section, the mesh is generated over the domain as in Fig. 3. Total of three model

were analyzed; the first model was for the domain without any shutter, second model was for the

shutter with the elevation of 1 meter and the third model was for the domain the elevation of 3

meters. Another model was analyzed with the shutter with elevation of 5 meters.

The boundary conditions assigned to the shutter was Island Barrier. The length of the shutter is

about 4.6 km and the thickness of the shutter was about 65 meter. The thickness of the shutter

was limited by the selected scale of the model.

The Island Barrier boundary condition considers the flow if the barrier is overtopped and zero

normal flow is assumed if the barrier is not overtopped. For this boundary condition two

nodestrings are required with an equal number of nodes.

Figure 3. Mesh generation

Recording stations for water surface elevation contour was specified with and without shutter in

the figure 5 and 4, respectively. The recording station in the left and right are specified as the

“Back Recording Station” and “Right Recording Station”, respectively.


I-19

Figure 4. Recording stations on water surface elevation contour (without shutter)

Figure 5. Recording stations for water surface elevation contour (with shutter)

In the Fig. 6, water surface elevation for the model 1 (without any shutter) was compared in the

two recording stations. The surface elevations were pretty close.

Front

Recording station Back

Recording station

Recording Stations


I-20

Figure 6. Water surface elevation for the model 1 (without shutter)

In the Fig. 7, water surface elevation for model 2 (shutter elevation of 1 meter) are compared in

the “Back Recording Station” and “Right Recording Station”.

Figure 7. Water surface elevation for the model 2 (H=1 m)

In the Fig. 8, water surface elevation for model 3 (shutter with the elevation of 3 meter) are

compared in the two recording stations.

Front

Recording Stations

Back

Recording Stations


I-21

Figure 8. Water surface elevation for the model 3 (H=3 m)

In Fig. 9, the water surface elevation at the “Back Recording Station” for the three model are

compared. When the shutter elevation was 1 meter, the water surface elevation was reduced from

2.2 meter to 1.8 meter, a reduction of 18 %. When the shutter was 3 meter, the back water

elevation was 1.3 meter, a reduction of 41 %.

Figure 9. Comparison of water surface elevation for model 1, 2 and 3 in the “Back Recording Station”

The depth-average velocity contour in the model 3 (H=3 m) is shown in the following figure.

No shutter

H=1 m shutter

H=3 m shutter

Ele

vat

ion (

m)

Time (min)


I-22

Figure 10. Depth-average velocity contour (with shutter model 3: H=3 m)

Figure 11. Comparison of depth-averaged velocity model 1, 2 and 3 in the “Back Recording Station” (time =

106.5 hr)

No shutter

H=1 m shutter

H=3 m shutter

Time (min)

Dep

th-a

ver

aged

vel

oci

ty (

m/s

)


I-23

Based on three models it was observed that the maximum depth-average velocity (time = 106.5

hr) decreased by 60% at the “Back Recording Station” during the storm surge. When the shutter

height was 3 meter (model 3).

Figure 12. The elevation and velocity of waves at the critical time of t=106.5 hr in the “Back Recording

Station”

As shown in Fig. 12, the shutter (H=3 m), at the critical time of t=106.5 hr decreased the

elevation by 50% and the velocity by 70%.

Conclusion

It is crucial to review potential protection system of texas coast, especially Galveston and

Houston ship channel, against hurricane storm surge to avoid major losses and an economic

catastrophy. ADCIRC program is a very useful tool for parametric study of potential solution.

Embedded shutter with varying heights were placed at one location in Galveston Bay and

analyzed with hurricane Ike simulation. The shutter height was sensitive to the velocity and

elevation of storm surge waves.

in Galveston Bay is a great option to protect Texas coast, especially Galveston and Houston ship

channel, against hurricane storm surge to avoid an economical catastrophy. The velocity and

elevation of waves decreased

Acknowledgment

The ADCIRC, PC and parallel, code were provided by Dr Luettich, Professor at University of

North Carolina at Chapel Hill. Thanks to TLC2 (Texas Learning and Computer Center) for the

training to use of the supercomputer at the University of Houston.

References


I-24

Chu, P., Blain, C. A. and Linzell R. (2010) “Development, Implementation and Validation of an

ADCIRC-based Operational Coast Forecast System”. 14th

ADCIRC Model Workshop, April

20-21, 2010.

Coastal Portal (2010) “International Network for Storm Surge Barrier Managers”.

http://www.coastalwiki.org/coastalwiki/International_Network_for_Storm_Surge_BarrierMa

nagers.

Dresback, K. M., Kolar, R. L., Blain, C. A., Szpilka, C. M., Szpilka, A. M. and Luettich, R.

(2010) “Development of the Couple HYCOPM and ADCIRC Models with an Application in

the Northern Gulf of Mexico”. 14th

ADCIRC Model Workshop, April 20-21, 2010.

FEMA, URS and USZ Army Corps of Engineers (2003) “SLOSH Display Training”. September

2003, 95p.

Luettich, R. and Westerink, J. (2004) “Formulation and Numerical Implementation of the 2D/3D

ADCIRC Finite Element Model Version 44.XX”. Published on December 8th

2004, 74p.

Public Broadcasting Service (PBS) (2010) “Storm that drowned a city”.

http://www.pbs.org/wgbh/nova/orleans/proo-nf.html, July 2010.

Vipulanandan, C (2010), “Innovative Shutter Concept for Coastal Protection,” Proceedings,

THC-2010 (THC-IT wesite).

Vipulanandan, C and Jeannot Ahossin Guezo, Y. (2011), “Investigate Innovative Coastal

Protection Methods (IKE Dike and Shutter) for Houston Ship Channel and Galveston

Coastline Using Numerical Models (Hybrid of ADCIRC)” Proceedings, THC-2010 (THC-IT

wesite).

Vipulanandan, C and Jeannot Ahossin Guezo, Y. (2012), “Coastal Protection Systems and

Hurricane IKE Storm Surge Modeling Using ADCIRC” Proceedings, THC-2010 (THC-IT

wesite).

http://www.coastalwiki.org/coastalwiki/International_Network_for_Storm_Surge_BarrierManagers

http://www.coastalwiki.org/coastalwiki/International_Network_for_Storm_Surge_BarrierManagers

http://www.pbs.org/wgbh/nova/orleans/proo-nf.html


I-25

Energy Security on a Barrier Island

Marcel Blanchard, CEM, CEP, Assistant Vice President, Utility Operations

Lynn Crawford, PE, Market Leader, Energy and Utilities Infrastructure Affiliated Engineers

University of Texas Medical Branch Galveston

Abstract On September 12, 2008, Hurricane Ike reached Galveston Island with 110 mph winds and wide-scale

flooding, causing the immediate failure of all facility systems and utilities at the University of Texas

Medical Branch (UTMB).

Hurricane Ike, a Category 2 hurricane with a

storm surge above normal high tide levels,

moved across the Louisiana and Texas gulf

coasts on September 13, 2008. Maximum

sustained winds at landfall were estimated at

85 miles per hour (mph) and on Galveston

Island winds reached 110 mph with gusts of

125 mph. The largest storm surge was

estimated at 17 feet and possibly 20 feet in

some Galveston Island areas. Hurricane Ike

was the third most expensive disaster in

FEMA history, behind Katrina and Andrew,

and resulted in the largest evacuation of

Texans in the state's history. President Bush

declared a major disaster for the State of Texas

due to damages from Hurricane Ike and signed

a disaster declaration on September 13, 2008, authorizing FEMA to provide federal assistance in

designated areas of Texas.


I-26

Over the course of the following weeks, UTMB’s current District

Heating and Cooling System essential to all UTMB operations,

remained submerged in salt water, sustaining extensive permanent

damage before mechanical pumping efforts could even begin.

During the efforts to restart the system by UTMB after the storm,

the steam and condensate piping installed below grade exhibited

the presence of saltwater within the outer steel jacket that

surrounds the carrier piping. Saltwater was validated boiling out

of the jacket vents and drains as the steam and condensate carrier

piping was brought back online. This jacket and an external

cathodic protection system provided initial corrosion protection for

the buried piping. With the jacket compromised by saltwater and

the cathodic protection system damaged by the storm, there was no

longer any corrosion protection for the carrier piping. The initial

damage evaluation by UTMB of the steam and condensate system

was that of a complete loss of the underground steam and

condensate piping.

The existing District Heating and Cooling System is a system of

interdependent individual components linked in operation, like a

chain. The failure of any individual component will cause the

failure of the entire system. While addressing risks to single

components provides isolated value, it is only by providing similar protection to all the links in the

“chain” that repeated failure of the entire system can be prevented. Affiliated Engineers (AEI),

working closely with UTMB, developed a Three Step Solution to ensure UTMB would remain

resilient during a similar event:

Step One: Go Away from Buried Steam Pipe;

Step Two: Elevate or Protect the Boilers and Chillers;

Step Three: Produce On-Site Electricity via Combined Heat and Power.

Combined heat and power, is the production of electricity and heat from a single fuel source.

Considered highly efficient, co-generation captures heat lost during the production of electricity and

converts it into useful thermal energy, usually in the form of steam or hot water. Co-generation

systems are typically 60-80 percent efficient which is significantly more efficient than the traditional

power plant efficiency of approximately 30 percent.

These efficiency gains also result in cost savings, reduced air pollution and greenhouse gas emissions,

increased power reliability and quality, reduced grid congestion and avoided distribution losses.


I-27

Adapting to Climate Change: Lessons from Natural Hazards Planning

Gavin Smith, Executive Director, Department of Homeland Security

Coastal Hazards Center of Excellence

Associate Research Professor

Department of City and Regional Planning

University of North Carolina at Chapel Hill

Chapel Hill, NC 27599-3140

Adapting to climate change poses a major challenge for nations and communities around the world.

Many will have to contend with unprecedented risks and impacts due to rising sea levels, temperature

and rainfall shifts, and more intense coastal storms. Rural and urban livelihoods will be profoundly

affected. Poor and marginalized groups will be especially hard hit. Increasing attention is therefore

being focused on how to adapt to climate change. But much remains to be done to understand the

underlying challenges and opportunities. Moreover, there is a compelling need to take practical steps

to build community sustainability and resilience in the face of climate change.

Much has been learned from experience and scholarship in the field of natural hazards planning that

is directly relevant to climate change adaptation. This presentation synthesizes this scholarship and

distils lessons from case studies around the world to provide practical guidance for communities to

plan for and adapt to climate change. Natural hazards events become disasters when a physical peril,

such as an extreme weather event, exceeds the coping capacity of the imperiled community, nation or

region, such as the New Orleans levee failure and dismal response to Hurricane Katrina in 2005 or the

devastating impacts of the 2004 Indian Ocean tsunami. Natural hazards planning scholars and

practitioners have learned that disaster can be averted if proactive steps are taken to reduce social

vulnerability, maintain the natural functions of healthy ecosystems (such as coastal ecosystems that

reduce the impacts of coastal storms) and avoid land-use choices that put people in harm’s way.

These lessons have particular relevance for understanding and addressing the barriers to and

opportunities for adapting to climate change.

The materials used in this presentation draw from the soon to be published book, Adapting to Climate

Change: Lessons from Natural Hazards Planning which is co-edited by Gavin Smith and Bruce

Glavovic. This is the first book to provide climate change policy-makers, scholars, students, and

practitioners with a rigorous understanding of natural hazards planning scholarship and experience to

overcome these barriers and unlock opportunities for building communities that are sustainable and

resilient to climate change.

Improving the ability to draw from what we know about natural hazards and disasters and apply this

knowledge in practice to the challenges associated with climate change adaptation is the purpose of

this book. We do this by first sharing lessons derived from the study and practice of natural hazards

risk management across a global selection of case studies. The use of a case study approach allows

for a more in-depth and critical review of lessons across differing hazards and spatial and geo-

political scales, including those lessons that highlight key problems. Examples include the pitfalls of

failing to proactively plan for and reduce the potentially damaging effects of natural hazards on

human settlements; the limited attention placed on conveying risk to policy makers, communities, and


I-28

individuals in a manner that resonates with them and leads to targeted action; and the perpetuation of

vulnerability through poor recovery and reconstruction strategies including the lack of pre-event

planning for post-disaster recovery. Lessons also underscore the importance of establishing broad,

supportive networks that fuel the power of governance and reflexive learning across formal and

informal institutions; fostering an inclusive dialogue that links climate change adaptation strategies

and post-disaster assistance, including the ability to maximize the use of available resources

following major disasters to achieve complementary aims such as risk reduction and climate change

adaptation, sustainable development, and disaster resilience; and the importance of addressing

endemic problems like low levels of institutional capacity and commitment, poverty, environmental

degradation, and fragile economies that are “exposed” during disasters.

The book is organized in the following manner. After the introductory chapter, we begin with a

section titled Climate Change Adaptation: Theory and Practice. This section draws on chapters

written by Jorn Birkmann and Joanna Pardoe (Climate Change Adaptation and Disaster Risk

Reduction: Conceptual Approaches, Synergies and Mismatches) who provide an overview of the

linkages and gaps between climate change adaptation and disaster risk reduction concepts and

associated policies. Gina Zievogel and Sue Parnell’s chapter, Tackling Barriers to Climate Change

Adaptation in South African Coastal Cities describes the issues facing Cape Town and eThekwini as

a way to begin addressing adaptation at the urban city-scale. Both chapters help to unpack important

topics that are found throughout the remainder of the book. These include the identification of key

barriers and opportunities to achieving adaptation, the importance of developing a broad institutional

framework for action that is cognizant of the growing base of knowledge (including that which is

locally informed), the need to recognize and account for varied temporal and spatial scales, and the

ability to develop an appropriate mix of flexible and holistic policies that address underlying issues

such as risk reduction and development.

The next section, titled The Nature of Disasters and the Role of Natural Hazards Planning in Building

Resilient Communities, is comprised of five chapters that highlight the importance of planning and

collective action. Anthony Oliver Smith’s chapter, Climate Change Adaptation and Disaster Risk

Reduction in Highland Peru describes adaptation as a long-standing cultural phenomenon closely

associated with the survival of societies over time. This perspective helps the reader to understand

the complexities of this largely reactive process and compares this with hazard mitigation which is

ideally proactive in nature. In chapter 5, Iain White (Castles on Sand: The Shifting Sources of Flood

Risk and the Implications for Flood Governance) explains the challenges facing cities and regions in

England as increased urbanization and repeated flood-related disasters has led to a change in thinking

from an approach driven by the adoption of flood defenses in the aftermath of an event to a more pre-

emptive focus on risk management. In Planning for Resilient Coastal Communities: Emerging

Practice and Future Directions, Timothy Beatley provides a vision of what it means to be a resilient

coastal community, discusses principles underlying this designation, and highlights ways that

communities have achieved this objective. Bill Simbieda’s chapter, Resilience and Adaptation: The

Emergence of Local Action in California, focuses on the cities of Berkeley and San Francisco,

California (USA) both of whom assume different approaches to achieving disaster resilience,

emphasizing physical and institutional methods respectively. Philip Berke, in Rising to the Challenge:

Planning for Adaptation in the Age of Climate Change, argues that scenario-based planning provides

a sound means to confront what still amounts to a great deal of uncertainty in our understanding of

climate change-related impacts while providing the flexibility needed to account for unexpected

outcomes and new information.


I-29

In the last major section, Case Studies: Lessons from Natural Hazards, we draw from a collection of

case studies from across the world, including Applying Hurricane Recovery Lessons in the US to

Climate Change Adaptation: Hurricanes Fran and Floyd in North Carolina (USA) (Gavin Smith); The

2004 Manawatu Floods, New Zealand: Integrating Flood Risk Reduction and Climate Change

Adaptation (Bruce Glavovic); Learning from Analyses of Policy Frames and Informal Institutions in

the Fire Management Sector of Victoria, Australia (Karen Bosomworth, John Handmer and Steven

Dovers); Natural Coastal Hazards Planning, the 2004 Tsunami and Lessons Learned for Climate

Change Adaptation in Samoa (Namouta Poutasi, Michele Daly, Jude Kohlhase and Filomena

Nelson); Recovering from the 2004 Tsunami: Lessons for Climate Change Response in India (Ahana

Lakshmi, R. Purvaja and R. Ramesh); Disaster Recovery in Coastal Mississippi (USA): Lesson

Drawing from Hurricanes Camille and Katrina (Gavin Smith); and Waves of Adversity, Layers of

Resilience: Floods, Hurricanes, Oil Spills, and Adapting to a Changing Climate in the Mississippi

Delta (Bruce Glavovic).

Each of the case studies addresses a number of topical areas, including the pre- and post-event setting

of the locales being discussed and how these conditions shape the policies, programs, and plans

developed and implemented in the face of natural hazards and disasters; lessons drawn from these

experiences; the identification of barriers and opportunities for mainstreaming hazard mitigation and

disaster recovery policies into climate change adaptation; and a set of recommendations for action. A

review of the chapters show that a number of common themes emerge, including the importance of

effective governance/collective action; the influence of pre-event conditions such as culture, wealth,

policy frameworks, and institutions on desired outcomes; the value of establishing good vertical

connectivity between national policy and local plans; adopting varied and flexible risk management

strategies; and viewing disasters as focusing events, including the ability to adopt new policies while

recognizing the importance of including less powerful groups in the decision making process.

The final chapter, Conclusions, Recommendations, and Next Steps: Integrating Planning for Natural

Hazards and Climate Change Adaptation consolidates policy recommendations drawn from

throughout the text and organizes them under a broad vision and set of thematic areas that are framed

as goal statements. The chapter concludes with an examination of how these goals can be

operationalized in both existing, and where necessary, new plans, policies, and collective

arrangements that span multi-institutional and multi-lateral governance frameworks.


I-30

Local Disaster Resilience across the Gulf Coast: Intersecting Capacities

for and Perceptions of Resilience

Ashley Ross

Assistant Professor of Political Science

Sam Houston State University

[email protected]

http://localdisresilience.com

1806 Avenue J, Huntsville, TX 77340

Abstract contains excerpts from the forthcoming book

Local Disaster Resilience: Administrative and Political Perspectives, Routledge.

Introduction

Perceptions are merely abstractions of reality; however, they profoundly shape the

world around us as what we perceive often influences our decision-making. In disaster

management, the intersection of resilience perceptions with tangible realities of resilience

reveals the conditions that either strengthen or impede its development on a local level. This

analysis explores how objective approximations of county-level adaptive capacity overlap

with perceptions of the adaptive process held by county emergency managers to identify the

county characteristics linked to high, moderate, and low disaster resilience.

What is disaster resilience?

Community resilience has two components – adaptive capacity and the adaptive

process – that theoretically should be mutually reinforcing. Adaptive capacities are the

strengths a community has for disaster response and recovery. There are multiple types of

capacity including:

Social capacity for resilience is the aggregation of a community’s characteristics

including age, education levels, wealth, and language capacity that translate to able,

mobile, and resource-enabled individuals in the event of a disaster.

Community capital refers to the connectedness of community members that enable

cooperation and collaboration in disaster planning, response, and recovery.

Economic capacity for resilience refers to the robustness and diversity of a

community’s economy.

Institutional capacity for resilience concerns the plans and preparations a community

has made for disasters.



http://localdisresilience.com/

http://localdisresilience.com/


I-31

Infrastructure capacity for resilience refers to a community’s basic public service

capacity in terms of shelter, roads, and medical facilities that may be needed in the

event of a disaster.

Ecological capacity for resilience speaks to how community development has affected

natural coastal boundaries such as wetlands.

A community with a high degree of adaptive capacity has a population with low social

vulnerability, a robust and diverse economy, reduced vulnerability to hazards as a result of

both project and process mitigation policies and programs, sound support systems to enable

evacuation in case of an emergency and speedy restoration of services following an event,

and protected natural barriers to hazards. Additionally, a community with considerable

adaptive capacities has a citizenry with high levels of social capital and community

competence, willing and able to participate in collective decision-making.

These adaptive capacities are translated into the adaptive process when a disaster

strikes. Very severe disasters may temporarily disable the community’s abilities, but once

restored the adaptive capacities developed prior to the event will imbue the community with

strengths to engage in the adaptive process. This process involves collaboration to pursue

recovery in a manner that improvises solutions to local problems, coordinates collective

action, engages the community, and works to formalize solutions to endure beyond the

short-term. This recovery process should produce outcomes that feedback into adaptive

capacities to buffer against future hazards.

Measuring Disaster Resilience

To assess resilience in its entirety we need approximations of both adaptive capacity and

the adaptive process. Adaptive capacities largely entail tangible products and observable

characteristics of a community. Disaster mitigation plans exist on paper. Flood insurance is

traceable through records. Education levels, age groups, and special needs populations are

recorded by government agencies. These factors as well as others comprise the components

of a community’s adaptive capacity for disaster resilience and are measurable through

secondary sources such as the U.S. Census Bureau and the Federal Emergency Management

Agency (FEMA).

While adaptive capacity is amendable to objective measurement because it involves

tangible policies and outcomes, the adaptive process is somewhat more elusive because it

can widely vary in the way it manifests across different communities. Assessments of the

process of adaption, therefore, have been based on perception in this study. These

perceptions are measured by survey responses from county emergency managers.

The sample studied in this analysis includes counties and parishes within 25 miles of the

Gulf of Mexico in the states of Alabama, Florida, Louisiana, Mississippi, and Texas, totaling

75 jurisdictions. Emergency management directors were contacted in each county and parish

and invited to take part in the project’s survey. A total of 56 counties and parishes

participated, as shown in Figure 1.


I- 32

Figure1: Study Sample

Adaptive Capacity for Resilience

Adaptive capacities were measured across six components – social, community

capital, economic, institutional, infrastructure, and ecological – using multiple indicators

from secondary sources, summarized in Table 1. These indicators were aggregated to create

a score for each county, ranging from one (very low capacity) to five (very high capacity).

Counties in Texas and Mississippi scored moderate to low while the scores in Louisiana and

Alabama were moderate to very high. Counties in Florida exhibited varied levels adaptive

capacities.

Perceptions of the Adaptive Process of Resilience

The adaptive process is measured as ratings of coordination and collaboration during

past disaster response and recovery. These ratings are taken from county emergency

manager survey responses and are averaged to represent the overall quality of the adaptive

process. The survey asked participants to rate coordination with the following groups: 1)

citizens and citizen groups; 2) private partners; 3.) non-profit partners including faith-based

and volunteer groups; 4) local elected officials including municipal and county government;

5) neighboring county emergency managers; 6) state emergency management officials; and

7) federal emergency management officials. Possible responses included “poor” (coded 1),

“adequate” (coded 2), “good” (coded 3), and “excellent (coded 4). The average coordination

ratings across the groups were: 3.18 for citizens, 3.35 for private partners, 3.49 for non-profit

partners, 3.51 for local government, 3.78 for neighboring county emergency managers, 3.47

for state emergency management, and 2.80 for federal emergency management.


I- 33

Table 1: Adaptive Capacity for Resilience Indicators by Component

Variable Definition ( Effect on Resilience) Data Source

So

cia

l R

esil

ien

ce

education Percent of the population with a college degree (+) American Communities Survey 2010

transportation access Percent of households with a vehicle (+) American Communities Survey 2011

communication

capacity Percent housing units with a telephone (+) American Communities Survey 2011

language competency Percent of the population over 5 years of age that speak English “very well” (+) American Communities Survey 2010

non-vulnerable

population

Percent non-elderly population (+) USA Counties 2009

Percent population without a physical disability (+) US Census 2000

health care coverage Percent population with health insurance (under 65 years of age) (+) USA Counties 2007

Co

mm

un

ity

Ca

pit

al

place attachment Net international migration per 1,000 population (-) American Communities Survey 2009

Percent of the population born in the state that resides in the state (+) American Communities Survey 2010

political engagement Percent voter turnout in 2008 presidential election (+) Secretary of State/Dept. of State 2008

social capital

Religious adherents per 1,000 (+) ASARB 2010

Civic organizations per 10,000 (+) County Business Patterns 2009

Social advocacy organizations per 10,000 (+) County Business Patterns 2009

Eco

no

mic

Res

ilie

nce

housing capital Percent owner occupied housing (+) US Census 2010

employment Percent of the population that is employed (+) American Communities Survey 2010

Percent of labor force that is female (+) American Community Survey 2010

income equality Quintiles of Gini Index (higher values = more equal) (+) American Communities Survey 2012

economic diversity Percent of the population not employed in farming, fishing, forestry, or

extractive industries (+) US Census 2012

business robustness Ratio of large to small business employees (+) US Census 2009

health care access Total physicians per 10,000 (+) USA Counties 2009

Inst

itu

tio

na

l R

esil

ien

ce

mitigation plans Percent population covered by a multi-hazard mitigation plan (+) FEMA 2012

mitigation

organizations and

activities

Percent population participating in Community Rating System (+) FEMA 2012

Percent population covered by Citizen Corps council (+) Citizen Corps 2012

Percent population in Storm Ready communities (+) NOAA 2012

emergency services Percent local government expenditures for health/hospitals, fire and police (+) USA Counties 2002

administrative

decentralization Number of municipalities, school districts, and special districts (-) US Census 2007

disaster experience Number of Presidential disaster declarations, 2002-2011 (+) FEMA 2012

Infr

ast

ruct

ure

Res

ilie

nce

housing vulnerability Percent of housing not mobile homes (+) American Communities Survey 2010

Percent housing units built 1970-94 (-) American Communities Survey 2010

evacuation capacity Primary and secondary road miles per square mile (+) US Census 2010

medical capacity Number of hospital beds per 10,000 (+) County and City Data Book 2007

shelter capacity Percent vacant rental units (+) US Census 2010

Number of hotels/motels per square mile (+) County Business Patterns 2009

service restoration Number of public schools per square mile (+) FEMA Hazus 2.0 2011

Eco

logic

al

Res

ilie

nce

wetland preservation Net change in percent wetland area between 1996 to 2006 (+) NOAA 2010

impervious surfaces Percent impervious surface in square miles of land area (-) National Land Cover Database 2006

floodplain

development Index of severe repetitive loss properties (higher values = more loss) (-) FEMA 2007


I- 34

Intersecting Capacities for and Perceptions of Resilience

For each county, the measures of adaptive capacities and the adaptive process are

paired and plotted. Adaptive capacity is broken into two categories: 1) high which includes

rankings of high and very high (numerical equivalent of 4 and 5); and 2) moderate to low

which incorporate rankings of moderate, low, and very low (numerical equivalent of 1, 2,

and 3). The adaptive process is also grouped into high and low categories. High includes

ratings that are on average the equivalent of “good” or “excellent” (numerically expressed as

3 or 4), and low includes average ratings that range from “poor” (numerical equivalent of 1)

to above “average” (2.99). This means that some cases with coordination rankings higher

than “average” but not quite the equivalent of “good” are considered low coordination.

These standards ensure comparability and set up groupings where the highest categories

represent the most developed attributes of resilience.

Four groups emerge from pairings of adaptive capacity and the adaptive process as

shown in Figure 2. Group 1 includes those cases that are ranked high on capacity and have

high average ratings of the adaptive process. There are 18 counties that fall into this group.

Group 2 also includes cases that have high ratings of coordination but moderate to very low

capacity.1 There are 29 counties that exhibit this combination of qualities. Group 3 is

characterized by high capacity but low ratings of coordination.2 There are only two cases in

this category. Finally, Group 4 includes those cases that have low ratings of coordination

and moderate to low adaptive capacities.3 There are six counties in this category. This group

faces the most challenges in developing resilient outcomes as they are deficient in both pre-

and post- disaster resilience.

Figure 2: County Adaptive Capacities Coupled with Ratings of the Adaptive Process

Note: Each point represents a county in terms of its adaptive capacity and adaptive process.

The points were “jittered” to offset overlapping cases for visual presentation. Similarly, the

vertical and horizontal reference lines are moved slightly to accommodate the offset points.


I- 35

Resilience Profiles

Which characteristics describe counties and parishes that exhibit the most resilience?

Four factors were analyzed to identify patterns that exist among the groups: state context,

rural-urban character, disaster severity, and fiscal and human resources. State context

conditions the environment in which resilience develops on the local level as states set up

institutions and rules that affect disaster management. While there are many dynamics at

play within states, one political institution that may affect local government is home rule.

States that allow counties to self-govern including Florida, Mississippi, and Louisiana may

exhibit different patterns of resilience than those that do not, namely Texas. The urban-rural

character of localities should also influence disaster management on the local level as urban

counties have a greater demand for emergency services but rural counties also face the

challenge of delivering services to a dispersed population.4 Disaster severity may matter for

local disaster resilience as it could set counties back that have suffered substantial damage;

on the other hand, disasters offer the opportunity for improving local conditions and

developing resilient capacities. Finally, a greater pool of resources in terms of staff,

expertise, and funding should enable counties to invest more in disaster management and

cultivate their resilience. A variety of data are used to measure these attributes.

Measuring Factors that Affect Resilience

Six categories to represent the urban-rural character of counties were adapted from the

Rural-Urban Continuum or Beale Codes from the United State Department of Agriculture.5

The first includes metropolitan counties6 with a population of one million or more. The

second category includes metropolitan counties with a population of 250,000 to one million;

the third group is comprised of metropolitan counties with a population less than 250,000.

The fourth group includes non-metropolitan but urban counties with populations of 20,000

or more while the fifth category includes urban counties with a population of 2,500 to

19,999. The final category includes completely rural counties with population less than

2,500.

Disaster severity is considered across three types of events – hurricanes, tornadoes, and

the BP Deepwater Horizon oil spill. Hurricane severity is measured as average hurricane

maximum property damage caused by hurricane events from 2002-2011 in millions of

dollars. These data were obtained from SHELDUS.7 Tornado severity is measured as

average maximum Fujita-scale (F-scale) value recorded for tornado events during the time

period 2002-2011. This information was obtained from NOAA’s National Weather Service

Storm Prediction Center.8 The BP oil spill variable was constructed from economic loss

zones and ranges from zero – no economic impact – to five – the most economic impact.

Higher values on all the disaster indicators indicate more severe disaster events.

Resources are measured in multiple ways. The first is external grants secured from the

Federal Emergency Management Agency. This includes the average number of Public

Assistance grants for all categories of work awarded during the time period 1998-2012.9

Also considered are the average number of Hazard Mitigation Grant Program projects

awarded to each county from 1989 to 2011.10

The second set of indicators reflects human

resources, namely the average number of emergency management staff by county. These

data were taken from surveys of county emergency managers. The third set of indicators in

the resource category represents the qualifications of the county emergency manager. This

includes average years of experience and percentage of those managers with college degrees.


I- 36

Information on emergency manager qualifications was gathered from responses to the

emergency manager survey.

These data were aggregated for each group to determine which patterns exist among

varying degrees of resilience. For some data averages were taken; for others the percentages

of the categories of the data were examined. This information is presented in Table 2. The

profiles that emerge are discussed in the following section beginning with the most resilient

category represented by Group 1 then moving to the least resilient class represented by

Group 4. Finally Groups 2 and 3 data are considered as indicative of a profile for moderate

resilience.

Most Resilient Profile

The most resilient counties are represented by Group 1’s characteristics. Over fifty

percent of the cases in this group are from Louisiana, and nearly one-third is from the state

of Florida. Both of these states permit home rule for county and parish governments, and

both have considerably reorganized and invested in their state emergency management

institutions and infrastructure following severe storms – Hurricane Andrew (1992) and

Hurricane Katrina (2005).

The majority of counties in the most resilient category are metropolitan areas, and none

are rural with populations under 2,500. The counties in this group have experienced

somewhat severe disaster damages. They have the highest tornado damage of all the groups

with an average F-scale of 1.3 and the second-highest hurricane property damage of $365.6

million. They also have the second to highest average BP economic loss claim zone – 2.1.

This indicates that the majority of the areas in these counties are in Zone C which is the third

tier of economic loss behind Zone A and B.

Regarding fiscal and human resources, Group 1 has secured the most Public Assistance

grants of the four groups and the second-most Hazard Mitigation grants from the Federal

Emergency Management Agency (FEMA). This group also has the highest average

emergency management office staff with an average of 4.6 employees and the highest

percentage (27.8%) of emergency management office staff numbering six employees or more.

Emergency managers in this group have the least amount of average experience (14.9 years)

but comprise the highest percentage of college graduates (66.7%).

Least Resilient Profile

The least resilient counties are represented by Group 4. They have moderate to low

adaptive capacities and low ratings of the adaptive process. Most of these counties are from

Texas (50%), but nearly a one-third is from Louisiana as well. Fifty percent of the cases in

this group are urban areas with small populations ranging from 2,500 to 19,999.

This group has had little disaster experience; in terms of property damage they have

been impacted by hurricanes the least of all the groups, averaging $315.2 million, and

tornado damage has been small as well with an average of 0.5 on the F-scale. They have also

been least affected economically by the BP oil spill. Their average claim zone score is 1.25

indicating that most of the counties in this group are in Zone D – the lowest category of

economic loss.


I- 37


I-38

Counties in this group have secured fewer FEMA Public Assistance and Hazard Mitigation

grants than Groups 1 and 2. Similarly, the number of employees in county emergency management

offices ranks third behind Groups 1 and 2 with an average of 3.8. Sixty-seven percent of the counties

in this group have emergency management office staff ranging from three to five, and nearly 17%

have staff of six or more. The average of county emergency manager experience is 18.5 years which

is the second highest of the four groups. Additionally, fifty percent of county emergency managers in

this category have a college education.

Moderate Resilience Profile

While Group 1 represents the most resilient cases and Group 4 the least resilient cases given

their adaptive capacity and process pairings, Groups 2 and 3 are indicative of moderate development

of resilience. Group 2 cases have high ratings of the adaptive process but moderate to low capacity

while Group 3 is the opposite – low ratings of the adaptive process but high capacity. This middle

ground is important because a majority (56%) of the cases fall into these categories.

The cases in Groups 2 and 3 are counties and parishes from all of the Gulf Coast states, except

Alabama.xi

Moreover, sixty-nine percent of Texas counties, 61% of Florida counties, and all of the

Mississippi counties studied are considered to have moderate resilience. By contrast only 35% of

the Louisiana parishes included in the sample are in this middle ground. There is also a mix of

urban-rural counties in these two groups. Approximately 50% of the cases in Groups 2 and 3 are

metropolitan counties with populations ranging from 250,000 to over one million. Another quarter

is metropolitan areas with populations less than 250,000. There are urban counties with smaller

populations as well, and the two counties in the study that are rural with populations less than 2,500

belong to Group 2.

These groups are mixed with regards to their disaster experience as well. They have the

highest hurricane property damage with $461.7 million for Group 2 and $791.7 million for Group 3.

However, Group 3 has not experienced recent tornadoes, and Group 2’s experience has been

moderate in comparison to the other groups with an average F-scale of 1.1. BP oil spill damages

have been greatest for Group 3 (average of 2.5 claim zone) while Group 2 has been largely

unaffected (average of 1.4 claim zone).

Group 2 has had success at securing Public Assistance and Hazard Mitigation grants from

FEMA. It ranks second in average PA grants and first for HMGP grants of the four groups. It also

has the second to highest average number of county emergency management office staff with 4.4

employees, and its emergency managers have the highest average experience – 21.7 years.

However, it has the lowest percentage of county emergency managers with higher education; less

than 40% have a college degree. Group 3, on the other hand, has the fewest number of grants and

emergency management staff. Its county emergency managers also have the less average years of

experience than Groups 2 and 4.

Resilience Patterns

The patterns that emerge from intersecting capacities for and perceptions of resilience indicate

that highly resilient cases are in states with home rule institutions. Further examination of this

political institution reveals that 55% of the most resilient cases (those in Group 1) are counties that

have adopted home rule while only 35% of those cases in the moderate category (Groups 2 and 3)

and none of the counties in the least resilient group (Group 4) have home rule charters.

Additionally, the majority of the cases in the most resilient group are parishes from Louisiana while

the majority in the least resilient group are counties from Texas. Clearly, there state dynamics affect

how resilience develops locally.


I-39

The urban-rural character of counties also matters; however, the patterns that emerge for this

factor are much more mixed. Each profile of resilience includes counties of varying sizes. The only

pattern that is evident is that the most resilient cases do not include rural counties, which indicates

that there are particular challenges for building resilience in rural settings.

Disaster experiences also emerged as having distinct patterns among the groups. The most

resilient cases have suffered some disaster damages but not the most severe. The most severe

disasters have hit the moderately resilient group while the least resilient group has little experience

with disasters. This indicates that disasters can offer the opportunity to build resilience but that they

also disadvantage communities by straining their capacities.

Resources are also clearly connected to resilience. The counties that exhibit the most

resilience have secured the greatest number of Public Assistance grants as well as a considerable

amount of Hazard Mitigation Grant Program funding from FEMA. Additionally, the most resilient

cases have emergency management offices with the greatest number of staff and have the highest

percentage of emergency managers with college degrees. The moderate to least resilient cases have

medium sized to small emergency management staff and have emergency managers who have

considerable years of experience but not the highest rate of college graduation.

These findings point out that the development of local disaster resilience is limited in rural

settings and communities that have experienced severe disasters. However, resilience is improved

where local emergency management offices are supported with fiscal and human resources. This

underscores the resilience does not simply occur; rather investment is needed to nurture the

capacities and collective action needed for resilient responses to disaster events.

1 Cases in this category scored a three or lower on the adaptive capacity scale and ranked average

coordination as three or higher.

2 Cases in this group scored a four or higher on the adaptive capacity scale and ranked average

coordination as less than three.

3 Cases in this category scored a three or lower on the adaptive capacity scale and ranked average

coordination as less than three.

4 William L. Waugh Jr., “Management Capacity and Rural Community Resilience,” in Disaster

Resiliency: Interdisciplinary Perspectives, ed. Naim Kapucu, Christopher V. Hawkins, and

Fernando I. Rivera (New York: Routledge, 2013) 297.

5 U.S. Department of Agriculture, Economic Research Service, 2003 Rural-Urban Continuum

Codes [Downloadable Data File] (Washington DC: USDA, 2004), http://www.ers.usda.gov/data-

products/rural-urban-continuum-codes.aspx#.Udj8CKzlf2w (accessed April 5, 2012).

6 U.S. Department of Agriculture defines metropolitan as: “one urbanized area of 50,000 or more

population plus adjacent territory and have a high degree of social and economic integration.”

7 Hazards & Vulnerability Research Institute, The Spatial Hazard Events and Losses Database for

the United States, Version 10.0 [Online Database] (Columbia, SC: University of South Carolina,

2013), http://www.sheldus.org (accessed September 5, 2012).

8 National Oceanic and Atmospheric Administration, Storm Prediction Center Severe Weather GIS

(SVRGIS) [Online Database] (Norman: Storm Prediction Center, 2012),

http://www.spc.noaa.gov/gis/svrgis/ (accessed October 5, 2012).

http://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx#.Udj8CKzlf2w

http://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx#.Udj8CKzlf2w

http://www.sheldus.org/

http://www.spc.noaa.gov/gis/svrgis/


I-40

9 Federal Emergency Management Agency Library, FEMA Public Assistance Funded Projects

Summary [Downloadable Data File] (Washington DC: FEMA, 2012),

http://www.fema.gov/library/viewRecord.do?id=6299.

10 Federal Emergency Management Agency Library, FEMA Hazard Mitigation Program Summary

[Downloadable Data File] (Washington DC: FEMA, 2012),

http://www.fema.gov/library/viewRecord.do?id=6293.

11 There are, however, only two counties from Alabama in the study, and only one responded to the

survey of county emergency managers.

Do not cite without author’s permission.

http://www.fema.gov/library/viewRecord.do?id=6299

http://www.fema.gov/library/viewRecord.do?id=6293

Documents

Port of Houston Challenges and Issues - University of Houstonhurricane.egr.uh.edu/sites/hurricane.egr.uh.edu/... · well as everything in-between. With advances in geospatial technology,