Climate Change in Coastal Areas in Florida:Sea Level Rise Estimation and

Economic Analysis to Year 2080

Dr. Julie Harrington, DirectorCenter for Economic Forecasting and Analysis

Florida State UniversityInnovation Park

2035 E. Paul Dirac Dr.Suite 129 Morgan BldgTallahassee Fl 32310

Dr. Todd L. Walton, Jr., DirectorBeaches and Shores Resource Center

Florida State UniversityInnovation Park

2035 E. Paul Dirac Dr.Suite 203 Morgan BldgTallahassee Fl 32310

February 2007

This research was supported by a grant

from the National Commission on Energy Policy.

Summary Results

The science and economics of sea level change has been researchedin the past, with primary focus based on erosion of the shoreline,and human adaptation. This study serves to add to sea level riseresearch from two perspectives: one, through the estimation of sealevel rise based on historical local tidal gage data, and the other per-spective, to examine storm event and corresponding cost damages(based on intensity and storm surge return period) and associatedchanges in property values in a six coastal county case site area ofFlorida (Dade, Duval, Monroe, Escambia, Dixie and Wakulla).

The following project had a two-pronged approach. The data col-lection and estimation of sea level change, based on historical tidalstation gage data, was performed by the FSU Beaches and ShoresResource Center. The approaches to SLR estimation examined theeffects of accelerated SLR, including subsidence. In addition, thisproject utilized projected changes in eustatic SLR for years 2030and 2080, based on the Intergovernmental Panel on Climate Change(IPCC) 2001 estimates. Based on the FSU Beaches and Shores Re-source Center findings and IPCC data, the FSU Center for EconomicForecasting and Analysis (CEFA) estimated storm event return pe-riod and cost damages, storm surge, and property values affected asa result of sea level rise, for the six county case sites in Florida.

• Summary Result

– Sea Level Rise Estimate

1. The FSU Beaches and Shores Resource Center foundthat although there was a wide distribution of differentgage sites over the Florida Peninsula, the projected sealevel rise in year 2080 does not vary substantially, thelargest value being 0.35 meters in St. Petersburg, FL,while the smallest value is 0.25 meters in Fernandina,FL.

2. This study is the first known work to explore sea levelrise forecasting methods beyond the traditional poly-nomial linear estimation forecasting methods utilizinggage data. The second order approach used in this

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study is in accordance with climate modeling scenariosthat project an exponential sea level rise due to green-house gas effects.

– Economic Analysis of Sea Level Rise Estimate

1. There will be an increasing return period frequencywith an associated sea level rise. That is, more se-vere storm events will become more frequent than inthe past. Cost damages associated with storm eventscan also be expected to increase with respect to sealevel rise. Based on the IPCC sea level rise estimates,regarding a number of coastal counties, people livingalong the coast are projected to experience propertylosses at twice the rate of normal.

2. The storm model findings convey a considerable lossof property, in terms of property values and land area,due to varying sea level rise for years 2030 and 2080.

This study was able to establish a reasonable range of low to highsea level rise estimates to year 2080, with FSU providing the lowerbound for sea level rise based on historical gage data, and the IPCC2001 sea level estimates providing the upper bound, based on cli-mate modeling scenarios. The results of this project underscore theimportance of including sea level rise as a critical component in thehazard preparedness and mitigation planning for coastal communi-ties. In addition, the SLR impact on property values was analyzedwith the GIS Storm Model Regressions. Finally, the damage costestimates of storm surge for each 10 cm SLR were compared to theproperty values at risk with similar SLR.

Key Words:Sea Level Rise (SLR), Property Value, Damage Cost, Storm Surge,Return Period

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Contents

1 Introduction 9

2 Projected Sea Level Rise (to 2080) in Florida, Basedon Tide Station Records 142.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 142.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . 162.3 Methodology For Sea Level Forecasts . . . . . . . . . 182.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . 23

3 Economic Analysis of Sea Level Rise 263.1 Regression Model(s) For Hurricane Return Years and

Damage Cost . . . . . . . . . . . . . . . . . . . . . . 273.2 Hurricane Return Year(s) . . . . . . . . . . . . . . . 293.3 Damage Cost Assessment . . . . . . . . . . . . . . . 403.4 Economic Analysis of Property Loss for the Six Florida

Coastal Counties . . . . . . . . . . . . . . . . . . . . 453.5 ”Storm Model” Results . . . . . . . . . . . . . . . . . 463.6 Storm Surge Results . . . . . . . . . . . . . . . . . . 473.7 Alternate SLR Impact Analysis Using the GIS Storm

Model Regression Models . . . . . . . . . . . . . . . . 483.8 Summary and Conclusions . . . . . . . . . . . . . . . 56

4 Appendix A 65

5 Appendix B 66

6 Appendix C 72

7 Appendix D 86

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List of Figures

1 Map of Six Florida Counties . . . . . . . . . . . . . . 132 Fernandina Gage Station Forecast Filtered Sea Level

Rise . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Key West Gage Station Forecast Filtered Sea Level

Rise . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 St. Petersburg Gage Station Forecast Filtered Sea

Level Rise . . . . . . . . . . . . . . . . . . . . . . . . 245 Cedar Key Gage Station Forecast Filtered Sea Level

Rise . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 Pensacola Gage Station Forecast Filtered Sea Level

Rise . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Reduction of Hurricane Return in Wakulla County

Year(s) by Elevation Based on IPCC and FSU SeaLevel Rise Estimates . . . . . . . . . . . . . . . . . . 31

8 Reduction of Hurricane Return Year(s) in Dade Countyby Elevation Based on IPCC and FSU Sea Level RiseEstimates . . . . . . . . . . . . . . . . . . . . . . . . 33

9 Reduction of Hurricane Return Year(s) in Dixie Countyby Elevation Based on IPCC and FSU Sea Level RiseEstimates . . . . . . . . . . . . . . . . . . . . . . . . 34

10 Reduction of Hurricane Return Year(s) in Duval Countyby Elevation Based on IPCC and FSU Sea Level RiseEstimates . . . . . . . . . . . . . . . . . . . . . . . . 36

11 Reduction of Hurricane Return Year(s) in EscambiaCounty by Elevation Based on IPCC and FSU SeaLevel Rise Estimates . . . . . . . . . . . . . . . . . . 38

12 Reduction of Hurricane Return Year(s) in MonroeCounty by Elevation Based on IPCC and FSU SeaLevel Rise Estimates . . . . . . . . . . . . . . . . . . 39

13 Damage Cost and Storm Surge Estimates in WakullaCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 42

14 Damage Cost and Storm Surge Estimates in DadeCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 42

15 Damage Cost and Storm Surge Estimates in DixieCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 43

16 Damage Cost and Storm Surge Estimates in DuvalCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 43

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17 Damage Cost and Storm Surge Estimates in Escam-bia County . . . . . . . . . . . . . . . . . . . . . . . . 44

18 Damage Cost and Storm Surge Estimates in MonroeCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 44

19 Property Values at Risk with Sea Level Rise of DadeCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 49

20 Storm Surge Damage Cost with Sea Level Rise ofDade County . . . . . . . . . . . . . . . . . . . . . . 49

21 Property Values at Risk with Sea Level Rise of DixieCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 50

22 Storm Surge Damage Cost with Sea Level Rise ofDixie County . . . . . . . . . . . . . . . . . . . . . . 50

23 Property Values at Risk with Sea Level Rise of DuvalCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 51

24 Storm Surge Damage Cost with Sea Level Rise ofDuval County . . . . . . . . . . . . . . . . . . . . . . 51

25 Property Values at Risk with Sea Level Rise of Es-cambia County . . . . . . . . . . . . . . . . . . . . . 52

26 Storm Surge Damage Cost with Sea Level Rise ofEscambia County . . . . . . . . . . . . . . . . . . . . 52

27 Property Values at Risk with Sea Level Rise of Mon-roe County . . . . . . . . . . . . . . . . . . . . . . . 53

28 Storm Surge Damage Cost with Sea Level Rise ofMonroe County . . . . . . . . . . . . . . . . . . . . . 53

29 Property Values at Risk with Sea Level Rise of WakullaCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 54

30 Storm Surge Damage Cost with Sea Level Rise ofWakulla County . . . . . . . . . . . . . . . . . . . . . 54

31 Hurricane Return Year(s) and Cost Damage in WakullaCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 66

32 Hurricane Return Year(s)and Cost Damages in DadeCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 66

33 Hurricane Return Year(s) and Cost Damages in DixieCounty . . . . . . . . . . . . . . . . . . . . . . . . . . 67

34 Hurricane Return Year(s) and Cost Damages in Du-val County . . . . . . . . . . . . . . . . . . . . . . . . 67

35 Hurricane Return Year(s) and Cost Damages in Es-cambia County . . . . . . . . . . . . . . . . . . . . . 68

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36 Hurricane Return Year(s) and Cost Damages in Mon-roe County . . . . . . . . . . . . . . . . . . . . . . . 68

37 Increase Damage Cost in Wakulla County . . . . . . 6938 Damage Cost and Return Period for Dade County . . 6939 Damage Cost and Return Period for Dixie County . . 7040 Damage Cost and Return Peroid for Duval County . 7041 Damage Cost and Return Period for Escambia County 7142 Damage Cost and Return Period for Monroe County 7143 Dade County . . . . . . . . . . . . . . . . . . . . . . 7244 Dixie County . . . . . . . . . . . . . . . . . . . . . . 7445 Duval County . . . . . . . . . . . . . . . . . . . . . . 7646 Monroe County . . . . . . . . . . . . . . . . . . . . . 8047 Wakulla County . . . . . . . . . . . . . . . . . . . . . 8248 Escambia County . . . . . . . . . . . . . . . . . . . . 84

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List of Tables

1 The Potential Cost of Sea-Level Rise Along the De-veloped Coastline of the United States (Billions of1990 Dollars) . . . . . . . . . . . . . . . . . . . . . . 10

2 Eustatic Sea Level Rise Scenarios (in meters) . . . . . 143 Florida Tide Stations Used with at Least 50 Years of

Historical Data . . . . . . . . . . . . . . . . . . . . . 174 Table 4. Forecast Relative Sea Level Rise From 2006

to 2080 by Florida County . . . . . . . . . . . . . . . 215 Forecast Relative Sea Level Rise from 2006 to 2080 . 226 Regression Results for Elevation Return Years by County 287 Hurricane Return Year(s) for Recent Hurricane Events

by County . . . . . . . . . . . . . . . . . . . . . . . . 298 Storm Events Totals (in 2006 $)by County of Loss

Occurrence, 2004 . . . . . . . . . . . . . . . . . . . . 409 Storm Event Totals (in 2006 $) by County of Loss

Occurrence, 2005 . . . . . . . . . . . . . . . . . . . . 4010 Damage Cost and Storm Surge Regression Equations

for Recent Hurricane Events by County . . . . . . . . 4111 Sea Level Rise, Cost Damage and Storm Surge Esti-

mates by County . . . . . . . . . . . . . . . . . . . . 4112 Land Value and Land Area at Risk from Four Sea

Level Rise Scenarios For Six Florida Counties . . . . 4713 Storm Surge Projections Based on FSU and IPCC

SLR Estimates . . . . . . . . . . . . . . . . . . . . . 4814 Regression Results of Property Values at Risk . . . . 5515 Comparison of Marginal Storm Surge Damage Cost

and Marginal Property Values at Risk Assessment . . 56

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1 Introduction

The economic impact of sea level change has been researched inthe past, with primary focus based on erosion of the shoreline, andhuman adaptation. This study will provide an estimate (to year2080) of sea level and hurricane prediction changes (based on inten-sity and storm surge return period) and projected economic analysisof affected property values in a six coastal county case site area ofFlorida (Dade, Duval, Monroe, Escambia, Dixie and Wakulla).

The US has approximately 12,400 miles of coastline and morethan 19,900 miles of coastal wetlands. Frequently cited studies ofthe ’quantitative’ economic impacts of global climate change includeNordhaus (1991), Cline (1992), Fankhauser (1995) and Tol (2002)1.These studies estimated the costs of climate change in the US in theareas of farming, forestry, fisheries, energy, water supply, ecosystemloss, human amenity, life /morbidity, migration, air pollution, waterpollution and natural hazards. The summary of the results showthat annual costs range from $48.6 to $121.3 billion with associatedtemperature rises from 2.5 to 4◦C, and with corresponding sea levelrises of one meter (to year 2100). Those annual costs are compa-rable with a loss of between 1-2.5% of US GDP. Based on a studyin 2000, by Neuman, et al., Table 1 illustrates the potential cost ofsea level rise along the developed coastline of the U.S., in 1990 dol-lars2. Another recent study (Greenpeace, 2006) estimated the costsof adapting to a one meter sea level rise in the US would amount toUS $156 Billion (3 percent of GDP).3

1Tol, R. S. J. 2002. Estimates of the Damage Costs of Climate Change. Part 1: BenchmarkEstimates . Environmental and Resource Economics 21: 41-73.

2Neumann, J.E., G. Yohe, R. Nicholls, M. Manion, 2000. Sea-Level Rise and GlobalClimate Change: A Review of Impacts to U.S. Coasts.

3http://www.greenpeace.org/international/campaigns/climate-change/impacts/sea level rise

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Table 1 — The Potential Cost of Sea-Level Rise Along theDeveloped Coastline of the United States (Billions of1990 Dollars)

Global Sea-Level Rise(source)

Measurement AnnualizedEstimate

CumulativeEstimate

AnnualEstimate in2065

100 cm (Yohe, 1989) Property at riskof inundation

n/a 321 1.37

100 cm (EPA,1989) Protection n/a 73-111 n/a100 cm (Nordhaus, 1991) Protection 4.9 n/a n/a100 cm (Fankhauser, 1994) Protection 1.0 62.6 n/a100 cm (Yohe, et al., 1996) Protection and

Abandonment0.16 36.1 0.33

50 cm (Yohe, 1989) Property at riskof inundation

n/a 138 n/a

50 cm (Fankhauser, 1994) Protection 0.57 35.6 n/a50 cm (Yohe et al., 1996) Protection and

Abandonment0.06 20.4 0.07

50 cm (Yohe andSchlesinger, 1998)

Expected Protec-tion and Aban-donment

0.11 n/a 0.12

100 cm (Yohe andSchlesinger, 1998)

Expected Protec-tion and Aban-donment

0.38 n/a 0.40

41 cm mean (Yohe andSchlesinger, 1998)

Protection andAbandonment

0.09 n/a 0.10

10 cm, 10th percentile (Yoheand Schlesinger, 1998)

Protection andAbandonment

0.01 n/a 0.01

81 cm, 90th percentile (Yoheand Schlesinger, 1998)

Protection andAbandonment

0.23 n/a 0.31

The costs of sea level rise can be expressed as the capital cost ofprotection plus the economic value of land and structures at loss orat risk. Agricultural impacts can be expressed as costs or benefitsto producers and consumers. Non-market impacts, such as the im-pacts on ecosystems or human health is more difficult to quantify,although there is a broad array of literature on valuation theoryand application, primarily in non-climate change journals (Tol etal., 2000).

Currently, economic impact projections based on sea level changehave not been reported for the state of Florida. However, there area number of climate change studies that have been conducted inFlorida. Yohe, et al., conducted sea level change research for cer-tain sites in Florida (Miami, Key West, Port Richey, Apalachicola,and St. Joseph) on ”no foresight” and ”perfect foresight” scenarios,

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regarding gradual erosion loss and adaptation of the market withrespect to the sea level predictions, using a benefit-cost decisionmaking framework for estimating the human response to sea levelrise. The Yohe estimates examined 33, 67, and 100 cm SLR scenar-ios, and relied on relatively low-resolution elevation data by today’sstandards. The first option assumes sufficient advance warning ofSLR and fairly rapid market response to the perceived threat. Thesecond option reacts to the imminent loss of property at the timeof inundation, while the last option accepts protection as given andsimply seeks to minimize its costs. In general, costs for the advancedforesight option are lower than for the wait-and see option, especiallyfor the two higher SLR scenarios, but this advantage requires moreprecise knowledge of the course of SLR and and effective market-based retreat policy. Costs are highest for permanent protection.For Florida, Roger Pielke, Jr. (1998) found that hurricane damagefor a 1926 Miami hurricane, in normalized 1992 dollars4, was $39billion.

Florida is particularly vulnerable to sea level change. Florida isthe fourth most populated state (17.5 million people in 2005) andprojected to increase 47% by the year 2025, according to the U.SBureau of Census. Approximately 4,500 square miles (of the to-tal 66,000 square miles) in Florida are within 4.5 feet of sea level.According to a recent EPA study, a one foot rise would erode mostFlorida beaches at least 100-200 feet unless mitigation measures wereused. The Florida South Water Management District, in a recentstudy of the impact of sea level rise on the water resources of theregion, found that a 15 cm sea level rise would result in flooding insoutheastern coastal Florida and a greater need for water use cut-backs. They also found that certain areas throughout the Districtwould need additional freshwater deliveries to offset the increasinglyhigher saline water intrusion. Another recent study conducted bythe National Wildlife Federation and Florida Wildlife Federation,of sea level rise (using a mid-range scenario of 15 inches) for ninecoastal areas in Florida found that about 50 percent (23,000 acres)of saltmarsh and 84 percent (167,000 acres) of tidal flats at thesesites would be lost by year 2100. The area of dry land is projected to

4Assumption was that losses are proportional to three factors: inflation, wealth, and pop-ulation.

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decrease by 14 percent (175,000 acres) and about 30 percent (1,000acres) of ocean beaches and 2/3 (5,880 acres) of estuarine beacheswould disappear.

The tourism industry is acutely at risk to sea level rise in Florida.Tourism is the number one industry in the state, primarily due to itsnatural resources, a favorable climate, an immense shoreline, themeparks, professional and major university sports, major airports andcruise industry ports, cultural events and retirement communities.The number of Florida tourists reached a record 84.6 million in2006, and is projected to grow to 89 million by 2010 (Visit Florida,2006). Currently, 1.3 million Florida jobs are directly or indirectlyrelated to tourism, and projected to grow to between 1.5 and 1.8million by 2010. The 2005 Florida Visitor Study reported that thestate collected $3.7 billion in tourism/recreation sales taxes in 2005.That is, $62 billion was infused into the state’s economy duringthe year through tourist expenditures. (Visit Florida, 2005). Inaddition to tourism, the loss of coral reefs, coastal estuaries and as-sociated fisheries due to sea level rise would also negatively impactFlorida’s economy. There are options for adaptation to sea levelrise in Florida including: elevating existing areas, building sea wallsand flood control structures, and encouraging relocation. In a re-cent study by the Natural Resources Defense Council, they foundthat beach re-nourishment alone would cost between $50-$60 billion(current dollars) over the next 100 years.

There was a two-pronged approach regarding this project. Thedata collection and estimation of sea level rise, based on historicaltidal station gage data, was performed by the FSU Beaches andShores Resource Center. The approaches to SLR estimation in-cluded the effects of accelerated SLR and subsidence. This studyalso included eustatic SLR for years 2030 and 2080, correspondingto the Intergovernmental Panel on Climate Change (IPCC) 2001 es-timates (Table 2). Based on the FSU Beaches and Shores ResourceCenter findings, the FSU Center for Economic Forecasting and Anal-ysis (CEFA) used the FSU SLR and IPCC estimates to examinevarious SLR scenarios with respect to associated hurricane/stormevent cost damages and property values for the six county case sitesin Florida (dependent on the level of forecasted inundation).

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In addition, this project utilized projected changes in eustaticSLR for years 2030 and 2080, based on the Intergovernmental Panelon Climate Change (IPCC) 2001 estimates (Table 2). In a recentstudy conducted by Rahmstorf, et al., (2007) of carbon dioxide con-centration, global-mean air temperature and sea level changes, theyfound that the IPCC may have underestimated, in particular, thesea level rise projections. They concluded that the rate of sea levelrise for the last 20 years is 25% higher than any other 20 year periodin the preceding 115 years. Although the time interval is relativelyshort and could be attributed to internal decadal climate variability,the authors do stress that the largest contribution to the rapid sealevel rise come from ocean thermal expansion and the melting fromnon-polar glaciers, and include increasing evidence that the ice sheetcontribution is also rapidly increasing.

Based on the FSU Beaches and Shores Resource Center findingsand IPCC data, the FSU Center for Economic Forecasting and Anal-ysis (CEFA) estimated storm event return period and cost damages,storm surge, and property values affected as a result of sea level rise,for the six county case sites in Florida.

Figure 1 — Map of Six Florida Counties

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Table 2 — Eustatic Sea Level Rise Scenarios (in meters)Year Low Middle High2030 0.05 0.10 0.152080 0.10 0.30 0.65

*IPCC Estimates, 2001

2 Projected Sea Level Rise (to 2080) in Florida,Based on Tide Station Records

2.1 Introduction

Rising sea level has important economic consequences for Floridawhich has a relatively low lying coastal zone. Rising sea level willinundate low coastal areas of Florida and cause salt water intrusioninto coastal aquifers and coastal estuaries. Additionally, beach anddune recession will occur as a result of the rising sea level by creatinga sediment budget deficit in the offshore area. This shore recessionis a function of the sea level rise rate, the active beach profile width,and the depth of closure as first postulated by Bruun (i.e. see Deanand Dalrymple (2002)). Just what sea level in Florida5 will looklike in 2080 is an unanswered question but deserving of scientificinvestigation.

Looking at the broader picture, a global rise in sea level is pre-dicted by various climatic modelers in the following references: Hoff-man et al.(1983), National Academy of Sciences (1983,1985) , EPA(1989), Barth and Titus (1984), National Research Council (1987),IPCC (1990), Houghton et al (1990), National Research Council(1990), Church et al (1991), Wigley and Raper (1992), Titus andNarayanan (1995), and U.S. National Report to IUGG (1995). Inmore recent findings noted above, the Intergovernmental Panel onClimate Change (IPCC) report (Houghton et al.(1990)) suggests ascenario of global warming and consequent global sea level changeof 0.18 meters by 2030 and 0.44 meters by 2070. An independentlyresearched climatic scenario of Church et al. (1991) has calculateda sea level rise of 0.35 meters by 2050. In 2001, the IPCC projectedthat sea level rise would increase by 0.09 to 0.88 meters by 2100 over1990. The range is mainly the result of uncertainties about green-

5Todd L. Walton Jr.Director, Beaches and Shores Resource Center, Institute of Scienceand Public Affairs, Florida State University

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house gas emissions scenarios, temperature sensitivity of the climatesystem, and glacial melt. Recent studies show that net melting fromGreenland and the West Antarctic Ice sheet may be happening (e.g.,Thomas et al., 2004; Cook et al., 2005; Chen et al., 2006; Luthcke etal., 2006; Velicogna and Wahr, 2006; Shepherd and Wingham, 2007.In 2007, the IPCC slightly lowered its estimate of sea level rise to0.18 to 0.59 meters. The lowering was mainly the result of changesin estimates of the contribution of thermal expansion of the oceansto sea level. The IPCC admitted that it is challenging to factorin the future contribution from melting ice sheets. Using an em-pirical approach (comparing observed sea level and temperatures)Rahmstorf (2006) projected that sea level will rise 0.5 to 1.4 metersby 2100 over 1900. Empirical approaches can be very sensitive torecent trends. These climatic modelers suggest that recent globalwarming due to greenhouse gas accumulation in the environmentfrom industrial consequences has brought about sea level rise ac-celeration. The U.S. National Report to IUGG (1995) notes that ”Recent analyses indicate that global sea level has risen at somethingclose to 2 mm per year for at least the last century or so” based onthe work of Peltier and Tushingham( 1989), Trupin and Wahr(1990)and Douglas(1991), and also states that ”.... for the next centuryvarious authors plausibly argue that global sea level will rise at amuch faster rate than at present because of global warming”. Thusmuch climatic modeling work is predicated on the working basisthat global sea level is not only rising but also accelerating due toincreasing levels of greenhouse gases in our environment.

Just how these global sea level projections translate to Floridaof vital importance to economic projections for both Florida coastaldevelopment decisions as well as population growth decisions.

The overall acceptance of an acceleration in sea level rise may notbe an agreeable conclusion to all scientists based on past water leveldata record analysis. For example, The IPCC (2007) in reviewinginfomration on observed sea level rise concluded that:* Since 1961, 80% of the climate system’s warming has been ab-sorbed by the oceans and warming has been observed up to 3000meters in depth.* Mountain glaciers (not included Antarctica and Greenland) and

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snow cover have decreased, contributing to sea level.* Since 1993, losses of glacial mass from Greenland and Antarcticahave contributed to sea level rise.* Global sea level rose at a faster rate in the 20th century than inthe 19th century. The rate of sea level appears to have increasedsince the early 1990s, although natural variability cannot be ruledout as a cause of the increase.

In fact, it is felt by the author that future prediction should bepragmatic in providing for worst case economic scenarios that in-clude the possibility of sea level rise acceleration and let the dataspeak for itself when it comes to future projections of sea level. Pre-vious water level data related estimates of ”relative” sea level risetrends have assumed a linear rise trend in sea level which does notallow for acceleration/deceleration in relative sea level with time.Such findings have primarily been based on statistical findings thatan acceleration component of sea level rise is not statistically dif-ferent from zero at a given confidence level (see for example Zervas(2001)). Although it can always be shown at some level of ”sta-tistical” significance that higher terms in a non-linear approach tosea level rise may be insignificant, it is rationalized here that from apragmatic standpoint due to the physics behind climatic modelingand due to the existing climatic studies suggesting acceleration ofsea level, a higher order trend should be considered in sea level mod-eling to assess forecasts based on the data while allowing for suchacceleration/deceleration. Additionally, in a relatively tectonicallystable area such as Florida, global sea level acceleration as providedby climatic modelers would translate to a relative sea level accelera-tion trend. It is on this basis that the following forecasts of sea levelrise for the year 2080 are estimated.

2.2 Data Sources

The data utilized in the present projected sea level rise scenar-ios is from the National Oceanic and Atmospheric Administration(NOAA) primary tide gage station network in Florida. Althoughnumerous coastal tide stations exist in Florida, most have opera-tional data for only short record periods and are not suitable for

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the analysis provided herein. Necessary length of data record is aconsideration in picking which stations to utilize in sea level riseanalysis as too short a record will not reflect a proper trend whiletoo long a record will allow for non-stationarity in the data seriesto hide important shorter term fluctuations that may govern theforecast period. Pugh (1987) demonstrated that 10 year trends at asite can have different signs, depending on the time interval chosen.In a similar approach Douglas (1991) used the San Francisco tidegage data ( the longest continuous record (140 years) in the U.S.)and found that 30 year trends computed anywhere in the entire se-ries varied from -2 to +5 mm per year using linear trend analysis.His findings are suggestive that a 30 year record would be too shortfor analysis (and consequent forecasting/extrapolation). In anotherfinding, Emery and Aubrey (1991) noted strong coherence of resultsfor sea level records longer than 40-50 years which might be sug-gestive that such a period is reasonable for forecasting future sealevels. Roemmich investigated sea level records at Bermuda andCharleston, S.C., and found that coastal and nearby mid-ocean sealevel trends differ markedly over several decades. His conclusionssuggest that 50 year records of sea level are necessary to understandthe fluctuations at a given coastal location. In concurrence with thefindings above, the tide stations utilized all have 50 years or longerof available historical data record. Stations and station numbersutilized in the analysis are listed below in Table 3.

Table 3 — Florida Tide Stations Used with at Least 50 Years ofHistorical Data

Station Name Station NumberFernandina, FL 8720030Key West, FL 8724580St.Petersburg, FL 8726520Cedar Key, FL 8727520Pensacola, FL 8729840

All of the tide station gages utilized are in somewhat protectedwaters, which is the reason for the more complete analysis recordsavailable. Although open coast tidal stations might be expected tohave a higher water level due to the effects of wave setup, since theanalysis herein is aimed at projecting differences in water level frompresent to the year 2080, this difference in water level is not im-

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portant for the purposes herein. The fact that the data records areless contaminated by wave setup effects is in fact a benefit for thepresent analysis which aims at projecting the low frequency waterlevel rise over an approximately 75 year period.

The monthly mean sea level series was utilized from each of theabove gages for the analysis herein. An additional gage with a longhistorical period of record is that of Mayport ,FL (Station Number8720220) but data was not available for the period 1999 through2005, hence this gage was not analyzed. Additionally, the proximityof the Mayport and Fernandina gages was investigated and it wasfound that the two gages had a strong linear correlation between thedata sets showing that either of the gages could be a proxy for theother. The Fernandina gage also had an extended period of datamissing (1960-1969) but as the data was missing from the middleportion of the historical series rather than at the end of the seriesit was felt that the Fernandina record would provide a more mean-ingful analysis period than the Mayport historical series.

2.3 Methodology For Sea Level Forecasts

To keep the sea level rise scenario projections on the same timeperiod footing, a starting date of January 1941 was used to providethe historical parameter fitting (except for the St. Petersburg se-ries for which available data started in 1947). This was the earliestmonthly mean data available for the Key West tide gage stationand hence the other series records were accordingly shortened (ex-cept for St. Petersburg) for the analysis provided herein. The fithistorical series period of record was from January 1941 (January1947 for St. Petersburg series) through December of 2005, while theforecast period of record was from 2006 to 2080 with the projectedestimates at year end 2080 provided. Since the historical period ofrecord utilized in fitting spanned approximately 69 years (63 yearsfor the St. Petersburg series) it is believed that projection (extrap-olation) to a forecast period of approximately 75 years is reasonable(i.e. roughly equal time spans of historical fit and future forecast).

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Although the investigated station series are complete for the mostpart, there are missing values in station records for some months(as shown in later graphics) which do not allow for analysis tech-niques such as linear or non-linear filtering which typically requirecomplete data series. Rather than attempt to provide estimates ofunmeasured data to fill in the incomplete series (i.e. see Walton(1996)), techniques utilized in the analysis were limited to both lin-ear and non-linear least squares analysis along with seasonal meanestimation which can be applied to incomplete series data.

As noted previously the model fitting is predicated on a workingassumption that global sea level (and consequently for Florida rela-tive sea level) is not only rising but also accelerating due to climaticinfluence of greenhouse gases. This scenario will later be comparedusing the same approach and data only with standard linear leastsquares estimation for the non-acceleration assumption.

As climatic modelers have provided the global sea level rise to bean exponential rise in form, the nature of the model for relative sealevel rise for the Florida sea level stations was chosen of a similarform, i.e.

y(t) = p1 + p2 ∗ exp(p3 ∗ t)

(1)

which can be expanded in series form to

y(t) = p1 + p2(1 + p3 ∗ t + (p3)2 ∗ t2/2 + ....higher order terms)

(2)

where t represents the time component (i.e. the monthly meansea level index) and where The modeled y is a seasonally filteredwater level developed by removing the seasonal (monthly) meansof the monthly mean sea level series. It should be noted that the

19

y(t) series being fit is not the raw data but rather the deseasonal-ized data residual where the actual fit or forecast data would be themodeled or forecast y(t) with the seasonal (month) average added.The use of the seasonal averaging filter was to reduce the noiseof the fit and hence provide more parameter stability, along withthe fact that missing data in the raw data series did not allow fortypical linear or non-linear filtering approaches without making ”es-timates” about the missing data. The approach utilized makes noapriori assumptions regarding missing (unavailable) data. It shouldbe noted here that although the model to be fit is assumed to be ofa non-stationary exponential form in this expansion approach, a se-ries expansion of a stationary harmonic model approach can also beshown to lead to a higher order polynomial model with dependentcoefficients.

Equation 2 can be simply reformulated as a linear higher orderpolynomial which in the case provided has been terminated withthe second order. The approaches for model fitting involved botha linear least squares approach to model parameter estimation aswell as a non-linear least squares approach to model parameter es-timation. As non-linear estimation routines require information re-garding starting parameter values, a linear approach was utilizedto formulate estimated starting values for model fitting in the non-linear least squares approach. It should be noted that non-linearestimation techniques are not guaranteed to provide stable fit pa-rameter values but as will be seen, in many of the water level gageseries fits to the data, the non-linear forecast sea level rise was foundto be very close to the linear second order forecast sea level rise thusconfirming the validity of the utilized approaches. Due to the factthat most of the gages fit provided comparable values by the twotechniques, the linear second order forecast sea level rise was chosenfor projecting final sea level rise scenarios in the year 2080. The lin-ear first order sea level rise is also provided for comparison purposes.

The fit y(t) series are shown as the ordinate values in Figures1 through 5 where the abscissa is the monthly time index (i.e. 1=January 1941, [ except for St. Petersburg where 1= January 1947]).The de-seasonalized monthly mean sea level data during the his-torical period are shown as points on the graphs and the solid line

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represents the de-seasonalized historical sea level data fit during thespan of the historical data and the de-seasonalized sea level forecastcurve during the forecast period. The estimate of sea level rise fromyears 2006 to 2080 is the difference in the solid line between thefinal forecast time (2080) and the final historical time (2005) andis summarized in Table 4. For informational purposes, a second setof forecast sea level rise values is also provided in Table 4 for theshorter time forecast horizon from 2006 to year 2030.

Table 4 — Table 4. Forecast Relative Sea Level Rise From 2006 to2080 by Florida County

County 2006-2030 2006-2080(in meters) (in meters)

Monroe 0.0845 0.310Escambia 0.0887 0.343Dade 0.0845 0.310Dixie 0.0714 0.275Duval 0.073 0.254Wakulla 0.0827 0.319

As a check on the utilized approach to filtering the data series byremoving the monthly means, a second approach was used on theKey West data series in which the data series was not de-seasonalized(i.e. monthly means were not removed), but the entire data set wasfit utilizing two additional parameters to represent the monthly se-ries as a harmonic. This approach considered a monthly cycle of theform with T=12 [months] for the yearly cycle and the two unknownsbeing Amp= Amplitude (m) , and Phase = Phase of cycle (radians).For the Key West series, a forecast to the year 2080 produced theexact same sea level rise forecast result (to 2 decimal places) as theprevious de-seasonalized approach and additionally produced simi-lar Gaussian residual magnitudes.

y(t) = Amp ∗ (cos(2 ∗ pi ∗ t/T − phase)) + p1m + p2m ∗ t + p3m ∗ t2

(3)

An interesting result from the analysis is that for the differentgage sites which are widely spaced over the Florida Peninsula, the

21

projected sea level rise in year 2080 does not vary by much withthe largest value being 0.35 meters in St. Petersburg, FL, while thesmallest value is 0.25 meters in Fernandina, FL. Additionally in allbut the Fernandina gage data, the second order non-linear term hasa parameter that is statistically different from zero (and positive)at a 95 percent significance level. It is believed that the Fernand-ina series provided a less than significant second order term due tothe large gap in the data and the higher tidal range that may beresponsible for magnification of error in the residual modeled.

Table 5 is provided to compare the sea level rise by the threemodeling approaches utilized (i.e. the linear first order, the linearsecond order, and the non-linear exponential)

Table 5 — Forecast Relative Sea Level Rise from 2006 to 2080

Station Relative SeaLevel Rise(inmeters) 1stOrder

Relative SeaLevel Rise(inmeters) 2ndOrder

Relative SeaLevel Rise(inmeters)Exponential

Fernandina, FL 0.16 0.25 0.27Key West, FL 0.15 0.31 0.28St.Petersburg, FL 0.18 0.35 0.36Cedar Key, FL 0.11 0.27 0.16*Pensacola, FL 0.13 0.34 0.21*

* parameter estimation suspect due to convergence problems

Table 4 suggests that for gages where non-linear estimation con-vergence was obtained, both the second order linear model and theexponential model were comparable as previously noted. This tablealso shows that the linear first order sea level rise estimates were onthe order of one half of the linear second order sea level rise esti-mates. Similar linear first order estimates can be projected from sealevel rise rates provided in Zervas (2001). The fact that the secondorder forecasts provided greater sea level rise than that providedby the linear ”standard” approach suggests that the scenario of anacceleration in sea level rise rather than a deceleration in sea levelrise is a more likely scenario on the basis of the actual data avail-

22

able. Residuals from the data fitting procedure for the Florida gagesare provided in Figures 1 through 5, and show that the data resid-uals for the methodology utilized provide reasonable Gaussian bellshaped curves suggestive that the higher order fitting is satisfactory.

2.4 Summary

Relative sea level rise has been forecast from the present (year2006) to the future year 2080 for long term water level gages aroundthe Florida Peninsula by three different methods. The second orderlinear approach is recommended in the final analysis for projectingeconomic scenarios of future costs due to sea level rise due to itsinclusion of a higher order term that allows for acceleration in sealevel rise in accord with climate modeling scenarios that project anexponential sea level rise due to greenhouse gas effects. Althoughthe present work is not definitive in regard to an accelerating sealevel rise, it is clear at least from the data available that trends areconsistent that there is not a deceleration in sea level rise and apragmatic approach to future economic planning should be in tunewith climatic model scenarios that suggest the strong possibility ofan accelerating sea level rise in Florida and future values of sea levelrise on the order of the magnitude herein.

Figure 2 — Fernandina Gage Station Forecast Filtered Sea LevelRise

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Figure 3 — Key West Gage Station Forecast Filtered Sea Level Rise

Figure 4 — St. Petersburg Gage Station Forecast Filtered Sea LevelRise

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Figure 5 — Cedar Key Gage Station Forecast Filtered Sea LevelRise

Figure 6 — Pensacola Gage Station Forecast Filtered Sea Level Rise

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3 Economic Analysis of Sea Level Rise

Property owners and visitors along hurricane-prone coastal areasshould be aware of hurricane information including path, intensityand potential corresponding damage in order to prepare for propertyprotection and survival. While precise hurricane forecast informa-tion from the National Hurricane Center is easily obtained, infor-mation is less readily available regarding potential damage costsassociated with hurricanes. Insurance and re-insurance companiesoften perform these types of analyses, however, their results are notfrequently released to the public. The public still does not have con-clusive and consensus evidence of increased intensity and frequencyof storms as a result of climate change. The estimation of dam-age cost is also variable, based on different methodologies. Windspeed along the hurricane path is generally used to predict damagecosts. In order to gain more precise damage estimates associatedwith sea level rise, the objective of this section was to link damagecost and storm return years (based on storm surge) as a means ofproviding beach protection options or mitigation strategies such asflood proofing, elevated structure, and building codes, among oth-ers. Since storm surge is positively correlated with wind speed, (CN-MOC, 2005) one can predict a positive relationship between flooddamage and wind damage. For this analysis, storm surge data basedon return period was linked to historical damage cost data to yielda potential future total damage cost for various size hurricanes6 foreach of the six counties. The economic analysis involved two var-ied methodologies to measuring cost damage associated with sealevel rise. One is termed ”Hurricane Return Years” and the otheris ”Damage Cost”7. The statistical methodology for ”HurricaneReturn Years” estimation and attendant confidence and predictionlimits has been detailed by Johnson and Watson (1998). The ”Dam-age Cost” analysis is based on insurance claims data provided by theFlorida Office of Insurance Regulation (FOIR). The FOIR empha-sizes that although they do analyze the insurance claimant data forcompleteness and reasonability, they do not formally audit nor verify

6Hurricane strength in terms of Category 1-5.7In order to fit both graphs in one figure, CEFA selected hurricanes that would fit most

appropriately on both the reduction of hurricane return year and hurricane cost damageassessment figures. Hence, a few counties figures will extend slightly beyond the slope on oneor two figures.

26

the data. It should be noted that the following results are location-specific for the six Florida counties and on an individual hurricanebasis, and are not representative in application to all storm eventsin all Florida counties.

3.1 Regression Model(s) For Hurricane Return Years andDamage Cost

From the late 1980’s to current years, the Federal Emergency Man-agement Agency (FEMA) estimated stillwater elevations and stormreturn year(s) for Florida counties. The storm surge stillwater eleva-tions are a function of tidal and wind setup effects, and contributionsfrom wave action. The storm surge elevations for the storm returnyear (10, 50, 100 and 500-year) floods were estimated by FEMA foreach of the six counties. FSU CEFA expressed this linear relation-ship as equation (4) for the study area(s).

The purpose of the FEMA Flood Insurance Studies was to in-vestigate the existence and severity of flood hazards in the areas ofFlorida Counties, and to aid in the administration of the NationalFlood Insurance Act of 1968 and the Flood Disaster Protection Actof 1973. FSU CEFA applied the Florida Office of Insurance Regu-lation data to generate storm surge and historical damage cost ofeight hurricanes in 2004 and 2005 (using interpolation and regres-sion) presented as equation (5). The data sources for Equation 5were obtained from the Hurricane Summary Data, Florida Office ofInsurance Regulation, 2006 (in 2006 Dollars) and the Tropical Cy-clone Report for 2005.

Hurricane Return Years8

Y = a + bX

(4)

whereY = Surge(ft)9, X = Y ear

8Return periods capture the essence of uncertainty in extreme meteorological phenomena(storm surge, wave, and wind) associated with hurricanes.

9Storm surge is simply water that is pushed toward the shore by the force of the windsswirling around the storm.

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Cost Damage Assessment10

Y = c + dM

(5)

whereY = Surge(ft), M = Damage Cost

For example, in Wakulla County, the regression equation forstorm surge and storm return period is presented as:

Y = 0.271412 + 5.90899logX

and the regression for storm surge and historical damage cost is

Y = 4.763688 + 0.922944M

(M in million dollars).

The regression summary results for regression equation 4 (elevationreturn years) are presented in Table 6.

Table 6 — Regression Results for Elevation Return Years by County

County T Stat P-Value R SquareDade Intercept 17.26 Intercept 0.003* 0.99

X variable 13.69 X variable 0.005*Dixie Intercept 4.89 Intercept 0.039* 0.99

X variable 14.89 X variable 0.004*Duval Intercept 2.30 Intercept 0.26 0.99

X variable 19.30 X variable 0.03*Escambia Intercept -1.39 Intercept 0.30 0.99

X variable 68.39 X variable 0.0002*Monroe Intercept 1.02 Intercept 0.42* 0.90

X variable 4.33 X variable 0.05*Wakulla Intercept 0.56 Intercept 0.63 0.99

X variable 23.69 X veriable 0.002*

Note:*95% Level of Significance

10Regression with 95% confidence

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3.2 Hurricane Return Year(s)

Table 7 illustrates the results of the storm surge elevation regres-sion(s) for current conditions, for the FSU SLR estimates to Year2080, and the corresponding IPCC SLR estimates to Year 2080,for the study area counties. FSU CEFA selected recent hurricaneevents which were representative of the counties, and with readilyavailable data for cost damage and storm surge elevations. This sec-tion highlights the reduction of hurricane return years findings foreach county. As can be expected, with both the FSU and IPCC es-timates for SLR, there is a consistent reduction in hurricane returnperiod. That is, one would expect that with SLR, a hurricane eventwith similar storm surge would occur more frequently. For example,a hurricane with surge of 7 feet would be expected under normalconditions to occur once every 76 years. Including FSU and IPCCSLR estimates, the same hurricane storm surge can be expected ev-ery 21 and 5 years, respectively.

Table 7 — Hurricane Return Year(s) for Recent Hurricane Eventsby County

County Hurricane Surge(ft)

ReturnPeriod(yrs)

SLR Estimates (ft) ReturnPeriod(yrs)

Dade Wilma 7.00 75.79 FSU 2030 0.28 51.252080 1.02 20.60

IPCC 2030 0.49 39.522080 2.13 5.18

Dixie Dennis 9.00 13.59 FSU 2030 0.06 13.282080 0.90 9.59

IPCC 2030 0.49 11.262080 2.13 6.00

Duval Frances 5.9 100.00 FSU 2030 0.24 80.172080 0.83 47.00

IPCC 2030 0.49 63.682080 2.13 14.03

Escambia Dennis 12 845.99 FSU 2030 0.29 731.802080 1.12 470.03

IPCC 2030 0.49 657.352080 2.13 272.41

Monroe Wilma 2.76 7.35 FSU 2030 0.28 6.042080 1.02 3.61

IPCC 2030 0.49 5.222080 2.13 1.65

Wakulla Dennis 9 29.96 FSU 2030 0.27 27.142080 1.05 20.40

IPCC 2030 0.49 25.052080 2.13 13.70

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Wakulla County -

The ground elevations are typically verylow throughout the county, ranging fromsea level to ten feet National Geodetic Ver-tical Datum (NGVD) near the coast togreater than 25 feet near the northern partof the county. As mentioned in the FEMAstudy11, the main flood hazard in terms ofdamage to Wakulla County is the inunda-tion of low-lying coastal areas during thepassage of a severe hurricane or tropicalstorm. The coastal area is very prone toextreme storm tides. The storm surge elevations are higher is cer-tain areas (west and south of Apalachee Bay) for two reasons; shal-low water depths extend a great distance offshore, thereby increas-ing the effect of bottom and wind friction which results in higherstorm surge elevations and second, storm generated winds out ofthe south-southeast create a flow of water in a north-west directionalong Florida’s west coast into Apalachee Bay.

A hurricane with an elevation of 12.5 feet, would have a return pe-riod12 of 100 years (i.e., that hurricane with associated storm surge12.5 feet in elevation would most likely occur once every 100 years).Hurricane Dennis resulted in a 9 feet high surge in Wakulla County.Based on FEMA’s study, it was classified as a 30 year event hur-ricane (using aforementioned Equation 4). For a sea level rise of0.27-feet and 1.05-feet scenarios (FSU estimates to year 2030 and2080), the same hurricane storm surge as Dennis will be reducedfrom 30 years to 27.14 years and 20.40 years respectively; for 0.49-feet and 2.13-feet scenarios (IPCC estimates to year 2030 and 2080),the same hurricane storm surge will be reduced to 25.05 years and13.70 years. Figure 7 indicates the scenario of reduction of hurricanereturn year(s) in Wakulla County, according to the IPCC and FSUSLR estimates to year 2080 . We can expect an increasing return pe-

11Federal Emergency Management Agency, Flood Insurance Study, Wakulla County Unin-corporated Areas, 1986. Community Number - 120315.

12Hurricane Return Years capture the essence of uncertainty in extreme meteorologicalphenomena (storm surge, wave, and wind) associated with hurricanes.

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riod frequency with a higher sea level rise scenario. This translatesinto a significant impact for coastal area residents. Since frequencyof the same extent storm return is increasing, the importance of en-suring adequate housing and beach protection seems critical in thethreatened areas.

Figure 7 — Reduction of Hurricane Return in Wakulla CountyYear(s) by Elevation Based on IPCC and FSU SeaLevel Rise Estimates

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Dade County -

The Dade County Flood InsuranceStudy was used for developing actuarialflood insurance rates, to update existingfloodplain regulations and by local and re-gional planners to further promote soundland use and floodplain development13. Thestudy encompassed all of Dade County,with the exception of the Everglades Na-tional Park (about 1/3 of the county). DadeCounty is flat and low with elevations gen-erally below ten feet NGVD. The westernand southern areas are marshy with a mean elevation of around 5feet mean sea level (MSL).

Hurricane Wilma resulted in a seven feet high surge in DadeCounty. Based on FEMA’s study, it was classified approximately asa 75.80 year event hurricane. For a sea level rise of 1-feet scenario(historical tidal gauge records FSU SLR estimate to year 2080 of 31cm), the same density hurricane as Wilma would be reduced froma 75.80 year to a 20 year event; for the IPCC SLR estimate to year2008, 2.132 ft (65 cm), it will reduced to be 5.20 years (Figure 8).Additionally, for a sea level rise of 0.28-feet scenario (FSU estimatesto year 2030), the same hurricane storm surge as Wilma will be re-duced from 75.80 years to 51.25 years; for 0.49-feet scenario (IPCCestimates to year 2030), the same hurricane storm surge will be re-duced to 39.52 years.

13Federal Emergency Management Agency, Flood Insurance Study, Dade County and In-corporated Areas, 1994. Community Number - 120635 - 120661.

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Figure 8 — Reduction of Hurricane Return Year(s) in Dade Countyby Elevation Based on IPCC and FSU Sea Level RiseEstimates

Dixie County -

The purpose of the Dixie County FloodInsurance Study was to investigate the exis-tence and severity of flood hazards in DixieCounty, and to aid in the administration ofthe National Flood Insurance Act of 1968and the Flood Disaster Protection Act of197314. The study was also used to con-vert Dixie County to the regular program offlood insurance by the FEMA. In addition,local and regional planners use the studyto promote sound flood plain management.The study covered all of the unincorporated areas of Dixie County.Dixie County is on the North Florida Gulf Coast, about 90 milessouth of Tallahassee, and about 100 miles north of Tampa. DixieCounty is low in elevation, with gently sloping and poorly drained

14Federal Emergency Management Agency, Flood Insurance Study, Dixie County and Un-incorporated Areas, 1983. Community Number - 120336.

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marshy areas. The elevations range from 10 feet NGVD to higherareas in the northern portion of the county, extending to 60 feetNGVD. Hurricane Dennis resulted in a 9 feet high surge in DixieCounty. Based on FEMA’s study, it was classified approximately asa 15 year event hurricane. For a sea level rise of 1-feet scenario (his-torical tidal gauge records FSU SLR estimate to year 2080 of 31 cm),the same hurricane storm surge as Dennis would be reduced from a15 year to less than a 10 year event; for the IPCC SLR estimate toyear 2008, 2.132 ft (65 cm), it will reduced to be 9.60 years (Figure9). For a sea level rise of 0.06-feet scenario (FSU estimates to year2030), the same hurricane storm surge as Dennis will be reducedfrom 13.60 years to 13.28 years; for 0.49-feet and 2.13-feet scenarios(IPCC estimates to year 2030 and 2080), the same hurricane stormsurge will be reduced to 11.26 years and 6 years.

Figure 9 — Reduction of Hurricane Return Year(s) in Dixie Countyby Elevation Based on IPCC and FSU Sea Level RiseEstimates

34

Duval County -

The flood insurance study for DuvalCounty was used by the community to up-date existing floodplain regulations as partof the regular phase of the National FloodInsurance Program15. The information wasalso used by local and regional planner topromote sound land use and floodplain de-velopment. Due to flat terrain, many ar-eas inland experience shallow flooding af-ter a heavy rainfall. The City of NeptuneBeach is partially protected from the At-lantic Ocean by a seawall.

Hurricane Frances resulted in a 5.9 feet high surge in DuvalCounty. Based on FEMA’s study, it was classified as a 100 yearevent hurricane. For a sea level rise of 0.24-feet and 0.83-feet sce-narios (FSU estimates to year 2030 and 2080), the same hurricanestorm surge as Frances will be reduced from 100 years to 80.17years and 47 years respectively; for 0.49-feet and 2.13-feet scenar-ios (IPCC estimates to year 2030 and 2080), the same hurricanestorm surge will be reduced to 63.68 years and 14.03 years. Figure10 indicates the scenario of reduction of hurricane return year(s) inDuval County, according to the IPCC and FSU SLR estimates toyear 2080 .

15Federal Emergency Management Agency, Flood Insurance Study, City of Neptune BeachDuval County 1989. Community Number - 120079.

35

Figure 10 — Reduction of Hurricane Return Year(s) in DuvalCounty by Elevation Based on IPCC and FSU SeaLevel Rise Estimates

Escambia County -

The goal of the Escambia County FloodInsurance Study was used for developingactuarial flood insurance rates, to updateexisting floodplain regulations and by lo-cal and regional planners to further pro-mote sound land use and floodplain devel-opment, and to aid in the administration ofthe National Flood Insurance Act of 1968and the Flood Disaster Protection Act of197316. The study covered the entire geo-graphic area of Escambia County. The ter-

16Federal Emergency Management Agency, Flood Insurance Study, Escambia County andIncorporated Areas, 2000. Community Number - 120080,120082,120084,125138.

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rain in Escambia County is highly variable. Level to moderatelysloping terrain are indicative of the southwest portion of the county(west of Pensacola). The soils here are somewhat impermeable,and poorly drained. In addition, in the southwestern area are flat,low and marshy areas. In the central and northern portions of thecounty, there are rolling forested hills and moderately steep slopes.Elevations in these areas may reach up to 300 feet. Flooding inEscambia County is normally a result of tidal surge and overflow ofstreams and swamps associated with rainfall runoff.

Hurricane Dennis resulted in a 12 feet high surge in EscambiaCounty. Based on FEMA’s study, it was classified as approximatelya 900 year event hurricane. For a sea level rise of 0.29-feet and1.12-feet scenarios (FSU estimates to year 2030 and 2080), the samehurricane storm surge as Dennis will be reduced from 855 years to731.80 years and 470.03 years respectively; for 0.49-feet and 2.13-feet scenarios (IPCC estimates to year 2030 and 2080), the samehurricane storm surge will be reduced to 657.35 years and 272.41years. Figure 11 indicates the scenario of reduction of hurricanereturn year(s) in Escambia County, according to the IPCC and FSUSLR estimates to year 2080 .

37

Figure 11 — Reduction of Hurricane Return Year(s) in EscambiaCounty by Elevation Based on IPCC and FSU SeaLevel Rise Estimates

Monroe County -

The flood insurance study (FIS) forMonroe County was used to aid in the ad-ministration of the National Flood Insur-ance Act of 1968 and the Flood Disasterprotection Act of 1973. The FIS was alsoused to develop flood risk data for variousareas of the county that were used to estab-lish actuarial flood insurance rates and as-sist the county to promote sound floodplainmanagement17. The FIS covered the entiregeographic area of Monroe County. Resi-dential, commercial and industrial development in Monroe Countyoccurs mainly along the Florida Keys. The mainland remains largely

17Federal Emergency Management Agency, Flood Insurance Study, Monroe County andIncorporated Areas, 2005. Community Number - 12087CV000A.

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undeveloped and includes the Big Cypress National Preserve andEverglades National Park. Coastal areas bordering the AtlanticOcean and Gulf of Mexico are subject to storm surge flooding dueto hurricanes and tropical storms. Flood protection measures arenot known to exist in Monroe County.

Hurricane Wilma resulted in a 2.76 feet high surge in MonroeCounty. Based on FEMA’s study, a 100 year event hurricane wouldbe associated with a 6.75 feet high storm surge. For a sea level riseof 0.28-feet and 1.02-feet scenarios (FSU estimates to year 2030 and2080), the same hurricane storm surge as Wilma will be reducedfrom 7.30 years to 6.04 years and 3.61 years respectively; for 0.49-feet and 2.13-feet scenarios (IPCC estimates to year 2030 and 2080),the same hurricane storm surge will be reduced to 5.22 years and1.65 years. Figure 12 indicates the scenario of reduction of hurricanereturn year(s) in Monroe County, according to the IPCC and FSUSLR estimates to year 2080 .

Figure 12 — Reduction of Hurricane Return Year(s) in MonroeCounty by Elevation Based on IPCC and FSU SeaLevel Rise Estimates

39

3.3 Damage Cost Assessment

There are significant cost damages associated with hurricane events.FSU CEFA compiled data from the latest two years (2004 and 2005)from the Hurricane Summary Data Reports, presented in Tables 8and 9. Although the storm events listed below were of the strongercategory rating, the level of corresponding cost damages was highlyvariable. The cost damages were a function of storm intensity andproximity of the study area county to the associated hurricane.

Table 8 — Storm Events Totals (in 2006 $)by County of LossOccurrence, 2004

County Charley Frances Ivan JeanDade $3,008,721 $70,468,075 $2,865,950 $16,170,268Dixie $36,408 $4,945,128 $63,237 $971,682Duval $5,906,950 $72,322,498 $1,649,646 $22,404,237Escambia $1,001,182 $12,980,961 $2,010,001,983 $19,105,056Monroe $663,804 $4,945,128 $363,295 $133,665Wakulla $14,047 $1,854,422 $214,588 $193,451

Table 9 — Storm Event Totals (in 2006 $) by County of LossOccurrence, 2005

County Dennis Katrina Rita WilmaDade $5,976,177 $585,157,998 $4,396,620 $2,152,438Dixie $59,559 $1,742 $661 $33,104Duval $361,426 $831,764 $151,072 $1,055,752Escambia $70,706,486 $11,341,048 $150,867 $283,996Monroe $4,400,998 $27,907,960 $11,329,370 $215,335,831Wakulla $4,418,483 $588,457 $1,274 $28,279

*Hurricane Summary Data, Florida Office of Insurance Regulation, 2006

As mentioned previously in the model description and regressionequation(s), the regression for storm surge and historical damagecost is Y = 4.76+0.92M (in $2006 dollars). Overall, the damagecost estimates for recent hurricanes range from thousands to billionsfor the six counties. By using storm surge data and damage costestimates data; the FSU SLR estimates (which varied by county)and the IPCC standard SLR estimates of 2.13 feet (or 65 cm) wereapplied to the initial storm surge level to express a new regressionfor the damage cost estimates. The damage cost estimate equationsare presented in Table 10.

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Table 10 — Damage Cost and Storm Surge Regression Equationsfor Recent Hurricane Events by County

County Regression EquationWakulla Y = 4.76+0.92MMonroe Y = 0.86+0.02MDade Y = 1.40+2.67MDuval Y = 0.87+0.07MDixie Y = 2.82+0.1MEscambia Y = 4.93+0.10M*Y=Storm Surge(feet), M=$2006 Dollar

Table 11 — Sea Level Rise, Cost Damage and Storm SurgeEstimates by County

County Hurricane Surge(ft)

DamageCost

SLR FSUEsti-mates(ft)18

DamageCost

SLRIPCCEsti-mate(ft)

DamageCost

Dade Wilma 7.00 $2.10B 1.02 $2.48B 2.132 $2.90BDixie Dennis 9.00 $61

Thous0.91 $70.90

Thous2.132 $83.12

ThousDuval Frances 5.90 $69

Thous0.82 $80.15

Thous2.132 $98.00

ThousEscambia Dennis 12.00 $73

Thous1.12 $84.51

Thous2.132 $95.00

ThousMonroe Wilma 2.76 $233M 1.02 $298.40M 2.132 $370.13MWakulla Dennis 9.00 $4.59M 1.05 $5.73M 2.132 $6.90M

A storm similar to Hurricane Dennis in Wakulla County couldbring 50% more damage from $4.59 million to almost $7 million,as depicted in Figure 12. Dade County, from a Hurricane Wilmadamage cost perspective, would bring about a 40% increase in costdamages to around $3 billion (Figure 13). Dixie County experi-enced relatively low cost damages (based on filed reports) so wouldexperience a 37% increase in cost damages based on damages associ-ated with Hurricane Dennis (Figure 14). Duval County (HurricaneFrances strength hurricane) would result in a 42% increase (Figure15). Escambia County (Hurricane Dennis size) would result in a30% increase, to $95 Million (Figure 16). Monroe County (Hurri-cane Wilma, Figure 17) would experience a 60% increase in costdamages associated with a Wilma-sized hurricane in the year 2080(according to IPCC estimates of 65 cm).

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Figure 13 — Damage Cost and Storm Surge Estimates in WakullaCounty

Figure 14 — Damage Cost and Storm Surge Estimates in DadeCounty

42

Figure 15 — Damage Cost and Storm Surge Estimates in DixieCounty

Figure 16 — Damage Cost and Storm Surge Estimates in DuvalCounty

43

Figure 17 — Damage Cost and Storm Surge Estimates in EscambiaCounty

Figure 18 — Damage Cost and Storm Surge Estimates in MonroeCounty

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3.4 Economic Analysis of Property Loss for the Six FloridaCoastal Counties

The objective of this phase of the project was to determine thevalue of land that would be affected by SLR over time for the sixFlorida coastal counties19, based on the 2030 and 2080 sea level risescenarios. CEFA used both the FSU Beaches and Shores historicaltide gage data (for years 2030 and 2080), and the IPCC highest es-timates (for years 2030 and 2080). The four sea level rise scenariosprovide a range of property values affected by sea level rise over time.

FSU CEFA and Industrial Economics Inc., under the guidanceof James Neumann, created a GIS ArcView Model Builder tool toprocess Florida parcel data20 and combine it with Digital ElevationModel (DEM) elevation data (assigned to a tax parcel centroid).The model was termed the ”’storm model”’. DEM is a digital rep-resentation of ground surface topography or terrain.

The DEM data was downloaded from http://seamless.usgs.gov/for each of the six counties. For some of the counties, 10 meterDEM was available for only certain sections of the county. FSUCEFA decided that if 10M was not available for the entire county,then 30 meter resolution DEM would be feasible21. Using the DEMdata, FSU CEFA next generated a set of contour maps for eachcounty. CEFA used a surface analysis tool (3D Analyst) to createthe contours. The first contour was baselined at zero with a Z factor(increment) of one, then the next contour was drawn for one meter,and so forth. The parcel data map was then placed (overlaid) overthe contour map. A centroid points file (with unique identifier) wasnext created using X Tools Pro. One centroid was attributed to eachparcel.22 was then used to generate the parcel elevation file and to

19Dade, Duval, Dixie, Escambia, Monroe and Wakulla Counties20Florida Department of Revenue DR-590 (12D.8) parcel data. CEFA did not ver-

ify/validate the FDOR data, thus, the results of this study are based on the assumptionof the quality or correct topology of the FDOR parcel data. CEFA used the variable ”JustValue” to best reflect property values. The Department of Revenue uses just value for prop-erty tax assessment and recommended it’s use (personal communication, Dr. Ke-tsai Wu,FDOR Statistician)

21Dade, Escambia and Wakulla County were available for 10 meter DEM. Dixie and MonroeCounty were available for 30 meter DEM. Duval County only had a small portion of the countyavailable in 10 meter DEM, primarily inland.

22The storm model derives centroid elevation values from a spatial analyst function usingDEM data (parcels that are not within the shoreline range of the DEM are deleted). The

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process the output based on user defined sea level conditions. Themodel works for any SLR or storm surge value, within the limits ofthe DEM data, and appears to be returning both reasonable val-ues and maps of inundated areas. The results presented in Table 6,provide an estimate of land value and land area23at risk, for the sixcounty-wide data based on sea level rise scenarios.

3.5 ”Storm Model” Results

The results presented in Table 12, provide an estimate of landvalue and land area at risk for the six county-wide data based onsea level rise scenarios. The storm model findings convey a consider-able loss of property due to varying sea level rise for years 2030 and2080. For example, Monroe County will incur significant damages inall four scenarios, ranging from approximately $1 billion (Year 2030,FSU Beaches and Shores SLR estimate) to $5.7 billion (Year 2080,IPCC SLR estimate), in 2007 dollars. Similarly, according to theIPCC 2080 SLR scenario, Duval County will experience $644 mil-lion in damages. In general, concerning the more rural areas, suchas Wakulla and Dixie Counties, one would expect lower propertyvalues compared to the more urbanized counties. However, with sealevel rise, all the counties experience substantial property losses overtime.

The total land acres were much more difficult to quantify. Forexample, Duval County, during that same time period (2030-2050),can expect to lose approximately 31,000 land acres, based on thedata provided to FSU CEFA by the Department of Revenue landparcel database that was used for this study. This translates to ap-proximately 6.3% of total land area of Duval County.

model joins centroid (with DEM values) to the parcel data based upon parcel ID. It creates aselect statement that is user defined, such that parcels below a given elevation are lost (andthis value is calculated). Lastly, the model produces a shapefile of lost values for both justvalues and land values.

23Based on the parcel database and the associated errors with land values, these resultsare strictly based on the quality of the parcel database. Dade, Dixie, Escambia and MonroeCounties are using year 2005 parcel database; Duval County is using year 2004 parcel database;Wakulla County is using year 2007 parcel database.

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Table 12 — Land Value and Land Area at Risk from Four Sea LevelRise Scenarios For Six Florida Counties

County SLR Estimates PropertyValuesat Risk*($2007)

TotalLandatRisk(Acres)

%Cov-eredbyDEM

% ofLandAreaatRisk

CountyLandArea(Acres)

Dixie FSU 2030 0.0174m $1,953,152 93 100 0.02 450,5602080 0.275m $2,753,065 140 0.03

IPCC 2030 0.15m $2,656,830 122 0.032080 0.65m $4,275,514 190 0.04

Escambia FSU 2030 0.0887m $141,101,223 1,107 39.4 0.26 423,9362080 0.343m $279,520,579 3,196 0.75

IPCC 2030 0.15m $151,447,789 1,741 0.412080 0.65m $977,130,041 10,580 2.50

Monroe FSU 2030 0.0845m $922,092,573 4450 100 0.70 638,0162080 0.310m $3,264,205,082 14,671 2.30

IPCC 2030 0.15m $1,528,820,629 6,319 0.992080 0.65m $5,706,227,005 22,756 3.57

Wakulla FSU 2030 0.0827m $269,960 47 68.5 0.01 388,2882080 0.319m $1,055,766 83 0.02

IPCC 2030 0.15m $409,620 57 0.012080 0.65m $2,230,401 181 0.05

Dade FSU 2030 0.0845m $14,835,863 148 57.8 0.01 1,245,5042080 0.310m $56,980,539 195 0.02

IPCC 2030 0.15m $29,852,956 159 0.012080 0.65m $128,065,286 243 0.02

Duval** FSU 2030 0.073m $35,010,480 4,354 $100 0.88 495,1682080 0.254m $263,831,552 11,284 2.28

IPCC 2030 0.15m $171,618,164 6,307 1.272080 0.65m $643,562,482 30,945 6.25

* Based on Florida Department of Revenue DR-590 (12D.8) Parcel Data Just Values for2005.** Duval County had only a small portion of 10 meter DEM data available, mostly inland.

3.6 Storm Surge Results

Table 13 presents storm surge estimates based on the flood in-surance studies and for the insurance regulation data (for the sixcounties) for sea level rise estimates to 2080.

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Table 13 — Storm Surge Projections Based on FSU and IPCC SLREstimates

County Hurricane Surge(ft)

SLRFSUEsti-mates(ft)

ProjectionsBasedon FSUSLR Es-timates(ft)

SLRIPCCEsti-mate(ft)

ProjectionsBased onIPCCSLR Esti-mates

Dade Wilma 7.00 1.02 8.02 2.132 9.13Dixie Dennis 9.00 0.91 9.91 2.132 11.13Duval Frances 5.90 0.82 6.72 2.132 8.03Escambia Dennis 12.00 1.12 13.12 2.132 14.13Monroe Wilma 2.76 1.02 3.78 2.132 4.89Wakulla Dennis 9.00 1.05 10.05 2.132 11.13

Appendix A describes the project data sources for the GIS map-ping portion of this study, in greater detail. Appendix B (Figures31 through 42) depict the joint representation of hurricane returnperiod and associated cost damages based on SLR estimates. Ap-pendix C (Figures 43 through 48) illustrate sample coastal area sec-tions of the six county storm model results for years 2030 and 2080,based on FSU Beaches and Shores and IPCC sea level rise estimates.As can be expected with higher and variable SLR estimates, greaternumbers of land parcels become inundated over time.

3.7 Alternate SLR Impact Analysis Using the GIS StormModel Regression Models

To better illustrate the marginal increase rate of property valuesat risk with associated SLR, we implemented the aforementioned’Storm Model’. Staff used 10 cm as the interval to measure howmuch value of property became inundated with each 10 cm increaseof sea level rise; at 10 cm, 20 cm, 30 cm, 40 cm, 50 cm and 60cm increments of sea level rise. Appendix D describes the detailedmethodology. Scenarios greater than 60 cm are of course possible,but would not lead to a greater qualitative insight, as the highestIPCC estimate of SLR for year 2080, is 65 cm.

Although the potential economic developments in the next fewdecades are difficult to predict, an approximation of basic imple-mentation costs is possible. The following calculations are roughapproximations, however, they provide a useful first estimate and

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guideline for future data collection efforts. Figures 19 through 30show the marginal or added cost of property values at risk of each 10cm sea level rise increase of each county. In addition, a comparisonwas made regarding property values at risk and the previous sectiondamage assessment analysis on the cost of storm surge by (relativeto each 10 cm of sea level rise). Both property values at risk anddamage cost are measured in 2007 Dollars.

Figure 19 — Property Values at Risk with Sea Level Rise of DadeCounty

Figure 20 — Storm Surge Damage Cost with Sea Level Rise ofDade County

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Figure 21 — Property Values at Risk with Sea Level Rise of DixieCounty

Figure 22 — Storm Surge Damage Cost with Sea Level Rise ofDixie County

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Figure 23 — Property Values at Risk with Sea Level Rise of DuvalCounty

Figure 24 — Storm Surge Damage Cost with Sea Level Rise ofDuval County

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Figure 25 — Property Values at Risk with Sea Level Rise ofEscambia County

Figure 26 — Storm Surge Damage Cost with Sea Level Rise ofEscambia County

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Figure 27 — Property Values at Risk with Sea Level Rise ofMonroe County

Figure 28 — Storm Surge Damage Cost with Sea Level Rise ofMonroe County

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Figure 29 — Property Values at Risk with Sea Level Rise ofWakulla County

Figure 30 — Storm Surge Damage Cost with Sea Level Rise ofWakulla County

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Among the six counties, Monroe County has the highest marginalproperty value at risk. For each 10 cm increase in sea level, the prop-erty values at risk increase by $ 797.27 Million. The lowest marginalvalue at risk is in Dixie County. An equivalent rise in sea level wouldincrease the property values at risk by $168.92 Thousand. Based onthe results from the GIS ’Storm Model,’ the property values at riskwith sea level rise of each county are presented in Table 14.

Table 14 — Regression Results of Property Values at RiskCounty Regression Equation R-SquareDade Property Values at Risk = -1.69 + 205.91 Sea Level Rise 0.98Dixie Property Values at Risk = 2.01 + 1.40 Sea Level Rise + (2.9

E-06) (Sea Level Rise2̂)0.94

Duval Property Values at Risk = -36.39 + 1060.67 Sea Level Rise 0.96Escambia Property Values at Risk = 201.22 - 974.36 Sea Level Rise +

0.003 (Sea Level Rise2̂)0.98

Monroe Property Values at Risk = 340.50 + 7972.70 Sea Level Rise 0.99Wakulla Property Values at Risk = -0.04 + 3.40 Sea Level Rise 0.99

*Y: Sea Level Rise in Meters; M: Property Values at Risk in million 2007 $.

The damage cost from a storm surge would also increase after arise in sea level, as compared to an equivalent storm surge in theabsence of a sea level rise. Comparing with the increase in prop-erty values at risk, the previous damage cost analysis with similarSLR, for Dade County, the percentage is1%. For Dixie County, thepotential increase in damage costs associated with a storm surgeassessment, mentioned in the previous section, ranges from 0.2% to0.5% of increase in property values at risk. For Duval County, thepotential increase in previous storm surge damage costs estimatesranges from 0.003% to 0.009% of total increase in property values atrisk. For Escambia County, the percentage of potential increase inprevious storm surge damage costs estimates comparing to increasein property values at risk ranges from 0.002% to 0.005%. For Mon-roe County, the potential increase in previous storm surge damagecosts estimates ranges from 1%-2% of the total increase in propertyvalues at risk. For Wakulla County, the potential increase in previ-ous storm surge damage costs estimates of an equivalent storm surgein the absence of a sea level rise is about 0.1% of increase in prop-erty values at risk with same SLR. Table 15 details the percentageof comparing the previous storm surge damage costs analysis withproperty values at risk associated with a 10 cm sea level rise.

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Table 15 — Comparison of Marginal Storm Surge Damage Cost andMarginal Property Values at Risk Assessment

Sea LevelRise

Dade Dixie Duval Escambia Monroe Wakulla

0-10 cm 1% 0.2% 0.009% 0.003% 1% 0.1%11-20 cm 1% 0.2% 0.005% 0.004% 2% 0.1%21-30 cm 1% 0.4% 0.003% 0.005% 2% 0.1%31-40 cm 1% 0.5% 0.004% 0.005% 2% 0.1%41-50 cm 1% 0.5% 0.005% 0.003% 2% 0.1%51-60 cm 1% 0.5% 0.004% 0.002% 2% 0.1%

3.8 Summary and Conclusions

Sea Level Rise Estimation - The FSU Beaches and Shores Re-source Center performed the sea level rise estimation based on site-specific tidal gage records data (historical 60 years) for the followingsix counties in Florida: Dade, Duval, Monroe, Escambia, Dixie andWakulla; for years 2030 and 2080. An interesting result from theanalysis is that although there was a wide distribution of differentgage sites over the Florida Peninsula, the projected sea level rise inyear 2080 does not vary substantially, the largest value being 0.35meters in St. Petersburg, FL, while the smallest value is 0.25 metersin Fernandina, FL.

This study is the first known work to explore sea level rise fore-casting methods beyond the traditional polynomial linear estimationforecasting methods utilizing gage data. The second order linear ap-proach is recommended in the final analysis for projecting economicscenarios of future costs due to sea level rise. The second orderlinear approach includes a higher order term that accounts for ac-celeration effects in sea level rise. The second order approach is inaccordance with climate modeling scenarios that project an expo-nential sea level rise due to greenhouse gas effects. Although thepresent work is not definitive in regard to an accelerating sea levelrise, it is clear at least from the data available that trends are con-sistent and that there is no deceleration in sea level rise over time. Apragmatic approach to future economic planning should be in tunewith climatic model scenarios that suggest the strong possibility ofan accelerating sea level rise in Florida.

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Economic Analysis of Sea Level Rise - Given a 65 cm(Year 2080, IPCC estimate) sea level rise scenario, a similar stormevent will appear again sooner rather than later. This translates toa situation in a number of coastal counties, for storm events suchas hurricanes, where people living along the coastal areas will ex-perience property losses at twice the current rate. For example,Hurricane Dennis resulted in a 9 feet high surge in Wakulla Countyand according to FEMA’s study, was classified as a 30 year eventhurricane. For a sea level rise of 2.13-feet scenario (IPCC estimateto year 2080 of 65 cm), the same hurricane storm surge as Denniswill be reduced from a 30 year to a 13.7 year event. There will bean increasing return period frequency with an associated sea levelrise.

Cost damages associated with storm events can also be expectedto increase with respect to sea level rise. For example, for Dade andMonroe Counties, a Wilma-like hurricane with corresponding SLRof 65 cm in Year 2080, can expect to generate a 40% increase and a60% increase, respectively, in cost damages.

The storm model findings convey a considerable loss of propertydue to varying sea level rise for years 2030 and 2080. For example,Monroe County will incur significant damages in all four scenar-ios, ranging from approximately $1 billion (Year 2030, FSU Beachesand Shores SLR estimate) to $5.7 billion (Year 2080, IPCC SLRestimate), in 2007 dollars. Duval County will lose approximately31,000 land acres (or 6.2% of the total county area) due to sea levelrise by Year 2080. In general, concerning the more rural areas, suchas Wakulla and Dixie Counties, one would expect lower propertyvalues compared to the more urbanized counties. However, with sealevel rise, all the counties experience substantial property losses overtime.

The GIS Storm Model Regressions analyzed the SLR impact onproperty values. Each 10 cm of SLR will result in a substantialnumber of properties at risk of inundation. In addition, the damagecost estimates of storm surge for each 10 cm SLR were compared tothe property values at risk with similar SLR. For example, each 10cm of SLR will result in $ 797.27 million of property values at risk

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for Monroe County. Damage Cost associated with a same scenariostorm surge can be expected to increase by $13.75 million.

According to a recent report by the Southeast Regional Assess-ment Team on Preparing for a Changing Climate, changes in dis-turbance patterns (e.g., hurricanes, floods, etc.) are possibly moresignificant in terms of potential economic losses than longer termchanges in precipitation and temperature. Facing gradual sea wa-ter level rise, coastal residents should certainly employ mitigationstrategies regarding protection of their coastal properties. Thisstudy was able to establish a reasonable range of low to high sea levelrise estimates to year 2080, with FSU providing the lower bound forsea level rise based on historical gage data, and the IPCC 2001 sealevel estimates providing the upper bound, based on climate model-ing scenarios. The results of this project underscore the importanceof including sea level rise as a critical component in the hazard pre-paredness and mitigation planning for coastal communities.

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4 Appendix A

Project Data Sources

At the project onset, it was determined that the USGS DEM datahad some reliability issues and did not provide resolution below 10meter elevation (pertaining to the counties for this study). Due tothese concerns, FSU CEFA opted to initially work with LiDAR datagiven its better resolution. LiDAR, or Laser Imaging Detection andRanging, is an optical remote sensing technology which measuresproperties of scattered light to find range and/or other informationof a target at a distance. CEFA tested one county (Escambia) us-ing LiDAR data, and spent a considerable amount of time errorchecking and data cleaning, and creating numerous TIN files (in or-der to overlay the parcel id data). The year, 2005, proved to befraught with errors (based on the Army Corps not providing suf-ficient quality checks before release to the public). CEFA decidedto use 2004 data instead since it proved to be more accurate. FSUCEFA (and in consulting with FSU FREAC) found that the LiDARdata was raw; i.e., not each value in the file reflected ”bare earth”and in some cases, building roof tops, tree lines, etc. were captured.Therefore, manually cleaning this data was determined to not be areasonable solution given the time and financial constraints of theproject. In many cases, along the bay side of the island, the LiDARvalues started at 5 or 6 feet (likely due to mangrove or other vege-tation along the shoreline). Currently24, the LiDAR data used forcoastal counties in Florida is used primarily for beach erosion andbeach profile measurements by the Florida Department of Environ-mental Protection (FDEP) Beaches and Shores. Hence, they areinterested in the mean water level to the edge of the parcel data,and not very concerned with data any further inland. The end resultwas that it was not useful for this study and a decision was made toinstead use the National Elevation Dataset (NED) data from USGS.

24The Florida Division of Emergency Management, Department of Community Af-fairs, has issued a contract to perform LiDAR flyover(s) for all coastal counties inFlorida. FDEM will catalog all existing LiDAR data and related products (bathymetryand orthophotography) within areas of the state affected by hurricane surge. See:http://www.floridadisaster.org/gis/lidar/ for latest updates.

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5 Appendix B

Joint representation of hurricane return period and associ-ated cost damages based on SLR estimates

Figure 31 — Hurricane Return Year(s) and Cost Damage inWakulla County

1 Hurricane summary data, Florida Office of Insurance Regulation, 20062 Flood Insurance study, FEMA, 2000

Resource: CEFA, Florida State University, 2006

Figure 32 — Hurricane Return Year(s)and Cost Damages in DadeCounty

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Figure 33 — Hurricane Return Year(s) and Cost Damages in DixieCounty

Figure 34 — Hurricane Return Year(s) and Cost Damages in DuvalCounty

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Figure 35 — Hurricane Return Year(s) and Cost Damages inEscambia County

Figure 36 — Hurricane Return Year(s) and Cost Damages inMonroe County

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Figure 37 — Increase Damage Cost in Wakulla County

Figure 38 — Damage Cost and Return Period for Dade County

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Figure 39 — Damage Cost and Return Period for Dixie County

Figure 40 — Damage Cost and Return Peroid for Duval County

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Figure 41 — Damage Cost and Return Period for Escambia County

Figure 42 — Damage Cost and Return Period for Monroe County

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6 Appendix C

Areas of Sea Level Rise Inundation for Year 2030 and 2080,Based on FSU and IPCC Sea Level Rise Estimates25

Figure 43 — Dade CountyYear 2030, FSU Estimates

Year 2030, IPCC Estimates

25Since SLR is more accentuated in some counties, the figures depict representative coastalsections of the county. In more high lying areas, the entire county is displayed.

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Year 2080, FSU Estimates

Year 2080, IPCC Estimates

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Figure 44 — Dixie CountyYear 2030, FSU Estimates

Year 2030, IPCC Estimates

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Year 2080, FSU Estimates

Year 2080, IPCC Estimates

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Figure 45 — Duval CountyYear 2030, FSU Estimates

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Year 2030, IPCC Estimates

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Year 2080, FSU Estimates

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Year 2080, IPCC Estimates

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Figure 46 — Monroe CountyYear 2030, FSU Estimates

Year 2030, IPCC Estimates

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Year 2080, FSU Estimates

Year 2080, IPCC Estimates

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Figure 47 — Wakulla CountyYear 2030, FSU Estimates

Year 2030, IPCC Estimates

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Year 2080, FSU Estimates

Year 2008, IPCC Estimates

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Figure 48 — Escambia CountyYear 2030, FSU Estimates

Year 2030, IPCC Estimates

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Year 2080, FSU Estimates

Year 2080, IPCC Estimates

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7 Appendix D

Methodology of Alternate SLR Impact Analysis Using theGIS Storm Model Regression Models

To better illustrate the marginal increase rate of property valuesat risk with Sea Level Rise, the aforementioned ’Storm Model’ wasimplemented. As mentioned previously, the ’Storm Model’ derivescentroid elevation values from a spatial analyst function using DEMdata of each county. DEM data is also widely known as a digitalterrain model (DTM). A DEM can be represented as a raster (agrid of squares) or as a triangular irregular network (TIN). DEMsare commonly built using remote sensing techniques, however, theymay also be built from land surveying. A DEM implies that eleva-tion can be accessed at each location in the study area. Staff used’Storm Model’ to join the centroid (with DEM values) to the parceldata based upon parcel ID and then created select constraint state-ments defined by a specified sea level rise, such that parcels belowa given elevation were categorized as property values at risk.

The interval was set to 10 cm in the GIS Storm equation. Staffset the ’select constraint statement’ in the ’Storm Model’ to be10 cm, 20 cm, 30 cm, 40 cm, 50 cm and 60 cm 26. Elevations wereexported if it was lower than the sea level rise and a new data layerwas created. A database of property values at risk was created byexporting the new data layer.

For Dade, Duval, Monroe and Wakulla Counties, the linear re-gression model was implemented:

M = a + bY

Where M: Property Values at Risk (in million 2007 $)Y: Sea Level Rise (in Meter)

26As the highest sea level rise estimates from IPCC is 65 cm(2080), we kept the sea levelrise below this to make the result to be reasonable.

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For Dixie and Escambia Counties, the quadratic regression modelwas implemented:

M = a + bY + c(Y 2)

Where M: Property Values at Risk (in million 2007 $)Y: Sea Level Rise (in Meter)

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