Internet-based Modeling, Mapping, and Analysis 

for the Greater Everglades (IMMAGE; Version 1.0): 

Web-based Tools to Assess the Impact of Sea 

Level Rise in South Florida

By Paul Hearn, David Strong, Eric Swain, and Jeremy Decker

Open-File Report 2013–1185

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013 

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Suggested citation:

Hearn, Paul, Strong, David, Swain, Eric, and Decker, Jeremy, 2013, Internet-based Modeling, 

Mapping, and Analysis for the Greater Everglades (IMMAGE; Version 1.0)—Web-based tools 

to assess the impact of sea level rise in south Florida: U.S. Geological Survey Open-File 

Report 2013–1185, 17 p., ht tp://pubs.usgs.gov/of/2013/1185. 

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

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Although this information product, for the most part, is in the public domain, it also may 

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iii

Contents

Abstract .............................................................................................................................................1

Statement of Problem .......................................................................................................................1

Objectives .........................................................................................................................................2

Study Area ........................................................................................................................................3

Methodology .....................................................................................................................................4

Web-Site Layout............................................................................................................................4

Model Output Accessible Through IMMAGE.....................................................................................7

Flow and Transport in a Linked Overland/Aquifer Density-Dependent System simulator and 

BIscayne South Eastern Coastal Transport model........................................................................7

Habitat and Species Dispersal Models............................................................................................10

Sea, Lake, and Overland Surges from Hurricanes Model ...........................................................12

Multihazard Loss Analysis Program Loss Analyses ........................................................................12

Conclusions ....................................................................................................................................15

References Cited ............................................................................................................................15

Figures

1. Generalized schematic of the U.S. Geological Survey's Internet-based Modeling, 

Mapping, and Analysis for the Greater Everglades Web-based model interface. ................3

2. Internet-based Modeling, Mapping, and Analysis for the Greater Everglades study area 

and coverage of the BIscayne SouthEastern Coastal Transport; Habitat Suitability; Sea, 

Lake, and Overland Surges from Hurricanes; and the Federal Emergency Management 

Agency’s multihazard loss analysis program models...........................................................4

 3. The U.S. Geological Survey's Internet-based Modeling, Mapping, and Analysis for the 

Greater Everglades Web-site entry page .............................................................................5

 4. The U.S. Geological Survey's Internet-based Modeling, Mapping, and Analysis for the 

Greater Everglades saltwater intrusion applet......................................................................6

 5. The U.S. Geological Survey's Internet-based Modeling, Mapping, and Analysis for the 

Greater Everglades full GIS Web interface provides more advanced users with a suite 

of tools to display model output on maps or imagery, or in tabular or graphical format........6

 6. This screenshot of the U.S. Geological Survey's Internet-based Modeling, Mapping, 

and Analysis for the Greater Everglades full GIS Web interface shows a BIscayne 

South Eastern Coastal Transport model analysis of the impact of a 60-cm sea level 

rise on the areal extent of saltwater intrusion in the area around central Miami, Florida....10

 7. Screenshot of the U.S. Geological Survey's Internet-based Modeling, Mapping, and 

Analysis for the Greater Everglades Habitat Suitability applet showing forecasted 

amphibian species richness for a sea level rise of 30 cm ..................................................11

 8. Screenshot of the U.S. Geological Survey's Internet-based Modeling, Mapping, and 

Analysis for the Greater Everglades Sea, Lake, and Overland Surges from Hurricanes 

(SLOSH) storm surge applet, showing the spatial extent and depth of surge forecasted 

in the Miami area for a Category 5 hurricane .....................................................................14
iv

 9. Screenshot of the U.S. Geological Survey's Internet-based Modeling, Mapping, and 

Analysis for the Greater Everglades multihazard loss analysis program Surge Loss 

applet, showing the distribution of forecasted losses from daytime damages to vehicles 

in the greater Miami area for a Category 5 hurricane.........................................................15

Table

1. Input parameters for the BIscayne SouthEastern Coastal Transport (BISECT)...................8
v 

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

yard (yd) 0.9144 meter (m)

SI to Inch/Pound 

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 
1 

Internet-based Modeling, Mapping, and Analysis 

for the Greater Everglades (IMMAGE; Version 1.0): 

Web-based Tools to Assess the Impact of Sea 

Level Rise in South Florida

By Paul Hearn, David Strong, Eric Swain, and Jeremy Decker

Abstract

South Florida's Greater Everglades area is particularly vulnerable to sea level rise, 

due to its rich endowment of animal and plant species and its heavily populated urban 

areas along the coast. Rising sea levels are expected to have substantial impacts on inland 

flooding, the depth and extent of surge from coastal storms, the degradation of water 

supplies by saltwater intrusion, and the integrity of plant and animal habitats. Planners 

and managers responsible for mitigating these impacts require advanced tools to help 

them more effectively identify areas at risk. The U.S. Geological Survey's (USGS) 

Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE) 

Web site has been developed to address these needs by providing more convenient access 

to projections from models that forecast the effects of sea level rise on surface water and 

groundwater, the extent of surge and resulting economic losses from coastal storms, and 

the distribution of habitats. IMMAGE not only provides an advanced geographic 

information system (GIS) interface to support decision making, but also includes topicbased modules that explain and illustrate key concepts for nontechnical users. The 

purpose of this report is to familiarize both technical and nontechnical users with the 

IMMAGE Web site and its various applications. 

Statement of Problem 

Recent analyses using current Intergovernmental Panel on Climate Change 

(IPCC) projections of sea level rise and Light Detection and Ranging (lidar) data for the 

eastern portions of Miami-Dade County suggest that coastal areas in South Florida will 

be subjected to the degradation of coastal habitats, contamination of municipal water 

supplies by the intrusion of saltwater into coastal aquifers, alteration of groundwater flow 

patterns, and increased risk of surge-related flooding and wind damage from coastal 

storms (Doyle and others, 2007; Renken, 2005). It is critical that water planners, as well 

as park managers and municipal authorities, have the best tools available to assess the 

societal risks and economic impacts that these adverse environmental changes could have 

on nearby communities, protected lands, and the region as a whole. 

Furthermore, development and water management policies being debated today in 

South Florida will commit public and private capital to infrastructure and facilities with 
2 

design lives (the periods of time during which infrastructure and facilities are expected by 

their designers to operate within their specified parameters) extending well into the 

period of time when the impacts of sea level rise are expected to occur. Surface-water 

control canals, municipal water distribution networks, and wastewater and storm water 

collection systems have design lives on the order of 30 to 75 years. Sewage treatment and 

wastewater reclamation facilities have design lives close to 50 years. The anticipated rise 

in sea level during the next century may compromise the functioning of these engineered 

systems and may stress the ability of associated natural systems to adapt. 

Recently, numerical models have been developed to forecast the impact of sea 

level rise on saltwater intrusion, inland flooding, surge from coastal storms, and the 

resulting impact on the suitability of habitat for key species in the Greater Everglades. 

These models are among our most powerful tools in forecasting future trends and 

evaluating alternative restoration and preservation policies. Unfortunately, the full 

potential of these models is often not realized. If an analyst wants to evaluate how 

changing the input parameters of a model affects the results, the model will typically 

need to be run again using the new parameters. Since many models take hours or even 

days to run, evaluating a large number of parameter sets can be a time-consuming and 

tedious process. Furthermore, due to their inherent complexity, it is usually not practical 

to make them available to the general public.

Objectives

IMMAGE's principal objective is to develop a coupled GIS-enabled Web-based 

framework to provide interactive model-based scenarios to evaluate the potential impact 

of sea level rise on water supply, inland flooding, storm surge, and habitat management 

in South Florida. The framework will allow scientists, local planners, and resource 

managers to evaluate the impact of sea level rise on: 

1. saltwater intrusion into coastal water well fields,

2. the optimal use of canals to impede the inland movement of saline groundwater, 

3. urban flooding, 

4. the risk to populated areas and natural habitat from catastrophic storm surge, 

5. wetland inundation periods and depths, and 

6. habitat distribution.

The IMMAGE Web site provides a framework of online GIS-based interfaces to 

four selected models, thereby enhancing their usability and making them available to a 

broad user community. The approach is relatively simple: by running models in advance, 

using the maximum likely range of input parameters, all the necessary model output can 

be stored in a server database (fig. 1). Web applications can then be developed to allow 

users to select the desired input parameters, retrieve the necessary model output from the 

database, and display the output in a map viewer with other relevant data—all online. 

Without the need to actually run the models online, this process is relatively fast, 

allowing the user to evaluate multiple scenarios in a short period of time. 
3 

Figure 1. Generalized schematic of the U.S. Geological Survey's Internet-based Modeling, 

Mapping, and Analysis for the Greater Everglades (IMMAGE) Web-based model interface.

IMMAGE's Web interface provides access to the following four models: 

1. The BIscayne SouthEastern Coastal Transport (BISECT) model, a coupled 

groundwater and surface-water model developed by the U.S. Geological Survey 

(USGS) (Lohmann and others, 2010). 

2. Habitat distribution models, which utilize output from BISECT, developed at the 

Everglades National Park (Pearlstine and others, 2006). 

3. Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model developed by 

the National Oceanic and Atmospheric Administration’s (NOAA) National 

Hurricane Center (NOAA/NHC) 

(http://www.nhc.noaa.gov/ssurge/ssurge_slosh.shtml), and 

4. The Federal Emergency Management Agency’s (FEMA) multihazard loss 

analysis program [HAZUS-MH (HAZUS)] (http://www.fema.gov/hazus). 

Study Area 

The area covered by this project includes the Everglades National Park and 

Biscayne National Park (ENP and BNP), Miami-Dade and Monroe Counties, and the 

eastern portions of Broward and Palm Beach Counties. Output from the BISECT and 

habitat suitability models is available for the ENP and BNP, and coastal areas of Monroe 
4 

and Miami Dade County. SLOSH and HAZUS model output is available for MiamiDade, Broward, and Palm Beach Counties (fig. 2). 

Figure 2. Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE) 

study area and coverage of the BIscayne SouthEastern Coastal Transport (BISECT); Habitat 

Suitability; Sea, Lake, and Overland Surges from Hurricanes (SLOSH); and the Federal 

Emergency Management Agency’s (FEMA) multihazard loss analysis program (HAZUS) models.

The dashed black-and-white lines delineate county boundaries.

Methodology

Web-Site Layout

To accommodate interest on the part of the public without a technical background 

in the subject matter, including Federal, State, and local officials, academia, journalists, 

and the general public, IMMAGE provides several examples of how model output can be 
5 

applied (fig. 3). Each example includes a brief overview of the specific issue and a GISbased application (applet) demonstrating how the relevant model can be utilized to 

support decision making (fig. 4). For more advanced users, IMMAGE's complete GIS 

Web interface provides access to output from all four of the models, as well as various 

tools to view and display data (fig. 5). 

Figure 3. The U.S. Geological Survey's Internet-based Modeling, Mapping, and Analysis for the 

Greater Everglades (IMMAGE) Web-site entry page. Tabs at the top of the page link to applets 

demonstrating how model output can be applied to decisionmaking related to specific issues.
6 

Figure 4. The U.S. Geological Survey's Internet-based Modeling, Mapping, and Analysis for the 

Greater Everglades (IMMAGE) saltwater intrusion applet.

Figure 5. The U.S. Geological Survey's Internet-based Modeling, Mapping, and Analysis for the 

Greater Everglades (IMMAGE) full GIS Web interface provides more advanced users with a suite 

of tools to display model output on maps or imagery, or in tabular or graphical format.
7 

Model Output Accessible Through IMMAGE

Flow and Transport in a Linked Overland/Aquifer Density-Dependent System 

simulator and BIscayne South Eastern Coastal Transport model

The Flow and Transport in a Linked Overland/Aquifer Density-Dependent 

System (FTLOADDS) simulator has been developed over a number of years. It began 

with a surface-water application to the coastal area along Florida Bay using the 

Simulation of Surface-Water Integrated Flow and Transport in Two Dimensions model 

(SWIFT2D) (Schaffranek, 2004; Swain and others, 2004; Swain, 2005). Groundwater 

flow was incorporated into this simulation by coupling SWIFT2D with SEAWAT, a 

generic simulator of groundwater flow and transport (Langevin and others, 2005). This 

coupled scheme is called FTLOADDS. The model area was expanded to encompass the 

whole Everglades National Park (ENP) area and referred to as the Tides and Inflows in 

the Mangrove Everglades (TIME) model (Wang and others, 2007). This model has been 

used to represent hydrologic restoration scenarios for the Comprehensive Everglades 

Restoration Plan (CERP). 

The FTLOADDS simulator was additionally applied to the Ten Thousand Islands 

area (Swain and Decker, 2009) and to the Biscayne Bay coast (Wolfert-Lohmann and 

others, 2008; Swain and others, 2009). The Biscayne Bay model was then combined with 

the TIME model to produce the BIscayne South Eastern Coastal Transport (BISECT) 

model. The BISECT model incorporates some of the most important natural and urban 

areas in South Florida and is useful in examining the effects of hydrologic factors. 

Applications include evaluation of the impact of sea level rise (SLR) on future forecasts 

with varying levels of sea level rise in conjunction with CERP restoration changes and 

hindcast simulation to represent historical and transient conditions. 

Experiments on various SLR scenarios have been performed with BISECT, which 

uses the FTLOADDS simulator for flow and transport in a coupled hydrodynamic 

surface-water/groundwater system. The existing conditions simulation runs for the 7-year 

period January 1, 1996 through December 31, 2002 . BISECT is a combination of two 

model domains that were developed separately; the TIME domain west of the L-31N 

Canal (Wang and others, 2007) and the Biscayne domain from the L-31N Canal to 

offshore Biscayne Bay (Swain and others, 2009). This combination allows the 

examination of the entire coastal region from the Barron River in the western side of the 

study area to the C-9 Canal outlet just north of Fort Lauderdale. The input parameters 

used in the BISECT runs, plus temporal and spatial resolutions, are provided in table 1. 
8 

Table 1. Input parameters for the BIscayne SouthEastern Coastal Transport (BISECT).

[ft, feet; h, hours; m, meters; min, minutes]

Input parameter Temporal 

resolution

Spatial resolution Source

Topography/bathymetry Static 500 m

 [1,640 feet (ft)]. 

Airborne helicopter and

 digital elevation models. 

Aquifer dimensions and

 conductivity. 

Static 500 m

 (1,640 ft)

 horizontal and 

 2 m (6.6 ft)

 vertical. 

Field studies and

 documentation. 

Surface-water frictional

 resistance. 

Static 500 m Field studies and

 calibration. 

Groundwater/surface-

 water leakage

 coefficients. 

Static 500 m

 (1,640 ft). 

Aquifer properties and

 calibration. 

Groundwater boundaries Daily 500 m

 (1,640 ft). 

Field well levels.

Surface-water inflows Daily 500 m

 (1,640 ft). 

Hydraulic structure

 flow measurements. 

Tidal boundaries 30 min Variable Tidal components

 derived from Fourier

 analysis of offshore

 stations. 

Precipitation 6 h Eight zones

 covering entire

 area. 

Interpolated rain-gage

 measurements. 

Solar radiation

 (evapotranspiration). 

30 min One value for

 area. 

Pyranometer.

Air temperature and

 humidity

 (evapotranspiration). 

30 min One value for

 area. 

Weather station.

Albedo

 (evapotranspiration). 

Static 500 m

 (1,640 ft). 

Based on depth below

 sea level from

 experimentation. 

Canal levels Daily Variable Hydraulic structure

 stage measurements. 

Wind 30 min One value for

 area. 

Weather station.

Groundwater pumpage Daily For each major

 well field. 

Municipal well records.
9 

IMMAGE allows users to display BISECT output on imagery or map 

backgrounds, or as vertical profiles in tabular or graphical form. BISECT output includes 

the following parameters: 

1. Inland flooding (as maximum depth, percent inundation or percent continuous 

inundation), 

2. Groundwater salinity, 

3. Groundwater conductivity,

4. Depth to water table, and

5. Hydrostatic pressure.

Model input parameters include future sea level (0 to 60 cm; 0 to 2 ft) and CERP 

scenarios (CERP A, currently planned; CERP B, more rapid progress; and CERP C, no 

change from current conditions). 

Figure 6 shows a screenshot of BISECT model projections of groundwater 

salinities for an SLR of 60 cm (2 ft). Purpose contour lines around municipal water-well 

fields depict groundwater travel times from various points; blue lines and black icons 

represent canals and surface-water control structures, respectively. Forecasted 

groundwater salinities, depicted in the map view as the vertical average of salinities 

within the Biscayne aquifer, indicate a clear threat to the two easternmost well fields. On 

the right side of figure 6, a more precise impact assessment is shown by the vertical 

profiles plotted by IMMAGE for the point indicated by the red cross highlighted in the 

middle of the map. These profiles reflect BISECT forecasts of salinity profiles for CERP 

Plans A, B, and C (designated as Schemes A, B, and C in figure 6). BISECT forecasts the 

CERP C (no action) scenario to result in dramatically increased groundwater salinities, 

reflecting the absence of freshwater routed through canals to impede the inland migration 

of saline groundwater. While BISECT predicts substantially lower salinities for CERP A 

and B scenarios, the impact may still be sufficient to require mitigation.
10

Figure 6. This screenshot of the U.S. Geological Survey's Internet-based Modeling, Mapping, 

and Analysis for the Greater Everglades (IMMAGE) full GIS Web interface shows a BIscayne 

South Eastern Coastal Transport (BISECT) model analysis of the impact of a 60-cm (2-ft) sea level 

rise on the areal extent of saltwater intrusion in the area around central Miami, Florida. 

Habitat and Species Dispersal Models 

The Everglades are endowed with habitats for several unique species of plants and 

animals, many of which are rare or endangered. Due to its extremely low relief, this area 

is quite vulnerable to the effects of SLR. Relatively small increases in sea level are 

expected to lead to large increases in inundation periods for broad areas in the park, and 

have already influenced both surface and subsurface saltwater intrusion (Stabenau and 

others, 2011). The inland migration of saline surface and groundwater has most likely 

increased due to lower freshwater discharge associated with upstream development over 

the past century. These hydrologic changes put pressure on the ecosystem, causing a 

variety of impacts, such as inland migration of plants, variation in species composition, 

and disruptions of predator-prey relationships (Pearlstine and others, 2010). Mangrove 

species resistant to higher salinities have already migrated approximately 3 km (2 mi) 

inland since the 1940s in parts of the national park, presumably in response to rising sea 

level (Ross and others, 2000). The coastal habitats of Everglades National Park are at the

end of the hydrologic restoration chain, but are the first areas to be impacted by SLR. 

Consequently, both restoration and SLR may cause substantial spatial changes in habitat 

availability and distribution. 

The ability of species to migrate among core habitat areas is impacted by changes 

in South Florida habitat connectivity resulting from urban growth, sea level rise, and 

restoration-stimulated habitat succession. The USGS, in cooperation with the South 

Florida Natural Resources Center, Everglades National Park (SFNRC/ENP) (Labiosa and 

others, 2009), is developing and testing metrics for ranking the potential for wildlife 

species dispersal under alternative changes to the natural landscape and urban growth. 
11

The Circuitscape model (McRae and others, 2008) provides the mechanism for 

evaluation of species dispersal. Circuitscape algorithms adapt electronic circuit theory to 

predict patterns of movement, gene flow, and genetic differentiation among plant and 

animal populations in heterogeneous landscapes. 

Coastal models for juvenile spotted seatrout (Cynoscion nebulosus), blue crab 

(Callinectes sapidus), and turtle grass (Thalassia testudinum) have been developed at the 

SFNRC/ENP (fig. 7). The models are coupled to the BISECT hydrologic model to 

simulate changes in habitat suitability under scenarios of restoration and SLR (Pearlstine 

and others, 2008, 2010). The graphical user interface provided by IMMAGE is not only 

an effective and practical way for scientists and managers to view data without the need 

for their own GIS software; it also provides a convenient tool to educate the nontechnical 

public on the issue of habitat vulnerability. The spotted seatrout, blue crab, and turtle 

grass models were originally developed for the Caloosahatchee estuary and applied to 

Florida Bay. These models are not a perfect fit, but were available and serve to 

demonstrate the utility of IMMAGE in addressing habitat change. For application to 

South Florida, these models should be considered early implementations because 

validation studies have not been conducted yet. IMMAGE provides a modular approach 

that will be of value to newer habitat models as they become available.

Figure 7. Screenshot of the U.S. Geological Survey's Internet-based Modeling, Mapping, and 

Analysis for the Greater Everglades (IMMAGE) Habitat Suitability applet showing forecasted 

amphibian species richness for a sea level rise of 30 cm (1 ft). 
12

Sea, Lake, and Overland Surges from Hurricanes Model 

Sea, Lake, and Overland Surges from Hurricanes (SLOSH) is a numerical model 

developed by the NOAA/NHC to estimate storm surge heights and winds resulting from 

historical, hypothetical, or predicted hurricanes [Federal Emergency Management 

Association (FEMA), 2003a, 2003b]. SLOSH uses the hydrodynamic flow equations and 

requires input of bathymetry, coastal topography, surface characteristics, and tidal levels 

(Jelesnianski and others, 1992). The coastline is represented as a physical boundary. 

Subgrid-scale water features (cuts, chokes, sills, and channels), and vertical obstructions 

(levees, roads, spoil banks, and so forth) are parameterized. The SLOSH model does not 

include rainfall amounts, river flow, or wind-driven waves; however, these are combined 

with the model results in the final analysis of at-risk areas. 

Using methodologies developed by Longenecker (2008, 2009) to incorporate the 

NHC SLOSH model into the HAZUS-MH Coastal Flood Model (CFM), FEMA Region 

IV developed a Coastal Flood Loss Atlas (CFLA) to improve risk assessments and 

mitigation planning for hurricane storm surges on the eastern coast of the United States. 

In order to establish a baseline “dictionary” to reference hurricane surge losses, the study 

uses SLOSH to establish maximum potential storm surge depths by hurricane category

maximum of maximum's (MOM's). SLOSH divides the coastline into basins and 

produces a water-surface-elevation grid used to determine depths by subtracting ground 

elevation. The SLOSH MOM grid is first converted to a raster surface elevation through 

centroid point interpolation; then USGS 30-meter National Elevation Dataset data are 

subtracted to determine depth over land. The display resolution of the SLOSH model is 

30 meters (98.4 ft). SLOSH model output accuracy varies from ±5 to 20 percent, 

depending on the accuracy of high-water-mark data [National Oceanic and Atmospheric 

Administration (NOAA), 2013)]. A good overview of the SLOSH model and the factors 

affecting its accuracy and resolution are provided in Glahn and others (2009). 

Multihazard Loss Analysis Program Loss Analyses 

At the present time, the only nationally applicable and standardized methodology 

for estimating potential losses from floods, earthquakes, and hurricanes is contained 

within FEMA’s multihazard loss analysis program [HAZUS-MH (HAZUS)] loss analysis 

software (http://www.fema.gov/hazus). HAZUS uses geographic information system 

(GIS) technology to estimate the physical, economic, and social impacts of disasters. It is 

used for mitigation and recovery as well as for preparedness and response. HAZUS 

includes a comprehensive, nationwide database of population and infrastructure at risk. In 

its fifth release since its introduction in 1992, HAZUS now has an extensive user group, 

which includes Federal, State, and local government agencies, as well as public utilities, 

the private sector, and academia. 

The HAZUS-MH Flood Information Tool allows rapid analysis of a wide variety 

of stream discharge data and elevation data to determine flood depth, extent, and 

frequencies over entire flood plains (http://www.fema.gov/hazus/hazus-flood-informationtool). The flood model also includes a library of more than 900 damage curves for use in 

estimating damage to various types of buildings and infrastructure. Using these damage 

estimates, the flood model also estimates shelter requirements and indirect economic 

losses due to floods. It also allows users to analyze the effects of flood warning, and the 

benefits of follow-on remapping studies, levees, and elevation of structures. 
13

In order to use SLOSH model data in HAZUS, a GIS process was developed by 

Longenecker (2008, 2009) to subtract the topography from the SLOSH water surface 

elevation in order to determine storm surge water depth over land. By executing this 

process, a raster-based water-depth grid is supplied to HAZUS that represents storm 

surge inundation for a particular storm track (forecast/response scenario), for a particular 

storm approach direction or speed (hypothetical scenario), or for entire evacuation areas 

based on MOM delineation (maximum of maximum possible event for planning 

purposes).

The accuracy of HAZUS loss estimates is dependent on the quality of input data 

used. Level 1 analyses are conducted using the best available surge maps and asset data 

from the U.S. Census Bureau data included in the HAZUS national database and are 

typically the least accurate. More accurate level 2 loss estimates are produced by 

including more accurate surge maps and (or) by replacing the default asset inventories 

with more accurate local inventories of buildings, essential facilities, and other

infrastructure. The highest accuracy level 3 analyses include the hazard and inventory 

improvements in a level 2 study in addition to expert refinement of loss functions and 

other analysis parameters. HAZUS users typically choose an analysis level based on the 

costs entailed and the corresponding value of higher accuracy results. 

For the IMMAGE project, SLOSH MOM inundation predictions and the 

accompanying HAZUS surge loss analyses were provided to USGS by FEMA Region IV 

for Miami-Dade, Broward, and Palm Beach Counties. Asset values from HAZUS's 

national database were used. The resulting loss analyses were then loaded into the 

IMMAGE map viewer (figs. 8 and 9). Users are able to access and display several 

different HAZUS loss categories, including economic losses for several building types 

and vehicles (daytime and night), displaced population, and accumulated debris. 
14

Figure 8. Screenshot of the U.S. Geological Survey's Internet-based Modeling, Mapping, and 

Analysis for the Greater Everglades (IMMAGE) Sea, Lake, and Overland Surges from Hurricanes 

(SLOSH) storm surge applet, showing the spatial extent and depth of surge forecasted in the 

Miami area for a Category 5 hurricane. Modeled surge depths for all storm categories can be 

accessed for a specific census block using the Point Info tool.
15

Figure 9. Screenshot of the U.S. Geological Survey's Internet-based Modeling, Mapping, and 

Analysis for the Greater Everglades (IMMAGE) multihazard loss analysis program [HAZUS-MH 

(HAZUS)] Surge Loss applet, showing the distribution of forecasted losses from daytime damages 

to vehicles in the greater Miami area for a Category 5 hurricane. The Point Info tool allows data for 

all storm categories to be displayed for a selected census block.

Conclusions

This report documents the development of an advanced Web interface to provide 

greater awareness and access to modeled data relevant to sea level rise in the Greater 

Everglades, and its potential impact on local water supplies; the extent of inland flooding, 

surge, and associated losses from coastal storms; and habitat distribution. The IMMAGE 

Web site is designed to serve a broad spectrum of users, including scientists, elected 

officials, government agencies, academia, and the general public. It is hoped that the 

advanced functionality provided by IMMAGE will serve as a template for the 

development of similar Web sites to increase public awareness of USGS science and its 

application to decisionmaking for other issues facing our Nation. 

References Cited

Doyle, R., Bailey, K., and Matheny, 2007, Adaptive response planning to sea level rise 

planning in Florida and implications for comprehensive and public facilities planning: 

Department of Urban and Regional Planning, Florida State University, 49 p. 
16

Federal Emergency Management Agency [FEMA], (2003a), Multi-hazard loss estimation 

methodology, flood model, HAZUS, technical manual: Washington, D.C. 

Federal Emergency Management Agency [FEMA], (2003b), HAZUS-MH User Manual: 

Washington, D.C. 

Glahn, B., Taylor, A., Kurkowski, N., and Shaffer, W.A., 2009, The role of the SLOSH 

model in National Weather Service storm surge forecasting: National Weather Digest, 

v. 33, no. 1, p. 3–14. 

Jelesnianski, C.P., Chen, J., and Shaffer, W.A., 1992, SLOSH: Sea, Lake, and Overland 

Surges from Hurricanes: NOAA Technical Report NWS 48, National Oceanic and 

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