Four essays of environmental risk-mitigation

Expected damages of environmental risks depend both on their intensities and probabilities. There is very little control over probabilities of climate related disasters such as hurricanes. Therefore, researchers of social science are interested identifying preparation and mitigation measures that build human resilience to disasters and avoid serious loss. Conversely, environmental degradation, which is a process through which the natural environment is compromised in some way, has been accelerated by human activities. As scientists are finding effective ways on how to prevent and reduce pollution, the society often fails to adopt these effective preventive methods. Researchers of psychological and contextual characterization offer specific lessons for policy interventions that encourage human efforts to reduce pollution. This dissertation addresses four discussions of effective policy regimes encouraging pro-environmental preference in consumption and production, and promoting risk mitigation behavior in the face of natural hazards. The first essay describes how the speed of adoption of environment friendly technologies is driven largely by consumers' preferences and their learning dynamics rather than producers' choice. The second essay is an empirical analysis of a choice experiment to understand preferences for energy efficient investments. The empirical analysis suggests that subjects tend to increase energy efficient investment when they pay a pollution tax proportional to the total expenditure on energy consumption. However, investments in energy efficiency seem to be crowded out when subjects have the option to buy health insurance to cover pollution related health risks. In context of hurricane risk mitigation and in evidence of recently adopted My Safe Florida Home (MSFH) program by the State of Florida, the third essay shows that households with home insurance, prior experience with damages, and with a higher sense of vulnerability to be affected by hurricanes are more likely to allow home inspection to seek mitigation information. The fourth essay evaluates the impact of utility disruption on household well being based on the responses of a household-level phone survey in the wake of hurricane Wilma. Findings highlight the need for significant investment to enhance the capacity of rapid utility restoration after a hurricane event in the context of South Florida.

Development of effective approaches to the large-scale aerodynamic testing of low-rise buildings

Low-rise buildings are often subjected to high wind loads during hurricanes that lead to severe damage and cause water intrusion. It is therefore important to estimate accurate wind pressures for design purposes to reduce losses. Wind loads on low-rise buildings can differ significantly depending upon the laboratory in which they were measured. The differences are due in large part to inadequate simulations of the low-frequency content of atmospheric velocity fluctuations in the laboratory and to the small scale of the models used for the measurements. A new partial turbulence simulation methodology was developed for simulating the effect of low-frequency flow fluctuations on low-rise buildings more effectively from the point of view of testing accuracy and repeatability than is currently the case. The methodology was validated by comparing aerodynamic pressure data for building models obtained in the open-jet 12-Fan Wall of Wind (WOW) facility against their counterparts in a boundary-layer wind tunnel. Field measurements of pressures on Texas Tech University building and Silsoe building were also used for validation purposes. The tests in partial simulation are freed of integral length scale constraints, meaning that model length scales in such testing are only limited by blockage considerations. Thus the partial simulation methodology can be used to produce aerodynamic data for low-rise buildings by using large-scale models in wind tunnels and WOW-like facilities. This is a major advantage, because large-scale models allow for accurate modeling of architectural details, testing at higher Reynolds number, using greater spatial resolution of the pressure taps in high pressure zones, and assessing the performance of aerodynamic devices to reduce wind effects. The technique eliminates a major cause of discrepancies among measurements conducted in different laboratories and can help to standardize flow simulations for testing residential homes as well as significantly improving testing accuracy and repeatability. Partial turbulence simulation was used in the WOW to determine the performance of discontinuous perforated parapets in mitigating roof pressures. The comparisons of pressures with and without parapets showed significant reductions in pressure coefficients in the zones with high suctions. This demonstrated the potential of such aerodynamic add-on devices to reduce uplift forces.

Paleoenvironmental change in wetlands of the Florida Everglades, southeast USA

In this special issue, we report on efforts to reconstruct paleoclimate/paleolimnology of the Florida Everglades, applying a wide range of techniques including sedimentological, micropaleontological and biogeochemical approaches. The papers included here describe results obtained by studies conducted in Everglades National Park and the greater South Florida Everglades by Florida Coastal Everglades Long Term Ecological Research Program (FCE LTER) collaborators. This multi-investigator project contrasts nutrient dynamics in two inland-to-marine transects aligned along separate drainages in southern Florida that differ in their susceptibility to coastal pressures and in volume of freshwater delivery. This effort focuses on the paleoecological aspects of FCE LTER research that address scales of ecosystem transformations driven by climate variability and change and human activities. The central question addressed by this body of work is “How is the shape of the freshwater-to-marine gradient in the Florida coastal Everglades controlled by changes in climate, freshwater inflow (i.e. through human activities), and disturbance (i.e. sea level rise, hurricanes, fire)?”

Allocation of Biomass and Net Primary Productivity of Mangrove Forests along Environmental Gradients in the Florida Coastal Everglades, USA

Vegetation patterns of mangroves in the Florida Coastal Everglades (FCE) result from the interaction of environmental gradients and natural disturbances (i.e., hurricanes), creating an array of distinct riverine and scrub mangroves across the landscape. We investigated how landscape patterns of biomass and total net primary productivity (NPPT), including allocation in above- and below-ground mangrove components, vary inter-annually (2001–2004) across gradients in soil properties and hydroperiod in two distinct FCE basins: Shark River Estuary and Taylor River Slough. We propose that the allocation of belowground biomass and productivity (NPPB) relative to aboveground allocation is greater in regions with P limitation and permanent flooding. Porewater sulfide was significantly higher in Taylor River (1.2 ± 0.3 mM) compared to Shark River (0.1 ± 0.03 mM) indicating the lack of a tidal signature and more permanent flooding in this basin. There was a decrease in soil P density and corresponding increase in soil N:P from the mouth (28) to upstream locations (46–105) in Shark River that was consistent with previous results in this region. Taylor River sites showed the highest P limitation (soil N:P > 60). Average NPPT was double in higher P environments (17.0 ± 1.1 Mg ha−1 yr−1) compared to lower P regions (8.3 ± 0.3 Mg ha−1 yr−1). Root biomass to aboveground wood biomass (BGB:AWB) ratio was 17 times higher in P-limited environments demonstrating the allocation strategies of mangroves under resource limitation. Riverine mangroves allocated most of the NPPT to aboveground (69%) while scrub mangroves showed the highest allocation to belowground (58%). The total production to biomass (P:B) ratios were lower in Shark River sites (0.11 yr−1); whereas in Taylor River sites P:B ratios were higher and more variable (0.13–0.24 yr−1). Our results suggest that the interaction of lower P availability in Taylor River relative to Shark River basin, along with higher sulfide and permanent flooding account for higher allocation of belowground biomass and production, at expenses of aboveground growth and wood biomass. These distinct patterns of carbon partitioning between riverine and scrub mangroves in response to environmental stress support our hypothesis that belowground allocation is a significant contribution to soil carbon storage in forested wetlands across FCE, particularly in P-limited scrub mangroves. Elucidating these biomass strategies will improve analysis of carbon budgets (storage and production) in neotropical mangroves and understanding what conditions lead to net carbon sinks in the tropical coastal zone.

Evaluating the Distribution of Terrestrial Dissolved Organic Matter in a Complex Coastal Ecosystem Using Fluorescence Spectroscopy

The coastal zone of the Florida Keys features the only living coral reef in the continental United States and as such represents a unique regional environmental resource. Anthropogenic pressures combined with climate disturbances such as hurricanes can affect the biogeochemistry of the region and threaten the health of this unique ecosystem. As such, water quality monitoring has historically been implemented in the Florida Keys, and six spatially distinct zones have been identified. In these studies however, dissolved organic matter (DOM) has only been studied as a quantitative parameter, and DOM composition can be a valuable biogeochemical parameter in assessing environmental change in coastal regions. Here we report the first data of its kind on the application of optical properties of DOM, in particular excitation emission matrix fluorescence with parallel factor analysis (EEM-PARAFAC), throughout these six Florida Keys regions in an attempt to assess spatial differences in DOM sources. Our data suggests that while DOM in the Florida Keys can be influenced by distant terrestrial environments such as the Everglades, spatial differences in DOM distribution were also controlled in part by local surface runoff/fringe mangroves, contributions from seasgrass communities, as well as the reefs and waters from the Florida Current. Application of principal component analysis (PCA) of the relative abundance of EEM-PARAFAC components allowed for a clear distinction between the sources of DOM (allochthonous vs. autochthonous), between different autochthonous sources and/or the diagenetic status of DOM, and further clarified contribution of terrestrial DOM in zones where levels of DOM were low in abundance. The combination between EEM-PARAFAC and PCA proved to be ideally suited to discern DOM composition and source differences in coastal zones with complex hydrology and multiple DOM sources.

Development of test-based wind-driven rain intrusion model for hurricane-induced building interior and contents damage

Major portion of hurricane-induced economic loss originates from damages to building structures. The damages on building structures are typically grouped into three main categories: exterior, interior, and contents damage. Although the latter two types of damages, in most cases, cause more than 50% of the total loss, little has been done to investigate the physical damage process and unveil the interdependence of interior damage parameters. Building interior and contents damages are mainly due to wind-driven rain (WDR) intrusion through building envelope defects, breaches, and other functional openings. The limitation of research works and subsequent knowledge gaps, are in most part due to the complexity of damage phenomena during hurricanes and lack of established measurement methodologies to quantify rainwater intrusion. This dissertation focuses on devising methodologies for large-scale experimental simulation of tropical cyclone WDR and measurements of rainwater intrusion to acquire benchmark test-based data for the development of hurricane-induced building interior and contents damage model. Target WDR parameters derived from tropical cyclone rainfall data were used to simulate the WDR characteristics at the Wall of Wind (WOW) facility. The proposed WDR simulation methodology presents detailed procedures for selection of type and number of nozzles formulated based on tropical cyclone WDR study. The simulated WDR was later used to experimentally investigate the mechanisms of rainwater deposition/intrusion in buildings. Test-based dataset of two rainwater intrusion parameters that quantify the distribution of direct impinging raindrops and surface runoff rainwater over building surface — rain admittance factor (RAF) and surface runoff coefficient (SRC), respectively —were developed using common shapes of low-rise buildings. The dataset was applied to a newly formulated WDR estimation model to predict the volume of rainwater ingress through envelope openings such as wall and roof deck breaches and window sill cracks. The validation of the new model using experimental data indicated reasonable estimation of rainwater ingress through envelope defects and breaches during tropical cyclones. The WDR estimation model and experimental dataset of WDR parameters developed in this dissertation work can be used to enhance the prediction capabilities of existing interior damage models such as the Florida Public Hurricane Loss Model (FPHLM).^

Ecohydrology, evapotranspiration and hydrogeochemistry of carbonate mangrove wetlands

Coastal environments can be highly susceptible to environmental changes caused by anthropogenic pressures and natural events. Both anthropogenic and natural perturbations may directly affect the amount and the quality of water flowing through the ecosystem, both in the surface and subsurface and can subsequently, alter ecological communities and functions. The Florida Everglades and the Sian Ka'an Biosphere Reserve (Mexico) are two large ecosystems with an extensive coastal mangrove ecotone that represent a historically altered and pristine environment, respectively. Rising sea levels, climate change, increased water demand, and salt water intrusion are growing concerns in these regions and underlies the need for a better understanding of the present conditions. The goal of my research was to better understand various ecohydrological, environmental, and hydrogeochemical interactions and relationships in carbonate mangrove wetlands. A combination of aqueous geochemical analyses and visible and near-infrared reflectance data were employed to explore relationships between surface and subsurface water chemistry and spectral biophysical stress in mangroves. Optical satellite imagery and field collected meteorological data were used to estimate surface energy and evapotranspiration and measure variability associated with hurricanes and restoration efforts. Furthermore, major ionic and nutrient concentrations, and stable isotopes of hydrogen and oxygen were used to distinguish water sources and infer coastal groundwater discharge by applying the data to a combined principal component analysis-end member mixing model. Spectral reflectance measured at the field and satellite scales were successfully used to estimate surface and subsurface water chemistry and model chloride concentrations along the southern Everglades. Satellite imagery indicated that mangrove sites that have less tidal flushing and hydrogeomorphic heterogeneity tend to have more variable evapotranspiration and soil heat flux in response to storms and restoration. Lastly, water chemistry and multivariate analyses indicated two distinct fresh groundwater sources that discharge to the phosphorus-limited estuaries and bays of the Sian Ka'an Biopshere Reserve; and that coastal groundwater discharge was an important source for phosphorus. The results of the study give us a better understanding of the ecohydrological and hydrogeological processes in carbonate mangrove environments that can be then be extrapolated to similar coastal ecosystems in the Caribbean.

Tree Islands in Everglades Landscapes: Current Status, Historical Changes, and Hydrologic Impacts on Population Dynamics and Moisture Relations, First Annual Report

In 2005 we initiated a project designed to better understand tree island structure and function in the Everglades and the wetlands bordering it. Focus was on the raised portions at the upstream end of the islands, where tropical hardwood species adapted to well-drained conditions usually are the most prominent component of the vegetation. The study design is hierarchical, with four levels; in general, a large number of sites is to be surveyed once for a limited set of parameters, and increasingly small sets of islands are to be sampled more intensively, more frequently, and for more aspects of ecosystem function. During the first year of the 3-year study, we completed surveys of 41 Level 1 (i.e., the least intensive level) islands, and established permanent plots in two and three islands of Levels 2 and 4 intensity, respectively. Tree species richness and structural complexity was highest in Shark Slough “hammocks”, while islands in Northeast Shark Slough and Water Conservation Area 3B, which receive heavy human use, were simpler, more park-like communities. Initial monitoring of soil moisture in Level 4 hammocks indicated considerable local variation, presumably associated with antecedent rainfall and current water levels in the adjacent marsh. Tree islands throughout the study area were impacted significantly by Hurricanes Katrina and Wilma in 2005, but appear to be recovering rapidly. As the project continues to include more islands and repeated measurements, we expect to develop a better grasp of tree island dynamics across the Everglades ecosystem, especially with respect to moisture relations and water levels in the adjacent marsh. The detailed progress report which follows is also available online at http://www.fiu.edu/~serp1/projects/treeislands/tree_islands_2005_annual_report.pd

Essays in Environmental Economic Valuation and Decision Making in the Presence of an Environmental Disaster

The first essay developed a respondent model of Bayesian updating for a double-bound dichotomous choice (DB-DC) contingent valuation methodology. I demonstrated by way of data simulations that current DB-DC identifications of true willingness-to-pay (WTP) may often fail given this respondent Bayesian updating context. Further simulations demonstrated that a simple extension of current DB-DC identifications derived explicitly from the Bayesian updating behavioral model can correct for much of the WTP bias. Additional results provided caution to viewing respondents as acting strategically toward the second bid. Finally, an empirical application confirmed the simulation outcomes. The second essay applied a hedonic property value model to a unique water quality (WQ) dataset for a year-round, urban, and coastal housing market in South Florida, and found evidence that various WQ measures affect waterfront housing prices in this setting. However, the results indicated that this relationship is not consistent across any of the six particular WQ variables used, and is furthermore dependent upon the specific descriptive statistic employed to represent the WQ measure in the empirical analysis. These results continue to underscore the need to better understand both the WQ measure and its statistical form homebuyers use in making their purchase decision. The third essay addressed a limitation to existing hurricane evacuation modeling aspects by developing a dynamic model of hurricane evacuation behavior. A household’s evacuation decision was framed as an optimal stopping problem where every potential evacuation time period prior to the actual hurricane landfall, the household’s optimal choice is to either evacuate, or to wait one more time period for a revised hurricane forecast. A hypothetical two-period model of evacuation and a realistic multi-period model of evacuation that incorporates actual forecast and evacuation cost data for my designated Gulf of Mexico region were developed for the dynamic analysis. Results from the multi-period model were calibrated with existing evacuation timing data from a number of hurricanes. Given the calibrated dynamic framework, a number of policy questions that plausibly affect the timing of household evacuations were analyzed, and a deeper understanding of existing empirical outcomes in regard to the timing of the evacuation decision was achieved.

Surface Mean Flow and Turbulence Structure in Tropical Cyclone Winds

Hurricanes are one of the deadliest and costliest natural hazards affecting the Gulf coast and Atlantic coast areas of the United States. An effective way to minimize hurricane damage is to strengthen structures and buildings. The investigation of surface level hurricane wind behavior and the resultant wind loads on structures is aimed at providing structural engineers with information on hurricane wind characteristics required for the design of safe structures. Information on mean wind profiles, gust factors, turbulence intensity, integral scale, and turbulence spectra and co-spectra is essential for developing realistic models of wind pressure and wind loads on structures. The research performed for this study was motivated by the fact that considerably fewer data and validated models are available for tropical than for extratropical storms. Using the surface wind measurements collected by the Florida Coastal Monitoring Program (FCMP) during hurricane passages over coastal areas, this study presents comparisons of surface roughness length estimates obtained by using several estimation methods, and estimates of the mean wind and turbulence structure of hurricane winds over coastal areas under neutral stratification conditions. In addition, a program has been developed and tested to systematically analyze Wall of Wind (WoW) data, that will make it possible to perform analyses of baseline characteristics of flow obtained in the WoW. This program can be used in future research to compare WoW data with FCMP data, as gust and turbulence generator systems and other flow management devices will be used to create WoW flows that match as closely as possible real hurricane wind conditions. Hurricanes are defined as tropical cyclones for which the maximum 1-minute sustained surface wind speeds exceed 74 mph. FCMP data include data for tropical cyclones with lower sustained speeds. However, for the winds analyzed in this study the speeds were sufficiently high to assure that neutral stratification prevailed. This assures that the characteristics of those winds are similar to those prevailing in hurricanes. For this reason in this study the terms tropical cyclones and hurricanes are used interchangeably.

Present-day Pattern Variations in the Central and Eastern Pacific El Nino

El Niño and the Southern Oscillation (ENSO) is a cycle that is initiated in the equatorial Pacific Ocean and is recognized on interannual timescales by oscillating patterns in tropical Pacific sea surface temperatures (SST) and atmospheric circulations. Using correlation and regression analysis of datasets that include SST’s and other interdependent variables including precipitation, surface winds, sea level pressure, this research seeks to quantify recent changes in ENSO behavior. Specifically, the amplitude, frequency of occurrence, and spatial characteristics (i.e. events with maximum amplitude in the Central Pacific versus the Eastern Pacific) are investigated. The research is based on the question; “Are the statistics of ENSO changing due to increasing greenhouse gas concentrations?” Our hypothesis is that the present-day changes in amplitude, frequency, and spatial characteristics of ENSO are determined by the natural variability of the ocean-atmosphere climate system, not the observed changes in the radiative forcing due to change in the concentrations of greenhouse gases. Statistical analysis, including correlation and regression analysis, is performed on observational ocean and atmospheric datasets available from the National Oceanographic and Atmospheric Administration (NOAA), National Center for Atmospheric Research (NCAR) and coupled model simulations from the Coupled Model Inter-comparison Project (phase 5, CMIP5). Datasets are analyzed with a particular focus on ENSO over the last thirty years. Understanding the observed changes in the ENSO phenomenon over recent decades has a worldwide significance. ENSO is the largest climate signal on timescales of 2 - 7 years and affects billions of people via atmospheric teleconnections that originate in the tropical Pacific. These teleconnections explain why changes in ENSO can lead to climate variations in areas including North and South America, Asia, and Australia. For the United States, El Niño events are linked to decreased number of hurricanes in the Atlantic basin, reduction in precipitation in the Pacific Northwest, and increased precipitation throughout the southern United Stated during winter months. Understanding variability in the amplitude, frequency, and spatial characteristics of ENSO is crucial for decision makers who must adapt where regional ecology and agriculture are affected by ENSO.

Present-day Pattern Variations in the Central and Eastern Pacific El Nino

El Niño and the Southern Oscillation (ENSO) is a cycle that is initiated in the equatorial Pacific Ocean and is recognized on interannual timescales by oscillating patterns in tropical Pacific sea surface temperatures (SST) and atmospheric circulations. Using correlation and regression analysis of datasets that include SST’s and other interdependent variables including precipitation, surface winds, sea level pressure, this research seeks to quantify recent changes in ENSO behavior. Specifically, the amplitude, frequency of occurrence, and spatial characteristics (i.e. events with maximum amplitude in the Central Pacific versus the Eastern Pacific) are investigated. The research is based on the question; “Are the statistics of ENSO changing due to increasing greenhouse gas concentrations?” Our hypothesis is that the present-day changes in amplitude, frequency, and spatial characteristics of ENSO are determined by the natural variability of the ocean-atmosphere climate system, not the observed changes in the radiative forcing due to change in the concentrations of greenhouse gases. Statistical analysis, including correlation and regression analysis, is performed on observational ocean and atmospheric datasets available from the National Oceanographic and Atmospheric Administration (NOAA), National Center for Atmospheric Research (NCAR) and coupled model simulations from the Coupled Model Inter-comparison Project (phase 5, CMIP5). Datasets are analyzed with a particular focus on ENSO over the last thirty years. Understanding the observed changes in the ENSO phenomenon over recent decades has a worldwide significance. ENSO is the largest climate signal on timescales of 2 - 7 years and affects billions of people via atmospheric teleconnections that originate in the tropical Pacific. These teleconnections explain why changes in ENSO can lead to climate variations in areas including North and South America, Asia, and Australia. For the United States, El Niño events are linked to decreased number of hurricanes in the Atlantic basin, reduction in precipitation in the Pacific Northwest, and increased precipitation throughout the southern United Stated during winter months. Understanding variability in the amplitude, frequency, and spatial characteristics of ENSO is crucial for decision makers who must adapt where regional ecology and agriculture are affected by ENSO.

Influence of Increased Human Presence in the Mills River Basin on Water Availability and Drought

Periods of drought and low streamflow can have profound impacts on both human and natural systems. People depend on a reliable source of water for numerous reasons including potable water supply and to produce economic value through agriculture or energy production. Aquatic ecosystems depend on water in addition to the economic benefits they provide to society through ecosystem services. Given that periods of low streamflow may become more extreme and frequent in the future, it is important to study the factors that control water availability during these times. In the absence of precipitation the slower hydrological response of groundwater systems will play an amplified role in water supply. Understanding the variability of the fraction of streamflow contribution from baseflow or groundwater during periods of drought provides insight into what future water availability may look like and how it can best be managed. The Mills River Basin in North Carolina is chosen as a case-study to test this understanding. First, obtaining a physically meaningful estimation of baseflow from USGS streamflow data via computerized hydrograph analysis techniques is carried out. Then applying a method of time series analysis including wavelet analysis can highlight signals of non-stationarity and evaluate the changes in variance required to better understand the natural variability of baseflow and low flows. In addition to natural variability, human influence must be taken into account in order to accurately assess how the combined system reacts to periods of low flow. Defining a combined demand that consists of both natural and human demand allows us to be more rigorous in assessing the level of sustainable use of a shared resource, in this case water. The analysis of baseflow variability can differ based on regional location and local hydrogeology, but it was found that baseflow varies from multiyear scales such as those associated with ENSO (3.5, 7 years) up to multi decadal time scales, but with most of the contributing variance coming from decadal or multiyear scales. It was also found that the behavior of baseflow and subsequently water availability depends a great deal on overall precipitation, the tracks of hurricanes or tropical storms and associated climate indices, as well as physiography and hydrogeology. Evaluating and utilizing the Duke Combined Hydrology Model (DCHM), reasonably accurate estimates of streamflow during periods of low flow were obtained in part due to the model’s ability to capture subsurface processes. Being able to accurately simulate streamflow levels and subsurface interactions during periods of drought can be very valuable to water suppliers, decision makers, and ultimately impact citizens. Knowledge of future droughts and periods of low flow in addition to tracking customer demand will allow for better management practices on the part of water suppliers such as knowing when they should withdraw more water during a surplus so that the level of stress on the system is minimized when there is not ample water supply.

Reanalysis of the 1954-1963 Atlantic Hurricane Seasons

HURDAT is the main historical archive of all tropical storms and hurricanes in the North Atlantic Basin, which includes the Caribbean Sea and Gulf of Mexico, from 1851 to the present. HURDAT is maintained and updated annually by the National Hurricane Center at Miami, Florida. Today, HURDAT is widely used by research scientists, operational hurricane forecasters, insurance companies, emergency managers and others. HURDAT contains both systematic biases and random errors. Thus, the reanalysis of HURDAT is vital. For this thesis, HURDAT is reanalyzed for the period of 1954-1963. The track and intensity of each existing tropical cyclone in HURDAT is assessed in the light of 21st century understanding and previously unrecognized tropical cyclones are detected and analyzed. The resulting changes will be recommended to the National Hurricane Center Best Track Change Committee for inclusion in HURDAT.

Endogenous Risk Perception, Geospatial Characteristics and Temporal Variation in Hurricane Evacuation Behavior

The main focus of this thesis was to gain a better understanding about the dynamics of risk perception and its influence on people’s evacuation behavior. Another major focus was to improve our knowledge regarding geo-spatial and temporal variations of risk perception and hurricane evacuation behavior. A longitudinal dataset of more than eight hundred households were collected following two major hurricane events, Ivan and Katrina. The longitudinal survey data was geocoded and a geo-spatial database was integrated to it. The geospatial database was composed of distance, elevation and hazard parameters with respect to the respondent’s household location. A set of Bivariate Probit (BP) model suggests that geospatial variables have had significant influences in explaining hurricane risk perception and evacuation behavior during both hurricanes. The findings also indicated that people made their evacuation decision in coherence with their risk perception. In addition, people updated their hurricane evacuation decision in a subsequent similar event.