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Nearly one third of the world's population live in coastal areas, and ten of the fifteen most populous cities in the world lie on a coast. Inhabitants of the Low Elevation Coastal Zone (LECZ) - defined as the contiguous area along the coast that is less than 10 meters above sea level - make up 10% of the world's population and 13% of the world's urban population. Sea level rise, coastal inundation and associated shoreline retreat have emerged as one of the primary threats to these populations and the resources located near the coastal fringe. To meet the needs of governments, planners and managers, researchers are continually faced with the challenge of integrating large volumes of complex environmental and spatial-temporal data. Typically the spatial and temporal components of data sets are underutilized because methods for effectively handling these data have not been available. To address these issues, Eonfusion, a 4-Dimensional software solution, is easily incorporated into the geospatial workflow to significantly enhance the ease with which we can now integrate and explore complex spatially and temporally variant data sets. This paper explores case studies along prominent coastal regions in which, as example, models are developed to predict the impact of rising sea levels on low-lying coastal areas focusing on: (1) Coastal Inundation (2) Coastal Vulnerability (3) Property Devaluation. The inundation is spatially modeled as a function of time and enables the visualization of sea level rise scenarios to assess the extent and impact. The coastal vulnerability mapping highlights the analytical power of Eonfusion, through the efficient integration of inundation and vulnerability models to demonstrate the universal application of the software to the field of climate change research. The third model fuses the cadastral layer and a simple property valuation model to complete the scenario, thus demonstrating a powerful pathway for the estimation and visualization of - the impact of these climate change events. The data for these case studies include: (1) Sea level rise scenarios from IPCC stage 4 (2) LID AR Elevation Data (3) Cadastral Parcels and Value Indicators (4) Storm Surge Information (5) Vulnerability Mapping. The processing steps required to integrate, analyze and visualize these models are: (1) Generation of 3D terrain model from LID AR data (2) Adjustments for any Height Data (AHD) discrepancies (3) Integrate IPCC sea level rises and storm surges into Sea level rise timeseries (4) Identification of sinks using Eonfusion API application (5) Calculate inundation levels and tipping points at which sinks get filled (6) Fuse Cadastre and value model with 3D surface - value decreases as % of title flooded (7) Airphoto drape (8) Set up visualization scene with integrated graph for all scenarios. The outcomes from this work include the identification of powerful pathways through the employment of new visualization and spatial-temporal analysis tools for: (1) Dynamic scenario based modeling for assessing cost and environmental impacts from climate change; (2) The provision of a mechanism enabling the visualization of the complex spatial and temporal patterns from a wide range of data derived empirically and from models. This enables key stakeholders to rapidly assess scenarios and their likely impacts (3) Modeling of this type could be used in a number of areas including fire, flood, tsunami, hurricanes, etc.
Date of Conference: 26-29 Oct. 2009