Sea level rise (SLR) is one of the most damaging impacts associated with climate change. The objective of this study is to develop a comprehensive framework to identify the spatial patterns of sea level in the historical records, project regional mean sea levels in the future, and assess the corresponding impacts on the coastal communities. The first part of the study suggests a spatial pattern recognition methodology to characterize the spatial variations of sea level and to investigate the sea level footprints of climatic signals. A technique based on artificial neural network is proposed to reconstruct average sea levels for the characteristic regions identified. In the second part of the study, a spatial dynamic system model (DSM) is developed to simulate and project the changes in regional sea levels and sea surface temperatures (SST) under different development scenarios of the world. The highest sea levels are predicted under the scenario A1FI, ranging from 71 cm to 86 cm (relative to 1990 global mean sea level); the lowest predicted sea levels are under the scenario B1, ranging from 51 cm to 64 cm (relative to 1990 global mean sea level). Predicted sea levels and SST's of the Indian Ocean are significantly lower than those of the Pacific and the Atlantic Ocean under all six scenarios. The last part of this dissertation assesses the inundation impacts of projected regional SLR on three representative coastal U.S. states through a geographic information system (GIS) analysis. Critical issues in the inundation impact assessment process are identified and discussed.
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Spatial analysis of sea level rise associated with climate change
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