LDR   05814nam^^22002773a^4500
001        FI15062039_00001
005        20160201121626.0
006        m^^^^^o^^d^^^^^^^^
007        cr^^n^---ma^mp
008        150720n^^^^^^^^xx^||||^o^^^^^|||^u^eng^d
245 00 |a Spatial Analysis of Sea Level Rise Associated with Climate Change |h [electronic resource] / |c It is the users responsibility to check the usage rights for material collected and disseminated from the SMARTech Site. The rights statement can be found by following the link included in "Host" material.
260        |c 2013.
506        |a Please contact the owning institution for licensing and permissions. It is the user's responsibility to ensure use does not violate any third party rights.
520 3    |a Sea level rise (SLR) is one of the most damaging impacts associated with climate change. An important aspect of SLR analysis is to characterize its spatial variability, so that potential threats of SLR to local regions of interest can be assessed more accurately. Despite various studies on geographical pattern identification of sea level change, the related physical, empirical, and stochastic models are still in a fairly preliminary stage. 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. In the first part of the study, a spatial pattern recognition methodology is developed to characterize the spatial variations of sea level and to investigate the sea level footprints of climatic signals. Utilizing clustering algorithms, this methodology is capable of grouping sea level data with changing magnitude of spatial variations over time into separate regions, and it also has the functionality to assess the relative strengths of different climate phenomena’s sea level footprints. When applied to a spatial sea level dataset for the period of 1950 to 2001, the pattern recognition methodology identified spatial patterns in the data that are potentially associated with climate phenomena such as El Nino-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO). ENSO was evaluated as the strongest spatial signal in the data, which supports related findings of previous studies. A technique based on artificial neural network is subsequently proposed to reconstruct average sea levels for the characteristic regions identified. Utilizing the correlative relationship between sea level and sea surface temperature (SST), the neural network takes regional average SST’s and global average sea level as input variables, and it generates regional average sea levels as outputs. By applying this neural network approach, regional average sea levels were reconstructed for the characteristic regions identified by the pattern recognition technique, as well as regions based on major ocean basins. 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. Among the four marker scenarios and two illustrative scenarios proposed by the Intergovernmental Panel on Climate Change (IPCC), the highest and the lowest projected SST's occur under scenarios A1FI and B1, respectively, responding to the highest and the lowest predicted global mean CO2 concentrations. 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. Sea levels projected by the spatial DSM models are generally lower than those by previous semiempirical sea level models, which reflect the importance of feedback mechanisms to the dynamic system of sea level and SST. The third 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, namely Florida, Georgia and New Jersey. Remarkably different magnitudes of land inundation were projected for these three study regions, which reflect the variations among their land topography. The projected total area of land inundation from 2010 to 2100 is about 3,000 square miles for Florida under all six IPCC SRES scenarios, making it the most severely affected region among the three. The corresponding value for Georgia ranges from 201 to 376 square miles, while that range for the state of New Jersey is from 142 to 202 square miles. These projections correspond to about 5.4%, 0.3% - 0.6%, and 1.9% - 2.7% of the current total land area of Florida, Georgia, and New Jersey. The importance of consistent elevation datum referencing and data accuracy was demonstrated through the example of Florida, suggesting the necessity of examining the reference datum issue and establishing high accuracy elevation data for future research.
533        |a Electronic reproduction. |c Florida International University, |d 2015. |f (dpSobek) |n Mode of access: World Wide Web. |n System requirements: Internet connectivity; Web browser software.
650    0 |a Climate Change.
650    0 |a Sea Level Rise.
720 1    |a Chang, Biao.
773 0    |t Rights statement
830    0 |a dpSobek.
830    0 |a Sea Level Rise.
852        |a dpSobek |c Sea Level Rise
856 40 |u http://dpanther.fiu.edu/dpService/dpPurlService/purl/FI15062039/00001 |y Click here for full text
856 42 |3 Host material |u https://smartech.gatech.edu/page/terms |y Rights statement
992 04 |a http://dpanther.fiu.edu/sobek/content/FI/15/06/20/39/00001/FI15062039thm.jpg
997        |a Sea Level Rise


The record above was auto-generated from the METS file.