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|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. |
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|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. |
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|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. |
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|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. |
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|a dpSobek |c Sea Level Rise |
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|u http://dpanther.fiu.edu/dpService/dpPurlService/purl/FI15062039/00001 |y Click here for full text |
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|3 Host material |u https://smartech.gatech.edu/page/terms |y Rights statement |
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|a http://dpanther.fiu.edu/sobek/content/FI/15/06/20/39/00001/FI15062039thm.jpg |
