Abstract:
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In order to model sea level rise, we examine altimetry, water temperature, water salinity, and El NiƱo southern oscillation (ENSO) 3.4 data to model sea level changes in coastal Florida. The variations in reginal and global sea surface height anomalies (SSHA) were used for predicting the local changes of SSHA. The sea level changes were modeled using both multiple regression and generalized additive model (GAM). The global mean sea level (GMSL), regional SSHA, water temperature, water salinity, year, and ENSO were identified as significant factors to predict local sea. Our future work will be focused on extending and refining the proposed model by including other factors such as average monthly winds, atmospheric pressures, and coastal currents.
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