Generalized Additive Models for Predicting Sea Level Rise in Coastal Florida
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Published:2023-10-16
Issue:10
Volume:13
Page:310
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ISSN:2076-3263
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Container-title:Geosciences
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language:en
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Short-container-title:Geosciences
Author:
Vaidya Hanna N.1, Breininger Robert D.2, Madrid Marisela3ORCID, Lazarus Steven4, Kachouie Nezamoddin N.2
Affiliation:
1. Department of Mathematical Statistics, Wake Forest University, Winston-Salem, NC 27109, USA 2. Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA 3. Turtle Mountain Tribal College, Belcourt, ND 58316, USA 4. Department of Ocean Engineering and Marine Sciences, Florida Institute of Technology, Melbourne, FL 32901, USA
Abstract
Within the last century, the global sea level has risen between 16 and 21 cm and will likely accelerate into the future. Projections from the Intergovernmental Panel on Climate Change (IPCC) show the global mean sea level (GMSL) rise may increase to up to 1 m (1000 mm) by 2100. The primary cause of the sea level rise can be attributed to climate change through the thermal expansion of seawater and the recession of glaciers from melting. Because of the complexity of the climate and environmental systems, it is very difficult to accurately predict the increase in sea level. The latest estimate of GMSL rise is about 3 mm/year, but as GMSL is a global measure, it may not represent local sea level changes. It is essential to obtain tailored estimates of sea level rise in coastline Florida, as the state is strongly impacted by the global sea level rise. The goal of this study is to model the sea level in coastal Florida using climate factors. Hence, water temperature, water salinity, sea surface height anomalies (SSHA), and El Niño southern oscillation (ENSO) 3.4 index were considered to predict coastal Florida sea level. The sea level changes across coastal Florida were modeled using both multiple regression as a broadly used parametric model and the generalized additive model (GAM), which is a nonparametric method. The local rates and variances of sea surface height anomalies (SSHA) were analyzed and compared to regional and global measurements. The identified optimal model to explain and predict sea level was a GAM with the year, global and regional (adjacent basins) SSHA, local water temperature and salinity, and ENSO as predictors. All predictors including global SSHA, regional SSHA, water temperature, water salinity, ENSO, and the year were identified to have a positive impact on the sea level and can help to explain the variations in the sea level in coastal Florida. Particularly, the global and regional SSHA and the year are important factors to predict sea level changes.
Subject
General Earth and Planetary Sciences
Reference49 articles.
1. Sea-Level Rise from the Late 19th to the Early 21st Century;Church;Surv. Geophys.,2011 2. Probabilistic reanalysis of twentieth-century sea-level rise;Hay;Nature,2015 3. A Future Submerged: Implications of Sea Level Rise for South Florida;Peruyera;Fla. A M Univ. Law Rev.,2012 4. Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., and Gomis, M.I. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press. 5. European Environment Agency (2023, January 26). Projected Change of Global Mean Sea Level, Available online: https://www.eea.europa.eu/data-and-maps/daviz/projected-global-mean-sea-level#tab-dashboard-01.
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