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Path Analysis of Sea-Level Rise and Its Impact

Author

Listed:
  • Jean Chung

    (Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
    These authors contributed equally to this work.)

  • Guanchao Tong

    (Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
    These authors contributed equally to this work.)

  • Jiayou Chao

    (Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
    These authors contributed equally to this work.)

  • Wei Zhu

    (Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA)

Abstract

Global sea-level rise has been drawing increasingly greater attention in recent years, as it directly impacts the livelihood and sustainable development of humankind. Our research focuses on identifying causal factors and pathways on sea level changes (both global and regional) and subsequently predicting the magnitude of such changes. To this end, we have designed a novel analysis pipeline including three sequential steps: (1) a dynamic structural equation model (dSEM) to identify pathways between the global mean sea level (GMSL) and various predictors, (2) a vector autoregression model (VAR) to quantify the GMSL changes due to the significant relations identified in the first step, and (3) a generalized additive model (GAM) to model the relationship between regional sea level and GMSL. Historical records of GMSL and other variables from 1992 to 2020 were used to calibrate the analysis pipeline. Our results indicate that greenhouse gases, water, and air temperatures, change in Antarctic and Greenland Ice Sheet mass, sea ice, and historical sea level all play a significant role in future sea-level rise. The resulting 95% upper bound of the sea-level projections was combined with a threshold for extreme flooding to map out the extent of sea-level rise in coastal communities using a digital coastal tracker.

Suggested Citation

  • Jean Chung & Guanchao Tong & Jiayou Chao & Wei Zhu, 2021. "Path Analysis of Sea-Level Rise and Its Impact," Stats, MDPI, vol. 5(1), pages 1-14, December.
    • Handle: RePEc:gam:jstats:v:5:y:2021:i:1:p:2-25:d:710128
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    References listed on IDEAS

    as
    1. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    2. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    3. Søren Johansen, 2010. "The Analysis of Nonstationary Time Series Using Regression, Correlation and Cointegration with an Application to Annual Mean Temperature and Sea Level," Discussion Papers 10-27, University of Copenhagen. Department of Economics.
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