Path Analysis of Sea-Level Rise and Its Impact

Stats

Jiayou Chao¹  Guanchao Tong¹  Jean Chung¹  Wei Zhu¹ 

[1] Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA;

关键词: unified (dynamic) structural equation model;    generalized additive model;    vector autoregression model;    global mean sea level;   

DOI  :  10.3390/stats5010002

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown