Abstract
Climate change and the associated sea level rise (SLR) are presenting newfound challenges to the port systems and coastal transportation infrastructure of southeast Texas. This paper introduces a Geographic Information Systems (GIS) based model designed to simulate inundation scenarios under various sea-level projections, aiming to assess the vulnerabilities of both port facilities and road networks. The study area encompasses a specific region within Jefferson County, southeast Texas, encompassing three major ports: Port Arthur, Beaumont, and Orange. Utilizing a high-resolution (1-m) Digital Elevation Model (DEM) derived from the 2017 LiDAR dataset, this model is integrated with NASA’s sea-level rise projections to compute the extent and volume of inundation across low, medium, and high SLR scenarios. Drawing from monthly mean sea level data spanning from 1958 to 2020, the lowest SLR projections, derived from the relative sea-level trend measured at the Sabine Pass, TX gauge station, indicate a yearly increase of 6.16 mm, with a 95% confidence interval of +/- 0.74 mm. Projections for 2050 and 2,100 show the lowest SLR at 0.17 m and 0.48 m, respectively. In contrast, the medium to high RSLR projections under the IPCC SSP3-7.0 scenario for 2050 and 2,100 stand at 0.54 m and 1.34 m, respectively. The findings reveal that, under medium to high SLR scenarios, the extent of inundated areas in the study region is expected to expand by 12.4% in 2050 and 19.9% in 2,100, compared to the lowest SLR projection. Additionally, the length of submerged roadways is predicted to increase by 6.9% in 2050 and 13.3% in 2,100, in comparison to the lowest SLR projection. It is worth noting that some margin of error may be introduced due to factors such as the width of the port area and access roads, the high-resolution DEM, and the alignment of computed inundated areas with the existing topography. Overall, the manuscript highlights the urgency of proactive planning and underscores the importance of safeguarding critical infrastructure in the context of climate change and SLR.