DOI: 10.17603/ds2vt3t
title: Sea level rise projection at Sewells Point (6838610) in Norfolk
title: in Advancing Resilience of Transportation Infrastructure Systems to Land Subsidence (LS), Sea-Level Rise (SLR) and Storm Surge in Coastal Areas

description: From NWLON station 6838610 at Sewells Point in Norfolk, Virginia, the relative mean SLR rate is 3.40 mm/yr. from 1928 to 1992. From subsidence contours, LS rate at the station to be 2.4 mm/yr from 1940 to 1971, which will be further calibrated later by simulated annual LS values with groundwater flow model and COMPAC. Local sea level rise could be determined to be -0.7 mm/yr. (= 3.40 -2.4 -1.70). The regional mean SLR in Chesapeake Bay from satellite altimetry sounds in range of -1.0 to 0 mm/yr. from 1992 to 2009. Our preliminary SLR rate of 1 mm/yr. (= -0.70 + 1.70) looks higher than this range, which would be further calibrated with data from other stations 8637624, 8632200 and 8638660 in this region and LS simulation results at corresponding stations. Our SLR rate also will be compared to mean SLR trends in other NWLON stations along the Atlantic coast for further justification. The Intermediate High Scenario is comparable to an average of the high end of ranges of global mean SLR reported by several studies using semi-empirical approaches. The Intermediate Low Scenario is comparable to the global mean SLR projection from the IPCC AR4. The observed SLR during 1993 to 2017 preliminarily shows how the projected SLR during this period fits the observed.
description: Vulnerability of critical infrastructure, especially the transportation system and high value assets to direct and indirect damage is increasing in the region, as relative SLR, shoreline retreat and the magnitude and frequency of near-shoreline coastal flooding increases.  Many of the transportation infrastructure have been designed within the area without consideration of LS and SLR, it is thus critical to close the gaps between the convectional designs and the phasing of the innovative designs that realistically anticipate these treats.  Adaptive engineered systems that integrate with and leverage natural infrastructure are urgently needed to mitigate these impacts and promote sustainable and resilient coasts. 

The overall goal of the proposed project is to develop approaches and methods that will enable design and management of more sustainable, resilient multi-modal transportation infrastructure in coastal areas that are subject to the combined effects of LS and SLR.  Two important U.S. coastal areas that are experiencing rapid relative SLR because of LS and SLR will be studied (Chesapeake Bay and Huston Galveston Area).  A combined research team from Morgan State University, the lead institution located in Baltimore, MD, and Carnegie Mellon University will work collaboratively on the project.

In this project, we seek to address the following high-level research questions:

1)     In the defined areas of study, what are the components of the multi-modal transportation network(s) that are most vulnerable to SLR and LS?

2)     With prioritization of enhancing resilience, how can coastal transportation networks be adapted optimally to mitigate the potential damages from the vulnerabilities?

3)     Which innovative designs and approaches can best support these adaptation strategies, in terms of an integrated approach to multi-modal transportation management?  

The proposed project will support development of resilient built infrastructures in coastal areas, through better monitoring, mapping, prediction/forecasting, and modeling of SLR and LS. Chesapeake Bay in Maryland and Galveston Bay in Texas are proposed as test beds for study of needs and opportunities for deployment of modern sensing, monitoring, modeling and control technologies for continuous assessment and management of LS and SLR, and integration of this information in adaptive approaches for design and management of infrastructure. The project addresses the vulnerability of transportation infrastructure to LS and SLR and will yield new approaches for designing resilient and sustainable coastal infrastructure. The project will also offer educational opportunities about LS and SLR to minority students.  Additionally, the project will ultimately create more informed strategies for policy makers, planners and engineers as scientific understanding of LS, SLR and storm surge is critical for making informed decisions about public investment and management of vulnerable coastal communities.

LS would be computed with COMPAC (Helm, 1975) based on CMP2006 simulated groundwater level and compaction parameter values from Pope and Burbey (2004) and further analysis. The local mean SLR projection would be downscaled from global mean SLR projection with a quadratic SLR equation (Parris et. al., 2012; UACE, 2013) based on local NWLON station relative SLR analysis and removing impacts of LS.