Disappearing cities on US coasts

Global climate change is causing more frequent and intense extreme weather events, impacting freshwater resources and exacerbating sea-level rise (SLR). Globally, SLR is accelerating, posing a substantial socioeconomic challenge, particularly for coastal populations and infrastructure. Coastal cities often experience land subsidence, compounding the effects of SLR and increasing coastal hazards. This study focuses on the US coast, where SLR is rising faster than the global average. The US, a coastal nation with a significant coastal population and economy, faces substantial socioeconomic losses from SLR. Short-term vulnerability assessments are crucial for developing adaptation strategies. However, accurate projections require high-resolution measurements of vertical land motion (VLM), which are currently lacking in many areas of the USA. This study aims to address this data gap and provide more accurate inundation hazard models for US coastal cities.

Existing literature highlights the consequences of SLR on coastal communities globally, including increased flooding, erosion, and saltwater intrusion. Studies have shown the acceleration of SLR and its varied impacts across different regions. The compounding effect of land subsidence on SLR and coastal hazards has also been documented. Previous research emphasizes the need for high-resolution VLM data for accurate coastal vulnerability assessments but points to the significant data gap in this area for the USA. Some existing projections of flooding fail to account for spatially variable land elevation changes, leading to inaccurate estimations of risk and exposure. Additionally, the literature highlights the disproportionate impacts of climate change on vulnerable communities, emphasizing the need for equitable adaptation strategies.

This study integrates high-resolution VLM data (millimeter-level accuracy using InSAR) with geocentric SLR projections (from the Shared Socioeconomic Pathway 2-4.5 scenario) and LiDAR digital elevation models (DEMs) to forecast relative SLR rates and create detailed inundation maps for 32 major US coastal cities. The 2010 US census data provides baseline estimates for population and property exposure, while the Zillow Home Value Index (ZHVI) is used to estimate property values. Two scenarios are considered: one without flood-defense structures and another that accounts for existing structures. The analysis quantifies the increase in exposed land area, population, properties, and home values due to relative SLR by 2050, using 2020 as a baseline. The study also compares the InSAR-derived VLM data with VLM rates from the IPCC Sixth Assessment Report to assess the impact of spatially variable VLM on exposure estimates. Finally, racial and economic disparities in exposure are analyzed using US census data.

By 2050, relative SLR could expose an additional 1,334–1,813 km² in the 32 cities if no flood defenses are in place, impacting 176,000–518,000 people and 94,000–288,000 properties with an estimated home value of US$32–109 billion. The Atlantic coast is projected to experience the most significant increase in exposed area (773–951 km²) and population (59,000–263,000), with Miami facing the greatest share of exposure. The Gulf coast is also at high risk (528–826 km² of additional exposed area and 110,000–225,000 people affected). New Orleans already has substantial areas below sea level and is highly vulnerable. The Pacific coast shows comparatively lower inundation hazards (20–40 km² additional exposed area). Land subsidence is a critical driver of coastal hazards, contributing significantly to relative SLR, especially on the Atlantic and Gulf coasts. In some Gulf Coast locations, subsidence currently outpaces geocentric SLR. A comparison with IPCC projections reveals discrepancies in exposure estimates due to the limitations of tide-gauge data in capturing spatially variable VLM within cities. The InSAR data provides higher resolution and more accurate estimates. Analysis of racial disparities reveals that minority groups are disproportionately impacted, particularly on the Gulf coast. Economic disparities are also evident in New Orleans and Port Arthur. The study also models a 'defended' scenario, accounting for existing flood control structures, reducing the impact, but still showing considerable risk, particularly on the Atlantic coast.

The findings highlight the crucial role of incorporating high-resolution VLM data in coastal flood risk assessments. The discrepancy between InSAR-derived and IPCC-derived exposure estimates underscores the importance of using more spatially refined VLM data for accurate projections. The study emphasizes the urgency for adaptation measures in US coastal cities. The disproportionate impact on minority and low-income communities highlights the need for equitable and inclusive adaptation strategies. Ignoring subsidence leads to underestimation of risk and unequal impact.

This study demonstrates the significant threat of relative SLR to US coastal cities by 2050, particularly considering land subsidence. Incorporating high-resolution VLM data is essential for accurate risk assessment. The study highlights the need for proactive adaptation strategies, including coastal defenses, subsidence control, and land-use planning, with a focus on equity and resilience. Future research should focus on refining VLM models, improving projections under different climate scenarios, and developing more effective and equitable adaptation strategies.

The study uses a specific SLR projection scenario (SSP2-4.5) and assumes linear projections of VLM rates. The analysis does not include all potential future adaptation measures or economic impacts beyond property values. The accuracy of the inundation models relies on the quality of the VLM and elevation data. The analysis of social vulnerability is limited to racial and economic disparities, and other factors should be considered.