This study investigated the rate of natural subsidence along the Texas coast using multidecadal to century tide gauge (TG) and global positioning system (GPS) data sets. The rates of land subsidence and sea level rise are aligned to the Gulf of Mexico (GOM) Reference Frame 2020 (GOM20), which is tied to the stable portion of the Gulf Coastal Plain. GOM20 provides a robust reference for ruling out regional ground movements associated with regional tectonics and glacial isostatic adjustment (GIA) and highlighting natural subsidence in the Gulf Coast Aquifer region. According to this study, the mean sea level rise rate within the GOM was $2.6\mathrm{mm}/\mathrm{year}$ with respect to GOM20 from the 1970s to the 2010s. Present land subsidence along the Texas coast is dominated by the natural subsidence varying from $0.7\mathrm{mm}/\mathrm{year}$ in the central coastal area (Port Mansfield, Corpus Christi, and Rockport) to $1.6\mathrm{mm}/\mathrm{year}$ in the southern coastal area (South Padre Island) to $1.5–3.5\mathrm{mm}/\mathrm{year}$ in the northern coastal area (Freeport, Galveston Island, Texas City, and Sabine Pass). The average natural subsidence rate along the 600-km Texas coastline is $1.4\mathrm{mm}/\mathrm{year}$ with respect to GOM20. Four scenarios (lowest, medium-low, medium-high, and highest) for future coastal submergence were developed by integrating the natural subsidence and sea level rise along the Texas coast with the global sea level scenarios. Our analysis projects that the average submergence along the Texas coastline from 2020 to 2100 will be greater than 0.3 m, and likely between 0.6 and 1.2 m, but is unlikely to exceed 2.0 m.

Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of a soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach for the Tiffany Run watershed, Baltimore City. This study utilized different extreme storm events, a high-resolution (1 m) LiDAR Digital Terrain Model (DTM), building footprints, and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, the City Catchment Analysis Tool (CityCAT), to simulate urban flood dynamics. The pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 m in most areas, with hydrologic soil groups C and D, especially downstream of the study area. The post-soil rehabilitation simulation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 m. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following the rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. The validation using a contingency matrix demonstrated true-positive rates of 0.75, 0.50, 0.64, and 0 for the selected events, confirming the model’s capability at capturing real-world flood occurrences.

This study reconstructs and projects relative sea-level change (RSLC) along Chesapeake Bay, a global hotspot for sea-level rise, from 1900 to 2100 by statistically extrapolating observed tide gauge trends, rather than employing climate model-based scenarios. The approach integrates global mean sea-level rise (GMSLR), regional sea-level rise (RSLR), and local land subsidence (LS) to evaluate both past and future behavior. Tide gauge data reveal that Chesapeake Bay’s sea level has accelerated at 0.099 ± 0.013 mm/year² since 1992, with a linear rate of 1.26 mm/year since 1900, slightly outpacing global averages. LS, primarily driven by glacial isostatic adjustment (GIA) and sediment compaction, has been the dominant contributor to RSLC since the early 20th century, accounting for up to 71% of the RSLC prior to 1992 across 15 tide gauge stations. However, with GMSLR accelerating at 0.120 ± 0.025 mm/year², the relative contribution of LS to RSLC is projected to decline to 31–43% by 2100. The reconstructed RSLC for the 20th century ranges between 32 and 44 cm, while extrapolated projections for the 21st century indicate a further increase of 53–99 cm. By 2100, GMSLR is expected to contribute to 60–70% of total RSLC. Spatial variability in RSLC across 15 tide gauge stations reflects differing geological conditions and anthropogenic influences such as groundwater withdrawal and construction-induced subsidence. These findings highlight the critical need for adaptive strategies to mitigate the impact of rising sea levels on coastal communities and infrastructure in the Chesapeake Bay region. Continued monitoring, improved modeling, and targeted resilience planning are essential to address the accelerating threats posed by sea-level rise and to ensure the sustainability of vulnerable coastal areas.

The Chesapeake Bay (CB) region faces significant risks from relative sea-level change (RSLC), driven by global mean sea-level rise (GMSLR), regional sea-level rise (RSLR), and local land subsidence (LS). This study introduces a methodology to decipher RSLC trends in the CB area by integrating these components. We develop trend equations spanning 1900–2100, incorporating acceleration for GMSLR and RSLR since 1992, with linear LS estimation using tide gauge, satellite altimetry, and InSAR data. Our approach employs dynamic RSLC equations, Maclaurin series expansions, and inverse simulations to project RSLC trends through 2100. Stable RSLC rates require over 122 years of data for reliable linear trend estimation, with the Baltimore tide gauge providing the necessary long-term dataset. Similarity in monthly mean sea-level variations within a coastal region enables a new method to identify LS from short-term tide gauge data by correlating it with corresponding long-term data at Baltimore. LS is categorized into bedrock-surface subsidence (BSS) and compaction subsidence (CS), with methods proposed to map BSS contours and estimate CS. CS is further classified into primary consolidation, secondary consolidation, construction-induced, and negative subsidence to determine specific compaction types. The projection model highlights the dominant influence of GMSLR acceleration since 1992, with local LS and RSLR influenced by ocean circulation, density changes, and gravitational, rotational, and deformational (GRD) effects. This integrated approach enhances understanding and predictive reliability for RSLC trends, supporting resilience planning and infrastructure adaptation in coastal CB communities.