Subtle Land Subsidence Elevates Future Storm Surge Risks Along the Gulf Coast of the United States.

We developed a robust InSAR processing strategy that can effectively mitigate severe decorrelation noise in a large volume of InSAR data. We mapped the average land subsidence rate (2017–2020) over the 131,572 km2 Upper Texas and Louisiana coasts from Sentinel‐1 data, with ∼2 mm/yr accuracy based on independent GPS and tide gauge validation at 189 locations. The improved InSAR observations reveal widespread subsidence that was previously undetected in coastal wetlands and rural areas with small communities. Our InSAR surface deformation map is at the spatial scale that overlaps with the scale of hydrodynamic model grids. This allows us to integrate InSAR observations into operational storm surge models to analyze future flooding risks due to relative sea level change. We found that these subtle millimeter‐to‐centimeter subsidence features can substantially increase hurricane‐induced inundation, and passive flood mapping (known as the "bathtub" approach) can lead to inaccurate flood risk predictions. Plain Language Summary: The analysis of future storm surge risks along the Gulf Coast often focuses only on projected sea level rise estimates due to the lack of spatially dense land subsidence data. Here we show advances in spaceborne Interferometric Synthetic Aperture Radar (InSAR) techniques can enable the retrieval of coastal subsidence patterns over a 131,572 km2 region along the Gulf Coast of the United States, a challenging terrain for InSAR analysis due to the presence of dense vegetation coverage. Our results reveal widespread subsidence features due to oil and gas production, groundwater pumping, and wetland degradation that were previously undetected. Physics‐based storm surge modeling shows that the detected subtle millimeter‐to‐centimeter land subsidence can substantially elevate flood water depth and flood extent. We demonstrate that the relationship between coastal subsidence and flood depth increase is complex, and communities vulnerable to future flood risk are highly variable along the Gulf Coast of the United States. Key Points: A robust InSAR processing algorithm enables accurate retrieval of subtle surface deformation signals over densely vegetated Gulf CoastInSAR reveals many coastal subsidence features that were previously undetected by existing GPS and tide gauge stationsStorm surge modeling suggests that passive flood mapping based on topography can lead to inaccurate flood risk predictions [ABSTRACT FROM AUTHOR]