Remote Sensing of Tidal Dynamics in North Carolina Using the Surface Water and Ocean Topography Mission

Current methods for measuring water levels along the North Carolina coast often rely on NOAA tide gauges, but with only six in the state, the large gaps between gauges leave spatial patterns poorly understood. To measure water surface elevations (WSE), we used 100 m raster data from the Surface Water and Ocean Topography (SWOT) mission and compared them with NOAA tide-gauge water-level data. Our results showed high positive correlation coefficients (0.94-0.99) and mean absolute errors of 0.04-0.06 m between SWOT and NOAA data. Using the maximum lagged correlation between SWOT WSE and gauge data, we identified the gauge and lag time that best captured water levels. Comparing our findings against gauge water-level data from SECOORA, our results showed that by combining SWOT WSE with gauge data, we can provide a fast, simple way to estimate tidal effects on water level across the entire region.

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Publication Details

Journal
UNC Libraries
Published
2026-08-20
DOI
https://doi.org/10.17615/wzb8-v973
Primary Topic
Flood Risk Assessment and Management
Type
article
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article

Remote Sensing of Tidal Dynamics in North Carolina Using the Surface Water and Ocean Topography Mission

A. Smith
UNC Libraries
Flood Risk Assessment and Management
article

Remote Sensing of Tidal Dynamics in North Carolina Using the Surface Water and Ocean Topography Mission

A. Smith
article en

Abstract

Current methods for measuring water levels along the North Carolina coast often rely on NOAA tide gauges, but with only six in the state, the large gaps between gauges leave spatial patterns poorly understood. To measure water surface elevations (WSE), we used 100 m raster data from the Surface Water and Ocean Topography (SWOT) mission and compared them with NOAA tide-gauge water-level data. Our results showed high positive correlation coefficients (0.94-0.99) and mean absolute errors of 0.04-0.06 m between SWOT and NOAA data. Using the maximum lagged correlation between SWOT WSE and gauge data, we identified the gauge and lag time that best captured water levels. Comparing our findings against gauge water-level data from SECOORA, our results showed that by combining SWOT WSE with gauge data, we can provide a fast, simple way to estimate tidal effects on water level across the entire region.

UNC Libraries
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Flood Risk Assessment and Management
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