Land Subsidence with tide Gauge, Radar Altimetry and GNSS: A Case Study at Subsiding Coast in Texas

Global sea-level is rising at an unprecedented rate in recent decades largely due to glacier/ice-sheet melting and ocean warming expansion, which is referred to as the absolute sea-level rise (ASLR). The combination of vertical land motion (VLM) and ASLR leads to a faster sea-level rise relative to the land, which is referred to as the relative sea-level rise (RSLR). The Texas Gulf Coast is one of the leading hotspots subject to VLM and/or RSLR issues in the United States. GNSS has long been used to monitor long-term and accurate VLM with a continuous GNSS (cGNSS) observation network. However, the observation network density offered by cGNSS stations is often limited. This study investigated the feasibility of estimating VLM using the sea-level change differences between the ASLR and RSLR data observed by tide gauges and satellite radar altimetry using 5-day, 15-day, and 30-day time windows. The results were validated by comparing against the GNSS measurements processed by GipsyX software. Independent time-series of VLM processed by using sea-level change differences and GNSS were obtained at Galveston and Rockport, two areas of the Gulf Coast experiencing substantial subsidence. The trends of the VLM time-series were estimated with Hector software. At Galveston, Texas, the VLM trend from GNSS (-3.9 ± 0.4 mm/yr) matched well with that from sea-level change difference estimated at the tide gauge (-3.8 ± 0.7 mm/yr with a 15-day mean window). At Rockport, Texas, an approximately 1.0 mm/yr VLM difference was observed between GNSS (-4.2 ± 0.3 mm/yr) and sea-level change difference (-5.2 ± 1.1 mm/yr with a 15-day mean window) within the confidence intervals of the two methods. This preliminary study demonstrated the feasibility of VLM estimates with sea-level change differences in coastal areas where longterm static cGNSS observations are absent. However, further investigation is needed to model error sources and improve noise mitigation for reliable VLM trend estimate.