Abstract The Gulf of Mexico (GOM) region faces acute challenges from coastal subsidence, sea-level rise, and fault-related ground deformation, necessitating a geodetic reference frame capable of isolating minor regional- and local-scale ground deformation from the background signal of rigid plate rotation and translation. We present the Gulf of Mexico Reference Frame 2025 (GOM25), a temporally stable and spatially consistent datum aligned with the International Global Navigation Satellite Systems (GNSS) Service Reference Frame 2020 (IGS20). GOM25 is realized through a densified network of 150 continuous GNSS stations, located in the northern GOM region and anchored to the stable interior of the North American Plate. The frame achieves a stability of 0.3 mm / year horizontally and 0.5 mm / year vertically, providing a reliable reference foundation for the greater Gulf region (15°–37° N; 110°–75° W). To ensure practical accessibility, we provide a one-set seven-parameter transformation for Earth-centered, Earth-fixed (ECEF)-XYZ coordinates, from which we derive the corresponding transformations for displacements and site velocities between IGS20 and GOM25. Applying this regional frame yields quantitative insights relevant to infrastructure and hazard management; two examples exemplify its utility: (1) a mean sea-level rise rate of 2.7 ± 0.4 mm / year during the last half century along the GOM coast and South Atlantic Bight, and (2) an average uplift rate of 1.3 ± 0.6 mm / year in the southern High Plains over the last two decades—both measured relative to GOM25, physically the stable Gulf coastal plain. GOM25 provides a critical foundation for reconciling sea-level measurements, constraining postglacial rebound models, and monitoring the structural health of onshore and offshore infrastructures, supporting interdisciplinary efforts in coastal flooding and inundation mapping, wetland loss mitigation, urban subsidence assessment, and resilient planning across the region.
Euler pole parameters (EPPs) were estimated for the North American Terrestrial Reference Frame of 2022 (NATRF2022), which is a modernized plate-fixed reference frame based on the International Terrestrial Reference Frame of 2020 (ITRF2020), replacing the North American Datum of 1983 (NAD83). NATRF2022 is aligned with ITRF2020 at epoch 2020.0 with the same geocentric origin that is compatible with GNSS products and applications. The EPP estimation was based on a spherical model of Earth, using station velocities derived from the latest velocity estimates: version 8 from the Canadian Geodetic Survey and the Multi-Year CORS Solution version 3.0 from the United States National Geodetic Survey, both referenced to ITRF2020. This paper details the collaborative development of the definitional EPPs for NATRF2022, presents its values, and discusses the results. EPP estimates are close to other Euler pole estimates for North America plate motion; however, it further reduces the frame velocities sampled at more than 4,000 stations within the rigid part of North American plate, thus providing more stable coordinates over time. Since the EPP estimation was simplified by assuming that unit vectors of vertical velocities are perpendicular to the surface of Earth, we examine the effect of this simplification on the north and up velocities as well as the geodetic ellipsoidal heights. We show that ellipsoidal heights will change with a maximum subsidence rate of -0.07 mm/year over western and southern parts of Alaska transitioning to an uplift rate of +0.04 mm/year over St. John's and southeast of Greenland. These values are well below the Global Geodetic Observing System recommendation for the stability of reference frames (0.1 mm/year) and therefore are negligible. The presented estimates are the official values of EPPs that will be implemented in Canada and the United States for NATRF2022 by their respective geodetic agencies.
In recent years, many global geopotential models (GGMs) have been produced by various international organizations by combining satellite, airborne, terrestrial gravity, marine gravity anomaly, and altimetry data. These models provide a consistent mathematical description of Earth's gravity field, enabling the calculation of geoid undulation and the conversion of global navigation satellite system (GNSS) ellipsoidal heights to orthometric heights. Over the past few decades, continuous advancements in gravity field modeling have significantly enhanced our understanding of the Earth's heterogeneous mass distribution, both at the surface and within the subsurface. As a result, GGMs have emerged as a valuable resource in geodesy and geophysics, providing reliable insights into global and regional gravity variations while supporting high-accuracy geoid determination and the unification of height systems. Therefore, this research investigates the efficacy of 19 state-of-the-art and widely adopted GGMs and identifies the appropriate GGM and truncation degree for free-air gravity anomaly reduction using newly acquired 190 GNSS/leveling points and 3,102 gravity anomaly points covering an approximately 363,000 sq. km region with varied topography in the northern Indian region. In this study, the performance of different GGMs was assessed using two independent datasets augmented with the Residual Terrain Model (RTM): geoid heights obtained from GNSS/leveling observations and terrestrial gravity anomaly measurements. XGM2019e_2159 yields the best geoid undulation results over northern India, with an root mean square error (RMSE) of +/- 0.199 m at degree 2,160, reduced to 0.143 m after applying a four-parameter corrective surface to account for datum biases, tilts, and drifts inherent to GNSS/leveling comparisons. The spectral analysis further shows that model ranking varies across the low, medium, and high frequency bands, and no single model exhibits uniform superiority across all degrees, while EIGEN-6C4 resulted in the most reliable model for filling data gaps, with an RMSE of +/- 29.16 mGal (1 Gal = 1 cm/s2 = 0.01 m/s2; therefore 1 mGal = 1 & times; 10-5 m/s2).
The positioning accuracy of global navigation satellite systems (GNSS) is important for conducting effective fieldwork, designing geodetic networks, and achieving desired deformation rates. Over the years, the positioning accuracy of the global positioning system (GPS) has been extensively studied, and mathematical models for the north, east, and up coordinate components have been developed. Today researchers have turned their attention to studying the accuracy of multiGNSS (MGEX) positioning. NASA's GNSS software GIPSY-X provides a framework capable of producing positioning information for GPS and European satellite navigation system (GALILEO) data. A positioning-accuracy model is also available for GIPSY-X solutions; however, it only uses GPS data, and is outdated today. In order to determine the accuracy of GIPSY-X GNSS positioning, we use the processing products of NASA's Jet Propulsion Laboratory (JPL) in which the data are also available for Global Navigation Satellite System of Russia (GLONASS), and this multiGNSS product set was available only for a short period of time in 2019. We assess the accuracy performance of the GPS-only solution (G) against each of the various combinations of these three constellations (GRE, GR, GE) for continuous data. Using the publicly available data from the NASA 2019 experiment, the accuracy of positioning from short observation sessions (i.e., considered to be 4- and 8-h GNSS campaigns) is also assessed against 24-h continuous measurements. The PPP module of GIPSY-X is employed to produce the position solutions. Furthermore, we derive accuracy prediction models based on the observation session duration for each of the various GNSS constellation combinations. As expected, the best accuracy is obtained by the combination of all three constellations (GRE solution), and the GRE solution, on average, is superior to the GPS-only solution by about 9%, 16%, and 14% for north, east, and up coordinates. The impact is slightly greater on the GNSS campaign solutions and hence is promising for the studies conducting episodic GNSS measurements to monitor natural hazards and engineering structures.
Digital elevation models (DEMs) are essential for infrastructure design and flood modeling, yet publicly available DEMs typically exhibit vertical accuracies of one to three meters, insufficient for these applications. This study develops and systematically evaluates a constraint-based DEM enhancement approach that integrates sparse, high-accuracy light detection and ranging (LiDAR) reference data collected at bridge locations to improve regional DEM quality. The methodology employs nearest-neighbor spatial interpolation to generate elevation correction surfaces from constraint points (points of known accurate elevations), followed by Gaussian smoothing to preserve topographic continuity. The approach was evaluated using LiDAR data from 15 bridge locations in the Austin metropolitan area in Central Texas. Four spatial interpolation methods (nearest neighbor, inverse distance weighting, natural neighbor, and linear interpolation) were compared, with nearest neighbor achieving optimal performance with a 28.79% improvement in mean Root Mean Square Error (RMSE) within the study area. Gaussian filtering with an optimized smoothing parameter (sigma=0.3 m) further enhanced accuracy, achieving a mean RMSE to 0.105 m. Spatial configuration analysis across six-, nine-, and 15-bridge configurations revealed critical dependencies: distributed constraint arrangements consistently achieved optimal accuracy with six to eight constraint locations, while clustered configurations (constraint points clustered inside the area of interest) with peripheral constraints exhibited performance degradation despite increasing constraint count. This performance resulted from nearest-neighbor interpolation's reliance on geometric proximity without terrain similarity consideration. The findings provide practical guidance for transportation agencies and flood management programs seeking to leverage existing infrastructure-derived LiDAR surveys for regional DEM enhancement, demonstrating that strategic constraint placement throughout the area of interest is essential for maximizing performance.
