Detection of storm surge-induced non-tidal ocean loading deformation in Hong Kong using sub-daily GNSS observations

Storm surges occurring over short periods can cause abrupt changes in ocean mass near the coast， leading to transient crustal subsidence known as Non-Tidal Ocean Loading (NTOL). Existing NTOL products provided by various institutions are typically derived from oceanic gravity or fluid dynamic models combined with Earth’s elastic response theory. However， these model-based products lack validation against in-situ observations， raising concerns about their reliability under extreme conditions. In contrast， geodetic Global Navigation Satellite Systems (GNSS) observations offer high accuracy， high spatiotemporal resolution， and near real-time availability. In this study， we analyze sub-daily GNSS vertical displacements from 12 stations in Hong Kong during a storm surge in October 2021 and compare them with NTOL predictions from three global models (GFZ， IMLS， and GGFC) to assess their consistency in a complex coastal environment. Results show that the IMLS and GGFC NTOL products are more reliable than the GFZ product for detecting sub-daily loading deformations in a small region. The GFZ product suffers from spurious NTOL displacement signals (max≈15 mm) during the peak storm surge period. Among the correlation coefficients between 3-h GNSS vertical displacements and the three NTOL products， the IMLS product achieves the highest correlation (max=0.67)， followed by the GGFC product， while the GFZ product exhibits the lowest correlation. Extending the GNSS solution time span (from 3-h to 6-h or 9-h) improves its correlation with NTOL. However， since storm surge effects typically last only tens of minutes to a few hours， a 6-h or 9-h GNSS solution may not capture the full details of the storm surge-induced deformation. Our work demonstrates that sub-daily GNSS observations can detect the transient NTOL-induced subsidence caused by storm surges， thereby validating the accuracy and applicability of these NTOL models and filling the gap left by the lack of observational support in previous model-based studies.