Global Navigation Satellite System (GNSS) observations are critical for establishing high-precision terrestrial reference frames (TRF), but the environmental loading effects, particularly hydrological loading deformation (HYLD), remain unaccounted in existing TRF like ITRF2020, limiting their accuracy. This study evaluates the performance of multiple HYLD datasets derived from GRACE (mascon and spherical harmonic (SH) products) and four hydrological models (LSDM, ERA5, GLDAS2, and MERRA2) in explaining seasonal and non-seasonal GNSS displacements globally using IGS Repro3 and Repro2 datasets. Among these six HYLD datasets, we demonstrate that the GRACE mascon solution achieves superior performance in explaining the seasonal and non-seasonal GNSS displacements, by quantifying the amplitude reduction ratio (AMPR) and root mean square reduction ratio (RMSR) induced by HYLD corrections, respectively. The mascon-derived HYLD achieves better correction, particularly with the vertical median AMPR of 35.1% and RMSR of 4%. In contrast, hydrological models and SH product have relatively lower performance in explaining GNSS displacements, with ERA5 achieving only 24.7% for the vertical AMPR. The HYLDs of coastal stations generally exhibit worse performance with lower AMPR and more negative RMSR distributions, likely reflecting the influence of ocean loading and their limitations in accurately isolating the land water signal within land boundaries; whereas the mascon result shows minimal differences between inland and coastal stations, benefitting from the reduced leakage of land water into the oceans. Furthermore, the transition from Repro2 to the improved reprocessing strategy in Repro3 enhances the overall consistency between HYLDs and GNSS displacements, specifically with a 7% improvement in the vertical AMPR with MERRA2.