Evolution of GNSS-Derived Precipitable Water Vapor and Its Driving Factors During the “Dragon Boat Water” Rainfall Event in Guangxi, China

Precipitable water vapor (PWV) is an important indicator for quantifying atmospheric water vapor, and its evolution is intrinsically linked to the formation and development of extreme weather. As a type of heavy rainfall occurring during the Dragon Boat Festival in southern China, the phenomenon known as “Dragon Boat Water” (DBW) has caused a series of disasters in Guangxi Province. Therefore, it is crucial to investigate PWV evolution and contributing factors. The PWV retrieved by Global Navigation Satellite System (GNSS) technology offers a reliable method due to its advantages of high-temporal resolution, high-precision, and weather-independent. In this study, the GNSS-derived PWV at the stations was obtained based on the GNSS data from 2020 to 2022 at 121 GNSS stations as well as the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) atmospheric reanalysis data. To assess the performance of the GNSS-derived PWV, it was compared with Radiosondes, demonstrating its high accuracy. Additionally, combined with ERA5 reanalysis data, the evolution of GNSS-retrieval PWV during the DBW event in Guangxi was analyzed, and the results showed that the direction of the water vapor transport pathway was from the southeast to the northwest. The overall trend of PWV decreased from southeast to northwest, with higher values observed in coastal areas compared to inland areas, and greater concentrations in plains than in mountains. Further investigation revealed that the evolution of GNSS-derived PWV was governed by the synergistic effects of mean sea level pressure (MSLP), horizontal wind speed (u-wind), vertical wind speed (v-wind), and 2m temperature (T2M). The direction of the wind field generally aligned with the direction of PWV movement, and the magnitude of PWV corresponded with wind field intensity. The distribution of PWV was found to be negatively correlated with MSLP and positively correlated with T2M. These findings could deepen the understanding of PWV dynamics and improve the prediction of extreme precipitation events.