Global Navigation Satellite System (GNSS) signals can be exploited to monitor atmospheric moisture and surface hydrology in tropical regions, where conventional observation networks are sparse. Tropospheric water vapour, expressed as precipitable water vapour (PWV), can be derived from GNSS signal propagation with high temporal resolution, providing a cost-effective, automated, and weather-independent approach to investigating moisture dynamics across multiple timescales. Using 16 years of data from 42 GNSS permanent stations across tropical continents and islands, combined with satellite precipitation (TRMM) and cloud-top temperature observations, we analysed the diurnal cycle of deep convection. We find that the afternoon maximum in integrated atmospheric moisture coincides with precipitation maxima and cloud-top temperature minima at most locations. Furthermore, the afternoon decrease in cloud-top temperature and the development of convective precipitation consistently follow the diurnal increase in tropospheric moisture, particularly over continental and large-island stations. We also found that even small daily changes in integrated moisture (up to 2 mm) were sufficient to trigger convective rainfall, with a mono-modal diurnal distribution largely consistent across seasons.

On the other hand, GNSS reflectometry (GNSS-R), which analyses signals reflected from Earth’s surface, provides a natural extension of GNSS-based atmospheric studies. This stems from the fact that GNSS-R can readily distinguish ocean from land, estimate wind speed over open water, and identify permanent or transient surface water bodies over land. The latter capability enables the monitoring of temporal variability in land surface inundation and, consequently, the identification of flood events. Unlike many satellite-based flood detection techniques, GNSS-R relies on relatively low-cost small-satellite constellations, whose rapidly growing number—including systems operated by the private sector—substantially expands the available data pool. This is particularly important in the tropics, where flood observations remain sparse, especially for small and short-lived events, limiting our ability to characterise flood dynamics across spatial and temporal scales and to better understand the relationship between flooding and precipitation patterns. Using the Cyclone GNSS (CYGNSS) small-satellite constellation, we assess small- to regional-scale floods over Sumatra by comparing inundation estimates with independent flood databases. Case studies and a bulk analysis of 555 documented flood events demonstrate that CYGNSS can identify flooded periods and track day-to-day changes in inundation extent, providing insight into flood dynamics. These results establish a pathway towards linking precipitation patterns with flood development and evolution, highlighting GNSS’s unique capability for high-resolution monitoring of atmospheric and surface processes in the tropics.

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