A GNSS-based standardized index for near-real-time monitoring of the spatiotemporal evolution of droughts

Yunnan Province, China, a region characterized by complex topography and seasonal monsoon-driven hydrology, which is vulnerable to extreme droughts that impact agriculture, ecosystems, and water resources. This study introduces a GNSS-based Standardized Terrestrial Water Storage Anomaly Index (GNSS-STWSAI) that leverages daily vertical displacements from a dense GNSS network in Yunnan to quantify hydrological droughts with high temporal (∼daily) and spatial (∼0.7°) resolution. A Gaussian Mixture Model (GMM) is applied to objectively classify drought severity, providing a systematic framework for distinguishing droughts. We evaluate the method against conventional meteorological indices and analyze drought events from 2019 to 2022 in relation to atmospheric circulation patterns that influence monsoon moisture transport. GNSS-STWSAI reveals the lagged response of hydrological drought to meteorological forcing, enabling detailed characterization of drought onset, propagation, and recovery. This approach provides continuous, near-real-time monitoring at local scales, and the integration of GMM offers a statistically robust, automated classification of drought severity. These innovations allow for timely, region-specific water resource management and enhance understanding of monsoon-driven drought dynamics in Yunnan Province. We propose GNSS-STWSAI, a novel index using daily GNSS-derived terrestrial water storage anomalies and probabilistic modeling for near-real-time hydrological drought monitoring. Applied to Yunnan Province, GNSS-STWSAI captures spatiotemporal drought patterns influenced by large-scale ocean–atmosphere interactions, providing enhanced insights into drought propagation dynamics. • Novel GNSS-STWSAI enables real-time hydrological drought monitoring. • Ocean-atmosphere interactions drive extreme droughts in Yunnan. • Cascading dynamics from meteorological to hydrological drought revealed.