Insights into hydrological drought characteristics using GNSS-inferred large-scale terrestrial water storage deficits

The hydrological loading displacements measured by continuous Global Navigation Satellite System (GNSS) networks can provide critical constraints on total terrestrial water storage (TWS) anomalies. We invert sparsely distributed GNSS vertical positions for daily large-scale heights based Slepian basis functions and devise a novel GNSS-based drought severity index (GNSS-DSI) dataset characterization in Brazil. spatiotemporal patterns of GNSS-inferred estimates agree with the TWS observations derived from Gravity Recovery Climate Experiment (GRACE) spherical harmonic solutions. Both GRACE capture notable annual oscillations Amazon River Basin, an amplitude close to 500 mm, larger than that 100–200 mm other geographical divisions. newly-developed monthly GNSS-DSI time series correlate well well-accepted GRACE-DSI dataset, 95% stations feature moderate-to-strong correlations (greater 0.50) between these two DSI data sets. new monitoring tool solely deviations succeeds identifying well-documented historical droughts provides quantitative extremes four large river basins Our results demonstrate instrumented network could be taken as independent remotely monitor variations quantitatively characterize regional-scale extremes.