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

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