Tracking the water storage and runoff variations in the Paraná basin via GNSS measurements.

The Paraná basin is the second largest river basin in South America and provides abundant water resources globally. However, current research lacks hydrological investigation of the region. The vertical crustal deformation recorded by the Global Navigation Satellite System (GNSS) can be used to accurately estimate regional-scale terrestrial water storage (TWS). Therefore, we utilized the daily vertical displacement time series data at 102 GNSS stations to recover the water storage variations in the Paraná basin from 2013 to 2020. To recognize primary spatiotemporal features of TWS changes, we applied the principal component analysis (PCA) method in the inversion strategy. Results indicate that the TWS variations inferred from GNSS generally align in spatiotemporal patterns with estimates from both the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS). However, some discrepancies are evident at local scales. The TWS changes derived from both GNSS and GRACE exhibited generally larger magnitude of oscillations than those estimated by GLDAS, while the GRACE results neglected the evident seasonal oscillation of the water mass in the southeast of the basin. Given the challenge of capturing large-scale runoff variations through in-situ observations, we innovatively applied GNSS and water budget closure method to provide a novel runoff estimate for the Paraná basin. The GNSS-inferred runoff exhibited a strong correlation (correlation coefficient of 0.72) with in-situ observations. Overall, our study fills the critical knowledge gap in geodesy-based hydrological investigation in the Paraná basin. We aim to highlight the immense potential of GNSS for hydrological parameter estimation and provide valuable reference data for regional hydrological research and for water resources management.

Hydrological drought characterization based on GNSS imaging of vertical crustal deformation across the contiguous United States.

Continuous Global Navigation Satellite System (GNSS) measurements allow us to track subtle elastic crustal deformation in the response to hydrological mass variations and provide an additional tool to independently characterize hydrological extremes (e.g., droughts and floods). In this study, we develop a time-varying GNSS imaging strategy that depends on the principal component analysis of GNSS-sensed vertical crustal displacement (VCD) in 2006-2020 and the monthly images of hydrology-induced deformation are generated for drought characterization across the contiguous United States. The first 12 principal components are selected in our time-varying imaging system, which account for 85% of the data variance. Considering that surface water loads are inversely correlated with the induced elastic vertical motions, we reverse the signs of the GNSS-imaged time series in all grids in subsequent studies (referred to as negative VCD (NVCD)). The GNSS-NVCD data generally correlate well with the water estimates from the Gravity Recovery and Climate Experiment (GRACE) and North American Land Data Assimilation System (NLDAS). Using the GNSS-imaged gridded NVCD products, we produce a GNSS-based drought severity index (GNSS-DSI) based on the climatological methodology, which is implemented by standardizing the GNSS NVCD anomalies that deviate from climatological normal. In most regions, strong linear correlations are accessible for GNSS-DSI relative to GRACE-DSI and the self-calibrating Palmer Drought Severity Index (scPDSI). The new drought monitoring tool, which is based solely on GNSS-measured vertical positions, is used for hydrological drought characterization (onset, end, duration, magnitude, intensity, and recovery); it succeeds in identifying well-documented historical droughts from the US drought monitor (USDM). Our study presents a new drought characterization framework using solely GNSS-measured hydrological loading displacements from a dense GNSS network, which has great potential to strengthen operational drought monitoring and assessment.

Crop production response to soil moisture and groundwater depletion in the Nile Basin based on multi-source data.

Soil moisture (SM) and groundwater (GW) depletion triggered by anthropogenic and natural climate change are influencing food security via crop production per capita decrease in the Nile River Basin (NRB). However, to the best of our understanding, the causes and impact of SM and GW depletion have not been studied yet comprehensively in the NRB. In this study, GW is derived from the Gravity Recovery and Climate Experiment (GRACE) mission, and SM was estimated using the Triple Collocation Analysis (TCA). SM/GW depletion causes were evaluated via the Land Use Land Cover (LULC) and rainfall/temperature change analysis, whereas impact analysis focused on crop production per capita reduction (food insecurity) during SM depletion. The major findings of this study are 1) TCA analyzed SM show a decreasing trend (-0.06 mm/yr) in agricultural land while increasing (+0.21 mm/yr) in forest land, 2) LULC analysis indicated a vast increment of agricultural land (+9%) and bareland (+9%) although the decreasing pattern of forest (-1.5%) and shrubland (-6.9%) during 1990-2019; 3) the impact of SM depletion on crop production per capita caused food insecurity during a drought year, 4) agriculture drought indices and crop production per capita show high correlations (R2 = 0.86 to 0.60) demonstrated that Vegetation Supply Water Index (VSWI) could provide strategic warning of drought impacts on rainfed agricultural regions. In conclusion, SM and GW depletions are mainly caused by human-induced and climate change factors imposing food insecurity challenges in the NRB coupled with increasing temperature and excessive water extraction for irrigation. Therefore, it is highly recommended to rethink and reverse SM/GW depletion causing factors to sustain food security in NRB and similar basins.