Quantifying the amount of precipitation and its uncertainty is a challenging task all over the world, particularly over the African continent, where rain gauge (RG) networks are poorly distributed. In recent decades, several satellite remote sensing (SRS)‐based precipitation products have become available with reasonable spatial and temporal resolutions to be applied in hydrological and climate studies. However, uncertainties of these products over Africa are largely unknown. In this study, the generalized ‘three‐cornered‐hat’ (TCH) method is applied to estimate uncertainties of gridded precipitation products over the entire African continent, without being dependent to the choice of a reference dataset. Six widely used SRS‐based precipitation products (at monthly scales) were evaluated over the entire continent during the period of 2003–2010. The TCH results are further compared to those of the classical evaluation using the Global Precipitation Climatology Centre (GPCC) over entire Africa, as well as to the RG observations over the Greater Horn of Africa (GHA). Overall, for the study period (2003–2010), the TCH results indicate that the RG‐merged products contain smaller error amplitudes compared to the satellite‐only products, consistent with the GPCC‐based evaluation. A multiple comparison procedure ranking, which was applied based on signal‐to‐noise ratios (SNRs), indicated that PERSIANN contains the highest SNR and thus suitable over most of Africa, followed by ARCv2, TRMM, CMORPH, TAMSAT, and GSMaP. To extract the main spatio‐temporal variability of rainfall over Africa, complex empirical orthogonal function technique was applied, from which the extracted patterns of GPCC, TRMM, PERSIANN, and ARCv2 were found to be similar but different from those of TAMSAT, CMORPH, and GSMaP. Finally, the TCH and RG‐based validation methods were found to provide similar evaluations for the SRS‐only products (CMORPH and GSMaP) over GHA, with CMORPH emerging to be the most suitable product, consistent with previous studies.

Global warming is emerging as an important predictor of water availability and future water supplies across the world through inducing the frequency and severity in hydrological extremes. These extremes (e.g., drought) have potential impacts on groundwater, environmental flows, as well as increase social inequalities (limited access to water by the poor), among a range of other issues. Understanding the influence of global climate on groundwater systems is thus critical to help reshape global water markets through policies underpinned by the knowledge of climatic processes driving the water cycle and freshwater supply. The main aim of this study is to improve understanding of the influence of climate variability on global groundwater using statistical methods (e.g., multi-linear regression and wavelet analyses). The response of groundwater to climate variability are assessed and the feasibility of identifying climatic hotspots of groundwater-climate interactions are explored (2003-2017). Generally, climate variability plays a major role in the distribution of groundwater recharge, evidenced in the groundwater-rainfall relationship (r ranging from 0.6 to 0.8 with lags of 1-5 months) in several regions (Amazon and Congo basins, West Africa, and south Asia). Some of the areas where no relationship exists coincide with major regional aquifer systems (e.g., Nubian sand stone in north Africa) in arid domains with fossil groundwater. Our results also show that groundwater fluxes across the world are driven by global climate teleconnections. Notable among these climate teleconnections are PDO, ENSO, CAR, and Nino 4 with PDO showing the strongest relationship (r= 0.80) with groundwater in some hotspots (e.g. in South America). The explicit role of the Pacific ocean in regulating groundwater fluxes provides an opportunity to improve the prediction of climate change impact on global freshwater systems. As opposed to remarkably large productive hydrological systems (Amazon and Congo basins), in typically arid domains, groundwater could be restricted during prolonged drought, constraining the persistence of surface water in the maintenance of a healthy surface-groundwater interactions.

Aimed at mapping time variations in the Earth’s gravity field, the Gravity Recovery and Climate Experiment (GRACE) satellite mission is applicable to access terrestrial water storage (TWS), which mainly includes groundwater, soil moisture (SM), and snow. In this study, SM and accumulated snow water equivalent (SWE) are simulated by the Global Land Data Assimilation System (GLDAS) land surface models (LSMs) and then used to isolate groundwater anomalies from GRACE-derived TWS in Pennsylvania and New York States of the Mid-Atlantic region of the United States. The monitoring well water-level records from the U.S. Geological Survey Ground-Water Climate Response Network from January 2005 to December 2011 are used for validation. The groundwater results from different combinations of GRACE products (from three institutions, CSR, GFZ and JPL) and GLDAS LSMs (CLM, NOAH and VIC) are compared and evaluated with in-situ measurements. The intercomparison analysis shows that the solution obtained through removing averaged simulated SM and SWE of the three LSMs from the averaged GRACE-derived TWS of the three centers would be the most robust to reduce the noises, and increase the confidence consequently. Although discrepancy exists, the GRACE-GLDAS estimated groundwater variations generally agree with in-situ observations. For monthly scales, their correlation coefficient reaches 0.70 at 95% confidence level with the RMSE of the differences of 2.6 cm. Two-tailed Mann-Kendall trend test results show that there is no significant groundwater gain or loss in this region over the study period. The GRACE time-variable field solutions and GLDAS simulations provide precise and reliable data sets in illustrating the regional groundwater storage variations, and the application will be meaningful and invaluable when applied to the data-poor regions.

Currently, various satellite processing centers produce extensive data, with different solutions of the same field being available. For instance, the Gravity Recovery and Climate Experiment (GRACE) has been monitoring terrestrial water storage (TWS) since April 2002, while the Center for Space Research (CSR), the Jet Propulsion Laboratory (JPL), the GeoForschungsZentrum (GFZ), and the Groupe de Recherche de Géodésie Spatiale (GRGS) provide individual monthly solutions in the form of Stokes coefficients. The inverted TWS maps (or the regionally averaged values) from these coefficients are being used in many applications; however, as no ground truth data exist, the uncertainties are unknown. Consequently, the purpose of this work is to assess the quality of each processing center by estimating their uncertainties using a generalized formulation of the three-cornered hat (TCH) method. Overall, the TCH results for the study period of August 2002 to June 2014 indicate that at a global scale, the CSR, GFZ, GRGS, and JPL presented uncertainties of 9.4, 13.7, 14.8, and 13.2 mm, respectively. At a basin scale, the overall good performance of the CSR was observed at 91 river basins. The TCH-based results were confirmed by a comparison with an ensemble solution from the four GRACE processing centers.