The GRACE‐FO satellites launched in May 2018 are able to quantify the water mass deficit in Central Europe during the two consecutive summer droughts of 2018 and 2019. Relative to the long‐term climatology, the water mass deficits were −112 ± 10.5 Gt in 2018 and −145 ± 12 Gt in 2019. These deficits are 73% and 94% of the mean amplitude of seasonal water storage variations, which is so severe that a recovery cannot be expected within 1 year. The water deficits in 2018 and 2019 are the largest in the whole GRACE and GRACE‐FO time span. Globally, the data do not show an offset between the two missions, which proves the successful continuation of GRACE by GRACE‐FO and thus the reliability of the observed extreme events in Central Europe. This allows for a joint assessment of the four Central European droughts in 2003, 2015, 2018, and 2019 in terms of total water storage deficits.

The application of terrestrial water storage (TWS) data observed with GRACE and GRACE‐FO often requires realistic uncertainties. For gridded TWS data, this requires the knowledge of the covariances, which can be derived from the formal, i.e., formally estimated in the parameter estimation, variance‐covariance matrix provided together with the Stokes coefficients. However, the propagation of monthly variance‐covariance matrices to TWS data is computationally expensive, so we apply a spatial covariance model for TWS data. The covariance model provides non‐homogeneous (location depending), non‐stationary (time depending), and anisotropic (orientation depending) covariances between any two given points. Further, the model accommodates wave‐like behavior of East‐West‐directed covariances, which residuals of GRACE striping errors can cause. The main application of such spatial covariances is the estimation of uncertainties for mean TWS time series for arbitrary regions such as river basins. Alternatively, regional uncertainties can be derived from the above mentioned formal variance‐covariance matrices of the Stokes coefficients. This study compares modeled basin uncertainties for GFZ RL06 and ITSG‐Grace2018 TWS data with the formal basin uncertainties from the ITSG‐Grace 2018 solution. The modeled and formal uncertainties fit both in the spatial and temporal domain. We further evaluate the modeled uncertainties by comparison to empirical uncertainties over arid regions. Here, again the appropriateness of the modeled uncertainties is shown. The results, namely the TWS uncertainties for global river basins, are available via the GravIS portal. Further, we provide a Python toolbox, which allows computing uncertainties and covariance matrices.

We investigate the potential of two independent component analysis (ICA) methods, i.e., the temporal and spatiotemporal ICA, for separating geophysical signals in Gravity Recovery and Climate Experiment (GRACE) data. These methods are based on the assumption of the statistical independence of the signals and thus separate the GRACE‐observed mass changes into maximal independent signals. These two ICA methods are compared to the conventional principal component analysis (PCA) method. We test the three methods with respect to their ability to separate a periodic hydrological signal from a trend signal originating in the solid Earth or the cryosphere with simulated and Center for Space Research GRACE mass changes for the time period of January 2003 to December 2010. In addition, we investigate whether the methods are capable of separating hydrological annual and semiannual mass variations. It is shown that both ICA methods are superior to PCA when non‐Gaussian mass variations are analyzed. Furthermore, the spatiotemporal ICA resolves successfully the lack of full temporal and spatial independence of the geophysical signals observed by GRACE both in global and regional simulation scenarios. Although the temporal and spatiotemporal ICA are nearly equivalent, both superior to PCA in the global GRACE analysis, the spatiotemporal ICA proves to be more efficient in regional applications by recover more reliably the postglacial rebound trend in North America and the bimodal total water storage variability in Africa.

This study investigates the potential of satellite altimetry for water level time series estimation of smaller inland waters where only very few measurements above the water surface are available. A new method was developed using off-nadir measurements to estimate the parabola generated by the hooking effect. For this purpose, a new waveform retracker was used as well as an adopted version of the RANdom SAmple Consensus (RANSAC) algorithm. The method is applied to compute time series of the water levels height of the Mekong River and some of its tributaries from Envisat high-frequency data. Reliable time series can be obtained from river crossings with widths of less than 500 m and without direct nadir measurements over the water. The expected annual variations are clearly depicted and the time series well agree with available in situ gauging data. The mean RMS value is 1.22 m between the resulting time series and in situ data, the best result is 0.34 m, the worst 2.26 m, and 80% of the time series have an RMS below 1.5 m.

The US–German GRACE (Gravity Recovery and Climate Experiment, 2002–2017) and GRACE-FO (GRACE Follow-On, since 2018) satellite missions observe terrestrial water storage (TWS) variations. Over 20 years of data allow for investigating interannual variations beyond linear trends and seasonal signals. However, the origin of observed TWS changes cannot be determined solely with GRACE and GRACE-FO observations. This study focuses on the northern part of the East African Rift around the lakes of Turkana, Victoria, and Tanganyika. It aims to characterise and analyse the interannual TWS variations compared to meteorological and geodetic observations of the water storage compartments (surface water, soil moisture, and groundwater). We apply the STL (Seasonal-Trend decomposition using LOESS) method to decompose the signal into a seasonal signal, an interannual signal, and residuals. By clustering the interannual TWS dynamics for the African continent, we define the exact outline of the study region. We observe a TWS decrease until 2006, followed by a steady rise until 2016, and then the most significant TWS gain in Africa in 2019 and 2020. Besides meteorological variability, surface water storage variations in the lakes explain large parts of the TWS decrease before 2006. The storage dynamics of Lake Victoria alone contribute up to 50 % of these TWS changes. On the other hand, the significant TWS increase around 2020 can be attributed to nearly equal rises in groundwater and surface water storage, which coincide with a substantial precipitation surplus. Soil moisture explains most of the seasonal variability but does not influence the interannual variations. As Lake Victoria dominates the surface water storage variations in the region, we further investigate the lake and the downstream Nile River. The Nalubaale Dam regulates Lake Victoria's outflow. Water level observations from satellite altimetry reveal the impact of dam operations on downstream discharge and on TWS decreases in the drought years before 2006. On the other hand, we do not find evidence for an impact of the Nalubaale Dam regulations on the strong TWS increase after 2019.