For 15 years, the Gravity Recovery and Climate Experiment (GRACE) mission have monitored total water storage (TWS) changes. The GRACE mission ended in October 2017, and 11 months later, the GRACE Follow-On (GRACE-FO) mission was launched in May 2018. Bridging the gap between both missions is essential to obtain continuous mass changes. To fill the gap, we propose a new approach based on a remove–restore technique combined with an autoregressive (AR) prediction. We first make use of the Global Land Data Assimilation System (GLDAS) hydrological model to remove climatology from GRACE/GRACE-FO data. Since the GLDAS mis-models real TWS changes for many regions around the world, we further use least-squares estimation (LSE) to remove remaining residual trends and annual and semi-annual oscillations. The missing 11 months of TWS values are then predicted forward and backward with an AR model. For the forward approach, we use the GRACE TWS values before the gap; for the backward approach, we use the GRACE-FO TWS values after the gap. The efficiency of forward–backward AR prediction is examined for the artificial gap of 11 months that we create in the GRACE TWS changes for the July 2008 to May 2009 period. We obtain average differences between predicted and observed GRACE values of at maximum 5 cm for 80% of areas, with the extreme values observed for the Amazon, Alaska, and South and Northern Asia. We demonstrate that forward–backward AR prediction is better than the standalone GLDAS hydrological model for more than 75% of continental areas. For the natural gap (July 2017–May 2018), the misclosures in backward–forward prediction estimated between forward- and backward-predicted values are equal to 10 cm. This represents an amount of 10–20% of the total TWS signal for 60% of areas. The regional analysis shows that the presented method is able to capture the occurrence of droughts or floods, but does not reflect their magnitudes. Results indicate that the presented remove–restore technique combined with AR prediction can be utilized to reliably predict TWS changes for regional analysis, but the removed climatology must be properly matched to the selected region.

We propose a novel approach to classify sets of Global Navigation Satellite System (GNSS) permanent stations as benchmarks for hydrogeodesy. Benchmarks are trusted sets of GNSS stations whose displacements are classified as significantly and positively correlated with hydrospheric changes and identified in a three temporal‐scales: short‐term, seasonal and long‐term. We use 63 vertical displacement time series processed at the Nevada Geodetic Laboratory for the period 1998–2021 from stations located within Amazon basin and show that estimates of trends and annual signals, including the annual phase maximum, are very coherent with water surface levels provided by altimetry missions. We compute vertical displacements from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gravity missions and predict those also from Global Land Water Storage (GLWS) v2.0 data set which values are produced by assimilation of GRACE into WaterGAP Global Hydrological Model (WGHM). We divide vertical displacements from the three data sets into the pre‐defined temporal‐scales of short‐term, seasonal and long‐term, using non‐parametric wavelet analysis. For each temporal‐scale, correlation coefficients are computed between GNSS‐measured and GRACE‐derived/GLWS‐predicted displacements. We present the benefits of applying high‐resolution GRACE‐assimilating hydrology model to benchmark GNSS stations, which are particularly evident when using spherical harmonic coefficients higher than 120. Their increase causes the number of stations included in the benchmarks to rise by up to 15% for short‐term. Benchmarking allows hydrogeodesy to take advantage of a broader set of GNSS stations that were previously omitted, such as earthquake‐affected sites and those where a possible poroelastic response is observed.

Hydrogeodesy can benefit greatly from the use of Global Positioning System (GPS) displacements to analyse local changes in the hydrosphere, which the commonly used Gravity Recovery and Climate Experiment (GRACE) mission is unable to provide due to coarse spatial resolution. Hydrosphere changes recorded by GPS are unfortunately hidden among the other signals to which the system is also sensitive so that the sensitivity of GPS to changes in the hydrosphere on temporal-scales from pluri-annual to decadal is questionable. We focus on hydrosphere signatures present on these long-term temporal-scales as observed by GPS through the vertical displacement time series (DTS) of 122 permanent stations over Europe and compare them to the DTS derived from GRACE for GPS locations. Our methodology is based on the weighted Savitzky-Golay (S-G) filter, an underestimated filter in the field of geodetic time series analysis. We show that the correspondence between GPS and GRACE on long-term temporal-scales is generally strong, but decreases for coastal regions and regions where the coarse gridding of GRACE does not capture local hydrosphere effects. Further, the negative correlation with El Niño Southern Oscillations (ENSO) is confirmed for Europe.

Earth’s crust deforms in various time and spatial resolutions. To estimate them, geodetic observations are widely employed and compared to geophysical models. In this research, we focus on the Earth’s crust deformations resulting from hydrology mass changes, as observed by GRACE (Gravity Recovery and Climate Experiment) gravity mission and modeled using WGHM (WaterGAP Global Hydrological Model) and GLDAS (Global Land Data Assimilation System), hydrological models. We use the newest release of GRACE Level-2 products, i. e. RL06, provided by the CSR (Center for Space Research, Austin) analysis center in the form of a mascon solution. The analysis is performed for the European area, divided into 29 river basins. For each basin, the average signal is estimated. Then, annual amplitudes and trends are calculated. We found that the eastern part of Europe is characterized by the largest annual amplitudes of hydrology-induced Earth’s crust deformations, which decrease with decreasing distance to the Atlantic coast. GLDAS largely overestimates annual amplitudes in comparison to GRACE and WGHM. Hydrology models underestimate trends, which are observed by GRACE. For the basin-related average signals, we also estimate the non-linear variations over time using the Singular Spectrum Analysis (SSA). For the river basins situated on the southern borderline of Europe and Asia, large inter-annual deformations between 2004 and 2009 reaching a few millimeters are found; they are related to high precipitation and unexpectedly large drying. They were observed by GRACE but mismodelled in the GLDAS and WGHM models. Few smaller inter-annual deformations were also observed by GRACE between 2002-2017 for central and eastern European river basins, but these have been also well-covered by the WGHM and GLDAS hydrological models.

Gravity Recovery and Climate Experiment (GRACE) mission data is widely used in various fields of science. GRACE explored changes of the gravity field regularly from April 2002 to June 2017. In the following research, we examine variance of signal contained in two different formats of GRACE data: standard spherical harmonics and mass concentration blocks (so-called “mascons”) solutions, both provided in the most recent releases. For spherical harmonics-based solution, we use monthly gravity field solutions provided up to degree and order (d/o) 96 by three different computing centers, i.e. the NASA’s Jet Propulsion Laboratory (JPL), the German Research Center for Geosciences (GFZ) and the Center for Space Research (CSR). For the mass concentration blocks, we use values of total water storage provided by the CSR, JPL and the Goddard Space Flight Center (GSFC) computing centers, which we convert to spherical harmonic coefficients up to d/o 96. We show that using the anisotropic DDK3 filter to smooth the north-south stripes present in total water storage obtained from standard spherical harmonics solution leaves more information than common isotropic Gaussian filter. In the case of mascons, GSFC solution contains much more information than the CSR and JPL releases, relevant for corresponding d/o. Differences in variance of signal arise from different background models as well as various shape and size of mascons used during processing of GRACE observations.

The Gravity Recovery and Climate Experiment (GRACE) mission and its successor, the GRACE Follow-On mission, have been continuously observing changes in Total Water Storage (TWS) since 2002. These global, monthly, two-decade changes are conventionally modelled using a harmonic regression function, assuming a linear trend and seasonal signals; the former is extremely important because it indicates the long-term loss or gain of water masses. However, current climate change, sudden, unpredictable floods, or prolonged droughts, increased human water withdrawals due to increased demand in drought-affected areas or excessive population growth, make the actual long-term changes occurring in the TWS time series significantly different from the linear trend. For the first time, we parameterize these deviations globally, supplementing the conventional model with a polynomial function of the third, fourth and fifth degree; the optimal degree is chosen separately for each region. We demonstrate that the new parameterized augmented deterministic model of the TWS has advantages, as the previously unparameterized nonlinearities lead to improvements in the root-mean-square (RMS) values of up to 50%, especially for areas where nonlinearity is most pronounced. Furthermore, it allows for the assessment of the nature of the residuals of the TWS series, previously considered white noise, and leads to more reliable interpretations of short-term events such as droughts or floods.