The unprecedented 2021–2022 drought in Europe has caused significant interannual declines in terrestrial water storage (TWS) across the Alps. However, hydrological modeling in mountainous regions remains challenging due to limited in situ data and complex physiographic setting. In these regions, the coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE), combined with its sensitivity to leakage errors, limits its ability to resolve localized water variations. Meanwhile, the existing Global Navigation Satellite System (GNSS) network, with a typical spacing of 50 km, cannot capture fine-scale hydrological heterogeneity. To address these limitations, we assessed the feasibility of integrating GNSS and Interferometric Synthetic Aperture Radar (InSAR) displacement data for TWS analysis in the Eastern European Alps. We inverted TWS variations on a 0.1∘ × 0.1∘ grid during the drought by applying a regularized inversion for an elastic Earth model. The results indicate topography-correlated declines in equivalent water thickness ranging from 0.2 to 1.2 m yr−1 in most of the region. These water losses show pronounced spatial heterogeneity that is not captured by state-of-the-art TWS model products, which exhibit spatially smoother trends of below 0.5 m yr−1. These findings demonstrate that joint GNSS–InSAR inversion can capture localized hydrological anomalies. With more publicly available InSAR products, the framework used in this paper offers considerable potential for monitoring water storage changes during extreme climatic events. Plain Language Summary The 2021–2022 drought in Europe caused a noticeable loss of water stored in the Alps. Because the Alps act as a major freshwater reservoir for large parts of Europe, understanding how much water is lost—and where—is essential for water resource management and climate impact assessment. Studying these changes is difficult because mountain regions have limited measurement stations compared to their complex conditions. Satellite gravity observations provide large-scale estimates but cannot detect local variations, and existing Global Navigation Satellite System (GNSS) networks are too sparse to capture fine details of water mass variations.

Abstract. The Global Gravity-based Groundwater Product (G3P) provides observations of global groundwater storage (GWS) variations, calculated from GRACE/-FO-derived terrestrial water storage (TWS) by subtracting the contributions of root zone soil moisture, glaciers, surface water storage, and snow water equivalent. As such, G3P provides the first globally consistent, publicly available groundwater dataset from satellite gravimetry for continental-scale trend assessment. Such data are a crucial observational constraint for assessing global groundwater depletion, recharge, and water storage trends related to climate change and human activities. A challenge is the reliable separation and quantification of long-term trends from stochastic signals attributable to natural climate variability (“climate noise”) and observational system errors. To address this, we introduce a trend-analysis framework that uses calibrated time-series models to account for trends, seasonal, and stochastic variations. The approach requires minimal assumptions about underlying processes and enables the separation of significant long-term trends of GWS and TWS from stochastic variability. Applying this framework to 21.5 years of data, our results show (1) that groundwater depletion dominates freshwater decline at continental scales – most prominently in Asia (-55 km3 yr-1) – whereas ice mass loss remains the largest global contributor by component, and (2) reveal previously unobserved trends, including increasing groundwater storage in large parts of Africa (+37 km3 yr-1) and declining trends attributed to droughts, e.g., in Southern Africa, Asia, and parts of Europe. Our global aggregation of statistically significant trends indicates net volumetric GWS changes of -27 km3 yr-1 and TWS changes of -145 km3 yr-1 (excluding Antarctica and Greenland). We also find that many regions in the Northern Hemisphere are prone to climate-induced drying, with parts of Europe close to persistent long-term groundwater decline.

50 years-long closed loop simulations of the future satellite gravimetry missions GRACE-C and NGGM/MAGIC are used in this study to rigorously assess the abilities of these upcoming missions to track wet extreme events from space on a global scale. The simulations are based on modeled terrestrial water storage variations from a GFDL-CM4 climate model run that took part in CMIP6. In addition to nominal monthly gravity field solutions, experimental 5-daily solutions were processed for both missions. Besides offering the ability to study return levels of extreme events in a statistical sense, the simulations also offer the opportunity to test the proper identification of various naturally occurring wet extremes with varying magnitude, location, as well as temporal and spatial extent. While the double pair constellation MAGIC always outperforms GRACE-C in any metric tested, we note that the 5-daily MAGIC solution is found to be advantageous over its monthly solution for the estimation of statistical return levels of wet extremes, but not with respect to the identification of individual wet extreme events as measured by recall, precision, and F1-score. Reasons for the inferior performance of the 5-daily solutions are spatial leakage, residual noise, and adverse effects from a sub-optimal combination of data from the polar and inclined pair that manifest as artifacts polewards of 70°. Our results therefore call for further refinements in the MAGIC processing schemes to further optimize in particular the novel 5-daily solutions so that the added-value of those sub-monthly products can be fully exploited.

Abstract Satellite gravimetry, such as data from the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO (Follow-On), provides insights into temporal variations in Earth’s gravity field, which are linked to mass redistribution processes like ice sheet melting and groundwater depletion. However, measurement noise and limited spatial resolution require filtering techniques to extract meaningful signals. In this context, Slepian functions—optimal basis functions for spatially localized signal representation—offer a way to mitigate signal leakage and improve regional mass change estimates. In addition, integrating Slepian functions within a Kalman-filter framework enables simultaneous filtering and localization of the dataset. Following the work of Wöhnke et al. (Int J Geomath. 16:2, 2025), who combined radial basis functions with a Kalman filter in a closed-loop simulation, a similar setup has been developed that incorporates Slepian functions into the Kalman filter. We test this setup with simulated gridded water storage estimates for a region in central Europe, based on the European Space Agency (ESA) Earth System Model (ESM) as well as realistic GRACE-like noise. The differences between the simulated observations and results from the closed-loop simulation of the Slepian functions and Kalman filter are compared with results from applying the Kalman filter alone, as well as with the simulation presented in Wöhnke et al. (Int J Geomath. 16:2, 2025). The comparison indicates that Slepian functions perform similarly to radial basis functions, and generally better than using a Kalman filter only.

Abstract We present a global framework to infer sub-seasonal subsurface water storage dynamics from daily satellite surface soil moisture by optimizing the time constant of an exponential filter used for the depth extrapolation against GRACE and GRACE-FO terrestrial water storage (TWS) anomalies. The approach uses ESA CCI v9.1 surface and root-zone soil moisture products together with daily ITSG-Grace2018 gravity fields on a 1° global grid. For each grid cell, the time constant T of the filter is optimized to maximize the correlation between exponentially filtered soil moisture and GRACE-based TWS from which snow, surface-water, and seasonal components have been removed before. On average, the global area-weighted correlation increases from 0.19 for unfiltered surface soil moisture to 0.39 after optimization. The optimal T values decrease systematically with the depth of the soil moisture layer used as input and show physically consistent patterns related to climatic and hydrogeological controls such as aridity, soil characteristics, and depth to the groundwater table. In contrast to existing approaches that use in-situ soil moisture data to compute globally uniform T parameters, our approach allows to capture spatially varying infiltration dynamics into deeper soil layers. The resulting global T field thus provides an observation-driven proxy for subsurface storage dynamics at weekly-to-monthly time scales, offering a simple and transferable approach for linking satellite surface soil moisture to terrestrial water storage variations.

Terrestrial water storage (TWS) integrates snow, soil moisture, surface water, and groundwater and is a key indicator of how climate variability and human activity reshape the global water cycle. The GRACE and GRACE-FO satellite missions provide the only direct, globally consistent observations of TWS change, but their record only begins in 2002 which is too short for many climate-scale analyses. We present a deep learning application that reconstructs monthly GRACE-like TWS anomalies (TWSA) back to 1940 by learning the relationship between daily ERA5 meteorological forcing (precipitation, evapotranspiration, runoff) and monthly GRACE observations. In contrast to prior reconstruction approaches based on grid-cell-wise regression, CNNs, or LSTMs, we adapt a multi-variate time series graph neural network (MTGNN) architecture, which was originally developed for mobility and traffic forecasting on urban sensor networks to this satellite-geodesy task. Spatial dependencies are encoded in a static, interpretable hybrid adjacency matrix that combines geodesic proximity with lagged correlations of climatic time series, capturing both local hydrological coupling and large-scale teleconnections. The reconstruction achieves a grid-cell Pearson correlation of 0.69, a basin-mean correlation of 0.94, and a near-zero bias, and it reproduces the spatial fingerprints of the 2015/16 El Niño and 2020/21 La Niña events. A systematic comparison with established reconstruction approaches (GTWS-MLrec, RM-REC, GRAiCE) shows that the graph-based model is statistically competitive at basin scale, reaching a correlation within 0.025 of the best baseline while using only roughly half to a tenth of the predictors the other models require and revealing characteristic weaknesses in arid regions in all models. The complete implementation is publicly available at github.com/hcu-cml/MTGNN-TWS-Reconstruction-GRACE

Changes in soil water storage can be studied on a global scale using a variety of satellite observations. With active or passive microwave remote sensing, we can study the upper few centimeters of the soil, while satellite gravimetry allows us to detect changes in the entire column of terrestrial water storage (TWS). The combination of both types of data can provide valuable insight into hydrological dynamics in different soil depths towards a better understanding of changes in subsurface water storage. We use daily Gravity Recovery and Climate Experiment (GRACE) data and satellite soil moisture data to identify extreme hydroclimatic events, focusing on prolonged droughts. To enhance our comprehension of the subsurface, we utilize not just surface soil moisture data but also integrate information on root zone soil moisture. Original level-3 surface soil moisture data sets of SMAP and SMOS are compared to post-processed level-4 data products (both surface and root zone soil moisture) and a multi-satellite product provided by the ESA CCI. We analyse the correspondence between high and low percentiles in TWS and soil moisture time series, which allows us to identify extreme events in different integration depths and storage compartments. Furthermore, we compute the rate of change of anomalies to assess how quickly the system accumulates storage deficits during drought conditions and recovers from them for different soil depths. Our investigation focuses on the temporal dynamics of near-surface soil moisture and TWS, highlighting the cascading effects that propagate from the surface into the subsurface. The results we obtained indicate characteristic patterns of the temporal dynamics of drought recovery in varying soil depths. Specifically, our analysis shows that surface soil moisture recovers faster than TWS, and that this recovery process slows down as soil integration depth increases.

Satellite gravimetry as realized with GRACE and GRACE-FO provides a novel opportunity to study extreme deviations from annually varying terrestrial water storage (TWS) in all continental areas of our planet. By utilizing the generalized extreme value (GEV) distribution, we estimate return levels for events that are expected to happen once every 10 (i.e., 1-in-10) years. With two GRACE-like reconstructions spanning over 40 and 114 years, respectively, we show that the currently available data record of 20 years is already sufficiently long to derive robust estimates of those return levels. When contrasting the GRACE/-FO results to model experiments from the CMIP6 archive extending until the year 2100 by concatenating historical runs and climate projections under the SSP5-8.5 socioeconomic pathway, we find that (a) the multi-model median from CMIP6 has the overall best agreement with the satellite data, thereby nicely confirming the validity of a central assumption of many climate-related studies that heavily rely on ensemble statistics. We also find that (b) CMIP6 model runs contain only modest deviations of 1-in-10 years return levels from the beginning of the 20th century when compared to present-day, but predict stronger changes toward more extreme return levels by the end of the 21st century. On the other hand, we also find substantial differences between satellite data and individual model experiments, which opens new opportunities to inform, validate and/or calibrate numerical climate models with satellite gravimetry data from GRACE, GRACE-FO, and in future also GRACE-C.

Water mass changes at and below the surface of the Earth cause changes in the Earth’s gravity field which can be observed by at least three geodetic observation techniques: ground-based point measurements using terrestrial gravimeters, space-borne gravimetric satellite missions (GRACE and GRACE-FO) and geometrical deformations of the Earth’s crust observed by GNSS. Combining these techniques promises the opportunity to compute the most accurate (regional) water mass change time series with the highest possible spatial and temporal resolution, which is the goal of a joint project with the interdisciplinary DFG Collaborative Research Centre (SFB 1464) "TerraQ – Relativistic and Quantum-based Geodesy". A method well suited for data combination of time-variable quantities is the Kalman filter algorithm, which sequentially updates water storage changes by combining a prediction step with observations from the next time step. As opposed to the standard way of describing gravity field variations by global spherical harmonics, we introduce space-localizing radial basis functions as a more suitable parameterisation of high-resolution regional water storage change. An estimation environment has been set up for the combination of GRACE/-FO satellite gravimetry with GNSS station displacements. The feasibility and stability of the approach is first demonstrated in a closed-loop simulation to test the setup and tune the algorithm. Subsequently, it is applied to real GRACE and GNSS observations to sequentially update the parameters of a regional gravity field model for Central Europe. The implementation was designed to flexibly include further observation techniques (e.g. terrestrial gravimetry) at a later stage. This presentation will outline the Kalman filter framework and regional parameterisation approach, and addresses challenges such as the relative weighting between the GRACE and GNSS data, and the appropriate choice of the Kalman filter process model and radial basis function parameterisation.

We evaluate simulations for single-, double- and multiple-pair satellite gravimetry missions with respect to applications in hydrology, sea level budgeting, and solid Earth science. We begin with the retrieval of weekly spherical harmonic solutions from GRACE-FO and MAGIC-like inter-satellite laser tracking in the presence of realistic aliasing, as well as from more distant scenarios that would involve flying quantum accelerometers on satellite pairs in various orbital planes of different inclination. To account for realistic applications, we simulate the impact of such data products in basin-averaged total water storage recovery, in the retrieval of water storages via assimilation into global and regional models, in global and regional ocean mass estimation also in combination with radar altimetry, and in the monitoring of Earthquakes and submarine volcano growth. While we find that the MAGIC simulation provides the largest improvement step with respect to our GRACE-FO simulation, the more advanced scenarios add sensitivity in particular in applications where gravity and mass change data can be directly equated to observable phenomena. It is more challenging to judge the benefit of advanced missions with scientific applications that involve combination with model ensembles and additional remote sensing data, as their uncertainties may determine the noise floor and will need to be projected into the future, which we did not attempt at here.

In this study a regional modelling framework for water mass changes is developed. The approach can introduce geodetic observation types of varying temporal and spatial resolution including their correlated error information. For this purpose a Kalman filter process was set up using a regional parameterisation by space-localising radial basis functions and a process model based on stochastic prediction. The feasibility of the approach is confirmed in a closed-loop simulation experiment using gridded water storage estimates derived from simulated monthly solutions of the GRACE satellite gravimetry mission and considering realistic error patterns. The resulting mass change time series exhibit strongly reduced noise and a very high agreement with the reference model. The modelling framework is designed to flexibly allow a future extension towards combining satellite gravimetry with other geodetic observations such as GNSS station displacements or terrestrial gravimetry.

The increasing frequency, intensity, and duration of extreme heat and drought events in a warming climate make it crucial to understand the relationship between surface and subsurface water storage dynamics during these events. Changes in water storage can be studied globally using satellite observations. Microwave remote sensing observes the upper few centimeters of the soil, while satellite gravimetry detects changes in the entire column of terrestrial water storage. We use daily data of the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), satellite-based surface soil moisture data and root zone products from Soil Moisture Ocean Salinity, Soil Moisture Active Passive, and European Space Agency Climate Change Initiative on a harmonized 1 degrees ${}<^>{\circ}$ global grid to study the evolution of water storage deficits across different soil layers. The joint analysis of the three types of data provides valuable insight into the hydrological dynamics in different soil depths and subsurface water storage compartments. To identify different dynamics, we compute the rate of change from de-seasonalized water storage anomaly time series to assess how quickly the system accumulates storage deficits during drought conditions and recovers from them for different integration depths in the subsurface. The results indicate characteristic patterns of the temporal dynamics of drought recovery with fast fluctuations and short recovery times for surface soil moisture, a prolonged behavior in the root-zone, and an even slower response in the entire water column. This highlights that the cascading propagation of drought dynamics from the surface to the subsurface can be quantified by remote sensing data with daily resolution at the global scale.

Under the assumption that a warming climate leads to an intensification of the global water cycle, it can be hypothesized that also the occurrence frequency and severity of extreme events such as droughts or floods will increase in the upcoming decades to centuries. Global coupled climate models, which project the future evolution of various variables of the Earth's climate system are important tools for the analysis of such expected changes. To assess the reliability of the models and to identify possible systematic discrepancies, it is essential to evaluate the model output against observations.In this study, present and future occurrences of extreme events are analysed in water storage time series simulated by coupled global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and compared against spatio-temporal changes in water mass derived from GRACE and GRACE-FO. This comparison is based on Extreme Value Theory, as the exact timing of modelled extreme events cannot be assessed by observations due to the stochastic behavior of climate variability in unconstrained model experiments. From estimated extreme value distributions return levels are calculated, a quantity describing the magnitude or frequency of extreme values. Challenges that have to be overcome in the analysis are the non-stationary data and the relatively short time span of the GRACE observations. The latter issue is addressed by additionally assessing GRACE-based water storage reconstructions available over many decades.This study provides insights into the ability of global climate models to model the occurrence of TWS extremes, namely unusual dry and wet phases. It also examines whether the climate model projections predict an increasing intensity of extreme events.

Since 2002, the GRACE and GRACE-FO satellite gravity missions have been observing changes in the Earth’s gravity field. ESA and NASA are currently planning a double-pair satellite constellation MAGIC, which promises an enhanced spatial and temporal resolution compared to GRACE/-FO. After MAGIC, in the long-term post-2040-time frame, a gravity mission constellation with multiple satellite pairs equipped with novel quantum sensor instrumentation is considered as a promising candidate concept to improve the observation time series even further. It has the potential to acquire unprecedented data on key Earth processes and is expected to significantly expand the potential range of applications. Within the ongoing ESA project “Quantum Space Gravimetry for monitoring Earth’s Mass Transport Processes” (QSG4EMT) an online questionnaire was created to assess user requirements for such a future quantum mission concept. We will present the results of this community assessment based on 135 answers from various user groups (hydrology, oceanography, glaciology, atmospheric and climate sciences, solid earth sciences, and geodesy). In addition to application-driven demands of the different disciplines regarding the required spatial and temporal resolution, accuracy, and latency, we discuss the expected added benefits of hypothetical future mission scenarios and outline possible new application fields.

Every year, natural climate variability leads to droughts and floods which have significant impacts for ecosystems and societies. Water reservoirs like soil moisture, lakes, and groundwater act as natural buffers and balance these fluctuations by providing water supply during dry conditions and by storing water surplus after rain and snow events. Such natural fluctuations unfold over time scales that can reach several decades, making it challenging to assess the extent to which trends in water reservoirs observed over the recent past are caused by anthropogenic modifications. Such modifications can themselves be further partitioned into different terms. For instance, one can contrast the contribution of regional land and water management on the one hand, and the contribution of climate change on the other. Another frequent framework is to causally relate changes in water storage to individual changes in precipitation, evapotranspiration, and runoff. In this contribution, we review the strengths and weaknesses of recent approaches used to causally attribute observed as well as projected changes in water availability. Ensembles of model simulations and factorial experiments typically represent a powerful way of assessing individual responses to drivers and developing a plausible and mechanistic understanding. However, contradictions also quickly emerge between global hydrological model simulations, which typically represent water reservoirs and water management more thoroughly, and Earth system (climate) model simulations, which include biogeochemical effects, like CO2 fertilization, that are typically neglected by hydrological models. We will show that these two incomplete modeling worlds can be reconciled with large-scale satellite observations in only a few regions, while very large uncertainties remain in other parts of the world and in particular over tropical areas.

SUMMARY The joint ESA/NASA Mass-change And Geosciences International Constellation (MAGIC) has the objective to extend time-series from previous gravity missions, including an improvement of accuracy and spatio-temporal resolution. The long-term monitoring of Earth’s gravity field carries information on mass change induced by water cycle, climate change and mass transport processes between atmosphere, cryosphere, oceans and solid Earth. MAGIC will be composed of two satellite pairs flying in different orbit planes. The NASA/DLR-led first pair (P1) is expected to be in a near-polar orbit around 500 km of altitude; while the second ESA-led pair (P2) is expected to be in an inclined orbit of 65°–70° at approximately 400 km altitude. The ESA-led pair P2 Next Generation Gravity Mission shall be launched after P1 in a staggered manner to form the MAGIC constellation. The addition of an inclined pair shall lead to reduction of temporal aliasing effects and consequently of reliance on de-aliasing models and post-processing. The main novelty of the MAGIC constellation is the delivery of mass-change products at higher spatial resolution, temporal (i.e. subweekly) resolution, shorter latency and higher accuracy than the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO). This will pave the way to new science applications and operational services. In this paper, an overview of various fields of science and service applications for hydrology, cryosphere, oceanography, solid Earth, climate change and geodesy is provided. These thematic fields and newly enabled applications and services were analysed in the frame of the initial ESA Science Support activities for MAGIC. The analyses of MAGIC scenarios for different application areas in the field of geosciences confirmed that the double-pair configuration will significantly enlarge the number of observable mass-change phenomena by resolving smaller spatial scales with an uncertainty that satisfies evolved user requirements expressed by international bodies such as IUGG. The required uncertainty levels of dedicated thematic fields met by MAGIC unfiltered Level-2 products will benefit hydrological applications by recovering more than 90 per cent of the major river basins worldwide at 260 km spatial resolution, cryosphere applications by enabling mass change signal separation in the interior of Greenland from those in the coastal zones and by resolving small-scale mass variability in challenging regions such as the Antarctic Peninsula, oceanography applications by monitoring meridional overturning circulation changes on timescales of years and decades, climate applications by detecting amplitude and phase changes of Terrestrial Water Storage after 30 yr in 64 and 56 per cent of the global land areas and solid Earth applications by lowering the Earthquake detection threshold from magnitude 8.8 to magnitude 7.4 with spatial resolution increased to 333 km.

AbstractWe evaluate trends in terrestrial water storage over 1950–2100 in CMIP6 climate models against a new global reanalysis from assimilating GRACE and GRACE-FO satellite observations into a hydrological model. To account for different timescales in our analysis, we select regions in which the influence of interannual variability is relatively small and observed trends are assumed to be representative of the development over longer periods. Our results reveal distinct biases in drying and wetting trends in CMIP6 models for several world regions. Specifically, we see high model consensus for drying in the Amazon, which disagrees with the observed wetting. Other regions show a high consensus of models and observations suggesting qualitatively correctly simulated trends, e.g., for the Mediterranean and parts of Central Africa. A high model agreement might therefore falsely indicate a robust trend in water storage if it is not assessed in light of the observed developments. This underlines the potential use of maintaining an adequate observational capacity of water storage for climate change assessments.

Global coupled climate models are in continuous need for evaluation against independent observations to reveal systematic model deficits and uncertainties. Changes in terrestrial water storage (TWS) as measured by satellite gravimetry missions GRACE and GRACE-FO provide valuable information on wetting and drying trends over the continents. Challenges arising from a comparison of observed and modelled water storage trends are related to gravity observations including non-water related variations such as, for example, glacial isostatic adjustment (GIA). Therefore, correcting secular changes in the Earth's gravity field caused by ongoing GIA is important for the monitoring of long-term changes in terrestrial water from GRACE in particular in former ice-covered regions. By utilizing a new ensemble of 56 individual realizations of GIA signals based on perturbations of mantle viscosities and ice history, we find that many of those alternative GIA corrections change the direction of GRACE-derived water storage trends, for example, from gaining mass into drying conditions, in particular in Eastern Canada. The change in the sign of the TWS trends subsequently impacts the conclusions drawn from using GRACE as observational basis for the evaluation of climate models as it influences the dis-/agreement between observed and modelled wetting/drying trends. A modified GIA correction, a combined GRACE/GRACE-FO data record extending over two decades, and a new generation of climate model experiments leads to substantially larger continental areas where wetting/drying trends currently observed by satellite missions coincide with long-term predictions obtained from climate model experiments. The satellite gravimetry missions GRACE and GRACE-FO measure changes in the lateral distribution of water stored on Earth. These data give important insight into climate-driven wetting and drying on the continents and can thus be applied to evaluate the output of climate models designed to simulate current and future changes in the Earth system. However, GRACE/-FO are not only sensitive to changes in water storage, but also to other mass change processes such as glacial isostatic adjustment (GIA). GIA refers to the uplift of the land surface, which is still ongoing after the melting of the heavy ice cover from the last ice age. To isolate climate-induced water storage trends, the GIA effect has to subtracted from the GRACE/-FO time series. With 56 different GIA models, we show that the choice of this correction affects the direction of the GRACE-derived water storage trend from wetting to drying (or vice versa). Therefore, it is crucial for the dis-/agreement of the observed trend direction and climate models, particularly in the previously ice-covered regions of Canada. With the revised data processing scheme, the satellite observations document long-term changes in terrestrial water storage in various regions that are in line with climate model projections. A glacial isostatic adjustment (GIA) model ensemble characterizes the uncertainty of GIA corrections on GRACE-derived water storage trends The GIA correction critically impacts the dis-/agreement between GRACE and climate model wetting/drying trends in Eastern Canada Spatially coherent regions of agreement between observed trends and climate model predictions have grown substantially over earlier studies

SUMMARY Gravity field satellite missions are unique observation systems to directly measure mass transport processes on Earth and to gather valuable information for climate research. Next Generation Gravity Missions (NGGMs) are expected to be launched within this decade, setting high anticipation for an enhanced monitoring capability that will improve the spatial and temporal resolutions of gravity observations significantly. They will allow for an evaluation of long-term trends in the Terrestrial Water Storage (TWS) signal. The results of this study are based on a time-series of global changes in soil moisture and snow obtained from future climate projections until the year 2100 of a coupled climate model taking part in the CMIP6 (Coupled Model Intercomparison Project Phase 6). For different mission concepts, namely in-line single-pair missions and a Bender double-pair mission, the recoverability of a time variable mass signal is evaluated, considering realistic noise assumptions, simulated over several decades. The results show that a single-pair mission can fulfill the target requirements for the long-term trend, set by the user community, after 70 yr while a double pair already achieves it after 30 yr of observation. After 100 yr of double-pair constellations the globally averaged RMS (polar areas excluded) improves, compared to a single-pair mission, by a factor of 5 for the linear trend, 2.5 for annual amplitude, and 1.8 for the phase observation. In addition, regional investigations indicate that the simple parameter model consisting of offset, linear trend, and annual signal coefficients, as it was used in this study, in several cases might not be able to capture the whole time-variable signal sufficiently, due to the presence of interannual signals. Hence, advanced, locally more adaptable parameter models need to be considered for a better parametrization of local effects in the future.

&lt;p&gt;Regularly updated information about states, trends and dynamics of water storage in different spatio-temporal scales has gained increasing importance, especially with a perspective on hydrological extreme events as well as water management issues. Monitoring these storage dynamics is challenging due to the spatial heterogeneity and the contribution of different storage compartments (e.g., near-surface soil moisture, deep unsaturated zone, groundwater). A promising monitoring technique is gravimetry, well suited for the integral observation of different storage compartments. While satellite gravimetry (GRACE, GRACE-FO) provides information on storage variations at a spatially large scale with low spatial and temporal resolution, the opposite is true for terrestrial gravimetry. Ways to combine both satellite and terrestrial gravimetry are addressed and evaluated within the German Collaborative Research Centre TerraQ. For the terrestrial approach, several gravimeters were deployed for continuous monitoring at different locations within Germany. The work presented here takes as an example a forest site within the TERENO observatory of north-eastern Germany, with continuous observations &amp;#160;of a superconducting gravimeter (iGrav 033) since 2017.&lt;br /&gt;The signal footprint of such a gravimeter typically covers a radius of 0.5 to 2 km, depending on local topography, although most of the signal originates from the direct vicinity of the instrument. Also, the device can sense mass changes beyond this distance, depending on their magnitude (e.g., tides, atmosphere or global hydrological effects). In hydro-gravimetric studies, all non-desired signals are typically removed, resulting in residuals that are representative for the local hydrological effects only. Towards comparing and combining these terrestrial measurements with satellite products, one open question is how representative the terrestrial gravity residuals are in a regional context. With the goal to assess this spatial representativeness, we conducted seasonal relative gravity surveys with 2 CG-6 gravimeters in an extent of roughly 25 by 30 km around the iGrav installation. The survey data were combined with spatial information about topography and land-use. Water storage changes could thus be attributed to each survey point. A joint analysis with the continuous measurements of the superconducting gravimeter at the permanent installation site allowed for mapping the spatial patterns and similarities among all sites.&lt;/p&gt; &lt;p&gt;This study is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) &amp;#8211; Project-ID 434617780 &amp;#8211; SFB 1464&lt;/p&gt;