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Information on water storage changes in the soil can globally be obtained from different types of satellite observations. While microwave remote sensing is limited to the upper few centimeters of the soil, satellite gravimetry can detect changes in the full column of terrestrial water storage (TWS), but cannot distinguish between storage variations in different soil depths. Jointly analyzing both data types promises insights into the underlying hydrological dynamics and may enable a better process understanding of water storage change in the subsurface. In this study, we investigate the global relationship of (1) several satellite soil moisture (SM) products and (2) non-standard daily TWS data from the GRACE/-FO satellite gravimetry missions. The analysis of these data on a daily basis could be beneficial for identifying hydro-climatic extreme events such as heavy precipitation or flood events. Therefore, we evaluate the correspondence of particularly high and low percentiles in both TWS and SM time series to identify extreme events. We discuss the different temporal dynamics in near-surface SM vs. TWS before and after flood and drought events to identify antecedent conditions leading to floods and recovery times after droughts in different storage compartments. Furthermore, temporally high-frequency variations are discussed using sub-monthly SM and TWS variations derived by high-pass filtering. We find increasingly large correlations between the TWS and SM for deeper SM integration depths (root zone vs. surface layer). Even for high-pass-filtered (sub-monthly) variations, significant correlations of up to 0.6 can be found in regions with large high-frequency variability., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

In 2019 the Combination Service for Time-variable Gravity fields (COST-G) started operation with the release of the complete time-series of combined GRACE monthly gravity fields COST-G GRACE RL01. Meanwhile additional time-series of monthly GRACE gravity fields have become available, mainly from Chinese analysis centers. The COST-G quality control confirms the high quality of these solutions that are derived with independent analysis tools.In the frame of the Horizon 2020 project Global Gravity-based Groundwater Product (G3P) the weighting scheme of the monthly GRACE-FO gravity fields was adapted to take into account the limitations on the solution space imposed by the specific observation geometry of the GRACE/GRACE-FO missions.We present the new COST-G GRACE RL02 combination, based on an extended set of time-series including COST-G's new Chinese partners, and combined applying the G3P weighting scheme., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

International audience; The German Research Unit GlobalCDA has the goal to improve the predictive skills of hydrological models by combining remote sensing information using a calibration/data assimilation (C/DA) approach. In order to validate model results and to assess the success of the C/DA efforts, independent data sets are crucially needed, such as in-situ groundwater observations to assess the ability of the model to describe groundwater storage changes. The main challenge arising from such comparisons is to capture basin-scale groundwater storage from a set of in-situ GW observations settled in highly heterogeneous lithologies with irregular & non-homogeneous sampling. Furthermore, the conversion from groundwater (GW) level measurements to storage variations requires information on specific yield, ideally given site-specific for each monitoring well. However, this information is largely not available and difficult to estimate in areas with highly heterogeneous geology.In our study we use a data set of groundwater level observations at about 3000 groundwater monitoring wells in France. Based on a high-resolution hydro-geological information system provided by the French geological survey (BRGM) and water authorities (BDLISA), we assign the borehole data to individual hydro-geological units. For the upscaling to river basin averages, we (i) aggregate the measurements that originate from the same unit and (ii) account for the areal fractions of the hydro-geological units within the river basin. For the interpretation of GW level variations to GW storage changes, we tested several approaches to estimate specific yield values for the individual hydro-geological units. Wherever possible, we use the specific yield values explicitly provided in the BDLISA data base, mostly estimated from pumping test analysis. When not available, we assign literature-based specific yield values based on the detailed lithological information provided for each unit. This method is compared to global porosity information provided by low resolution geological data from GHYLMPS (Gleeson et al., 2014)Besides presenting the methodological approach, this presentation shows the resulting groundwater storage time series, averaged for individual river basins in France and for individual 0.5° grid cells. Additionally, comparisons to simulated groundwater storage variations of the WaterGAP Global Hydrology Model (WGHM) will be presented. We discuss the sensitivity of basin averaged GW storage time series to different choices of specific yield for individual boreholes.How to cite: Hsu, K.-H., Eicker, A., Hasan, M., Güntner, A., and Longuevergne, L.: Estimating groundwater storage changes for major river basins in France using a regional groundwater data set , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7127, https://doi.org/10.5194/egusphere-egu22-7127, 2022.

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 behaviour 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 modelled basin uncertainties for GFZ RL06 and ITSG-Grace2018 TWS data with the formal basin uncertainties from the ITSG-Grace2018 solution. The modelled and formal uncertainties fit both in the spatial and temporal domain. We further evaluate the modelled uncertainties by comparison to empirical uncertainties over arid regions. Here, again the appropriateness of the modelled 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.

The International Combination Service for Time-variable Gravity Fields (COST-G) is a Product Center of IAG’s International Gravity Field Service (IGFS). COST-G provides consolidated monthly global gravity fields in terms of spherical harmonic coefficients and thereof derived grids of surface mass changes by combining existing solutions or normal equations from COST-G analysis centers (ACs) and partner analysis centers (PCs). The COST-G ACs adopt different analysis methods but apply agreed-upon consistent processing standards to deliver time-variable gravity field models from GRACE/GRACE-FO low-low satellite-to-satellite tracking (ll-SST) and from Swarm GPS high-low satellite-to-satellite tracking (hl-SST). The poster presents the organizational structure of COST-G and results from the first releases of combined monthly GRACE and GRACE-FO solutions. In addition, the perspectives from the provision of deterministic signal models (DSM), allowing for a few months prediction and therefore application in operational precise orbit determination activities of low Earth satellites, are discussed.

The satellite missions GRACE and GRACE-FO, dedicated to the observation of the time-variable Earth gravity field, provide invaluable insight into continental total water storage, continental ice mass change and ocean mass change at spatial scales of 200-400 km and monthly time-resolution, covering the time-period 2002 to present. The Combination Service for Time-Variable Gravity Fields (COST-G) of the International Association of Geodesy (IAG) collects the monthly gravity fields of its associated or partner Analysis Centers (ACs) and performs a weighted combination to provide a consolidated time-series of monthly gravity fields at Level-2 (spherical harmonic coefficients) and user-friendly Level-3 (post-processed global grids) products. Gaps in the GRACE or GRACE-FO time-series may be bridged by monthly gravity fields derived from orbits of the Swarm satellites, dedicated to the observation of the Earth magnetic field, at the cost of significantly reduced spatial resolution. COST-G performs quality control and harmonization of the contributing GRACE, GRACE-FO or Swarm time-series. The combined gravity fields undergo consistency checks and internal validation by the COST-G validation centers at GFZ and GRGS. An independent board of experts in hydrological, oceanic and cryosphere applications irregularly performs external validations. A combined re-processed GRACE time-series COST-G GRACE RL01 is available at the International Center for Global Earth Models ICGEM (L2-products) or the Gravity Information Service GRAVIS (L3-products), both operated by GFZ. The operational GRACE-FO time-series is updated regularly with a latency of less than 3 months, Swarm gravity fields are operationally combined in a quarterly processing scheme.