To fully exploit data from the Gravity Recovery and Climate Experiment (GRACE), we separate geophysical signals observed by GRACE in Antarctica by deriving high-spatial resolution maps for present-day glacial isostatic adjustment (GIA) and ice-mass changes with the least possible noise level. For this, we simultaneously (i) improve the postprocessing of gravity data and (ii) consistently combine them with high-resolution data from Ice Cloud and land Elevation Satellite altimeter (ICESat) and Regional Atmospheric Climate Model 2.3 (RACMO). We use GPS observations to discriminate between various candidate spatial patterns of vertical motions caused by GIA. The ICESat-RACMO combination determines the spatial resolution of estimated ice-mass changes. The results suggest the capability of the developed approach to retrieve the complex spatial pattern of present-day GIA, such as a pronounced subsidence in the proximity of the Kamb Ice Stream and pronounced uplift in the Amundsen Sea Sector.@en

An accurate estimation of water resources dynamics is crucial for proper management of both agriculture and the local ecology, particularly in semi-arid regions. Imperfections in model physics, uncertainties in model land parameters and meteorological data, as well as the human impact on land changes often limit the accuracy of hydrological models in estimating water storages. To mitigate this problem, this study investigated the assimilation of terrestrial water storage variation (TWSV) estimates derived from the Gravity Recovery And Climate Experiment (GRACE) data using an ensemble Kalman filter (EnKF) approach. The region considered was the Hexi Corridor in northern China. The hydrological model used for the analysis was PCR-GLOBWB, driven by satellite-based forcing data from April 2002 to December 2010. The impact of the GRACE data assimilation (DA) scheme was evaluated in terms of the TWSV, as well as the variation of individual hydrological storage estimates. The capability of GRACE DA to adjust the storage level was apparent not only for the entire TWSV but also for the groundwater component. In this study, spatially correlated errors in GRACE data were taken into account, utilizing the full error variance-covariance matrices provided as a part of the GRACE data product. The benefits of this approach were demonstrated by comparing the EnKF results obtained with and without taking into account error correlations. The results were validated against in situ groundwater data from five well sites. On average, the experiments showed that GRACE DA improved the accuracy of groundwater storage estimates by as much as 25 %. The inclusion of error correlations provided an equal or greater improvement in the estimates. In contrast, a validation against in situ streamflow data from two river gauges showed no significant benefits of GRACE DA. This is likely due to the limited spatial and temporal resolution of GRACE observations. Finally, results of the GRACE DA study were used to assess the status of water resources over the Hexi Corridor over the considered 9-year time interval. Areally averaged values revealed that TWS, soil moisture, and groundwater storages over the region decreased with an average rate of approximately 0.2, 0.1, and 0.1 cm yr^-1 in terms of equivalent water heights, respectively. A particularly rapid decline in TWS (approximately -0.4 cm yr^-1) was seen over the Shiyang River basin located in the southeastern part of Hexi Corridor. The reduction mostly occurred in the groundwater layer. An investigation of the relationship between water resources and agricultural activities suggested that groundwater consumption required to maintain crop yield in the growing season for this specific basin was likely the cause of the groundwater depletion.

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We present a high resolution model of the linear trend in the Earth’s mass variations based on DMT-2 (Delft Mass Transport model, release 2). DMT-2 was produced primarily from K-Band Ranging (KBR) data of the Gravity Recovery And Climate Experiment (GRACE). It comprises a time series of monthly solutions complete to spherical harmonic degree 120. A novel feature in its production was the accurate computation and incorporation of stochastic properties of coloured noise when processing KBR data. The unconstrained DMT-2 monthly solutions are used to estimate the linear trend together with a bias, as well as annual and semi-annual sinusoidal terms. The linear term is further processed with an anisotropic Wiener filter, which uses full noise and signal covariance matrices. Given the fact that noise in an unconstrained model of the trend is reduced substantially as compared to monthly solutions, the Wiener filter associated with the trend is much less aggressive compared to a Wiener filter applied to monthly solutions. Consequently, the trend estimate shows an enhanced spatial resolution. It allows signals in relatively small water bodies, such as Aral sea and Ladoga lake, to be detected. Over the ice sheets, it allows for a clear identification of signals associated with some outlet glaciers or their groups. We compare the obtained trend estimate with the ones from the CSR-RL05 model using (i) the same approach based on monthly noise covariance matrices and (ii) a commonly-used approach based on the DDK-filtered monthly solutions. We use satellite altimetry data as independent control data. The comparison demonstrates a high spatial resolution of the DMT-2 linear trend. We link this to the usage of high-accuracy monthly noise covariance matrices, which is due to an accurate computation and incorporation of coloured noise when processing KBR data. A preliminary comparison of the linear trend based on DMT-2 with that computed from GSFC global mascons v01 reveals, among other, a high concentration of the signal along the coast for both models in areas like the ice sheets, Gulf of Alaska, and Iceland.@en

In this study, satellite observations including gravity (GRACE), terrestrial reflectance (MODIS), and global precipitation (TRMM) data, along with the output from the PCR-GLOBWB hydrological model, are used to generate monthly and sub-monthly terrestrial water storage (TWS) estimates and quantify flood events over the Tonlé Sap basin between 2002 and 2014. This study is the first time GRACE data have been used to investigate the hydrological processes over the Tonlé Sap basin. To improve the accuracy of the TWS estimates from GRACE, a signal restoration method was applied in an effort to recover the signal loss (i.e., signal leakage) inherent in the standard GRACE post-processing scheme. The method applies the correction based on the GRACE observations only, requiring no external data or hydrological models. The effectiveness of the technique over the Tonlé Sap basin was validated against several independent data sets. Based on the GRACE observations since 2002, the 2011 and 2013 flood events were clearly identified, and measured to have basin-averaged TWS values of 42 cm (40% higher than the long-term mean peak value) and 36 cm (34% higher) equivalent water height, respectively. Those same years also coincide with the largest observed flood extents, estimated from the MODIS data as 6561 km² (91% above the long-term mean peak value) and 5710 km² (66% above), respectively. Those flood events are also linked to the observed inter-annual variations of water storage between 2010 and 2014. It was shown that those inter-annual variations mainly reflect the variations in the surface water and groundwater storage components, influenced by the change of the precipitation intensity. In addition, this study presents a new approach for deriving monthly and sub-monthly TWS variations over a regularly inundated area by using MODIS reflectance data in addition to GRACE solutions. The results of this study show that GRACE data can be considered as an effective tool for monitoring certain small-scale (82,000 km²) hydrological basins.

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There has been considerable research in the literature focused on computing and forecasting sea-level changes in terms of constant trends or rates. The Antarctic ice sheet is one of the main contributors to sea-level change with highly uncertain rates of glacial thinning and accumulation. Geodetic observing systems such as the Gravity Recovery and Climate Experiment (GRACE) and the Global Positioning System (GPS) are routinely used to estimate these trends. In an effort to improve the accuracy and reliability of these trends, this study investigates a technique that allows the estimated rates, along with co-estimated seasonal components, to vary in time. For this, state space models are defined and then solved by a Kalman filter (KF). The reliable estimation of noise parameters is one of the main problems encountered when using a KF approach, which is solved by numerically optimizing likelihood. Since the optimization problem is non-convex, it is challenging to find an optimal solution. To address this issue, we limited the parameter search space using classical least-squares adjustment (LSA). In this context, we also tested the usage of inequality constraints by directly verifying whether they are supported by the data. The suggested technique for time-series analysis is expanded @en

In this study we address the question of whether regional gravity field modeling techniques of GRACE data can offer improved resolution over traditional global spherical harmonic solutions@en