AbstractWe investigate the benefits of future quantum accelerometers based on cold atom interferometry (CAI) on current and upcoming satellite gravity mission concepts. These mission concepts include satellite‐to‐satellite tracking (SST) in a single‐pair (GRACE‐like) and double‐pair constellation as well as satellite gravity gradiometry (SGG, single satellite, GOCE‐like). Regarding instruments, four scenarios are considered: current‐generation electrostatic (GRACE‐, GOCE‐like), next‐generation electrostatic, conservative hybrid/CAI and optimistic hybrid/CAI. For SST, it is shown that temporal aliasing poses currently the dominating error source in simulated global gravity field solutions independent of the investigated instrument and constellation. To still quantify the advantages of CAI instruments on the gravity functional itself, additional simulations are performed where the impact of temporal aliasing is synthetically reduced. When neglecting temporal aliasing, future accelerometers in conjunction with future ranging instruments can substantially improve the retrieval performance of the Earth's gravity field (depending on instrument and constellation). These simulation results are further investigated regarding possible benefit for hydrological use cases where these improvements can also be observed (when omitting temporal aliasing). For SGG, it is demonstrated that, with realistic instrument assumptions, one is still mostly insensitive to time‐variable gravity and not competitive with the SST principle. However, due to the improved instrument sensitivity of quantum gradiometers compared to the GOCE mission, static gravity field solutions can be improved significantly.

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.

SUMMARY More than 20 yr of measurement data of the gravity missions GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow-On) allow detailed investigations of long-term trends in continental terrestrial water storage (TWS). However, the spatial resolution of conventional GRACE/GRACE-FO data products is limited to a few hundred kilometres which restrains from investigating hydrological trends at smaller spatial scales. In this study GRACE and GRACE-FO data have been used to calculate TWS trends with maximized spatial resolution. Conventionally, GRACE/GRACE-FO is presented as a series of either unconstrained gravity fields post-processed with spatial low pass filters or constrained inversions commonly known as Mascon products. This paper demonstrates that both approaches to suppress spatially correlated noise are mathematically equivalent. Moreover, we demonstrate that readily inverting all available sensor data from GRACE/GRACE-FO for a single TWS trend map, together with annual variations and a mean gravity field, provides additional spatial detail not accessible from the standard products. The variable trade-off between spatial and temporal resolution as a unique feature of satellite gravimetry allows for gravity products that are tailored towards specific geophysical applications. We show additional signal content in terms of long-term water storage trends for four dedicated examples (Lake Victoria, Northwest India, Bugachany Reservoir and High Plains Aquifer) for which external information from other remote sensing instruments corroborates the enhanced spatial resolution of the new mean-field trend product.

As changes in gravity are directly related to mass variability, satellite missions observing the Earth's time varying gravity field are a unique tool for observing mass transport processes in the Earth system, such as the water cycle, rapid changes in the cryosphere, oceans, and solid Earth processes, on a global scale. The observation of Earth's gravity field was successfully performed by the GRACE and GOCE satellite missions, and will be continued by the GRACE Follow-On mission. A comprehensive team of European scientists proposed the next-generation gravity field mission MOBILE in response to the European Space Agency (ESA) call for a Core Mission in the frame of Earth Explorer 10 (EE10). MOBILE is based on the innovative observational concept of a high-low tracking formation with micrometer ranging accuracy, complemented by new instrument concepts. Since a high-low tracking mission primarily observes the radial component of gravity-induced orbit perturbations, the error structure is close to isotropic. This geometry significantly reduces artefacts of previous along-track ranging low-low formations (GRACE, GRACE-Follow-On) such as the typical striping patterns. The minimum configuration consists of at least two medium-Earth orbiters (MEOs) at 10000 km altitude or higher, and one low-Earth orbiter (LEO) at 350-400 km. The main instrument is a laser-based distance or distance change measurement system, which is placed at the LEO. The MEOs are equipped either with passive reflectors or transponders. In a numerical closed-loop simulation, it was demonstrated that this minimum configuration is in agreement with the threshold science requirements of 5 mm equivalent water height (EWH) accuracy at 400 km wavelength, and 10 cm EWH at 200 km. MOBILE provides promising potential future perspectives by linking the concept to existing space infrastructure such as Galileo next-generation, as future element of the Copernicus/Sentinel programme, and holds the potential of miniaturization even up to swarm configurations. As such MOBILE can be considered as a precursor and role model for a sustained mass transport observing system from space.

In large Tropical River basins such as the Amazon, groundwater plays a major role in the water and ecological cycles with large influences on the rainforest ecosystems and climate variability. However, due to the lack of monitoring networks, Amazon groundwater storage and its variability remain poorly known. Here, we provide an unprecedented direct estimate of the spatio-temporal variations of the anomaly of groundwater storage over the period January 2003 -September 2010 in the Amazon Basin by decomposing the total terrestrial water storage measured by the Gravity Recovery and Climate Experiment (GRACE) mission into the individual contributions of other hydrological reservoirs, using multi-satellite data for the surface waters and floodplains and models outputs for the soil moisture. We show that the seasonal variations of groundwater storage represent between 20 and 35% of the terrestrial water storage seasonal volume variations of the Amazon. Larger seasonal amplitudes of groundwater storage (>450 mm) are found in the Alter do Chao and Ica aquifers in the central part of the Amazon Basin. Anomalies of groundwater storage exhibit a strong interannual variability (STD reaching 120 mm along the central corridor) during the study period in response to hydrologic variability and climatic events such as the extreme drought that occurred in 2005.

We present a MatlabTM/Octave-based software tool mGlobe to compute the effect of atmospheric, continental water storage, and non-tidal ocean mass variations on surface gravity. These effects must be considered or reduced prior to any analysis of geophysical phenomena using observations of superconducting gravimeters. Contrary to the alternative providers, mGlobe allows the computation for an arbitrary location worldwide, supports a larger number of input models and offers more flexibility in terms of computation settings. The high number of supported models is important for assessment of model uncertainties. Discrepancies exceeding 75% were found. The continental water storage effect showed low sensitivity to spatial and temporal resolution. The deficient temporal resolution affects the non-tidal loading and atmospheric effects significantly. The same holds true for the influence of the spatial resolution on atmospheric effects. To compensate this effect, we introduce a site-specific correction factor based on differences between the real topography and model's orography.

For the estimation of soil loss by erosion in the strongly affected Chinese Loess Plateau we applied the Universal Soil Loss Equation (USLE) using a number of input data sets (monthly precipitation, soil types, digital elevation model, land cover and soil conservation measures). Calculations were performed in ArcGIS and SAGA. The large-scale soil erosion in the Loess Plateau results in a strong non-hydrological mass change. In order to investigate whether the resulting mass change from USLE may be validated by the gravity field satellite mission GRACE (Gravity Recovery and Climate Experiment), we processed different GRACE level-2 products (ITG, GFZ and CSR). The mass variations estimated in the GRACE trend were relatively close to the observed sediment yield data of the Yellow River. However, the soil losses resulting from two USLE parameterizations were comparatively high since USLE does not consider the sediment delivery ratio. Most eroded soil stays in the study area and only a fraction is exported by the Yellow River. Thus, the resultant mass loss appears to be too small to be resolved by GRACE.

In data sparse mountainous regions it is difficult to derive areal precipitation estimates. In addition, their evaluation by cross validation can be misleading if the precipitation gauges are not in representative locations in the catchment. This study aims at the evaluation of precipitation estimates in data sparse mountainous catchments. In particular, it is first tested whether monthly precipitation fields from downscaled reanalysis data can be used for interpolating gauge observations. Secondly, precipitation estimates from this and other methods are evaluated by comparing simulated and observed discharge, which has the advantage that the data are evaluated at the catchment scale. This approach is extended here in order to differentiate between errors in the overall bias and the temporal dynamics, and by taking into account different sources of uncertainties. The study area includes six headwater catchments of the Karadarya Basin in Central Asia. Generally the precipitation estimate based on monthly precipitation fields from downscaled reanalysis data showed an acceptable performance, comparable to another interpolation method using monthly precipitation fields from multi-linear regression against topographical variables. Poor performance was observed in only one catchment, probably due to mountain ridges not resolved in the model orography of the regional climate model. Using two performance criteria for the evaluation by hydrological modelling allowed a more informed differentiation between the precipitation data and showed that the precipitation data sets mostly differed in their overall bias, while the performance with respect to the temporal dynamics was similar. Our precipitation estimates in these catchments are considerably higher than those from continental-or global-scale gridded data sets. The study demonstrates large uncertainties in areal precipitation estimates in these data sparse mountainous catchments. In such regions with only very few precipitation gauges but high spatial variability of precipitation, important information for evaluating precipitation estimates may be gained by hydrological modelling and a comparison to observed discharge.

The GRACE (Gravity Recovery and Climate Experiment) satellite mission provides global time-series of the Earth's gravity field. In view of limited resolution and noise from the GRACE data, various filtering techniques have been developed to extract an optimal signal. There is no conclusion on the best filter method so far, however. On the other hand, terrestrial gravity observations from superconducting gravimeters (SGs) provide variations of the gravity field with very high accuracy and time resolution, but only at single points. The aim of this study is to compare GRACE-derived temporal gravity variations with gravity time-series within a network of six Central European SG stations. Empirical orthogonal functions (EOF) analysis was applied to detect common signal characteristics over a 3 yr period (2004–2006). rms Differences between the time-series of several GRACE solutions amount to 60 per cent of the rms variability of the individual data sets. The rms differences between the SG and GRACE time-series are about 70 per cent of the rms value of the SG observations. The best agreement between SG and GRACE is obtained when using a Gaussian filter with filter lengths of 800–1250 km for the GRACE data. With the EOF analysis, a common regional signal can be deduced from all gravity data sets. Nevertheless, differences in the first EOF among the GRACE solutions were up to 40 per cent, and differences of up to 50 per cent were found between the SG-based terrestrial and the GRACE-based satellite observations.

Time variable gravity field models derived from the satellite mission GRACE have been demonstrated to be consistent with water mass variations in the global hydrological cycle. Independent observations are provided by terrestrial measurements. In order to achieve a maximum of reliability and information gain, ground-based gravity observations may be deployed for comparison with the gravity field variations derived from the GRACE satellite mission. In this context, the data of the network of superconducting gravimeters (SG) of the 'Global Geodynamics Project' (GGP) are of particular interest. This study is focused on the dense SG network in Central Europe with its long-term gravity observations. It is shown that after the separation and reduction of local hydrological effects in the SG observations especially for subsurface stations, the time-variable gravity signals from GRACE agree well with the terrestrial observations from the SG station cluster.Station stability of the SG sites with respect to vertical deformations was checked by GNSS based observations. Most of the variability can be explained by loading effects due to changes in continental water storage, and, in general, the stability of all stations has been confirmed.From comparisons based on correlation and coherence analyses in combination with the root mean square (RMS) variability of the time series emerges, that the maximum correspondence between the SG and GRACE time series is achieved when filtering the GRACE data with Gaussian filters of about 1000 km filter length, which is in accordance with previous publications.Empirical Orthogonal Functions (EOF) analysis was applied to the gravity time series in order to identify common characteristic spatial and temporal patterns. The high correspondence of the first modes for GRACE and SG data implies that the first EOF mode represents a large-scale (Central European) time-variable gravity signal seen by both the GRACE satellites and the SG cluster. (C) 2011 Elsevier Ltd. All rights reserved.

Regional and global variations in soil moisture are commonly assessed by remote sensing and/or hydrological models. Less attention has been paid to the associated large-scale mass changes detectable with satellite gravimetry. Here we analyse variations in soil moisture from remote sensing (satellite sensor AMSR-E) and hydrological modelling (WaterGAP Global Hydrology Model) with respect to mass changes in continental water storage from space gravimetry (satellite mission GRACE). The study is performed for a test area in Central Asia, where mass change in soil moisture is the dominant contributor to the total water storage (TWS). The different data sets are compared with respect to their spatio-temporal characteristics via principal component analyses. Results show high correlation in the annual cycles of all data sets. The high level agreement between the spatial patterns of TWS changes from GRACE and soil moisture variations from AMSR-E suggests an important potential of satellite gravimetry in studies related to regional variations in soil moisture.

Temporal variations of surface water volume over inundated areas of the Lower Ob' Basin in Siberia, one of the largest contributor of freshwater to the Arctic Ocean, are estimated using combined observations from a multisatellite inundation dataset and water levels over rivers and floodplains derived from the TOPEX/POSEIDON (T/P) radar altimetry. We computed time-series of monthly maps of surface water volume over the common period of available T/P and multisatellite data (1993–2004). The results exhibit interannual variabilities similar to precipitation estimates and river discharge observations. This study also presents monthly estimates of groundwater and permafrost mass anomalies during 2003–2004 based on a synergistic analysis of multisatellite observations and hydrological models. Water stored in the soil is isolated from the total water storage measured by GRACE when removing the contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the snow estimated by inversion of GRACE measurements. The time variations of groundwater and permafrost are then obtained when removing the water content of the root zone reservoir simulated by hydrological models.

Abstract. The aim of this study is to provide an improved global simulation of continental water storage variations by calibrating the WaterGAP Global Hydrology Model (WGHM) for 28 of the largest river basins worldwide. Five years (January 2003–December 2007) of satellite-based estimates of the total water storage changes from the GRACE mission were combined with river discharge data in a multi-objective calibration framework that uses the most sensitive WGHM model parameters. The uncertainty and significance of the calibration results were analysed with respect to errors in the observation data. An independent simulation period (January 2008–December 2008) was used for validation. The contribution of single storage compartments to the total water budget before and after calibration was analysed in detail. A multi-objective improvement of the model states was obtained for most of the river basins, with mean error reductions of up to 110 km3/month for discharge and up to 24 mm of a water mass equivalent column for total water storage changes, such as for the Amazon basin. Errors in the phase and signal variability of seasonal water mass changes were reduced. The calibration is shown to primarily affect soil water storage in most river basins. The variability of groundwater storage variations was reduced on a global scale after calibration. Structural model errors were identified from a small contribution of surface water storage including wetlands in river basins with large inundation areas, such as the Amazon or the Mississippi. Our results demonstrate the value of both the GRACE data and the multi-objective calibration approach for improving large-scale hydrological simulations, and they provide a starting-point for improving model structures. The integration of complimentary observation data to further constrain the simulation of single storage compartments is encouraged.

Current soil erosion and reservoir sedimentation modelling at the meso-scale is still faced with intrinsic problems with regard to open scaling questions, data demand, computational efficiency and deficient implementations of retention and re-mobilisation processes for the river and reservoir networks. To overcome some limitations of current modelling approaches, the semi-process-based, spatially semi-distributed modelling framework WASA-SED (Vers. 1) was developed for water and sediment transport in large dryland catchments. The WASA-SED model simulates the runoff and erosion processes at the hillslope scale, the transport and retention processes of suspended and bedload fluxes in the river reaches and the retention and remobilisation processes of sediments in reservoirs. The modelling tool enables the evaluation of management options both for sustainable land-use change scenarios to reduce erosion in the headwater catchments as well as adequate reservoir management options to lessen sedimentation in large reservoirs and reservoir networks. The model concept, its spatial discretisation scheme and the numerical components of the hillslope, river and reservoir processes are described and a model application for the meso-scale dryland catchment Isábena in the Spanish Pre-Pyrenees (445 km2) is presented to demonstrate the capabilities, strengths and limits of the model framework. The example application showed that the model was able to reproduce runoff and sediment transport dynamics of highly erodible headwater badlands, the transient storage of sediments in the dryland river system, the bed elevation changes of the 93 hm3 Barasona reservoir due to sedimentation as well as the life expectancy of the reservoir under different management options.

The gravimetric time series achieved from the combination of superconducting and absolute gravimeters are characterized by highest precision and long-term stability. If the effects of Earth and ocean tides, atmosphere and polar motion are removed, the residual curve is dominated by hydrological mass variations. A major source of these variations is water storage changes in the vicinity of the sensor. However, global variations contribute to the signal significantly. For three stations of superconducting gravimeters, a comparison of the principal components obtained from the residual gravity curve on the one hand and continental water storage from the WaterGAP Global Hydrological Model (WGHM) on the other hand is carried out. The results demonstrate a coherence of seasonal variations but a difference in the contribution of the local zone at the individual stations, which point out the need for a careful and site-specific examination of local effects.

Approximately seven years of time-variable gravity data from the satellite mission Gravity Recovery and Climate Experiment (GRACE) are available to quantify present-day mass variations on and near the Earth's surface. Mass variations caused by the continental water cycle are the dominant signal component after subtracting contributions from atmosphere and oceans. This makes hydrology a primary area of application of GRACE data. To derive water storage variations at the scale of large river basins, appropriate filter techniques have to be applied to GRACE gravity fields given in a global spherical harmonic representation. A desirable filter technique minimises both GRACE data error and signal leakage across the border of the region of interest. This study evaluates the performance of six widely used filter methods (isotropic filters, anisotropic filters and decorrelation methods) and their parameter values to derive regionally averaged water mass variations from GRACE data. To this end, filtered time series from GRACE for 22 of the world's largest river basins were compared to continental water mass variations from a multimodel mean of three global hydrological models (WGHM, GLDAS and LaD). Filter-induced biases for seasonal amplitudes and phases of water storage variations, as well as satellite and leakage error budgets, were quantified for each river basin and explained in terms of storage variations in and around the basin. The optimum filter types and filter parameters were identified for each basin. The best results were provided by a decorrelation method that uses GRACE orbits for the filter design. Our ranking between all filter types and parameters depended on the geographical location, shape and signal characteristics of the specific river basin. Based on a multicriterial evaluation of satellite and leakage error, as well as an error assessment of the hydrological data, the filter selection and parameter optimisation results were shown to be reliable for 17 river basins. The results serve as a guideline for the optimal filtering of GRACE global spherical harmonic coefficients for hydrological applications.

For the period 2003–2004 and for six large river basins, the present study compares monthly time series of multi‐satellite‐derived surface water extent with other independent global data sets related to land water dynamics, such as water mass variations monitored by GRACE, simulated surface and total water storage from WGHM, water levels from altimetry, and GPCP precipitation estimates. In general, the datasets show a strong agreement with each other at seasonal timescale. In particular, over the Amazon and the Ganges basins, analysis of seasonal phase differences and hysteresis behavior between surface water extent, water level and storage reveal the complex relations between water extent and storage variations and the different effects of water transport processes within large river basins. The results highlight the value of combining multi‐satellite techniques for retrieving surface water storage dynamics.

In hydrological and soil erosion modelling at large spatial scales, semi‐distributed approaches may use representative hillslope profiles to reproduce landscape variability. Until now, the process of delineating landscape units as homogeneous parts of the landscape with regard to their terrain, vegetation, and soil properties required expert knowledge and familiarity with the study area. In addition, the delineation procedure was often highly time‐consuming and included a high degree of subjectivity. This paper presents a novel, semi‐automated approach for the delineation of landscape units, the derivation of representative toposequences, and their partitioning into terrain components. It incorporates an algorithm to retrieve representative catenas and their attributes for elementary hillslope areas based on elevation and other key spatial data frequently required as environmental model input, e.g. vegetation and soil data. An example application for the Ésera catchment in Spain illustrates that with the presented approach, upscaling of hillslope properties becomes feasible for environmental modelling at large scales while ensuring reproducible results.

We analyze spatiotemporal variations of surface mass anomalies induced by hydrological mass redistributions at the Earth's surface. To this end, we use a suite of global hydrological models as well as products from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. As a novelty we identify dominating periodic patterns that are not restricted to the fundamental annual frequency and its overtones, using a method that combines conventional empirical orthogonal functions (EOF) analysis with a determination of sine waves of arbitrary periods from the principal components. We assess the significance of the derived spectra in view of correlated errors of the GRACE data by means of a Monte Carlo technique. This allows us to create filtered GRACE time series including only the significant terms, which will serve for basin‐specific calibration of hydrological models with respect to the dominant periodic water storage variations. The study reveals that besides dominating annual signals, semiannual (found only in a few basins), and also long‐periodic waves in the range of 2.1 to 2.5 years contribute to periodic water storage variations. An interpretation and a preliminary explanation of these spectra is included. Comparisons of the spectra obtained from GRACE and global hydrological models exhibit in many river basins a systematic advance of the phases of annual terms of the hydrological models as compared to GRACE in the range of 1 to 6 weeks. This indicates deficiencies of the hydrological models with regard to runoff routing in the river network and/or water retention in lakes and wetlands.

The increase of surface runoff at the plot scale caused by soil water repellency is a generally accepted phenomenon. However, to improve the understanding of the effect of water repellency on runoff at the catchment scale, spatio-temporal dynamics of water repellency have to be analysed in more detail.The experimental setup of this study allowed the investigation of the relationship between water repellency and runoff generation on Quaternary and Tertiary sandy substrates while ensuring similar conditions in terms of terrain characteristics, meteorological and vegetation-free conditions on both areas. Measurements of water drop penetration time and contact angle were carried out over a period from September 2003 to December 2005. Spatial variability of actual soil water repellency was related to heterogeneity of substrate and geomorphologic units, variations in time were related with the seasons and their meteorological conditions. To relate variable degrees of actual water repellency to surface runoff generation, both variables were measured in parallel at the plot scale (I m x I m) and at the hillslope scale from September 2004 to December 2005. Soil water repellency of the Tertiary sands showed a temporal variability depending on the season, with the highest degree during summer and autumn. Variation of hydrophobicity between the seasons caused higher runoff coefficients in summer and autumn. Spatial heterogeneity of the soil water repellency revealed lower values in fine-textured erosion rills and higher values for interrills and top areas. The measured runoff coefficients decreased from the scale of microplots to the hillslope scale due to infiltration in hydrophilic rills on the hillslope.The results suggest that improved hydrological modelling approaches on water-repellent soils can be based on a geomorphological subdivision of the catchment area and seasonally varying infiltration parameters. Copyright (c) 2007 John Wiley & Sons, Ltd.