[1] We detect the acceleration of ice mass depletion in Greenland for the period 1991–2011 from the low-degree gravity field up to degree and order 4 derived from the Satellite Laser Ranging (SLR) data. Between 2003 and 2011, during the era when the GRACE (Gravity Recovery and Climate Experiment) satellite data are available, our SLR results of gravity changes agree well with GRACE showing significant negative patterns in Greenland. Prior to that, the SLR linear trend maps show a near balance in Greenland ice mass during 1991–2002 (after the glacial isostatic adjustment is accounted for using model values). We further confirm the consistency of our SLR results with the vertical crustal uplift of the region observed by the Global Positining System (GPS) which manifests ice mass loading/unloading. Thus, the SLR data series constitute a continuous benchmark for the time history of the Greenland ice mass changes for over two decades.

[1] We describe Earth's mass flux from April 2003 through November 2008 by deriving a time series of mascons on a global 2° × 2° equal-area grid at 10 day intervals. We estimate the mass flux directly from K band range rate (KBRR) data provided by the Gravity Recovery and Climate Experiment (GRACE) mission. Using regularized least squares, we take into account the underlying process dynamics through continuous space and time-correlated constraints. In addition, we place the mascon approach in the context of other filtering techniques, showing its equivalence to anisotropic, nonsymmetric filtering, least squares collocation, and Kalman smoothing. We produce mascon time series from KBRR data that have and have not been corrected (forward modeled) for hydrological processes and find that the former produce superior results in oceanic areas by minimizing signal leakage from strong sources on land. By exploiting the structure of the spatiotemporal constraints, we are able to use a much more efficient (in storage and computation) inversion algorithm based upon the conjugate gradient method. This allows us to apply continuous rather than piecewise continuous time-correlated constraints, which we show via global maps and comparisons with ocean-bottom pressure gauges, to produce time series with reduced random variance and full systematic signal. Finally, we present a preferred global model, a hybrid whose oceanic portions are derived using forward modeling of hydrology but whose land portions are not, and thus represent a pure GRACE-derived signal.

[1] 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.

[1] The differences between mass concentration (mascon) parameters and standard Stokes coefficient parameters in the recovery of gravity information from gravity recovery and climate experiment (GRACE) intersatellite K-band range rate data are investigated. First, mascons are decomposed into their Stokes coefficient representations to gauge the range of solutions available using each of the two types of parameters. Next, a direct comparison is made between two time series of unconstrained gravity solutions, one based on a set of global equal area mascon parameters (equivalent to 4° × 4° at the equator), and the other based on standard Stokes coefficients with each time series using the same fundamental processing of the GRACE tracking data. It is shown that in unconstrained solutions, the type of gravity parameter being estimated does not qualitatively affect the estimated gravity field. It is also shown that many of the differences in mass flux derivations from GRACE gravity solutions arise from the type of smoothing being used and that the type of smoothing that can be embedded in mascon solutions has distinct advantages over postsolution smoothing. Finally, a 1 year time series based on global 2° equal area mascons estimated every 10 days is presented.

[1] The Community Land Model version 3 (CLM3) is the land component of the Community Climate System Model (CCSM). CLM3 has energy and water biases resulting from deficiencies in some of its canopy and soil parameterizations related to hydrological processes. Recent research by the community that utilizes CLM3 and the family of CCSM models has indicated several promising approaches to alleviating these biases. This paper describes the implementation of a selected set of these parameterizations and their effects on the simulated hydrological cycle. The modifications consist of surface data sets based on Moderate Resolution Imaging Spectroradiometer products, new parameterizations for canopy integration, canopy interception, frozen soil, soil water availability, and soil evaporation, a TOPMODEL-based model for surface and subsurface runoff, a groundwater model for determining water table depth, and the introduction of a factor to simulate nitrogen limitation on plant productivity. The results from a set of offline simulations were compared with observed data for runoff, river discharge, soil moisture, and total water storage to assess the performance of the new model (referred to as CLM3.5). CLM3.5 exhibits significant improvements in its partitioning of global evapotranspiration (ET) which result in wetter soils, less plant water stress, increased transpiration and photosynthesis, and an improved annual cycle of total water storage. Phase and amplitude of the runoff annual cycle is generally improved. Dramatic improvements in vegetation biogeography result when CLM3.5 is coupled to a dynamic global vegetation model. Lower than observed soil moisture variability in the rooting zone is noted as a remaining deficiency.

[1] High-degree and high-order spherical harmonics of time-variable gravity fields observed by the Gravity Recovery and Climate Experiment (GRACE) gravity mission are dominated by noise. We develop two smoothing methods that suppress these high-degree and high-order errors with results superior to more commonly used Gaussian smoothing. These optimized smoothing methods considerably improve signal-to-noise levels of GRACE terrestrial water storage estimates relative to residual signal and noise over the oceans and show significantly better spatial resolution and lower leakage error. On the basis of analysis using an advanced land surface model, the equivalent spatial resolution from these optimized smoothing estimates is about 500 km, compared to the roughly 800–1000 km Gaussian smoothing that is required to suppress high-degree noise in the GRACE fields.

[1] We show that spherical harmonic (SH) solutions of the Gravity Recovery and Climate Experiment (GRACE) are now of sufficient quality to observe effects of co-seismic and post-seismic deformation due to the rupture from the Mw = 9.3 Sumatra-Andaman earthquake on December 26, 2004, and its companion Nias earthquake (Mw = 8.7) on March 28, 2005. The improved GGM 03 SH (Level 2) solutions, and improved filtering methods provide estimates with spatial resolution comparable to earlier estimates from range-rate (Level 1) GRACE data.The gravityfield disturbance extends over 1800 km along Andaman and Sunda subduction zones, and changes with time following events. Gravity changes may be due to afterslip, viscoelastic relaxation, or other processes associated with dilatation. Satellite gravity measurements from GRACE provide a unique new measure of deformation and post-seismic processes associated with major earthquakes, especially in areas which are primarily oceanic. Citation: Chen, J. L., C. R. Wilson, B. D. Tapley, and S. Grand (2007), GRACE detects coseismic and postseismic deformation from the SumatraAndaman earthquake, Geophys. Res. Lett., 34, L13302,

[1] The Gravity Recovery and Climate Experiment (GRACE) satellite gravity mission provides a new capability for measuring extreme climate events, such as floods and droughts associated with large‐scale terrestrial water storage (TWS) change. GRACE gravity measurements show significant TWS increases in the lower Amazon basin in the first half of 2009, clearly associated with the exceptional flood season in that region. The extended record of GRACE monthly gravity solutions reveals the temporal and spatial evolution of both nonseasonal and interannual TWS change in the Amazon basin over the 7 year mission period from April 2002 to August 2009. GRACE observes a very dry season in 2002–2003 and an extremely wet season in 2009. In March 2009 (approximately the peak of the recent Amazon flood), total TWS surplus in the entire Amazon basin is ∼624 ± 32 Gt, roughly equal to U.S. water consumption for a year. GRACE measurements are consistent with precipitation data. Interannual TWS changes in the Amazon basin are closely connected to ENSO events in the tropical Pacific. The 2002–2003 dry season is clearly tied to the 2002–2003 El Nino and the 2009 flood to the recent La Nina event. The most significant contribution of this study in the area of water resources is to confront the hydrological community with the latest results of the GRACE satellite mission and further demonstrates the unique strength of GRACE and follow‐up satellite gravity observations for measuring large‐scale extreme climate events.

[1] In this study, we estimate a time series of geocenter anomalies from a combination of data from the GravityRecoveryandClimateExperiment(GRACE)satellitemissionandthe outputfromoceanmodels.Amatrixequationisderivedrelating totalgeocentervariations to the GRACE coefficients of degrees two and higher and to the oceanic component of the degree one coefficients. We estimate the oceanic component from two state-of-the-art ocean models. Results are compared to independent estimates of geocenter derived from other satellite data, such as satellite laser ranging and GPS. Finally, we compute degree one coefficients that are consistent with the processing applied to the GRACE Level-2 gravity field coefficients. The estimated degree one coefficients can be used to improve estimates of mass variability from GRACE, which alone cannot provide them directly.

Zunyi Xie, Alfredo Huete, Xuanlong Ma, Natalia Restrepo-Coupe, Rakhesh Devadas, Kenneth Clarke, Megan Lewis

With the warming of the global climate, the mass loss of the Greenland ice sheet is intensifying, having a profound impact on the rising of the global sea level. Here, we used Gravity Recovery and Climate Experiment (GRACE) RL06 data to retrieve the time series variations of ice sheet mass in Greenland from January 2003 to December 2015. Meanwhile, the spatial changes of ice sheet mass and its relationship with land surface temperature are studied by means of Theil–Sen median trend analysis, the Mann–Kendall (MK) test, empirical orthogonal function (EOF) analysis, and wavelet transform analysis. The results showed: (1) in terms of time, we found that the total mass of ice sheet decreases steadily at a speed of −195 ± 21 Gt/yr and an acceleration of −11 ± 2 Gt/yr2 from 2003 to 2015. This mass loss was relatively stable in the two years after 2012, and then continued a decreasing trend; (2) in terms of space, the mass loss areas of the Greenland ice sheet mainly concentrates in the southeastern, southwestern, and northwestern regions, and the southeastern region mass losses have a maximum rate of more than 27 cm/yr (equivalent water height), while the northeastern region show a minimum rate of less than 3 cm/yr, showing significant changes as a whole. In addition, using spatial distribution and the time coefficients of the first two models obtained by EOF decomposition, ice sheet quality in the southeastern and northwestern regions of Greenland show different significant changes in different periods from 2003 to 2015, while the other regions showed relatively stable changes; (3) in terms of driving factors temperature, there is an anti-phase relationship between ice sheet mass change and land surface temperature by the mean XWT-based semblance value of −0.34 in a significant oscillation period variation of 12 months. Meanwhile, XWT-based semblance values have the largest relative change in 2005 and 2012, and the smallest relative change in 2009 and 2010, indicating that the influence of land surface temperature on ice sheet mass significantly varies in different years.

With evidence of an accelerated water cycle over the past few decades, we make inferences on the spatial variability of interannual evaporation and precipitation patterns from 2003 to 2014 gravity anomalies, using the Gravity Recovery and Climate Experiment (GRACE) satellite mascon data set. Comparison of the mascon solution with an ensemble harmonic solution is conducted, along with validation over the oceans via sea surface height from multimission altimetry minus Argo floats data/GECCO2 [the GECCO2 ocean synthesis is the German contribution to Estimating the Circulation and Climate of the Ocean project (www.ecco-group.org)] steric sea level. The mascon solution was consistently more accurate than its spherical harmonic counterpart across large spatial and temporal scales, due mainly to the inherent smoothing from the mascon cells. Comparison of GRACE with both GECCO2 + altimetry and Argo + altimetry mass estimates revealed an offset in phase with regard to the annual cycle, but yielded an rmse of only 5.6 mm in the interannual signal after phase correction. This paper furthers evidence of an accelerated water cycle at a rate of 1.5% ± 1.1% at low latitudes, and provides a means of validation for oceanic freshwater budget studies based on salinity measurements.

We present an improved mascon approach to transform monthly spherical harmonic solutions based on GRACE satellite data into mass anomaly estimates in Greenland. The GRACE-based spherical harmonic coefficients are used to synthesize gravity anomalies at satellite altitude, which are then inverted into mass anomalies per mascon. The limited spectral content of the gravity anomalies is properly accounted for by applying a low-pass filter as part of the inversion procedure to make the functional model spectrally consistent with the data. The full error covariance matrices of the monthly GRACE solutions are properly propagated using the law of covariance propagation. Using numerical experiments, we demonstrate the importance of a proper data weighting and of the spectral consistency between functional model and data. The developed methodology is applied to process real GRACE level-2 data (CSR RL05). The obtained mass anomaly estimates are integrated over five drainage systems, as well as over entire Greenland. We find that the statistically optimal data weighting reduces random noise by 35–69%, depending on the drainage system. The obtained mass anomaly time-series are de-trended to eliminate the contribution of ice discharge and are compared with de-trended surface mass balance (SMB) time-series computed with the Regional Atmospheric Climate Model (RACMO 2.3). We show that when using a statistically optimal data weighting in GRACE data processing, the discrepancies between GRACE-based estimates of SMB and modelled SMB are reduced by 24–47%.

We analyse spatio-temporal 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 w.r.t. runoff routing in the river network and/or water retention in lakes and wetlands. GeoForschungsZentrum, Potsdam, Germany Copyright 2008 by the American Geophysical Union. 0148-0227/08/$9.00

This work was supported by DOE (DE-SC0006773), NASA (NNX13AK82A), and NSF (AGS-0944101). L. R. Leung was supported by the DOE Office of Science Biological and Environmental Research Earth System Modeling program. Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. We thank the Jet Propulsion Laboratory for providing the GRACE data, which were processed by Sean Swenson under support from the NASA MEaSUREs Program. High-performance computing support was provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation, through computing time on Yellowstone (http://n2t.net/ark:/85065/d7wd3xhc) and on The University of Arizona Research Computing

This study explores the feasibility of an entirely satellite remote sensing (RS)-based hydrologic budget model for a ground data-constrained basin, the Rufiji basin in Tanzania, from the balance of runoff (Q), precipitation (P), storage change (ΔS), and evapotranspiration (ET). P was determined from the Tropical Rainfall Measuring Mission, ΔS from the Gravity Recovery and Climate Experiment, and ET from the Moderate Resolution Imaging Spectroradiometer, the surface radiation budget, and the Atmosphere Infrared Radiation Sounder. Q was estimated as a residual of the water balance and tested against measured Q for a sub-basin of the Rufiji (the Usangu basin) where ground measurements were available (R2 = 0.58, slope = 1.9, root mean square error = 29 mm/month, bias = 14%). We also tested a geographical information system (GIS)-driven (ArcCN-runoff) runoff model (R2 = 0.64, slope = 0.43, root mean square error = 39 mm/month). We conducted an error propagation analysis from each of the model's hydrologic components (P, ET, and ΔS). We find that the RS-based model amplitude is most sensitive to ET and slightly less so to P, whereas the model's seasonal trends are most sensitive to ∆S. Although RS–GIS-driven models are becoming increasingly used, our results indicate that long-term water resource assessment policy and management may be more appropriate than ‘instantaneous’ or short-term water resource assessment. However, our analyses help develop a series of tools and techniques to progress our understanding of RS–GIS in water resource management of data-constrained basins at the level of a water resource manager. Copyright © 2012 John Wiley & Sons, Ltd.

The terrestrial water storage changes (TWSC) for global scale as well as area scale can be estimated by GRACE (Gravity Recovery And Climate Experiment) mission, while hydrologic and climate model named GLDAS (Global Land Data Assimilation System) provide variations of soil moisture and snows which can also derive the TWSC. These new technologies provide new methods to monitor global terrestrial water storage variations and climate change. The global monthly TWSC have been derived from GRACE observations and GLDAS model estimates respectively, it shows there have high There have high correlations between TWSC derived from GRACE observations and GLDAS model estimates, while due to lack of information from GLDAS models, TWSC derived from GRACE observations is more reliable. The monthly mean TWSC from April 2002 to August 2011 have been derived from GRACE observations, and there has a steady condition of the global TWSC with a slowly growing rapid of 0.142±0.0028cm/year in this period. while the change range of Northern Hemisphere of TWSC is more obvious than those of Southern Hemisphere. Due to the unbalance of regional rainfall, the TWSC in the middle latitude regions are changing more significantly than those in high-and-low latitude regions. It shows great correlations between global TWSC and global land surface temperature with correlation coefficient of 0.91, while the change of land surface temperature always occurred about two months later than the TWSC, and both of them have obvious change with annual period.

The satellite mission GRACE (Gravity Recovery and Climate Experiment) provides monthly solutions of the global gravity field of the Earth. Temporal variations in gravity reflect mass redistributions over the Earth. Those mass movements are caused by physical phenomena such as ice sheet dynamics, accumulation of hydrological masses over land, the dynamics in the solid Earth and the dynamics in the atmosphere and ocean. This thesis work focuses on the validation of the oceanic mass variations derived from GRACE. The goal of the project is to compare the oceanic estimates from the space-borne instrument with independent data from pressure recorders (BPRs), deployed on the bottom of the deep ocean in the southern ocean. Although those datasets seem to be of a completely different nature they measure the same quantity. Namely, the variations of mass in the atmosphere and ocean, which in their turn influence the dynamics of the ocean. In this study the GRACE data is compared to the following two quantities: - Local bottom pressure at the BPR stations - Pairwise differences of local bottom pressure (a measure of the oceanic transport) The processing methodology applied to the BPR records is as follows: The raw BPR data is de-tided using a harmonic analysis method. The data is then averaged (over a month) to make them equivalent to the GRACE solutions. GRACE data from three different computing centers1 are compared. The spherical harmonic coefficients are converted to equivalent water height and geostrophic flow, indicators of the variations in the ocean and atmosphere. The solution is filtered to remove correlated errors, after which the de-aliasing models are added back to the solutions. The final solution is obtained by applying a spatial averaging filter. The bottom pressure comparison showed good agreements at locations in the southern Indian ocean, the south Atlantic and the Argentine basin. The best correlations obtained values over 0.8 and typical errors of below 1.5 cm. The analysis showed an apparent connection between phenomena in the Argentine basin and near Crozet. A two monthly mode appeared to be present in both areas. The capabilities of GRACE to detect this mode is marginal and further investigation is required. The comparison for the geostrophic flow (pressure differences) showed a significant agreement in the south Indian ocean, with correlations over 0.7 and errors as low as 7 mm/s. Correlations in other areas were weak or even non existent. We conclude that, for dedicated areas, GRACE shows the ability to measure realistic large scale variations in bottom pressure and geostrophic flow on spatial scales yet barely explored.

The presence of ice in soil dramatically alters soil hydrologic and thermal properties. Despite this important role, many recent studies show that explicitly including the hydrologic effects of soil ice in land surface models degrades the simulation of runoff in cold regions. This paper addresses this dilemma by employing the Community Land Model version 2.0 (CLM2.0) developed at the National Center for Atmospheric Research (NCAR) and a simple TOPMODEL-based runoff scheme (SIMTOP). CLM2.0/ SIMTOP explicitly computes soil ice content and its modifications to soil hydrologic and thermal properties. However, the frozen soil scheme has a tendency to produce a completely frozen soil (100% ice content) whenever the soil temperature is below 0°C. The frozen ground prevents infiltration of snowmelt or rainfall, thereby resulting in earlier- and higher-than-observed springtime runoff. This paper presents modifications to the above-mentioned frozen soil scheme that produce more accurate magnitude and seasonality of runoff and soil water storage. These modifications include 1) allowing liquid water to coexist with ice in the soil over a wide range of temperatures below 0°C by using the freezing-point depression equation, 2) computing the vertical water fluxes by introducing the concept of a fractional permeable area, which partitions the model grid into an impermeable part (no vertical water flow) and a permeable part, and 3) using the total soil moisture (liquid water and ice) to calculate the soil matric potential and hydraulic conductivity. The performance of CLM2.0/SIMTOP with these changes has been tested using observed data in cold-region river basins of various spatial scales. Compared to the CLM2.0/SIMTOP frozen soil scheme, the modified scheme produces monthly runoff that compares more favorably with that estimated by the University of New Hampshire–Global Runoff Data Center and a terrestrial water storage change that is in closer agreement with that measured by the Gravity Recovery and Climate Experiment (GRACE) satellites.

The essence of this paper is to undertake an investigation of the recent Canadian Prairie drought by employing total water storage anomalies obtained from gravity recovery and climate experiment (GRACE) remote sensing satellite mission. In order to successfully retrieve average terrestrial water storages from gravity measurements, a necessary procedure is to undertake the transformation of the GRACE geopotential spherical harmonic coefficients into spatially varying time series of geopotential heights that were subsequently converted into water equivalent amounts. These obtained GRACE-based total water storages were thereafter validated using storages estimated from the atmospheric-based water balance P - E computation in conjunction with the measured streamflow records for the Saskatchewan River Basin at its Grand Rapids outlet in Canada. Interestingly, the results from this study corroborate the potential of GRACE-based technique as a veritable tool for the characterization of the 2002/2003 Canadian Prairie droughts. Especially, this approach would prove resourceful for other regions globally where soil moisture availability is sparing or worst still, inexistent thereby making such studies impossible.