Gravity Recovery And Climate Experiment Mass Research Articles

Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States

The Gravity Recovery And Climate Experiment (GRACE) and GRACE-FollowOn (GRACE(−FO)) satellites have been monitoring Earth’s changes in terrestrial water storage (TWS) or surficial mass changes at monthly sampling and a spatial scale longer than ∼330 km (half wavelength) over the past two decades. At monthly sampling or revisit time, the use of satellite gravimetry is difficult to effectively monitor abrupt extreme weather events which are high-frequency, including the climate-induced hurricanes/cyclones, flash floods and droughts. The majority of the contemporary studies have focused on satellite gravimetry spatial downscaling, and not on reducing the temporal resolution of Earth’s mass change. Here, we developed a Deep Learning (DL) algorithm to downscale monthly GRACE/GRACE(−FO) Mass Concentration (Mascon) TWS anomaly (TWSA) solutions to daily sampling over the Contiguous United States (CONUS), with the aim of monitoring rapidly evolving natural hazard episodes. The simulative performance of the DL algorithm is validated by comparing the modeling to an independent observation and the land hydrology model (LHM) predicted TWSA. To confirm that our daily and monthly simulations captured the climatic variations, we first compared our simulations with El Niño/La Niña Southern Oscillation (ENSO) circulation system index, which has a dominant climatological and socioeconomic impact across CONUS, and results reveal high correlations which are statistically significant. Next, we assessed the feasibilities to detect long- and short-term variations in the TWSA signals triggered by hydrological extremes, including the 2011 and 2019 Missouri River Floods, the August 2017 Atlantic Hurricane Harvey landfalls in Texas, the 2012–2017 drought in California, and the flash drought in the Northern Great Plains in 2017. Additional validation results using independent in situ observations reveal that our DL-aided gravimetry downscaled daily simulations are capable of elucidating hazards and water cycle evolutions at high temporal resolution.

Spatio-temporal evolution and teleconnection factor analysis of groundwater drought based on the GRACE mascon model in the Yellow River Basin

Gravity Recovery and Climate Experiment (GRACE) gravity satellite can provide data for large-scale drought assessment, making up for the disadvantage that the traditional groundwater drought monitoring methods cannot accurately obtain groundwater drought information. In this study, the GRACE mass concentration (mascon) model was adopted for exploring the multi-timescale groundwater drought variations. Taking the Yellow River Basin (YRB) as the study area, the spatiotemporal evolution law and trend of groundwater drought during 2003–2020 were comprehensively investigated by the model; the relations between groundwater drought and teleconnection factors were disclosed by the cross wavelet transform technology, in which the analysis highlighted four key findings: (1) the data obtained by GRACE were verified to be credible, contributing to the evaluation of groundwater drought; (2) the standardized groundwater drought index (SGDI) was –0.65 in 2006, indicating the serious groundwater drought appeared in that year; (3) the groundwater droughts in both monthly and seasonal scales were increasing during 2003–2020; and (4) the influence of teleconnection factors, especially the El Nino-Southern Oscillation (ENSO), cannot be ignored in formation of groundwater drought in the YRB. The evaluation of groundwater drought variations was realized by GRACE mascon model, providing a technical reference for similar studies in other watersheds.

Using GRACE to Detect Groundwater Variation in North China Plain after South-North Water Diversion.

The gravity recovery and climate experiment (GRACE) and its Follow-On mission provide a versatile tool for monitoring groundwater depletion in North China Plain (NCP). However, intermittent data gaps and inherent coarse spatial resolution have restricted the continuous detection of regional groundwater storage anomaly (GWSA) after 2014, the period of interest during the implementation of the south-to-north water diversion middle route project (SNWDP). Here, we investigated the spatiotemporal changes of GWSA in the NCP during 2004 to 2020 based on continuous downscaled GRACE data. First, we derived the continuous terrestrial water storage anomaly from six GRACE and Follow-On solutions (i.e., spherical harmonics (SH) and mass concentration [mascon] solutions). Second, we employed a long short-term memory (LSTM) model and water balance equation to downscale GWSA (i.e., 0.25° × 0.25°). Lastly, we investigated its spatiotemporal characteristics before (2004 to 2014) and after (2015 to 2020) the SNWDP operation. We show the applicability of the continuous downscaled GWSA to capture the characteristics of in situ measurements. The GWSA detects groundwater depletion at a significant (p < 0.05) rate of -17.09 ± 1.80 (SH) and -17.87 ± 1.65 (mascon) mm/a during 2004 to 2014, but a recovering trend of 7.18 ± 3.98 (SH) and 8.23 ± 4.99 (mascon) during 2015 to 2018. The subsequent groundwater extraction and precipitation reduction from 2019 to 2020, resulted in the decreasing trend of GWSA from 2015 to 2020, which is -19.11 ± 8.75 (SH) and -19.72 ± 9.08 mm/a (mascon), respectively. Spatially, the overall depletion trends become nonsignificant along the canals of SNWDP compared to the period 2004 to 2014, and groundwater recovering with trends <6 mm/a near Beijing and Tianjin are detected by the mascon solution during 2015 to 2020.

Hydro-Geophysical Evaluation of the Regional Variability of Senegal’s Terrestrial Water Storage Using Time-Variable Gravity Data

The Gravity Recovery and Climate Experiment (GRACE) satellite data retrieval experiment has been instrumental in characterizing the global fluctuations in terrestrial water storage (ΔTWS) over the past 20 years. Given the limited availability of hydrological data, GRACE measurements are frequently combined with other climatic models, standardized precipitation index (SPI), and standardized temperature index (STI) data to examine the likelihood of such impacts on hydrology and calculate the groundwater storage changes (ΔGWS). The characterization of the intensity and variability of drought events has been identified based on the Terrestrial Water Storage Deficit Index (TWSI), derived from GRACE mass concentration blocks (mascons) over Senegal during the studied period (April 2002–December 2021). The results are: (1) The average annual precipitation (AAP) rate for the entire period was calculated at 692.5 mm/yr. (2) The GRACE-derived ΔTWS variations were calculated at +0.89 ± 0.34, +0.07 ± 0.36, +1.66 ± 1.20, and +0.63 ± 0.08 cm/yr for Periods I (April 2002–December 2009), II (January 2010–December 2017), III (January 2018–December 2021), and the entire period (April 2002–December 2021), respectively. (3) The ΔGWS changes were estimated to be +0.89 ± 0.31, +0.085 ± 0.33, +1.64 ± 1.11, and +0.63 ± 0.08 cm/yr for Periods I, II, II, and the entire period, respectively. (4) There is good agreement in some years and seasons according to the investigation of the link between the GRACE dataset, STI, and SPI. (5) Senegal’s groundwater storage is increasing at a rate of 0.63 ± 0.08 cm/yr (1.24 ± 0.16 km3/yr) between April 2002 and December 2021. (6) Considering the yearly extraction rates of 1.13 ± 0.11 cm/yr (2.22 ± 0.22 km3/yr), an average recharge rate of +1.76 ± 0.14 cm/yr (+3.46 ± 0.28 km3/yr) was calculated for the studied area. The integrated strategy is instructive and economical.

Identifying the status of groundwater drought from a GRACE mascon model perspective across China during 2003–2018

The traditional groundwater drought monitoring method relies on the ground observation data, which is difficult to reflect the large-scale groundwater drought information. Thus, Gravity Recovery and Climate Experiment (GRACE) gravity satellite technology is applied to estimate large-scale groundwater drought condition, which can provide a new data source for drought investigation. However, the dynamic variations of GRACE-based groundwater drought were still unclear across China. In this study, the evolutions of groundwater drought were investigated from a high-resolution GRACE mass concentration (mascon) model perspective. The spatio-temporal variations and gridded trend characteristics of groundwater drought were comprehensively identified across China during 2003–2018. Subsequently, the relationships between groundwater and meteorological drought were quantitatively revealed. The results indicated that: (1) the verification results of GRACE were credible and reliable for groundwater drought evaluation; (2) the most serious groundwater drought occurred in April 2011, with an average groundwater drought index (GDI) value of –0.86; (3) the gridded drought trend characteristic showed that groundwater drought presented an upward trend during 2003–2018; and (4) the propagation dynamics from meteorological to groundwater drought were shorter in summer (5–6 months), and longer in winter (14–15 months) across China. This study sheds new viewpoints into groundwater drought variations from a perspective of high-resolution GRACE mascon model, which can also be applied in other areas.

Improving the Accuracy of Water Storage Anomaly Trends Based on a New Statistical Correction Hydrological Model Weighting Method

The Gravity Recovery and Climate Experiment (GRACE) satellite solutions have been considerably applied to assess the reliability of hydrological models on a global scale. However, no single hydrological model can be suitable for all regions. Here, a New Statistical Correction Hydrological Model Weighting (NSCHMW) method is developed based on the root mean square error and correlation coefficient between hydrological models and GRACE mass concentration (mascon) data. The NSCHMW method can highlight the advantages of good models compared with the previous average method. Additionally, to verify the effect of the NSCHMW method, taking the Haihe River Basin (HRB) as an example, the spatiotemporal patterns of Terrestrial Water Storage Anomalies (TWSA) in HRB are analyzed through a comprehensive comparison of decadal trends (2003–2014) from GRACE and different hydrological models (Noah from GLDAS-2.1, VIC from GLDAS-2.1, CLSM from GLDAS-2.1, CLSM from GLDAS-2.0, WGHM, PCR-GLOBWB, and CLM-4.5). Besides, the NSCHMW method is applied to estimate TWSA trends in the HRB. Results demonstrate that (1) the NSCHMW method can improve the accuracy of TWSA estimation by hydrological models; (2) the TWSA trends continue to decrease through the study period at a rate of 15.7 mm/year; (3) the WGHM and PCR-GLOBWB have positive reliability with respect to GRACE with r &gt; 0.9, while all the other models underestimate TWSA trends; (4) the NSCHMW method can effectively improve RMSE, NES, and r with 3–96%, 35–282%, 1–255%, respectively, by weighting the WGHM and PCR-GLOBWB. Indeed, groundwater depletion in HRB also proves the necessity of the South-North Water Diversion Project, which has already contributed to groundwater recovery.

Evaluation of the temporal variations of groundwater storage and its interactions with climatic variables using GRACE data and hydrological models: A study from Turkey

AbstractMonitoring of the fluctuations of groundwater storage is particularly important in arid and semi‐arid regions where water scarcity brings about various challenges. Remote sensing data and techniques play a preponderant role in developing solutions to environmental problems. The launch of Gravity Recovery and Climate Experiment (GRACE) satellites has eased the remote monitoring and evaluation of groundwater resources with an unprecedented precision over large scales. Within the scope of the current study, the latest release (RL06) of GRACE mass concentrations (Mascons) from Jet Propulsion Laboratory (JPL) dataset as well as Global Land Data Assimilation System (GLDAS) models of Noah and Catchment Land Surface Model (CLSM) were used to provide Groundwater Storage Anomalies (GWSA) over Turkey. The temporal interactions of the estimated GWSA with the climatic variables of precipitation and temperature (derived from the reanalysis datasets of CHELSA [Climatologies at High resolution for the Earth's Land Surface Areas] and FLDAS [the Famine Early Warning Systems Network Land Data Assimilation System], respectively) were investigated statistically. The results suggest that there is a descending trend (from 2003 to 2016) for Terrestrial Water Storage Anomalies (TWSA) and GWSA over Turkey with a total loss of 11 and 6 cm of water, respectively. The statistical analysis results also indicate that the monthly variations of GWSA over Turkey are highly correlated with precipitation and temperature at 2‐month lag. The analysis of the climatology (long‐term) values of monthly GWSA, precipitation and temperature also revealed high agreement between the variables.

Attributing Terrestrial Water Storage Variations across China to Changes in Groundwater and Human Water Use

AbstractThis study investigated the attribution of terrestrial water storage (TWS) variations across China to changes in groundwater and human water use. As one vital storage component, the groundwater storage (GWS) derived from the Jet Propulsion Laboratory’s GRACE (Gravity Recovery and Climate Experiment) mass concentration solution compared reasonably well with the in situ groundwater table depth, with the correlation coefficients ranging from −0.83 to −0.18, all of which were statistically significant at the 95% confidence level. About 71% of the trends in derived GWS had the same sign as those of observations, without systematic deviation, across China. The GWS variation contributed a large portion of the TWS trend in most regions of China, and the majority of contribution values reached 50%–150% in the Hai River basin, the Loess Plateau, and the middle portion of the Yangtze River basin. The dominant role of GWS is closely related to the detected long-term “memories” in both TWS and GWS. The increase of irrigation consumption accelerated the TWS depletion trend by 13.4% in the Huai River basin, while the decrease of consumptive agricultural water use alleviated the TWS decline rate by 4.1% in the Hai River basin. Importantly, the correlation coefficients reached 0.74–0.95 between the TWS change and the residual of precipitation, evapotranspiration, flow into the sea, and irrigation consumption in the four river basins of particular interest. The findings of this study are helpful for understanding regional water cycles in China.

Spatio-Temporal Evaluation of Water Storage Trends from Hydrological Models over Australia Using GRACE Mascon Solutions

The Gravity Recovery and Climate Experiment (GRACE) data have been extensively used to evaluate the total terrestrial water storage anomalies (TWSA) from hydrological models. However, which individual water storage components (i.e., soil moisture storage anomalies (SMSA) or groundwater water storage anomalies (GWSA)) cause the discrepancies in TWSA between GRACE and hydrological models have not been thoroughly investigated or quantified. In this study, we applied GRACE mass concentration block (mascon) solutions to evaluate the spatio-temporal TWSA trends (2003–2014) from seven prevailing hydrological models (i.e., Noah-3.6, Catchment Land Surface Model (CLSM-F2.5), Variable Infiltration Capacity macroscale model (VIC-4.1.2), Water—Global Assessment and Prognosis (WaterGAP-2.2d), PCRaster Global Water Balance (PCR-GLOBWB-2), Community Land Model (CLM-4.5), and Australian Water Resources Assessment Landscape model (AWRA-L v6)) in Australia and, more importantly, identified which individual water storage components lead to the differences in TWSA trends between GRACE and hydrological models. The results showed that all of the hydrological models employed in this study, except for CLM-4.5 model, underestimated the GRACE-derived TWSA trends. These underestimations can be divided into three categories: (1) ignoring GWSA, e.g., Noah-3.6 and VIC-4.1.2 models; (2) underrating both SMSA and GWSA, e.g., CLSM-F2.5, WaterGAP-2.2d, and PCR-GLOBWB-2 models; (3) deficiently modeling GWSA, e.g., AWRA-L v6 model. In comparison, CLM-4.5 model yielded the best agreement with GRACE but overstated the GRACE-derived TWSA trends due to the overestimation of GWSA. Our results underscore that GRACE mascon solutions can be used as a valuable and efficient validation dataset to evaluate the spatio-temporal performance of hydrological models. Confirming which individual water storage components result in the discrepancies in TWSA between GRACE and hydrological models can better assist in further hydrological model development.

Introducing an Improved GRACE Global Point-Mass Solution—A Case Study in Antarctica

In the so-called point-mass modeling, surface densities are represented by point masses, providing only an approximated solution of the surface integral for the gravitational potential. Here, we propose a refinement for the point-mass modeling based on Taylor series expansion in which the zeroth-order approximation is equivalent to the point-mass solution. Simulations show that adding higher-order terms neglected in the point-mass modeling reduces the error of inverted mass changes of up to 90% on global and Antarctica scales. The method provides an alternative to the processing of the Level-2 data from the Gravity Recovery and Climate Experiment (GRACE) mission. While the evaluation of the surface densities based on improved point-mass modeling using ITSG-Grace2018 Level-2 data as observations reveals noise level of approximately 5.77 mm, this figure is 5.02, 6.05, and 5.81 mm for Center for Space Research (CSR), Goddard Space Flight Center (GSFC), and Jet Propulsion Laboratory (JPL) mascon solutions, respectively. Statistical tests demonstrate that the four solutions are not significant different (95% confidence) over Antarctica Ice Sheet (AIS), despite the slight differences seen in the noises. Therefore, the estimated noise level for the four solutions indicates the quality of GRACE mass changes over AIS. Overall, AIS shows a mass loss of −7.58 mm/year during 2003–2015 based on the improved point-mass solution, which agrees with the values derived from mascon solutions.

Mass Balance of the Greenland Ice Sheet from GRACE and Surface Mass Balance Modelling

The Greenland Ice Sheet (GrIS) is losing mass at a rate that represents a major contribution to global sea-level rise in recent decades. In this study, we use the Gravity Recovery and Climate Experiment (GRACE) data to retrieve the time series variations of the GrIS from April 2002 to June 2017. We also estimate the mass balance from the RACMO2.3 and ice discharge data in order to obtain a comparative analysis and cross-validation. A detailed analysis of long-term trend and seasonal and inter-annual changes in the GrIS is implemented by GRACE and surface mass balance (SMB) modeling. The results indicate a decrease of −267.77 ± 8.68 Gt/yr of the GrIS over the 16-year period. There is a rapid decline from 2002 to 2008, which accelerated from 2009 to 2012 before declining relatively slowly from 2013 to 2017. The mass change inland is significantly smaller than that detected along coastal regions, especially in the southeastern, southwestern, and northwestern regions. The mass balance estimates from GRACE and SMB minus ice discharge (SMB-D) are very consistent. The ice discharge manifests itself mostly as a long-term trend, whereas seasonal mass variations are largely attributed to surface mass processes. The GrIS mass changes are mostly attributed to mass loss during summer. Summer mass changes are highly correlated with climate changes.

Reciprocal comparison of geodetically sensed and modeled vertical hydrological loading products

The Global Positioning System (GPS) permanent stations at the equatorial and southern sub-tropical hydrobelts of South America undergo the highest seasonality on the Earth due to hydrological loadings. Fortunately, there are products that account for such variations, although some of them have not been properly evaluated. For instance, global solutions of Gravity Recovery and Climate Experiment (GRACE) are band-limited to lower frequencies; therefore, comparisons with GPS data must account for such spectral inconsistencies. It is proposed to spatially average 39 GPS sites by applying Gaussian smoothing, which allows comparisons with long-wavelength part of GRACE solutions by Center for Space Research (CSR), GeoForschungszentrum, and Jet Propulsion Laboratory. Comparisons are also carried out with loadings from Noah-driven Global Land Data Assimilation System (GLDAS) and GRACE mass concentration (mascon) solution by Goddard Space Flight Center. Results show that CSR best reduces the variances of the radial displacements considering both spatially filtered (70%) and unfiltered (53%) GPS data covering the period from Jan 2010 to Dec 2015. However, GLDAS-Noah underestimates the amplitudes of vertical loadings, which might be due to unmodeled inland water and groundwater storages. While acknowledging that a denser distribution of GPS stations is needed, the findings still shed light on the quality of the global hydrological loading products based on GRACE and GLDAS datasets, which might be of interest to the respective science teams.

Evaluating GRACE Mass Change Time Series for the Antarctic and Greenland Ice Sheet—Methods and Results

Satellite gravimetry data acquired by the Gravity Recovery and Climate Experiment (GRACE) allows to derive the temporal evolution in ice mass for both the Antarctic Ice Sheet (AIS) and the Greenland Ice Sheet (GIS). Various algorithms have been used in a wide range of studies to generate Gravimetric Mass Balance (GMB) products. Results from different studies may be affected by substantial differences in the processing, including the applied algorithm, the utilised background models and the time period under consideration. This study gives a detailed description of an assessment of the performance of GMB algorithms using actual GRACE monthly solutions for a prescribed period as well as synthetic data sets. The inter-comparison exercise was conducted in the scope of the European Space Agency’s Climate Change Initiative (CCI) project for the AIS and GIS, and was, for the first time, open to everyone. GMB products generated by different groups could be evaluated and directly compared against each other. For the period from 2003-02 to 2013-12, estimated linear trends in ice mass vary between −99 Gt/yr and −108 Gt/yr for the AIS and between −252 Gt/yr and −274 Gt/yr for the GIS, respectively. The spread between the solutions is larger if smaller drainage basins or gridded GMB products are considered. Finally, findings from the exercise formed the basis to select the algorithms used for the GMB product generation within the AIS and GIS CCI project.

Long-term and inter-annual mass changes of Patagonia Ice Field from GRACE

Using more than 14 years of GRACE (Gravity Recovery and Climate Experiment) satellite gravimetry observations, we estimate the ice loss rate for the Patagonia Ice Field (PIF) of South America. After correcting the effects of glacier isostatic adjustment (GIA) and hydrological variations, the ice loss rate is −23.5 ± 8.1 Giga ton per year (Gt/yr) during the period April 2002 through December 2016, equivalent to an average ice thickness change of −1.3 m/yr if evenly distributed over PIF. The PIF ice mass change series also show obvious inter-annual variations during the entire period. For the time spans April 2002 to December 2007, January 2008 to December 2012 and January 2013 to December 2016, the ice loss rates are −26.4, −9.0 and −25.0 Gt/yr, respectively, indicating that the ice melting experienced significant slowing down and accelerating again in the past decade. Comparison with time series from temperature and precipitation data over PIF suggests that the inter-annual ice losses might not be directly correlated with the temperature changes and precipitation anomalies, and thus their interrelation is intricate. However, the dramatic ice loss acceleration in 2016 (with more than 100 Gt within the first half of the year) appears closely related with the evident temperature increase and severe precipitation shortage over 2016, which are likely correlated with the strong El Niño event around 2016. Moreover, we compare the GRACE spherical harmonic (SH) and mass concentration (Mascon) solutions in estimating the PIF ice loss rate, and find that the Mascon result has larger uncertainty in leakage error correction, while the SH solutions can better correct leakage errors based on a constrained forward modeling iterative method. Thus the GRACE SH solutions with constrained forward modeling recovery are recommended to evaluating the ice mass change of PIF or other glacier regions with relatively smaller spatial scales.

Improved GRACE regional mass balance estimates of the Greenland ice sheet cross-validated with the input–output method

Abstract. In this study, we use satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) to estimate regional mass change of the Greenland ice sheet (GrIS) and neighboring glaciated regions using a least squares inversion approach. We also consider results from the input–output method (IOM). The IOM quantifies the difference between the mass input and output of the GrIS by studying the surface mass balance (SMB) and the ice discharge (D). We use the Regional Atmospheric Climate Model version 2.3 (RACMO2.3) to model the SMB and derive the ice discharge from 12 years of high-precision ice velocity and thickness surveys. We use a simulation model to quantify and correct for GRACE approximation errors in mass change between different subregions of the GrIS, and investigate the reliability of pre-1990s ice discharge estimates, which are based on the modeled runoff. We find that the difference between the IOM and our improved GRACE mass change estimates is reduced in terms of the long-term mass change when using a reference discharge derived from runoff estimates in several subareas. In most regions our GRACE and IOM solutions are consistent with other studies, but differences remain in the northwestern GrIS. We validate the GRACE mass balance in that region by considering several different GIA models and mass change estimates derived from data obtained by the Ice, Cloud and land Elevation Satellite (ICESat). We conclude that the approximated mass balance between GRACE and IOM is consistent in most GrIS regions. The difference in the northwest is likely due to underestimated uncertainties in the IOM solutions.

Implications of Groundwater Depletion for Aquifer Geomatrix Deformation and Water Availability

Worsening water storage depletion contributes to environmental degradation, land subsidence and earthquake or even disrupts food production/security and social stability. There is also the need for efficient water use strategies in populated regions, especially when such regions also have intensive agricultural and industrial activities. The North China Plain (NCP) is one such region which is not only the seat of power, but also a major agricultural and industrial with a severe water storage depletion. Thus this study integrates satellite, model and field data products to investigate water storage depletion and land subsidence in the Beijing Environs of NCP. In the first step, GRACE (Gravity Recovery and Climate Experiment) mass rates are analyzed for water storage depletion in the region. Next, GRACE total water storage (TWS) is corrected for soil moisture storage (SMS) to derive groundwater storage (GWS) using GLDAS (Global Land Data Assimilation System) data products. The derived GWS is compared with GWS obtained from field-measured groundwater level to show water storage depletion in the study area. Then GPS (Global Positioning System) data of relative land surface change are used to show land subsidence due to water storage depletion. A total of ~96 near-consecutive months (Jan. 2002 through Dec. 2009) of datasets are used in the study. Based on GRACE mass rates, TWS depletion is 36.54±1.74 mm yr^-1 or 6.34±0.29 km³ yr^-1 for the 169 000 km² study area. Analysis of relative land surface change shows the occurrence of land subsidence at 7.29±0.35 mm yr^-1 in the Beijing Environ of NCP. About 7.50% (2.74±0.18 mm yr^-1 or 0.46±0.03 km³ yr^-1) of the depletion in TWS and 5.25% (1.52±0.07 mm yr^-1 or 0.26±0.01 km³ yr^-1) of that in GWS are attributed to storage reductions due to the land subsidence. Storage loss in the region justifies the current south-north water transfer efforts in the region. The concurrence of water storage depletion and land subsidence could have adverse implications for the hydrology, ecology, food security and social stability of the region. It is important to devise efficient measures to avert the negative effects of water storage depletion in the study area.

Effect of GIA models with 3D composite mantle viscosity on GRACE mass balance estimates for Antarctica

Seismic data indicate that there are large viscosity variations in the mantle beneath Antarctica. Consideration of such variations would affect predictions of models of Glacial Isostatic Adjustment (GIA), which are used to correct satellite measurements of ice mass change. However, most GIA models used for that purpose have assumed the mantle to be uniformly stratified in terms of viscosity. The goal of this study is to estimate the effect of lateral variations in viscosity on Antarctic mass balance estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data. To this end, recently-developed global GIA models based on lateral variations in mantle temperature are tuned to fit constraints in the northern hemisphere and then compared to GPS-derived uplift rates in Antarctica.We find that these models can provide a better fit to GPS uplift rates in Antarctica than existing GIA models with a radially-varying (1D) rheology. When 3D viscosity models in combination with specific ice loading histories are used to correct GRACE measurements, mass loss in Antarctica is smaller than previously found for the same ice loading histories and their preferred 1D viscosity profiles. The variation in mass balance estimates arising from using different plausible realizations of 3D viscosity amounts to 20 Gt/yr for the ICE-5G ice model and 16 Gt/yr for the W12a ice model; these values are larger than the GRACE measurement error, but smaller than the variation arising from unknown ice history. While there exist 1D Earth models that can reproduce the total mass balance estimates derived using 3D Earth models, the spatial pattern of gravity rates can be significantly affected by 3D viscosity in a way that cannot be reproduced by GIA models with 1D viscosity. As an example, models with 1D viscosity always predict maximum gravity rates in the Ross Sea for the ICE-5G ice model, however, for one of the three preferred 3D models the maximum (for the same ice model) is found near the Weddell Sea. This demonstrates that 3D variations in viscosity affect the sensitivity of present-day uplift and gravity rates to changes in the timing of the ice history. In particular, low viscosities (<1019 Pas) found in West Antarctica make the mantle very sensitive to recent changes in ice thickness.

Assessment of the capabilities of the temporal and spatiotemporal ICA method for geophysical signal separation in GRACE data

AbstractWe investigate the potential of two independent component analysis (ICA) methods, i.e., the temporal and spatiotemporal ICA, for separating geophysical signals in Gravity Recovery and Climate Experiment (GRACE) data. These methods are based on the assumption of the statistical independence of the signals and thus separate the GRACE‐observed mass changes into maximal independent signals. These two ICA methods are compared to the conventional principal component analysis (PCA) method. We test the three methods with respect to their ability to separate a periodic hydrological signal from a trend signal originating in the solid Earth or the cryosphere with simulated and Center for Space Research GRACE mass changes for the time period of January 2003 to December 2010. In addition, we investigate whether the methods are capable of separating hydrological annual and semiannual mass variations. It is shown that both ICA methods are superior to PCA when non‐Gaussian mass variations are analyzed. Furthermore, the spatiotemporal ICA resolves successfully the lack of full temporal and spatial independence of the geophysical signals observed by GRACE both in global and regional simulation scenarios. Although the temporal and spatiotemporal ICA are nearly equivalent, both superior to PCA in the global GRACE analysis, the spatiotemporal ICA proves to be more efficient in regional applications by recover more reliably the postglacial rebound trend in North America and the bimodal total water storage variability in Africa.

Late Quaternary glacial history constrains glacio-isostatic rebound in Enderby Land, East Antarctica

Measurements of the loss or gain of ice mass from large ice sheets are presently achieved through satellite-based techniques such as GRACE (Gravity Recovery and Climate Experiment). The accuracy of these satellite-based measurements to changes in modern ice sheet mass depends on our knowledge of present-day glacio-isostatic crustal uplift rates caused by past ice sheet changes. To improve models of glacio-isostatic rebound in East Antarctica, we investigated ice histories along Rayner Glacier, Enderby Land, and a little explored sector of the ice sheet where GRACE data had suggested significant mass gain during the last decade. Observations from a recent glacial geomorphic reconnaissance coupled with cosmogenic nuclide dating indicate that in the lower part of the Rayner Glacier, Enderby Land, ice heights lowered by at least 300 m and the calving margin retreated by at least 10 km in the early Holocene (~6 to 9 ka B.P.). The magnitude and timing of deglaciation are consistent with ice histories used to model the postglacial rebound corrections for present-day GRACE mass trends. These observations strengthen the body of evidence that suggests ice mass gain in Enderby Land is presently partly offsetting mass loss in other parts of Antarctica.

Evaluating Greenland glacial isostatic adjustment corrections using GRACE, altimetry and surface mass balance data

Glacial isostatic adjustment (GIA) represents a source of uncertainty for ice sheet mass balance estimates from the Gravity Recovery and Climate Experiment (GRACE) time-variable gravity measurements. We evaluate Greenland GIA corrections from Simpson et al (2009 Quat. Sci. Rev. 28 1631–57), A et al (2013 Geophys. J. Int. 192 557–72) and Wu et al (2010 Nature Geosci. 3 642–6) by comparing the spatial patterns of GRACE-derived ice mass trends calculated using the three corrections with volume changes from ICESat (Ice, Cloud, and land Elevation Satellite) and OIB (Operation IceBridge) altimetry missions, and surface mass balance products from the Regional Atmospheric Climate Model (RACMO). During the period September 2003–August 2011, GRACE ice mass changes obtained using the Simpson et al (2009 Quat. Sci. Rev. 28 1631–57) and A et al (2013 Geophys. J. Int. 192 557–72) GIA corrections yield similar spatial patterns and amplitudes, and are consistent with altimetry observations and surface mass balance data. The two GRACE estimates agree within 2% on average over the entire ice sheet, and better than 15% in four subdivisions of Greenland. The third GRACE estimate corrected using the (Wu et al 2010 Nature Geosci. 3 642–6)) GIA shows similar spatial patterns, but produces an average ice mass loss for the entire ice sheet that is 64 − 67 Gt yr−1 smaller. In the Northeast the recovered ice mass change is 46–49 Gt yr−1 (245–270%) more positive than that deduced from the other two corrections. By comparing the spatial and temporal variability of the GRACE estimates with trends of volume changes from altimetry and surface mass balance from RACMO, we show that the Wu et al (2010 Nature Geosci. 3 642–6) correction leads to a large mass increase in the Northeast that is inconsistent with independent observations.