Gravity Recovery And Climate Experiment Research Articles

Inversion of Regional Groundwater Storage Changes Based on the Fusion of GNSS and GRACE Data: A Case Study of Shaanxi–Gansu–Ningxia

This paper aims to address the limitations of the distribution number and uniformity of Continuously Operating Reference Stations (CORS) and their impact on the reliability of inverting regional groundwater storage (GWS) based on Green’s function method and using global navigation satellite system (GNSS) data. A fusion method on the inversion of regional GWS changes from GNSS and the Gravity Recovery and Climate Experiment (GRACE) was proposed in this paper. Taking the Shaanxi–Gansu–Ningxia (SGN) region as an example, the in situ groundwater level data from ten CORS stations and eight wells were used for test analyses. In this paper, an atmospheric pressure model from the European Centre for Medium-Range Weather Forecasts (ECMWF), a global land data assimilation system (GLDAS), a WaterGAP global hydrology model (WGHM), and mean sea level anomaly (MSLA) data were used to quantitatively monitor the influence of vertical deformation caused by non-tidal environmental load. After deducing these loading deformations from the filtered time series of non-linear monthly geodetic height from the GNSS, the GWS changes in the SGN region from 2011 to 2014 were inverted. Meanwhile, the change in surface water storage from the GLDAS and WGHM models were removed from the terrestrial water storage (TWS) changes derived from GRACE. On this basis, the remove–restore theory in the Earth’s gravity field was introduced to both fuse the inversion results and obtain the regional GWS changes based on the fusion method. The results showed the following: (1) The local characteristics from the fusion results were more prominent than those of GRACE on the spatial scale, such as in the southwest and northeast in the study area. In addition, the fusion results were more uniform than those from GNSS, especially for the sparse and missing areas in which CORS stations were located, and the local effect was weakened. (2) On the time scale, compared with GRACE, the trends in GWS changes obtained from the fusion method and from GNSS inversion were roughly the same as the in situ groundwater level changes. (3) For the in situ groundwater wells “6105010031” and “6101260010”, the correlation coefficients of the fusion result were 0.53 and 0.56, respectively. The accuracy of the fusion method was slightly higher than that from GNSS, which indicates that the fusion method may be more effective for areas where CORS stations are missing or sparsely distributed. The methods in this paper can provide significant reference material for hydrodynamic research, sustainable management of water resources, and the dynamic maintenance of height data.

Joint inversion of GNSS and GRACE/GFO data for terrestrial water storage changes in the Yangtze River Basin

SUMMARYSatellite geodetic technologies, such as the Global Navigation Satellite System (GNSS), Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO), have complementary advantages in inferring terrestrial water storage (TWS) changes at regional and basin scales. We introduced a joint inversion strategy to infer TWS changes using GNSS- and GRACE/GFO-derived vertical displacements based on Green's function theory in the Yangtze River Basin (YRB) from January 2011 to December 2020. Additionally, we investigated the performance of variance component estimation (VCE) and Akaike's Bayesian Information Criterion (ABIC) to determine the optimal relative weights of different observation data. The performance of our joint inversion strategy was verified through a closed-loop simulation and multi-source hydrometeorological data [i.e. the time derivative of TWS changes (${\rm d}S/{\rm d}t$) from precipitation (P), evapotranspiration (ET) and run-off (R) based on the water balance equation, called P-ET-R]. The closed-loop simulation shows that the TWS changes from joint inversion have better consistencies with the synthetic signals than those of GNSS- and GRACE-only estimates, and the corresponding root mean square error (RMSE) decreased 1.43−6.28 mm and correlation coefficient (CC) increased 3−10 per cent. The ABIC was more suitable for the joint inversion of measured GRACE/GFO and GNSS data for TWS changes in the YRB. Analysis from the measured data shows that the spatial patterns and seasonal characteristics in TWS changes derived from GNSS, GRACE/GFO and their joint inversion are in good agreement in the YRB. The contribution of GNSS observations to the joint inversion in the upstream of the YRB is greater than that of GRACE/GFO due to the relatively densely distributed GNSS stations, but the opposite is true in the downstream. Furthermore, the joint inversion results have better agreements with P and P-ET-R compared to GNSS- and GRACE/GFO-only estimates in the upstream, and the corresponding CCs increased 5−7 per cent (for P) and 2−5 per cent (for P-ET-R), respectively, which further demonstrates the effectiveness of our joint inversion strategy. Our estimation strategy provides a new insight for joint inversion of GNSS and GRACE/GFO data to obtain more reliable TWS changes.

Inversion of terrestrial water storage changes from GNSS vertical displacements using a priori constraint: A case study of the Yunnan Province, China

Global Navigation Satellite System (GNSS) is an effective means to monitor surface deformations associated with changes in terrestrial water storage (TWS). In this study, we introduced a priori constraint matrix and estimated the regularization parameter through an iterative least-squares method to infer the TWS changes using GNSS vertical displacements from December 2010 to February 2021 in the Yunnan Province (YNP), China. The GNSS-inferred TWS changes were validated through the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) estimates, the Global Land Data Assimilation System (GLDAS) hydrological model, and the meteorological data (i.e., precipitation and Drought Severity Index (DSI)). The results demonstrate that the spatial resolution of TWS changes derived from 45 GNSS stations can reach 2°×2° in the YNP, and the a priori constraint is better than the traditional Laplacian matrix constraint for solving the discrete ill-conditioned problem of GNSS inversion. The GNSS-inferred TWS changes are consistent with the TWS changes derived from GRACE/GFO and GLDAS estimates in the spatio-temporal domains over the YNP, but the GNSS inversion results show stronger amplitudes. Furthermore, the ground-based GNSS observations are more sensitive to the TWS changes, and the correlation between precipitation and GNSS-inferred TWS changes is improved by about 11 % and 5 % compared to GRACE/GFO and GLDAS estimates in the YNP, respectively. The GNSS-inferred DSI can well reveal the two severe drought events in the YNP during the study period, which is consistent with the DSI derived from GRACE/GFO and GLDAS estimates, as well as the published self-calibrating Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). The GNSS observations can compensate the limitations of GRACE/GFO observations (e.g., bridging the data gap between GRACE and GFO missions), and help to better investigate the TWS changes in the YNP, which has significant potentials for regional water resource management and extreme climate changes monitoring.

Coseismic Gravity Changes and Crustal Deformation Induced by the 2018 Fiji Deep-Focus Earthquake Observed by GRACE and GRACE-FO Satellites

Earthquakes at depths of ≥300 km are generally called deep-focus earthquakes. Only two deep-focus earthquakes with Mw 8.0 or more have occurred in this century—the 2013 Okhotsk earthquake (Mw 8.3) and the 2018 Fiji earthquake (Mw 8.2) on 19 August 2018. However, the 2018 Fiji earthquake was only reported on seismographs, and the related crustal deformations were not observed by the Global Navigation Satellite System because the observation network did not exist around the epicenter. This study analyzed the time series of gravity data observed by the Gravity Recovery And Climate Experiment (GRACE) and its successor, GRACE Follow-On, and detected the spatial distribution of coseismic gravity changes mainly due to crustal deformation by the 2018 Fiji earthquake. The results in this study were not consistent with the numerical calculation of gravity changes when using the fault parameters estimated by the data of seismic waves. Thus, numerical calculations were used to construct a uniform slip rectangle fault model to explain coseismic gravity changes and provide a spatial distribution map of crustal deformation. However, this fault model is only based on gravity changes; thus, new research combining satellite gravimetry and seismic wave data will be necessary in the future.

Distinctive response of thermospheric cooling to ICME and CIR-driven geomagnetic storms

The temporal response of thermospheric CO2 and NO cooling emissions is investigated during ICME and CIR-driven geomagnetic storms by using data from the SABER instrument onboard the TIMED, GRACE, and DMSP satellites. The superposed epoch analysis reveals that the cooling emissions experience a strong enhancement and quick recovery to pre-event value within 3–4 days during CME storms. Whereas, it shows slower recovery that lasts for more than 6–7 days during CIR-driven storms. We performed detailed study of NO cooling emission owing to the fact that the production of NO depends on the external energy input. The different response of thermospheric NO cooling during CME and CIR storms can be attributed to differences in precipitation of particle (electron and ion) fluxes. A strong correlation with a positive timelag is observed between NO cooling emission and Dst index, coupling functions and particle flux. Further, the correlation between NO cooling flux and particle flux displays a distinct and stronger correlation during CIR storms as compared to CME. This study also shows that the Newell coupling function (normalized cross-correlation, r = 0.90 for CME and r = 0.92 for CIR) and the Akasofu parameter (r = 0.92 for CME, r = 0.76 for CIR) are better correlated with NO cooling flux, respectively, during CIR- and ICME-driven storms.

Spatiotemporal Analysis of Future Trends in Terrestrial Water Storage Anomalies at Different Climatic Zones of India Using GRACE/GRACE-FO

This work is a climatological evaluation of terrestrial water storage anomalies (TWSAs), which act as driving forces for sustainable development, in one of the most populous countries of the world. The objective of this work is to evaluate RL06 mascon data from the GRACE and GRACE-FO satellite missions over India to explore seasonal and interannual changes in terrestrial water storage, encompassing an area of ~3.29 million km2 with 285 grid points, from 2002 through to 2020. Several statistical tests are performed to check the homogeneity (i.e., Pettitt’s test, the BRT, the SNHT, and the VNRT). Most of the homogeneous data are found in winter, pre-monsoon, and post-monsoon, approximately above 42% to 47%, and the least are found in monsoons and annual with only 33%, at a 95% significance level. According to Pettitt’s test, the majority of the breakpoints are present in 2014 for winter, 2012 for pre-monsoon, 2011 for monsoons and post-monsoon, and 2008 as well as 2011 for annual. Furthermore, to detect trends and magnitudes we employed the nonparametric MK test, the MMK test, Sen’s slope estimator, and the parametric SLR test. According to the MK and MMK tests, the most significant negative and positive trends indicate the chances of droughts and floods, respectively. The Indo–Gangetic region shows the highest declination. According to Sen’s slope and the SLR test, the most declining magnitude is found in Delhi, Panjab, Uttrakhand, the northern part of Rajasthan, and Uttar Pradesh. Based on our findings, the average declining rate of yearly terrestrial water storage data from the MK, MMK, and SLR tests is −0.0075 m (−0.75 cm/year) from 2002 to 2020. Koppen–Geiger climate zones are also used to depict the seasonal and interannual descriptive statistics of TWSA trends. Interestingly, the annual means of arid desert cold (−0.1788 cm/year) and tropical savanna (−0.1936 cm/year) have the smallest declining trends when compared to other climatic zones. Northern Indian regions’ temperate dry winter, hot/warm summer, and dry arid steppe hot regions show the maximum declining future trend. This study could be useful in planning and managing water resources, agriculture, and the long-term growth of the country by using an intelligent water delivery system.

Predicting Short-Term Deformation in the Central Valley Using Machine Learning

Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers’ integrity.

Groundwater recharge estimation using in-situ and GRACE observations in the eastern region of the United Arab Emirates

The intensive agricultural expansion and rapid urban development in Abu Dhabi Emirate, United Arab Emirates (UAE) have resulted in a major decline in local and regional groundwater levels. By using the latest release (RL06) of Gravity Recovery and Climate Experiment (GRACE) satellite measurements and Global Land Data Assimilation System (GLDAS) products, the groundwater storage change was computed and compared with the time series of in-situ monitoring wells over the period of 2010–2016. The RL06 GRACE products from Jet Propulsion Laboratory (JPL), University of Texas Center for Space Research (CSR), German Research Center for Geosciences (GFZ), and JPL mass concentrations (MASCON) were assessed and have shown satisfactory agreements with the monitoring wells. The JPL MASCON reflected the in-situ groundwater storage change better than the other GRACE products (R = 0.5, lag =1 month, RMSE = 13 mm). The groundwater recharge is estimated for the study area and compared with the in-situ recharge method that considers multi recharge components from the rainfall, irrigation return flow and internal fluxes. The results show that the agreements between in-situ and GRACE-derived recharge estimates are highly agreeable (e.g., R2 = 0.91, RMSE = 1.5 Mm3 to 7.8 Mm3, and Nash-Sutcliff Efficiency = 0.7). Using the Mann-Kendall trend test and Sen's slope, the analyses of policies, number of wells, and farm areal expansion with groundwater time series derived from GRACE helped to validate GRACE and emphasize the importance of regulations for sustainable development of groundwater resources. The impacts of subsidy cuts after 2010 can be captured from the GRACE data in the eastern region of Abu Dhabi Emirate. The linear trend of groundwater storage anomaly obtained from GRACE over the period from 2003 to 2010 is −6.36 ± 0.6 mm/year while it showed a decline trend of −1.2 ± 0.6 mm/year after the subsidy cut. The proposed approach has a potential application for estimating groundwater recharge in other arid regions where in-situ monitoring wells are limited or absent.

GRACE and mass budget method reveal decelerated ice loss in east Greenland in the past decade

Excellent progress has been made in characterizing and understanding ice loss in Greenland, but most existing studies used a constant model to describe the long-term trend, preventing a detailed and rigorous understanding of change in the trend. Moreover, the impacts of atmospheric forcing on the trend of ice mass change are not clear. To address these limitations, we propose a piecewise trajectory model to detect the breaking points in the trend of ice mass change and estimate the optimal segmented trends. A breaking point located around 2012.6 was detected in the CW, SW, NE, SE, and the whole Greenland based on Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (FO) data and the mass budget method (MBM). Piecewise trend estimates from both methods suggest that the ice loss decelerated dramatically in east Greenland in 2012.6–2021 compared to 2002–2012.6. The GRACE results indicate that the mass loss rate decelerated by 70% (22.1 ± 0.8 to 6.6 ± 1.0 Gt/yr) in the NE and 38% (89.0 ± 1.5 to 55.4 ± 0.7 Gt/yr) in the SE. Though the rates slightly increased in some regions, the Greenland ice loss rate overall decreased from 269.9 ± 1.8 Gt/yr to 217.0 ± 3.3 Gt/yr. Mass budget analysis suggests that this deceleration was mainly caused by increased precipitation in east Greenland, which was associated with persistent positive North Atlantic Oscillation (NAO) phase in 2013–2021. During this positive NAO period, the enhanced westerlies and low-pressure anomaly off southeast Greenland advected more wet warm air to east Greenland and thus caused more precipitation. As the NAO exhibited a strong positive trend in this century, if it persists into the future, precipitation will have a high chance to increase in east Greenland, benefitting Greenland retaining ice.

Analysis of Groundwater Storage Fluctuations Using GRACE and Remote Sensing Data in Wadi As-Sirhan, Northern Saudi Arabia

Human activity has led to a rise in the demand for water, prompting Saudi Arabia to search for alternative groundwater supplies. Wadi As-Sirhan is one area that has experienced extensive agricultural growth and the severe over-exploitation of its groundwater resources. The groundwater drawn from the wadi should be continuously monitored to determine the best management options for groundwater resources and economic growth. The most recent Gravity Recovery and Climate Experiment (GRACE) mission and outputs of land surface models were combined to estimate the depletion rate of the groundwater of the Wadi As-Sirhan drainage basin in the northern region of Saudi Arabia throughout the period of April 2002–December 2021. The findings are: (1) the average GRACE-derived terrestrial water storage variation (ΔTWS) was calculated at −13.82 ± 0.24 mm/yr; (2) the soil moisture storage variation was averaged at +0.008 ± 0.004 mm/yr; (3) the GRACE-derived groundwater depletion rate was estimated at −13.81 ± 0.24 mm/yr; (4) the annual precipitation data over the Wadi As-Sirhan was averaged at 60 mm/yr; (5) The wadi has a minimal recharge rate of +2.31 ± 0.24 mm/yr, which may partially compensate for a portion of the groundwater withdrawal; (6) the sediment thickness shows an increase from 0 m at the southern igneous and volcanic rocks to more than 3000 m close to the Saudi–Jordanian border; (7) The wadi’s eastern, southern, and western portions are the sources of its tributaries, which ultimately drain into its northwestern portion; (8) change detection from the Landsat photos reveals considerable agricultural expansions over recent decades. The integrated method is useful for analyzing changes to groundwater resources in large groundwater reservoirs and developing environmentally appropriate management programs for these resources.

Effects of climate change and agricultural expansion on groundwater storage in the Amur River Basin

Since the 1990s, the climate in the Amur River Basin (ARB) has changed, and large-scale wetlands in the region have been reclaimed for paddy fields. The study of the influence of climate change and agricultural expansion on groundwater storage is of great significance to the evaluation of regional water resource balance and the promotion of ecological protection and agricultural development. In this work, the groundwater storage anomaly (GWSA) in the ARB and its drivers were analyzed for the period 2003–2016 using Gravity Recovery and Climate Experiment (GRACE) satellite data, a Global Land Data Assimilation System model, and in situ observations of groundwater levels. Results indicated that 1) the GWSA in the ARB increased at a rate of 2.0–2.4 mm/yr from 2003 to 2016; the GWSA in the upper reaches of the ARB increased, whereas the GWSA in the middle and lower reaches decreased during the study period. 2) The GWSA in the middle and lower reaches of the ARB was greatly influenced by temperature (Tmp) and evapotranspiration (ET). Tmp was positively correlated with GWSA, whereas ET was negatively correlated with GWSA (p < 0.05). 3) Extreme rainfall had a delayed effect on groundwater recharge. Wetland degradation and agricultural development were the main factors causing the decrease of the GWSA in the middle and lower reaches of the ARB. In summary, temperature and evapotranspiration affect groundwater storage by regulating the water–heat balance, wetland reclamation reduces the regional storage capacity, and the irrigation required for reclaimed farmland is the main source of groundwater loss.

Review of In-Situ and Remote Sensing-Based Indices and Their Applicability for Integrated Drought Monitoring in South Africa

The devastating socioeconomic impacts of recent droughts have intensified the need for improved drought monitoring in South Africa (SA). This study has shown that not all indices can be universally applicable at all regions worldwide, and there is no single index that can represent all aspects of droughts. The aim of this study was to review the performance and applicability of the Palmer drought severity index (PDSI), surface water supply index (SWSI), vegetation condition index (VCI), standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), standardised streamflow index (SSI), standardised groundwater index (SGI), and GRACE (Gravity Recovery and Climate Experiment)-based drought indices in SA and provide guidelines for selecting feasible candidates for integrated drought monitoring. The review is based on the ‘2016 World Meteorological Organisation (WMO) Handbook of Drought Indicators and Indices’ guidelines. The PDSI and SWSI are not feasible in SA, mainly because they are relatively complex to compute and interpret and cannot use readily available and accessible data. Combining the SPI, SPEI, VCI, SSI, and SGI using multi-index or hybrid methods is recommended. Hence, with best fitting probability distribution functions (PDFs) used, and with an informed choice between parametric and non-parametric approaches, this combination has the potential for integrated drought monitoring. Due to the scarcity of groundwater data, investigations on the use of GRACE-based groundwater drought indices must be carried out. These findings may contribute to improved drought early warning and monitoring in SA.

Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods

The Urmia lake in north-west Iran has dried up to perilously low levels in the past two decades. In this study, we investigate the drivers behind the decline in lake water level with the help of in-situ and remote sensing data. We use total water storage (TWS) changes from the gravity recovery and climate experiment (GRACE) satellite mission. TWS from GRACE includes all the water storage compartments in a column and is the only remote sensing product that can help in estimating groundwater storage (GWS) changes. The coarse spatial (approx. 300 km) resolution of GRACE does not allow us to identify local changes that may have led to the Urmia lake disaster. In this study, we tackle the poor resolution of the GRACE data by employing three machine learning (ML) methods including random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP). The methods predict the groundwater storage anomaly (GWSA), derived from GRACE, as a function of hydro-climatic variables such as precipitation, evapotranspiration, land surface temperature (LST) and normalized difference vegetation index (NDVI) on a finer scale of 0.25° × 0.25°. We found that i) The RF model exhibited highest R (0.98), highest NSE (0.96) and lowest RMSE (18.36 mm) values. ii) The RF downscaled data indicated that the exploitation of groundwater resources in the aquifers is the main driver of groundwater storage and changes in the regional ecosystem, which has been corroborated by few other studies as well. The impact of precipitation and evapotranspiration on the GWSA was found to be rather weak, indicating that the anthropogenic derivers had the most significant impact on the GWSA changes. iii) We generally observed a significant negative trend in GWSA, having also significant positive correlations with the well data. However, over regions with dam construction significant negative correlations were found.

Basin-scale terrestrial water storage changes inferred from GRACE-based geopotential differences: a case study of the Yangtze River Basin, China

SUMMARY The Gravity Recovery and Climate Experiment (GRACE) mass concentration (mascon) solutions provide enhanced signal and spatial resolution of surface mass changes by using regularization techniques to reduce striping errors. To further improve the computational efficiency and capture the same benefits as GRACE mascon solutions, we presented an estimation of regional mascon solutions from GRACE-based geopotential differences by using spatio-temporal constraints with the unconstrained spherical harmonic solutions as a priori information. As a case study, the changes in the basin-scale terrestrial water storage (TWS) over the Yangtze River Basin (YRB) on 2° × 2° grids at monthly intervals were estimated using GRACE-based geopotential differences, for the period of 2003 January—2013 December. The estimates were validated through official GRACE mascon solutions and in situ observations (i.e. time derivative of TWS change derived from precipitation, evapotranspiration and river run-off based on the water mass balance equation). The results demonstrate that the spatial and temporal patterns of TWS changes in the YRB inferred from geopotential differences adequately agree with the official mascon solutions; however, differences in amplitudes can be observed at the subbasin scale because of different regularizations applied in different solutions. In situ validations demonstrate that seasonal changes of mascon solutions and in situ observations agree well in the YRB; however, there are evident discrepancies in amplitudes over the subbasins owing to leakage biases in mascon solutions. For the entire YRB, the statistical evaluation and cross-wavelet transform demonstrate that our regional mascon solutions appear more consistent with in situ observations than the official mascon solutions. In addition, compared with the results estimated by spatial constraints, regional mascon solutions estimated by spatio-temporal constraints using observations from three consecutive months adjacent to the given month were improved. Our method provides an alternative option to use different regularization constraints, which is helpful for fine-tuning analysis of basin-scale TWS changes.

Dayside Upper‐Thermospheric Density Fluctuations as Observed by GRACE and GRACE‐FO at ∼500 km Height

AbstractWe statistically investigate fluctuation amplitudes (normalized to the background values) of dayside low‐/mid‐latitude upper‐thermospheric mass density as observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE‐Follow‐On (GRACE‐FO) spacecraft at ∼500 km altitude between 2002 and 2022. There are three new findings in our results. First, the climatology closely replicates previous studies on stratospheric and upper‐thermospheric gravity waves (GWs) below the GRACE(‐FO) altitudes. For example, in low‐latitude regions, the fluctuations are stronger above continents than in the oceanic area. Mid‐latitude fluctuations prefer the local winter hemisphere to the summer, and the South American/Atlantic region in June solstice hosts stronger fluctuations than in any other low‐/mid‐latitude locations or seasons. Fluctuations are more intense under lower solar activity. The above‐mentioned consistency of the GRACE(‐FO) results with previous lower‐altitude GW studies confirms that GWs can penetrate up to 500 km. Second, the anti‐correlation of upper‐thermospheric GW with solar activity, which has been earlier reported for multi‐year time scales, can also be identified on the scale of the solar rotation period (∼27 days). Third, we demonstrate asymmetry between pre‐noon and post‐noon GWs. The former exhibits stronger GW activity, which may result from the colder thermosphere being more favorable for intense mass density fluctuations via secondary/tertiary GW generation.

Filling the gap between GRACE and GRACE-FO data using a model integrating variational mode decomposition and long short-term memory: a case study of Northwest China

Terrestrial water storage anomalies (TWSA) play an important role in the regional and global hydrological cycle. The Gravity Recovery and Climate Experiment (GRACE) (from April 2002 to June 2017) and GRACE-Follow on (GRACE-FO) (from June 2018 to now) provide monthly global TWSA on a large scale, which contributes to the development of hydrology, but there is an 11-month data gap (i.e., from July 2017 to May 2018) between them due to the launch period. Therefore, based on the Center for Space Research (CSR) spherical harmonics (SH) and Mascons solution from April 2002 to June 2017, this study develops a method based on the Variational Mode Decomposition (VMD) with the Long Short-Term Memory (LSTM) model to fill the data gap of GRACE dataset and then tests over Northwest China (NWC). The result shows that integrated VMD–LSTM model achieve significantly higher prediction accuracy as a whole over NWC (NSE = 0.974, CC = 0.989, and RMSE = 0.808 cm) and exhibit higher reliability compared to the single LSTM models. The proposed method outperforms compared to previous similar studies. The current study offers an accurate and effective means of bridging the gap in GRACE-based TWSA data, especially in poor dataset regions like NWC, which may contribute to further understanding of the hydrological cycle and water resources management.

GRACE time-variable gravity and its application to geoscience: Quantitative analysis of relevant literature

The Gravity Recovery and Climate Experiment (GRACE) is the most important gravity satellite to date in human history. Since its launch in 2002, GRACE time-varying gravity has had an unprecedented impact on earth science and has generated revolutionary changes. Because of natural phenomena such as climate warming, glacial melting, sea level rise, and earthquakes, earth science research has become an increasingly popular discipline in recent years. This article summarizes the importance of GRACE time-varying gravity, its application to geoscience, and its development. We analyzed the historical development and current status of GRACE time-varying gravity as well as research hotspots by searching the literature in the core collection databases of the China National Knowledge Infrastructure and the Web of Science over the past 20 years. The CiteSpace and VOSviewer software packages were applied with reference to the principle of literature metrology. Our investigation and analysis of characteristic indexes, such as the numbers of publications, co-occurrence of keywords, and co-citation of documents, uncovered the wide application and promotion of gravity satellites, especially GRACE time-varying gravity, in earth science. The results showed that the number of publications addressing GRACE data and time-varying gravity theory is increasing annually and that the USA, China, and Germany are the main producers. The Chinese Academy of Sciences, the National Aeronautics and Space Administration of the United States, and the Helmholtz Association of German Research Centres rank among the top three institutions in the world in terms of producing the most publications on this topic. We found that GRACE time-varying gravity plays unique roles in measuring changes in terrestrial water storage changes, ice and snow melting and sea level changes, and (co)seismic gravity changes, as well as in promoting other disciplines.

Interplay of Altitude, Ground Track Coverage, Noise, and Regularization in the Spatial Resolution of GRACE Gravity Field Models

AbstractModels of the temporal gravity field derived from space gravity missions are typically produced with monthly temporal resolution and ∼300‐km spatial resolution. However, variations in instrument performance and altitude of the Gravity Recovery and Climate Experiment (GRACE) mission impact the spatial resolution that can be achieved month‐to‐month. As the altitude of the orbits of the twin spacecraft vary throughout the mission, so does the ability of the observations to recover certain components of the temporal gravity field. The spatial resolution of GRACE observations should increase as the altitude decreases throughout the mission because the reduced altitude intensifies the gravity signals acting on the satellites. Simulations using actual GRACE altitude and ground track coverage and realistic noise levels confirm this predicted influence of the altitude of the satellites on the accuracy of the estimated solutions. Solutions with larger mass concentration elements (mascons) are more numerically stable as the satellite altitude decreases but they suffer from greater error caused by the inability to properly represent spatial variations of signals within mascons, referred to as intramascon variability. Mascons as small as ∼150 × 150 km (i.e., ∼1.5 arc‐degree) reduce the intramascon variability and, with appropriate regularization, yield the most accurate solutions, especially during the low‐altitude periods of the GRACE mission. Importantly, unlike spherical harmonic solutions, regularized mascon solutions are not degraded during resonant orbit months, and are of comparable quality to months with full ground track coverage.

Spatiotemporal correlation and multivariate analysis between vegetation health, terrestrial water storage and precipitation

Vegetation health is an essential indicator in the global hydrologic cycle as it is interrelated with the hydrological components. In tropical areas where vegetation dominates, analysing their correlation at a regional scale helps forecast the hydrologic cycle and understand vegetation’s response to climate change. However, the interactions between vegetation, terrestrial water storage and climate factors such as precipitation remain poorly understood in this region. Therefore, using Landsat and Gravity Recovery and Climate Experiment (GRACE) remote sensing and observed precipitation data, this study analysed the spatiotemporal correlation of Normalized Difference Vegetation Index (NDVI), Terrestrial Water Storage Anomaly (TWSA) and precipitation for the whole Peninsular Malaysia. The correlation coefficient (R) was used to assess the temporal variability of NDVI with TWSA and precipitation separately. Furthermore, a Geographically Weighted Regression (GWR) model was constructed to evaluate the spatial non-stationarity and heterogeneity relationships between the multi variables. The findings revealed complex interactions between the variables, where the strength of the correlations varied depending on the localised region and study period. The results suggest that downscaled GRACE-derived TWSA data would be helpful for detailed vegetation modelling and water resources management.

Comparison of GRACE/GRACE-FO Spherical Harmonic and Mascon Products in Interpreting GNSS Vertical Loading Deformations over the Amazon Basin

We compute the vertical displacements in the Amazon Basin using the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) observations, including both the gravity spherical harmonic (SH) solutions from the Center for Space Research (CSR), GeoForschungsZentrum (GFZ) and Jet Propulsion Laboratory (JPL) and mascons from CSR, JPL and Goddard Space Flight Center (GSFC). The correlation coefficients, annual amplitude and root mean squares (RMS) reductions are calculated to assess the agreements between the GRACE/GRACE-FO and Global Navigation Satellite System (GNSS) vertical displacements at 22 selected GNSS stations. For the six GRACE/GRACE-FO products (i.e., CSR SH, GFZ SH, JPL SH, CSR mascon, GSFC mascon and JPL mascon), the mean annual amplitude reductions are 77.6%, 76.4%, 76.3%, 78.6%, 78.5% and 76.6%, respectively, the corresponding mean RMS reductions are 63.2%, 61.7%, 62.3%, 64.9%, 65.3% and 63.8%, respectively, and the mean correlation coefficients are all over 0.93. On the whole, mascon solutions agree slightly better with GNSS solutions than SH solutions do. The CSR SH and the GSFC mascon solutions show the best agreements with the GNSS solution among the 3 SH and 3 mascon products, respectively. We estimate GRACE/GRACE-FO noises using the three-cornered hat (TCH) method and find that the CSR SH and GSFC mascons also have the smallest noise variances among the SH and mascon products, respectively. By analyzing the GNSS stations from the central and southern Amazon Basin, we find that: (1) the RMS reductions when the mascon solutions are removed from GNSS height series are slightly larger than those using the SH solutions in the center, while in south all the RMS reductions are fairly close; (2) for both SH solutions and mascon solutions, the correlation coefficients in the center are slightly larger than those in the south, but conversely, the mean annual amplitude reductions in the center are much smaller than those in the south.