Gravity Recovery And Climate Experiment Observations Research Articles

Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations

Over the past two decades, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor, GRACE-follow on (GRACE-FO), have played a vital role in climate research. However, the absence of certain observations during and between these missions has presented a persistent challenge. Despite numerous studies attempting to address this issue with mathematical and statistical methods, no definitive optimal approach has been established. This study introduces a practical solution using Linear Regression Analysis (LRA) to overcome data gaps in both GRACE data types—mascon and spherical harmonic coefficients (SHCs). The proposed methodology is tailored to monsoon patterns and demonstrates efficacy in filling data gaps. To validate the approach, a global analysis was conducted across eight basins, monitoring changes in total water storage (TWS) using the technique. The results were compared with various geodetic products, including data from the Swarm mission, Institute of Geodesy and Geoinformation (IGG), Quantum Frontiers (QF), and Singular Spectrum Analysis (SSA) coefficients. Artificial data gaps were introduced within GRACE observations for further validation. This research highlights the effectiveness of the monsoon method in comparison to other gap-filling approaches, showing a strong similarity between gap-filling results and GRACE’s SHCs, with an absolute relative error approaching zero. In the mascon approach, the coefficient of determination (R2) exceeded 91% for all months. This study offers a readily usable gap-filling product—SHCs and smoothed gridded observations—with accurate error estimates. These resources are now accessible for a wide range of applications, providing a valuable tool for the scientific community.

Optimizing Parameters in the Common Land Model by Using Gravity Recovery and Climate Experiment Satellite Observations

Terrestrial water storage (TWS) is pivotal in understanding environmental dynamics, climate change, and human impacts. Despite the utility of land surface models, uncertainties persist in their parameterization schemes. This study employs GRACE (Gravity Recovery and Climate Experiment) satellite data to optimize the runoff parameterization scheme within the Common Land Model by a data assimilation and parameter optimization method. The optimization algorithm sets an adjustment factor that varies with time and space for runoff simulation and updates it along with the running of the land surface model. The evaluation reveals that there are improved correlation coefficients and reduced root mean square errors compared to GRACE observations. Independent assessments by using in situ river discharge observations demonstrate enhanced model performance, particularly in mountainous regions such as western North America. This study underscores the efficacy of integrating GRACE data to improve land surface model parameterization, offering more accurate predictions of TWS changes.

Drought susceptibility mapping in Iraq using GRACE/GRACE-FO, GLDAS, and machine learning algorithms

Drought susceptibility mapping is crucial for mitigating water scarcity and its cascading effects. This study utilizes machine learning (ML) and Gravity Recovery and Climate Experiment (GRACE) data to identify drought-prone areas in Iraq. Terrestrial water storage anomaly (TWSA) data derived from GRACE observations was utilized, in conjunction with various meteorological and topographical factors (temperature, precipitation, wind speed, potential evapotranspiration, aridity index, normalized difference vegetation index, groundwater storage, root zone moisture, elevation, and slope) sourced from diverse global datasets, primarily those comprising the Global Land Data Assimilation System (GLDAS). These combined datasets served as input for five machine learning models to identify the drought prone area of Iraq: linear discriminant analysis, classification and regression trees, support vector machine, random forest, and k-nearest neighbor. All models exhibited exceptional performance (>0.9 accuracy, >0.8 kappa values, >0.9 areas under the receiver operating characteristic curve) highlighting the effectiveness of the combined TWSA and multi-factor approach. The analysis identified five drought susceptibility zones across Iraq, ranging from very low to very high. An area of approximately 160,650 km2, primarily concentrated in the northeastern and Mesopotamian plains of central and southern Iraq, was identified as exhibiting high to very high drought susceptibility, highlighting the necessity for targeted mitigation strategies. This study pioneers the use of GRACE-derived TWSA and GLDAS data for drought susceptibility mapping in Iraq, paving the way for data-driven water management interventions.

High‐Resolution Terrestrial Water Storage Estimates From GRACE and Land Surface Models

AbstractTerrestrial Water Storage (TWS) changes have been estimated at basin to continental scales from gravity variations using data from the Gravity Recovery and Climate Experiment (GRACE) satellites since 2002. The relatively low spatial resolution (∼300 km) of GRACE observations has been a main limitation in such studies. Various data processing strategies, including mascons, forward modeling, and constrained linear deconvolution (CLD), have been employed to address this limitation. Here we develop a revised CLD method to obtain a TWS estimate that combines GRACE observations with much higher spatial resolution land surface models. The revised CLD constrains model estimates to agree with GRACE TWS when smoothed. As an example, we apply the method to obtain a high spatial resolution TWS estimate in Australia. We assess the accuracy of the approach using synthetic GRACE data.

A physical/statistical data-fusion for the dynamical downscaling of GRACE data at daily and 1 km resolution

GRACE (Gravity Recovery And Climate Experiment) is one of the most important satellite instrument for terrestrial hydrology and water cycle analysis. It is the unique source of information for the Total Water Storage Change (TWSC) that includes the mostly unknown groundwater storage change. Its use for many socio-economic applications is however drastically reduced by its spatial (≃200 km) and temporal (≃ monthly) resolutions. A statistical/physical dynamical downscaling is proposed here to obtain High Resolution (HR) 1 km and daily TWSC estimates, using auxiliary information from other satellite and modeling data (i.e. precipitation, evaporation and river network direction from topography). This scheme utilizes the Water Budget (WB) closure, an hydrological model and a set of statistical tools. The quality of the results is demonstrated over the Po river basin, a very challenging basin for GRACE observations due to its relatively small size and complex contrasting surface types. It is shown that terrestrial runoff and river discharge are well disentangled in the obtained HR TWSC fields. The new TWSC data can also show the impact of weather events such as strong precipitations. This study demonstrates how the optimal combination of multiple Earth data can help us better describe the terrestrial water cycle at higher space/time resolutions.

Total water storage anomalies reconstruction using noise-augmented u-shaped network: A case study in the Yangtze River Basin

The satellite mission of Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) have characterized global total water storage anomalies (TWSA) with unprecedented accuracy. However, the data gap between GRACE and GRACE-FO from July 2017 to May 2018 represents challenges for interpreting long-term water storage changes. In this study, we present a deep learning model, a noise-augmented u-shaped network (NA-UNet), to bridge the gap over the Yangtze River Basin (YRB). This model has been trained on precipitation, temperature, and hydrological model data. We show that the NA-UNet model achieves a state-of-the-art TWSA reconstruction, outperforming the standard UNet model and previous studies. At the basin scale, the NA-UNet model agrees favorably well with GRACE observations, with a correlation coefficient (CC) of 0.99, Nash-Sutcliff efficient (NSE) of 0.97, and normalized root-mean-square error (NRMSE) of 0.04 during the testing period. At the grid-cell scale, our model has a much more stable performance with median CC/NSE/NRMSE values of 0.99/0.96/0.05. This significant improvement in the gap-filling ability addresses the issue with discontinuous TWSA observations while laying the foundation for predicting future changes in total water storage.

Improved Drought Characteristics in the Pearl River Basin Based on Reconstructed GRACE Solution with Enhanced Temporal Resolution

As global warming intensifies, the damage caused by drought cannot be disregarded. Traditional drought monitoring is often carried out with monthly resolution, which fails to monitor the sub-monthly climatic event. The GRACE-based drought severity index (DSI) is a drought index based on terrestrial water storage anomalies (TWSA) observed by the gravity recovery and climate experiment (GRACE) satellite. DSI has the ability to monitor drought effectively, and it is in good consistency with other drought monitoring methods. However, the temporal resolution of DSI is limited by that of GRACE observations, so it is necessary to obtain TWSA with a higher temporal resolution to calculate DSI. We use a statistical method to reconstruct the TWSA, which adopts precipitation and temperature to obtain TWSA on a daily resolution. This statistical method needs to be combined with the time series decomposition method, and then the parameters are simulated by the Markov chain Monte Carlo (MCMC) procedure. In this study, we use this TWSA reconstruction method to obtain high-quality TWSA at daily time resolution. The correlation coefficient between CSR–TWSA and the reconstructed TWSA is 0.97, the Nash–Sutcliffe efficiency is 0.93, and the root mean square error is 16.57. The quality of the reconstructed daily TWSA is evaluated, and the DSI on a daily resolution is calculated to analyze the drought phenomenon in the Pearl River basin (PRB). The results show that the TWSA reconstructed by this method has high consistency with other daily publicly available TWSA products and TWSA provided by the Center for Space Research (CSR), which proves the feasibility of this method. The correlation between DSI based on reconstructed daily TWSA, SPI, and SPEI is greater than 0.65, which is feasible for drought monitoring. From 2003 to 2021, the DSI recorded six drought events in the PRB, and the recorded drought is more consistent with SPI-6 and SPEI-6. There was a drought event from 27 May 2011 to 12 October 2011, and this drought event had the lowest DSI minimum (minimum DSI = −1.76) recorded among the six drought events. The drought monitored by the DSI is in line with government announcements. This study provides a method to analyze drought events at a higher temporal resolution, and this method is also applicable in other areas.

Assessing Terrestrial Water Storage Variations in Southern Spain Using Rainfall Estimates and GRACE Data

This paper investigates the relationship between rainfall, groundwater and Gravity Recovery and Climate Experiment (GRACE) data to generate regional-scale estimates of terrestrial water storage variations in the Andalucía region of southern Spain. These estimates can provide information on groundwater depletion (caused by periods of low rainfall or droughts) and groundwater recovery. The spatial distribution of groundwater bodies in southern Spain is complex and current in situ groundwater monitoring methods are deficient, particularly in terms of obtaining representative samples and in implementing and maintaining groundwater monitoring networks. The alternative approach proposed here is to investigate the relationship between precipitation time series and changes in the terrestrial water storage estimated from GRACE observations. The results were validated against the estimated fluctuation in regional groundwater. The maximum correlation between the mean groundwater level and the GRACE observations is 0.69 and this occurs at a lag of one month because the variation in gravity is immediate, but rainfall water requires around one month to travel across the vadose zone before it reaches the groundwater table. Using graphical methods of accumulated deviations from the mean, we show that, in general, groundwater storage follows the smooth, multi-year trends of terrestrial water storage but with less short-term trends; the same is true of rainfall, for which the local trends are more pronounced. There is hysteresis-like behaviour in the variations in terrestrial water storage and in the variations of groundwater. In practical terms, this study shows that, despite the abnormal dryness of the Iberian Peninsula during the 2004–2010 drought, the depleted groundwater storage in Andalucía recovered almost to its pre-drought level by 2016. In addition, groundwater storage and terrestrial water storage show very similar trends but with a delay in the groundwater trend.

A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) have facilitated highly accurate observations of changes in total water storage anomalies (TWSA). However, limited observations of TWSA derived from GRACE in the Yangtze River Basin (YRB) have hindered our understanding of its long-term variability. In this paper, we present a deep learning model called RecNet to reconstruct the climate-driven TWSA in the YRB from 1923 to 2022. The RecNet model is trained on precipitation, temperature, and GRACE observations with a weighted mean square error (WMSE) loss function. The performance of the RecNet model is validated and compared against GRACE data, water budget estimates, hydrological models, drought indices, and existing reconstruction datasets. The results indicate that the RecNet model can successfully reconstruct historical water storage changes, surpassing the performance of previous studies. In addition, the reconstructed datasets are utilized to assess the frequency of extreme hydrological conditions and their teleconnections with major climate patterns, including the El Niño-Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and North Atlantic Oscillation. Independent component analysis is employed to investigate individual climate patterns' unique or combined influence on TWSA. We show that the YRB exhibits a notable vulnerability to extreme events, characterized by a recurrent occurrence of diverse extreme dry/wet conditions throughout the past century. Wavelet coherence analysis reveals significant coherence between the climate patterns and TWSA across the entire basin. The reconstructed datasets provide valuable information for studying long-term climate variability and projecting future droughts and floods in the YRB, which can inform effective water resource management and climate change adaptation strategies.

Determining the Day-to-Day Occurrence of Low-Latitude Scintillation in Equinoxes at Sanya during High Solar Activities (2012–2013)

Plasma irregularity in the equatorial and low-latitude ionosphere, which leads to ionospheric scintillation, can threaten the operation of radio-based communication and navigation systems. A method for forecasting scintillation activity is still pending. In this study, we examined the performance of ionospheric parameters, including the critical frequency (foF2), peak height of the F2-layer (hmF2), scale height (Hm) and virtual height (h’F), around local sunset from ground-based ionosonde observations, and also the characteristics of Equatorial Ionization Anomaly (EIA) derived from Gravity Recovery and Climate Experiment (GRACE) observations in equinoctial months (March–April and September–October) during high solar activities (2012–2013) at a low-latitude station at Sanya (18.3° N, 109.6° E; dip lat.: 12.8° N), China. Furthermore, the simplified linear growth rate of Rayleigh–Taylor (R–T) instability inferred from ionosonde measurements and EIA strength derived from GRACE observations were used to estimate the day-to-day occurrence of post-sunset scintillation. The results indicate that it is not adequate to determine whether scintillation in a low-latitude region would occur or not based on one ionospheric parameter around sunset. The simplified growth rate of R–T instability can be a good indicator for the day-to-day occurrence of scintillation, especially in combination with variations in EIA strength. An index including the growth rate and EIA variations for the prediction of the post-sunset occurrence of irregularity and scintillation is proposed; the overall prediction accuracy could be about 90%. Our results may provide useful information for the development of a forecasting model of the day-to-day variability of irregularities and scintillation in equatorial and low-latitude regions.

Characterizing hydrological droughts within three watersheds in Yunnan, China from GNSS-inferred terrestrial water storage changes constrained by GRACE data

SUMMARY The spatiotemporal evolution of drought is often modulated by climate and watershed characteristics. While numerous drought studies using space geodesy have been conducted in Yunnan, the scarcity and limited sensitivity of observation instruments have hindered the development of watershed-scale drought analyses. This study aims to accurately characterize hydrological droughts within three watersheds in Yunnan from 2011 January to 2021 May by a Global Navigation Satellite System (GNSS) inversion constrained by Gravity Recovery and Climate Experiment (GRACE) data. Initially, we employed Variational Bayesian Independent Component Analysis to reconstruct the 3-D crustal deformations at 43 GNSS stations resulting from hydrological loadings. We then computed the time-series of vertical displacements caused by GRACE Mascon water products. Subsequently, utilizing the method of least squares, we derived the scaling factors between the vertical crustal displacements (VCD) obtained from GNSS observations and the synthetic displacements derived from GRACE data. By combing scaling-factor-adjusted VCDs derived from GRACE with the GNSS data, we have obtained accurate estimates of water storage for three Yunnan watersheds. Finally, we identified drought events characterized by abnormal decreases in water storage and used climatological methods to quantitatively describe the severity, extent and recovery of these drought extremes. Additionally, we evaluated the influence of various earth elastic structures on the scaling factors, and demonstrated their advantageous contribution to aligning GNSS and GRACE observations. In conclusion, our study introduces a novel approach to integrate GNSS and GRACE retrievals, allowing for accurate characterization of droughts in data-scarce regions, which cannot be achieved by GNSS or GRACE individually. Moreover, our results underscore the potential benefits of watershed-scale drought monitoring and analysis for effective water resource management.

Applying Reconstructed Daily Water Storage and Modified Wetness Index to Flood Monitoring: A Case Study in the Yangtze River Basin

The terrestrial water storage anomaly (TWSA) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provides a new means for monitoring floods. However, due to the coarse temporal resolution of GRACE/GRACE-FO, the understanding of flood occurrence mechanisms and the monitoring of short-term floods are limited. This study utilizes a statistical model to reconstruct daily TWS by combining monthly GRACE observations with daily temperature and precipitation data. The reconstructed daily TWSA is utilized to monitor the catastrophic flood event that occurred in the middle and lower reaches of the Yangtze River basin in 2020. Furthermore, the study compares the reconstructed daily TWSA with the vertical displacements of eight Global Navigation Satellite System (GNSS) stations at grid scale. A modified wetness index (MWI) and a normalized daily flood potential index (NDFPI) are introduced and compared with in situ daily streamflow to assess their potential for flood monitoring and early warning. The results show that terrestrial water storage (TWS) in the study area increases from early June, reaching a peak on 19 July, and then receding till September. The reconstructed TWSA better captures the changes in water storage on a daily scale compared to monthly GRACE data. The MWI and NDFPI based on the reconstructed daily TWSA both exceed the 90th percentile 7 days earlier than the in situ streamflow, demonstrating their potential for daily flood monitoring. Collectively, these findings suggest that the reconstructed TWSA can serve as an effective tool for flood monitoring and early warning.

Similarities and Differences in the Earth’s Water Variations Signal Provided by Grace and AMSR-E Observations Using Maximum Covariance Analysis at Various Land Cover Data Backgrounds

ABSTRACT The study presents a compatibility analysis of gravimetric observations with passive microwave observations. Monitoring the variability of soil water content is one of the essential issues in climate-related research. Total water storage changes (ΔTWS) observed by Gravity Recovery and Climate Experiment (GRACE), enables the creation of many applications in hydrological monitoring. Soil moisture (SM) is a critical variable in hydrological studies. Advanced Microwave Scanning Radiometer (AMSR-E) satellite products provided unique observations on this variable in near-daily time resolutions. The study used maximum covariance analysis (MCA) to extract principal components for ΔTWS and SM signals. The analysis was carried out for the global area, dividing the discussion into individual continents. The amplitudes of gravimetric and microwave signals were computed via the complex empirical orthogonal function (EOF) and the complex conjugate EOF* to determine the regions for detailed comparison. Similarities and differences in signal convergence results were compared with land cover data describing soil conditions, vegetation cover, urbanization status, and cultivated land. Convergence was determined using Pearson correlation coefficients and cross-correlation. In order to compare ΔTWS and SM in individual seasons, ΔTWS observations were normalized. Results show that naturally forested areas and large open spaces used for agriculture support the compatibility between GRACE and AMSRE observations and are characterized by a good Pearson correlation coefficient >0.8. Subpolar regions with permafrost present constraints for AMSR-E observations and have little convergence with GRACE observations.

Reconstructing a long-term water storage-based drought index in the Yangtze River Basin

Drought is a prolonged dry period in the natural climate cycle, and is one of the most costly weather events. The Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water storage anomalies (TWSA) have been widely used to assess drought severity. However, the relatively short cover period of GRACE and GRACE Follow-On limit our knowledge about the characterization and evolution of drought over decades time scale. This study proposes a standardized GRACE reconstructed TWSA index (SGRTI) to assess the drought severity based on a statistical reconstruction method calibrated by GRACE observations. Results show that the SGRTI correlates well with 6-month scale SPI and SPEI, with correlation coefficients reaching 0.79 and 0.81 in the YRB from 1981 to 2019. Soil moisture can capture drought condition like the SGRTI, while cannot further reflect deeper water storage depletion. The SGRTI is also comparable to the SRI and in-situ water level. As a case study for the Yangtze River Basin, its three sub-basins experience more frequent droughts, shorter drought duration, and lower severity drought, as identified by SGRTI during 1992–2019 relative to 1963–1991. The presented SGRTI in this study can provide a valuable supplement to the drought index before the GRACE era.

Reconstructing terrestrial water storage anomalies using convolution-based support vector machine

Study regionThe Congo River basin in west-central Africa Study focusTraditional machine learning algorithms are recently being replaced by integrated learning techniques in pattern recognitions and predictions. These updated tools (or techniques), which attempt to explore higher dimensions and uncover hidden patterns in considerably non-linear datasets are the new normal in small, medium and large dataset even in higher orders. This study investigates the performance of a convolution-based support vector machine in hydrological analysis to optimize forecast of terrestrial water storage anomalies (TWSA). The linear, polynomial, and radial basis function (RBF) kernels were explored in reconstructing the three variants of TWSA obtained from the Gravity Recovery and Climate Experiment (GRACE) level 3 products. Using the original input training datasets, we built and trained the convolution-neural network (CNN) which is composed of convolution and fully connected layers, and was integrated into the traditional support vector machine (SVM). The network was trained with twenty-two (22) datasets. The original predictor datasets are composed of hydrological fluxes (HF), climate indices (CI), and sea surface temperature (SST) datasets whose influence on GRACE-TWSA was analyzed in the body of our work. Our results show that the polynomial-kernel based on the convolution-based support vector machine (CSVM) outperformed the other regression models in the reconstruction of all variants of TWSA. The high accuracy achieved using the CSVM demonstrates its promising potential to fill the gaps in the missing GRACE observations through its refined reconstruction capabilities. New hydrological insight for the regionIn this study, (i) SST variants or components (e.g., those of east tropical Atlantic) were leading predictors of TWSA, (ii) increasing polynomial order does not increase accuracy in all polynomial kernel operations, especially in cases of severe non-linearity between predictors and predictands, (iii) for the RBF kernels, intermediate gamma values ranging from 0.05 to 0.5 are ideal for climatic analysis, as very small or very large gamma values will either behave like a linear model or over fit, respectively, and (iv) overall, the polynomial kernels reconstructed and predicted TWSA better than the other kernels. The other conventional machine learning procedures used to compare the fit of the CSVM show significant insight for future TWSA reconstruction processes. Their robustness can be explored by varying the independent variables fed into the machine learning framework and tuning their hyper-parameters to result in better fitting index, which promises to be much more useful than traditional learning methods.

Water cycle science enabled by the GRACE and GRACE-FO satellite missions

Satellite observations of the time-variable gravity field revolutionized the monitoring of large-scale water storage changes, beginning with the 2002 launch of the Gravity Recovery and Climate Experiment (GRACE) mission. Most hydrologists were sceptical of the satellite gravimetry approach at first, but validation studies assuaged their concerns and high-profile, GRACE-based groundwater depletion studies caused an explosion of interest. The importance of GRACE observations for hydrologic and cryospheric science became so great that GRACE Follow-On (GRACE-FO) jumped the National Aeronautics and Space Administration’s Earth science mission queue and launched in 2018. A third mass change mission is currently under development. Here, we review key milestones in satellite gravimetry’s progression from the fringes of hydrology to being a staple of large-scale water cycle and water resources studies and the sole source of observations of what is now an ‘essential climate variable’, terrestrial water storage. The story of satellite gravimetry’s progression from the fringes of hydrology to being a staple of large-scale water cycle and water resources science and the sole source of global observations of terrestrial water storage now an ‘essential climate variable’.

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.

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.

High-resolution surface mass loads in the Amazon Basin combining GRACE and river routing model

SUMMARYUnderstanding water redistribution on Earth's surface is essential to hydrological applications and water management. Variations in water mass loads have been observed by the Gravity Recovery and Climate Experiment (GRACE), but the low spatial resolution of GRACE limits determination of their distribution in detail. Hydrologic models provide higher spatial resolution water mass loads, but may include larger uncertainties. In this study, we develop high-resolution surface mass loads over the Amazon basin using forward modelling by combining GRACE data and a hydrologic model. River routing discharge is also included as a priori information because of the large water volume changes on relatively narrow channels in the Amazon basin. These high-resolution surface mass loads constrained by river routing agree with GRACE observations when spatially smoothed. Vertical deformation estimated from these high-resolution loads agree with Global Navigation Satellite System (GNSS) observations, at both seasonal and inter-annual timescales. In particular the most improved agreement is obtained at the NAUS GNSS station, close to the main channel of the Amazon, relative to predictions made using GRACE data. At two other stations (APSA and MAPA) near the main channel, the estimated vertical deformations apparently differ from observation, but much of the discrepancy is reduced when river path is corrected in river-routing model, indicating the importance of water loads on river channel to understand crustal displacement in the area.

Low-Frequency Dynamic Ocean Response to Barometric-Pressure Loading

Abstract Changes in dynamic manometric sea level ζm represent mass-related sea level changes associated with ocean circulation and climate. We use twin model experiments to quantify magnitudes and spatiotemporal scales of ζm variability caused by barometric pressure pa loading at long periods ( month) and large scales () relevant to Gravity Recovery and Climate Experiment (GRACE) ocean data. Loading by pa drives basin-scale monthly ζm variability with magnitudes as large as a few centimeters. Largest ζm signals occur over abyssal plains, on the shelf, and in marginal seas. Correlation patterns of modeled ζm are determined by continental coasts and H/f contours (H is ocean depth and f is Coriolis parameter). On average, ζm signals forced by pa represent departures of and from the inverted-barometer effect ζib on monthly and annual periods, respectively. Basic magnitudes, spatial patterns, and spectral behaviors of ζm from the model are consistent with scaling arguments from barotropic potential vorticity conservation. We also compare ζm from the model driven by pa to ζm from GRACE observations. Modeled and observed ζm are significantly correlated across parts of the tropical and extratropical oceans, on shelf and slope regions, and in marginal seas. Ratios of modeled to observed ζm magnitudes are as large as ∼0.2 (largest in the Arctic Ocean) and qualitatively agree with analytical theory for the gain of the transfer function between ζm forced by pa and wind stress. Results demonstrate that pa loading is a secondary but nevertheless important contributor to monthly mass variability from GRACE over the ocean.