Gravity Recovery And Climate Experiment Products Research Articles

Tracking shallow and deep groundwater storage changes in North China Plain with improved fusion method and hybrid spectral analysis approach

The North China Plain (NCP) is a critical agricultural hub in China, confronting a significant issue of groundwater depletion. However, different terrestrial water storage (TWS) change products from the Gravity Recovery and Climate Experiment (GRACE) satellites differ considerably in estimating groundwater depletion rates in the NCP. It is urgent to optimize these different products to obtain an elevated understanding of regional groundwater storage (GWS) changes. The generalized three-cornered hat (GTCH) method is used to quantify uncertainties of different GRACE products, and this prior information is integrated with the Bayesian model averaging (BMA) method to fuse GRACE products. Results demonstrate that the GTCH + BMA method can generate uniform GWS products with heightened precision, enhanced coherence, and diminished uncertainty in the NCP. After that, the shallow and deep GWS are meticulously differentiated based on the high-coverage in-situ data, and the shallow GWS (2005.1–2016.8) (-13.7 ± 4.6 mm/yr) declined faster than deep GWS (-6.05 ± 3.9 mm/yr) in the NCP. To further explore the primary factors driving these changes, the latest Hankel Spectrum Analysis (HSA) method combined with ridge regression simulation is employed to decouple the change components of human and natural factors in the shallow and deep GWS. The first attempt to give the contribution of human and natural factors to shallow GWS is 77.18 %/22.82 %, and to deep GWS is 87.59 %/12.41 %.

Comparison of Terrestrial Water Storage Changes in the Tibetan Plateau and Its Surroundings Derived from Gravity Recovery and Climate Experiment (GRACE) Solutions of Different Processing Centers

The GRACE twin satellite gravity mission from 2002 to 2017 has considerably improved investigations on global and regional hydrological changes. However, there are different GRACE solutions and products available which may yield different results for certain regions despite applying the same postprocessing and time span. This is especially the case for the Tibetan Plateau (TP) with its special hydrological conditions represented by localized but strong signals that can overlap or merge with signals inside the plateau, which can falsify the determination of terrestrial water storage (TWS) changes in the TP area. To investigate the effect of GRACE solution selection on inverted TWS changes, we analyze quantitatively the secular and monthly changes for 14 glacier areas and 10 water basins in and around the TP area that have been calculated from 16 different available GRACE solutions. Our analysis provides expectable results. While trend results from different spherical harmonic (SH) GRACE solutions match well, there are significant differences to and between mascon GRACE solutions. This is related to the different processing concepts of mascon solutions and their forced handling in our comparisons. SH solution time series match each other when mass changes are strong with a large amplitude and regular periodicity. However, for regions where small TWS changes are associated with small amplitudes, trends, and/or unstable signal periods, SH solutions can also yield different results. Such behavior is known from a time series analysis. Interestingly though, we find that the COST-G and ITSG SH GRACE solutions are closest to the average of all solutions. Therefore, these solutions appear to be preferable for TWS investigations in regions with highly variable hydrological conditions, such as in the Tibetan Plateau and its surroundings. This also indicates that combined solutions such as COST-G provide a promising pathway for an improved TWS analysis, which should be further elaborated.

Quantifying Multi-Source Uncertainties in GRACE-Based Estimates of Groundwater Storage Changes in Mainland China

The Gravity Recovery and Climate Experiment (GRACE) satellites have been widely used to estimate groundwater storage (GWS) changes, yet their uncertainties related to the multi-source datasets used are rarely investigated. This study focuses on quantifying the uncertainties of GRACE GWS estimates in mainland China during 2003–2015, by generating a total of 3456 solutions from the combinations of multiple GRACE products and auxiliary datasets. The Bayesian model averaging (BMA) approach is used to derive the optimal estimates of GWS changes under an uncertainty framework. Ten river basins are further identified to analyze the estimated annual GWS trends and uncertainty magnitudes. On average, our results show that the BMA-estimated annual GWS trend in mainland China is −1.93 mm/yr, whereas its uncertainty reaches 4.50 mm/yr. Albeit the estimated annual GWS trends and uncertainties vary across river basins, we found that the high uncertainties of annual GWS trends are tied to the large differences between multiple GRACE data and soil moisture products used in the GWS solutions. These findings highlight the importance of paying more attention to the existence of multi-source uncertainties when using GRACE data to estimate GWS changes.

Downscaling Simulation of Groundwater Storage in the Beijing, Tianjin, and Hebei Regions of China Based on GRACE Data

Gravity Recovery and Climate Experiment (GRACE)-derived groundwater storage anomalies (GWSA) have been used to highlight groundwater depletion in regional aquifer systems worldwide. However, the use of GRACE products in smaller areas is limited owing to the coarse spatial resolution of the data product. This study utilized a dynamic downscaling method to improve the GWSA resolution from 1° to 0.05° by constructing a groundwater storage numerical model in the Beijing, Tianjin, and Hebei regions of China (BTH). The results indicate that: (1) the GRACE-derived and calculated GWSA had a good match with an average root mean squared error (RMSE) of 2.61 cm equivalent water height (EWH) and an average Nash–Sutcliffe efficiency coefficient (NSE) of 0.84 for the calibration period. (2) The hydraulic gradient coefficient and specific yield had the highest sensitivity, and transmissivity had the lowest sensitivity; however, different forcing data had no obvious influence on the GWSA. (3) The downscaled results not only exhibited time series variations that were consistent with those of the GRACE-derived solutions but also revealed a finer spatial heterogeneity of the GWSA along with increasing correlation coefficients between the GRACE-derived GWSA and the in situ measurements of groundwater levels by 0.06 and reducing the RMSE by 8.85%. (4) The downscaled results reflected the spatiotemporal change characteristics of groundwater storage in different hydrogeological units and administrative regions well. This study demonstrates the potential applications of the proposed downscaling method for both regional and local groundwater resource management.

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.

Improving Spatial Resolution of GRACE-Derived Water Storage Changes Based on Geographically Weighted Regression Downscaled Model

Obtaining high-resolution products that can accurately estimate the spatiotemporal changes in regional water resources is essential for the rational utilization of water resources. The temporal gravity field model derived from the Gravity Recovery and Climate Experiment (GRACE) satellite can effectively monitor the regional terrestrial water storage (TWS) changes. Nevertheless, the main of GRACE products is their coarse spatial resolution, which limits their applicability to small-scale regions. Herein, we propose a geographically weighted regression downscaled model (GWRDM) to improve the spatial resolution of TWS from 0.5° to 0.1° and validate it against the downscaled results provided by the spatial global regression downscaled model (SGRDM) and temporal grid regression downscaled model (TGRDM). The results show that the downscaled products based on GWRDM outperform those based on SGRDM and TGRDM. The GWRDM not only effectively improves the spatial resolution of GRACE products but also maintains the intensity of the original signal. Similarly, the GWRDM is further used to downscale groundwater storage (GWS) and validated against the in situ observations. The downscaled GWS based on GWRDM agrees well with in situ observations. On the monthly scale, the average correlation coefficient (CC) for both is 0.53 and above 0.70 for some in situ observations. On the annual scale, 81.25% and 37.50% of the in situ observations have CC values larger than 0.60 and 0.90, respectively, which further verifies the reliability of GWRDM. Consequently, the GWRDM provides a practical algorithm for obtaining high-resolution water storage estimates, which is expected to provide a reference for small-scale regional water resources management.

Bridging the gap between GRACE and GRACE-FO using a hydrological model

Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), two successive satellite-based missions starting in 2002, have provided an unprecedented way of measuring global terrestrial water storage anomalies (TWSA). However, a temporal gap exists between GRACE and GRACE-FO products from July 2017 to May 2018, which introduces bias and uncertainties in TWSA calculations and modeling. Previous studies have incorporated hydroclimatic factors as predictors for filling the gap, but most of them utilized artificial intelligence or pure statistical models that generally de-trended TWSA and had no physical foundation. Thus, a physically-based reconstruction is required for increasing robustness. In this study, we bridge the temporal gap by developing an empirical hydrological model. The “abcd” model, a T-based snow component, and linear correction are utilized to represent runoff generation, snow dynamics, and long-term trends. The testing results indicate that our hydrological model can successfully reconstruct TWSA in tropical, temperature, and continental climates, although further improvement is needed for arid climates. Our reconstruction for the gap achieves high accuracy and robustness as shown by the evaluations against sea-level budget and GLDAS-derived TWSA. Compared to previous studies using artificial intelligence or statistical techniques, our hydrological model performs similarly in the gap filling but does not involve de-trended or de-seasonalized transformations, which will facilitate the combination of GRACE and GRACE-FO products and improve the physical understanding of global TWSA.

Improving the Spatial Resolution of GRACE-Derived Terrestrial Water Storage Changes in Small Areas Using the Machine Learning Spatial Downscaling Method

Gravity Recovery and Climate Experiment (GRACE) satellites can effectively monitor terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse resolution of GRACE products, there is still a large number of deficiencies that need to be considered when investigating TWS changes in small-scale areas. Hence, it is necessary to downscale the GRACE products with a coarse resolution. First, in order to solve this problem, the present study employs modeling windows of different sizes (Window Size, WS) combined with multiple machine learning algorithms to develop a new machine learning spatial downscaling method (MLSDM) in the spatial dimension. Second, The MLSDM is used to improve the spatial resolution of GRACE observations from 0.5° to 0.25°, which is applied to Guantao County. The present study has verified the downscaling accuracy of the model developed through the combination of WS3, WS5, WS7, and WS9 and jointed with Random Forest (RF), Extra Tree Regressor (ETR), Adaptive Boosting Regressor (ABR), and Gradient Boosting Regressor (GBR) algorithms. The analysis shows that the accuracy of each combined model is improved after adding the residuals to the high-resolution downscaled results. In each modeling window, the accuracy of RF is better than that of ETR, ABR, and GBR. Additionally, compared to the changes in the TWS time series that are derived by the model before and after downscaling, the results indicate that the downscaling accuracy of WS5 is slightly more superior compared to that of WS3, WS7, and WS9. Third, the spatial resolution of the GRACE data was increased from 0.5° to 0.05° by integrating the WS5 and RF algorithm. The results are as follows: (1) The TWS (GWS) changes before and after downscaling are consistent, decreasing at −20.86 mm/yr and −21.79 mm/yr (−14.53 mm/yr and −15.46 mm/yr), respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (CC) values of both are above 0.99 (0.98). (2) The CC between the 80% deep groundwater well data and the downscaled GWS changes are above 0.70. Overall, the MLSDM can not only effectively improve the spatial resolution of GRACE products but also can preserve the spatial distribution of the original signal, which can provide a reference scheme for research focusing on the downscaling of GRACE products.

Accounting for GIA signal in GRACE products

SUMMARY The Gravity Recovery and Climate Experiment (GRACE) observes gravitational potential anomalies that include the effects of present-day surface mass change (PDSMC)- and glacial isostatic adjustment (GIA)-driven solid Earth mass redistribution. Therefore, GIA estimates from a forward model are commonly removed from GRACE to estimate PDSMC. There are several GIA models and to facilitate users in using a GIA model of their choice, both GRACE and GIA products are made available in terms of global gridded fields representing mass anomaly. GRACE-observed gravitational potential anomalies are represented in terms of equivalent water height (EWH) with a relation that accounts for an elastic solid Earth deformation due to PDSMC. However, for obtaining GIA EWH fields from GIA gravitational potential fields, two relations are being used: one that is similar to that being used for GRACE EWH and the other that does not include an elastic deformation effect. This leaves users with the possibility of obtaining different values for PDSMC with a given GRACE and GIA field. In this paper, we discuss the impact of this problem on regional mass change estimates and highlight the need for consistent treatment of GIA signals in GRACE observations.

Comparison of decadal water storage trends from common GRACE releases (RL05, RL06) using spatial diagnostics and a modified triple collocation approach

GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-On) satellites have provided unique insights into the evolution of Terrestrial Water Storage (TWS) in space and time. Despite such advancements, various GRACE solutions produced by different data centers display uneven spatial attributes with varying associated uncertainties. Via spatial diagnostics tools and a modified triple collocation (MTC) approach, this research evaluates the TWS (terrestrial water storage) trend estimations “on the grid-scale” from 11 gridded GRACE products of RL05 and RL06 releases between 2002 and 2017. Distinct from classic TCA (triple collocation analysis), the MTC employs a GWR (geographically weighted regression) scaling scheme with distinctive spatial coefficients. The spatial diagnostics analyses identified different autocorrelation patterns, clustering tendencies of hot (positive) and cold (negative) spots agglomeration at varying spatial width, and unique frequency distributions. The results indicated that within a 10-degree spatial radius the SHs (Spherical Harmonics) of RL05 and RL06 are highly autocorrelated compared to the mascons (mass concentration blocks) solutions. The spatial clustering results revealed that many solutions agreed on the overall directions and distribution of the hot and cold spots. The clustering among mascon products, however, reflected more localized mass anomalies. At the scale of drainage basins, the trend magnitude, as well as their associated uncertainties appeared to be driven by the occurrence of spatial clusters within the basin area. The MTC results showed that the uncertainty patterns follow the same spatial extent within each cluster. The MTC analysis underscored the added benefits of cluster analysis and the GWR scaling over the classic OLS approach.

Integrated flood potential index for flood monitoring in the GRACE era

By utilizing Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomaly (TWSA) and remote sensing precipitation data, Flood Potential Index (FPI) has been widely used in large-scale flood monitoring. However, divergent post-processing dynamics of different GRACE solutions result in substantial uncertainties in GRACE TWSA and thus affecting predictive skills of FPI. To overcome this, this study develops an Integrated Flood Potential Index (IFPI) by linking the FPI derived from six GRACE products. The Gaussian copula is employed to establish the joint distribution of FPI from six spherical harmonic (SH) products and mass concentration blocks solutions. One of the most flood-prone regions, Yangtze River basin in China, is selected as a case study. We have identified and characterized the floods with different intensities using IFPI, which is evaluated against standardized discharge observations as well as the Total Storage Deficit Index (TSDI), Water Storage Deficit Index (WSDI) and Combined Climatologic Deviation Index (CCDI). Results show that the area under curve (AUC) values of IFPI for different levels of floods are generally greater than FPIs and their ensemble mean, implying the better predictive skill for the large-scale flood events. During the three severest floods in 2010, 2015, and 2016, IFPI captures the flood variability exhibited by TSDI, WSDI, and CCDI, as well as hydrological observations. This proposed approach might provide reference for flood monitoring and from multi-mission satellite data.

Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data

Terrestrial water storage changes (TWSCs) retrieved from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have been extensively evaluated in previous studies over large basin scales. However, monitoring the TWSC at small basin scales is still poorly understood. This study presented a new method for calculating TWSCs at the small basin scales based on the water balance equation, using hydrometeorological and multi-source data. First, the basin was divided into several sub-basins through the slope runoff simulation algorithm. Secondly, we simulated the evapotranspiration (ET) and outbound runoff of each sub-basin using the PML_V2 and SWAT. Lastly, through the water balance equation, the TWSC of each sub-basin was obtained. Based on the estimated results, we analyzed the temporal and spatial variations in precipitation, ET, outbound runoff, and TWSC in the Ganjiang River Basin (GRB) from 2002 to 2018. The results showed that by comparing with GRACE products, in situ groundwater levels data, and soil moisture storage, the TWSC calculated by this study is in good agreement with these three data. During the study period, the spatial and temporal variations in precipitation and runoff in the GRB were similar, with a minimum in 2011 and maximum in 2016. The annual ET changed gently, while the TWSC fluctuated greatly. The findings of this study could provide some new information for improving the estimate of the TWSC at small basin scales.

Spatiotemporal changes of terrestrial water storage and possible causes in the closed Qaidam Basin, China using GRACE and GRACE Follow-On data

Terrestrial water storage (TWS) is a crucial indicator of regional water balance and water resources changes. Due to limited hydrological observations, we combined the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) products using the Long Short-Term Memory (LSTM) neural network to monitor the TWS changes from April 2002 to March 2020 over the closed Qaidam Basin in northwest China and examined the impacts of climate and meteorological changes on TWS variations. The results indicated that the LSTM model, driven by the cumulative precipitation, temperature, and Global Land Data Assimilation System datasets, was reliable for use in reconstruction of the GRACE products in the closed basin. The TWS variations featured seasonal variation characteristics and a significant upward trend at internal-annual scales, which were tested via linear statistics and a modified Mann–Kendall method. The increasing trend is likely to remain strongly sustainable in the near future with a Hurst index over 0.75 in most regions. Moreover, the TWS oscillation has a periodicity and nonlinearity increase trend of 0.43 mm/month as observed using ensemble empirical mode decomposition analysis, and the TWS components (including snow water equivalent, soil moisture, and groundwater) demonstrate discordant increasing trends in the basin. Under climate change conditions, teleconnection factors have strong impacts on TWS variability, particularly for the Pacific Decadal Oscillation index with a significant negative correlation by cross wavelet transform technology. Nonetheless, the increase in TWS is primarily influenced by precipitation increases and is more sensitive to the accumulated precipitation in this region. In this study, the GRACE products in combination with GRACE-FO data may help us to better understand the spatiotemporal characterization of TWS in Qaidam Basin, which will provide an important support for the water resource management and ecological environment protection in such data-scarce regions.

GRACE products and land surface models for estimating the changes in key water storage components in the Nile River Basin

The Nile River Basin (NRB) is facing extreme demand for its water resources due to an alarming increase in population and the changing climate. The NRB is not compatible with ground-based in-situ observations owing to its large basin area size and limited hydrological data access from basin countries. Thus, it lends itself to remotely sensed approaches with high spatial resolution and extended temporal coverage. The Gravity Recovery and Climate Experiment (GRACE) avails a unique opportunity to investigate the changes in key components of terrestrial water storage (TWS). GRACE TWS solutions have specific tuning parameters and processing strategies that result in regionally specific variations and error patterns. We explored the TWS time series spatiotemporal changes, trends, uncertainties, and signal-to-noise ratio among different GRACE TWS data. We had also investigated the key terrestrial water storage components such as surface water, soil moisture, and groundwater storage changes. The results show that GRACE spherical harmonic solutions' uncertainty is higher than the mass concentration (mascon) over the NRB, and the Center for Space Research-mascons had the best performance. The evapotranspiration correlation (R2 = 0.85) has the highest correlation with GRACE’s TWS, whereas the normalized difference vegetation index (R2 = 0.82) has the second highest correlation. Notably, significant long-term (2003–2017) negative groundwater and soil moisture trends demonstrate a potential depletion of the NRB. Despite an increase in precipitation and the TWS time series, the rate of decline increased rapidly after 2008, thereby indicating the possibility of human-induced change (e.g. for irrigation purposes). Therefore, the results of this study provide a guide for future studies related to hydro-climatic change over the NRB and similar basins.

GRACE reveals depletion of water storage in northwestern South America between ENSO extremes

Dynamics of terrestrial water storage are determinant for many natural and social phenomena, with implications for water security and environmental sustainability. Here we use 2002–2017 data from the Gravity Recovery and Climate Experiment (GRACE) to study these dynamics in the Magdalena-Cauca river basin in northwestern South America. Through comparison with water balance-based estimates we assess the performance of multiple GRACE products in representing water storage dynamics in the basin, identifying the Mascon product from the Jet Propulsion Laboratory as the best suited for further analysis. We then investigate the existence of long term trends and show that terrestrial water storage in general and groundwater storage in particular have been gradually depleting in the basin since around the end of 2010. GRACE data reveal that this trend is not uniform across the basin but exhibits a clear-cut pattern in which the water depletion rate is more pronounced in the lower parts of the basin than it is in the upper basin. We explore the mechanisms behind the identified temporal trends and spatial patterns and show that water storage depletion largely coincides with a period between the La Niña and El Niño extreme phases of ENSO. Likewise, the pronounced contrast between depletion rates in the lower and higher parts of the basin largely coincides with marked biophysical differences between these regions, including the presence of major wetland systems in the lowlands, and the highlands of the Andean mountains.

A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran

This study presents a new strategy to predict the monthly groundwater level with short- and long-lead times over the Rafsanjan aquifer in Iran using an ensemble machine learning method called Gradient Boosting Regression (GBR). In this way, the satellite-based products, including the Tropical Rainfall Measuring Mission (TRMM) and the Gravity Recovery and Climate Experiment (GRACE) datasets, as well as the pumping rate, are used as the predictive variables in different lag times. To obtain the optimal input combinations, the Gamma Test (GT) is employed as a non-linear feature selection technique. The spatial analysis of the performance prediction is performed using several error metrics (e.g., R2, and NRMSE). Results indicate that the GBR provides a high prediction performance to predict the GWL, whereas the GRACE product is an accurate predictive variable. The correlation analysis between the predicted and observed GWL shows that the coefficient of determination values vary in the range of 0.66 to 0.94 over the different lead times. The spatial pattern of the prediction accuracy indicates that the regions with higher water depth and pumping rate offer higher performance. Moreover, the increasing trends in performance accuracy are observed from the north to south and the west to east of the Rafsanjan aquifer. In general, the proposed approach provides a reliable insight for water resources planner to make a decision based on the accurate modeling results.

Development of a Daily GRACE Mascon Solution for Terrestrial Water Storage

AbstractThe Gravity Recovery and Climate Experiment (GRACE) and GRACE‐Follow On missions have provided a global history of terrestrial water storage changes since 2002. Traditional GRACE products resolve monthly time‐variable gravity at spatial resolutions of 300–500 km, with many recent efforts focusing on regularized mass concentration (mascon) solutions to better resolve signals spatially. However, monthly resolution inhibits the applicability of GRACE to investigations of sub‐monthly signals. This study presents a new daily mass change solution estimated as deviations from the NASA Goddard Space Flight Center (GSFC) converged monthly mascon product and quantifies the fundamental trade‐off between temporal and spatial resolution in GRACE‐only solutions. As an iteration of the GSFC product, this daily solution represents a mixing of monthly information at higher relative spatial resolution with daily information at lower relative spatial resolution, resolving each temporal scale at the best resolution achievable without requiring hydrological model dependencies in the estimation. The resolution of the daily mascons is 300–400 km at high latitudes and 600–1,000 km lower, depending on each daily orbital track and the proximity of each mascon to constraint region boundaries (e.g., coastlines). Through simulations and model comparisons, we demonstrate that daily signals over areas larger than 400,000 km are recoverable, with basins larger than 800,000 km exhibiting strong signal recovery relative to leakage. This analysis establishes baselines for daily signal recovery from GRACE in the context of longer time scale solutions, characterizes leakage inherent in daily GRACE information, and creates new opportunities for applying GRACE to investigations of sub‐monthly signals.

GROUNDWATER STORAGE CHANGE ESTIMATION USING GRACE SATELLITE DATA IN INDUS BASIN

Over exploitation of Ground Water (GW) has resulted in lowering of water table in the Indus Basin. While waterlogging, salinity and seawater intrusion has resulted in rising of water table in Indus Basin. The sparse piezometer network cannot provide sufficient data to map groundwater changes spatially. To estimate groundwater change in this region, data from Gravity Recovery and Climate Experiment (GRACE) satellite was used. GRACE measures (Total Water Storage) TWS and used to estimate groundwater storage change. Net change in storage of groundwater was estimated from the change in TWS by including the additional components such as Soil Moisture (SM), Surface water storage (Qs) and snowpack equivalent water (SWE). For the estimation of these components Global Land Data Assimilation system (GLDAS) Land Surface Models (LSMs) was used. Both GRACE and GLDAS produce results for the Indus Basin for the period of April 2010 to January 2017. The monitoring well water-level records from the Scarp Monitoring Organization (SMO) and the Punjab Irrigation and Drainage Authority (PIDA) from April 2009 to December 2016 were used. The groundwater results from different combinations of GRACE products GFZ (GeoforschungsZentrum Potsdam) CSR (Center for Space Research at University of Texas, Austin) JPL (Jet Propulsion Laboratory) and GLDAS LSMs (CLM, NOAH and VIC) are calibrated (April 2009-2014) and validated (April 2015-April 2016) with in-situ measurements. For yearly scale, their correlation coefficient reaches 0.71 with Nash-Sutcliffe Efficiency (NSE) 0.82. It was estimated that net loss in groundwater storage is at mean rate of 85.01 mm per year and 118,668.16 Km3 in the 7 year of study period (April 2010-Jan 2017). GRACE TWS data were also able to pick up the signals from the large-scale flooding events observed in 2010 and 2014. These flooding events played a significant role in the replenishment of the groundwater system in the Indus Basin. Our study indicates that the GRACE based estimation of groundwater storage changes is skillful enough to provide monthly updates on the trend of the groundwater storage changes for resource managers and policy makers of Indus Basin.

Spatial Downscaling of GRACE TWSA Data to Identify Spatiotemporal Groundwater Level Trends in the Upper Floridan Aquifer, Georgia, USA

Accurate assessments of groundwater resources in major aquifers across the globe are crucial for sustainable management of freshwater reservoirs. Observations from the Gravity Recovery and Climate Experiment (GRACE) satellite have become invaluable as a means to identify regions groundwater change. While there is a large body of research that focuses on downscaling coarse (1°) GRACE products, few studies have attempted to spatially downscale GRACE to produce fine resolution (5 km) maps that are more useful to resource managers. This study trained a boosted regression tree model to statistically downscale GRACE total water storage anomaly to monthly 5 km groundwater level anomaly maps in the karstic upper Floridan aquifer (UFA) using multiple hydrologic datasets. Evaluation of spatial predictions with existing groundwater wells indicated satisfactory performance (R = 0.79, NSE = 0.61). Results demonstrate that groundwater levels were stable between 2002–2016 but varied seasonally. The data also highlights areas where groundwater pumping is exacerbating UFA water-level declines. While results demonstrate the applicability of machine learning based methods for spatial downscaling of GRACE data, future studies should account for preferential flowpaths (i.e., conduits, lineaments) in karstic systems.

Multiple-data-based monthly geopotential model set LDCmgm90

While the GRACE (Gravity Recovery and Climate Experiment) satellite mission is of great significance in understanding various branches of Earth sciences, the quality of GRACE monthly products can be unsatisfactory due to strong longitudinal stripe-pattern errors and other flaws. Based on corrected GRACE Mascon (mass concentration) gridded mass transport time series and updated LDCgam (Least Difference Combination global angular momenta) data, we present a new set of monthly gravity models called LDCmgm90, in the form of Stokes coefficients with order and degree both up to 90. The LDCgam inputs are developed by assimilating degree-2 Stokes coefficients from various versions of GRACE (including Mascon products) and SLR (Satellite Laser Ranging) monthly gravity data into combinations of outputs from various global atmospheric, oceanic, and hydrological circulation models, under the constraints of accurately measured Earth orientation parameters in the Least Difference Combination (LDC) scheme. Taking advantages of the relative strengths of the various input solutions, the LDCmgm90 is free of stripes and some other flaws of classical GRACE products.