Gravity Recovery And Climate Experiment Estimates Research Articles

Machine learning assessment of hydrological model performance under localized water storage changes through downscaling

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) data has limited its application in the management of local-scale water resources. To address this limitation, we developed a new downscaling approach using predictors from regional and global hydrological models for a 15-year period (2002–2017) and tested it in the northern Great Artesian Basin, Australia. We used four different machine learning algorithms (support vector machine, partial least squares, gaussian process and random forest) to downscale the original GRACE estimate of 0.5° to a spatial grain size of 0.1° (global) and 0.05° (regional). This was based on precipitation, evapotranspiration and runoff estimates from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) and Australian Water Outlook (AWO) hydrological models, respectively. The downscaled products were validated using 42 in-situ precipitation observations spread across the test region. We further evaluated which of the downscaled products best mimicked local-scale hydrology using a range of statistical metrics. Our results showed that regional hydrological models best characterized the dynamics of local scale hydrology (rainfall v. downscaled product), and the gaussian process regression algorithm made the best predictions for both models. The correlation coefficients for the raw values varied from 0.45 to 0.49 while that of the standardized values varied from 0.46 to 0.52 with the random forest model providing the best fitting for the regional-based products. The regional downscaling approach employed in this study may be readily integrated into local water resources planning programs.

An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model

AbstractThe gravity recovery and climate experiment (GRACE) observations have so far been utilized to detect and trace the variations of hydrological extremes worldwide. However, applying the coarse resolution GRACE estimates for local‐scale analysis remains a big challenge. In this study, a new version of the fine resolution (1 km) Famine early warning systems network Land Data Assimilation System (FLDAS) model data was integrated into a machine learning model along with the GRACE data to evaluate the subbasin‐scale variations of water storage, and drought. With a correlation of and a root mean square error (RMSE) of of its results, the downscaling model turned out to be very successful in modelling the finer resolution variations of TWSA. The water storage deficit (WSD) and Water Storage Deficit Index (WSDI) were used to determine the episodes and severity of drought events. Accordingly, two severe droughts (January 2008 to March 2009 and September 2019 to December 2020) were discerned in the Kizilirmak Basin (KB) located in Central Türkiye. The characterization of droughts was evaluated based on WSDI, scPDSI, and model‐based drought indices of the soil moisture storage percentile (SMSP) and groundwater storage percentile (GWSP). The results indicated discrepancies in the drought classes based on different indices. However, the WSDI turned out to be more correlated with GWSP, suggesting its high ability to monitor groundwater droughts as well.

A shrinkage-free approach for fusing GRACE-based total water storage changes with models using wavelet multiresolution analysis

The observations from Gravity Recovery and Climate Experiment (GRACE) satellites provide total water storage changes (TWSC) at ∼300 km or larger spatial scales. To obtain information at a higher spatial resolution, suitable for management purposes, previous studies combined GRACE TWSC maps with information from hydrological models via assimilation techniques (e.g. CLSM-DA), or with external observations via scaling and machine learning approaches. Both techniques may introduce shrinkage errors, a reduction in amplitude of long spatial wavelength signals, and distorting water budgets at basin scale best observed by GRACE. Here, we present a fusion approach based on wavelet multiresolution analysis to combine long-wavelength GRACE TWSC observations with short-wavelength measurements of TWSC from the GLDAS Noah model in the contiguous United States (CONUS). We decompose TWSC maps into building blocks at different spatial wavelengths, examine their statistical characteristics, and combine complementary components at wavelet coefficient levels. The fused TWSC is then obtained by inverse wavelet transform. A spectral analysis indicates that the fused TWSC comprises frequency content that balances spectral characteristics of both input datasets. The fused TWSC dataset possesses enhanced details in the spatial domain, while it accurately quantifies the water budget and its long-term spatial trend at basin scale during 2003–2015, showing a good agreement with GRACE estimates. Independent validation against elastic hydrologic loading deformation measured at ∼2000 GNSS stations across CONUS shows similar overall performance for GRACE and fused datasets, while it outperforms GRACE in modeling groundwater storage changes in CONUS when compared to CLSM-DA.

Statistical downscaling of GRACE TWSA estimates to a 1-km spatial resolution for a local-scale surveillance of flooding potential

The Gravity Recovery and Climate Experiment (GRACE) paved the way for large-scale monitoring of the hydrological extremes. However, local scale analysis is aslo challenging due to the coarse resolution of the GRACE estimates. The feasibility of the downscaled GRACE data for the flood monitoring in the Kizilirmak Basin (KB) in Türkiye is investigated in this study by integrating the GRACE and hydrological model outputs of a random forest approach. Results suggest that the TWSA, over the Asagi Kizilirmak Basin (AKB), is ascending with an annual rate of +3.51mm/yr; while the Orta Kizilirmak Basin (OKB), Yukari Kizilirmak Basin (YKB), Delice Basin (DB), Develi Kapali Basin (DKB), and Seyfe Kapali Basin (SKB) showed descending trend respectively as -1.15mm/yr, -1.58mm/yr, -1.14mm/yr, -2.34mm/yr, and -1.31mm/yr. The hydrological status of the basin showed that in 2003, 2005, 2010–2013, and 2015–2016 periods the study area was prone to the inundation. Hence, by validating the Flood Potential Index (FPI) rates acquired from the downscaled GRACE data, it was shown that the best correlation coefficient (0.73) between FPI and streamflow (Q) is associated with the SKB. It is also concluded that the downscaled TWSA associated with the fine-resolution models depicts acceptable accuracy in determination of the flood potential at local scales.

Satellite-based investigation of water stress at the basin scale: an integrated analysis of downscaled GRACE estimates and remotely sensed data

Abstract Freshwater availability is a very determining issue, especially in semiarid and arid regions, for sustainable development and secured food production. In this premise, the detection and assessment of water stress are of utmost importance. In this study, the satellite-based Potential Available Water Storage (PAWS) index is used to test its feasibility for a basin-scale analysis of water stress in the Western Mediterranean Basin (WMB) in Türkiye. The coarse-resolution GRACE (Gravity Recovery And Climate Experiment) estimates were downscaled based on the Random Forest (RF) model and then were integrated with fine-resolution precipitation data to derive fine-resolution PAWS values. The accuracy of the index was validated against the net water flux (NWF) and water storage deficit (WSD) values over the basin. The results revealed a good performance for the PAWS index for a local scale evaluation of water stress. The PAWS variations turned out to be highly correlated with the NWF (r = 0.72) and WSD (r = 0.66). The PAWS indicates that the WMB has suffered from a critical hydrological situation from 2003 to 2020 during which the basin has been under stress with the most critical situation in 2018 when the per capita water has fallen below 500 m3 suggesting an absolute water stress status.

Remote sensing-based monitoring and evaluation of the basin-wise dynamics of terrestrial water and groundwater storage fluctuations.

The recent dynamics of terrestrial water storage (TWS) and groundwater storage (GWS) fluctuations were investigated based on the Gravity Recovery And Climate Experiment (GRACE) observations over 25 basins of Türkiye. Coarse-resolution GRACE estimates were downscaled based on the Random Forest algorithm. The impacts of precipitation (P) and evapotranspiration (ET) on the variations of water storage were also assessed. The findings demonstrated good performance for the RF model in simulating finer resolution estimates of TWS. The results indicated a diminishing trend of TWS and its hydrologic components over all the basins from 2003 to 2020. The Doğu Akdeniz Basin with the annually decreasing TWS and GWS of [Formula: see text] and [Formula: see text] was the most critical basin of Türkiye. The least storage loss was observed in the Batı Karadeniz Basin with the annual TWS and GWS loss of [Formula: see text] and [Formula: see text], respectively. Based on the results, Türkiye has lost, on average, an estimated [Formula: see text] and [Formula: see text] of its TWS and GWS, respectively, which are equivalent to the total storage loss of [Formula: see text] and [Formula: see text] of TWS and GWS during the last 18years. The results also indicated that P and ET interact differently with the variations of TWS and GWS. The net water flux was revealed to be partially correlated with the total water storage fluctuations, suggesting the governing role of other deriving forces particularly the anthropogenic factors in the spatiotemporal variations of Türkiye's water storage; therefore, a sector-specific analysis of the water storage variations is crucial for the country, particularly by concentrating more on the dynamics of GWS.

Modified flood potential index (MFPI) for flood monitoring in terrestrial water storage depletion basin using GRACE estimates

The Gravity Recovery and Climate Experiment (GRACE) and Follow-On (GRACE-FO) missions offer great potential for large-scale flood event monitoring. However, the effectiveness of current GRACE-based indices may be limited in areas with significant Terrestrial Water Storage (TWS) decline. In this study, a Modified Flood Potential Index (MFPI) was proposed to improve the monitoring capability for flood events in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB). First, the GRACE/FO Terrestrial Water Storage Anomaly (TWSA) data gap was filled with a Multilayer Perceptron Artificial Neural Network Model (MLP-ANN). Based on the gap-filled data, the performance of the MFPI was compared with five selected indices: Flood Potential Index (FPI), Total Storage Deficit Index (TSDI), Modified Total Storage Deficit Index (MTSDI), Water Storage Deficit Index (WSDI), and Combined Climatologic Deviation Index (CCDI). Finally, the spatiotemporal monitoring ability of the MFPI was evaluated for typical and atypical flood events. We found that (1) MLP-ANN was able to predict the GRACE/FO data gap with a performance of ‘Very Good’; (2) the MFPI outperformed the FPI, TSDI, MTSDI, WSDI, and CCDI in capturing flood events, and could also reflect their spatiotemporal processes; and (3) the MFPI avoids long-term TWSA trend effects on flood event monitoring and is available for areas with significant TWSA trends. This study can provide a reference for using GRACE data to monitor large-scale flood events.

Comparison of the shallow groundwater storage change estimated by a distributed hydrological model and GRACE satellite gravimetry in a well-irrigated plain of the Haihe River basin, China

Groundwater is a key component of the global hydrological cycle. Quantitative approaches to estimate the spatial and temporal changes in groundwater storage (GWS) could provide solid foundation for sustainable management. This study made a comprehensive comparison of GWS changes based on two independent approaches: the Soil and Water Assessment Tool (SWAT) hydrological model and Gravity Recovery and Climate Experiment (GRACE) satellite mission data. The feasibility of using these two estimation methods to quantify shallow groundwater variations in the Haihe River basin of China, a globally representative overexploited area, was assessed. The GRACE-derived GWS changes were calculated based on independent component analysis and a joint inversion method. The results showed that the GRACE-estimated shallow GWS anomalies during 2003–2012 agree well with the SWAT-simulated data. For the variations in the long-term GWS depletion rate, we found that heterogeneity at 22 subbasin (approximately 500 to 1500 km2) scales was detected by the GRACE data, and these results were spatially coherent with the SWAT simulations based on the distributed modeling units and multiple inputs. The GRACE estimates could adequately replicate the general trends of GWS depletion in the winter wheat (Triticum aestivum L.) growth period and recovery in the summer maize (Zea mays L.) growth period. Specifically, comparisons between GRACE-based and SWAT-simulated results indicated that the signals of pumping for winter wheat irrigation in several key water requirement periods in April, May and November were identified by GRACE, as were seasonal changes in shallow GWS (e.g., an obvious depletion in spring). Despite an overestimation of the scope of the fluctuations in GRACE estimates compared with those of SWAT simulations from the perspective of the water flux, the effective identification of the impact of anthropogenic exploitation on shallow GWS in GRACE suggests that this monitoring tool could be applied in the groundwater management of this plain. This study demonstrated a good agreement between SWAT-simulated and GRACE-based shallow GWS changes from the perspectives of long-term trends and variations in crop seasons. Additionally, satisfactory consistency was observed from the perspective of the water flux in this semiarid and semihumid study region considering the influence of human activities.

Bridging the data gap between GRACE and GRACE-FO using artificial neural network in Greenland

The Gravity Recovery and Climate Experiment (GRACE) provided an entirely new way to measure mass changes on the Earth at unprecedented accuracy and resolution. However, the delayed launch of the GRACE Follow On (FO) mission led to an approximately 1-year gap between GRACE and GRACE-FO data, breaking the continuity of observation and hampering data analysis. Efforts have been made to bridge this gap for the major river basins, but little has been done on ice sheets. To address this limitation, we evaluated multiple linear regression (MLR), a back propagation neural network (BPNN), and a deep belief network (DBN) to fill the data gap in Greenland and its six sub-regions. We employed these methods to establish the relationships between precipitation, runoff, evapotranspiration, and ice discharge and GRACE-estimated ice mass changes. A sliding window testing method shows that the BPNN outperformed the two other methods with a root mean square (RMS) error of 1.5 cm in the metrics for the equivalent water height for the whole of Greenland and 1.5–3.6 cm (2.5 cm in average) for the six sub-regions. This accuracy is rather high given an uncertainty of ∼ 2 cm in GRACE estimates, but higher for the whole of Greenland than in the sub-regions. This difference was caused by mass leakage error between adjacent sub-regions in the GRACE observations, and can be mitigated by merging these sub-regions. The BPNN predicted ice mass changes agree with the GRACE estimates for long-term and seasonal signals, but are less accurate and precise for interannual signals. Based on the GRACE observations and the BPNN predicted values, we re-estimated the ice mass change in Greenland finding that ice loss on Greenland decelerated after mid-2012, primarily due to deceleration in SW, SE, and NE regions.

Components of 21 years (1995–2015) of absolute sea level trends in the Arctic

Abstract. The Arctic Ocean is at the frontier of the fast-changing climate in the northern latitudes, and sea level trends are a bulk measure of ongoing processes related to climate change. Observations of sea level in the Arctic Ocean are nonetheless difficult to validate with independent measurements, and this is globally the region where the sea level trend (SLT) is most uncertain. The aim of this study is to create a satellite-independent reconstruction of Arctic SLT, as it is observed by altimetry and tide gauges (TGs). Previous studies use Gravity Recovery and Climate Experiment (GRACE) observations to estimate the manometric (mass component of) SLT. GRACE estimates, however, are challenged by large mass changes on land, which are difficult to separate from much smaller ocean mass changes. Furthermore, GRACE is not available before 2003, which significantly limits the period and makes the trend more vulnerable to short-term changes. As an alternative approach, this study estimates the climate-change-driven Arctic manometric SLT from the Arctic sea level fingerprints of glaciers, Greenland, Antarctica and glacial isostatic adjustment (GIA) with the addition of the long-term inverse barometer (IB) effect. The halosteric and thermosteric components complete the reconstructed Arctic SLT and are estimated by interpolating 300 000 temperature (T) and salinity (S) in situ observations. The SLT from 1995–2015 is compared to the observed SLT from altimetry and 12 selected tide gauges (TGs) corrected for vertical land movement (VLM). The reconstructed estimate manifests the salinity-driven halosteric component as dominating the spatial SLT pattern with variations between −7 and 10 mm yr−1. The manometric SLT in comparison is estimated to be 1–2 mm yr−1 for most of the Arctic Ocean. The reconstructed SLT shows a larger sea level rise in the Beaufort Sea compared to altimetry, an issue that is also identified by previous studies. There is a TG-observed sea level rise in the Siberian Arctic in contrast to the sea level fall from the reconstructed and altimetric estimate. From 1995–2015 the reconstructed SLT agrees within the 68 % confidence interval with the SLT from observed altimetry in 87 % of the Arctic between 65∘ N and 82∘ N (R=0.50) and with 5 of 12 TG-derived (VLM-corrected) SLT estimates. The residuals are seemingly smaller than results from previous studies using GRACE estimates and modeled T–S data. The spatial correlation of the reconstructed SLT to altimetric SLT during the GRACE period (2003–2015) is R=0.38 and R=0.34/R=0.37 if GRACE estimates are used instead of the constructed manometric component. Thus, the reconstructed manometric component is suggested as a legitimate alternative to GRACE that can be projected into the past and future.

Land deformation and sinkhole occurrence in response to the fluctuations of groundwater storage: an integrated assessment of GRACE gravity measurements, ICESat/ICESat-2 altimetry data, and hydrologic models

ABSTRACT Uncontrolled extraction of water from groundwater aquifers causes groundwater depletion, which in turn triggers the formation of sinkholes in many parts of the world. Monitoring and detection of these geomorphologic features are of utmost importance and priority for the decision-makers to minimize significant environmental as well as socio-economic implications of land deformation. In this study, a systematic approach is proposed to investigate the spatio-temporal associations of groundwater storage changes with sinkhole evolution and land deformation by using a number of remotely sensed and modeled data as well as in-situ observations. The proposed approach is implemented and tested in Konya Closed Basin (KCB), Turkey, which is one of the most critical areas in central Turkey concerning sinkhole formation. The results of GRACE (Gravity Recovery and Climate Experiment) estimates suggest that there is a descending trend in the temporal variations of TWSA (Terrestrial Water Storage Anomalies) and GWSA (Groundwater Storage Anomalies) over KCB with an average storage depletion of 4.12 ± 0.34 cm/yr and 3.40 ± 0.61 cm/yr, respectively. The analysis of land deformation from ICESat/ICESat-2 (Ice, Cloud, and Land Elevation Satellite) altimetry data also indicates a descending trend with an estimated average vertical displacement of 5 cm/yr for the study area, which seems to be in rational accord with the sinkhole evolution over KCB. The results further suggest that the sinkhole evolution over KCB has an acceptable association with the variations of groundwater storage, groundwater use, and precipitation. Compared with previous works implemented over KCB, the findings of this study manifest a good performance for the proposed methodology. The integrated analysis of GRACE gravity measures, ICESat/ICESat-2 altimetry data, modeled groundwater abstraction, and in-situ observations of precipitation yields acceptable results for the detection and monitoring of sinkhole events provided that appropriate distribution of the elevation points from ICESat/ICESat-2 is accessible for the given study area.

Uncertainty Assessments of Load Deformation from Different GPS Time Series Products, GRACE Estimates and Model Predictions: A Case Study over Europe

A good understanding of the accuracy of the Global Positioning System (GPS) surface displacements provided by different processing centers plays an important role in load deformation analysis. We estimate the noise level in both vertical and horizontal directions for four representative GPS time series products, and compare GPS results with load deformation derived from the Gravity Recovery and Climate Experiment (GRACE) gravity measurements and climate models in Europe. For the extracted linear trend signals, the differences among different GPS series are small in all the three (east, north, and up) directions, while for the annual signals the differences are large. The mean standard deviations of annual amplitudes retrieved from the four GPS series are 3.54 mm in the vertical component (69% of the signal itself) and ~ 0.3 mm in the horizontal component (30% of the signal itself). The Scripps Orbit and Permanent Array Center (SOPAC) and MEaSUREs series have the lowest noise level in vertical and horizontal directions, respectively. Through consistency/discrepancy analysis among GPS, GRACE, and model vertical series, we find that the Jet Propulsion Laboratory (JPL) and Nevada Geodetic Laboratory (NGL) series show good consistency, the SOPAC series show good agreements in annual signal with the GRACE and model, and the MEaSUREs series show substantially large annual amplitude. We discuss the possible reasons for the notable differences among GPS time series products.

RECOG RL01: correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes

Abstract. Observations of changes in terrestrial water storage (TWS) obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models, spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighbouring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology-related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes and reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a relocation of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction, RECOG-EQ, includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above Mw9. We discuss that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available on an open-access basis via the Pangaea database (RECOG-LR: Deggim et al., 2020a, https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al., 2020b, https://doi.org/10.1594/PANGAEA.921923).

Coherent Satellite Monitoring of the Water Cycle Over the Amazon. Part 2: Total Water Storage Change and River Discharge Estimation

AbstractIn companion paper 1, the SAtellite Water Cycle (SAWC) satellite data set integration approach was presented. SAWC accounts for (1) the closure of the water budget at the sub‐basin scale by (2) using upstream/downstream dependencies. Here, the SAWC database is used to reconstruct a missing water component. The total water storage change (dS) can be reconstructed prior to the Gravity Recovery and Climate Experiment (GRACE) period. In terms of seasonal and interannual variations, SAWC provides long‐term dS estimates that are highly consistent with the GRACE estimate (2002–2015). When compared to the extent of inundation, correlation of anomalies reaches 0.6 over sub‐basins presenting large floodplain. A strong relationship (correlation of 0.7) exists between the monthly dS and the El Niño index. The river discharge R can also be estimated using the SAWC approach during the GRACE period. A scheme was developed to estimate R continuously along the river through the indirect interpolation of discharge station measurements by using satellite estimates of dS, precipitation P, and evaporation E. These R estimates along the river were evaluated with 80 independent measurements and had a correlation of 0.86 and a Kling‐Gupta efficiency index of 0.71. A comprehensive monthly river discharge estimate was proposed for the first time across the Amazon basin over 13 years (2002–2015) at a 0.25° resolution. The SAWC framework is a pure observation tool for integrating a large range of satellite estimates, potentially facilitating the assimilation of such data into land surface models.

Missing Hydrological Contribution to Sea Level Rise

AbstractOver the past decade, the rate of global mean sea level (GMSL) rise is about 3.5 mm/year. Terrestrial water/ice mass loss to the oceans and ocean volume expansion explain about 3.1 mm/year, indicating that the GMSL budget is not been fully understood. Past estimates from Gravity Recovery and Climate Experiment (GRACE) data have indicated that terrestrial water storage (TWS) is increasing and is thus a mitigating contributor to GMSL rise. However, TWS estimates from GRACE are uncertain mostly due to limitations in GRACE estimates of degree‐1 and degree‐2 order‐0 spherical harmonic coefficients. We obtain an improved estimate of the TWS contribution to GMSL change using revised GRACE estimates of these low‐degree coefficients. For the period 2005–2015, we find that TWS makes an additional contribution to GMSL rise of about 0.32 ± 0.02 mm/year, mostly associated with a TWS decrease. This revised estimate is sufficient to nearly balance the budget of GMSL rise.

GRAMAT: a comprehensive Matlab toolbox for estimating global mass variations from GRACE satellite data

In this paper, we robustly analyze the noise reduction methods for processing spherical harmonic (SH) coefficient data products collected by the Gravity Recovery and Climate Experiment (GRACE) satellite mission and devise a comprehensive GRACE Matlab Toolbox (GRAMAT) to estimate spatio-temporal mass variations over land and oceans. Functions in GRAMAT contain: (1) destriping of SH coefficients to remove “north-to-south” stripes, or geographically correlated high-frequency errors, and Gaussian smoothing, (2) spherical harmonic analysis and synthesis, (3) assessment and reduction of the leakage effect in GRACE-derived mass variations, and (4) harmonic analysis of regional time series of the mass variations and assessment of the uncertainty of the GRACE estimates. As a case study, we analyze the terrestrial water storage (TWS) variations in the Amazon River basin using the functions in GRAMAT. In addition to obvious seasonal TWS variations in the Amazon River basin, significant interannual TWS variations are detected by GRACE using the GRAMAT, which are consistent with precipitation anomalies in the region. We conclude that using GRAMAT and processing the GRACE level-2 data products, the global spatio-temporal mass variations can be efficiently and robustly estimated, which indicates the potential wide range of GRAMAT’s applications in hydrology, oceanography, cryosphere, solid Earth and geophysical disciplines to interpret large-scale mass redistribution and transport in the Earth system. We postulate that GRAMAT will also be an effective tool for the analysis of data from the upcoming GRACE-Follow-On mission.

Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface: implications for the global water and sea level budgets

Observations from the Gravity Recovery and Climate Experiment (GRACE) satellite missionprovide quantitative estimates of the global water budget components. However, these estimatesare uncertain as they show discrepancies when different parameters are used in the processing ofthe GRACE data. We examine trends in ocean mass, ice loss from Antarctica, Greenland, arcticislands and trends in water storage over land and glaciers from GRACE data (2005–2015) andexplore the associated uncertainty. We consider variations in six different GRACE processingparameters, namely the processing centre of the raw GRACE solutions, the geocentre motion,the Earth oblateness, the filtering, the leakage correction and the glacial isostatic adjustment(GIA). Considering all possible combinations of the different processing parameters leads toan ensemble of 1500 post-processed GRACE solutions, which is assumed to cover a significantpart of the uncertainty range of GRACE estimates. The ensemble-mean trend in all globalwater budget components agree within uncertainties with previous estimates based on differentsources of observations. The uncertainty in the global water budget is±0.27 mm yr−1[at the 90per cent confidence level (CL)] over 2005–2015. We find that the uncertainty in the geocentremotion and GIA corrections dominate the uncertainty in GRACE estimate of the globalwater budget. Their contribution to the uncertainty in GRACE estimate is respectively±0.21and±0.12 mm yr−1(90 per cent CL). This uncertainty in GRACE estimate implies anuncertainty in the net warming of the ocean and the Earth energy budget of±0.25 W m−2(90per cent CL) when inferred using the sea level budget approach.

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

NASA’s Gravity Recovery and Climate Experiment (GRACE) has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km2). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km2). Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km2) dataset of groundwater storage changes over a portion of California’s Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM)-derived slope, and Natural Resources Conservation Service (NRCS) soil type). The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE) values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively). Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model’s simulated maps have the potential for application to local groundwater management initiatives in the region.

The Impact of Atmospheric Modeling Errors on GRACE Estimates of Mass Loss in Greenland and Antarctica

AbstractSystematic errors in Gravity Recovery and Climate Experiment (GRACE) monthly mass estimates over the Greenland and Antarctic ice sheets can originate from low‐frequency biases in the European Centre for Medium‐Range Weather Forecasts (ECMWF) Operational Analysis model, the atmospheric component of the Atmospheric and Ocean Dealising Level‐1B (AOD1B) product used to forward model atmospheric and ocean gravity signals in GRACE processing. These biases are revealed in differences in surface pressure between the ECMWF Operational Analysis model, state‐of‐the‐art reanalyses, and in situ surface pressure measurements. While some of these errors are attributable to well‐understood discrete model changes and have published corrections, we examine errors these corrections do not address. We compare multiple models and in situ data in Antarctica and Greenland to determine which models have the most skill relative to monthly averages of the dealiasing model. We also evaluate linear combinations of these models and synthetic pressure fields generated from direct interpolation of pressure observations. These models consistently reveal drifts in the dealiasing model that cause the acceleration of Antarctica's mass loss between April 2002 and August 2016 to be underestimated by approximately 4 Gt yr−2. We find similar results after attempting to solve the inverse problem, recovering pressure biases directly from the GRACE Jet Propulsion Laboratory RL05.1 M mascon solutions. Over Greenland, we find a 2 Gt yr−1 bias in mass trend. While our analysis focuses on errors in Release 05 of AOD1B, we also evaluate the new AOD1B RL06 product. We find that this new product mitigates some of the aforementioned biases.

Long-Term Water Storage Changes of Lake Volta from GRACE and Satellite Altimetry and Connections with Regional Climate

Satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) provides a quantitative measure of terrestrial water storage (TWS) change at different temporal and spatial scales. In this study, we investigate the ability of GRACE to quantitatively monitor long-term hydrological characteristics over the Lake Volta region. Principal component analysis (PCA) is employed to study temporal and spatial variability of long-term TWS changes. Long-term Lake Volta water storage change appears to be the dominant long-term TWS change signal in the Volta basin. GRACE-derived TWS changes and precipitation variations compiled by the Global Precipitation Climatology Centre (GPCC) are related both temporally and spatially, but spatial leakage attenuates the magnitude of GRACE estimates, especially at small regional scales. Using constrained forward modeling, we successfully remove leakage error in GRACE estimates. After this leakage correction, GRACE-derived Lake Volta water storage changes agree remarkably well with independent estimates from satellite altimetry at interannual and longer time scales. This demonstrates the value of GRACE estimates to monitor and quantify water storage changes in lakes, especially in relatively small regions with complicated topography.