Recovery Mission Research Articles

Benefits and Pitfalls of GRACE and Streamflow Assimilation for Improving the Streamflow Simulations of the WaterGAP Global Hydrology Model

AbstractDistribution and change of freshwater resources is often simulated with global hydrological models. However, owing to process representation limitations and forcing data uncertainties, these model simulations have shortcomings. Combining them with observations via data assimilation, for example, with data from the Gravity Recovery and Climate Experiment (GRACE) mission or streamflow measured at in situ stations is considered to improve the realism of the simulations. We assimilate gridded total water storage anomaly (TWSA) from GRACE into the WaterGAP Global Hydrology Model (WGHM) over the Mississippi River basin via an Ensemble Kalman Filter. Our results agree with previous studies where assimilating GRACE observations nudges TWSA simulations closer to the observations, reducing the root mean square error (RMSE) by 21% compared to an uncalibrated model. However, simulations of streamflow show degeneration at more than 90% of all gauge stations for metrics such as RMSE and correlations; only the annual phase of simulated streamflow improves at half the stations. Therefore, for the first time, we instead assimilated streamflow observations into the WGHM, which improved simulated streamflow at up to nearly 80% of the stations, with normalized RMSE showing improvements of up to 0.1, while TWSA was well‐simulated in all metrics. Combining both approaches, that is, jointly assimilating GRACE‐derived TWSA and streamflow observations, leads to a trade‐off between a good fit of both variables albeit skewed to the GRACE observations. Overall, we speculate that our findings point to limitations of process representation in WGHM hindering consistent flux simulation from the storage history, especially in dry regions.

A Probabilistic Approach to Characterizing Drought Using Satellite Gravimetry

AbstractIn the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.

Motion generation for a tracked robot going over an unfixed obstacle on a slope using reinforcement learning

Tracked robots are useful in disaster recovery missions owing to their high traversability. However, even tracked robots find traversing terrains with obstacles such as rocks difficult. Particularly, unfixed obstacles pose problems. Therefore, this study aims to generate motion for tracked robots to go over unfixed obstacles on slopes by utilizing a reinforcement learning (RL) approach. In RL, rewards are important to make robots learn optimal policies for various tasks. Hence, a new definition of going over an unfixed obstacle is proposed. Based on the proposed definition, a sparse reward is designed to motivate the robot to go over the obstacle while avoiding the large sliding-down phenomenon responsible for the failure of going over and the large directional deviation of the robot. In our experiment, a robot is trained in a dynamic simulator and it attempts to go over a spherical unfixed obstacle under various environmental conditions: radius of the obstacle, slope degree, and initial position of the obstacle. We verify that the success rate converges to a high value and the robot successfully generates motions to go over the spherical unfixed obstacle.

Multivariate variational mode decomposition to extract the stripe noise in GRACE harmonic coefficients

SUMMARY In this work, a novel method has been developed to remove the north–south stripe noise in the Level-2 spherical harmonic coefficient products collected by the Gravity Recovery and Climate Experiment (GRACE) mission. The proposed method extracts the stripe noise from the equivalent water height (EWH) map via the Multivariate Variational Mode Decomposition algorithm. The idea behind our method is to extract the cofrequency mode in multiple-channel series in the longitude direction. The parameters of our method are empirically determined. The investigation in a closed-loop simulation proves the improvement of our methods compared with the Singular Spectrum Analysis Spatial (SSAS) filter. Subsequently, the spatial-domain and spectral-domain investigations are conducted by using real GRACE data. Our method only suppresses stripe noise at low latitudes (30°S–30°N) and imposes an order-dependent impact on spherical harmonic coefficients but with potential oversmoothing. Meanwhile, the well-documented water level proves that our method further reduces outliers in a time-series of localized mass variations compared with the SSAS filter. More importantly, users are allowed to reduce the filtering strength of our method to preserve small-scale strong signals while suppressing stripe noise. Moreover, noise levels over the ocean at low latitudes are evaluated as well. The noise level of our method using empirical parameters is 32.48 mm of EWH, with 31.54 and 53.52 mm for DDK6 and SSAS, respectively. Our work introduces a novel method to address the issue of north–south stripe noise in the spatial domain.

An investigation of ocean mass budget in the East China Sea during the GRACE era

The Gravity Recovery and Climate Experiment (GRACE) mission provides uniquely high-precision observations for monitoring ocean mass changes (OMC), allowing for the establishment and evaluation of the ocean mass budget in conjunction with satellite altimetry and temperature and salinity observations. However, it is challenging to perform OMC closed-loop validation in the East China Sea (ECS) due to potential biases in the individual model and the lack of certain data processing. In this study, we comprehensively analyze the ocean mass budget in the ECS during the GRACE era (2005–2015) by utilizing multiple datasets, mainly consisting of three official GRACE RL06 solutions, three altimetry products, and four ocean reanalysis products. The effect of ocean bottom deformation, neglected in previous studies, is −0.38 ± 0.06 mm/yr, and we estimate a more accurate ensemble sea level change to be 4.05 ± 1.50 mm/yr in the ECS from the altimetry products. There are discrepancies between leakage-corrected GRACE OMC observations and steric-corrected altimeter OMC estimations in both the seasonal signals and the long-term trends (e.g., 6.25 mm/yr vs. 4.22 mm/yr). These discrepancies are strongly correlated with sediment runoff from the Yangtze River and in-situ sediment observations, suggesting that ocean sediment accumulation should be considered in the ocean mass budget in the ECS. Since in-situ sediment data are estimated over ∼100 years, we employ an empirical estimation method to determine the corresponding data during the period 2005–2015, to avoid potential biases caused by inconsistencies in observational timespans. The results show that sediment mass changes can explain about 96 % of residual trends. Our results emphasize the significant impact of sediment on improving the ocean mass budget in the ECS, offering a novel perspective for estimating ocean mass changes in other coastal regions.

Boundaries identification of geological structure in lunar Oceanus Procellarum region using full gravity gradient tensor methodology

Boundary recognition visually delineates the horizontal extent of subsurface anomalies, providing a foundational basis for interpreting gravity data. Compared to gravity observations, gravity gradient data offers the benefits of multiple components and the enhancement of shortwave information in graphical representations. In our study, we investigated the delineation of geological structure boundaries in the lunar Oceanus Procellarum region using gravity gradient techniques. First, based on the observations from the Gravity Recovery and Interior Laboratory (GRAIL) mission, we obtained high-precision synthetic full tensor values of the gravity gradient as our primary research data. Moreover, to reduce curvature errors during large-scale terrain forward modeling, we applied the spherical prism of tesseroid to eliminate the effects of near-surface topography. Grounded on the Bouguer anomalies of gravity gradient, four distinct boundary recognition methods have been employed to explore the geological structure of the lunar Oceanus Procellarum region. It includes the Theta map method, the directional Theta map method, the combination of total horizontal derivative and the modulus of full tensor gravity gradient, and the improved edge detection method based on the Theta map method.The common feature of these methods is the utilization of the multi-component data combination from gravity gradient tensors, which can enhance the accuracy of edge detection. From the investigation results presented in the paper, we have found the following: 1) The improved edge detection method, based on the Theta map principle, enables better identification of the center of subsurface anomaly bodies during boundary recognition, utilizing a consistent single color in graphic displays. 2) According to the results of boundary recognition, numerous strip anomalies exist in the Oceanus Procellarum area that show less correlation with topographic relief. One possible explanation for these graphical anomalies of gravity gradient is that they serve as channels for the upwelling of mantle material during lunar evolution.

SODA - a tool to predict storm induced orbit decays for low Earth orbiting satellites

Due to the rapidly increasing technological progress in the last decades, the issue of space weather and its influences on our everyday life gains more and more importance. Today, satellite-based navigation plays a key role in aviation, logistics and transportation systems. With the strong rise of the current solar cycle 25 the number and intensity of solar eruptions increased. The forecasting tool SODA (Satellite Orbit DecAy) is based on an interdisciplinary analysis of space geodetic observations and solar wind in situ measurements. It predicts the effect of coronal mass ejections from their in-situ measured counterparts (ICME) on the altitude of low Earth orbiting satellites at 490 km with a lead time of about 20 hours. Additionally, it classifies the severeness of the expected geomagnetic storm in the form of the Space Weather G–scale from the National Oceanic and Atmospheric Administration (NOAA). For the establishment and validation of SODA, we examined 360 ICME events over a period of 21 years. Appropriated variations in the thermospheric neutral mass density, were derived mainly from measurements of the Gravity Recovery and Climate Experiment (GRACE) satellite mission. Related changes in the interplanetary magnetic field component Bz were investigated from real-time measurements using data from spacecraft located at the Lagrange point L1. The analysis of the ICME induced orbit decays and the interplanetary magnetic field showed a strong correlation as well as a time delay between the ICME and the associated thermospheric response. The derived results are implemented in the real- forecasting tool SODA, which integrated in the Space Safety Program Ionospheric Weather Expert Service Center; I.161 of the Space Agency (ESA).

Colored noise in GRACE total water storage time series: Its impact on trend significance in the Türkiye region and major world river basins

Temporal correlations are prevalent in most geophysical time series data, leading to what is known as colored noise, which become more apparent in the frequency domain rather than time domain. Neglecting this type of noise in modeling can result in underestimated standard errors for the parameters of the model used to analyze the time series. As a result, statistical tests misinterpret the significance of these parameters, such as the overall trend rate, because the “signal to noise” ratio becomes incorrectly larger than it should be. In this study, we investigate the temporal correlations in the time series data from the Gravity Recovery and Climate Experiment (GRACE) mission. GRACE and its successor mission GRACE Follow-On (GRACE-FO) have been successfully employed to monitor global total water storage anomalies over two decades since its initial launch in 2002. We primarily utilize monthly GRACE mascon (mass concentration) solutions provided by NASA Goddard Space Flight Center (GSFC), examining both regional and global scales. The power spectrum density of the residuals, calculated after applying standard harmonic regression to each mascon time series on Earth’s land, reveals an average spectral index of κ = −1. This indicates the predominance of flicker noise, one of the most common forms of colored noise in geodetic time series. Notably, the noise becomes more Brownian (κ < −1) in regions with intense hydrological events, while it tends to be more white noise (κ → 0) in areas with less water circulation compared to other land regions. This observation suggests that unmodelled hydrological events, such as droughts, floods, and ones associated with ice-melting, contribute to the residuals as colored noise in the time series. To account for this noise in our time series modeling, we primarily consider autoregressive (AR) moving average (MA) process, commonly known as ARMA, in addition to the standard variance component estimation for noise amplitudes. We focus on individual mascon blocks in the Türkiye Region and mascon solutions for 24 world’s major river basins. Our findings demonstrate that an ARMA(1,1) model is sufficient for describing the noise in these regions. The results underscore the importance of considering colored noise, as it leads to approximately three times larger standard errors for the overall trend rate. This substantially impacts the significance of the trend rates.

Assessment of the Added Value of the GOCE GPS Data on the GRACE Monthly Gravity Field Solutions

The time-varying gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission suffer from pronounced longitudinal stripe errors in the spatial domain. A potential way to mitigate such errors is to combine GRACE data with observations from other sources. In this study, we investigate the impacts on GRACE monthly gravity field solutions of incorporating the GPS data collected by the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) mission. To that end, we produce GRACE/GOCE combined monthly gravity field solutions through combination on the normal equation level and compare them with the GRACE-only solutions, for which we have considered the state-of-the-art ITSG-Grace2018 solutions. Analysis in the spectral domain reveals that the combined solutions have a notably lower noise level beyond degree 30, with cumulative errors up to degree 96 being reduced by 31%. A comparison of the formal errors reveals that the addition of GOCE GPS data mainly improves (near-) sectorial coefficients and resonant orders, which cannot be well determined by GRACE alone. In the spatial domain, we also observe a significant reduction by at least 30% in the noise of recovered mass changes after incorporating the GOCE GPS data. Furthermore, the signal-to-noise ratios of mass changes over 180 large river basins were improved by 8–20% (dependent on the applied Gaussian filter radius). These results demonstrate that the GOCE GPS data can augment the GRACE monthly gravity field solutions and support a future GOCE-type mission for tracking more accurate time-varying gravity fields.

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.

Sediment transport in South Asian rivers high enough to impact satellite gravimetry

Abstract. Satellite gravimetry is used to study the global hydrological cycle. It is a key component in the investigation of groundwater depletion on the Indian subcontinent. Terrestrial mass loss caused by river sediment transport is assumed to be below the detection limit in current gravimetric satellites of the Gravity Recovery and Climate Experiment Follow-On mission. Thus, it is not considered in the calculation of terrestrial water storage (TWS) from such satellite data. However, the Ganges and Brahmaputra rivers, which drain the Indian subcontinent, constitute one of the world's most sediment-rich river systems. In this study, we estimate the impact of sediment mass loss within their catchments on local trends in gravity and consequential estimates of TWS trends. We find that for the Ganges–Brahmaputra–Meghna catchment sediment transport accounts for (4 ± 2) % of the gravity decrease currently attributed to groundwater depletion. The sediment is mainly eroded from the Himalayas, where correction for sediment mass loss reduces the decrease in TWS by 0.22 cm of equivalent water height per year (14 %). However, sediment mass loss in the Brahmaputra catchment is more than twice that in the Ganges catchment, and sediment is mainly eroded from mountain regions. Thus, the impact on gravimetric TWS trends within the Indo–Gangetic Plain – the main region identified for groundwater depletion – is found to be comparatively small (&lt; 2 %).

Assimilating Space‐Based Thermospheric Neutral Density (TND) Data Into the TIE‐GCM Coupled Model During Periods With Low and High Solar Activity

AbstractThe global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space‐based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space‐based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm‐C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm‐B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE‐GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.

Relevance of near-surface soil moisture vs. terrestrial water storage for global vegetation functioning

Abstract. Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of its rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture, and terrestrial water storage present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterize vegetation functioning through the near-infrared reflectance of vegetation (NIRv) and compare its relation to (i) near-surface soil moisture from the ESA's Climate Change Initiative (CCI) and (ii) total water storage from the Gravity Recovery and Climate Experiment (GRACE) mission at a monthly timescale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy- and other water-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than that of near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. At the same time, we note that this comparison is hampered by different noise levels in these satellite data streams. In line with this, an attribution analysis that examines the relative importance of soil water storage for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of the ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation–water interactions in land surface models to support more accurate climate change projections.

Application of gravity and remote sensing data to groundwater storage variation in Wadi Al Dawasir, Saudi Arabia

In order to deal with the increase in human-caused impacts, Saudi Arabia is exploring new groundwater sources. Significant agricultural development in Wadi Al Dawasir has resulted in an overuse of groundwater resources. Constant monitoring of the wadi's groundwater is necessary in order to make informed decisions about the future of the wadi's groundwater resources and the local economy. The rate of groundwater depletion in the Wadi Al Dawasir drainage basin in northern Saudi Arabia was calculated by combining data from the advanced Gravity Recovery and Climate Experiment (GRACE) mission with outputs from a land surface model. The analysis covered the period from April 2002 to December 2021. The results are as follows: (1) the average terrestrial water storage fluctuation (ΔTWS) was calculated at −0.48 ± 0.02 cm yr−1; (2) the average soil moisture storage change (ΔSMS) was calculated at + 0.0008 ± 0.0002 cm yr−1; (3) the average groundwater depletion rate was computed at −0.48 ± 0.02 cm yr−1; (4) the average yearly rainfall data for the Wadi Al Dawasir was 90.1 mm; (5) The surface relief is creating eastward streams that carry surface water downstream; (6) Significant agricultural expansions over the past few decades can be observed through Landsat change detection; (7) Higher sediment accumulation varying from 400 to more than 3000 m is observed in the east, near the Wadi's downstream, where the Wajid aquifer can be found. The integrated approach is a cost-effective and efficient tool for accurately evaluating the variability of groundwater resources over large regions.

Characterization of groundwater drought and understanding of climatic impact on groundwater resources in Korea

Although South Korea is frequently subjected to monsoons, the shortage of summer monsoons and relatively dry spring and winter seasons cause droughts in the Korean Peninsula. Since the late 1960s, severe droughts in South Korea have encouraged extensive groundwater development for agricultural and economic growth. Therefore, identifying and estimating the impact of droughts on the country’s groundwater resources is crucial for establishing policies and regulations on groundwater exploitation and recharge. Current drought management of the country relies on a meteorological drought index. However, these estimates lack a comprehensive understanding of absolute drought severity and propagation. This study aims to develop a quantitative method for calculating the frequency and severity of groundwater droughts based on the Groundwater Deficit (GWD) derived from NASA's Gravity Recovery and Climate Experiment (GRACE) mission data. The estimated annual GWD in mass over the country ranges from -0.07 km3 to 0.04 km3, with a trend in the mass of -1.60×10−3 km3/year from 2002 to 2021. Thirteen groundwater drought events were identified based on GWD. Among those, the highest drought severities recorded in 2015 and 2019 were -34.27 km3 months and -55.38 km3 months, respectively. Monthly deficits define the quantity of water required to restore normal groundwater storage conditions. Hence, the minimum and average days to recover from groundwater droughts were estimated based on the strength of the storage-based approaches. The GWD approach aligns with meteorological drought databases with a time lag. It illustrates the transition from meteorological drought to groundwater drought, along with their onset, cessation, propagation, instantaneous severity, and peak magnitude.

Temporal and Spatial Variation Analysis of Groundwater Stocks in Xinjiang Based on GRACE Data

Situated in China’s arid and semi-arid zones, the Xinjiang region heavily relies on groundwater for its freshwater supply. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, covering the years 2003 to 2021, to quantitatively evaluate the temporal and spatial changes in groundwater storage anomalies (GWSA) in the Xinjiang region. Furthermore, we incorporate the HydroSHEDS dataset to examine the spatial variations in groundwater storage anomalies across watersheds of varying scales. Based on our findings, the GWSA decreased during the study period at a mean rate of −0.381 mm/month, marked by a consistent trend and notable interannual variability. In addition, significant regional disparities are observed; while groundwater storage in the southeastern watersheds is on an upward trend, a general decline is noted in the northern and central regions. The most pronounced depletion is detected in the northwest, especially in the Ili River basin and along the western slopes of the Tianshan Mountains. These changes are intricately linked to anthropogenic factors, including population growth and escalating water demands. In response, the study advocates for the development and enforcement of more rigorous and scientifically informed groundwater management strategies to promote sustainable water use in Xinjiang.

Globally consistent estimates of high-resolution Antarctic ice mass balance and spatially resolved glacial isostatic adjustment

Abstract. A detailed understanding of how the Antarctic ice sheet (AIS) responds to a warming climate is needed because it will most likely increase the rate of global mean sea level rise. Time-variable satellite gravimetry, realized by the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions, is directly sensitive to AIS mass changes. However, gravimetric mass balances are subject to two major limitations. First, the usual correction of the glacial isostatic adjustment (GIA) effect by modelling results is a dominant source of uncertainty. Second, satellite gravimetry allows for a resolution of a few hundred kilometres only, which is insufficient to thoroughly explore causes of AIS imbalance. We have overcome both limitations by the first global inversion of data from GRACE and GRACE-FO, satellite altimetry (CryoSat-2), regional climate modelling (RACMO2), and firn densification modelling (IMAU-FDM). The inversion spatially resolves GIA in Antarctica independently from GIA modelling jointly with changes of ice mass and firn air content at 50 km resolution. We find an AIS mass balance of −144 ± 27 Gt a−1 from January 2011 to December 2020. This estimate is the same, within uncertainties, as the statistical analysis of 23 different mass balances evaluated in the Ice sheet Mass Balance Inter-comparison Exercise (IMBIE; Otosaka et al., 2023b). The co-estimated GIA corresponds to an integrated mass effect of 86 ± 21 Gt a−1 over Antarctica, and it fits better with global navigation satellite system (GNSS) results than other GIA predictions. From propagating covariances to integrals, we find a correlation coefficient of −0.97 between the AIS mass balance and the GIA estimate. Sensitivity tests with alternative input data sets lead to results within assessed uncertainties.

Detection of Subsurface Density Structures of the Aristarchus Plateau by Gravity Inversion

AbstractThe Aristarchus plateau, located at the center of Oceanus Procellarum, exhibits one of the most complex volcanic features on the Moon. To understand the subsurface three‐dimensional density distribution under the Aristarchus plateau, we performed gravity inversion using high‐resolution gravity data obtained from the Gravity Recovery and Interior Laboratory mission. Our inversion results indicate the presence of a strong lateral density differentiation, with some positive and negative density anomalies possibly exhibiting a correlation with the volcanic features observed on the surface. A linear high‐density anomaly near the Cobra Head magma source and an elliptical high‐density anomaly both exactly match the surface basaltic exposures observable in the remote sensing data. We executed the density separation to extract low‐density anomalies of the whole plateau and removed most of the density artifacts. The low‐density anomalies display elevated terrain evidence of multiple “semi‐ring” structures, suggesting the location of buried remnants of crater rims. The Aristarchus crater has a central low‐density anomaly in the exact size and shape of the later‐formed impact crater. This anomaly is consistent with the high crater porosity produced by extensive impact‐generated fracturing and dilatant bulking, though the observed gravity and density anomalies are greater in magnitude than expected for this process. The remote sensing data expose high‐silica material in the crater rim, implying that the young crater excavated an underlying layer containing both plagioclase and Si‐rich materials, and indicating that the local uplift of feldspathic and/or silicic materials also contributes to the high amplitude of the low‐density anomaly in the Aristarchus crater.

Bridging the spatiotemporal ice sheet mass change data gap between GRACE and GRACE-FO in Greenland using machine learning method

The Gravity Recovery and Climate Experiment (GRACE) mission terminated its operation in October 2017, and its successor, the GRACE Follow‐On mission, started operation in May 2018, leading to a 11-months data gap. As a prospective way to evaluate the Greenland ice sheet (GrIS) mass change, it would be vital to bridge the data gap. Despite efforts to bridge the gap with machine learning (ML) techniques, there are still two flaws in terms of the lack of spatial distribution and selecting forcing variables. Firstly, we applied the partial least squares regression (PLSR) model to assess the most explanatory variables of GRACE-derived ice mass change, and thus selected satellite altimetry elevation, and runoff, sublimation, rainfall, and snowfall from MAR v3.12 (Modèle Atmosphérique Régionale) model as predictors for predictive models. SVM (Support Vector Machine), BPNN (Back Propagation Neural Network), and MLR (Multiple Linear Regression) are utilized to reconstruct the GRACE-like gridded data. The SVM outperforms the other two predictive models, with 25.6 % of grid cells having Nash-Sutcliffe efficiency (NSE) coefficient above 0.75 and Scaled Root Mean Square Error (RSR) above 0.50. The average RMSE values for all testing periods are 3.86, 4.37, and 5.57 cm, for SVM, BPNN, and MLR, respectively. The reconstructed GRACE-like grid data demonstrated accurate spatiotemporal information with less noise, and exhibited a higher mean correlation of 0.93 across six sub-regions of GrIS, indicating a stronger agreement with original GRACE data compared to other methods. Furthermore, we investigated the changes in precipitation, runoff, and temperature pattern driven by the North Atlantic Oscillation (NAO), Greenland Blocking Index (GBI) and Atmospheric Rivers (ARs) to corroborate the shifts in GrIS mass loss feature. It was found that there was a correlation between precipitation, runoff, and ARs frequency, with mean correlation of 0.48 and 0.51 for wintertime and summertime, respectively. The moderate mass loss rate during the data gap can be attributed to the positive NAO and higher ARs frequency in GrIS. In general, the reconstructed data presented in this study provided a better comprehension of ice mass change and valuable information on how the ice mass responds to climate variations.

GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present

Abstract. Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940–2022) and relatively high-resolution (i.e., 0.25∘) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine-learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land–ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10 168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability such as strong El Niño events. The GTWS-MLrec dataset consists of three reconstructions based on (a) mascons of the Jet Propulsion Laboratory of the California Institute of Technology, the Center for Space Research at the University of Texas at Austin, and the Goddard Space Flight Center of NASA; (b) three detrended and de-seasonalized reconstructions; and (c) six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management. GTWS-MLrec is available on Zenodo through https://doi.org/10.5281/zenodo.10040927 (Yin, 2023).