Gravity Recovery And Climate Experiment Research Articles

Application of GRACE-based satellite estimates in the assessment of flood potential: A case study of Gangetic-Brahmaputra basin, India.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

Separation of earthquake and hydrology signals from GRACE satellites data via independent component analysis: a case study in the Sumatra region

SUMMARY The Gravity Recovery and Climate Experiment (GRACE) satellites have observed mass migrations caused by megathrust earthquakes. Extracting earthquake-related signals from GRACE data is still a challenge due to the interference from non-earthquake sources such as terrestrial hydrology. Instead of reducing hydrological signals by potentially biased hydrological models, in this study we apply a model-free technique of independent component analysis (ICA), to separate earthquake and non-earthquake signals from non-Gaussian GRACE data. We elucidate the principles and mechanisms of ICA for the separation of earthquake and hydrology signals, employing simulated data to demonstrate the process. Our findings demonstrate that both spatial ICA and temporal ICA are highly effective in discerning earthquake related to 2004 Mw 9.2 event and hydrological signals from GRACE data in the Sumatra region. This stands in stark contrast to principal component analysis, which often encounters challenges with signal intermingling. The utility of ICA is evident in its ability to distinctly delineate coseismic and post-seismic behaviours associated with megathrust events, including the 2004 Sumatra, the 2010 Maule, and the 2011 Tohoku earthquakes. ICA effectively mitigates the potential for misestimation of earthquake signals, an issue that can carry substantial implications. Therefore, employing ICA facilitates the accurate extraction of earthquake-related data from satellite gravity observations—a critical process for refining earthquake source parameters and understanding Earth's rheological properties, especially when non-earthquake signals are significant and cannot be disregarded.

Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020

In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

Predicting groundwater level based on remote sensing and machine learning: a case study in the Rabat-Kénitra region

ABSTRACT Groundwater is essential for sustaining water needs, industrial growth, agriculture, and ecosystems, particularly in arid regions. This study uses data from GRACE and MODIS satellites, integrating environmental variables like land surface temperature, soil moisture, terrestrial water storage, precipitation, and vegetation indices to predict groundwater levels in Morocco’s Rabat-Salé Kenitra region. These environmental variables serve as input parameters, with the output being the predicted groundwater level. Advanced machine learning models, including Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Random Forest (RF), and Decision Tree (DT) were employed to capture the relationships between these variables and groundwater levels. The GBR model showed superior performance with an R2 value of 0.99, a Mean Absolute Error (MAE) of 1.94, and a Root Mean Squared Error (RMSE) of 2.98, significantly improving over traditional methods that struggle with non-linear relationships and data noise. Compared to existing methods, our approach offers enhanced accuracy and robustness due to the GBR model’s ability to handle complex and non-linear relationships. This study demonstrates the advantages of integrating diverse environmental datasets with advanced machine learning techniques, improving groundwater management strategies and prediction reliability, especially in regions facing significant water scarcity and climate change impacts.

Machine learning downscaling of GRACE/GRACE-FO data to capture spatial-temporal drought effects on groundwater storage at a local scale under data-scarcity

The continued threat from climate change and human impacts on water resources demands high-resolution and continuous hydrological data accessibility for predicting trends and availability. This study proposes a novel threefold downscaling method based on machine learning (ML) which integrates: data normalization; interaction of hydrometeorological variables; and the application of a time series split for cross-validation that produces a high spatial resolution groundwater storage anomaly (GWSA) dataset from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission, GRACE Follow-On (GRACE-FO). In the study, the relationship between the terrestrial water storage anomaly (TWSA) from GRACE and other land surface and hydrometeorological variables (e.g., vegetation coverage, land surface temperature, precipitation, and in situ groundwater level data) is leveraged to downscale the GWSA. The predicted downscaled GWSA datasets were tested using monthly in situ groundwater level observations, and the results showed that the model satisfactorily reproduced the spatial and temporal variations in the GWSA in the study area, with Nash-Sutcliffe efficiency (NSE) correlation coefficient values of 0.8674 (random forest) and 0.7909 (XGBoost), respectively. Evapotranspiration was the most influential predictor variable in the random forest model, whereas it was rainfall in the XGBoost model. In particular, the random forest model excelled in aligning closely with the observed groundwater storage patterns, as evidenced by its high positive correlations and lower error metrics (Mean Absolute Error (MAE) of 54.78 mm; R-squared (R²) of 0.8674). The downscaled 5 km GWSA data (based on random forest) showed a decreasing trend in storage associated with variability in the rainfall pattern. An increase in drought severity during El Niño lengthened the full recovery time of groundwater based on historical storage trends. Furthermore, the time lag between the occurrence of precipitation and recharge was likely controlled by the drought intensity and the spatial recharge characteristics of the aquifer. Projected increases in drought severity could further increase groundwater recovery times in response to droughts in a changing climate, resetting storage to a new tipping condition. Therefore, climate change adaptation strategies must recognise that less groundwater will be available to supplement the surface water supply during droughts.

Residual and Unmodeled Ocean Tide Signal From 20+ Years of GRACE and GRACE‐FO Global Gravity Field Models

AbstractWe analyze remaining ocean tide signal in K/Ka‐band range‐rate (RR) postfit residuals, obtained after estimation of monthly gravity field solutions from 21.5 years of Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On sensor data. Low‐pass filtered and numerically differentiated residuals are assigned to grids and a spectral analysis is performed using Lomb‐Scargle periodograms. We identified enhanced amplitudes at over 30 ocean tide periods. Spectral replicas revealed several tides from sub‐semidiurnal bands. Increased ocean tide amplitudes are located in expected regions, that is, in high‐latitude, coastal and shallow water regions, although some tides also show distinct patterns over the open ocean. While most identified tides are considered during processing, and therefore the amplitudes represent residual signal w.r.t. the ocean tide model, several unmodeled tides were found, including astronomical degree‐3 tides , , , , and radiational and/or compound tides , , , and . The astronomical degree‐3 tides were observed on a global level for the first time a few years ago in altimeter data. We are unaware of any global data‐constrained solutions for the other tides. The amplitude patterns of these tides exhibit similarities to purely hydrodynamic solutions, and altimeter observations (astronomical degree‐3 only). The sensitivity of the satellites to these rather small tidal effects demands their inclusion into the gravity field recovery processing to reduce orbit modeling errors and a possible aliasing. The conducted study shows enormous potential of RR postfit residuals analysis for validating ocean tide models and improving gravity field recovery processing strategies.

A Monte Carlo Propagation of the Full Variance‐Covariance of GRACE‐Like Level‐2 Data With Applications in Hydrological Data Assimilation and Sea‐Level Budget Studies

AbstractUnderstanding mass (re‐)distribution within the Earth system, and addressing global challenges such as the impact of climate change on water resources requires global time‐variable terrestrial water storage (TWS) estimates along with reasonable uncertainty fields. The Gravity Recovery and Climate Experiment (GRACE) and GRACE‐FO satellite missions provide time‐variable gravity fields with full variance‐covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance‐Covariance (MCFVC) error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE‐like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(‐FO) data assimilation for hydrological applications and sea‐level budget estimation are presented that indicate the importance of accounting for the full covariance information in these studies.

Forecasting Next Year's Global Land Water Storage Using GRACE Data

AbstractExisting approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real‐time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision‐makers to improve current drought and water resource management.

Limited sensitivity of Antarctic GIA mass change estimates to lateral viscosity variations

The Gravity Recovery and Climate Experiment (GRACE) has revealed spatiotemporal mass changes in the Antarctic Ice Sheet. However, GRACE data must be corrected for the gravity changes due to glacial isostatic adjustment (GIA). Here we investigate the sensitivity of GIA-induced gravity changes in Antarctica to the lithospheric thickness and upper mantle viscosity using a one-dimensional (1-D) model that assumes a radially varying Earth structure. The sensitivity is assessed using several Antarctic ice-history models that have been widely used to correct GRACE data. The results indicate a trade-off between lithospheric thickness and upper mantle viscosity in evaluating the Antarctic GIA correction. This trade-off exists for all ice-history models; however, the reason for the trade-off differs among models. Furthermore, since there is a sharp contrast in the Earth structure between West and East Antarctica, the adopted ice histories are separated into West and East Antarctic components to examine their contributions to the Antarctic GIA correction. We consider 1-D Earth structures that are averaged from the seismically derived three-dimensional Earth structure for West and East Antarctica. These results indicate that the contributions of the East and West Antarctic loads do not significantly affect the GIA corrections for the West and East Antarctic regions, respectively, and that the trade-off between lithospheric thickness and upper mantle viscosity results in minimal divergence in the assessment of the Antarctic GIA correction between the averaged Earth models of West and East Antarctica. Therefore, the contrast in Earth structure beneath Antarctica may have a limited effect on the ice-mass change estimates for the entire Antarctic Ice Sheet.

GRACE based groundwater drought evaluation of Ganga Basin and analysis of drought propagation using wavelet based quantitative approach

The Ganga River Basin which is the home of almost half a billion people have plunged into groundwater drought due to anthropogenic activities. Hence groundwater drought assessment and its propagation from the meteorological drought is highly required for the Ganga River Basin. This paper focuses on the evaluation of historical groundwater drought using Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) dataset, further to obtain the drought propagation times. Traditionally the drought propagation time is obtained from the correlation between groundwater drought time series and various scales of meteorological time series. However, the GRACE-derived groundwater drought index (GGDI) showed lesser correlation with the Standardized Precipitation Index (SPI) / Standardized Precipitation Evapotranspiration Index (SPEI) due to the presence of consistent trend in the GGDI series. Hence, a novel quantitative approach using Cross Wavelet Transform (XWT) is introduced to determine the drought propagation time which can devoid of the contribution of anthropogenic activities. Extracting the significant power area of XWT of GGDI with SPI/SPEI of different scales led to the determination of groundwater drought propagation time. The results showed Groundwater Storage Anomaly (GWSA) has a steep downtrend for Upper Gangetic Basin (UGB) (−26.2 mm/year) and Yamuna Chambal Basin (YCB) (−21.8 mm/year). It was observed that UGB and YCB faced groundwater drought from 2017 to 2022. The wavelet analysis showed that the drought propagation time of YCB is 14 months, UGB is 17 months, and the Lower Gangetic Basin (LGB) is 21 months. The frequency domain analysis of the drought signals suggested YCB had a faster response to the meteorological forcing, and LGB had the slowest response.

Significance testing for cross correlation: A critical examination of correlations between ENSO and GRACE-derived terrestrial water storage variabilities

The cross correlation has a wide range of applications in geophysical fields for measuring linear connections or relationships among physical quantities. Nonetheless, there remains a dearth of comprehensive discourse regarding its statistical significance testing, which is crucial for differentiating meaningful outcomes from those merely stemming from fortuity or pure randomness. Conventional theoretical methods for significance testing, commonly used in statistical analysis tools such as SPSS and MATLAB, are only applicable when dealing with idealized circumstances such as white noise. In discretionary application of these methods to analyze geophysical signals with, say, red noise may result in potentially unjustified conclusions. This study aims to develop a comprehensive approach based on the t-distribution within a rigorous statistical context, aiming to facilitate significance tests of cross correlation for general signals with specified time shifts or ranges. Extensive Monte Carlo experiments substantiate its robustness, thereby paving the way for accurate and expeditious identification of statistically (hence potentially physically) meaningful correlations in general. As an example, we examine critically the previously purported significant correlations between ENSO (El Niño Southern Oscillation) and global terrestrial water storage variations derived from the GRACE (Gravity Recovery and Climate Experiment) satellite mission, demonstrating that they are subject to questioning in the absence of complete significance testing.

Increasing vulnerability of vegetation to Meteorological and Groundwater drought: A Case study in Argentina

Study RegionContinental Argentina, South America. Study FocusThis study integrates multiple data sources to analyze the response of vegetation to meteorological and groundwater drought events in Argentina. It utilizes GRACE satellite data to monitor water storage variations, the GLDAS hydrological model to track soil moisture levels, and NDVI to assess vegetation health, categorized by different vegetation types. Meteorological data facilitated the computation of drought severity using the Standardized Precipitation-Evapotranspiration Index (SPEI). Anomalies in groundwater storage, derived from these data, informed the calculation of the Grace Groundwater Drought Index (GGDI). A comprehensive statistical analysis was conducted to examine the impact of meteorological (SPEI) and groundwater (GGDI) droughts on various vegetation types. New Hydrological Insights for the RegionThe study reveals that from 2002 to 2022, GRACE-TWSA and GLDAS-TWSA displayed consistent trends in Argentina. Meteorological droughts, characterized by higher frequency and intensity, predominantly affect croplands and grasslands. Compared to lagged effects, the cumulative impact of droughts was found to be more significant. Shrublands demonstrated the greatest resistance to drought, whereas vegetation in humid regions showed lower drought tolerance. Notably, the duration associated with the lag effects of meteorological droughts decreased from 2.16 months in 2002–1.52 months in 2020, suggesting an increasing sensitivity of vegetation to these droughts. This underscores the urgency of developing targeted drought resilience and water resource management strategies.

Assimilating multivariate remote sensing data into a fully coupled subsurface-land surface hydrological model

Hydrological models play a crucial role in tracking and predicting terrestrial water storage, yet they face challenges due to uncertainties and inaccuracies caused by various factors such as meteorological processes and data limitations. To refine these models, data assimilation has emerged as a valuable tool, utilizing a new source of data to update model states while considering associated uncertainties, thereby enhancing our comprehension and predictive capabilities in hydrological processes. In this context, satellite data are receiving increasing attention because they can cover large areas and are useful in detecting spatial and temporal variability of water.In most existing studies related to satellite data assimilation in hydrological models, one source satellite data is used in the analysis. This study focuses on improving subsurface water storage model accuracy by assimilating data from different satellite sources. In particular, we used data from the Soil Moisture and Ocean Salinity (SMOS) satellite and terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE). The data are assimilated into a fully coupled subsurface-surface hydrological model, developed with ParFlow-CLM (PARallel FLOW- Community Land Model). The investigation is conducted in Iran. Employing an Ensemble Kalman Filter, three assimilation scenarios are explored: (i) GRACE, (ii) SMOS, and (iii) the combined assimilation of both GRACE and SMOS data (joint). Findings are validated against the Soil Moisture Active Passive (SMAP) and in-situ groundwater data by using a novel probabilistic reliability framework, demonstrating the advantages of joint data assimilation.The study highlights the influence of assimilated remote sensing data type on the effectiveness of data assimilation. Assimilating GRACE data enhances groundwater level estimations. However, SMOS data positively impacts topsoil moisture estimations, but adversely affects groundwater level estimates. Importantly, the assimilation of both GRACE and SMOS data through multivariate (joint) data assimilation significantly improves accuracy for both soil moisture estimation and groundwater level estimation.

Estimation of groundwater-level changes based on GRACE satellite and GLDAS assimilation data in the Songnen Plain, China

Estimation of groundwater-level changes based on GRACE satellite and GLDAS assimilation data in the Songnen Plain, China

A novel Slepian approach for determining mass-term sea level from GRACE over the South China Sea

Treatment of land-to-ocean leakage is crucial in modeling the mass-term sea level (MSL) using the Gravity Recovery and Climate Experiment (GRACE) satellite gravity measurements. In this study, we utilized the Spherical Slepian functions (SSFs) to determine MSL variations in the South China Sea. A sensitivity simulation in terms of trend, annual amplitude, and phase revealed that the SSF solution with a 1° coastal buffer zone provides a better land-to-ocean leakage correction than traditionally used Spherical Harmonics (SH). It was also found that an additional smoothing procedure for SSF with low concentrating energy could significantly reduce the high-frequency noise in GRACE (e.g., the north–south strips). The spatiotemporal characteristics of the true MSL were further compared among GRACE SH and Mascon solutions, model-predicted ocean bottom pressure, and steric-corrected altimetric data (i.e., satellite-altimetric sea level minus steric effect). Results revealed that despite the SSF-inverted regional MSL solution being generally similar to other results, this technique notably recovered the realistic magnitude of detailed features. The apparent MSL signal was, for instance, precisely recovered in the central part of the Gulf of Thailand and the Sunda Shelf by the SSF, while the SH significantly underestimated it due to smoothing. In addition to the seasonality, the interannual signal decomposed from the SSF-inverted residual MSL (by removing the trend and periodic signals) was the strongest and well cross-correlated with the Southern Oscillation Index. With more detailed spatial patterns revealed by MSL from GRACE SSF, our findings demonstrate that SSF is more suitable for regional-scale studies.

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.

Assessing GNSS hydrological monitoring capability across different climatic settings in China

Vertical position changes of ground-based Global Navigation Satellite System (GNSS) stations have been used to study regional terrestrial water storage (TWS) changes. However, the feasibility is still unclear in many areas due to diverse local effects. This study aims to evaluate the capability of GNSS vertical displacements in monitoring hydrological variations in four climate settings over Chinese mainland. The spatial and temporal variations of hydrological load-induced (HYDL) vertical displacements at 208 GNSS sites during 2011–2020 were analyzed by comparing with Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GFO) and Global Land Data Assimilation System (GLDAS) derived TWS changes. The results indicate that GNSS vertical positions show different capabilities in capturing seasonal and non-seasonal hydrological dynamics in different climate regions. Among the four climatic settings, the subtropical monsoon climate (SMC) region, with the largest deformation fluctuation (the regional mean root mean square (RMS) is 7.97 mm), has the highest regional mean HYDL-GRACE and HYDL-GLDAS anti-correlation coefficients (CCs) of −0.47 and −0.45 at the seasonal scale, respectively. For the individual GNSS site, the number of the sites with CC <−0.40 between HYDL and GRACE/GLDAS-derived TWS changes accounts for 55.1% and 55.1% (SMC), 13.0% and 7.4% (temperate monsoon climate, TMC), 6.7% and 13.3% (temperate continental climate, TCC), 32.3% and 38.7% (plateau climate, PC), respectively. For the non-seasonal term, although the proportion with CC <−0.40 in each climate type decreases mainly due to the influence of local geodynamic and human activities, especially in the SMC and PC regions, GNSS site vertical deformations still show good capability in monitoring hydrological extremes. The results provide valuable information for better application of GNSS to hydrology.

Assessing terrestrial water storage variations in Afghanistan using GRACE and FLDAS-Central Asia data

Study regionAfghanistan, Central Asia. Study focusIn this study, we evaluated the terrestrial water storage dynamics in Afghanistan and its five major river basins using the terrestrial water storage anomalies (TWSA) from three Gravity Recovery and Climate Experiment (GRACE) mascons observations from JPL, CSR, and GSFC processing centers, and the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System – Central Asia (FLDAS-CA) simulation. New hydrological insights for the regionsSince 2008, due to intense and prolonged drought conditions and groundwater overexploitation, TWS in Afghanistan has been decreasing at an alarming rate. The average slopes of the TWSA trend in Afghanistan for the GRACE period (2003–2016) of the three products range between − 3.6 to − 4.8 mm/year. The decrease in TWS is further exacerbated during the GRACE-FO period (2019–2022), ranging between − 20.4 to − 30 mm/year. Because groundwater is heavily relied on in the country but human-induced change (i.e., groundwater extraction) is not simulated in FLDAS-CA, a significant difference could be observed between GRACE observations and FLDAS-CA results, especially following after each severe drought event (e.g., 2008, 2018) when substantial groundwater extraction occurred. The assimilation of GRACE observations into the FLDAS-CA framework will undoubtedly have a positive impact for decision-makers and local stakeholders in preparing and mitigating the impacts of drought and groundwater overexploitation in Afghanistan and Central Asia.

Quantifying groundwater depletion in Arabian Peninsula transboundary aquifer systems: Understanding natural and anthropogenic drivers

Groundwater is the primary source of freshwater for domestic, agricultural, and industrial usage in the Arabian Peninsula countries. It is increasingly becoming a limited resource due to human activities leading to excessive depletion and contamination. Thus, sustainable management of groundwater resources in this region is critical. The groundwater in the Arabian Peninsula countries is primarily found in transboundary systems such as the Wajid, Umm Er Radhuma, and Wasia Aquifers shared between Saudi Arabia, Iraq, Syria, Yemen, and Oman. These systems have no groundwater-sharing agreements, which leads to a lack of data sharing, unsustainable and uncoordinated development, rapid water depletion, water quality deterioration, and land subsidence. This study examines the Wajid, Umm Er Radhuma, and Wasia aquifer systems from April 2002 to May 2021 by analyzing monthly gravity field solutions from GRACE and GRACE-FO satellite data, other remote sensing observations, information from the Global Land Data Assimilation System (GLDAS), as well as field data to determine how regional water resources are changing over time and to identify the factors that influence these resources. The sharp decline in Total Water Storage Anomalies (TWSA) and the Groundwater Storage Anomalies (GWSA) across all three systems is caused by a combination of climatic and human factors. The observed decline in Total Water Storage can be partly attributed to a decrease in regional rainfall, whereas the depletion of Groundwater Storage has a strong correlation with the rise in groundwater extraction for irrigation purposes in the 2010s. The recent rise is groundwater depletion in specific areas of central Saudi Arabia may be attributed to agricultural irrigation and rapid urban development. The results are insightful for monitoring water storage in management plans and decision-making processes to preserve and efficiently use groundwater resources.

Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with Random Forests

The Gravity Recovery and Climate Experiment (GRACE) satellites launched in 2002, which were followed by the GRACE Follow-On mission in 2018, represented a significant advancement in global groundwater storage monitoring. However, the inherent limitation of a relatively low spatial resolution confines its applicability primarily to large river basins and aquifers. This study introduces a novel statistical downscaling approach using Random Forests (RF) to enhance the spatial resolution from 3° × 3° to 0.25° × 0.25° for GRACE-derived groundwater storage (GWS) changes. Focusing on sub-Saharan Africa, the Middle East, and South Asia across diverse climates and human interventions, the analysis spans from February 2003 to December 2022. Predictors encompass hydrological fluxes and their accumulation, including IMERG precipitation, GLEAM evapotranspiration, and GLDAS runoff, alongside GLDAS soil moisture and MODIS NDVI. RF models show robust performance across 114 aquifers, with a Pearson’s correlation coefficient (CC) exceeding 0.92 for training sets and 0.85 for validation sets. Validation against in-situ groundwater level observations from wells in India reveals satisfactory performance, with positive Spearman’s CC values between in-situ groundwater levels and downscaled terrestrial water storage anomaly (TWSA) for a majority of dug and bore wells. This study concludes with the generation of monthly downscaled GWS changes (0.25° × 0.25°) spanning from February 2003 to December 2022. These datasets serve as a foundation for facilitating finer-scale hydrological studies and formulating local groundwater management strategies.