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Groundwater withdrawal can cause localized and rapid poroelastic subsidence, spatially broad elastic uplift of low amplitude, and changes in the gravity field. Constraining groundwater loss in Mexico City, we analyze data from the Gravity Recovery and Climate Experiment and its follow‐on mission (GRACE/FO) and Synthetic Aperture Radar (SAR) Sentinel‐1A/B images between 2014 and 2021. GRACE/FO observations yield a groundwater loss of 0.85–3.87 km3/yr for a region of ∼300 × 600 km surrounding Mexico City. Using the high‐resolution interferometric SAR data set, we measure >35 cm/yr subsidence within the city and up to 2 cm/yr of uplift in nearby areas. Attributing the long‐term subsidence to poroelastic aquifer compaction and the long‐term uplift to elastic unloading, we apply respective models informed by local geology, yielding groundwater loss of 0.86–12.57 km3/yr. Our results suggest Mexico City aquifers have been depleting at faster rates since 2015, exacerbating the socioeconomic and health impacts of long‐term groundwater overdrafts. Plain Language Summary Groundwater overdraft in Mexico City results from excessive freshwater demand and unsustainable water resource management in a subtropical environment with warm summers and dry winters. Groundwater depletion can result in ground surface deformation and changes in the gravity field, observable by Sentinel‐1 and GRACE satellites. Here, we examine data from both satellite missions between November 2014 and October 2021 to determine groundwater volume loss. Using GRACE, which has a footprint of ∼350 km, we quantify groundwater volume loss to a rate of 0.85–3.87 km3 per year in the broader area surrounding Mexico City. Analysis of high‐resolution Sentinel‐1 synthetic aperture radar images shows land sinks at a rate of 35 cm/yr within the city and surrounding areas uplifts at a rate of ∼2 cm/yr. While the subsidence is a consequence of aquifer compaction, the uplift represents an elastic unloading response of the Earth's crust to water mass loss. Using geophysical models informed by local geology, we show that the region loses groundwater at rates of 0.86–12.57 km3/yr. Our results emphasize the need for groundwater monitoring in Mexico City to assist with managing freshwater resources. Key Points A subsidence rate of >35 cm/yr within Mexico City, surrounded by ∼2 cm/yr of uplift, is observed using space‐borne synthetic aperture radar Groundwater loss of 0.86–12.57 km3/yr in Mexico City causes poroelastic subsidence, a broad‐scale elastic uplift, and gravity field change Mexico City aquifers have been depleting at least since 2015, exacerbating groundwater overdrafts' socioeconomic and health impacts

The Iberian region relies heavily on groundwater and is highly vulnerable to climate variability, making it crucial to understand factors influencing water availability. The aim of this research was to assess how large-scale climate patterns affect total water storage anomalies (TWSAs) in Iberia, particularly in relation to persistent droughts and floods. To address this, I analyzed TWSAs derived from a reconstructed dataset (GRACE-REC) spanning from 1980 to 2019, first at the scale of the entire Iberian Peninsula and then across its main river basins. The links between the North Atlantic Oscillation (NAO), East Atlantic (EA) and Scandinavian (SCAND) patterns, TWSAs, and hydrological extremes were quantified using wavelet and principal component analysis. The results reveal that the NAO exerts the strongest multiyear influence on TWSAs, with periodicities of approximately 10 and 6.5 years, particularly in the southern river basins (Tagus, Guadiana, and Guadalquivir). EA and SCAND have stronger influences in the northern basins (Douro, Minho, and Ebro), driving 2- to 3.5-year cycles. Coupled phases of climate patterns, such as NAO+ and EA− (or SCAND−), correspond to extreme droughts, whereas NAO− and EA+ (or SCAND+) correspond to wet conditions.

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002–2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH,MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤−0.5 km³/y) and increasing (≥0.5 km³/y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km³/y, whereas most models estimate decreasing trends (−71 to 11 km³/y). Land water storage trends, summed over all basins, are positive for GRACE (∼71–82 km³/y) but negative for models (−450 to −12 km³/y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and humaninduced water storage changes may be underestimated.

Groundwater is an important part of water storage and one of the important sources of agricultural irrigation, urban living, and industrial water use. The recent launch of Gravity Recovery and Climate Experiment (GRACE) Satellite has provided a new way for studying large-scale water storage. The application of GRACE in local water resources has been greatly limited because of the coarse spatial resolution, and low temporal resolution. Therefore, it is of great significance to improve the spatial resolution of groundwater storage for regional water management. Based on the method of random forest (RF), this study combined six hydrological variables, including precipitation, evapotranspiration, runoff, soil moisture, snow water equivalent, and canopy water to conduct downscaling study, aiming at downscaling the resolution of the total water storage and groundwater storage from 1° (110 km) and to 0.25° (approximately 25 km). The results showed that, from the perspective of long time series, the prediction results of the RF model are ideal in the whole research area and the observations wells area. From the perspective of space, the detailed changes of water storage could be captured in greater detail after downscaling. The verification results show that, on the monthly scale and annual scale, the correlation between the downscaling results and the observation wells is 0.78 and 0.94, respectively, and they both reach the confidence level of 0.01. Therefore, the RF downscaling model has great potential for predicting groundwater storage.

We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5 ∘ , covering the time frame 2003 to 2019 without gaps, and including monthly uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with a version of the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the average over more than 1000 stations globally, GLWS2.0 correlates better with GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands than GRACE/-FO. While some differences exist, overall GLWS2.0 agrees reasonably well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases. Highlights We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies over the global land with a spatial resolution of 0.5 ∘ , covering the period 2003 to 2019 without gaps, and including uncertainty quantification. GLWS2.0 synthesizes monthly GRACE/-FO mass change maps with daily precipitation and radiation data via the WaterGAP model framework, taking data and model uncertainty into account. Here we describe the methods and data sets that went into GLWS2.0 and its validation from a geodetic applications perspective. We find that, in the global average, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading.

Understanding the spatiotemporal characteristics of hydrological components and their impacts on vegetation are critical for comprehending hydrological, climatological, and ecological processes under environmental change and solving future water management challenges. Innovative methods need to be developed in semiarid areas to analyze the special hydrological factors in the water resource systems of these areas. Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) were applied with the normalized difference vegetation index (NDVI) data in this paper to analyze spatiotemporal changes of hydrological factors in the Xiliaohe River Basin (XRB). The results showed that precipitation (P), evapotranspiration (ET) and temperature (T) had similar seasonal change patterns at rates of 0.05 cm/yr., 0.01 cm/yr. and −0.05 °C/yr., respectively. Total water storage change (TWSC) was consistent with the change trend of soil moisture change (SMC) and showed a fluctuating trend. Groundwater change (GWC) showed a decreasing trend at a rate of −0.43 cm/yr. P and ET had a greater impact on GLDAS data (R = 0.634, P < 0.05 and R = 0.686, P < 0.01, respectively) than on other factors. GWC was more sensitive to changes in T (R = 0.570, P < 0.05). Furthermore, a lag period of 0 to 1 months was observed for the effects of P and ET on TWSC and GLDAS. NDVI showed an upward trend at a rate of 0.001 yr−1 between 2002 and 2014. A spatial distribution of NDVI was heterogeneous in the study area. ET, GLDAS and GWC in growing season limited vegetation growth and were more important than other factors in XRB. The results may contribute to an understanding of the relationships between the hydrological cycle and climate change and provide scientific support for local environmental management.

Remote-sensing hydrological data provided by the Gravity Recovery and Climate Experiment (GRACE/GRACE Follow-On) and reanalyzed ERA5-Land data allow for construction of a holistic picture of freshwater availability and successive wet/dry periods in the Middle East, a region with ground-data scarcity. The aim of this study is to conduct a comprehensive spatiotemporal and sustainability analysis of long-term (21 years, 2002–2022) GRACE-derived groundwater storage anomalies (GWSA) at 0.25° resolution over the Middle East. The time series of GWSA in each pixel is calculated after subtracting the soil-moisture anomaly, snow-water equivalent anomaly, and surface-water anomaly (all obtained from the ERA5-Land dataset) from the GRACE-based total water storage anomaly (TWSA) values. The Thiel-Sen slope method was used to detect the spatiotemporal patterns of GWSA over the region. An analytical groundwater sustainability index was developed based on three indicators—reliability, resiliency, and vulnerability—for the study area, on the basin scale. The statistical analyses revealed an average decline of –5.93 mm/year (–37.29 km3/year) for groundwater storage over the Middle East during the study period. The results suggest that most of the basins in the Middle East (59.14%) are operating extremely and severely unsustainably, while 34.41 and 6.45% of basins are slightly unsustainable and moderately sustainable, respectively. The results of this study uncover a holistic picture of groundwater variations and their sustainability over the Middle East, which can help to mitigate drought risks in the region, characterized by few publicly available ground-data sources.

In 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. Plain Language Summary Total Water Storage (TWS) is defined as the sum of water stored as surface water (e.g., lakes and rivers), groundwater, soil moisture, snow, ice, and vegetation biomass. Since its launch in 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided unique TWS change measurements with many applications in hydrology, including characterizing drought events. Scientists have been using satellites like GRACE and its successor, GRACE‐FO, to understand drought by measuring the Total Water Storage Anomaly (TWSA). However, previous methods didn't consider uncertainties from satellite orbits, models, and data errors. This study offers a novel probabilistic approach for characterizing drought, Probabilistic Storage‐based Drought Index (PSDI), which acknowledges the uncertainties in the GRACE TWS change. We use simulations to create different drought scenarios, offering probabilities for each category instead of one fixed category. Comparing PSDI to traditional methods, we found that traditional methods tend to overestimate drought severity. We tested PSDI across different regions, and it consistently proved to be a reliable way to understand drought conditions, offering a more comprehensive perspective. Our probabilistic approach offers a more realistic view of TWS drought, making it suitable for adaptive strategies and risk management. Key Points A novel probabilistic framework is introduced to characterize drought using Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On observations and propagating their stochastics Our study suggests a tendency of deterministic approaches to overestimate storage‐based drought severity The probabilistic approach captures global hydrological droughts while delivering more realistic results suited for risk management

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GRACE/‐FO) satellite missions have provided unprecedented measurements of terrestrial water storage changes (TWSC). These data are essential for monitoring the global water cycle, supporting drought and flood risk management, and informing water‐related decision‐making. However, GRACE products are typically released with a latency of several months, limiting their utility for real‐time and operational forecasting applications. In this study, we use machine learning to forecast GRACE‐like TWSC up to 12 months ahead, relying solely on observational and reanalysis‐based inputs. The observation‐driven forecast approach is evaluated over the period 2010–2024 and benchmarked against seasonal forecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF)’s new long‐range forecasting system (SEAS5). Our results show that the developed method offers improved accuracy and robustness compared to the ECMWF forecasts, providing a viable data‐driven alternative for operational TWSC forecasting. We generate global forecast data sets at 1° resolution, creating a robust, publicly available resource that extends GRACE‐like insights into the near future. The study addresses the latency of GRACE/‐FO products by offering real‐time TWSC forecasts to support applications such as drought early warning, sea level prediction, hydrological model validation, and geodetic applications such as forecasting Earth orientation parameters via hydrological angular momentum excitation or estimating loading corrections in GNSS and altimetry data analysis. The hindcast data set (2010–2024) evaluated in this study and the regularly updated semi‐operational forecast data set (from 2024 onward) are publicly available at: and .

The importance of high-resolution and continuous hydrologic data for monitoring and predicting water levels is crucial for sustainable water management. Monitoring Total Water Storage (TWS) over large areas by using satellite images such as Gravity Recovery and Climate Experiment (GRACE) data with coarse resolution (1°) is acceptable. However, using coarse satellite images for monitoring TWS and changes over a small area is challenging. In this study, we used the Random Forest model (RFM) to spatially downscale the GRACE mascon image of April 2020 from 0.5° to ~5 km. We initially used eight different physical and hydrological parameters in the model and finally used the four most significant of them for the final output. We executed the RFM for Mississippi Alluvial Plain. The validating data R2 for each model was 0.88. Large R2 and small RMSE and MAE are indicative of a good fit and accurate predictions by RFM. The result of this research aligns with the reported water depletion in the central Mississippi Delta area. Therefore, by using the Random Forest model and appropriate parameters as input of the model, we can downscale the GRACE mascon image to provide a more beneficial result that can be used for activities such as groundwater management at a sub-county-level scale in the Mississippi Delta.