The dataset, created by Chen Zhang, Yifei Fan, Wenxia Han, Yingchun Ge, Jiabei Zhu and Tao Che from institutes of the Chinese Academy of Sciences, was built by combining satellite observations, high-resolution environmental data and machine learning. The starting point is the GRACE and GRACE-FO mascon solutions from the University of Texas Center for Space Research, which provide monthly terrestrial water storage anomalies at 0.25 degrees. Because the GRACE missions suffered 35 months of missing observations, including an 11-month gap between the two missions after July 2017, the team first used Singular Spectrum Analysis to fill the temporal gaps. This technique decomposes the trajectory matrix of each pixel’s time series, extracts dominant modes and reconstructs the missing values through diagonal averaging, with cross-validation experiments masking entire years to quantify reconstruction errors. The median error was 1.156 cm for short gaps and 1.679 cm globally for the longer gap-filling phase, rising to 2.708 cm over land.

Once the terrestrial water storage record was temporally continuous, the researchers isolated the groundwater component through a vertical water balance. Monthly soil water storage from the four Noah soil layers of NASA’s GLDAS Noah V2.1 product, together with snow water equivalent and vegetation canopy water storage, were subtracted from the GRACE signal. All components were converted to centimetres of equivalent water height and expressed as anomalies relative to a 2004-2009 baseline to match the GRACE definition. Permanent water bodies were masked using the Global Surface Water Explorer, and glaciers and ice sheets were masked with the Randolph Glacier Inventory and the Greenland Ice Mapping Project, reducing contamination from signals that do not belong to groundwater at all.

The heart of the work is the downscaling framework. The team assembled a database of 19 environmental predictors at 1 km resolution, including monthly temperature and precipitation from CHELSA, evapotranspiration from PML_V2, daily surface soil moisture from a jointly inverted ERA5 and ESA-CCI product, elevation from GMTED2010, eight soil attributes from SoilGrids, and vegetation index and land surface temperature from MODIS. Two additional temporal coding variables derived from sine and cosine transformations capture seasonal characteristics. Separate machine learning models were trained for each aquifer category defined by the WHYMAP global aquifer map, recognising that hydrogeological conditions differ profoundly between major groundwater basins, complex structures and local shallow aquifers. Two candidate architectures were evaluated: XGBoost, a gradient-boosted tree ensemble, and a deep neural network built as a multilayer perceptron with three to six layers of 128 to 768 units, trained with the Adam optimizer and early stopping.

XGBoost won the production role on the strength of consistent performance across aquifer categories. When the 1 km reconstructions were aggregated back to the original 0.25 degree GRACE scale, the product agreed closely with the original groundwater storage anomalies, achieving a coefficient of determination of 0.972 and a root mean square error of 2.10 cm, compared with 0.961 and 2.661 cm for the neural network. The tree ensemble also outperformed the deep network in 10 of 11 evaluated aquifer categories in internal tests. The researchers are careful to note that the random 70/30 training-test split used for model development does not constitute a spatially or temporally independent generalization test, which is why the independent validation against monitoring wells carries so much weight.

That validation is arguably the most ambitious part of the study. The team drew on a global archive of 152,069 groundwater monitoring wells compiled by Jasechko and colleagues, with annual records spanning 2002 to 2020, and partitioned the network into 1 degree spatial blocks, using roughly 70 percent of blocks for calibration and holding out 30 percent for blind validation. Because groundwater levels and storage have different physical dimensions, an effective specific yield coefficient was estimated only from calibration blocks and transferred to held-out blocks without refitting. On the common 0.25 degree grid, the XGBoost product achieved a coefficient of determination of 0.538, a root mean square error of 0.378 cm per year and a bias of 0.106 cm per year against the held-out observations, with trend-sign consistency of 0.73. Sensitivity experiments perturbing the conversion coefficient by up to 30 percent showed that correlation and trend-direction agreement were robust, while amplitude-related metrics such as the regression slope responded more strongly.

Conversion-free comparisons using independently standardized time series revealed where the product shines and where it struggles. In climate-driven basins such as the Stampriet-Kalahari Basin, with 7,849 wells, the correlation reached 0.82, accurately reproducing multi-year depletion and recovery. Strong agreement also appeared in the Cuvelai-Upper Zambezi Basin, the Russian Platform Basins and the Ganges-Brahmaputra Basin. In intensively managed aquifers, however, the picture diverges: in the Northern Great Plains Aquifer the correlation was negative, with the satellite-constrained reconstruction indicating climate-driven recovery while local wells showed sustained depletion, a decoupling the authors interpret as a signal potentially consistent with anthropogenic pumping rather than a direct estimate of extraction. Monthly validation across five representative aquifers showed that the reconstruction reproduces long-term variability better than seasonal fluctuations, with mean correlations of 0.55 for trends versus 0.39 for seasonal components.

Regional benchmarks against published estimates added further nuance. Agreement was strongest in the North China Plain, with a correlation of 0.963, while depletion magnitudes were weaker than published values in northwestern India and California. In the High Plains Aquifer, the reconstructed trend during the published 2003-2013 window had the opposite sign to the reference estimate, a mismatch the authors flag clearly rather than explain away. Experiments using alternative target products, including the JPL mascon solution and the H08 and WaterGAP global hydrological models, showed that reconstruction performance and predictor importance depend on the input source, with static physiography and soil variables dominating for satellite targets while temporal encoding gained weight for model targets.

The global climatology that emerges from the dataset is striking. Significant depletion hotspots concentrate in the Indus-Ganges Basin, the North China Plain, the Arabian Peninsula, the Caspian Sea region and the High Plains of the United States, while surplus zones align with tropical rainforest areas under the Intertropical Convergence Zone and mid-to-high-latitude regions. A meridional mean profile reveals a pronounced depletion trough between 25 and 45 degrees north, the belt combining the descending branch of the Hadley Cell with the world’s densest irrigation. Seasonally, more than half of the global land area, 52.30 percent, is governed by recurrent intra-annual cycles, while arid interiors such as North Africa, the Arabian Peninsula and Central Asia are dominated by persistent non-seasonal deficits. Statistically, about 38.84 percent of effective land area shows significant depletion and 49.16 percent significant recharge, with only 12 percent in stable equilibrium.

The authors are explicit about the limits of their creation. The kilometre-scale patterns are predictor-informed reconstructions under coarse-scale GRACE constraints, not independent satellite observations, and residual surface-water variability may remain in the estimates, especially in tropical and floodplain-dominated regions. Local attribution of the fine-scale textures, such as gradients aligned with agricultural boundaries around New Delhi or the Zhangye oasis in the hyper-arid Heihe River Basin, requires additional observations of groundwater levels, abstraction, recharge and aquifer properties. Within those constraints, the openly available dataset, hosted by the National Cryosphere Desert Data Center and previewable through Google Earth Engine, offers scientists a powerful new lens for regional groundwater assessment, hydrological model evaluation and studies of how the world’s largest accessible freshwater resource responds to climatic and human pressures.

Subject of Research: High-resolution global groundwater storage anomalies: a 1 km downscaled dataset

Article Title: High-resolution global groundwater storage anomalies: a 1 km downscaled dataset

Article References: Zhang, C., Fan, Y., Han, W., Ge, Y., Zhu, J., & Che, T. (2026). High-resolution global groundwater storage anomalies: a 1 km downscaled dataset. Earth System Science Data, 18(10), 7593-7626. https://doi.org/10.5194/essd-18-7593-2026

Image Credits: AI Generated

DOI: 10.5194/essd-18-7593-2026

Keywords: groundwater, GRACE, machine learning, XGBoost, water security, aquifer depletion, remote sensing, downscaling, hydrology, Earth observation, data science, sustainability

Specific yield, usually written as Sy, describes the fraction of water that an unconfined aquifer releases when it drains freely under gravity. A sandy aquifer might give up a large share of its stored water, while dense fractured rock releases only a trickle. Because Sy controls the conversion between water level changes and actual storage volumes, errors in this single number propagate directly into national groundwater budget estimates, recharge calculations, and depletion assessments. Despite its importance, reliable Sy values have remained scarce at regional scales, forcing practitioners to rely on hydrogeological maps or on values calibrated inside large models, both of which carry substantial uncertainty and often fail to capture the true spatial variability of aquifer properties.

A team led by Maya Raghunath Suryawanshi and Kuruva Satish Kumar at the Indian Institute of Science in Bengaluru, working with colleagues from ISRO centres and other IISc departments, took a different route. They applied the water table fluctuation method, a classical technique that normally works at the scale of a single well or field site, but scaled it up to whole mascon regions of roughly three degrees across India. The logic is elegant: if you know how much water storage changed over an area, and you know how much the water table rose or fell, the ratio between the two gives you the specific yield. The challenge has always been obtaining trustworthy storage changes over large regions, and this is where satellites enter the story.

The Gravity Recovery and Climate Experiment, GRACE, and its successor GRACE Follow-On measure tiny variations in Earth’s gravity field caused by the movement of water between reservoirs. By subtracting surface water, soil moisture, and other non-groundwater compartments, estimated here using the WaterGAP Global Hydrology Model, the researchers isolated the groundwater component of the satellite signal for each mascon. They then paired these storage anomalies with quality-controlled groundwater level records from the India Water Resources Information System, covering the period from 2003 to 2022. The well data had previously been rigorously filtered by the same group, who published the cleaned dataset in Scientific Data, ensuring that faulty or unrepresentative measurements would not contaminate the regression between storage change and water level change.

The resulting map of specific yield across India spans values from 0.003 to 0.15, a factor of fifty between the driest and most productive aquifers. The highest values appear in the north-eastern states and parts of peninsular India, where alluvial and weathered formations can store and release substantial water volumes. Western India, by contrast, shows the lowest values, consistent with hard rock terrains where the effective porosity accessible to the water table is small. These patterns matter enormously for water managers: a one meter decline in the water table removes far less water from a low-Sy hard rock aquifer than from a high-Sy alluvial one, so identical level trends imply very different depletion rates depending on location.

To validate their estimates, the team compared the satellite-derived Sy values against reference values taken from India’s national hydrogeological map. The agreement was close for many of the 39 mascons analysed, but a few showed notable deviations, which the authors interpret as evidence of genuine spatial heterogeneity within those large footprint areas rather than simple method failure. A mascon cell of roughly 300 kilometres across can encompass alluvial plains, crystalline uplands, and coastal sediments simultaneously, so a single map value may average over enormous internal diversity. Crucially, when the researchers propagated the uncertainties in both the GRACE storage changes and the water level observations, they found that 29 of the 39 mascon estimates fell within the uncertainty bounds of the reference values, a result that lends statistical credibility to the framework.

One of the most intriguing findings concerns resolution. GRACE mascon solutions are coarse, and a natural question is whether downscaling the satellite product to 0.5 degrees would sharpen the Sy estimates. The team tested a downscaled GRACE-FO product and found negligible improvement, indicating that the limiting factor is not the resolution of the gravity data alone but the combined signal-to-noise of the storage and level observations. For regional to continental scale applications, the native mascon framework is therefore adequate, and the extra complexity of downscaling brings no measurable benefit for this particular parameter. This is a practical lesson for the growing community working with GRACE derivatives: finer grids do not automatically mean better hydrogeology.

The authors are careful about the limits of their approach. The framework is constrained by the coarse spatial resolution of GRACE observations and by the limited temporal availability of groundwater level measurements, so it is intended for regional-scale rather than local-scale aquifer characterisation. A district water manager seeking to plan well placement cannot use a three-degree mascon value directly, but a state or national agency assessing basin-wide storage trends now has an observationally grounded Sy estimate where previously only map-based guesses existed. The method also requires that water level fluctuations genuinely reflect storage changes rather than being distorted by pumping patterns, well density, or local confining conditions, assumptions that the quality control of the well dataset helps to safeguard but cannot fully guarantee.

The broader significance of the study lies in what it enables. India is the largest consumer of groundwater in the world, and its agricultural economy, particularly the post-Green Revolution cereal systems, depends heavily on aquifer extraction. Previous satellite-based assessments of Indian groundwater depletion, including the landmark 2009 Nature study by Rodell and colleagues, had to assume Sy values from existing maps, embedding whatever errors those maps contained into the depletion estimates. With observationally derived, spatially varying Sy now available for the whole country, the conversion from water level trends to storage volumes can be revisited with greater confidence, and global hydrological models that represent groundwater dynamics can be calibrated against a new empirical benchmark.

Looking forward, the same framework can be applied wherever GRACE coverage overlaps with dense well monitoring networks, potentially extending to other major groundwater economies facing similar data scarcity. As the GRACE and GRACE-FO record lengthens and new missions are planned, and as national well databases grow more complete, the marriage of satellite gravimetry and in situ hydrogeology promises to keep refining our picture of the invisible water reserves on which hundreds of millions of people depend. For a country whose aquifers are being drawn down at rates that alarm scientists and policymakers alike, knowing precisely how much water each meter of decline represents is not an academic detail; it is the difference between sustainable management and silent depletion.

Subject of Research: Estimation of specific yield for Indian unconfined aquifers using GRACE satellite storage changes and in situ groundwater levels

Article Title: Estimating specific yield from GRACE-based storage change and groundwater levels across India

Article References: Suryawanshi, M. R., Kumar, K. S., Shaw, B., Varadaganahalli Anandagowda, C., Sukumaran, V., Kour, R., Kochar, A., Chander, S., Nikam, B. R., Dasika, N. K., & Vishwakarma, B. D. (2026). Estimating specific yield from GRACE-based storage change and groundwater levels across India. Hydrogeology Journal. https://doi.org/10.1007/s10040-026-03195-z

Image Credits: AI Generated

DOI: 10.1007/s10040-026-03195-z

Keywords: GRACE, specific yield, groundwater, India, hydrogeology, satellite gravimetry, water table fluctuation method, aquifer storage, GRACE Follow-On, groundwater depletion, mascon, WaterGAP

The Algeciras Fault System stretches roughly 540 kilometers in a northeast-southwest direction along the eastern flank of Colombia’s Central Cordillera, cutting through the departments of Meta, Huila, Cauca, Nariño, Caquetá, and Putumayo. It is a transpressional structure, meaning it combines sideways strike-slip motion with vertical compression, and it marks part of the boundary zone where the Nazca and South American plates grind against each other. The fault system passes beneath towns including Uribe, Algeciras, Neiva, Gigante, Pitalito, Sibundoy, Mocoa, and Ipiales, across terrain that rises from 600 meters to more than 3,000 meters above sea level. Historical records document destructive earthquakes in the Huila-Caquetá region, and instrumental monitoring confirms persistent moderate-to-strong seismicity along its trace.

What makes the new study unusual is its data sources. The researchers drew on monthly gravity solutions from NASA’s GRACE mission and its successor GRACE Follow-On, which measure tiny changes in Earth’s gravitational field from orbit. Because those satellites also see the weight of water moving through rivers, soils, and aquifers, the team subtracted hydrological contributions using the Global Land Data Assimilation System, or GLDAS, a land-surface model driven by meteorological and satellite observations. Even after this correction, the authors caution that residual non-tectonic signals cannot be fully excluded, so the gravity signals they interpret must be treated carefully.

To check the quality of the satellite gravity data, the researchers compared it against airborne gravimetric surveys flown by Colombia’s National Hydrocarbons Agency in 2006 and 2009 over the Yari-Caguán and Putumayo basins. At coarse regional scales, the agreement was remarkably tight: when the airborne data were smoothed with a 500-kilometer upward-continuation filter, average differences from the satellite solutions dropped to between roughly 0.49 and 1.54 percent. This cross-check supported the team’s decision to use the GFZ-processed RL06 solutions at full 96-by-96 spherical harmonic resolution, which preserve small-amplitude spatial variations that stronger filtering would erase.

The seismic side of the analysis drew on 14,592 earthquakes recorded by the Colombian Geological Survey between May 2002 and July 2020. The team converted moment magnitudes to local magnitudes where necessary and estimated the energy released by each event. When they sorted earthquakes by whether they occurred in areas of gravity increase or gravity decrease, a clear asymmetry emerged: 52.1 percent of events fell in gravity-decrease zones, but those zones accounted for 93.1 percent of the total released seismic energy. Stronger, shallower earthquakes were especially concentrated where gravity was falling.

The most dramatic result came from combining radar interferometry with gravity. Using 85 processed Sentinel-1 satellite images and the Small Baseline Subset technique, the researchers mapped vertical ground motion across the fault corridor and compared it month by month with GRACE-derived gravity variations. They identified two kinds of domains: direct-correlation areas, where gravity and elevation move up or down together, and inverse-correlation areas, where one rises as the other falls. When they tallied the earthquakes falling within each domain type, they found that areas of simultaneous decrease in both gravity and elevation accounted for approximately 99 percent of the total seismic energy released in the analyzed subset. For earthquakes of magnitude 3.0 or larger at depths of 20 kilometers or less, 89 percent of events occurred in these gravity-and-elevation-decrease zones.

This pattern is geophysically meaningful. Simultaneous gravity loss and ground subsidence can indicate mass redistribution at depth, whether through compaction, fluid migration, or the elastic response of the crust to tectonic loading. The authors suggest that such zones may reflect a closer spatial association between mass redistribution and crustal deformation during seismically active periods. Comparable gravity-deformation relationships have been documented in other settings, including Mount Etna in Italy and regions of nonlinear land subsidence, suggesting the pattern is not unique to the Colombian Andes but may be a general feature of actively deforming crust.

The team also cross-correlated gravity variations with ground displacements measured at nine permanent GNSS stations from Colombia’s GeoRED network. Correlation coefficients ranged from nearly zero to 0.61, with time lags stretching up to 45 months. The vertical component showed the strongest correlations of any measured quantity, with the LAB1 station recording a maximum direct correlation of 0.6143 at a four-month lag and a maximum inverse correlation of -0.5593 at six months. Stations close to the main fault trace, like LAB1, tended to show rapid responses consistent with elastic deformation of the upper crust, while stations farther away, like QUIL and OVSC, showed longer-delayed correlations that may reflect deeper viscoelastic processes or rheological differences along the fault corridor.

As a case study, the researchers examined the magnitude 6.0 Mesetas earthquake of December 24, 2019, which struck at a depth of about 13 kilometers. In the months surrounding the event, the GRACE-derived gravity time series displayed a distinctive W-shaped fluctuation, bounded by the two most abrupt gravity variations in the entire 2002-2020 record. At the earthquake location, the team noted a temporal coincidence between a gravity decrease of 0.461 milligal and an elevation change of -0.062 meters. Spectral analysis using the Fast Fourier Transform showed that most of the signal’s power was concentrated at low frequencies, with a dominant peak at roughly 0.2 cycles per month, corresponding to a period of about five months. The authors interpret this sequence as potentially compatible with stress accumulation, rupture, and post-event crustal readjustment, though they emphasize that the coarse spatial resolution of GRACE and possible residual hydrological effects mean the observation is exploratory rather than conclusive.

The study’s authors, Edwin Mauricio Pinzon Monroy and Carlos Alberto Vargas Jiménez, are careful to frame their results as associations rather than causal proof. Satellite gravimetry resolves features hundreds of kilometers wide, far larger than the rupture zone of a magnitude 6 earthquake, and hydrological models cannot perfectly remove water-related signals. GNSS time series vary in length and continuity, and InSAR measurements can be affected by atmospheric conditions and topographic artifacts. Yet the convergence of independent data streams, satellites weighing the Earth from orbit, radar measuring the ground’s rise and fall, and seismometers tallying where energy is released, offers a template that can be applied to other active fault systems worldwide. As gravity missions improve in resolution and longer geodetic records accumulate, this kind of multi-source approach may become a standard tool for identifying which segments of a fault are quietly loading up, and which are already letting go.

Subject of Research: Integration of satellite gravimetry, GNSS, InSAR, and seismicity to analyze the Algeciras Fault System in Colombia

Article Title: Seismic – Gravimetric analysis of the Algeciras fault system (Colombia): spatio-temporal correlations from GRACE and GNSS-InSAR

Article References: Pinzon Monroy, E. M., & Vargas Jiménez, C. A. (2026). Seismic – Gravimetric analysis of the Algeciras fault system (Colombia): spatio-temporal correlations from GRACE and GNSS-InSAR. Frontiers in Earth Science, 14, Article 1938095. https://doi.org/10.3389/feart.2026.1938095

Image Credits: AI Generated

DOI: 10.3389/feart.2026.1938095

Keywords: Algeciras Fault System, GRACE, GRACE Follow-On, GNSS, InSAR, satellite gravimetry, seismicity, Colombia, tectonic deformation, Mesetas earthquake, GLDAS, seismic hazard

The product, called GMM-SWE v1.0, covers the Northern Hemisphere between 35° and 85° north latitude for every month from 2003 to 2022. Rather than relying on a single measurement technique, the framework fuses two very different streams of information: satellite measurements of tiny changes in Earth’s gravity field from the GRACE and GRACE-FO missions, and high-resolution meteorological data from the ERA5-Land reanalysis. The two are combined within a physics-constrained data assimilation scheme built around an adaptive Kalman filter, a mathematical engine that continuously reconciles model predictions with observations by weighting each according to its uncertainty. The result is a spatially continuous, physically consistent record of three fundamental snow properties: snow water equivalent, snow depth, and snow density.

The gravitational component is the conceptual heart of the approach. The GRACE satellites, flying in tandem, detect minuscule variations in the distance between one another as they pass over regions of changing mass on the surface below. When snow accumulates, the ground beneath it becomes fractionally heavier, and the satellites register the shift. The signal, however, is not clean. Changes in total terrestrial water storage also include groundwater fluctuations, soil moisture, surface water, and other hydrological components. To isolate the snow contribution, the researchers explicitly subtracted these non-snow terms, derived from an ensemble of land surface and hydrological models. This mass-balance approach means the snow estimates are anchored to actual physical mass rather than to statistical relationships that may break down outside the conditions in which they were calibrated.

Gravity alone, however, cannot tell the whole story. The GRACE measurements are coarse in space and time, and they say nothing directly about how deep the snowpack is or how dense it has become. GMM-SWE therefore incorporates high-resolution meteorological forcing and global topographic information to capture elevation-dependent melt dynamics and temperature thresholds, a refinement that matters enormously in complex mountain terrain where snow behavior can change dramatically over a few hundred meters of altitude. Once the assimilated snow mass has been estimated, it is further refined with a Rauch–Tung–Striebel smoother, a technique that reduces temporal noise and ensures that the seasonal evolution of the snowpack remains physically coherent rather than jittering from month to month.

Perhaps the most innovative element of the dataset is its dynamic decoupling of snow mass and snow volume. Traditional large-scale products often assume a static relationship between depth and density, an assumption that ignores the fact that a snowpack is a living, evolving material. Fresh powder may sit at densities below 100 kilograms per cubic meter, while a spring snowpack that has survived repeated melt–freeze cycles can exceed 500. GMM-SWE retrieves snow depth and density by coupling the smoothed snow water equivalent estimates with a physics-based SNOW-17 compaction scheme resolved at sub-daily time steps. This allows the dataset to explicitly represent densification and metamorphic processes within the snowpack, capturing how grains round, settle, and transform under their own weight and through temperature fluctuations, without leaning on fixed density assumptions that vary wildly across climates and seasons.

The validation effort behind the dataset is substantial. The researchers compared their estimates against an extensive network of 13,135 in-situ ground stations spanning a wide range of climatic and topographic regimes, as well as against established products including ERA5-Land, GLDAS, WGHM, and GlobSnow. The results show consistently strong performance, with particular skill in the high-latitude continental interiors of Siberia and North America, where seasonal snow dominates the annual water cycle. Crucially, the framework substantially improves the representation of snow water equivalent in complex mountain regions, such as high mountain Asia, where passive microwave satellite retrievals, the backbone of many conventional snow products, are severely limited by deep snowpacks, wet snow conditions, and rugged topography that scramble the microwave signal.

Sensitivity analyses add an important layer of credibility. The researchers found that the large-scale patterns of snow water equivalent and their year-to-year variability are governed primarily by the physical mass-balance constraints at the core of the method, rather than by finely tuned parameter choices. In other words, the dataset’s performance does not hinge on arbitrary calibration decisions that might not generalize; it emerges from the underlying physics of mass conservation. This robustness is exactly what is needed for a product intended to serve hydrological applications and climate-scale assessments of cryospheric change, where users must be able to trust that the numbers reflect reality rather than modeler preference.

The practical implications reach far beyond the scientific community. Snow water equivalent is the single most important variable for seasonal water forecasting in snow-dominated basins: it determines how much meltwater will arrive in rivers and reservoirs months later. Water managers across the western United States, Central Asia, and the Himalayan headwaters make decisions worth billions of dollars based on snow estimates, and errors in those estimates propagate directly into flood risk, drought planning, and hydropower generation. A unified, physically informed dataset that spans two decades and the entire hemisphere gives these communities a consistent baseline, and gives climate scientists the long, homogeneous record they need to detect trends in seasonal snow storage and to evaluate how well climate models capture them.

The dataset also arrives at a moment of rapid change. Warming temperatures are altering the timing of snowfall, shortening the snow season, and shifting precipitation from snow to rain in many mid-latitude regions, with pronounced regional differences in how seasonal snow storage is evolving. Disentangling those regional trajectories requires observations that are consistent across borders and biomes, something no network of ground stations alone can provide. By combining the global reach of satellite gravimetry with the local realism of meteorological forcing and snow physics, GMM-SWE offers a template for how the next generation of cryospheric monitoring products might be built, and the authors have released both the dataset and the MATLAB production code openly, inviting scrutiny, reuse, and improvement by the wider community.

As with any ambitious synthesis, the work is a preprint currently under peer review, and its assumptions, particularly the model-based subtraction of non-snow water storage components, will face scrutiny from specialists. But the underlying idea is undeniably elegant: the same planet-watching satellites originally designed to track melting ice sheets and draining aquifers can, with the right physics bolted on, be repurposed to weigh the winter snowpack of an entire hemisphere. In doing so, they transform one of hydrology’s most stubborn uncertainties into a monthly, measurable quantity, one that may prove indispensable as the world’s snowlines climb and the freshwater stored in snow becomes an ever more precious and contested resource.

Subject of Research: A GRACE and meteorology data assimilation dataset of Northern Hemisphere snow water equivalent, snow depth, and snow density for 2003–2022

Article Title: GMM-SWE v1.0: A GRACE–Meteorology-Constrained Dataset of Snow Water Equivalent, Snow Depth, and Snow Density over the Northern Hemisphere, 2003–2022

Article References: Mohasseb, H., & Yi, S. (2026). GMM-SWE v1.0: A GRACE–Meteorology-Constrained Dataset of Snow Water Equivalent, Snow Depth, and Snow Density over the Northern Hemisphere, 2003–2022. https://doi.org/10.5194/essd-2026-675

Image Credits: AI Generated

DOI: 10.5194/essd-2026-675

Keywords: snow water equivalent, GRACE, GRACE-FO, data assimilation, snow density, snow depth, ERA5-Land, Northern Hemisphere, cryosphere, hydrology, satellite gravimetry, climate change

The concept of a sea level budget is deceptively simple. Satellite altimeters measure the total height of the sea surface by bouncing radar pulses off the water, a quantity that includes everything: the expansion of seawater as it warms, the dilution or concentration of salt, and the sheer addition or redistribution of water mass. Independent observing systems can estimate each ingredient separately. The Argo program, a global fleet of nearly 4000 autonomous profiling floats, measures temperature and salinity in the upper 2000 meters of the ocean, allowing scientists to compute the steric component, the part of sea level change caused by density changes. The GRACE and GRACE Follow-On satellite missions, jointly operated by NASA and the German Aerospace Center, track Earth’s shifting gravity field to reveal where water mass is accumulating or draining. If everything is measured correctly, altimetry minus steric change should equal ocean mass change, and the residual, the difference between the two sides of the ledger, should be statistically indistinguishable from zero.

At the global scale, this budget closes reasonably well. But a recent study by Bouih and colleagues, published in 2025 in the same journal, examined regional budgets basin by basin over the 2004 to 2022 period and found something troubling. In the Pacific, Indian and South Atlantic oceans, the residuals were small enough to fall within data uncertainties. In the North Atlantic, however, strong positive residuals appeared no matter which estimate of ocean mass was used. The region was gaining sea level faster than the sum of its measured parts could account for, and the discrepancy was far too large to dismiss as random error. That finding set the stage for the new investigation, which systematically tests every link in the observational chain to find where the accounting breaks down.

The team assembled an unusually comprehensive toolkit. For total sea level, they used the Copernicus Climate Change Service’s daily gridded altimetry product, corrected for known instrumental drifts and for the ongoing rebound of the crust following the last ice age, the so-called glacial isostatic adjustment. For ocean mass, they averaged the latest Release 6 mascon solutions from three processing centers: the Center for Space Research in Texas, the Jet Propulsion Laboratory in California and the Goddard Space Flight Center in Maryland. For the steric component, they compared three independent Argo-based gridded products from the Scripps Institution of Oceanography, JAMSTEC in Japan and the Met Office Hadley Centre in the United Kingdom. Crucially, they added a fourth source of information that no float can provide: the CIGAR ocean reanalysis, a 32-member ensemble simulation based on the NEMO ocean model, forced by ERA5 atmospheric data and constrained by in situ temperature and salinity profiles, but deliberately not assimilating satellite altimetry, which makes it an independent check on the observed record.

The first clue emerged from comparing the steric products themselves. In the North Atlantic, temperature and salinity effects largely cancel each other out, a phenomenon known as thermohaline compensation. All four products showed strong positive thermosteric trends in the western basin, offset by negative halosteric trends, leaving a total steric signal much smaller than either component alone. This compensation means that salinity changes, negligible for global mean sea level, cannot be ignored in this region. The team also identified a technical wrinkle: some Argo floats have suffered a spurious salinity drift since 2016, and only the Scripps product corrects for it, while the JAMSTEC and EN4 products show suspicious anomalies that may reflect this uncorrected instrument problem. The researchers therefore relied on the Scripps product for their baseline calculations.

The more surprising discovery concerned the manometric component, the ocean mass term. When the team compared GRACE-based mass trends with those derived from the CIGAR reanalysis, by subtracting the local steric signal from the reanalysis’s sterodynamic sea level and adding the gravitational, rotational and deformation fingerprints of modern ice melt, the two maps told opposite stories. GRACE showed significant negative mass trends across most of the basin, weakening from east to west. The reanalysis showed positive trends in the east and negative trends in the west, separated by a striking dividing line that appears to follow the Mid-Atlantic Ridge. When the researchers computed an independent estimate of mass change by simply subtracting full-depth steric sea level from altimetry, the result agreed with the reanalysis in the eastern Atlantic and contradicted GRACE there, pointing a finger at possible problems in the satellite gravity data itself.

A likely culprit has recently been identified by other researchers. The GRACE mass estimates must be corrected for glacial isostatic adjustment, the slow rebound of the solid Earth after the disappearance of the great ice sheets. Traditional corrections assume that the Earth’s mantle has the same viscosity everywhere, but a 2026 study by Hightower and colleagues developed a three-dimensional GIA model based on seismic tomography and showed that lateral variations in mantle rheology substantially alter the correction in many regions, particularly across the entire Northeast Atlantic. Since the new sea level budget study used GRACE data with the traditional correction, the discrepancy between the satellite gravity and reanalysis mass estimates in the eastern basin may well stem from an inadequate GIA correction, a possibility the authors flag as a priority for future work.

The second piece of the puzzle was the deep ocean. Argo floats profile only the upper 2000 meters, leaving the vast volume of water below unobserved by the float array. Using the CIGAR reanalysis, which models the full ocean depth, the team computed the thermosteric contribution of water deeper than 2000 meters and added it to the budget. The effect was dramatic. Including the deep ocean reduced the mean residual trend by about 30 percent when the GRACE mass estimate was used. But when the reanalysis was used for both the mass term and the full-depth steric term, the residuals collapsed by roughly 90 percent compared with the upper-ocean-only calculation. The remaining residual trend was 0.24 plus or minus 0.22 millimeters per year, small enough that the North Atlantic sea level budget can finally be considered closed within the data uncertainties. By contrast, using the Scripps upper-ocean steric data combined with reanalysis deep-ocean information still left a residual of 0.88 plus or minus 0.20 millimeters per year, above the uncertainty range.

The implications reach beyond regional bookkeeping. The North Atlantic hosts the meridional overturning circulation, the system of currents that helps regulate climate on both sides of the Atlantic, and it is a region where heat is actively pumped into the deep ocean. If a substantial fraction of regional sea level rise is hiding below 2000 meters, then observing systems that stop at float depth will systematically misattribute that rise, and climate models that underrepresent deep warming will misproject regional sea level futures. The findings lend strong support to the One Argo initiative, an international effort to extend the float array into the deep ocean with sensors rated for full-depth profiling by 2030. Only direct deep measurements, the authors caution, can confirm whether the reanalysis-based deep warming estimate is accurate, since a reanalysis, however sophisticated, remains a model-based reconstruction rather than an observation.

Open questions remain. The reanalysis mass component shows a change in trend around 2016 whose origin is not yet explained, and the east-west disagreement between GRACE and the reanalysis demands resolution, whether through improved three-dimensional GIA corrections or through refinements in the gravity processing itself. But the central message of the study is one of reassurance and method. The North Atlantic was never hiding mysterious physics; it was hiding missing observations. Once the mass term is estimated with a physics-consistent reanalysis and the deep ocean is allowed to speak, one of the most stubborn discrepancies in regional sea level science quietly dissolves into the noise floor of the instruments, leaving a budget that finally balances.

Subject of Research: Closure of the regional North Atlantic sea level budget using satellite altimetry, GRACE gravimetry, Argo data and ocean reanalysis over 2004–2022

Article Title: North Atlantic sea level budget revisited

Article References: Song, Z., Cazenave, A., Llovel, W., Storto, A., & Bouih, M. (2026). North Atlantic sea level budget revisited. Ocean Science, 22(5), 2903-2914. https://doi.org/10.5194/os-22-2903-2026

Image Credits: AI Generated

DOI: 10.5194/os-22-2903-2026

Keywords: sea level budget, North Atlantic, satellite altimetry, GRACE, Argo floats, ocean reanalysis, deep ocean warming, steric sea level, ocean mass, glacial isostatic adjustment, thermohaline compensation, One Argo

The study, led by Stephanie Bringeland of Queen’s University together with colleagues from Aquanty Inc., the Canadian Geodetic Survey, and Alberta Environment and Protected Areas, focused on the Alberta portion of the South Saskatchewan River Basin (SSRB), a vast drainage network spanning the eastern slopes of the Rocky Mountains across roughly 17 percent of Alberta’s land area. This is no sleepy backwater: the basin hosts the Bow, Oldman, and Red Deer Rivers, feeds one of Canada’s most productive agricultural regions, and supplies water to growing cities. Agriculture alone consumes 85 percent of all surface water used in the basin — compared with a national average of just 9 percent — and the Bow, Oldman, and South Saskatchewan Rivers have already reached their allocation limits, meaning no new water rights can be granted.

To track how the basin’s total water budget has shifted, the researchers turned to the Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-On. These twin-satellite missions measure tiny variations in Earth’s gravitational field caused by the movement of mass across the planet’s surface. When snow accumulates, soils saturate, or aquifers fill, the ground beneath them becomes fractionally heavier — and the satellites detect it. The result is a monthly estimate of terrestrial water storage anomaly (TWSA): the combined weight of surface water, soil moisture, groundwater, and snow, expressed in centimetres of equivalent water height relative to a 2004–2009 baseline. The team used two independent GRACE processing approaches — NASA’s Jet Propulsion Laboratory mascon solution and a custom method from the Canadian Geodetic Survey — to bracket the range of possible satellite estimates.

Satellite gravimetry, however, has a fundamental limitation: it sees only the sum of all water stores, not the individual components. To unpack that total, the researchers employed HydroGeoSphere (HGS), a fully integrated groundwater–surface water model that simultaneously solves the three-dimensional Richards equation for variably saturated subsurface flow, the two-dimensional diffusion wave equation for overland flow, and Manning’s equation for open-channel river flow. The SSRB implementation is staggering in scale: more than 666,000 nodes and 1.2 million finite elements across twelve mesh layers, 740 distinct porous media zones, and 14,125 kilometres of discretely resolved rivers, all driven by a 10-kilometre-resolution daily climate dataset. The model was validated against weekly naturalized river flows at four gauging stations and groundwater levels at 79 observation wells, showing strong agreement with both.

When the researchers compared the HGS-derived TWSA with the satellite estimates over the 2002–2019 period, the correspondence was striking. Both captured the same seasonal heartbeat — storage peaking in April and May just before spring snowmelt, then bottoming out in August and September — and both reproduced the same interannual swings, including the recovery from the severe 1999–2004 Prairie drought, the decline into the 2008–2010 drought, a wet spell after 2010, and a gradual descent that culminated in drought by spring 2019. The comparison also exposed a telling discrepancy: the GRACE-derived seasonal amplitude (9.2 to 11.1 centimetres of equivalent water height) exceeded the model-based estimates (7.5 to 7.8 centimetres), suggesting that global land surface models and even high-resolution regional models may underestimate seasonal storage swings by 15 to 32 percent.

With the model validated against the satellites, the team could finally dissect the basin’s water storage into its constituent parts. The results were illuminating. Soil moisture proved the most volatile component, swinging dramatically with the seasons and with drought years such as 2003 and 2017. Snowpack showed strong seasonal variability but smaller interannual differences. Groundwater, by contrast, behaved like a slow-moving reservoir — heavily dampened, with a muted seasonal signal but pronounced multi-year cycles. This separation matters enormously for water managers: it revealed, for instance, that the catastrophic 2013 floods in southern Alberta may have been worsened by years of positive groundwater anomalies, which left the landscape with less capacity to absorb the torrential rainfall that triggered the disaster.

The most surprising discovery emerged from a time-frequency analysis of the storage records. Using continuous wavelet transforms and Lomb-Scargle periodograms, the researchers detected a harmonic signal in the basin’s water storage with a period of roughly 2.7 to 3.0 years — and this interannual pulse was strongest not in soils or rivers, but in the groundwater component, which showed a spectral peak of 82 decibels at a 2.8-year period. When the team compared this signal against major oceanic climate indices, the Oceanic Niño Index (ONI) — a measure of sea surface temperature anomalies in the east-central tropical Pacific — emerged as the clear winner. The smoothed interannual component of the basin’s water storage showed a moderate negative correlation with the ONI (correlation coefficient of −0.38), and both series shared the same 2.7–3.0-year periodicity. In plain terms: when the tropical Pacific runs warm during El Niño conditions, southern Alberta tends to run dry, with reduced snowpack and negative water storage anomalies. The North Atlantic Oscillation, by contrast, showed no significant link to the basin’s hydrology.

The implications stretch well beyond academic curiosity. Climate projections indicate that the Canadian Prairies will face more frequent and severe drought as temperatures rise, with evapotranspiration increasing and snowpack declining — one study cited in the paper projects up to an 11 percent decrease in maximum annual snow water equivalent for every degree of warming in the region’s grassland basins. Because snowmelt from the Rocky Mountains supplies roughly 80 percent of the Bow River’s streamflow, shrinking snowpacks threaten the very foundation of the basin’s water supply. At the same time, the character of El Niño itself is changing: multi-year ENSO events are becoming more frequent, and central Pacific El Niño events are increasing relative to their eastern Pacific counterparts. If prolonged warm-phase ENSO episodes translate into extended dry spells in the SSRB, the region could face longer and harsher droughts than its water management systems were designed to withstand.

The researchers are candid about the limitations of their approach. GRACE data has no true ground-truth comparison, so errors can only be estimated theoretically, and any inaccuracies in the models used to correct for effects like glacial isostatic adjustment propagate into the final storage estimates. The HGS model, despite its sophistication, slightly overestimated late-summer storage relative to the satellites, and its cumulative-sum processing method may amplify small errors over time. Yet the study’s central methodological lesson stands: neither satellites nor models alone can fully characterize a complex hydrological system. The satellites provide independent, near-uniform coverage that no network of ground instruments could match in a sparsely populated region, while the model supplies the component-level detail that gravity measurements inherently lack. Together, they form a powerful validation loop.

For the farmers, municipalities, and ecosystems of southern Alberta, the work offers something increasingly precious in a warming world: a clearer picture of where the water is, how it moves, and what drives its comings and goings. By revealing that the basin’s groundwater pulses in step with the tropical Pacific, the study opens the door to longer-lead forecasting of water availability — potentially giving managers months of warning before drought conditions take hold. As the authors note, future work will extend the analysis to the basin’s distinct physiographic regions to identify the primary drivers of storage change in each. In a basin where every new water allocation is already off the table, understanding the invisible rhythms of water storage may be the difference between sustainable management and crisis.

Subject of Research: Terrestrial water storage change in the South Saskatchewan River Basin assessed with GRACE/GRACE-FO satellite gravimetry and integrated hydrological modelling

Article Title: Investigating terrestrial water storage change in a western Canadian river basin with GRACE/GRACE-FO and fully-integrated groundwater – surface water modelling

Article References: Bringeland, S., Frey, S. K., Fotopoulos, G., Crowley, J., Xu, S., Khader, O., Eum, H., Farjad, B., Erler, A. R., & Gupta, A. (2026). Investigating terrestrial water storage change in a western Canadian river basin with GRACE/GRACE-FO and fully-integrated groundwater – surface water modelling. Hydrology and Earth System Sciences, 30(18), 6057-6073. https://doi.org/10.5194/hess-30-6057-2026

Image Credits: AI Generated

DOI: 10.5194/hess-30-6057-2026

Keywords: GRACE, GRACE-FO, terrestrial water storage, groundwater, soil moisture, snowpack, South Saskatchewan River Basin, HydroGeoSphere, El Niño Southern Oscillation, drought, Canadian Prairies, hydrological modelling

The research, carried out by Dimitrios Tsoulis and Charalampos Chaidas of the Department of Geodesy and Surveying at Aristotle University of Thessaloniki, covers the period from 2002 to 2025 and relies on data from NASA’s Gravity Recovery and Climate Experiment, known as GRACE, and its successor mission GRACE Follow-On. These twin-satellite systems measure tiny variations in Earth’s gravity field caused by the movement of water across the planet’s surface. When a region gains water, its mass increases slightly and the satellites detect a marginally stronger gravitational pull; when water is lost, the signal weakens. By tracking these minute gravitational fluctuations month after month, scientists can estimate changes in terrestrial water storage with a precision that no ground-based network of wells and gauges can match over large regions.

To translate the raw gravity data into a usable drought indicator, the researchers combined the GRACE and GRACE-FO observations with the GLDAS land surface model, which simulates the exchange of water and energy between the land and the atmosphere. From this fusion they computed two satellite-based drought metrics: the GRACE Drought Severity Index, or GRACE-DSI, and the GRACE-based Groundwater Drought Index, known as GGDI. These were then compared against the conventional drought indices that hydrologists have used for decades, including the Standardized Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and the Standardized Runoff Index. Each of the traditional indices captures a different slice of the drought story, from rainfall deficits to the balance between precipitation and atmospheric demand for water, but none of them sees the full picture of what is actually stored beneath the surface.

The comparison produced one of the study’s most striking results. The GRACE-based indicators recorded a greater frequency and persistence of droughts in Greece than the conventional indices did. In other words, the satellites saw droughts that the traditional metrics missed, and they found that once a drought set in, it tended to linger longer than the rainfall-based measures suggested. This matters because drought is not simply a matter of a dry month or a hot spell. It is a slow-moving deficit that accumulates in aquifers and soils, and indices built only on precipitation can underestimate how deeply and how long a region’s water reserves have been depleted.

Behind the trend in total storage, the study identified the main culprits: groundwater depletion and declining soil moisture. Groundwater is the invisible backbone of Mediterranean water security, supplying agriculture, households, and ecosystems during the dry months when surface sources dwindle. When aquifers are pumped faster than rainfall can recharge them, the loss shows up unmistakably in the gravity record. The fact that soil moisture reduction also emerged as a key driver points to a double burden, in which less water infiltrates the ground and what does infiltrate is increasingly drawn down by evaporation in a warming climate.

The spatial pattern of the decline was equally revealing. The strongest negative changes in water storage were found in the continental regions of Greece, the inland basins and plains where agriculture concentrates and where aquifers have historically been exploited most intensively. This geography is consistent with earlier research on Greek aquifers, which has documented overexploitation in several major basins, including the Thessaly plain and the Lake Karla region, where decades of intensive abstraction have led to well-documented water table declines. The satellite record now places those local observations into a national, two-decade-long context, showing that the problem is not confined to a handful of stressed basins but is part of a broader, systematic trend.

To understand what was driving the variability in water storage, the researchers turned to wavelet analysis, a mathematical technique that examines how the relationship between two time series changes across both time and frequency. Unlike classical correlation, which assumes a relationship is stable over the entire record, wavelet coherence can reveal whether two variables move together only during certain periods or at certain rhythms, such as the annual cycle. Applied to the GRACE storage data and the hydrometeorological variables from the ERA5 reanalysis, the analysis showed that temperature and evapotranspiration were the most important factors affecting the variability of water storage in Greece. This is a significant finding, because it means the fate of Greek water reserves is increasingly governed not by how much rain falls, but by how hot it gets and how much water the atmosphere pulls out of the land.

ERA5, the reanalysis product of the Copernicus Climate Change Service, provided the atmospheric side of the equation. A reanalysis combines historical observations with a numerical weather model to produce a physically consistent record of past climate, and ERA5 offers monthly fields of air temperature, precipitation, potential evaporation, and surface runoff across the entire globe. By pairing this with ground-based precipitation and temperature observations from the Hellenic National Meteorological Service, the researchers could anchor their satellite-derived storage changes in the actual climatic conditions of the period. The consistency between the space-based gravity measurements and the independent meteorological data strengthens confidence that the observed decline is real rather than an artifact of any single dataset.

The study also examined drought persistence using Markov chain analysis, a statistical framework that treats drought classes as states and estimates the probabilities of transitioning between them. If a region is in drought this month, a Markov chain can quantify the likelihood that it will remain in drought the following month, revealing how sticky, or persistent, drought conditions tend to be. Combined with the wavelet coherence analysis, this approach showed that the satellite-based drought indices captured a more persistent drought regime than the conventional indices, reinforcing the conclusion that water storage deficits in Greece are not fleeting anomalies but conditions that build up and hold on.

The implications extend well beyond Greece. The Mediterranean is one of the regions identified by the Intergovernmental Panel on Climate Change as a climate change hotspot, where warming is projected to outpace the global average and where water scarcity is expected to intensify. What the Greek case demonstrates is that the traditional toolkit for monitoring drought, built largely on precipitation and temperature records, may systematically understate the severity and duration of hydrological drought in a warming world. Satellite gravimetry offers a way to close that gap, providing an integrated measure of the water actually available in the ground rather than the water that merely falls from the sky. As the GRACE and GRACE-FO archives continue to grow, and as similar analyses are extended to other water-stressed regions, the ability to weigh a nation’s water supplies from orbit may become one of the most important tools in managing the slow, invisible crisis of groundwater depletion that is unfolding across the Mediterranean and far beyond it.

Subject of Research: Satellite-based assessment of long-term terrestrial water storage decline and drought dynamics in Greece

Article Title: Assessing long-term water storage and drought relationships under climate variability using GRACE/GRACE-FO and ERA5 data

Article References: Tsoulis, D., & Chaidas, C. (2026). Assessing long-term water storage and drought relationships under climate variability using GRACE/GRACE-FO and ERA5 data. Theoretical and Applied Climatology, 157(10), Article 610. https://doi.org/10.1007/s00704-026-06542-y

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06542-y

Keywords: GRACE, GRACE-FO, drought, Greece, groundwater depletion, terrestrial water storage, ERA5, Mediterranean climate, soil moisture, wavelet analysis, Markov chain, water resources

The satellites in question belong to the Gravity Recovery and Climate Experiment, known as GRACE, a joint NASA and German Aerospace Center mission that operated from 2002 to 2017 and was later continued by its successor, GRACE Follow-On. Rather than photographing the surface, GRACE tracks the distance between two orbiting spacecraft with extraordinary precision. When the mass of water stored in soils, aquifers and surface reservoirs shifts beneath them, the resulting gravitational tug subtly alters that distance. By measuring these minute changes, scientists can calculate how much water a region gains or loses each month, a quantity called the change in terrestrial water storage. The technique has transformed hydrology, but it comes with a fundamental catch: the gravity signal is smeared across enormous areas, so a single GRACE data point represents a block of land roughly 150,000 to 200,000 square kilometers in size. For a basin where a farmer’s tube well in one district may be draining an aquifer while a neighboring district recharges after monsoon rains, that blur hides exactly the local detail that water managers need most.

In a study published in Environmental Monitoring and Assessment, Chethan Varadaganahalli Anandagowda, Bhaskar R. Nikam and Suresh Kannaujiya tackled this resolution problem by teaching statistical models to translate the coarse satellite signal into fine-grained maps. Their approach, known as downscaling, exploits the fact that water storage changes are closely linked to variables that satellites and models can observe at much finer scales, including rainfall, soil moisture, evapotranspiration, temperature and terrain. The researchers built monthly climatology-based regression models that relate these high-resolution environmental datasets to the GRACE measurements, then used the learned relationships to estimate water storage change on a grid of 0.25 degrees by 0.25 degrees, roughly 25 to 28 kilometers per side. That is a dramatic sharpening of the effective resolution, bringing the satellite picture down to a scale where individual districts and aquifer systems begin to separate.

The heart of the method was a comparison of several statistical and machine learning approaches, including multiple linear regression, partial least squares regression and random forest modeling. The random forest technique, an ensemble method that builds hundreds of decision trees and averages their predictions, emerged as the clear winner. It achieved a Pearson correlation coefficient of 0.78 against validation data, meaning it captured more than three quarters of the variance in the observed storage signal, with a root mean square error of 12.3 centimeters of equivalent water height. In practical terms, the model could reproduce the month-to-month ebb and flow of the basin’s water storage with an accuracy that makes the downscaled maps genuinely useful rather than merely illustrative. The team validated their results using k-fold cross validation, a technique that repeatedly trains the model on most of the data and tests it on the withheld remainder, guarding against the model simply memorizing patterns rather than learning real physical relationships.

With the sharpened storage maps in hand, the researchers turned to the question that matters most for the Ganga Basin: what is happening to its groundwater specifically. GRACE measures all water stored on and below the land surface, so isolating the groundwater component requires subtracting the contributions of soil moisture, surface water and canopy storage. The team used products from the Global Land Data Assimilation System, a NASA modeling framework that simulates land surface water fluxes, together with satellite soil moisture records from the European Space Agency’s Climate Change Initiative, to strip away these shallower reservoirs. What remained was an estimate of groundwater storage change, which they then compared against observations from monitoring wells across the basin, converted to storage terms using specific yield values for the aquifer material.

The agreement between the space-based and ground-based estimates was striking, with both showing a consistent declining trend across the basin. But the fine-resolution data revealed something the coarse GRACE footprint had smoothed away: the decline is not uniform, and in some places it is far steeper than previous studies had suggested. Over the Delhi National Capital Region and Western Uttar Pradesh, regions where explosive population growth and intensive irrigation place enormous pressure on the aquifers, the downscaled estimates showed groundwater storage falling at rates of 4.69 to 7.16 centimeters of equivalent water height per year. Those figures are more severe than the trends reported in earlier basin-wide analyses, and they pinpoint the western Ganga plains as the epicenter of the basin’s groundwater crisis. The finding matters because it demonstrates that coarse satellite data, by averaging heavily depleted zones with less stressed ones, can systematically understate the intensity of depletion in the worst-affected areas.

The study went beyond storage trends to tackle drought, using multiple drought indices computed from the water storage record to identify major hydrometeorological drought events across the basin. Drought indices condense complex hydrological information into standardized measures of severity, allowing events to be compared across time and space. The analysis identified several significant drought episodes during the satellite era, but one stood out above the rest: the period from August 2015 to October 2017, which the researchers identified as the most intense drought in the record, persisting for a remarkable 27 consecutive months. That two-and-a-quarter-year event, which coincided with consecutive weak monsoon seasons, placed sustained stress on both surface reservoirs and the aquifers that farmers turned to when canals and rivers ran low. Because the downscaled data resolves the drought’s footprint at district scale, it shows which parts of the basin bore the brunt of that prolonged deficit rather than blurring the event into a basin-wide average.

The implications extend well beyond academic hydrology. India’s Central Ground Water Board assesses the country’s groundwater resources annually, but its estimates depend on the density and quality of well observations, which vary widely across states. Satellite-derived storage change offers an independent check on those assessments, and the downscaling approach demonstrated here brings that check down to a scale where it can inform district-level management decisions, from regulating well permits to planning managed aquifer recharge. The researchers note that their findings enhance understanding of local-scale groundwater abstractions and can contribute to sustainable water resource management in a basin where climate change is expected to make the monsoon more erratic even as demand continues to grow. Every centimeter of equivalent water height in these maps represents billions of cubic meters, so a decline of seven centimeters per year in a heavily farmed region translates into a withdrawal of water that took centuries or millennia to accumulate.

There are also lessons here for how the global scientific community monitors water in data-sparse regions. The Ganga Basin is hardly unique: aquifers from the North China Plain to California’s Central Valley to the Middle East are being drawn down faster than they refill, and in many of these places the observational infrastructure is thin. The success of the random forest approach, which outperformed classical regression methods by capturing nonlinear relationships between climate variables and storage change, suggests a transferable recipe. The datasets the team relied on, including GRACE mass grids processed at the University of Texas Center for Space Research, CHIRPS rainfall estimates, GLDAS model outputs, ESA soil moisture records and Indian well observations, are all freely available, meaning the workflow could be replicated for other stressed basins at little cost. As the GRACE Follow-On mission continues to deliver gravity data and machine learning tools grow more capable, the gap between what satellites can see and what water managers need to know keeps narrowing. For the Ganga Basin, that narrowing gap has just exposed an uncomfortable truth: the water is leaving faster, and in more places, than the blur of the old maps ever let on.

Subject of Research: Downscaling GRACE satellite gravity data with machine learning to assess groundwater depletion and drought in the Ganga Basin

Article Title: Downscaling GRACE storage change to analyze groundwater dynamics and drought evaluation in the Ganga Basin

Article References: Anandagowda, C. V., Nikam, B. R., & Kannaujiya, S. (2026). Downscaling GRACE storage change to analyze groundwater dynamics and drought evaluation in the Ganga Basin. Environmental Monitoring and Assessment, 198(11), Article 1146. https://doi.org/10.1007/s10661-026-16002-9

Image Credits: AI Generated

DOI: 10.1007/s10661-026-16002-9

Keywords: GRACE, groundwater, Ganga Basin, downscaling, random forest, machine learning, drought, terrestrial water storage, remote sensing, Delhi-NCR, Uttar Pradesh, water resources

The research team, led by G. Botetano and including J. C. Espinoza, K. Takahashi, and colleagues from institutions in Peru, France, and Brazil, analyzed the period from 1982 to 2024, using satellite-derived precipitation from CHIRPS, temperature from the ERA5-Land reanalysis, sea surface temperatures from HadISST, and more than forty years of daily water level measurements from the Negro River gauge at the port of Manaus. The Manaus gauge is no ordinary data point. Because the Negro River responds to rainfall across roughly three million square kilometers of upstream Amazonia, its levels integrate the hydrological memory of the entire northwestern basin, lagging behind precipitation by about three months as floodplains and saturated soils buffer and release water.

The technical core of the study rests on empirical orthogonal function analysis, a statistical technique that decomposes complex climate fields into their dominant spatial patterns and corresponding time series. When the researchers applied this method to seasonal precipitation, temperature, and terrestrial water storage anomalies measured by the GRACE and GRACE-FO satellite gravimetry missions, they found something remarkable: the leading modes of all these variables were tightly coupled. Precipitation variability correlated with water storage at around 0.78 to 0.79, river levels tracked precipitation at roughly 0.76 to 0.81, and temperature—the sign-inverted, since heat accompanies drought—correlated with storage at 0.76 to 0.88. In other words, the Amazon does not experience a rainfall anomaly here and a temperature anomaly there; it moves as a coherent compound system.

AB-CHEX distills this coupling into a single number for each season. Negative phases below the 15th percentile mark dry–hot compound events, combining rainfall deficits, anomalous warmth, and falling rivers. Positive phases above the 85th percentile mark wet–cold events, the signature of the great floods. The index validated beautifully against history: it reproduces the documented droughts of 1998, 2005, 2010, 2015–16, and 2023–24, and the floods of 2009, 2012, and 2021–22. It also captures basin-scale water storage variability, correlating with the GRACE-derived storage component at 0.93 in the wet season and 0.88 in the dry season—evidence that the index reflects genuine basin-wide hydroclimatic dynamics rather than the quirks of any single dataset.

Then comes the headline finding. When the researchers constructed a trend-preserving version of the index, AB-CHEXLT, the wet and dry seasons revealed opposite long-term trajectories. During the extended wet season from November to April, the index shows a significant drift toward wetter and colder compound conditions, consistent with an intensifying hydrological cycle and the record flooding that has plagued northern Amazonia—the cumulative duration of flood emergencies at Manaus in the first two decades of this century already exceeds that of the entire twentieth century by about 20 percent. During the dry season from June to September, the index shows a sustained shift toward increasingly dry–hot conditions, and here the decomposition is damning: the linear trend explains nearly a quarter of total dry-season variance, and within that trend, warming alone accounts for 86.9 percent of the long-term signal.

And then there is 2024. Even after removing long-term trends, the AB-CHEX value for 2024 reached its lowest magnitude across both seasons in the entire 43-year record, marking an event the authors describe as unprecedented. The 2023–24 drought combined delayed rainfall onset, record-breaking heat, collapsing river levels, widespread canopy browning, and sharp declines in gross primary productivity, with pronounced soil moisture deficits and elevated vapor pressure deficit covering more than half the Amazon region during the austral springs of 2023 and 2024. Previous severe dry–hot events clustered in recognizable patterns—the wet-season extremes of 1998 and 2016 during strong El Niño years, the dry-season extremes of 2005, 2010, and 2023—but 2024 broke the mold in both seasons simultaneously.

To find the culprits, the team turned to composite analysis of atmospheric circulation and sea surface temperatures during extreme index phases. During the wet season, the story is fundamentally Pacific. Dry–hot years show pronounced mid-level subsidence between 300 and 600 hectopascals across equatorial Amazonia—a weakening of the ascending branch of the regional Hadley circulation that suppresses deep convection—paired with an eastward shift of the Walker-like zonal circulation that enhances ascent over the eastern Pacific. Sea surface composites reveal the classic El Niño fingerprint: significant warming over the Niño-3.4 region. Quantitatively, ENSO-related Pacific sea surface temperature anomalies explain 61 percent of wet-season AB-CHEX variability, while the regional Hadley circulation index alone correlates at 0.85, accounting for 72 percent. El Niño weakens the overturning cells and starves the basin of moisture; La Niña strengthens them, driving the great wet–cold floods.

The dry season tells a different and, in a warming world, more troubling story. Here the primary control is not the Pacific but the Caribbean–Tropical North Atlantic region. Dry–hot years show intensified subsidence between 500 and 800 hectopascals, a northward-shifted and strengthened descending branch of the regional Hadley cell, and marked warming over the Caribbean–TNA. Warm sea surface anomalies there weaken the meridional pressure gradient, slacken the trade winds, and choke off the Atlantic moisture inflow that sustains dry-season convection. The Caribbean–TNA sea surface temperature index correlates with dry-season AB-CHEX at 0.79, explaining 62 percent of its variability—and crucially, this relationship holds regardless of the Pacific state, remaining significant under warm, neutral, and cold Niño-1+2 conditions. When the researchers removed the shared variance between the two ocean basins using partial correlations, the Pacific influence on dry-season compound extremes vanished entirely, while the Caribbean–TNA influence on wet-season extremes persisted only as a secondary modulation of ENSO-driven variability.

The spatial consequences of these two forcing regimes differ profoundly. During wet-season dry–hot events, precipitation deficits exceeding 25 percent and temperature anomalies above 1.2 degrees Celsius concentrate over northeastern Amazonia, where ENSO’s Walker and Hadley circulation teleconnections bite hardest. Dry-season dry–hot events, driven by the more spatially uniform Caribbean–TNA forcing, produce rainfall reductions exceeding 45 percent and anomalies near plus one degree Celsius across the Peruvian, central, and southern Amazon—wider, deeper, and more severe. Land–atmosphere feedbacks amplify the damage: antecedent soil moisture deficits persist through hydrological memory, suppressing evapotranspiration and shifting the surface energy balance toward sensible heating, which reinforces both the warmth and the rainfall deficit. In total, roughly 68 percent of the basin exhibits significant compound coupling with the index during the wet season, rising to 76 percent during the dry season.

The implications reach beyond atmospheric science. The sustained warming of the Caribbean–TNA since the mid-2000s, the authors note, appears to be intensifying dry–hot compound conditions, strengthening land–atmosphere feedbacks, and amplifying hydroclimatic instability—layered on top of deforestation-driven delays in wet-season onset across southern Amazonia and rising tree mortality across the basin. Because all of AB-CHEX’s inputs are publicly available, routinely updated datasets, the index can be computed operationally by hydrometeorological services and early-warning centers across Amazonian countries, with the caveat that the two-to-three-month hydrological lag limits real-time applications. What the index makes unmistakably clear is that the Amazon’s future hinges on two oceans and a warming atmosphere acting in concert—and that the dry season, where warming already explains most of the long-term drift toward compound drought and heat, is where the rainforest’s margin of safety is eroding fastest.

Subject of Research: Large-scale ocean–atmosphere drivers of compound drought–heat and flood–cold extremes in the Amazon Basin from 1982 to 2024

Article Title: Large-scale climate drivers of extreme compound events in the Amazon from interannual to long-term timescales (1982–2024)

Article References: Botetano, G., Espinoza, J. C., Takahashi, K., Gutierrez Villarreal, R. A., Marengo, J., & Wongchuig, S. (2026). Large-scale climate drivers of extreme compound events in the Amazon from interannual to long-term timescales (1982–2024). Climate Dynamics, 64(10), Article 427. https://doi.org/10.1007/s00382-026-08382-y

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08382-y

Keywords: Amazon Basin, compound extremes, drought, ENSO, El Niño, Tropical North Atlantic, Caribbean sea surface temperature, Hadley circulation, Walker circulation, river levels, GRACE, hydroclimatic variability

The innovation at the heart of the study is what the authors call the groundwater storage fluctuation, or GSF, method. Traditional approaches to estimating recharge are notoriously fragmented. Some rely on chloride mass balance calculations that require detailed chemical sampling of rainfall and soil water. Others depend on numerical models that must be calibrated against local field data, or on water-table fluctuation techniques that demand dense networks of monitoring wells. Because each technique carries its own assumptions, biases and data requirements, global syntheses have struggled to reconcile regional estimates into a coherent planetary picture. The GSF method sidesteps much of this complexity by working directly with monthly fluxes of groundwater storage anomalies, meaning it requires no seasonal templates, no pre-defined recharge events and no region-specific calibration.

The raw material for the method comes from an unusual but powerful source: two satellites flying in formation hundreds of kilometres above Earth. The Gravity Recovery and Climate Experiment, known as GRACE, and its successor GRACE Follow-On detect minute changes in the distance between the two spacecraft as they pass over regions of varying mass. When aquifers fill, the added water tugs slightly more strongly on the satellites; when aquifers are drained by pumping or drought, the gravitational pull weakens. These gravity signals, processed into estimates of terrestrial water storage, are then fed into the Catchment Land Surface Model of NASA’s Global Land Data Assimilation System, version 2.2. The assimilation constrains the model’s simulated groundwater storage with real satellite observations, producing a monthly record of groundwater storage anomalies across the entire globe at a quarter-degree resolution.

From these storage anomalies, the GSF method derives three quantities that water managers care deeply about: gross recharge, total discharge and net recharge. Gross recharge represents the total inflow of water into aquifers, while total discharge captures all outflows, both natural processes such as baseflow to rivers and springs, and human-related withdrawals. Without additional data, the two types of discharge cannot be separated, a limitation the authors acknowledge explicitly. Net recharge, the difference between the two, indicates whether an aquifer is gaining or losing water overall. Because the method operates on month-to-month changes in storage rather than absolute values, it is insensitive to many of the systematic errors that plague model-based estimates, and it can be applied identically in every basin on Earth, from the humid tropics to the driest deserts.

The spatial pattern the method reveals is striking. Recharge rates climb above 300 millimetres per year in humid tropical regions where abundant rainfall percolates through permeable soils, while falling below 100 millimetres per year across arid zones where evaporation competes fiercely with infiltration. Globally, the study finds a trend of 0.34 millimetres per year over the two decades examined, but this trend is not statistically significant, suggesting that on average the planet’s total recharge has remained remarkably stable even as its distribution shifts. That stability, however, masks dramatic swings at the regional scale. Arid basins show the highest interannual variability of any climate zone, a finding with profound implications for the communities that depend on them.

Three basins illustrate the point vividly. Australia’s Murray-Darling Basin, the agricultural heartland of the continent, exhibits a coefficient of variation of 48 percent, meaning its annual recharge nearly doubles or halves from one year to the next. Brazil’s São Francisco basin follows at 40 percent, and the Colorado basin in the United States at 36 percent. In these water-stressed regions, a single wet year can replenish aquifers substantially, while consecutive dry years can leave them depleted for extended periods. For farmers, city planners and environmental regulators, such volatility means that average recharge figures are dangerously misleading; sustainable pumping limits must account for the boom-and-bust rhythm of arid-zone hydrology rather than assuming a steady annual supply.

Of course, a new method is only as good as its validation, and the authors subjected their estimates to rigorous testing against independent data. Across Australia, they compared their satellite-derived recharge map with a high-resolution dataset built from the chloride mass balance technique, an entirely independent approach that estimates recharge from the accumulation of atmospheric chloride in soil profiles. The spatial agreement between the two was strong, with a rank correlation of 0.82. Zooming into the Murray-Darling Basin, the agreement held firm at 0.83, with a standard deviation of 32.1 millimetres per year. These numbers indicate that a method relying solely on satellite gravity and model assimilation can reproduce the spatial texture of recharge that laborious field campaigns have documented.

The underlying groundwater storage estimates were also checked against reality, using records from 259 monitoring boreholes across the Murray-Darling Basin. The correlation between the satellite-constrained storage anomalies and the in-situ observations was 0.47, with a standard deviation of 33.9 millimetres, a value that falls within the bootstrap uncertainty limit of 38.2 millimetres. While a correlation of 0.47 is moderate rather than spectacular, it is respectable given the enormous difference in scale between a point measurement in a borehole and a satellite footprint spanning hundreds of kilometres. The comparison confirms that the GRACE-constrained product captures genuine storage dynamics rather than artefacts of the modelling chain, even in a basin with complex geology and intense irrigation.

The authors are careful to spell out the limits of their approach. GRACE’s effective spatial resolution is approximately 300 kilometres, which means that grid-cell estimates in data-sparse regions or areas with low signal-to-noise ratios should be interpreted with caution. Small aquifers, localized recharge hotspots and narrow alluvial corridors simply fall below the satellites’ resolving power. The method also inherits uncertainties from the land surface model itself, particularly in regions where soil properties, vegetation and irrigation practices are poorly mapped. Nevertheless, the consistency of the global total with independent model ensembles, combined with strong regional validation, suggests the GSF framework offers a robust new baseline for planetary-scale groundwater accounting.

The timing of this work could hardly be more significant. Global assessments have documented rapid groundwater declines in many of the world’s major aquifers, alongside isolated cases of recovery driven by deliberate management intervention. As climate change intensifies droughts and alters precipitation patterns, the balance between recharge and withdrawal in arid and semi-arid basins will become ever more precarious. A globally consistent, satellite-based recharge estimate gives hydrogeologists, governments and international agencies a common yardstick for the first time, one that can be updated as new satellite data arrive. If the world is to manage its hidden water reserves wisely, it must first know how fast those reserves are being replenished. This study brings that knowledge within reach, transforming a patchwork of incompatible local estimates into a single, coherent view of the water flowing silently into the aquifers beneath our feet.

Subject of Research: Global estimation of groundwater recharge using the groundwater storage fluctuation method applied to GRACE-constrained GLDAS-2.2 CLSM data

Article Title: Towards global groundwater recharge estimation from GRACE-constrained GLDAS-CLSM data

Article References: Ferreira, V., & Ndehedehe, C. (2026). Towards global groundwater recharge estimation from GRACE-constrained GLDAS-CLSM data. Hydrogeology Journal. https://doi.org/10.1007/s10040-026-03169-1

Image Credits: AI Generated

DOI: 10.1007/s10040-026-03169-1

Keywords: groundwater recharge, GRACE, GLDAS, CLSM, groundwater storage fluctuation, satellite gravimetry, hydrogeology, Murray-Darling Basin, chloride mass balance, water resources, arid basins, remote sensing