Two Decades of Human and Climate Induced Groundwater Storage Shifts in Brazil

Two Decades of Human and Climate Induced Groundwater Storage Shifts in Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Two Decades of Human and Climate Induced Groundwater Storage Shifts in Brazil Augusto Getirana, Clyvihk Camacho, Maria Antonieta Mourao, Otto Correa Rotunno Filho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7311212/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Brazil holds the world’s largest reserves of renewable freshwater—the country contributes ~ 20% of the planet’s inland water discharge into the oceans 1 —, yet recurrent water crises expose its growing vulnerability under extreme events 2 . While surface water dominates national supply, vast groundwater reserves remain underused and poorly monitored, representing a critical but untapped resource for climate resilience and national security. Here, we present a data-driven spatiotemporal reconstruction of Brazil’s groundwater behavior over the past two decades, integrating multi-satellite and in situ data into an artificial intelligence modeling framework. Results reveal groundwater variability, recharge, and trends under climatological and anthropogenic stressors across the nation’s ~ 8.5 million km² of land. Brazil’s 2002–2023 averaged aquifer recharge is 223 mm—12% of annual precipitation—totaling ~ 1,900 km 3 of annual renewable groundwater volume. Persistent depletion or no recharge is observed in heavily exploited aquifers in eastern Brazil, further impacted by prolonged droughts 1 , 2 and climate oscillations. Such aquifers present depletion trends mirroring patterns observed in intensively exploited aquifers in Bangladesh 3 , India 4 , Iran 5 and the U.S. 6 , 7 . As a world’s major breadbasket, Brazil plays a vital role in global food security. Results presented here are therefore critical to the nation’s sustaining agricultural productivity under increasing climate stress. Earth and environmental sciences/Hydrology Earth and environmental sciences/Climate sciences/Hydrology Figures Figure 1 Figure 2 Figure 3 Figure 4 Main Brazil possesses the world’s largest volume of renewable freshwater, contributing roughly 20% of global inland runoff to the oceans 1 . However, this abundance has not shielded the country from water insecurity. Over the past 25 years, Brazil has experienced three major nationwide water crises, each intensified by climate-driven drought events and the nation’s reliance on surface water sources. These crises underscore the urgent need to diversify water supplies and strengthen resilience through increased understanding and sustainable use of groundwater resources 2 . Beneath Brazil’s 8.5 million km² of land lies a vast, yet mostly underutilized, reserve of groundwater. Groundwater storage is coarsely estimated in 112,000 cubic kilometers 8 , or ~ 95% of Brazilian water reserves 9 . Although over half of Brazilian municipalities draw on aquifers to some extent, groundwater accounts for only a modest share of domestic and agricultural use 10 . Still, concerns about declining groundwater storage have grown, with recent studies highlighting regional depletion trends 1 , 11 – 14 . Despite this, Brazil’s national groundwater monitoring infrastructure remains limited—about 400 federal wells provide sparse and uneven coverage across the country 11 , 15 . This data scarcity hampers efforts to characterize the spatial and temporal dynamics of aquifer systems and to manage them sustainably. Spatiotemporal large-scale groundwater monitoring in Brazil has been limited to modeling attempts based on global datasets. Earth Observing datasets, such as NASA’s Gravity Recovery and Climate Experiment (GRACE 16 ), have been used as a proxy to quantify groundwater loss 2 , 17 . Such studies give us a first-order picture of Brazil’s groundwater dynamics, but uncertainties remain high due to little to no integration of local data. Appropriate hydrogeological monitoring at different scales faces difficulties related to geological complexity, diversity and corresponding structures. Other factors affecting monitoring are complexities related to hydraulic properties of aquifers, recharge zones, groundwater exploitation, land use and land cover change, as well as meteorological and climate variability 11 , 18 . Parameterizing such a large number of factors without their adequate spatial and temporal distribution is a challenge to groundwater numerical modeling 19 . Surrogate models, without physical coupling 20 , such as those based on artificial intelligence (AI), can simulate the behavior of groundwater without the need for in-depth recognition of the geological environment 19 , 21 – 23 with results that already surpass physical models 11 , 24 – 26 . Building upon recent developments combining GRACE data and hydrogeological measurements within an AI modeling framework 11 , we reconstructed 21 years of spatiotemporal groundwater variability across Brazil. Our AI modeling framework ingests multi-source hydrogeological and meteorological data, including a variety of ground and satellite measurements and Brazil’s hydrogeomorphology and water use information for a more precise groundwater monitoring across the country. The AI model is capable of filling the gaps between gauges, providing detailed groundwater variability across the country, never before observed. As a result, we were able to characterize Brazil’s groundwater storage dynamics, unraveling its spatially distributed emerging trends, and impacts of climatological events. Groundwater variability across Brazilian aquifers Groundwater storage (GWS) standard deviation (σ) serves as a proxy for temporal variability and provides insights into the interplay between hydrological processes and aquifer characteristics. Figure 1 shows the spatial distribution of GWS variability, including long-term maxima, minima, and seasonal patterns. High σ values are found in regions where both natural (e.g., climate, lithology, surface–subsurface connectivity) and anthropogenic factors (e.g., groundwater abstraction, land use) drive dynamic groundwater behavior. Notably, the highest GWS variability occurs in more transmissive alluvial deposits of northwestern Brazil, within the Amazon basin (σ = 50 mm), and in the fractured crystalline basement of central Brazil (σ = 37–43 mm), where aquifers are typically shallower due to their lithological properties and enhanced surface–groundwater connectivity 15 , 27 , 28 (see Supplementary Fig. 1 for GWS variability of selected aquifers and Supplementary Text 1 for their brief characterization). Within the Amazon basin, particularly at the confluence of the Solimões and Negro Rivers, near Manaus, GWS anomalies range from + 176 mm to − 143 mm (Fig. 1 b,c). This location is characterized by surface water level variations of 10–15 meters 29 , which likely exert strong influence on subsurface water storage due to intense surface–aquifer coupling 30 . Although the basin is predominantly composed of porous sedimentary aquifers, extensive crystalline bedrock regions—less favorable for groundwater retention—are also present 31 , 32 . These conditions, combined with the region’s flat topography, high hydrological seasonality, and strong river–aquifer interactions, may contribute to recent drought episodes in the Amazon’s river systems 33 . Our model captures these dynamics, showing finer-resolution river patterns with significantly elevated GWS variability (σ = 57 mm), compared to the remainder of the basin (σ = 22 mm). In contrast, lower GWS variability is observed across free and porous aquifers in central and northeastern Brazil, with aquifer-averaged standard deviations ranging from 10 to 13 mm. This reduced variability is likely associated with deeper static groundwater levels 27 , which confer a greater capacity to buffer seasonal or interannual hydrological fluctuations compared to shallow alluvial or unconsolidated sedimentary systems that display greater ΔGWS amplitudes. The AI-based modeling framework also reveals groundwater variability patterns linked to the South Atlantic Convergence Zone (ZCAS) 34 . ZCAS plays a key role in transporting moisture from the Amazon basin toward central and southeastern Brazil, particularly during the austral summer (December–January–February, DJF) 35 . This influence is evident in Fig. 1 d, which highlights a pronounced belt of wetter-than-average GWS conditions extending from the Amazon to southeastern Brazil during DJF months. Conversely, during March–April–May (MAM), widespread groundwater depletion occurs across central Brazil, driven by seasonal drought, baseflow discharge, and elevated evapotranspiration losses 36 . Groundwater recharge Aquifer recharge, defined as the annual variation in GWS, was estimated across Brazil using the Water Table Fluctuation (WTF) method 37 – 40 for each hydrological year between 2002 and 2023, focusing on unconfined aquifer systems. The national mean annual recharge during this period was 223 mm, corresponding to a total volume of approximately 1,899 km³.yr⁻¹ of renewable groundwater. This volume represents roughly 12% of the country’s average annual precipitation. Recharge rates exhibit significant spatial variability (Fig. 2 a). For instance, the carbonate portion of the Bambuí karst aquifers displays relatively high recharge efficiency, capturing up to 18% of local precipitation. In contrast, fractured aquifers exhibit lower recharge rates, capturing approximately 5% of rainfall, while granular aquifers yield an average recharge rate of 12.5%. Temporal variability in recharge is also pronounced. The lowest annual recharge was recorded in 2015, at 159 mm (1,354 km³.yr⁻¹), coinciding with a widespread drought that triggered a national water crisis 1 . Conversely, the highest recharge occurred in 2021, reaching 274 mm (2,333 km³.yr⁻¹). Although parts of the country were under severe drought conditions that year 2 , the Amazon basin—covering approximately 60% of Brazil’s territory—experienced anomalously high rainfall and historic flooding, which significantly contributed to the observed peak in national recharge. It is noteworthy that certain regions in Brazil exhibit zero annual groundwater recharge, indicating an absence of replenishment during specific hydrological years. These conditions are observed in several key aquifer systems, including the Urucuia, Bauru–Caiuá, Serra Geral Formation, and the exposed recharge zone of the Guarani Aquifer (Botucatu Formation) 11 , 13 , 41 . Additional zero-recharge zones include the Urucuia aquifer, Serra Grande Formation, and areas underlain by the Crystalline Basement (Fig. 2 a and Supplementary Fig. 2a). These patterns are primarily associated with reduced precipitation 42 and elevated evapotranspiration rates 43 – 45 , often exacerbated by intensive agricultural land use. Within the Paraguay River basin, a significant decline in recharge—particularly within the Pantanal aquifer (Fig. 2 g; Supplementary Figs. 3f, 3l)—is likely driven by a combination of recent multi-year droughts 46 , 47 and widespread land cover changes. The region, encompassing one of the world’s largest tropical wetlands, has been increasingly affected by seasonal wildfires and the encroachment of pasture lands over native vegetation 48 . It is important to emphasize that these estimates are subject to the influence of current groundwater extraction practices, which may suppress recharge signals, as observed in other global regions 3 . ENSO impacts on groundwater storage Figures 2 c-n highlight the timing of major El Niño–Southern Oscillation (ENSO) events and their relationship with anomalies in GWS and TWS, as well as estimated recharge rates. Sensitivity analysis of the model (Supplementary Material, Sensitivity to Input Data) identified precipitation seasonality as the most influential predictor of groundwater recharge, based on SHAP value distributions. Sustained trends in TWS are likely to reflect long-term changes in groundwater systems—particularly when hydrogeological processes, with their inherently slow response times, dominate (see Supplementary Fig. 5 for Mann-Kendall trend analysis of GWS across the country). For example, the consistent negative TWS trend observed between 2009 and 2019 (Figs. 2 e- 2 h) is indicative of extended GWS decline. To evaluate the hydrological impact of the 2015/2016 El Niño event on Brazil's major aquifer systems, GWS trends were analyzed across three periods: 2002–2023, 2002–2014 (pre-El Niño), and 2015–2023 (post-El Niño)—linear regressions for each period are shown in Fig. 2 and Supplementary Figs. 2–4, with corresponding trend values summarized in Supplementary Table 1. The analysis focuses on six major unconfined aquifers: Alter do Chão, Urucuia, Bauru–Caiuá, the exposed recharge zone of the Guarani aquifer, Pantanal, and Parecis. Results reveal that the El Niño event exerts considerable influence on GWS variability. Strong signals of hydrological response were observed in the Amazon (Alter do Chão), Tocantins–Araguaia, Paraná (Bauru–Caiuá and Guarani), Paraguay (Pantanal), São Francisco (Urucuia), and Uruguay basins. These regions, which lie away from coastal climatic moderation, appear more sensitive to the precipitation anomalies associated with these climate oscillations. A marked shift in groundwater behavior is evident following the 2015/2016 El Niño event. The transition from weakly positive or neutral trends to widespread negative trends in GWS across several basins highlights the capacity of extreme seasonal climate events to alter long-term groundwater dynamics. In contrast, coastal basins along the Atlantic exhibit positive trends in GWS, as shown in Fig. 2 and Supplementary Figs. 3 and 4. This increase may be linked to La Niña events, which are associated with enhanced precipitation along Brazil’s eastern and southeastern coastal regions 49 – 51 . Elevated groundwater levels in these basins can reduce the sub-surface's capacity to absorb excess rainfall, thereby increasing surface runoff and the likelihood of flooding 52 . Additionally, saturated soils can promote mass movement processes such as landslides, which have been documented during intense rainfall events in Brazil 53 . For example, in the South Atlantic basin, GWS increased during the study period, particularly leading into 2023–2024. This rise in subsurface saturation may have limited infiltration capacity and exacerbated the severity of the flooding events that struck southern Brazil during this period 54 , 55 . These findings are consistent with previous studies 56 , 57 , 58 , 33 that underscore the substantial influence of El Niño and La Niña events on hydrological processes in the Amazon and broader South American region. Emerging trends and attributions Spatial patterns of emerging GWS trends were identified using a Support Vector Machine (SVM) clustering approach 59 , resulting in seven distinct regions (Fig. 3 a; see Supplementary Fig. 6 for aquifer breakdown). Region 1 exhibited a strong positive storage trend, regions 2 and 4 showed moderate storage declines, and region 3 was characterized by severe storage depletion. No statistically significant trends were detected in regions 5 and 6. Region 7 shows a positive storage trend. Focusing on northern Brazil, particularly the Amazon River basin (Supplementary Fig. 3c), a positive precipitation trend is observed (Supplementary Fig. 7b), which corresponds with modest gains in groundwater recharge and storage in localized areas, such as the Alter do Chão aquifer (Fig. 2 , region 1, and Supplementary Fig. 7). TWS variation in the region may reflect broader climate-driven processes, with some studies predicting significant impacts on groundwater storage in the Southern Hemisphere 60 , particularly in high-storage regions like the Amazon basin 61 . These findings raise an important question: can distinct recharge and storage behaviors coexist within the same aquifer system or river basin? Comparing results presented in Figs. 2 and 3 a confirms this possibility, showing that aggregate basin-scale trends may indicate net losses even when substantial subregions experience storage gains. This divergence is particularly evident in the Alter do Chão, Bauru–Caiuá, Guarani, and Parecis aquifers (Fig. 2 ), as well as in major basins such as the Tocantins–Araguaia, Amazon, and Paraná (Supplementary Figs. 3 and 4). In region 1, which exhibits strong positive groundwater storage trends, increases are likely influenced by interannual climate variability, particularly El Niño and La Niña events, which modulate precipitation patterns in southern Brazil 62 . These events contribute to elevated recharge during wetter periods, reinforcing storage gains. Region 7 exhibits positive groundwater trends—not as strong as those in region 1—mostly driven by increased precipitation rates (see Supplementary Fig. 7b). In region 2, encompassing the southern Amazon River basin and much of the Tocantins–Araguaia basin, extensive conversion of tropical forest and Cerrado to agricultural land has intensified 63 – 65 , coinciding with significant declines in observed precipitation 43 , 65 , 66 . These combined land cover and climate shifts (see Supplementary Fig. 7) are linked to reduced groundwater recharge (Fig. 3 b) and the resulting storage losses evident in our SVMbased regional clustering. This area also represents Brazil’s new agricultural frontier, where escalating groundwater abstraction is documented 28 , 67 – 69 . Climate projections further predict decreasing recharge rates in northern Brazil under future warming scenarios 70 , while amplified seasonality 12 , 71 and largescale deforestation 72 , 73 continue to alter the hydrological cycle. Thus, storage declines in region 2 arise from the synergistic impact of intensified extraction, altered hydrological processes, and landuse change, rather than abstraction alone. Also in region 2, the decline in the Pantanal aquifer (see Fig. 2 g, m) is intensified during the 2019–2020 drought 74 and may reflect a shift in the recharge regime based on modelled trends. In this area, groundwater recharge is likely affected by both agricultural and hydrological droughts, often compounded by widespread fire activity 46 , 47 . Wildfires alter soil properties, reduce infiltration capacity, and disrupt recharge processes 75 , 76 . Moreover, drought conditions driven or intensified by fires 48 , 77 may exacerbate storage losses. Given these complex interactions, this region warrants further investigation to better understand the controls on groundwater behavior and resilience under compounding environmental stressors. In northeastern Brazil (region 2), declining GWS is primarily attributed to increased groundwater abstraction 28 , 68 , 69 , 78 and the occurrence of prolonged droughts 1 , 62 , 63 . Rodell et al.⁶¹ report a mean groundwater loss of − 16.7 ± 2.9 km³ yr⁻¹ in the region, with anomalously low rainfall observed in 2012, 2014, and 2015 1 . Climate projections suggest that recharge in this semi-arid region may decrease by up to 70% due to climate change impacts 70 . The most severe GWS declines are observed in central Brazil (region 3), with notable losses in the São Francisco, Paraná, Southeast Atlantic, and East Atlantic basins (Supplementary Figs. 3 and 4). These trends result from a compounding set of drivers, including intensive groundwater use 28 , 68 , 78 , 81 , severe droughts 1 , 2 , 82 , anthropogenic climate change 33 , 60 , 66 , 83 , and shifts in the regional hydrological cycle 12 , 71 . Collectively, these factors contribute to substantial and sustained depletion of groundwater resources across the region. In region 4, storage losses are likely associated with localized groundwater abstraction 10 , 68 and natural hydrological variability. Although less pronounced than in other regions, the observed decline suggests a combination of climatic and anthropogenic influences. Discussion Many aquifer systems globally are increasingly threatened by a combination of anthropogenic pressures and environmental change, with several regions approaching critical thresholds of physical sustainability 84 —where groundwater withdrawal rates exceed natural and artificial recharge. In Brazil, similar pressures are emerging, particularly in large aquifers 11 , 13 , 14 , 41 , mirroring patterns already documented in highly exploited systems such as the Central Valley 6 and High Plains 7 aquifers, in the U.S., the Lower Zayandeh-Rud, in Iran 5 , and aquifers in northern India and Bangladesh 85 , all of which exhibit severe groundwater storage declines. In this context, careful consideration is essential when evaluating the groundwater use potential of aquifers. Resource assessments that focus solely on extraction capacity, without incorporating regional hydrogeological behavior and the effects of climate variability, risk triggering long-term storage depletion. Such unsustainable practices have already led to persistent groundwater losses in central Brazil 86 , underscoring the need for integrated, climate-sensitive management strategies that go beyond volumetric availability. Our analysis reveals pronounced spatial heterogeneity in GWS variability across Brazil. The Amazon basin exhibits the largest seasonal fluctuations, a pattern linked to the region’s porous sedimentary aquifers and extensive alluvial deposits, combined with high rainfall seasonality and strong river–aquifer interactions 14 . In contrast, aquifers in central and northeastern Brazil display markedly lower seasonal variability, likely due to their lower transmissivity, limited surface water connectivity, and deeper static water levels relative to alluvial systems. The South Atlantic Convergence Zone plays a pivotal role in modulating recharge dynamics, particularly during the austral summer (DJF), when enhanced moisture flux supports widespread infiltration and recharge in central and southeastern Brazil 41 . During the dry season (MAM), GWS declines are prevalent, driven by minimal recharge and elevated evapotranspiration. These patterns are consistent with previous GRACE-based assessments 1 , 80 , 82 , 87 but offer enhanced spatial granularity, enabling the detection of localized GWS responses to seasonal and interannual climate forcing 33 , 66 , 74 , 88 , 89 . El Niño and La Niña events exert a significant influence on Brazil’s groundwater dynamics by modulating precipitation patterns and, consequently, aquifer recharge. The 2015–2016 El Niño event resulted in pronounced groundwater storage (GWS) declines across the country 1 , with the most substantial impacts observed in the Amazon 90 and Paraná basins. In contrast, La Niña episodes have been associated with enhanced recharge in coastal systems, particularly in the South Atlantic basin, due to increased precipitation 51 . Despite the Amazon basin’s vast hydrological reserves, it experienced record-low river levels during the 2023–2024 hydrological year, coinciding with a strong El Niño and persistent long-term climate trends. Our model captures this hydroclimatic sensitivity, highlighting how both extreme droughts and floods disrupt the regional groundwater equilibrium. Similarly, the Urucuia aquifer continues to exhibit sustained GWS losses 11 , largely attributable to recurrent drought conditions and intensive irrigation 91 . Together, these findings emphasize the importance of integrating local-scale observational data into national groundwater assessments, especially in regions facing increasing hydrological stress under climate change. Groundwater use and depletion Groundwater depletion is a complex and increasingly global concern, with far-reaching implications for water security, land subsidence 92 , reductions in river discharge and surface water availability discharge 5 , 85 , saltwater intrusion in coastal zones, increased pumping costs, and even potential perturbations in Earth’s rotational dynamics 93 . In Brazil, recent studies have linked groundwater losses primarily to meteorological droughts 1 , 2 . While this study does not establish causality between groundwater depletion and extraction rates, the spatial overlap between emerging negative GWS trends and areas of high socioeconomic demand suggests a likely correlation. Currently, groundwater serves as the sole source of water supply for approximately 40% of Brazilian municipalities 10 and meets a significant portion of the country’s agricultural irrigation demand 13 . The growing reliance on groundwater for irrigation has already exerted pressure on large aquifer systems 10 . Mining activities further exacerbate groundwater stress, particularly in regions where aquifer dewatering is required for mineral extraction (Supplementary Fig. 7). Both open-pit and underground mining operations can dramatically alter groundwater dynamics by functioning as hydraulic sinks, necessitating continuous dewatering. These processes not only deplete groundwater reserves but also disrupt natural flow regimes and may diminish surface water contributions to rivers and wetlands 94 , 95 . The long operational lifespan and vertical extent of many mining projects compound these effects. For instance, in the eastern portion of the Moeda Syncline (Minas Gerais state), groundwater extraction exceeds 150% of estimated recharge—amounting to 0.04 km³ per year over a 305 km² area 96 . This imbalance results in an average annual storage decline of 134 mm, measurable even by satellite gravimetry. Such hydrological disturbances are already apparent in the São Francisco River basin and may extend into the Southeast and East Atlantic basins (see Supplementary Figs. 3 and 4), where groundwater-surface water interactions are increasingly altered. To assess the multifactorial pressures on Brazil’s groundwater systems, we evaluated a suite of spatial indicators, including: the distribution of pivot irrigation points 68 , well density from the SIAGAS system, areas of natural vegetation loss 73 , mining footprints 97 (with emphasis on metallic mineral extraction), evapotranspiration and LAI trends from NASA’s MODIS, precipitation trends from NASA’s GPM, and TWS trends from NASA’s GRACE missions (Supplementary Fig. 7). This integrated analysis highlights the intersection of anthropogenic drivers and climate variability in shaping the country's evolving groundwater landscape. Conclusions This study provides the first nationwide, observation-based assessment of groundwater dynamics across Brazil. We integrated local well data, socioeconomic information, and satellite-based hydrological measurements (e.g., GRACE’s terrestrial water storage, GPM’s precipitation, MODIS’ leaf area index and evapotranspiration), and leaf area index (MODIS). within a machine learning framework to estimate monthly recharge over a 0.1° spatial grid between 2002 and 2023. Extensive sensitivity analyses were conducted on both model architecture and input features to ensure robustness. The final dataset encompasses nearly 20 million recharge estimates across 87,367 grid cells, enabling a fine-scale characterization of recharge variability and long-term trends across diverse climatic, geological, and socioeconomic settings—from individual aquifers to major river basins. Our findings indicate that average groundwater recharge in Brazil is approximately 12% of annual precipitation, though spatial variability is considerable. Certain aquifers, particularly in northeastern and southeastern Brazil—including the Urucuia 13 , Guarani, and Serra Geral 89 —experience zero recharge in some years, highlighting their vulnerability to interannual climate variability. The Pantanal aquifer presents a particularly critical case, with recharge decline attributed to a combination of land-use change 98 , precipitation anomalies, climate variability 99 , and recurrent wildfires 100 . These disturbances alter soil structure and reduce infiltration, compounding the effects of drought and further impairing recharge processes. Limitations must be acknowledged. Aquifers with limited historical monitoring pose challenges due to reduced data representativeness. Additionally, regions with high variability in physical characteristics—such as geology, land use, climate, and extraction intensity—may reduce model accuracy. The resolution of the model (0.1°, monthly) is inherently constrained by the spatial and temporal availability of satellite data. In crystalline terrains, although model performance is promising, further observational datasets are required to validate simulations and differentiate between regolith and bedrock contributions to storage. Furthermore, as with many current artificial intelligence models, the methodology is data-driven and does not explicitly incorporate physical process representations. The model's applicability to other regions or aquifers will depend on the availability and quality of input data and adherence to the method’s assumptions and limitations. Despite these constraints, the model demonstrates strong potential for capturing groundwater dynamics in diverse hydrogeological settings. The integration of satellite remote sensing and machine learning offers a scalable and transferable framework for large-scale groundwater monitoring. More broadly, this study provides new insights into the spatiotemporal behavior of Brazil’s groundwater systems and represents a milestone in advancing sustainable water resources management in the country. Methods The methodology developed to simulate variations in groundwater storage (GWS) in Brazil integrates satellite observations, in situ well data, and hydrogeological attributes into a data-driven artificial intelligence (AI) framework. The approach comprises four main steps: 1. Time Series Decomposition: Satellite datasets from GRACE (Gravity Recovery and Climate Experiment), MODIS (Moderate Resolution Imaging Spectroradiometer), and GPM (Global Precipitation Measurement) were decomposed into seasonal and wavelet components to capture dominant temporal patterns. Discrete wavelet transform components (D1, D2, D3) and seasonal (S) and trend (T) signals were extracted from each dataset. 2. Spline Interpolation: The decomposed components were resampled to their original temporal resolutions using spline interpolation, ensuring alignment with in situ data from Brazil’s national groundwater monitoring network (RIMAS 11 ). 3. Data Integration and Feature Mapping: The interpolated satellite signals were collocated with RIMAS well data in space and time (latitude, longitude, timestamp). Hydrogeological attributes, including stratigraphic units, aquifer turnover potential, and aquifer productivity, were obtained from the Hydrogeological Map of Brazil and integrated through spatial matching with SIAGAS and ANA datasets. Additional predictors included the number of SIAGAS wells per 0.1° grid cell and average groundwater abstraction rates from the ANA Water Atlas. 4. AI-Based Modeling: The final step involves simulating GWS using a decision-tree ensemble model architecture. Following an extensive sensitivity analysis, the model ensemble selected included Extreme Gradient Boosting (XGBoost 101 ), Light Gradient Boosting Machine (LGBM 102 ), and CatBoost (CtB 103 ), whose outputs were subsequently refined through linear regression. Feature importance was assessed using SHAP (SHapley Additive exPlanations 104 ) to quantify each input’s contribution to the model’s predictions. Fig. 4 schematizes the AI-based modeling framework. Model Training and Data Structure The final dataset consisted of 34,145 samples across 33 features, totaling 1,126,785 input values spanning January 2010 to October 2023. GRACE-derived terrestrial water storage anomalies and their long-term trends were assigned double weight (2×) in the input matrix, reflecting their broader representation of terrestrial water storage dynamics 105 . All other inputs were assigned unit weight (1×). Training and validation followed an 80/20 split, with training data further subdivided (80% for training, 20% for internal testing). Random stratified sampling ensured consistent representation across time and space. Groundwater Recharge Estimation To quantify groundwater recharge across Brazil, we applied the Water Table Fluctuation (WTF) method 37 , widely recognized for its effectiveness in regional-scale assessments of unconfined aquifers 106 . The method assumes that rises in groundwater levels are predominantly due to recharge inputs reaching the water table 40 . Recharge was computed over the period from July 2003 to October 2023 across 87,367 spatial points, with a monthly time step, resulting in nearly 20 million individual recharge simulations. Unlike some traditional implementations of the WTF method, effective porosity was not explicitly used in this study because model outputs are expressed in equivalent water column height. Instead, the analysis focused on temporal changes in groundwater level derived from the AI-based model simulations. To enhance the robustness of the recharge estimates, we tested four different approaches for applying the WTF method: linear, power-law, bin mean, and bin median. The final recharge estimates were derived using the bin mean method, based on the computational framework introduced by Heppner & Nimmo 38 . In this approach, the observed water table elevation range is divided into 200 evenly spaced elevation bins. Water level declines are grouped into these elevation intervals (or "compartments"), and the mean rate of decline for each bin is calculated. Recharge is then estimated by interpolating between these bin-based average decline rates to derive the recession constant for each time step. A sensitivity analysis to input data and model validation are presented in Supplementary Texts 2 and 3. This approach provides a consistent and spatially scalable estimation of recharge, accounting for local variations in water table dynamics and allowing for application in both monitored and unmonitored regions. The method is especially suited to large-scale hydrological modeling in data-sparse environments like Brazil. Datasets GRACE Terrestrial Water Storage We used Release 06 (RL06) Mascon solutions from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, processed by the Center for Space Research (CSR). These data span April 2002 to October 2023 at a 0.25° spatial resolution and monthly temporal resolution 107 . GRACE provides terrestrial water storage (TWS) anomalies with an estimated uncertainty of approximately 1 cm water equivalent for regions ≥ 400,000 km² 108 . Advances in GRACE processing—including the use of mascon (mass concentration) solutions—have substantially improved spatial accuracy over traditional spherical harmonic products 109 , 110 . While the GRACE resolution remains constrained by the nature of satellite gravimetry, recent applications demonstrate the reliability of these data for hydrological assessments at sub-basin scales 11 , 13 , 111 . MODIS Leaf Area Index and Evapotranspiration Leaf Area Index (LAI) and evapotranspiration (ET 112 ) were derived from the MODIS sensor aboard NASA’s Terra and Aqua satellites. LAI estimates have been validated against in situ field measurements. Resolution varies between 250 m and 500 m, depending on the specific MODIS product 113 . ET estimates integrate surface temperature, vegetation indices, and solar radiation inputs into the Penman–Monteith equation, accounting for energy partitioning and environmental controls on transpiration, such as stomatal conductance and vapor pressure deficit 114 . GPM Precipitation Precipitation fields were obtained from the Global Precipitation Measurement (GPM) mission via the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm 115 . IMERG offers global coverage since 2000, with a spatial resolution of 0.1° and a 30-min temporal resolution 116 – 118 . Despite recognized challenges in complex terrain and during calibration, GPM data have shown strong performance in Brazil, particularly when validated against ground-based observations 119 . In Situ Data: Hydrogeological Map, RIMAS, SIAGAS, and ANA datasets Hydrogeological attributes were extracted from the Hydrogeological Map of Brazil 120 , which characterizes the spatial distribution and productivity of groundwater-bearing formations. Key model inputs included the outcropping stratigraphic unit, aquifer turnover classification, and the productivity class of each hydrostratigraphic unit. Groundwater well data were sourced from SIAGAS, Brazil’s Integrated Groundwater Information System 28 , maintained by the Geological Survey of Brazil (SGB). SIAGAS includes well location, depth, and construction details. For this study, well counts were aggregated to a 0.1° grid (aligned with GPM), with zero assigned to grid cells lacking observations. Groundwater abstraction data were derived from the Atlas Águas platform, maintained by Brazil’s National Water and Sanitation Agency (ANA). We used maximum groundwater flow rates granted per municipality (in L.s⁻¹), which were then spatially interpolated and averaged over a 0.1° grid to match the spatial resolution of other input datasets. The RIMAS network is composed of 398 wells and monitors groundwater dynamics across approximately 2.84 million km², representing 34% of Brazil's territory. Data used in this study span the period from August 2010 to October 2023. The density of monitoring varies considerably, with densities varying from 180 km² per well to 130,000 km² per well. This spatial heterogeneity reflects prioritization criteria, including aquifer lithology (sedimentary preference), socioeconomic importance, public water supply dependence, natural vulnerability, spatial representativeness, and existing infrastructure availability 121 . Aquifer effective porosity values used in recharge estimation were obtained from the literature (see Camacho et al. 11 ), ranging from 0.03 to 0.18, depending on the aquifer. The network includes wells located in porous, unconfined, semi-confined, and crystalline rock settings, allowing the exploration of how hydrogeological diversity influences storage dynamics. This variability was explicitly accounted for in the training and application of the machine learning framework presented in this study. Geological and hydrogeological profiles for all monitored aquifers are available at http://rimasweb.cprm.gov.br/layout/apresentacao.php . Declarations Data availability All data used in this work is available through https://figshare.com/s/7f57468b3aae02380213. Code availability All software developed in this work is available through https://figshare.com/s/7f57468b3aae02380213. Acknowledgements This work was funded by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES), the Brazilian Council for Scientific and Technological Development (CNPq), and the Rio de Janeiro State Foundation for Research Support (FAPERJ). Author Contributions Statement A.G. and C.R.C. designed the study; C.R.C. processed datasets, developed models and performed experiments; A.G., C.R.C. M.A.A.M. analyzed data and interpreted model outputs; A.G. and O.C.R.F. co-supervised the project. A.G., and C.R.C. wrote the manuscript. All co-authors reviewed the manuscript. Competing Interests Statement Authors declare that they have no competing interests. References Getirana, A. Extreme water deficit in Brazil detected from space. Journal of Hydrometeorology 17 , (2016). Getirana, A., Libonati, R. & Cataldi, M. Brazil is in water crisis — it needs a drought plan. Nature 600 , 218–220 (2021). Getirana, A. et al. Deltaic freshwater scarcity driven by unsustainable groundwater-fed irrigation. Nat Sustain 1–11 (2025) doi:10.1038/s41893-025-01566-0. Rodell, M., Velicogna, I. & Famiglietti, J. S. Satellite-based estimates of groundwater depletion in India. Nature 460 , 999–1002 (2009). Jasechko, S. et al. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature 625 , 715–721 (2024). Liu, P.-W. et al. Groundwater depletion in California’s Central Valley accelerates during megadrought. Nat Commun 13 , 7825 (2022). Nie, W. et al. Assimilating GRACE Into a Land Surface Model in the Presence of an Irrigation‐Induced Groundwater Trend. Water Resources Research 55 , 11274–11294 (2019). Rebouças, A. C. Inserção da Água Subterrânea no Sistema Nacional de Gerenciamento. Revista Brasileira de Recursos Hídricos 7 , 39–50 (2002). FAO. Country Profile – Brazil . 20 https://openknowledge.fao.org/server/api/core/bitstreams/d1001017-94e1-4f32-afff-89180000a9b5/content (2015). ANA, A. N. de Á. Atlas Águas: Segurança Hídrica Do Abastecimento Urbano . Agência Nacional de Águas e Saneamento Básico (2021). Camacho, C. R., Getirana, A., Rotunno Filho, O. C. & Mourão, M. A. A. Large‐Scale Groundwater Monitoring in Brazil Assisted With Satellite‐Based Artificial Intelligence Techniques. Water Resources Research 59 , e2022WR033588 (2023). Chagas, V. B. P., Chaffe, P. L. B. & Blöschl, G. Climate and land management accelerate the Brazilian water cycle. Nature Communications 13 , 5136 (2022). Gonçalves, R. D., Stollberg, R., Weiss, H. & Chang, H. K. Using GRACE to quantify the depletion of terrestrial water storage in Northeastern Brazil: The Urucuia Aquifer System. Science of the Total Environment 705 , 135845 (2020). Uchôa, J. G. S. M. et al. Widespread potential for streamflow leakage across Brazil. Nature Communications 15 , 10211 (2024). RIMAS, S. G. do B. Rede Integrada de Monitoramento das Águas Subterrâneas (RIMAS). (2020). Tapley, B., Bettadpur, S., Watkins, M. & Reigber, C. The gravity recovery and climate experiment: Mission overview and early results. Geophysical Research Letters 31 , 1–4 (2004). Ndehedehe, C. E. et al. Global assessment of drought characteristics in the Anthropocene. Resources, Environment and Sustainability 12 , 100105 (2023). Moeck, C. et al. A global-scale dataset of direct natural groundwater recharge rates: A review of variables, processes and relationships. Science of The Total Environment 717 , 137042 (2020). Tao, H. et al. Groundwater level prediction using machine learning models: A comprehensive review. Neurocomputing 489 , 271–308 (2022). Razavi, S., Tolson, B. A. & Burn, D. H. Review of surrogate modeling in water resources. Water Resources Research 48 , (2012). Razavi, S. & Gupta, H. V. What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in Earth and Environmental systems models. Water Resources Research 51 , 3070–3092 (2015). Rajaee, T., Ebrahimi, H. & Nourani, V. A review of the artificial intelligence methods in groundwater level modeling. Journal of Hydrology 572 , 336–351 (2019). Nourani, V. et al. Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review. Groundwater for Sustainable Development 25 , 101113 (2024). Chen, C., He, W., Zhou, H., Xue, Y. & Zhu, M. A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin, northwestern China. Scientific Reports 10 , 1–13 (2020). Sun, A. Y., Scanlon, B. R., Save, H. & Rateb, A. Reconstruction of GRACE Total Water Storage Through Automated Machine Learning. Water Resources Research 57 , e2020WR028666 (2021). Sun, A. Y. Predicting groundwater level changes using GRACE data. Water Resources Research 49 , 5900–5912 (2013). Fan, Y., Li, H. & Miguez-Macho, G. Global Patterns of Groundwater Table Depth. Science 339 , 940–943 (2013). SIAGAS, S. G. do B. Sistema de Informações de Águas Subterrâneas (SIAGAS). http://siagasweb.cprm.gov.br/layout/ (2020). Getirana, A., Peters-Lidard, C., Rodell, M. & Bates, P. D. Trade-off between cost and accuracy in large-scale surface water dynamic modeling. Water Resources Research (2017) doi:10.1002/2017WR020519. Miguez-Macho, G. & Fan, Y. The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands. Journal of Geophysical Research Atmospheres 117 , 1–30 (2012). Fluid Flow in Fractured Rock: Theory and Application . (Springer Netherlands, Dordrecht, 1991). doi:10.1007/978-94-011-3628-0. Singhal, B. B. S. & Gupta, R. P. Applied Hydrogeology of Fractured Rocks . (Springer Netherlands, Dordrecht, 1999). doi:10.1007/978-94-015-9208-6. Espinoza, J. C. et al. The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features. Scientific Reports 14 , 1–12 (2024). Gan, M. A., Kousky, V. E. & Ropelewski, C. F. The South America Monsoon Circulation and Its Relationship to Rainfall over West-Central Brazil. J. Climate 17 , 47–66 (2004). Reboita, M. S., Gan, M. A., Rocha, R. P. da & Ambrizzi, T. Regimes de precipitação na América do Sul: uma revisão bibliográfica. Revista Brasileira de Meteorologia 25 , 185–204 (2010). Mueller, B. et al. Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophysical Research Letters 38 , n/a-n/a (2011). Healy, R. W. & Cook, P. G. Using groundwater levels to estimate recharge. Hydrogeology Journal 10 , 91–109 (2002). Heppner, C. S. & Nimmo, J. R. A Computer Program for Predicting Recharge with a Master Recession Curve . 8 https://pubs.usgs.gov/publication/sir20055172 (2005) doi:10.3133/sir20055172. Labrecque, G., Chesnaux, R. & Boucher, M.-A. Water-table fluctuation method for assessing aquifer recharge: application to Canadian aquifers and comparison with other methods. Hydrogeol J 28 , 521–533 (2020). Scanlon, B. R., Healy, R. W. & Cook, P. G. Choosing appropriate techniques for quantifying groundwater recharge. Hydrogeology Journal (2002). Donadelli Sacchi, M., Lilla Manzione, R. & Gastmans, D. How much rainwater contributes to a spring discharge in the Guarani Aquifer System: insights from stable isotopes and a mass balance model. Isotopes in Environmental and Health Studies (2024) doi:10.1080/10256016.2024.2397469. Stríkis, N. M. et al. Modern anthropogenic drought in Central Brazil unprecedented during last 700 years. Nat Commun 15 , 1728 (2024). Hofmann, G. S. et al. Changes in atmospheric circulation and evapotranspiration are reducing rainfall in the Brazilian Cerrado. Scientific Reports 13 , 11236 (2023). de Andrade, B. C. C., de Andrade Pinto, E. J., Ruhoff, A. & Senay, G. B. Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System. International Journal of Applied Earth Observation and Geoinformation 98 , (2021). Cunha, A. P. M. A., Buermann, W. & Marengo, J. A. Changes in compound drought-heat events over Brazil’s Pantanal wetland: an assessment using remote sensing data and multiple drought indicators. Climate Dynamics 62 , 739–757 (2024). Garcia, L. C. et al. Record-breaking wildfires in the world’s largest continuous tropical wetland: Integrative fire management is urgently needed for both biodiversity and humans. Journal of Environmental Management 293 , 112870 (2021). Libonati, R. et al. Drought–heatwave nexus in Brazil and related impacts on health and fires: A comprehensive review. Annals of the New York Academy of Sciences 1517 , 44–62 (2022). Kumar, S. et al. Changes in land use enhance the sensitivity of tropical ecosystems to fire-climate extremes. Scientific Reports 12 , 1–11 (2022). Barros, V. R., Grimm, A. M. & Doyle, M. E. Relationship between Temperature and Circulation in Southeastern South America and its Influence from El Nino and La Nina Events. Journal of the Meteorological Society of Japan. Ser. II 80 , 21–32 (2002). Grimm, A. M. The El Niño Impact on the Summer Monsoon in Brazil: Regional Processes versus Remote Influences. Journal of Climate 16 , 263–280 (2003). Grimm, A. M., Ferraz, S. E. T. & Gomes, J. Precipitation Anomalies in Southern Brazil Associated with El Niño and La Niña Events. Journal of Climate 11 , 2863–2880 (1998). Grigg, N. S. Two Decades of Integrated Flood Management: Status, Barriers, and Strategies. Climate 12 , 67 (2024). Cabral, V. et al. The consequences of debris flows in Brazil: a historical analysis based on recorded events in the last 100 years. Landslides 20 , 511–529 (2023). Célia dos Santos Alvalá, R. et al. Analysis of the hydrological disaster occurred in the state of Rio Grande do Sul, Brazil in September 2023: Vulnerabilities and risk management capabilities. International Journal of Disaster Risk Reduction 110 , 104645 (2024). Pillar, V. D. & Overbeck, G. E. Learning from a climate disaster: The catastrophic floods in southern Brazil. Science 385 , eadr8356 (2024). Chen, J. L., Wilson, C. R. & Tapley, B. D. The 2009 exceptional Amazon flood and interannual terrestrial water storage change observed by GRACE. Water Resources Research 46 , 1–10 (2010). Marengo, J. A. & Espinoza, J. C. Extreme seasonal droughts and floods in Amazonia: Causes, trends and impacts. International Journal of Climatology 36 , 1033–1050 (2016). Espinoza, J. C. et al. Climate variability and extreme drought in the upper Solimões River (western Amazon Basin): Understanding the exceptional 2010 drought. Geophysical Research Letters 38 , 1–6 (2011). Platt, J. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. Adv. Large Margin Classif. 10 , (2000). Pokhrel, Y. et al. Global terrestrial water storage and drought severity under climate change. Nature Climate Change 11 , 226–233 (2021). Hu, K. et al. Hydrogeological characterisation of groundwater over Brazil using remotely sensed and model products. Science of the Total Environment 599–600 , 372–386 (2017). Fernandes, L. G. & Rodrigues, R. R. Changes in the patterns of extreme rainfall events in southern Brazil. International Journal of Climatology 38 , 1337–1352 (2018). Baidya Roy, S. & Avissar, R. Impact of land use/land cover change on regional hydrometeorology in Amazonia. Journal of Geophysical Research: Atmospheres 107 , 1–7 (2002). Costa, M. H., Yanagi, S. N. M., Souza, P. J. O. P., Ribeiro, A. & Rocha, E. J. P. Climate change in Amazonia caused by soybean cropland expansion, as compared to caused by pastureland expansion. Geophysical Research Letters 34 , 2–5 (2007). Marengo, J. A., Jimenez, J. C., Espinoza, J.-C., Cunha, A. P. & Aragão, L. E. O. Increased climate pressure on the agricultural frontier in the Eastern Amazonia–Cerrado transition zone. Scientific Reports 12 , 457 (2022). Stríkis, N. M. et al. Modern anthropogenic drought in Central Brazil unprecedented during last 700 years. Nature Communications 15 , 1728 (2024). Cajazeiras, C. C. de A. & Mourão, M. A. A. Projeto Rede Integrada de Monitoramento das Águas Subterrâneas: relatório diagnóstico Sistema Aquífero Parecis no Estado de Rondônia, Bacia Sedimentar dos Parecis. Volume 8. 8 , 40 (2012). ANA. Área Irrigada - Pivôs Mapeados. https://dadosabertos.ana.gov.br/datasets/41c75b31124c41ca96c14d568ba17c85_0/about (2024). Hirata, R., Suhogusoff, A. V., Marcellini, S. S., Villar, P. C. & Marcellini, L. A Revolução Silenciosa Das Águas Subterrâneas No Brasil: Uma Análise Da Importância Do Recurso e Os Riscos Pela Falta de Saneamento . (2019). Hirata, R. & Conicelli, B. P. Groundwater resources in Brazil: a review of possible impacts caused by climate change. Anais da Academia Brasileira de Ciências 84 , 297–312 (2012). Nie, W. et al. Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene. Proceedings of the National Academy of Sciences 121 , (2024). Nobre, C. A. & Borma, L. D. S. ‘Tipping points’ for the Amazon forest. Current Opinion in Environmental Sustainability 1 , 28–36 (2009). Assis, L. F. F. G. et al. TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping. ISPRS International Journal of Geo-Information 8 , 513 (2019). Calim Costa, M., Marengo, J. A., Alves, L. M. & Cunha, A. P. Multiscale analysis of drought, heatwaves, and compound events in the Brazilian Pantanal in 2019–2021. Theoretical and Applied Climatology 155 , 661–677 (2024). Vahedifard, F. et al. Interdependencies Between Wildfire-Induced Alterations in Soil Properties, Near-Surface Processes, and Geohazards. Earth and Space Science 11 , (2024). Maples, S. R., Fogg, G. E. & Maxwell, R. M. Modeling managed aquifer recharge processes in a highly heterogeneous, semi-confined aquifer system. Hydrogeology Journal 27 , 2869–2888 (2019). Tollefson, J. You’re not imagining it: extreme wildfires are now more common. Nature (2024) doi:10.1038/d41586-024-02071-8. IBGE. Pesquisa Nacional de Saneamento Básico . (2020). Rodell, M. & Reager, J. T. Water cycle science enabled by the GRACE and GRACE-FO satellite missions. Nature Water 1 , 47–59 (2023). Li, B. et al. Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges. Water Resources Research 55 , 7564–7586 (2019). Hirata, R. et al. As águas subterrâneas e sua importância ambiental e econômica para o Brasil. (2019) doi:10.11606/9788563124074. Rodell, M. et al. Emerging trends in global freshwater availability. Nature 557 , 651–659 (2018). Calvin, K. et al. IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (Eds.)]. IPCC, Geneva, Switzerland. Atmosphere vol. 13 (2023). Condon, L. E. et al. Global Groundwater Modeling and Monitoring: Opportunities and Challenges. Water Resources Research 57 , 1–27 (2021). Getirana, A. et al. Avert Bangladesh’s looming water crisis through open science and better data. Nature 610 , 626–629 (2022). ACWI, A. C. on W. I. A National Framework for Ground-Water Monitoring in the United States . (2013). Li, B. & Rodell, M. Terrestrial water storage in 2023. Nature Reviews Earth & Environment 5 , 247–249 (2024). Espinoza, J.-C., Marengo, J. A., Schongart, J. & Jimenez, J. C. The new historical flood of 2021 in the Amazon River compared to major floods of the 21st century: Atmospheric features in the context of the intensification of floods. Weather and Climate Extremes 35 , 100406 (2022). Hirata, R., Goodarzi, L., Rörig, F. S., Alves, L. M. & Bertolo, R. Climate change impacts on groundwater: a growing challenge for water resources sustainability in Brazil. Environ Monit Assess 197 , 784 (2025). Satizábal-Alarcón, D. A., Suhogusoff, A. & Ferrari, L. C. Characterization of groundwater storage changes in the Amazon River Basin based on downscaling of GRACE/GRACE-FO data with machine learning models. Science of The Total Environment 912 , 168958 (2023). Rodrigues, A. F. et al. Has unsustainable groundwater use induced low flow regimes in the Urucuia Aquifer System? An urgent call for integrated water management. Journal of Environmental Management 370 , 122979 (2024). Konikow, L. F. & Kendy, E. Groundwater depletion: A global problem. Hydrogeology Journal 13 , 317–320 (2005). Seo, K. et al. Drift of Earth’s Pole Confirms Groundwater Depletion as a Significant Contributor to Global Sea Level Rise 1993–2010. Geophysical Research Letters 50 , 1–7 (2023). Prokop, G., Younger, P. L. & Roehl, K. E. Groundwater Management in Mining Areas . Groundwater Management in Mining Areas (2004). Lottermoser, B. Mine Water. in Mine Wastes vol. 32 83–141 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2003). Magalhães, C. C. D. C., Melo, M. C. de, Guiguer, N. & Paula, R. S. de. The Cauê Aquifer on the eastern limb of the Moeda Syncline: characterization, impacts, and flow in the western Iron Quadrangle (Quadrilátero Ferrífero), MG, Brazil. Brazilian Journal of Geology 52 , (2022). ANM, A. N. de M. Processos minerários ativos - Brasil. ANM https://dados.gov.br/dados/conjuntos-dados/sistema-de-informacoes-geograficas-da-mineracao-sigmine (2024). Pompeu, J. Cross-Boundary Drivers of Water Cover Reduction in the Pantanal Wetland and Implications for its Conservation. Wetlands 45 , 32 (2025). Caballero, C. B. et al. Decadal hydroclimatic changes in the Pantanal, the world’s largest tropical wetland. Sci Rep 15 , 17675 (2025). Garcia, L. C. et al. Record-breaking wildfires in the world’s largest continuous tropical wetland: Integrative fire management is urgently needed for both biodiversity and humans. Journal of Environmental Management 293 , 112870 (2021). Chen, T. & Guestrin, C. XGBoost. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining vols 13-17-Augu 785–794 (ACM, New York, NY, USA, 2016). Ke, G. et al. LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 2017-Decem , 3147–3155 (2017). Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: unbiased boosting with categorical features. Advances in Neural Information Processing Systems 2018-Decem , 6638–6648 (2017). Lundberg, S. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 2017-Decem , 4766–4775 (2017). Scanlon, B. R. et al. Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proceedings of the National Academy of Sciences 115 , E1080–E1089 (2018). Labrecque, G., Chesnaux, R. & Boucher, M.-A. Water-table fluctuation method for assessing aquifer recharge: application to Canadian aquifers and comparison with other methods. Hydrogeol J 28 , 521–533 (2020). Save, H., Bettadpur, S. & Tapley, B. D. High‐resolution CSR GRACE RL05 mascons. Journal of Geophysical Research: Solid Earth 121 , 7547–7569 (2016). Swenson, S., Wahr, J. & Milly, P. C. D. Estimated accuracies of regional water storage variations inferred from the Gravity Recovery and Climate Experiment (GRACE). Water Resources Research (2003) doi:10.1029/2002WR001808. Rowlands, D. D. et al. Global mass flux solutions from GRACE: A comparison of parameter estimation strategies—Mass concentrations versus Stokes coefficients. Journal of Geophysical Research 115 , B01403 (2010). Ditmar, P. Conversion of time-varying Stokes coefficients into mass anomalies at the Earth’s surface considering the Earth’s oblateness. Journal of Geodesy 92 , 1401–1412 (2018). Melati, M. D., Fleischmann, A. S., Fan, F. M., Paiva, R. C. D. & Athayde, G. B. Estimates of groundwater depletion under extreme drought in the Brazilian semi-arid region using GRACE satellite data: application for a small-scale aquifer. Hydrogeology Journal 27 , 2789–2802 (2019). Earth Science Data Systems, N. MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061 | NASA Earthdata. Earth Science Data Systems, NASA (2025). Myneni, R., Knyazikhin, Y. & Park, T. MODIS Collection 6 (C6) LAI/FPAR Product User’s Guide. NASA EOSDIS Land Processes DAAC 6 , https://doi.org/10.5067/MODIS/MOD15A2H.061 (2020). Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment 111 , 519–536 (2007). Huffman, G., Bolvin, D., Braithwaite, D., Hsu, K. & Joyce, R. NASA Global Precipitation Measurement ( GPM ) Integrated Multi-satellitE Retrievals for GPM ( IMERG ) - Algorithm Theoretical Basis Document ( ATBD ). Nasa 29 (2018). Falck, A. S., Maggioni, V., Tomasella, J., Vila, D. A. & Diniz, F. L. R. Propagation of satellite precipitation uncertainties through a distributed hydrologic model: A case study in the Tocantins-Araguaia basin in Brazil. Journal of Hydrology 527 , 943–957 (2015). Zubieta, R., Getirana, A., Espinoza, J. C., Lavado-Casimiro, W. & Aragon, L. Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin using GPM-IMERG satellite-based precipitation dataset. Hydrology and Earth System Sciences 21 , 3543–3555 (2017). Getirana, A. et al. Potential of GPM IMERG precipitation estimates to monitor natural disaster triggers in urban areas: The case of Rio de Janeiro, Brazil. Remote Sensing 12 , 1–20 (2020). Gadelha, A. N. et al. Grid box-level evaluation of IMERG over Brazil at various space and time scales. Atmospheric Research 218 , 231–244 (2019). Diniz, J. A. O. et al. Mapa Hidrogeológico do Brasil. Serviço Geológico do Brasil (2014). Mourão, M. A. A. Projeto - Implantação de Rede Integrada de Monitoramento das Águas Subterrâneas - Proposta Técnica. (2009). Additional Declarations There is NO Competing Interest. Supplementary Files supplementarymaterialv1.docx Supplementary Material: Two Decades of Human and Climate Induced Groundwater Storage Shifts in Brazil Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7311212","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":498885593,"identity":"bbe6bfd2-51c5-4450-82c3-02d0cdb28782","order_by":0,"name":"Augusto Getirana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYLCDAyCCnzi1CSCCGaJFsoEULWBgcICAYv4Z6dckf/6wYZB37z94uKLmjrzx7ebHH34w1Mrh0itxI6dMmichjcHwzGGGg2eOPTPcdueYmWQPw3FjnNbdyEmTZkg4zGA4I5nhYAPbYcZtNxLMGHgYjiXOxOEpeaAWyR8gLfMfA7X8O2y/eUb6549/8GgxuJF+TIIHqEVegpnhYGPb4cQNEjkG0jwMNYn9ONxleOYNszVPWhqPAU+ywcHGvsPJM0C+kzE4YIwrguSOpz+8+cPGRk6+/eDjjw3fDtv2z0jf/PFNRZ0cGw4tDAw8BhDyAKqDD+PUwMDA/gBMyTegCtfh0TIKRsEoGAUjDAAAxFthgKPEQ6oAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9635-7220","institution":"NASA Goddard Space Flight Center","correspondingAuthor":true,"prefix":"","firstName":"Augusto","middleName":"","lastName":"Getirana","suffix":""},{"id":498885594,"identity":"393979c2-3e4a-4788-9192-4cce9a53919c","order_by":1,"name":"Clyvihk Camacho","email":"","orcid":"https://orcid.org/0000-0003-2545-1118","institution":"Geological Survey of Brazil","correspondingAuthor":false,"prefix":"","firstName":"Clyvihk","middleName":"","lastName":"Camacho","suffix":""},{"id":498885595,"identity":"dbcaad2b-a2e8-4476-9ae9-80385911f04a","order_by":2,"name":"Maria Antonieta Mourao","email":"","orcid":"","institution":"Geological Survey of Brazil","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Antonieta","lastName":"Mourao","suffix":""},{"id":498885596,"identity":"caa87ac8-4f62-4ee9-9bc7-39c57faeb2d8","order_by":3,"name":"Otto Correa Rotunno Filho","email":"","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Otto","middleName":"Correa Rotunno","lastName":"Filho","suffix":""}],"badges":[],"createdAt":"2025-08-06 15:20:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7311212/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7311212/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88880117,"identity":"1a9a7a63-e9e7-4710-9b35-34ced3e6368b","added_by":"auto","created_at":"2025-08-12 10:55:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":253215,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial and seasonal variability of simulated groundwater storage anomalies (ΔGWS) in Brazil.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Standard deviation of monthly ΔGWS across the 2002–2023 simulation period. \u003cstrong\u003eb\u003c/strong\u003e, Minimum and \u003cstrong\u003ec\u003c/strong\u003e, maximum monthly ΔGWS values. \u003cstrong\u003ed–g\u003c/strong\u003e, Seasonal ΔGWS anomalies for austral summer (DJF), autumn (MAM), winter (JJA) and spring (SON), computed as the deviation from the long‑term seasonal mean (2002–2023). The main zone of South Atlantic Convergence (ZCAS) activity is outlined on the DJF map. Twelve major hydrographic basins are delineated in panel a: (1) Amazon, (2) Tocantins–Araguaia, (3) Western North‑East Atlantic, (4) Parnaíba, (5) North‑East Atlantic, (6) São Francisco, (7) Eastern Southeast Atlantic, (8) Southeast Atlantic, (9) South Atlantic, (10) Uruguay, (11) Paraná and (12) Paraguay.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7311212/v1/77b7d0d12b7d9d765520cb4b.png"},{"id":88880595,"identity":"734ec683-c0de-46e6-8287-83b9a0257a85","added_by":"auto","created_at":"2025-08-12 11:03:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":497193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroundwater recharge dynamics and storage anomalies across major Brazilian aquifers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Mean annual groundwater recharge (mm.yr\u003csup\u003e⁻\u003c/sup\u003e¹) from 2002 to 2023. \u003cstrong\u003eb\u003c/strong\u003e, Statistically significant recharge trends (mm.yr\u003csup\u003e⁻\u003c/sup\u003e²) over the same period, derived via linear regression. \u003cstrong\u003ec–h\u003c/strong\u003e, Terrestrial water storage (TWS) time series, groundwater storage (GWS) anomalies for six representative aquifers in Brazil: (1) Alter do Chão, (2) Urucuia, (3) Bauru–Caiuá, (4) Guarani recharge zone (exposed portion), (5) Pantanal, and (6) Parecis. \u003cstrong\u003ei–n\u003c/strong\u003e, Mean annual recharge for the same aquifers. Shaded regions indicate years corresponding to La Niña and El Niño events. Groundwater recharge estimates are based on the Water Table Fluctuation (WTF) method. Trend values for recharge are indicated in the respective time series panels. GWS and TWS trend estimates are provided in Supplementary Table 1 for two time intervals: 2002–2015 and 2015–2023.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7311212/v1/1727cec9753daa0593299e42.png"},{"id":88880120,"identity":"1363240c-e927-4147-9dee-b92ebdb2717b","added_by":"auto","created_at":"2025-08-12 10:55:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":287908,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEmerging groundwater storage trends in Brazil (July 2002–October 2023).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, classification of emerging GWS trends (Support Vector Machine). \u003cstrong\u003eb\u003c/strong\u003e, Groundwater storage trend (mm.yr\u003csup\u003e⁻\u003c/sup\u003e¹) derived from GWS anomalies.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7311212/v1/4e9acbe0a3917df483a49fff.png"},{"id":88881693,"identity":"fe6ecbe3-8336-4138-96ac-7d63280a3192","added_by":"auto","created_at":"2025-08-12 11:11:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":223373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProcessing workflow and architecture of the AI-based joint model for groundwater simulation in Brazil.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchematic representation of the modeling framework developed to simulate groundwater storage observations from Brazil’s national monitoring network (RIMAS). The workflow integrates remote sensing inputs (MODIS evapotranspiration and vegetation indices, GPM precipitation, and GRACE terrestrial water storage anomalies), hydrogeological and socioeconomic datasets (SIAGAS well density and ANA water use records), and \u003cem\u003ein situ \u003c/em\u003ewell observations. Data are preprocessed via time series decomposition (seasonal and wavelet components), spatial matching, and interpolation before entering a joint AI model ensemble composed of XGBoost, LightGBM, and CatBoost algorithms. The final output simulates groundwater storage dynamics at a national scale, validated against RIMAS observations.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7311212/v1/a157ed4841e751766b0e3254.png"},{"id":96251639,"identity":"b794a373-a13d-4d1e-b864-fdbbad46eaab","added_by":"auto","created_at":"2025-11-19 07:39:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2480305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7311212/v1/a9427aae-2c4c-419a-b961-4558dfdfeebe.pdf"},{"id":88880135,"identity":"530fcfb5-17c2-4ff5-9a96-99d51a597e99","added_by":"auto","created_at":"2025-08-12 10:55:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8550150,"visible":true,"origin":"","legend":"Supplementary Material: Two Decades of Human and Climate Induced Groundwater Storage Shifts in Brazil","description":"","filename":"supplementarymaterialv1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7311212/v1/d4f2ea05e841ad98b5d34b50.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Two Decades of Human and Climate Induced Groundwater Storage Shifts in Brazil","fulltext":[{"header":"Main","content":"\u003cp\u003eBrazil possesses the world\u0026rsquo;s largest volume of renewable freshwater, contributing roughly 20% of global inland runoff to the oceans\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, this abundance has not shielded the country from water insecurity. Over the past 25 years, Brazil has experienced three major nationwide water crises, each intensified by climate-driven drought events and the nation\u0026rsquo;s reliance on surface water sources. These crises underscore the urgent need to diversify water supplies and strengthen resilience through increased understanding and sustainable use of groundwater resources\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeneath Brazil\u0026rsquo;s 8.5\u0026nbsp;million km\u0026sup2; of land lies a vast, yet mostly underutilized, reserve of groundwater. Groundwater storage is coarsely estimated in 112,000 cubic kilometers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, or ~\u0026thinsp;95% of Brazilian water reserves\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Although over half of Brazilian municipalities draw on aquifers to some extent, groundwater accounts for only a modest share of domestic and agricultural use\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Still, concerns about declining groundwater storage have grown, with recent studies highlighting regional depletion trends\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Despite this, Brazil\u0026rsquo;s national groundwater monitoring infrastructure remains limited\u0026mdash;about 400 federal wells provide sparse and uneven coverage across the country\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This data scarcity hampers efforts to characterize the spatial and temporal dynamics of aquifer systems and to manage them sustainably.\u003c/p\u003e\u003cp\u003eSpatiotemporal large-scale groundwater monitoring in Brazil has been limited to modeling attempts based on global datasets. Earth Observing datasets, such as NASA\u0026rsquo;s Gravity Recovery and Climate Experiment (GRACE\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e), have been used as a proxy to quantify groundwater loss\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Such studies give us a first-order picture of Brazil\u0026rsquo;s groundwater dynamics, but uncertainties remain high due to little to no integration of local data.\u003c/p\u003e\u003cp\u003eAppropriate hydrogeological monitoring at different scales faces difficulties related to geological complexity, diversity and corresponding structures. Other factors affecting monitoring are complexities related to hydraulic properties of aquifers, recharge zones, groundwater exploitation, land use and land cover change, as well as meteorological and climate variability\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Parameterizing such a large number of factors without their adequate spatial and temporal distribution is a challenge to groundwater numerical modeling\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSurrogate models, without physical coupling\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, such as those based on artificial intelligence (AI), can simulate the behavior of groundwater without the need for in-depth recognition of the geological environment\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e with results that already surpass physical models\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Building upon recent developments combining GRACE data and hydrogeological measurements within an AI modeling framework\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, we reconstructed 21 years of spatiotemporal groundwater variability across Brazil. Our AI modeling framework ingests multi-source hydrogeological and meteorological data, including a variety of ground and satellite measurements and Brazil\u0026rsquo;s hydrogeomorphology and water use information for a more precise groundwater monitoring across the country. The AI model is capable of filling the gaps between gauges, providing detailed groundwater variability across the country, never before observed. As a result, we were able to characterize Brazil\u0026rsquo;s groundwater storage dynamics, unraveling its spatially distributed emerging trends, and impacts of climatological events.\u003c/p\u003e\n\u003ch3\u003eGroundwater variability across Brazilian aquifers\u003c/h3\u003e\n\u003cp\u003eGroundwater storage (GWS) standard deviation (σ) serves as a proxy for temporal variability and provides insights into the interplay between hydrological processes and aquifer characteristics. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the spatial distribution of GWS variability, including long-term maxima, minima, and seasonal patterns. High σ values are found in regions where both natural (e.g., climate, lithology, surface\u0026ndash;subsurface connectivity) and anthropogenic factors (e.g., groundwater abstraction, land use) drive dynamic groundwater behavior. Notably, the highest GWS variability occurs in more transmissive alluvial deposits of northwestern Brazil, within the Amazon basin (σ\u0026thinsp;=\u0026thinsp;50 mm), and in the fractured crystalline basement of central Brazil (σ\u0026thinsp;=\u0026thinsp;37\u0026ndash;43 mm), where aquifers are typically shallower due to their lithological properties and enhanced surface\u0026ndash;groundwater connectivity\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e (see Supplementary Fig.\u0026nbsp;1 for GWS variability of selected aquifers and Supplementary Text 1 for their brief characterization).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin the Amazon basin, particularly at the confluence of the Solim\u0026otilde;es and Negro Rivers, near Manaus, GWS anomalies range from +\u0026thinsp;176 mm to \u0026minus;\u0026thinsp;143 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb,c). This location is characterized by surface water level variations of 10\u0026ndash;15 meters\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, which likely exert strong influence on subsurface water storage due to intense surface\u0026ndash;aquifer coupling\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Although the basin is predominantly composed of porous sedimentary aquifers, extensive crystalline bedrock regions\u0026mdash;less favorable for groundwater retention\u0026mdash;are also present\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. These conditions, combined with the region\u0026rsquo;s flat topography, high hydrological seasonality, and strong river\u0026ndash;aquifer interactions, may contribute to recent drought episodes in the Amazon\u0026rsquo;s river systems\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Our model captures these dynamics, showing finer-resolution river patterns with significantly elevated GWS variability (σ\u0026thinsp;=\u0026thinsp;57 mm), compared to the remainder of the basin (σ\u0026thinsp;=\u0026thinsp;22 mm).\u003c/p\u003e\u003cp\u003eIn contrast, lower GWS variability is observed across free and porous aquifers in central and northeastern Brazil, with aquifer-averaged standard deviations ranging from 10 to 13 mm. This reduced variability is likely associated with deeper static groundwater levels\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, which confer a greater capacity to buffer seasonal or interannual hydrological fluctuations compared to shallow alluvial or unconsolidated sedimentary systems that display greater ΔGWS amplitudes.\u003c/p\u003e\u003cp\u003eThe AI-based modeling framework also reveals groundwater variability patterns linked to the South Atlantic Convergence Zone (ZCAS)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. ZCAS plays a key role in transporting moisture from the Amazon basin toward central and southeastern Brazil, particularly during the austral summer (December\u0026ndash;January\u0026ndash;February, DJF)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This influence is evident in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, which highlights a pronounced belt of wetter-than-average GWS conditions extending from the Amazon to southeastern Brazil during DJF months. Conversely, during March\u0026ndash;April\u0026ndash;May (MAM), widespread groundwater depletion occurs across central Brazil, driven by seasonal drought, baseflow discharge, and elevated evapotranspiration losses\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGroundwater recharge\u003c/h2\u003e\u003cp\u003eAquifer recharge, defined as the annual variation in GWS, was estimated across Brazil using the Water Table Fluctuation (WTF) method\u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e for each hydrological year between 2002 and 2023, focusing on unconfined aquifer systems. The national mean annual recharge during this period was 223 mm, corresponding to a total volume of approximately 1,899 km\u0026sup3;.yr⁻\u0026sup1; of renewable groundwater. This volume represents roughly 12% of the country\u0026rsquo;s average annual precipitation.\u003c/p\u003e\u003cp\u003eRecharge rates exhibit significant spatial variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). For instance, the carbonate portion of the Bambu\u0026iacute; karst aquifers displays relatively high recharge efficiency, capturing up to 18% of local precipitation. In contrast, fractured aquifers exhibit lower recharge rates, capturing approximately 5% of rainfall, while granular aquifers yield an average recharge rate of 12.5%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTemporal variability in recharge is also pronounced. The lowest annual recharge was recorded in 2015, at 159 mm (1,354 km\u0026sup3;.yr⁻\u0026sup1;), coinciding with a widespread drought that triggered a national water crisis\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Conversely, the highest recharge occurred in 2021, reaching 274 mm (2,333 km\u0026sup3;.yr⁻\u0026sup1;). Although parts of the country were under severe drought conditions that year\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, the Amazon basin\u0026mdash;covering approximately 60% of Brazil\u0026rsquo;s territory\u0026mdash;experienced anomalously high rainfall and historic flooding, which significantly contributed to the observed peak in national recharge.\u003c/p\u003e\u003cp\u003eIt is noteworthy that certain regions in Brazil exhibit zero annual groundwater recharge, indicating an absence of replenishment during specific hydrological years. These conditions are observed in several key aquifer systems, including the Urucuia, Bauru\u0026ndash;Caiu\u0026aacute;, Serra Geral Formation, and the exposed recharge zone of the Guarani Aquifer (Botucatu Formation)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Additional zero-recharge zones include the Urucuia aquifer, Serra Grande Formation, and areas underlain by the Crystalline Basement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Supplementary Fig.\u0026nbsp;2a). These patterns are primarily associated with reduced precipitation\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e and elevated evapotranspiration rates\u003csup\u003e\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, often exacerbated by intensive agricultural land use.\u003c/p\u003e\u003cp\u003eWithin the Paraguay River basin, a significant decline in recharge\u0026mdash;particularly within the Pantanal aquifer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg; Supplementary Figs.\u0026nbsp;3f, 3l)\u0026mdash;is likely driven by a combination of recent multi-year droughts\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and widespread land cover changes. The region, encompassing one of the world\u0026rsquo;s largest tropical wetlands, has been increasingly affected by seasonal wildfires and the encroachment of pasture lands over native vegetation\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIt is important to emphasize that these estimates are subject to the influence of current groundwater extraction practices, which may suppress recharge signals, as observed in other global regions\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eENSO impacts on groundwater storage\u003c/h3\u003e\n\u003cp\u003eFigures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-n highlight the timing of major El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation (ENSO) events and their relationship with anomalies in GWS and TWS, as well as estimated recharge rates. Sensitivity analysis of the model (Supplementary Material, Sensitivity to Input Data) identified precipitation seasonality as the most influential predictor of groundwater recharge, based on SHAP value distributions. Sustained trends in TWS are likely to reflect long-term changes in groundwater systems\u0026mdash;particularly when hydrogeological processes, with their inherently slow response times, dominate (see Supplementary Fig.\u0026nbsp;5 for Mann-Kendall trend analysis of GWS across the country). For example, the consistent negative TWS trend observed between 2009 and 2019 (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh) is indicative of extended GWS decline.\u003c/p\u003e\u003cp\u003eTo evaluate the hydrological impact of the 2015/2016 El Ni\u0026ntilde;o event on Brazil's major aquifer systems, GWS trends were analyzed across three periods: 2002\u0026ndash;2023, 2002\u0026ndash;2014 (pre-El Ni\u0026ntilde;o), and 2015\u0026ndash;2023 (post-El Ni\u0026ntilde;o)\u0026mdash;linear regressions for each period are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Figs.\u0026nbsp;2\u0026ndash;4, with corresponding trend values summarized in Supplementary Table\u0026nbsp;1. The analysis focuses on six major unconfined aquifers: Alter do Ch\u0026atilde;o, Urucuia, Bauru\u0026ndash;Caiu\u0026aacute;, the exposed recharge zone of the Guarani aquifer, Pantanal, and Parecis. Results reveal that the El Ni\u0026ntilde;o event exerts considerable influence on GWS variability. Strong signals of hydrological response were observed in the Amazon (Alter do Ch\u0026atilde;o), Tocantins\u0026ndash;Araguaia, Paran\u0026aacute; (Bauru\u0026ndash;Caiu\u0026aacute; and Guarani), Paraguay (Pantanal), S\u0026atilde;o Francisco (Urucuia), and Uruguay basins. These regions, which lie away from coastal climatic moderation, appear more sensitive to the precipitation anomalies associated with these climate oscillations. A marked shift in groundwater behavior is evident following the 2015/2016 El Ni\u0026ntilde;o event. The transition from weakly positive or neutral trends to widespread negative trends in GWS across several basins highlights the capacity of extreme seasonal climate events to alter long-term groundwater dynamics.\u003c/p\u003e\u003cp\u003eIn contrast, coastal basins along the Atlantic exhibit positive trends in GWS, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Figs.\u0026nbsp;3 and 4. This increase may be linked to La Ni\u0026ntilde;a events, which are associated with enhanced precipitation along Brazil\u0026rsquo;s eastern and southeastern coastal regions\u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Elevated groundwater levels in these basins can reduce the sub-surface's capacity to absorb excess rainfall, thereby increasing surface runoff and the likelihood of flooding\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Additionally, saturated soils can promote mass movement processes such as landslides, which have been documented during intense rainfall events in Brazil\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. For example, in the South Atlantic basin, GWS increased during the study period, particularly leading into 2023\u0026ndash;2024. This rise in subsurface saturation may have limited infiltration capacity and exacerbated the severity of the flooding events that struck southern Brazil during this period\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese findings are consistent with previous studies\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e that underscore the substantial influence of El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events on hydrological processes in the Amazon and broader South American region.\u003c/p\u003e\n\u003ch3\u003eEmerging trends and attributions\u003c/h3\u003e\n\u003cp\u003eSpatial patterns of emerging GWS trends were identified using a Support Vector Machine (SVM) clustering approach\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, resulting in seven distinct regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; see Supplementary Fig.\u0026nbsp;6 for aquifer breakdown). Region 1 exhibited a strong positive storage trend, regions 2 and 4 showed moderate storage declines, and region 3 was characterized by severe storage depletion. No statistically significant trends were detected in regions 5 and 6. Region 7 shows a positive storage trend.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFocusing on northern Brazil, particularly the Amazon River basin (Supplementary Fig.\u0026nbsp;3c), a positive precipitation trend is observed (Supplementary Fig.\u0026nbsp;7b), which corresponds with modest gains in groundwater recharge and storage in localized areas, such as the Alter do Ch\u0026atilde;o aquifer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, region 1, and Supplementary Fig.\u0026nbsp;7). TWS variation in the region may reflect broader climate-driven processes, with some studies predicting significant impacts on groundwater storage in the Southern Hemisphere\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, particularly in high-storage regions like the Amazon basin\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese findings raise an important question: can distinct recharge and storage behaviors coexist within the same aquifer system or river basin? Comparing results presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea confirms this possibility, showing that aggregate basin-scale trends may indicate net losses even when substantial subregions experience storage gains. This divergence is particularly evident in the Alter do Ch\u0026atilde;o, Bauru\u0026ndash;Caiu\u0026aacute;, Guarani, and Parecis aquifers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), as well as in major basins such as the Tocantins\u0026ndash;Araguaia, Amazon, and Paran\u0026aacute; (Supplementary Figs.\u0026nbsp;3 and 4).\u003c/p\u003e\u003cp\u003eIn region 1, which exhibits strong positive groundwater storage trends, increases are likely influenced by interannual climate variability, particularly El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events, which modulate precipitation patterns in southern Brazil\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. These events contribute to elevated recharge during wetter periods, reinforcing storage gains. Region 7 exhibits positive groundwater trends\u0026mdash;not as strong as those in region 1\u0026mdash;mostly driven by increased precipitation rates (see Supplementary Fig.\u0026nbsp;7b).\u003c/p\u003e\u003cp\u003eIn region 2, encompassing the southern Amazon River basin and much of the Tocantins\u0026ndash;Araguaia basin, extensive conversion of tropical forest and Cerrado to agricultural land has intensified\u003csup\u003e\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, coinciding with significant declines in observed precipitation\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. These combined land cover and climate shifts (see Supplementary Fig.\u0026nbsp;7) are linked to reduced groundwater recharge (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) and the resulting storage losses evident in our SVMbased regional clustering. This area also represents Brazil\u0026rsquo;s new agricultural frontier, where escalating groundwater abstraction is documented\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Climate projections further predict decreasing recharge rates in northern Brazil under future warming scenarios\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, while amplified seasonality\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e and largescale deforestation\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e continue to alter the hydrological cycle. Thus, storage declines in region 2 arise from the synergistic impact of intensified extraction, altered hydrological processes, and landuse change, rather than abstraction alone.\u003c/p\u003e\u003cp\u003eAlso in region 2, the decline in the Pantanal aquifer (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg, m) is intensified during the 2019\u0026ndash;2020 drought\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e and may reflect a shift in the recharge regime based on modelled trends. In this area, groundwater recharge is likely affected by both agricultural and hydrological droughts, often compounded by widespread fire activity\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Wildfires alter soil properties, reduce infiltration capacity, and disrupt recharge processes\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Moreover, drought conditions driven or intensified by fires\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e may exacerbate storage losses. Given these complex interactions, this region warrants further investigation to better understand the controls on groundwater behavior and resilience under compounding environmental stressors.\u003c/p\u003e\u003cp\u003eIn northeastern Brazil (region 2), declining GWS is primarily attributed to increased groundwater abstraction\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e and the occurrence of prolonged droughts\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Rodell et al.⁶\u0026sup1; report a mean groundwater loss of \u0026minus;\u0026thinsp;16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 km\u0026sup3; yr⁻\u0026sup1; in the region, with anomalously low rainfall observed in 2012, 2014, and 2015\u003csup\u003e1\u003c/sup\u003e. Climate projections suggest that recharge in this semi-arid region may decrease by up to 70% due to climate change impacts\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe most severe GWS declines are observed in central Brazil (region 3), with notable losses in the S\u0026atilde;o Francisco, Paran\u0026aacute;, Southeast Atlantic, and East Atlantic basins (Supplementary Figs.\u0026nbsp;3 and 4). These trends result from a compounding set of drivers, including intensive groundwater use\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, severe droughts\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, anthropogenic climate change\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, and shifts in the regional hydrological cycle\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Collectively, these factors contribute to substantial and sustained depletion of groundwater resources across the region.\u003c/p\u003e\u003cp\u003eIn region 4, storage losses are likely associated with localized groundwater abstraction\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e and natural hydrological variability. Although less pronounced than in other regions, the observed decline suggests a combination of climatic and anthropogenic influences.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMany aquifer systems globally are increasingly threatened by a combination of anthropogenic pressures and environmental change, with several regions approaching critical thresholds of physical sustainability\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e\u0026mdash;where groundwater withdrawal rates exceed natural and artificial recharge. In Brazil, similar pressures are emerging, particularly in large aquifers\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, mirroring patterns already documented in highly exploited systems such as the Central Valley\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and High Plains\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e aquifers, in the U.S., the Lower Zayandeh-Rud, in Iran\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, and aquifers in northern India and Bangladesh\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, all of which exhibit severe groundwater storage declines.\u003c/p\u003e\u003cp\u003eIn this context, careful consideration is essential when evaluating the groundwater use potential of aquifers. Resource assessments that focus solely on extraction capacity, without incorporating regional hydrogeological behavior and the effects of climate variability, risk triggering long-term storage depletion. Such unsustainable practices have already led to persistent groundwater losses in central Brazil\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e, underscoring the need for integrated, climate-sensitive management strategies that go beyond volumetric availability.\u003c/p\u003e\u003cp\u003eOur analysis reveals pronounced spatial heterogeneity in GWS variability across Brazil. The Amazon basin exhibits the largest seasonal fluctuations, a pattern linked to the region\u0026rsquo;s porous sedimentary aquifers and extensive alluvial deposits, combined with high rainfall seasonality and strong river\u0026ndash;aquifer interactions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In contrast, aquifers in central and northeastern Brazil display markedly lower seasonal variability, likely due to their lower transmissivity, limited surface water connectivity, and deeper static water levels relative to alluvial systems.\u003c/p\u003e\u003cp\u003eThe South Atlantic Convergence Zone plays a pivotal role in modulating recharge dynamics, particularly during the austral summer (DJF), when enhanced moisture flux supports widespread infiltration and recharge in central and southeastern Brazil\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. During the dry season (MAM), GWS declines are prevalent, driven by minimal recharge and elevated evapotranspiration. These patterns are consistent with previous GRACE-based assessments\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e but offer enhanced spatial granularity, enabling the detection of localized GWS responses to seasonal and interannual climate forcing\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEl Ni\u0026ntilde;o and La Ni\u0026ntilde;a events exert a significant influence on Brazil\u0026rsquo;s groundwater dynamics by modulating precipitation patterns and, consequently, aquifer recharge. The 2015\u0026ndash;2016 El Ni\u0026ntilde;o event resulted in pronounced groundwater storage (GWS) declines across the country\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, with the most substantial impacts observed in the Amazon\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e and Paran\u0026aacute; basins. In contrast, La Ni\u0026ntilde;a episodes have been associated with enhanced recharge in coastal systems, particularly in the South Atlantic basin, due to increased precipitation\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite the Amazon basin\u0026rsquo;s vast hydrological reserves, it experienced record-low river levels during the 2023\u0026ndash;2024 hydrological year, coinciding with a strong El Ni\u0026ntilde;o and persistent long-term climate trends. Our model captures this hydroclimatic sensitivity, highlighting how both extreme droughts and floods disrupt the regional groundwater equilibrium. Similarly, the Urucuia aquifer continues to exhibit sustained GWS losses\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, largely attributable to recurrent drought conditions and intensive irrigation\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTogether, these findings emphasize the importance of integrating local-scale observational data into national groundwater assessments, especially in regions facing increasing hydrological stress under climate change.\u003c/p\u003e\n\u003ch3\u003eGroundwater use and depletion\u003c/h3\u003e\n\u003cp\u003eGroundwater depletion is a complex and increasingly global concern, with far-reaching implications for water security, land subsidence\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, reductions in river discharge and surface water availability discharge\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, saltwater intrusion in coastal zones, increased pumping costs, and even potential perturbations in Earth\u0026rsquo;s rotational dynamics\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. In Brazil, recent studies have linked groundwater losses primarily to meteorological droughts\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While this study does not establish causality between groundwater depletion and extraction rates, the spatial overlap between emerging negative GWS trends and areas of high socioeconomic demand suggests a likely correlation.\u003c/p\u003e\u003cp\u003eCurrently, groundwater serves as the sole source of water supply for approximately 40% of Brazilian municipalities\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and meets a significant portion of the country\u0026rsquo;s agricultural irrigation demand\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The growing reliance on groundwater for irrigation has already exerted pressure on large aquifer systems\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMining activities further exacerbate groundwater stress, particularly in regions where aquifer dewatering is required for mineral extraction (Supplementary Fig.\u0026nbsp;7). Both open-pit and underground mining operations can dramatically alter groundwater dynamics by functioning as hydraulic sinks, necessitating continuous dewatering. These processes not only deplete groundwater reserves but also disrupt natural flow regimes and may diminish surface water contributions to rivers and wetlands\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. The long operational lifespan and vertical extent of many mining projects compound these effects. For instance, in the eastern portion of the Moeda Syncline (Minas Gerais state), groundwater extraction exceeds 150% of estimated recharge\u0026mdash;amounting to 0.04 km\u0026sup3; per year over a 305 km\u0026sup2; area\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. This imbalance results in an average annual storage decline of 134 mm, measurable even by satellite gravimetry. Such hydrological disturbances are already apparent in the S\u0026atilde;o Francisco River basin and may extend into the Southeast and East Atlantic basins (see Supplementary Figs.\u0026nbsp;3 and 4), where groundwater-surface water interactions are increasingly altered.\u003c/p\u003e\u003cp\u003eTo assess the multifactorial pressures on Brazil\u0026rsquo;s groundwater systems, we evaluated a suite of spatial indicators, including: the distribution of pivot irrigation points\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, well density from the SIAGAS system, areas of natural vegetation loss\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, mining footprints\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e (with emphasis on metallic mineral extraction), evapotranspiration and LAI trends from NASA\u0026rsquo;s MODIS, precipitation trends from NASA\u0026rsquo;s GPM, and TWS trends from NASA\u0026rsquo;s GRACE missions (Supplementary Fig.\u0026nbsp;7). This integrated analysis highlights the intersection of anthropogenic drivers and climate variability in shaping the country's evolving groundwater landscape.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides the first nationwide, observation-based assessment of groundwater dynamics across Brazil. We integrated local well data, socioeconomic information, and satellite-based hydrological measurements (e.g., GRACE\u0026rsquo;s terrestrial water storage, GPM\u0026rsquo;s precipitation, MODIS\u0026rsquo; leaf area index and evapotranspiration), and leaf area index (MODIS). within a machine learning framework to estimate monthly recharge over a 0.1\u0026deg; spatial grid between 2002 and 2023. Extensive sensitivity analyses were conducted on both model architecture and input features to ensure robustness. The final dataset encompasses nearly 20\u0026nbsp;million recharge estimates across 87,367 grid cells, enabling a fine-scale characterization of recharge variability and long-term trends across diverse climatic, geological, and socioeconomic settings\u0026mdash;from individual aquifers to major river basins.\u003c/p\u003e\u003cp\u003eOur findings indicate that average groundwater recharge in Brazil is approximately 12% of annual precipitation, though spatial variability is considerable. Certain aquifers, particularly in northeastern and southeastern Brazil\u0026mdash;including the Urucuia\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, Guarani, and Serra Geral\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e\u0026mdash;experience zero recharge in some years, highlighting their vulnerability to interannual climate variability. The Pantanal aquifer presents a particularly critical case, with recharge decline attributed to a combination of land-use change\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e, precipitation anomalies, climate variability\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e, and recurrent wildfires\u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. These disturbances alter soil structure and reduce infiltration, compounding the effects of drought and further impairing recharge processes.\u003c/p\u003e\u003cp\u003eLimitations must be acknowledged. Aquifers with limited historical monitoring pose challenges due to reduced data representativeness. Additionally, regions with high variability in physical characteristics\u0026mdash;such as geology, land use, climate, and extraction intensity\u0026mdash;may reduce model accuracy. The resolution of the model (0.1\u0026deg;, monthly) is inherently constrained by the spatial and temporal availability of satellite data. In crystalline terrains, although model performance is promising, further observational datasets are required to validate simulations and differentiate between regolith and bedrock contributions to storage. Furthermore, as with many current artificial intelligence models, the methodology is data-driven and does not explicitly incorporate physical process representations. The model's applicability to other regions or aquifers will depend on the availability and quality of input data and adherence to the method\u0026rsquo;s assumptions and limitations.\u003c/p\u003e\u003cp\u003eDespite these constraints, the model demonstrates strong potential for capturing groundwater dynamics in diverse hydrogeological settings. The integration of satellite remote sensing and machine learning offers a scalable and transferable framework for large-scale groundwater monitoring. More broadly, this study provides new insights into the spatiotemporal behavior of Brazil\u0026rsquo;s groundwater systems and represents a milestone in advancing sustainable water resources management in the country.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe methodology developed to simulate variations in groundwater storage (GWS) in Brazil integrates satellite observations, in situ well data, and hydrogeological attributes into a data-driven artificial intelligence (AI) framework. The approach comprises four main steps:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;Time Series Decomposition:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSatellite datasets from GRACE (Gravity Recovery and Climate Experiment), MODIS (Moderate Resolution Imaging Spectroradiometer), and GPM (Global Precipitation Measurement) were decomposed into seasonal and wavelet components to capture dominant temporal patterns. Discrete wavelet transform components (D1, D2, D3) and seasonal (S) and trend (T) signals were extracted from each dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;Spline Interpolation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe decomposed components were resampled to their original temporal resolutions using spline interpolation, ensuring alignment with in situ data from Brazil\u0026rsquo;s national groundwater monitoring network (RIMAS\u003csup\u003e11\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; \u0026nbsp;Data Integration and Feature Mapping:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interpolated satellite signals were collocated with RIMAS well data in space and time (latitude, longitude, timestamp). Hydrogeological attributes, including stratigraphic units, aquifer turnover potential, and aquifer productivity, were obtained from the Hydrogeological Map of Brazil and integrated through spatial matching with SIAGAS and ANA datasets. Additional predictors included the number of SIAGAS wells per 0.1\u0026deg; grid cell and average groundwater abstraction rates from the ANA Water Atlas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;AI-Based Modeling:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final step involves simulating GWS using a decision-tree ensemble model architecture. Following an extensive sensitivity analysis, the model ensemble selected included Extreme Gradient Boosting (XGBoost\u003csup\u003e101\u003c/sup\u003e), Light Gradient Boosting Machine (LGBM\u003csup\u003e102\u003c/sup\u003e), and CatBoost (CtB\u003csup\u003e103\u003c/sup\u003e), whose outputs were subsequently refined through linear regression. Feature importance was assessed using SHAP (SHapley Additive exPlanations\u003csup\u003e104\u003c/sup\u003e) to quantify each input\u0026rsquo;s contribution to the model\u0026rsquo;s predictions. Fig. 4 schematizes the AI-based modeling framework.\u003c/p\u003e\n\u003ch3\u003eModel Training and Data Structure\u003c/h3\u003e\n\u003cp\u003eThe final dataset consisted of 34,145 samples across 33 features, totaling 1,126,785 input values spanning January 2010 to October 2023. GRACE-derived terrestrial water storage anomalies and their long-term trends were assigned double weight (2\u0026times;) in the input matrix, reflecting their broader representation of terrestrial water storage dynamics\u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. All other inputs were assigned unit weight (1\u0026times;). Training and validation followed an 80/20 split, with training data further subdivided (80% for training, 20% for internal testing). Random stratified sampling ensured consistent representation across time and space.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGroundwater Recharge Estimation\u003c/h2\u003e\u003cp\u003eTo quantify groundwater recharge across Brazil, we applied the Water Table Fluctuation (WTF) method\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, widely recognized for its effectiveness in regional-scale assessments of unconfined aquifers\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. The method assumes that rises in groundwater levels are predominantly due to recharge inputs reaching the water table\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Recharge was computed over the period from July 2003 to October 2023 across 87,367 spatial points, with a monthly time step, resulting in nearly 20\u0026nbsp;million individual recharge simulations.\u003c/p\u003e\u003cp\u003eUnlike some traditional implementations of the WTF method, effective porosity was not explicitly used in this study because model outputs are expressed in equivalent water column height. Instead, the analysis focused on temporal changes in groundwater level derived from the AI-based model simulations.\u003c/p\u003e\u003cp\u003eTo enhance the robustness of the recharge estimates, we tested four different approaches for applying the WTF method: linear, power-law, bin mean, and bin median. The final recharge estimates were derived using the bin mean method, based on the computational framework introduced by Heppner \u0026amp; Nimmo\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In this approach, the observed water table elevation range is divided into 200 evenly spaced elevation bins. Water level declines are grouped into these elevation intervals (or \"compartments\"), and the mean rate of decline for each bin is calculated. Recharge is then estimated by interpolating between these bin-based average decline rates to derive the recession constant for each time step. A sensitivity analysis to input data and model validation are presented in Supplementary Texts 2 and 3.\u003c/p\u003e\u003cp\u003eThis approach provides a consistent and spatially scalable estimation of recharge, accounting for local variations in water table dynamics and allowing for application in both monitored and unmonitored regions. The method is especially suited to large-scale hydrological modeling in data-sparse environments like Brazil.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDatasets\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003eGRACE Terrestrial Water Storage\u003c/h2\u003e\u003cp\u003eWe used Release 06 (RL06) Mascon solutions from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, processed by the Center for Space Research (CSR). These data span April 2002 to October 2023 at a 0.25\u0026deg; spatial resolution and monthly temporal resolution\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. GRACE provides terrestrial water storage (TWS) anomalies with an estimated uncertainty of approximately 1 cm water equivalent for regions\u0026thinsp;\u0026ge;\u0026thinsp;400,000 km\u0026sup2; \u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e. Advances in GRACE processing\u0026mdash;including the use of mascon (mass concentration) solutions\u0026mdash;have substantially improved spatial accuracy over traditional spherical harmonic products\u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e,\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e. While the GRACE resolution remains constrained by the nature of satellite gravimetry, recent applications demonstrate the reliability of these data for hydrological assessments at sub-basin scales\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMODIS Leaf Area Index and Evapotranspiration\u003c/h2\u003e\u003cp\u003eLeaf Area Index (LAI) and evapotranspiration (ET\u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e) were derived from the MODIS sensor aboard NASA\u0026rsquo;s Terra and Aqua satellites. LAI estimates have been validated against \u003cem\u003ein situ\u003c/em\u003e field measurements. Resolution varies between 250 m and 500 m, depending on the specific MODIS product\u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e. ET estimates integrate surface temperature, vegetation indices, and solar radiation inputs into the Penman\u0026ndash;Monteith equation, accounting for energy partitioning and environmental controls on transpiration, such as stomatal conductance and vapor pressure deficit\u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eGPM Precipitation\u003c/h2\u003e\u003cp\u003ePrecipitation fields were obtained from the Global Precipitation Measurement (GPM) mission via the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm\u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e. IMERG offers global coverage since 2000, with a spatial resolution of 0.1\u0026deg; and a 30-min temporal resolution\u003csup\u003e\u003cspan additionalcitationids=\"CR117\" citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e. Despite recognized challenges in complex terrain and during calibration, GPM data have shown strong performance in Brazil, particularly when validated against ground-based observations\u003csup\u003e\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eIn Situ Data: Hydrogeological Map, RIMAS, SIAGAS, and ANA datasets\u003c/h2\u003e\u003cp\u003eHydrogeological attributes were extracted from the Hydrogeological Map of Brazil\u003csup\u003e\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e\u003c/sup\u003e, which characterizes the spatial distribution and productivity of groundwater-bearing formations. Key model inputs included the outcropping stratigraphic unit, aquifer turnover classification, and the productivity class of each hydrostratigraphic unit.\u003c/p\u003e\u003cp\u003eGroundwater well data were sourced from SIAGAS, Brazil\u0026rsquo;s Integrated Groundwater Information System\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, maintained by the Geological Survey of Brazil (SGB). SIAGAS includes well location, depth, and construction details. For this study, well counts were aggregated to a 0.1\u0026deg; grid (aligned with GPM), with zero assigned to grid cells lacking observations.\u003c/p\u003e\u003cp\u003eGroundwater abstraction data were derived from the \u003cem\u003eAtlas \u0026Aacute;guas\u003c/em\u003e platform, maintained by Brazil\u0026rsquo;s National Water and Sanitation Agency (ANA). We used maximum groundwater flow rates granted per municipality (in L.s⁻\u0026sup1;), which were then spatially interpolated and averaged over a 0.1\u0026deg; grid to match the spatial resolution of other input datasets.\u003c/p\u003e\u003cp\u003eThe RIMAS network is composed of 398 wells and monitors groundwater dynamics across approximately 2.84\u0026nbsp;million km\u0026sup2;, representing 34% of Brazil's territory. Data used in this study span the period from August 2010 to October 2023. The density of monitoring varies considerably, with densities varying from 180 km\u0026sup2; per well to 130,000 km\u0026sup2; per well. This spatial heterogeneity reflects prioritization criteria, including aquifer lithology (sedimentary preference), socioeconomic importance, public water supply dependence, natural vulnerability, spatial representativeness, and existing infrastructure availability\u003csup\u003e\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAquifer effective porosity values used in recharge estimation were obtained from the literature (see Camacho et al.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e), ranging from 0.03 to 0.18, depending on the aquifer. The network includes wells located in porous, unconfined, semi-confined, and crystalline rock settings, allowing the exploration of how hydrogeological diversity influences storage dynamics. This variability was explicitly accounted for in the training and application of the machine learning framework presented in this study. Geological and hydrogeological profiles for all monitored aquifers are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rimasweb.cprm.gov.br/layout/apresentacao.php\u003c/span\u003e\u003cspan address=\"http://rimasweb.cprm.gov.br/layout/apresentacao.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this work is available through https://figshare.com/s/7f57468b3aae02380213.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll software developed in this work is available through https://figshare.com/s/7f57468b3aae02380213.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES), the Brazilian Council for Scientific and Technological Development (CNPq), and the Rio de Janeiro State Foundation for Research Support (FAPERJ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.G. and C.R.C. designed the study; C.R.C. processed datasets, developed models and performed experiments; A.G., C.R.C. M.A.A.M. analyzed data and interpreted model outputs; A.G. and O.C.R.F. co-supervised the project. A.G., and C.R.C. wrote the manuscript. All co-authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGetirana, A. Extreme water deficit in Brazil detected from space. \u003cem\u003eJournal of Hydrometeorology\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, (2016).\u003c/li\u003e\n\u003cli\u003eGetirana, A., Libonati, R. \u0026amp; Cataldi, M. Brazil is in water crisis \u0026mdash; it needs a drought plan. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e600\u003c/strong\u003e, 218\u0026ndash;220 (2021).\u003c/li\u003e\n\u003cli\u003eGetirana, A. \u003cem\u003eet al.\u003c/em\u003e Deltaic freshwater scarcity driven by unsustainable groundwater-fed irrigation. \u003cem\u003eNat Sustain\u003c/em\u003e 1\u0026ndash;11 (2025) doi:10.1038/s41893-025-01566-0.\u003c/li\u003e\n\u003cli\u003eRodell, M., Velicogna, I. \u0026amp; Famiglietti, J. S. Satellite-based estimates of groundwater depletion in India. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e460\u003c/strong\u003e, 999\u0026ndash;1002 (2009).\u003c/li\u003e\n\u003cli\u003eJasechko, S. \u003cem\u003eet al.\u003c/em\u003e Rapid groundwater decline and some cases of recovery in aquifers globally. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e625\u003c/strong\u003e, 715\u0026ndash;721 (2024).\u003c/li\u003e\n\u003cli\u003eLiu, P.-W. \u003cem\u003eet al.\u003c/em\u003e Groundwater depletion in California\u0026rsquo;s Central Valley accelerates during megadrought. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 7825 (2022).\u003c/li\u003e\n\u003cli\u003eNie, W. \u003cem\u003eet al.\u003c/em\u003e Assimilating GRACE Into a Land Surface Model in the Presence of an Irrigation‐Induced Groundwater Trend. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 11274\u0026ndash;11294 (2019).\u003c/li\u003e\n\u003cli\u003eRebou\u0026ccedil;as, A. C. Inser\u0026ccedil;\u0026atilde;o da \u0026Aacute;gua Subterr\u0026acirc;nea no Sistema Nacional de Gerenciamento. \u003cem\u003eRevista Brasileira de Recursos H\u0026iacute;dricos\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 39\u0026ndash;50 (2002).\u003c/li\u003e\n\u003cli\u003eFAO. \u003cem\u003eCountry Profile \u0026ndash; Brazil\u003c/em\u003e. 20 https://openknowledge.fao.org/server/api/core/bitstreams/d1001017-94e1-4f32-afff-89180000a9b5/content (2015).\u003c/li\u003e\n\u003cli\u003eANA, A. N. de \u0026Aacute;. \u003cem\u003eAtlas \u0026Aacute;guas: Seguran\u0026ccedil;a H\u0026iacute;drica Do Abastecimento Urbano\u003c/em\u003e. \u003cem\u003eAg\u0026ecirc;ncia Nacional de \u0026Aacute;guas e Saneamento B\u0026aacute;sico\u003c/em\u003e (2021).\u003c/li\u003e\n\u003cli\u003eCamacho, C. R., Getirana, A., Rotunno Filho, O. C. \u0026amp; Mour\u0026atilde;o, M. A. A. Large‐Scale Groundwater Monitoring in Brazil Assisted With Satellite‐Based Artificial Intelligence Techniques. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, e2022WR033588 (2023).\u003c/li\u003e\n\u003cli\u003eChagas, V. B. P., Chaffe, P. L. B. \u0026amp; Bl\u0026ouml;schl, G. Climate and land management accelerate the Brazilian water cycle. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 5136 (2022).\u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves, R. D., Stollberg, R., Weiss, H. \u0026amp; Chang, H. K. Using GRACE to quantify the depletion of terrestrial water storage in Northeastern Brazil: The Urucuia Aquifer System. \u003cem\u003eScience of the Total Environment\u003c/em\u003e \u003cstrong\u003e705\u003c/strong\u003e, 135845 (2020).\u003c/li\u003e\n\u003cli\u003eUch\u0026ocirc;a, J. G. S. M. \u003cem\u003eet al.\u003c/em\u003e Widespread potential for streamflow leakage across Brazil. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 10211 (2024).\u003c/li\u003e\n\u003cli\u003eRIMAS, S. G. do B. Rede Integrada de Monitoramento das \u0026Aacute;guas Subterr\u0026acirc;neas (RIMAS). (2020).\u003c/li\u003e\n\u003cli\u003eTapley, B., Bettadpur, S., Watkins, M. \u0026amp; Reigber, C. The gravity recovery and climate experiment: Mission overview and early results. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1\u0026ndash;4 (2004).\u003c/li\u003e\n\u003cli\u003eNdehedehe, C. E. \u003cem\u003eet al.\u003c/em\u003e Global assessment of drought characteristics in the Anthropocene. \u003cem\u003eResources, Environment and Sustainability\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 100105 (2023).\u003c/li\u003e\n\u003cli\u003eMoeck, C. \u003cem\u003eet al.\u003c/em\u003e A global-scale dataset of direct natural groundwater recharge rates: A review of variables, processes and relationships. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e717\u003c/strong\u003e, 137042 (2020).\u003c/li\u003e\n\u003cli\u003eTao, H. \u003cem\u003eet al.\u003c/em\u003e Groundwater level prediction using machine learning models: A comprehensive review. \u003cem\u003eNeurocomputing\u003c/em\u003e \u003cstrong\u003e489\u003c/strong\u003e, 271\u0026ndash;308 (2022).\u003c/li\u003e\n\u003cli\u003eRazavi, S., Tolson, B. A. \u0026amp; Burn, D. H. Review of surrogate modeling in water resources. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, (2012).\u003c/li\u003e\n\u003cli\u003eRazavi, S. \u0026amp; Gupta, H. V. What do we mean by sensitivity analysis? The need for comprehensive characterization of \u0026ldquo;global\u0026rdquo; sensitivity in Earth and Environmental systems models. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 3070\u0026ndash;3092 (2015).\u003c/li\u003e\n\u003cli\u003eRajaee, T., Ebrahimi, H. \u0026amp; Nourani, V. A review of the artificial intelligence methods in groundwater level modeling. \u003cem\u003eJournal of Hydrology\u003c/em\u003e \u003cstrong\u003e572\u003c/strong\u003e, 336\u0026ndash;351 (2019).\u003c/li\u003e\n\u003cli\u003eNourani, V. \u003cem\u003eet al.\u003c/em\u003e Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review. \u003cem\u003eGroundwater for Sustainable Development\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 101113 (2024).\u003c/li\u003e\n\u003cli\u003eChen, C., He, W., Zhou, H., Xue, Y. \u0026amp; Zhu, M. A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin, northwestern China. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1\u0026ndash;13 (2020).\u003c/li\u003e\n\u003cli\u003eSun, A. Y., Scanlon, B. R., Save, H. \u0026amp; Rateb, A. Reconstruction of GRACE Total Water Storage Through Automated Machine Learning. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, e2020WR028666 (2021).\u003c/li\u003e\n\u003cli\u003eSun, A. Y. Predicting groundwater level changes using GRACE data. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 5900\u0026ndash;5912 (2013).\u003c/li\u003e\n\u003cli\u003eFan, Y., Li, H. \u0026amp; Miguez-Macho, G. Global Patterns of Groundwater Table Depth. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e339\u003c/strong\u003e, 940\u0026ndash;943 (2013).\u003c/li\u003e\n\u003cli\u003eSIAGAS, S. G. do B. Sistema de Informa\u0026ccedil;\u0026otilde;es de \u0026Aacute;guas Subterr\u0026acirc;neas (SIAGAS). http://siagasweb.cprm.gov.br/layout/ (2020).\u003c/li\u003e\n\u003cli\u003eGetirana, A., Peters-Lidard, C., Rodell, M. \u0026amp; Bates, P. D. Trade-off between cost and accuracy in large-scale surface water dynamic modeling. \u003cem\u003eWater Resources Research\u003c/em\u003e (2017) doi:10.1002/2017WR020519.\u003c/li\u003e\n\u003cli\u003eMiguez-Macho, G. \u0026amp; Fan, Y. The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 1\u0026ndash;30 (2012).\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eFluid Flow in Fractured Rock: Theory and Application\u003c/em\u003e. (Springer Netherlands, Dordrecht, 1991). doi:10.1007/978-94-011-3628-0.\u003c/li\u003e\n\u003cli\u003eSinghal, B. B. S. \u0026amp; Gupta, R. P. \u003cem\u003eApplied Hydrogeology of Fractured Rocks\u003c/em\u003e. (Springer Netherlands, Dordrecht, 1999). doi:10.1007/978-94-015-9208-6.\u003c/li\u003e\n\u003cli\u003eEspinoza, J. C. \u003cem\u003eet al.\u003c/em\u003e The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1\u0026ndash;12 (2024).\u003c/li\u003e\n\u003cli\u003eGan, M. A., Kousky, V. E. \u0026amp; Ropelewski, C. F. The South America Monsoon Circulation and Its Relationship to Rainfall over West-Central Brazil. \u003cem\u003eJ. Climate\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 47\u0026ndash;66 (2004).\u003c/li\u003e\n\u003cli\u003eReboita, M. S., Gan, M. A., Rocha, R. P. da \u0026amp; Ambrizzi, T. Regimes de precipita\u0026ccedil;\u0026atilde;o na Am\u0026eacute;rica do Sul: uma revis\u0026atilde;o bibliogr\u0026aacute;fica. \u003cem\u003eRevista Brasileira de Meteorologia\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 185\u0026ndash;204 (2010).\u003c/li\u003e\n\u003cli\u003eMueller, B. \u003cem\u003eet al.\u003c/em\u003e Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, n/a-n/a (2011).\u003c/li\u003e\n\u003cli\u003eHealy, R. W. \u0026amp; Cook, P. G. Using groundwater levels to estimate recharge. \u003cem\u003eHydrogeology Journal\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 91\u0026ndash;109 (2002).\u003c/li\u003e\n\u003cli\u003eHeppner, C. S. \u0026amp; Nimmo, J. R. \u003cem\u003eA Computer Program for Predicting Recharge with a Master Recession Curve\u003c/em\u003e. 8 https://pubs.usgs.gov/publication/sir20055172 (2005) doi:10.3133/sir20055172.\u003c/li\u003e\n\u003cli\u003eLabrecque, G., Chesnaux, R. \u0026amp; Boucher, M.-A. Water-table fluctuation method for assessing aquifer recharge: application to Canadian aquifers and comparison with other methods. \u003cem\u003eHydrogeol J\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 521\u0026ndash;533 (2020).\u003c/li\u003e\n\u003cli\u003eScanlon, B. R., Healy, R. W. \u0026amp; Cook, P. G. Choosing appropriate techniques for quantifying groundwater recharge. \u003cem\u003eHydrogeology Journal\u003c/em\u003e (2002).\u003c/li\u003e\n\u003cli\u003eDonadelli Sacchi, M., Lilla Manzione, R. \u0026amp; Gastmans, D. How much rainwater contributes to a spring discharge in the Guarani Aquifer System: insights from stable isotopes and a mass balance model. \u003cem\u003eIsotopes in Environmental and Health Studies\u003c/em\u003e (2024) doi:10.1080/10256016.2024.2397469.\u003c/li\u003e\n\u003cli\u003eStr\u0026iacute;kis, N. M. \u003cem\u003eet al.\u003c/em\u003e Modern anthropogenic drought in Central Brazil unprecedented during last 700 years. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1728 (2024).\u003c/li\u003e\n\u003cli\u003eHofmann, G. S. \u003cem\u003eet al.\u003c/em\u003e Changes in atmospheric circulation and evapotranspiration are reducing rainfall in the Brazilian Cerrado. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 11236 (2023).\u003c/li\u003e\n\u003cli\u003ede Andrade, B. C. C., de Andrade Pinto, E. J., Ruhoff, A. \u0026amp; Senay, G. B. Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System. \u003cem\u003eInternational Journal of Applied Earth Observation and Geoinformation\u003c/em\u003e \u003cstrong\u003e98\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003cli\u003eCunha, A. P. M. A., Buermann, W. \u0026amp; Marengo, J. A. Changes in compound drought-heat events over Brazil\u0026rsquo;s Pantanal wetland: an assessment using remote sensing data and multiple drought indicators. \u003cem\u003eClimate Dynamics\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 739\u0026ndash;757 (2024).\u003c/li\u003e\n\u003cli\u003eGarcia, L. C. \u003cem\u003eet al.\u003c/em\u003e Record-breaking wildfires in the world\u0026rsquo;s largest continuous tropical wetland: Integrative fire management is urgently needed for both biodiversity and humans. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e \u003cstrong\u003e293\u003c/strong\u003e, 112870 (2021).\u003c/li\u003e\n\u003cli\u003eLibonati, R. \u003cem\u003eet al.\u003c/em\u003e Drought\u0026ndash;heatwave nexus in Brazil and related impacts on health and fires: A comprehensive review. \u003cem\u003eAnnals of the New York Academy of Sciences\u003c/em\u003e \u003cstrong\u003e1517\u003c/strong\u003e, 44\u0026ndash;62 (2022).\u003c/li\u003e\n\u003cli\u003eKumar, S. \u003cem\u003eet al.\u003c/em\u003e Changes in land use enhance the sensitivity of tropical ecosystems to fire-climate extremes. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1\u0026ndash;11 (2022).\u003c/li\u003e\n\u003cli\u003eBarros, V. R., Grimm, A. M. \u0026amp; Doyle, M. E. Relationship between Temperature and Circulation in Southeastern South America and its Influence from El Nino and La Nina Events. \u003cem\u003eJournal of the Meteorological Society of Japan. Ser. II\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e, 21\u0026ndash;32 (2002).\u003c/li\u003e\n\u003cli\u003eGrimm, A. M. The El Ni\u0026ntilde;o Impact on the Summer Monsoon in Brazil: Regional Processes versus Remote Influences. \u003cem\u003eJournal of Climate\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 263\u0026ndash;280 (2003).\u003c/li\u003e\n\u003cli\u003eGrimm, A. M., Ferraz, S. E. T. \u0026amp; Gomes, J. Precipitation Anomalies in Southern Brazil Associated with El Ni\u0026ntilde;o and La Ni\u0026ntilde;a Events. \u003cem\u003eJournal of Climate\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 2863\u0026ndash;2880 (1998).\u003c/li\u003e\n\u003cli\u003eGrigg, N. S. Two Decades of Integrated Flood Management: Status, Barriers, and Strategies. \u003cem\u003eClimate\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 67 (2024).\u003c/li\u003e\n\u003cli\u003eCabral, V. \u003cem\u003eet al.\u003c/em\u003e The consequences of debris flows in Brazil: a historical analysis based on recorded events in the last 100 years. \u003cem\u003eLandslides\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 511\u0026ndash;529 (2023).\u003c/li\u003e\n\u003cli\u003eC\u0026eacute;lia dos Santos Alval\u0026aacute;, R. \u003cem\u003eet al.\u003c/em\u003e Analysis of the hydrological disaster occurred in the state of Rio Grande do Sul, Brazil in September 2023: Vulnerabilities and risk management capabilities. \u003cem\u003eInternational Journal of Disaster Risk Reduction\u003c/em\u003e \u003cstrong\u003e110\u003c/strong\u003e, 104645 (2024).\u003c/li\u003e\n\u003cli\u003ePillar, V. D. \u0026amp; Overbeck, G. E. Learning from a climate disaster: The catastrophic floods in southern Brazil. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e385\u003c/strong\u003e, eadr8356 (2024).\u003c/li\u003e\n\u003cli\u003eChen, J. L., Wilson, C. R. \u0026amp; Tapley, B. D. The 2009 exceptional Amazon flood and interannual terrestrial water storage change observed by GRACE. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 1\u0026ndash;10 (2010).\u003c/li\u003e\n\u003cli\u003eMarengo, J. A. \u0026amp; Espinoza, J. C. Extreme seasonal droughts and floods in Amazonia: Causes, trends and impacts. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 1033\u0026ndash;1050 (2016).\u003c/li\u003e\n\u003cli\u003eEspinoza, J. C. \u003cem\u003eet al.\u003c/em\u003e Climate variability and extreme drought in the upper Solim\u0026otilde;es River (western Amazon Basin): Understanding the exceptional 2010 drought. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 1\u0026ndash;6 (2011).\u003c/li\u003e\n\u003cli\u003ePlatt, J. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. \u003cem\u003eAdv. Large Margin Classif.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, (2000).\u003c/li\u003e\n\u003cli\u003ePokhrel, Y. \u003cem\u003eet al.\u003c/em\u003e Global terrestrial water storage and drought severity under climate change. \u003cem\u003eNature Climate Change\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 226\u0026ndash;233 (2021).\u003c/li\u003e\n\u003cli\u003eHu, K. \u003cem\u003eet al.\u003c/em\u003e Hydrogeological characterisation of groundwater over Brazil using remotely sensed and model products. \u003cem\u003eScience of the Total Environment\u003c/em\u003e \u003cstrong\u003e599\u0026ndash;600\u003c/strong\u003e, 372\u0026ndash;386 (2017).\u003c/li\u003e\n\u003cli\u003eFernandes, L. G. \u0026amp; Rodrigues, R. R. Changes in the patterns of extreme rainfall events in southern Brazil. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 1337\u0026ndash;1352 (2018).\u003c/li\u003e\n\u003cli\u003eBaidya Roy, S. \u0026amp; Avissar, R. Impact of land use/land cover change on regional hydrometeorology in Amazonia. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e \u003cstrong\u003e107\u003c/strong\u003e, 1\u0026ndash;7 (2002).\u003c/li\u003e\n\u003cli\u003eCosta, M. H., Yanagi, S. N. M., Souza, P. J. O. P., Ribeiro, A. \u0026amp; Rocha, E. J. P. Climate change in Amazonia caused by soybean cropland expansion, as compared to caused by pastureland expansion. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2\u0026ndash;5 (2007).\u003c/li\u003e\n\u003cli\u003eMarengo, J. A., Jimenez, J. C., Espinoza, J.-C., Cunha, A. P. \u0026amp; Arag\u0026atilde;o, L. E. O. Increased climate pressure on the agricultural frontier in the Eastern Amazonia\u0026ndash;Cerrado transition zone. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 457 (2022).\u003c/li\u003e\n\u003cli\u003eStr\u0026iacute;kis, N. M. \u003cem\u003eet al.\u003c/em\u003e Modern anthropogenic drought in Central Brazil unprecedented during last 700 years. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1728 (2024).\u003c/li\u003e\n\u003cli\u003eCajazeiras, C. C. de A. \u0026amp; Mour\u0026atilde;o, M. A. A. Projeto Rede Integrada de Monitoramento das \u0026Aacute;guas Subterr\u0026acirc;neas: relat\u0026oacute;rio diagn\u0026oacute;stico Sistema Aqu\u0026iacute;fero Parecis no Estado de Rond\u0026ocirc;nia, Bacia Sedimentar dos Parecis. Volume 8. \u003cstrong\u003e8\u003c/strong\u003e, 40 (2012).\u003c/li\u003e\n\u003cli\u003eANA. \u0026Aacute;rea Irrigada - Piv\u0026ocirc;s Mapeados. https://dadosabertos.ana.gov.br/datasets/41c75b31124c41ca96c14d568ba17c85_0/about (2024).\u003c/li\u003e\n\u003cli\u003eHirata, R., Suhogusoff, A. V., Marcellini, S. S., Villar, P. C. \u0026amp; Marcellini, L. \u003cem\u003eA Revolu\u0026ccedil;\u0026atilde;o Silenciosa Das \u0026Aacute;guas Subterr\u0026acirc;neas No Brasil: Uma An\u0026aacute;lise Da Import\u0026acirc;ncia Do Recurso e Os Riscos Pela Falta de Saneamento\u003c/em\u003e. (2019).\u003c/li\u003e\n\u003cli\u003eHirata, R. \u0026amp; Conicelli, B. P. Groundwater resources in Brazil: a review of possible impacts caused by climate change. \u003cem\u003eAnais da Academia Brasileira de Ci\u0026ecirc;ncias\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 297\u0026ndash;312 (2012).\u003c/li\u003e\n\u003cli\u003eNie, W. \u003cem\u003eet al.\u003c/em\u003e Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e, (2024).\u003c/li\u003e\n\u003cli\u003eNobre, C. A. \u0026amp; Borma, L. D. S. \u0026lsquo;Tipping points\u0026rsquo; for the Amazon forest. \u003cem\u003eCurrent Opinion in Environmental Sustainability\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 28\u0026ndash;36 (2009).\u003c/li\u003e\n\u003cli\u003eAssis, L. F. F. G. \u003cem\u003eet al.\u003c/em\u003e TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping. \u003cem\u003eISPRS International Journal of Geo-Information\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 513 (2019).\u003c/li\u003e\n\u003cli\u003eCalim Costa, M., Marengo, J. A., Alves, L. M. \u0026amp; Cunha, A. P. Multiscale analysis of drought, heatwaves, and compound events in the Brazilian Pantanal in 2019\u0026ndash;2021. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e \u003cstrong\u003e155\u003c/strong\u003e, 661\u0026ndash;677 (2024).\u003c/li\u003e\n\u003cli\u003eVahedifard, F. \u003cem\u003eet al.\u003c/em\u003e Interdependencies Between Wildfire-Induced Alterations in Soil Properties, Near-Surface Processes, and Geohazards. \u003cem\u003eEarth and Space Science\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, (2024).\u003c/li\u003e\n\u003cli\u003eMaples, S. R., Fogg, G. E. \u0026amp; Maxwell, R. M. Modeling managed aquifer recharge processes in a highly heterogeneous, semi-confined aquifer system. \u003cem\u003eHydrogeology Journal\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 2869\u0026ndash;2888 (2019).\u003c/li\u003e\n\u003cli\u003eTollefson, J. You\u0026rsquo;re not imagining it: extreme wildfires are now more common. \u003cem\u003eNature\u003c/em\u003e (2024) doi:10.1038/d41586-024-02071-8.\u003c/li\u003e\n\u003cli\u003eIBGE. \u003cem\u003ePesquisa Nacional de Saneamento B\u0026aacute;sico\u003c/em\u003e. (2020).\u003c/li\u003e\n\u003cli\u003eRodell, M. \u0026amp; Reager, J. T. Water cycle science enabled by the GRACE and GRACE-FO satellite missions. \u003cem\u003eNature Water\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 47\u0026ndash;59 (2023).\u003c/li\u003e\n\u003cli\u003eLi, B. \u003cem\u003eet al.\u003c/em\u003e Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 7564\u0026ndash;7586 (2019).\u003c/li\u003e\n\u003cli\u003eHirata, R. \u003cem\u003eet al.\u003c/em\u003e As \u0026aacute;guas subterr\u0026acirc;neas e sua import\u0026acirc;ncia ambiental e econ\u0026ocirc;mica para o Brasil. (2019) doi:10.11606/9788563124074.\u003c/li\u003e\n\u003cli\u003eRodell, M. \u003cem\u003eet al.\u003c/em\u003e Emerging trends in global freshwater availability. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e557\u003c/strong\u003e, 651\u0026ndash;659 (2018).\u003c/li\u003e\n\u003cli\u003eCalvin, K. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eIPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (Eds.)]. IPCC, Geneva, Switzerland.\u003c/em\u003e \u003cem\u003eAtmosphere\u003c/em\u003e vol. 13 (2023).\u003c/li\u003e\n\u003cli\u003eCondon, L. E. \u003cem\u003eet al.\u003c/em\u003e Global Groundwater Modeling and Monitoring: Opportunities and Challenges. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 1\u0026ndash;27 (2021).\u003c/li\u003e\n\u003cli\u003eGetirana, A. \u003cem\u003eet al.\u003c/em\u003e Avert Bangladesh\u0026rsquo;s looming water crisis through open science and better data. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e610\u003c/strong\u003e, 626\u0026ndash;629 (2022).\u003c/li\u003e\n\u003cli\u003eACWI, A. C. on W. I. \u003cem\u003eA National Framework for Ground-Water Monitoring in the United States\u003c/em\u003e. (2013).\u003c/li\u003e\n\u003cli\u003eLi, B. \u0026amp; Rodell, M. Terrestrial water storage in 2023. \u003cem\u003eNature Reviews Earth \u0026amp; Environment\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 247\u0026ndash;249 (2024).\u003c/li\u003e\n\u003cli\u003eEspinoza, J.-C., Marengo, J. A., Schongart, J. \u0026amp; Jimenez, J. C. The new historical flood of 2021 in the Amazon River compared to major floods of the 21st century: Atmospheric features in the context of the intensification of floods. \u003cem\u003eWeather and Climate Extremes\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 100406 (2022).\u003c/li\u003e\n\u003cli\u003eHirata, R., Goodarzi, L., R\u0026ouml;rig, F. S., Alves, L. M. \u0026amp; Bertolo, R. Climate change impacts on groundwater: a growing challenge for water resources sustainability in Brazil. \u003cem\u003eEnviron Monit Assess\u003c/em\u003e \u003cstrong\u003e197\u003c/strong\u003e, 784 (2025).\u003c/li\u003e\n\u003cli\u003eSatiz\u0026aacute;bal-Alarc\u0026oacute;n, D. A., Suhogusoff, A. \u0026amp; Ferrari, L. C. Characterization of groundwater storage changes in the Amazon River Basin based on downscaling of GRACE/GRACE-FO data with machine learning models. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e912\u003c/strong\u003e, 168958 (2023).\u003c/li\u003e\n\u003cli\u003eRodrigues, A. F. \u003cem\u003eet al.\u003c/em\u003e Has unsustainable groundwater use induced low flow regimes in the Urucuia Aquifer System? An urgent call for integrated water management. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e \u003cstrong\u003e370\u003c/strong\u003e, 122979 (2024).\u003c/li\u003e\n\u003cli\u003eKonikow, L. F. \u0026amp; Kendy, E. Groundwater depletion: A global problem. \u003cem\u003eHydrogeology Journal\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 317\u0026ndash;320 (2005).\u003c/li\u003e\n\u003cli\u003eSeo, K. \u003cem\u003eet al.\u003c/em\u003e Drift of Earth\u0026rsquo;s Pole Confirms Groundwater Depletion as a Significant Contributor to Global Sea Level Rise 1993\u0026ndash;2010. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 1\u0026ndash;7 (2023).\u003c/li\u003e\n\u003cli\u003eProkop, G., Younger, P. L. \u0026amp; Roehl, K. E. \u003cem\u003eGroundwater Management in Mining Areas\u003c/em\u003e. \u003cem\u003eGroundwater Management in Mining Areas\u003c/em\u003e (2004).\u003c/li\u003e\n\u003cli\u003eLottermoser, B. Mine Water. in \u003cem\u003eMine Wastes\u003c/em\u003e vol. 32 83\u0026ndash;141 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2003).\u003c/li\u003e\n\u003cli\u003eMagalh\u0026atilde;es, C. C. D. C., Melo, M. C. de, Guiguer, N. \u0026amp; Paula, R. S. de. The Cau\u0026ecirc; Aquifer on the eastern limb of the Moeda Syncline: characterization, impacts, and flow in the western Iron Quadrangle (Quadril\u0026aacute;tero Ferr\u0026iacute;fero), MG, Brazil. \u003cem\u003eBrazilian Journal of Geology\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eANM, A. N. de M. Processos miner\u0026aacute;rios ativos - Brasil. \u003cem\u003eANM\u003c/em\u003e https://dados.gov.br/dados/conjuntos-dados/sistema-de-informacoes-geograficas-da-mineracao-sigmine (2024).\u003c/li\u003e\n\u003cli\u003ePompeu, J. Cross-Boundary Drivers of Water Cover Reduction in the Pantanal Wetland and Implications for its Conservation. \u003cem\u003eWetlands\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 32 (2025).\u003c/li\u003e\n\u003cli\u003eCaballero, C. B. \u003cem\u003eet al.\u003c/em\u003e Decadal hydroclimatic changes in the Pantanal, the world\u0026rsquo;s largest tropical wetland. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 17675 (2025).\u003c/li\u003e\n\u003cli\u003eGarcia, L. C. \u003cem\u003eet al.\u003c/em\u003e Record-breaking wildfires in the world\u0026rsquo;s largest continuous tropical wetland: Integrative fire management is urgently needed for both biodiversity and humans. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e \u003cstrong\u003e293\u003c/strong\u003e, 112870 (2021).\u003c/li\u003e\n\u003cli\u003eChen, T. \u0026amp; Guestrin, C. XGBoost. in \u003cem\u003eProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u003c/em\u003e vols 13-17-Augu 785\u0026ndash;794 (ACM, New York, NY, USA, 2016).\u003c/li\u003e\n\u003cli\u003eKe, G. \u003cem\u003eet al.\u003c/em\u003e LightGBM: A highly efficient gradient boosting decision tree. \u003cem\u003eAdvances in Neural Information Processing Systems\u003c/em\u003e \u003cstrong\u003e2017-Decem\u003c/strong\u003e, 3147\u0026ndash;3155 (2017).\u003c/li\u003e\n\u003cli\u003eProkhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. \u0026amp; Gulin, A. CatBoost: unbiased boosting with categorical features. \u003cem\u003eAdvances in Neural Information Processing Systems\u003c/em\u003e \u003cstrong\u003e2018-Decem\u003c/strong\u003e, 6638\u0026ndash;6648 (2017).\u003c/li\u003e\n\u003cli\u003eLundberg, S. \u0026amp; Lee, S.-I. A Unified Approach to Interpreting Model Predictions. \u003cem\u003eAdvances in Neural Information Processing Systems\u003c/em\u003e \u003cstrong\u003e2017-Decem\u003c/strong\u003e, 4766\u0026ndash;4775 (2017).\u003c/li\u003e\n\u003cli\u003eScanlon, B. R. \u003cem\u003eet al.\u003c/em\u003e Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, E1080\u0026ndash;E1089 (2018).\u003c/li\u003e\n\u003cli\u003eLabrecque, G., Chesnaux, R. \u0026amp; Boucher, M.-A. Water-table fluctuation method for assessing aquifer recharge: application to Canadian aquifers and comparison with other methods. \u003cem\u003eHydrogeol J\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 521\u0026ndash;533 (2020).\u003c/li\u003e\n\u003cli\u003eSave, H., Bettadpur, S. \u0026amp; Tapley, B. D. High‐resolution CSR GRACE RL05 mascons. \u003cem\u003eJournal of Geophysical Research: Solid Earth\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e, 7547\u0026ndash;7569 (2016).\u003c/li\u003e\n\u003cli\u003eSwenson, S., Wahr, J. \u0026amp; Milly, P. C. D. Estimated accuracies of regional water storage variations inferred from the Gravity Recovery and Climate Experiment (GRACE). \u003cem\u003eWater Resources Research\u003c/em\u003e (2003) doi:10.1029/2002WR001808.\u003c/li\u003e\n\u003cli\u003eRowlands, D. D. \u003cem\u003eet al.\u003c/em\u003e Global mass flux solutions from GRACE: A comparison of parameter estimation strategies\u0026mdash;Mass concentrations versus Stokes coefficients. \u003cem\u003eJournal of Geophysical Research\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, B01403 (2010).\u003c/li\u003e\n\u003cli\u003eDitmar, P. Conversion of time-varying Stokes coefficients into mass anomalies at the Earth\u0026rsquo;s surface considering the Earth\u0026rsquo;s oblateness. \u003cem\u003eJournal of Geodesy\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 1401\u0026ndash;1412 (2018).\u003c/li\u003e\n\u003cli\u003eMelati, M. D., Fleischmann, A. S., Fan, F. M., Paiva, R. C. D. \u0026amp; Athayde, G. B. Estimates of groundwater depletion under extreme drought in the Brazilian semi-arid region using GRACE satellite data: application for a small-scale aquifer. \u003cem\u003eHydrogeology Journal\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 2789\u0026ndash;2802 (2019).\u003c/li\u003e\n\u003cli\u003eEarth Science Data Systems, N. MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061 | NASA Earthdata. Earth Science Data Systems, NASA (2025).\u003c/li\u003e\n\u003cli\u003eMyneni, R., Knyazikhin, Y. \u0026amp; Park, T. MODIS Collection 6 (C6) LAI/FPAR Product User\u0026rsquo;s Guide. \u003cem\u003eNASA EOSDIS Land Processes DAAC\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, https://doi.org/10.5067/MODIS/MOD15A2H.061 (2020).\u003c/li\u003e\n\u003cli\u003eMu, Q., Heinsch, F. A., Zhao, M. \u0026amp; Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 519\u0026ndash;536 (2007).\u003c/li\u003e\n\u003cli\u003eHuffman, G., Bolvin, D., Braithwaite, D., Hsu, K. \u0026amp; Joyce, R. NASA Global Precipitation Measurement ( GPM ) Integrated Multi-satellitE Retrievals for GPM ( IMERG ) - Algorithm Theoretical Basis Document ( ATBD ). \u003cem\u003eNasa\u003c/em\u003e 29 (2018).\u003c/li\u003e\n\u003cli\u003eFalck, A. S., Maggioni, V., Tomasella, J., Vila, D. A. \u0026amp; Diniz, F. L. R. Propagation of satellite precipitation uncertainties through a distributed hydrologic model: A case study in the Tocantins-Araguaia basin in Brazil. \u003cem\u003eJournal of Hydrology\u003c/em\u003e \u003cstrong\u003e527\u003c/strong\u003e, 943\u0026ndash;957 (2015).\u003c/li\u003e\n\u003cli\u003eZubieta, R., Getirana, A., Espinoza, J. C., Lavado-Casimiro, W. \u0026amp; Aragon, L. Hydrological modeling of the Peruvian\u0026ndash;Ecuadorian Amazon Basin using GPM-IMERG satellite-based precipitation dataset. \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 3543\u0026ndash;3555 (2017).\u003c/li\u003e\n\u003cli\u003eGetirana, A. \u003cem\u003eet al.\u003c/em\u003e Potential of GPM IMERG precipitation estimates to monitor natural disaster triggers in urban areas: The case of Rio de Janeiro, Brazil. \u003cem\u003eRemote Sensing\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1\u0026ndash;20 (2020).\u003c/li\u003e\n\u003cli\u003eGadelha, A. N. \u003cem\u003eet al.\u003c/em\u003e Grid box-level evaluation of IMERG over Brazil at various space and time scales. \u003cem\u003eAtmospheric Research\u003c/em\u003e \u003cstrong\u003e218\u003c/strong\u003e, 231\u0026ndash;244 (2019).\u003c/li\u003e\n\u003cli\u003eDiniz, J. A. O. \u003cem\u003eet al.\u003c/em\u003e Mapa Hidrogeol\u0026oacute;gico do Brasil. \u003cem\u003eServi\u0026ccedil;o Geol\u0026oacute;gico do Brasil\u003c/em\u003e (2014).\u003c/li\u003e\n\u003cli\u003eMour\u0026atilde;o, M. A. A. Projeto - Implanta\u0026ccedil;\u0026atilde;o de Rede Integrada de Monitoramento das \u0026Aacute;guas Subterr\u0026acirc;neas - Proposta T\u0026eacute;cnica. (2009).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7311212/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7311212/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrazil holds the world\u0026rsquo;s largest reserves of renewable freshwater\u0026mdash;the country contributes\u0026thinsp;~\u0026thinsp;20% of the planet\u0026rsquo;s inland water discharge into the oceans\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u0026mdash;, yet recurrent water crises expose its growing vulnerability under extreme events\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While surface water dominates national supply, vast groundwater reserves remain underused and poorly monitored, representing a critical but untapped resource for climate resilience and national security. Here, we present a data-driven spatiotemporal reconstruction of Brazil\u0026rsquo;s groundwater behavior over the past two decades, integrating multi-satellite and \u003cem\u003ein situ\u003c/em\u003e data into an artificial intelligence modeling framework. Results reveal groundwater variability, recharge, and trends under climatological and anthropogenic stressors across the nation\u0026rsquo;s\u0026thinsp;~\u0026thinsp;8.5\u0026nbsp;million km\u0026sup2; of land. Brazil\u0026rsquo;s 2002\u0026ndash;2023 averaged aquifer recharge is 223 mm\u0026mdash;12% of annual precipitation\u0026mdash;totaling\u0026thinsp;~\u0026thinsp;1,900 km\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e of annual renewable groundwater volume. Persistent depletion or no recharge is observed in heavily exploited aquifers in eastern Brazil, further impacted by prolonged droughts\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and climate oscillations. Such aquifers present depletion trends mirroring patterns observed in intensively exploited aquifers in Bangladesh\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, India\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, Iran\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and the U.S.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. As a world\u0026rsquo;s major breadbasket, Brazil plays a vital role in global food security. Results presented here are therefore critical to the nation\u0026rsquo;s sustaining agricultural productivity under increasing climate stress.\u003c/p\u003e","manuscriptTitle":"Two Decades of Human and Climate Induced Groundwater Storage Shifts in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 10:55:47","doi":"10.21203/rs.3.rs-7311212/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4111b04e-f50d-4048-bb16-8dbd8ce78314","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52988879,"name":"Earth and environmental sciences/Hydrology"},{"id":52988880,"name":"Earth and environmental sciences/Climate sciences/Hydrology"}],"tags":[],"updatedAt":"2025-11-18T10:37:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 10:55:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7311212","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7311212","identity":"rs-7311212","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}