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Global food consumption drives irrigation for crops, which depletes aquifers in some regions; here we quantify the volumes of groundwater depletion associated with global food production and international trade. International food trade causes water depletion (Dalin 21403, Phys Letter) International trade is increasingly transporting 'hidden' resources and environmental factors from one country to another. For example, the water used to produce a spear of asparagus eaten in London might come from irrigation in South America. Similarly, pollution generated in China might be traceable to consumer demand in the United States. Carole Dalin et al . now extend this idea to the non-renewable groundwater that is consumed for agricultural trade. They find that 11 per cent of groundwater extraction is linked to agricultural trade, with Pakistan, the United States and India accounting for two-thirds of the global totals. The research reveals the degree to which food consumption in one country can lead to groundwater depletion in others, highlighting the need to better consider issues of sustainability and equity in the international food trade. Recent hydrological modelling 1 and Earth observations 2 , 3 have located and quantified alarming rates of groundwater depletion worldwide. This depletion is primarily due to water withdrawals for irrigation 1 , 2 , 4 , but its connection with the main driver of irrigation, global food consumption, has not yet been explored. Here we show that approximately eleven per cent of non-renewable groundwater use for irrigation is embedded in international food trade, of which two-thirds are exported by Pakistan, the USA and India alone. Our quantification of groundwater depletion embedded in the world’s food trade is based on a combination of global, crop-specific estimates of non-renewable groundwater abstraction and international food trade data. A vast majority of the world’s population lives in countries sourcing nearly all their staple crop imports from partners who deplete groundwater to produce these crops, highlighting risks for global food and water security. Some countries, such as the USA, Mexico, Iran and China, are particularly exposed to these risks because they both produce and import food irrigated from rapidly depleting aquifers. Our results could help to improve the sustainability of global food production and groundwater resource management by identifying priority regions and agricultural products at risk as well as the end consumers of these products.

We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D–2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.

Groundwater is a primary source of fresh water in many parts of the world. Some regions are becoming overly dependent on it, consuming groundwater faster than it is naturally replenished and causing water tables to decline unremittingly. Indirect evidence suggests that this is the case in northwest India, but there has been no regional assessment of the rate of groundwater depletion. Here we use terrestrial water storage-change observations from the NASA Gravity Recovery and Climate Experiment satellites and simulated soil-water variations from a data-integrating hydrological modelling system4 to show that groundwater is being depleted at a mean rate of 4.0 1.0 cm yr-1 equivalent height of water (17.7 +/- 4.5 km3 yr-1) over the Indian states of Rajasthan, Punjab and Haryana (including Delhi). During our study period of August 2002 to October 2008, groundwater depletion was equivalent to a net loss of 109 km3 of water, which is double the capacity of India's largest surface-water reservoir. Annual rainfall was close to normal throughout the period and we demonstrate that the other terrestrial water storage components (soil moisture, surface waters, snow, glaciers and biomass) did not contribute significantly to the observed decline in total water levels. Although our observational record is brief, the available evidence suggests that unsustainable consumption of groundwater for irrigation and other anthropogenic uses is likely to be the cause. If measures are not taken soon to ensure sustainable groundwater usage, the consequences for the 114,000,000 residents of the region may include a reduction of agricultural output and shortages of potable water, leading to extensive socioeconomic stresses.

Increasing population, economic growth and changes in diet have dramatically increased the demand for food and water over the last decades. To meet increasing demands, irrigated agriculture has expanded into semi-arid areas with limited precipitation and surface water availability. This has greatly intensified the dependence of irrigated crops on groundwater withdrawal and caused a steady increase in groundwater withdrawal and groundwater depletion. One of the effects of groundwater pumping is the reduction in streamflow through capture of groundwater recharge, with detrimental effects on aquatic ecosystems. The degree to which groundwater withdrawal affects streamflow or groundwater storage depends on the nature of the groundwater–surface water interaction (GWSI). So far, analytical solutions that have been derived to calculate the impact of groundwater on streamflow depletion involve single wells and streams and do not allow the GWSI to shift from connected to disconnected, i.e. from a situation with two-way interaction to one with a one-way interaction between groundwater and surface water. Including this shift and also analysing the effects of many wells requires numerical groundwater models that are expensive to set up. Here, we introduce an analytical framework based on a simple lumped conceptual model that allows us to estimate to what extent groundwater withdrawal affects groundwater heads and streamflow at regional scales. It accounts for a shift in GWSI, calculates at which critical withdrawal rate such a shift is expected, and when it is likely to occur after withdrawal commences. It also provides estimates of streamflow depletion and which part of the groundwater withdrawal comes out of groundwater storage and which parts from a reduction in streamflow. After a local sensitivity analysis, the framework is combined with parameters and inputs from a global hydrological model and subsequently used to provide global maps of critical withdrawal rates and timing, the areas where current withdrawal exceeds critical limits and maps of groundwater and streamflow depletion rates that result from groundwater withdrawal. The resulting global depletion rates are compared with estimates from in situ observations and regional and global groundwater models and satellites. Pairing of the analytical framework with more complex global hydrological models presents a screening tool for fast first-order assessments of regional-scale groundwater sustainability and for supporting hydro-economic models that require simple relationships between groundwater withdrawal rates and the evolution of pumping costs and environmental externalities.

Managed aquifer recharge (MAR) is an effective approach to mitigate groundwater decline and spring depletion in karst systems impacted by excessive exploitation. However, the hydrogeological complexity of karst aquifers makes groundwater quantity and quality highly sensitive to human activities, posing challenges for MAR implementation. This study develops an integrated multi-method framework – combining isotopic analysis, flow monitoring, tracer tests, and numerical modeling – to evaluate the effects of MAR and groundwater extraction on karst aquifer dynamics, with a case study in the Baotu Spring system (Jinan, China). To enhance the accuracy of recharge rate quantification, an enhanced isotope mixing model that reduces uncertainties in estimating groundwater recharge ratios from multiple sources was developed, and the MAR rate settings were refined by establishing a quantitative relationship between effective MAR rates and water release rates through river flow monitoring. To improve the solute transport simulations' reliability, we conducted field tracer tests to constrain the effective porosity of the karst aquifer – a parameter typically poorly constrained in such systems. Furthermore, we validated the applicability of the equivalent porous media (EPM) model through rigorous hydrodynamic analysis, using field-measured fracture apertures to calculate Reynolds numbers and verify laminar flow conditions. The results demonstrate that surface water contributes > 80 % of recharge near MAR implementation zones, with MAR efficiency decreasing beyond critical river discharge thresholds. The karst aquifer exhibits laminar flow (effective porosity = 1.08 × 10−4), confirming the validity of the EPM approach. Modeling reveals that MAR significantly raises water tables, though efficiency varies by different MAR sources, and MAR-induced sulfate concentrations must be maintained below 56.5, 197.8, and 339.1 mg L−1 to meet China's Class I, II, and III groundwater standards, respectively. These findings provide practical guidelines for MAR implementation in temperate semi-arid fissured karst systems.

Reliable predictions of groundwater levels are crucial for sustainable groundwater resource management, which needs to balance diverse water needs and to address potential ecological consequences of groundwater depletion. Machine learning (ML) approaches for time series forecasting have shown promising accuracy for groundwater level prediction and, furthermore, offer scalability advantages over traditional numerical methods when sufficient data are available. Global ML architectures enable predictions across numerous monitoring wells concurrently using a single model, allowing predictions over a broad range of hydrogeological and meteorological conditions and simplifying model management. In this contribution, groundwater levels for 5288 monitoring wells across Germany were forecasted up to 12 weeks using two state-of-the-art ML approaches, the Temporal Fusion Transformer (TFT) and the Neural Hierarchical Forecasting for Time Series (N-HiTS) algorithm. The models were provided with historical groundwater levels, meteorological features, and a wide range of static features describing hydrogeological and soil properties at the monitoring sites. To determine the conditions under which the model achieves good performance and whether it aligns with hydrogeological system understanding, the model's performance was evaluated spatially and correlations with both static input features and time series features from hydrograph data were examined. The N-HiTS model outperformed the TFT model, achieving a median Nash–Sutcliffe efficiency (NSE) of 0.5 for the 12-week prediction over all 5288 monitoring wells. Performance varied widely: 25 % of wells achieved an NSE >0.68, while 15 % had an NSE <0 with the best N-HiTS model. A tendency for better predictions in areas with high data density was observed. Moreover, the models achieved higher performance in lowland areas with distinct seasonal groundwater dynamics, in monitoring wells located in porous aquifers, and at sites with moderate permeabilities, which aligns with theoretical expectations. Overall, the findings highlight that global ML models can facilitate accurate seasonal groundwater predictions over large, hydrogeological diverse areas, potentially informing future groundwater management practices at a national scale.

North China (NC) is experiencing significant groundwater depletion. We used GRACE and GRACE-FO RL06 Level-2 data with Mascon data from April 2002 to July 2022. We fused these two types of data through the generalized three-cornered hat method and further combined them with hydrological models, precipitation, in situ groundwater-level, and groundwater extraction (GWE) data to determine and verify temporal and spatial variations in groundwater storage (GWS) in NC. We quantitatively assessed groundwater sustainability by constructing a groundwater index in NC. We further explored the dynamic cyclic process of groundwater change and quantified the impact of the South-to-North Water Transfer Project (SNWTP) on GWS change in NC. The overall GWS shows a decreasing trend. The GRACE/GRACE-FO-derived GWS change results are consistent with those shown by the in situ groundwater-level data from the monitoring well. Groundwater in NC is in various states of unsustainability throughout the period 2002 to 2022. The SNWTP affected the water use structure to some extent in NC. This study elucidates the latest spatial–temporal variations in GWS, especially in the groundwater sustainability assessment and quantitative description of the effects of the SNWTP on changes in GWS in NC. The results may provide a reference for groundwater resource management.

In various regions worldwide, people rely heavily on groundwater as a significant water source for daily usage. The resulting large-scale depletion of groundwater has triggered surface deformation in densely populated urban areas. This paper aims to employ Persistent Scattered Interferometry Synthetic Aperture Radar (PS-InSAR) techniques to monitor and quantify the land surface deformation (LSD), assess the relationships between LSD and groundwater levels (GWL), and provide insights for urban planning in Lahore, Pakistan, as the research area. A series of Sentinel-1 images from the ascending track between 2017 and 2020 were analyzed. Moreover, the Mann–Kendall (MK) test and coefficient of determination were computed to analyze the long-term trends and spatial relationships between GWL depletion and line of sight (LOS) displacement. Our findings reveal significant increases in land subsidence (LS) and GWL from 2017 to 2020, particularly in the city center of Lahore. Notably, the annual mean subsidence during this period rose from −27 mm/year to −106 mm/year, indicating an accelerating trend with an average subsidence of −20 mm/year. Furthermore, the MK test indicated a declining trend in GWL, averaging 0.49 m/year from 2003 to 2020, exacerbating LS. Regions with significant groundwater discharge are particularly susceptible to subsidence rates up to −100 mm. The LS variation was positively correlated with the GWL at a significant level (p < 0.05) and accounted for a high positive correlation at the center of the city, where the urban load was high. Overall, the adopted methodology effectively detects, maps, and monitors land surfaces vulnerable to subsidence, offering valuable insights into efficient sustainable urban planning, surface infrastructure design, and subsidence-induced hazard mitigation in large urban areas.

In this paper, we use the small baseline set technology and the early geological hazard identification method based on the selection of Permanent Scatter (PS) and Distributed Scatter (DS) points to carry out the research on surface deformation monitoring caused by underground activities in mining cluster areas. We adopted the Small Baseline Subset InSAR (SBAS-InSAR) technique to process Sentinel-1A SAR images over the research area from March 2017 to May 2021. The deformation estimation technology based on the robustness of PS points and DS points can be used for early identification of high-density surface subsidence in a large area of mines. The surface subsidence information can be obtained quickly and accurately, and the advantages of using InSAR technology to monitor long-time surface subsidence in complex mining cluster areas was explored in this study. By comparing the monitoring data of the Global Navigation Satellite System (GNSS) ground monitoring equipment, the accuracy error of large-scale surface settlement information is controlled within 8 mm, which has high accuracy. Meanwhile, according to the spatial characteristics of cluster mining areas, it is analyzed that the relationship between adjacent mining areas through groundwater easily leads to regional associated large-area settlement changes. Compared with the D-InSAR (Differential InSAR) technology applied in mine monitoring at the early stage, this proposed method can monitor a large range of long time series and optimize the problem of decoherence to some extent in mining cluster areas. It has important reference significance for early monitoring and early warning of subsidence disaster evolution in mining intensive areas.

In water-stressed regions of the world, managed aquifer recharge (MAR), the process of intentionally recharging depleted aquifers, is an essential tool for combating groundwater depletion. Many groundwater-dependent regions, including the Central Valley in California, USA, are underlain by thick unsaturated zones (ca. 10 to 40 m thick), nested within complex valley-fill deposits that can hinder or facilitate recharge. Within the saturated zone, interconnected deposits of coarse-grained material (sands and gravel) can act as preferential recharge pathways, while fine-textured facies (silts and clays) accommodate the majority of the long-term increase in aquifer storage. However, this relationship is more complex within the vadose zone. Coarse facies can act as capillary barriers that restrict flow, and contrasts in matric potential can draw water from coarse-grained flow paths into fine-grained, low-permeability zones. To determine the impact of unsaturated-zone stratigraphic heterogeneity on MAR effectiveness, we simulate recharge at a Central Valley almond orchard surveyed with a towed transient electromagnetic system. First, we identified three outcomes of interest for MAR sites: infiltration rate at the surface, residence time of water in the root zone and saturated-zone recharge efficiency, which is defined as the increase in saturated-zone storage induced by MAR. Next, we developed a geostatistical approach for parameterizing a 3D variably saturated groundwater flow model using geophysical data. We use the resulting workflow to evaluate the three outcomes of interest and perform Monte Carlo simulations to quantify their uncertainty as a function of model input parameters and spatial uncertainty. Model results show that coarse-grained facies accommodate rapid infiltration rates and that contiguous blocks of fine-grained sediments within the root zone are >20 % likely to remain saturated longer than almond trees can tolerate. Simulations also reveal that capillary-driven flow draws recharge water into unsaturated, fine-grained sediments, limiting saturated-zone recharge efficiency. Two years after inundation, fine-grained facies within the vadose zone retain an average of 37 % of recharge water across all simulations, where it is inaccessible to either plants or pumping wells. Global sensitivity analyses demonstrate that each outcome of interest is most sensitive to parameters that describe the fine facies, implying that future work to reduce MAR uncertainty should focus on characterizing fine-grained sediments.