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Using publicly-available average monthly groundwater level data in 478 sub-basins and 30 basins in Iran, we quantify country-wide groundwater depletion in Iran. Natural and anthropogenic elements affecting the dynamics of groundwater storage are taken into account and quantified during the period of 2002–2015. We estimate that the total groundwater depletion in Iran to be ~ 74 km 3 during this period with highly localized and variable rates of change at basin and sub-basin scales. The impact of depletion in Iran’s groundwater reserves is already manifested by extreme overdrafts in ~ 77% of Iran’s land area, a growing soil salinity across the entire country, and increasing frequency and extent of land subsidence in Iran’s planes. While meteorological/hydrological droughts act as triggers and intensify the rate of depletion in country-wide groundwater storage, basin-scale groundwater depletions in Iran are mainly caused by extensive human water withdrawals. We warn that continuation of unsustainable groundwater management in Iran can lead to potentially irreversible impacts on land and environment, threatening country’s water, food, socio-economic security.

Population growth, economic development, and dietary changes have drastically increased the demand for food and water. The resulting expansion of irrigated agriculture into semi-arid areas with limited precipitation and surface water has greatly increased the dependence of irrigated crops on groundwater withdrawal. Also, the increasing number of people living in mega-cities without access to clean surface water or piped drinking water has drastically increased urban groundwater use. The result of these trends has been the steady increase of the use of non-renewable groundwater resources and associated high rates of aquifer depletion around the globe. We present a comprehensive review of the state-of-the-art in research on non-renewable groundwater use and groundwater depletion. We start with a section defining the concepts of non-renewable groundwater, fossil groundwater and groundwater depletion and place these concepts in a hydrogeological perspective. We pay particular attention to the interaction between groundwater withdrawal, recharge and surface water which is critical to understanding sustainable groundwater withdrawal. We provide an overview of methods that have been used to estimate groundwater depletion, followed by an extensive review of global and regional depletion estimates, the adverse impacts of groundwater depletion and the hydroeconomics of groundwater use. We end this review with an outlook for future research based on main research gaps and challenges identified. This review shows that both the estimates of current depletion rates and the future availability of non-renewable groundwater are highly uncertain and that considerable data and research challenges need to be overcome if we hope to reduce this uncertainty in the near future.

It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep-learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, shallow recurrent networks frequently seem to be excluded from the study design due to the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANNs, namely non-linear autoregressive networks with exogenous input (NARX) and popular state-of-the-art DL techniques such as long short-term memory (LSTM) and convolutional neural networks (CNNs). We compare the performance on both sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. Further, we also investigate the data dependency in terms of time series length of the different ANN architectures. For seq2val forecasts, NARX models on average perform best; however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL techniques especially when only small amounts of training data are available, where they can clearly outperform LSTMs and CNNs; however, LSTMs and CNNs might perform substantially better with a larger dataset, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

Groundwater provides critical freshwater supply, particularly in dry regions where surface water availability is limited. Climate change impacts on GWS (groundwater storage) could affect the sustainability of freshwater resources. Here, we used a fully-coupled climate model to investigate GWS changes over seven critical aquifers identified as significantly distressed by satellite observations. We assessed the potential climate-driven impacts on GWS changes throughout the 21 st century under the business-as-usual scenario (RCP8.5). Results show that the climate-driven impacts on GWS changes do not necessarily reflect the long-term trend in precipitation; instead, the trend may result from enhancement of evapotranspiration, and reduction in snowmelt, which collectively lead to divergent responses of GWS changes across different aquifers. Finally, we compare the climate-driven and anthropogenic pumping impacts. The reduction in GWS is mainly due to the combined impacts of over-pumping and climate effects; however, the contribution of pumping could easily far exceed the natural replenishment. Climate change may impact groundwater storage and thus the availability of freshwater resources. Here the authors use climate models to examine seven aquifers and find that storage changes are primarily the result of enhancement of evapotranspiration, reduction in snowmelt, and over-pumping rather than long-term precipitation changes.

Climate change disrupts groundwater levels (GWL) by modifying precipitation patterns, reducing recharge rates, and limiting water availability. Rising temperatures and evolving weather patterns further degrade surface and groundwater quality. These changes exacerbate competition for water resources, heightening allocation challenges and ecological disruptions. Groundwater fluctuations adversely affect ecosystems, causing habitat disturbances and biodiversity loss. This study explores the impacts of climate change on GWL using machine learning techniques to analyze 9,430 time series data points (1993–2021) from Northern China. Four distinct classes of top-performing machine learning models were evaluated. The CNN model demonstrated superior performance, achieving an R² value of 0.9924 and an RMSE of 0.1832, highlighting its efficacy in processing complex patterns. Pearson correlation analysis revealed that Average Annual Precipitation (AAP), Average Soil Moisture (ASM), and Evapotranspiration (EV) positively influence GWL, while Severe Wet Potential (SWP), Severe Drought Potential (SDP), and Temperature (T) exhibit negative correlations. Feature ranking identified AAP as the most critical factor for groundwater recharge, followed by ASM and EV, which also play significant roles in groundwater dynamics. These findings provide a robust understanding of the key drivers influencing groundwater recharge and storage, offering valuable insights to inform sustainable water resource management in the context of climate change. Highlights Climate change alters precipitation, reducing recharge and groundwater availability. Rising temperatures and weather changes impact surface and groundwater quality. Scarcity of groundwater leads to resource competition and allocation challenges. Groundwater fluctuations disrupt ecosystems, biodiversity, and habitats. Machine learning models, especially CNN, predict groundwater levels with high accuracy.

Large, confined aquifer systems play a vital role in sustaining human settlements and industries in many regions. Understanding the sustainability of these water resources requires the evaluation of groundwater storage change. Direct in‐situ observation of groundwater storage is limited by the distribution and availability of groundwater level and aquifer storativity data. Here, we use and compare two auxiliary methods, applied at basin and sub‐basin scales, to assess groundwater storage changes in the Great Artesian Basin (GAB), one of the World's largest confined aquifer systems. The first, the groundwater budget, derives storage change as the residual of fluxes in and out of the GAB, assuming they are all accounted for and accurately estimated. The second uses time‐variable gravity data from GRACE satellites to estimate temporal changes in groundwater mass, assuming that all other components of the terrestrial water mass change detected by GRACE are correctly subtracted. Despite the depletion observed during the 20th century, groundwater storage is mostly stable during 2002–2022. An increase in storage is detected in the Surat sub‐basin, a major recharge area. This increase is attributed to an over‐representation of large recharge events during the study period and/or storage recovery following rehabilitation of free‐flowing bores. The approach consisting in disaggregating GRACE data assumes that water storage changes in confined aquifers is dominated by changes in the GAB, and as such, it may overestimate the increase in the GAB by incorrectly attributing the increase occurring in overlying aquifers to the GAB. In contrast, the recharge estimates used in the groundwater budgets do not account for flood recharge and might underestimate storage increase in the GAB. Plain Language Summary Monitoring groundwater storage in large, confined aquifers is often impossible as it requires large groundwater level and lithological data sets that are often unavailable. However, monitoring is crucial for assessing and managing the sustainability of this resource and manage it appropriately. This study uses and compares two auxiliary methods, applied at basin and sub‐basin scales, to assess groundwater storage changes in the Great Artesian Basin (GAB), one of the World's largest confined aquifer systems. The groundwater budget approach estimates water storage changes by adding up the amounts of groundwater that goes in and out of the aquifer system. The satellite gravimetry approach uses the temporal changes of Earth's gravity field to infer changes in groundwater mass. Both methods agree that, despite the depletion observed during the 20th century, groundwater storage in the GAB was mostly stable during 2002–2022. An increase in groundwater storage is detected near major recharge areas. It is attributed to an over‐representation of large recharge events during the study period and/or groundwater storage recovery following capping of free‐flowing bores. Key Points GRACE and groundwater budgets agree that water storage in the Great Artesian Basin was stable for the period 2002–2022 Increased storage in the Surat sub‐basin is attributed to bore rehabilitation and/or increased recharge during the study period Within the Surat sub‐basin, increased storage may be overestimated by GRACE and/or underestimated by the groundwater budgets

Integrated agricultural water management (IAWM) encompasses multiple factors, necessitating the evaluation of performance across programs and involved entities and local consideration in different regions. This study analyzes the relation of allocation budgets and government agency programs in relation to the average annual availability of groundwater in Guanajuato State. Documentary investigation was conducted on public programs, aquifer availability, and agricultural land types over the period from 2017 to 2023. In the last six years, the amounts allocated to government programs from subsidies and donations have increased by 40%, leading to enhanced agricultural productivity in the state. Considering the agricultural types (rain-fed, irrigated, and protected) as separate variables, simple linear regression explains 97.8% of the variability in the DMA, indicating a decrease of 78.2 million m3 and an increase in irrigated agriculture. The estimator for the budget allocated to public programs is −2.21 × 10−7, indicating that even if the resources allocated to government programs related to the use and exploitation of water in the agricultural sector increase, the DMA will continue to decrease. Regarding the agriculture area type, the estimator has a value of −0.00237, indicating that each rain-fed or irrigated agriculture unit established would result in an approximate reduction of 2370 m3 of water in the DMA. Taking this into account, it is imperative to formulate strategies that consider intersectoral links, with a focus on prioritizing essential actions in rain-fed areas for water capture and/or irrigated agricultural areas for food production, which comprise 52% of the total land dedicated to the agricultural sector, and specifically targeting actions that promote groundwater management.

Billions of people rely on groundwater as being an accessible source of drinking water and for irrigation, especially in times of drought. Its importance will likely increase with a changing climate. It is still unclear, however, how climate change will impact groundwater systems globally and, thus, the availability of this vital resource. Groundwater recharge is an important indicator for groundwater availability, but it is a water flux that is difficult to estimate as uncertainties in the water balance accumulate, leading to possibly large errors in particular in dry regions. This study investigates uncertainties in groundwater recharge projections using a multi-model ensemble of eight global hydrological models (GHMs) that are driven by the bias-adjusted output of four global circulation models (GCMs). Pre-industrial and current groundwater recharge values are compared with recharge for different global warming (GW) levels as a result of three representative concentration pathways (RCPs). Results suggest that projected changes strongly vary among the different GHM–GCM combinations, and statistically significant changes are only computed for a few regions of the world. Statistically significant GWR increases are projected for northern Europe and some parts of the Arctic, East Africa, and India. Statistically significant decreases are simulated in southern Chile, parts of Brazil, central USA, the Mediterranean, and southeastern China. In some regions, reversals of groundwater recharge trends can be observed with global warming. Because most GHMs do not simulate the impact of changing atmospheric CO2 and climate on vegetation and, thus, evapotranspiration, we investigate how estimated changes in GWR are affected by the inclusion of these processes. In some regions, inclusion leads to differences in groundwater recharge changes of up to 100 mm per year. Most GHMs with active vegetation simulate less severe decreases in groundwater recharge than GHMs without active vegetation and, in some regions, even increases instead of decreases are simulated. However, in regions where GCMs predict decreases in precipitation and where groundwater availability is the most important, model agreement among GHMs with active vegetation is the lowest. Overall, large uncertainties in the model outcomes suggest that additional research on simulating groundwater processes in GHMs is necessary.

Current global-scale models of water resources do not generally represent groundwater lateral flows and groundwater–surface water interactions. But, models that do represent groundwater in more detail are becoming available and this raises the question of how estimates of water flow, availability, and impacts might change compared to previous global estimates. In this study, we provide the first global quantification of cell-to-cell groundwater flow (GWF) using a high-resolution global-scale GWF model and compare estimated impacts of groundwater pumping using two model setups: (a) with and (b) without including cell-to-cell GWFs and realistic simulation of groundwater–surface water interactions at the global scale (simulated over 1960–2010). Results show that 40% of the land–surface cell-to-cell flows are a notable part of the cell’s water budget and that globally large differences in the impact of groundwater pumping are estimatd between the two runs. Globally, simulated groundwater discharge to rivers and streams increased by a factor of 1.2–2.2 when GWFs and interactions between groundwater and surface water were included. For eight heavily pumped aquifers, estimates of groundwater depletion decrease by a factor of 1.7–22. Furthermore, our results show that GWFs and interactions between groundwater and surface water contribute to the volume of groundwater that can be pumped without causing notable changes in storage. However, in approximately 40% of the world’s watersheds where groundwater is used, groundwater is being pumped notably at the expense of river flow, and in 15% of the area globally depletion is increased as a result of nearby groundwater pumping. Evaluation of the model results showed that when groundwater lateral flows and groundwater–-surface water interactions were taken into account, the indirect observations of groundwater depletion and groundwater discharge were mimicked much better than when these fluxes were not included. Based on these findings, we suggest that including GWFs in large-scale water resources assessments will benefit a realistic assessment of groundwater availability worldwide, the estimation of impacts associated with groundwater pumping, especially when one is interested in the feedback between groundwater use and groundwater and surface water availability, and the impacts of current and future groundwater uses on the hydrological system.

The depletion of groundwater resources threatens food and water security in India. However, the relative influence of groundwater pumping and climate variability on groundwater availability and storage remains unclear. Here we show from analyses of satellite and local well data spanning the past decade that long-term changes in monsoon precipitation are driving groundwater storage variability in most parts of India either directly by changing recharge or indirectly by changing abstraction. We find that groundwater storage has declined in northern India at the rate of 2 cm/yr and increased by 1 to 2 cm/yr in southern India between 2002 and 2013. We find that a large fraction of the total variability in groundwater storage in north-central and southern India can be explained by changes in precipitation. Groundwater storage variability in northwestern India can be explained predominantly by variability in abstraction for irrigation, which is in turn influenced by changes in precipitation. Declining precipitation in northern India is linked to Indian Ocean warming, suggesting a previously unrecognized teleconnection between ocean temperatures and groundwater storage.