Rate Of Groundwater Depletion Research Articles

Application of gravity and remote sensing data to groundwater storage variation in Wadi Al Dawasir, Saudi Arabia

In order to deal with the increase in human-caused impacts, Saudi Arabia is exploring new groundwater sources. Significant agricultural development in Wadi Al Dawasir has resulted in an overuse of groundwater resources. Constant monitoring of the wadi's groundwater is necessary in order to make informed decisions about the future of the wadi's groundwater resources and the local economy. The rate of groundwater depletion in the Wadi Al Dawasir drainage basin in northern Saudi Arabia was calculated by combining data from the advanced Gravity Recovery and Climate Experiment (GRACE) mission with outputs from a land surface model. The analysis covered the period from April 2002 to December 2021. The results are as follows: (1) the average terrestrial water storage fluctuation (ΔTWS) was calculated at −0.48 ± 0.02 cm yr−1; (2) the average soil moisture storage change (ΔSMS) was calculated at + 0.0008 ± 0.0002 cm yr−1; (3) the average groundwater depletion rate was computed at −0.48 ± 0.02 cm yr−1; (4) the average yearly rainfall data for the Wadi Al Dawasir was 90.1 mm; (5) The surface relief is creating eastward streams that carry surface water downstream; (6) Significant agricultural expansions over the past few decades can be observed through Landsat change detection; (7) Higher sediment accumulation varying from 400 to more than 3000 m is observed in the east, near the Wadi's downstream, where the Wajid aquifer can be found. The integrated approach is a cost-effective and efficient tool for accurately evaluating the variability of groundwater resources over large regions.

Tracking shallow and deep groundwater storage changes in North China Plain with improved fusion method and hybrid spectral analysis approach

The North China Plain (NCP) is a critical agricultural hub in China, confronting a significant issue of groundwater depletion. However, different terrestrial water storage (TWS) change products from the Gravity Recovery and Climate Experiment (GRACE) satellites differ considerably in estimating groundwater depletion rates in the NCP. It is urgent to optimize these different products to obtain an elevated understanding of regional groundwater storage (GWS) changes. The generalized three-cornered hat (GTCH) method is used to quantify uncertainties of different GRACE products, and this prior information is integrated with the Bayesian model averaging (BMA) method to fuse GRACE products. Results demonstrate that the GTCH + BMA method can generate uniform GWS products with heightened precision, enhanced coherence, and diminished uncertainty in the NCP. After that, the shallow and deep GWS are meticulously differentiated based on the high-coverage in-situ data, and the shallow GWS (2005.1–2016.8) (-13.7 ± 4.6 mm/yr) declined faster than deep GWS (-6.05 ± 3.9 mm/yr) in the NCP. To further explore the primary factors driving these changes, the latest Hankel Spectrum Analysis (HSA) method combined with ridge regression simulation is employed to decouple the change components of human and natural factors in the shallow and deep GWS. The first attempt to give the contribution of human and natural factors to shallow GWS is 77.18 %/22.82 %, and to deep GWS is 87.59 %/12.41 %.

Accelerating Rate of Groundwater Depletion & Deteriorating Water Quality in Punjab

Groundwater depletion continues to be a cause for concern in Punjab as the State is amoung three states in India where groundwater extraction is more 100%. The nation average is 60%. The recently released Ground Water Resources Assessment for 2022 points the situation in Punjab being the most critical, with the agrarian state being the worst off in extracting underground water at 166% of its extractable groundwater resources.

A New Highly Parameterized Linear Inversion of Water Table Change and Groundwater Depletion Rate Tested With the High Plains Aquifer, U.S.A.

AbstractUnderstanding groundwater resource dynamics is limited by the sparsity of observations of water levels, pumping rates, and hydraulic properties relative to their spatiotemporal heterogeneity. To address some of this complexity, we proposed a new highly parameterized linear inverse method to quantify water table change and groundwater depletion rate in unconfined aquifers that does not require initial or boundary conditions. The method requires linearization, and we tested the performance of six proposed water table functions with three coordinate systems with synthetic models, finding that water table functions by the dimensionless method achieve the lowest errors. Our inverted water table changes and depletion rates remained stable and accurate with head measurement error of 5%. However, inversions became less accurate when head observations contained large temporal gaps. Next, we applied the inversion for water table changes and depletion rates in the Texas High Plains Aquifer (HPA) and the HPA during 2000–2015. To address sparse water levels and uncertain hydraulic conductivity measurements, two modifications were made to improve data constraints for inversion: (a) calculating winter‐time water levels for wells with >1 head observation by linear interpolation and (b) using hydraulic conductivity geostatistical realizations. The inverted water table change and depletion rate expected mean values and uncertainties were reasonable compared to the known pumping records of the Texas HPA. With relatively minor data curation, the new inverse method can quantify spatiotemporally continuous water table change, depletion rates, and their uncertainty for heterogeneous aquifers with temporally sparse and noisy water level observations.

Present and future groundwater depletion rates in Wadi Zabid, Tihama Coastal Plain, Yemen

Present and future groundwater depletion rates in Wadi Zabid, Tihama Coastal Plain, Yemen

Warming temperatures exacerbate groundwater depletion rates in India.

Climate change will likely increase crop water demand, and farmers may adapt by applying more irrigation. Understanding the extent to which this is occurring is of particular importance in India, a global groundwater depletion hotspot, where increased withdrawals may further jeopardize groundwater resources. Using historical data on groundwater levels, climate, and crop water stress, we find that farmers have adapted to warming temperatures by intensifying groundwater withdrawals, substantially accelerating groundwater depletion rates in India. When considering increased withdrawals due to warming, we project that the rates of net groundwater loss for 2041-2080 could be three times current depletion rates, even after considering projected increases in precipitation and possible decreases in irrigation use as groundwater tables fall. These results reveal a previously unquantified cost of adapting to warming temperatures that will likely further threaten India's food and water security over the coming decades.

Groundwater Depletion Rate Over China During 1965–2016: The Long‐Term Trend and Inter‐annual Variation

AbstractMany countries have experienced the increase of groundwater depletion rate (GWDR), posing great challenges to the environment and society. Given the slow variation of groundwater, long‐term estimation of GWDR at high‐resolution is critical for sustainable managements of regional water resources and risks of hydrological extremes in a changing climate. However, limited in‐situ and satellite observations and uncertainties from hydrological model simulations prohibit reliable estimation of the long‐term trend and inter‐annual variation of GWDR. Using survey‐based water withdrawal and water availability datasets, this study provided a spatially distributed estimation of annual GWDR over China at the 0.25° × 0.25° grid scale for the period 1965–2016 using a flux‐based approach. The estimated GWDR has higher spatial resolution and longer temporal coverage than previous products. With the newly compiled data, we found that groundwater over China was depleted at an average rate of 20.4 km3 year−1 during 1965–2016, especially in the arid and semi‐arid areas of northern China. The GWDR showed a significant increasing trend during 1965–2016, but such trend slowed down from 0.66 km3 year−1 during 1965–2001 to 0.06 km3 year−1 during 2001–2016, which was mainly attributed to the deceleration in human water withdrawal. Further analysis showed that over 40% of the inter‐annual variability of GWDR could be explained by precipitation variability, and the climate control increased over time. This study suggested a growing control of climate variations on GWDR over China as the human water withdrawal has been decelerating, and a need for climate change adaptation for a sustainable groundwater management.

Supplementary data for "Groundwater depletion rate over China during 1965-2016: the long-term trend and inter-annual variation"

Supplementary data for "Groundwater depletion rate over China during 1965-2016: the long-term trend and inter-annual variation"

Impact of Urbanization on Groundwater and Surface Temperature Changes: A Case Study of Lahore City

The over-exploitation of groundwater resources is a significant concern due to the potential risks associated with the depletion of this valuable freshwater source. Future planning must consider changes in groundwater availability and urban expansion which are critical for understanding urban growth patterns. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the Land surface temperature (LST) analysis has been performed to find the spatial spread of urbanization and its impact on surface temperature. The Gravity Recovery and Climate Experiment (GRACE) data for groundwater storage monitoring and Landsat data for land cover and LST mapping have been used. The GRACE-based Groundwater Storage (GWS) anomaly has been correlated with Tropical Rainfall Measuring Mission (TRMM)-based precipitation data. The GWS is further cross validated with the groundwater monitoring stations in the study area and the correlation of 0.7 is found. The time series analysis of GWS and the land cover maps with a decadal interval from 1990 to 2020 has been developed to find the impact of groundwater change due to urbanization. The results demonstrate a rapid increase in groundwater depletion and urbanization rates over the past decade. The LST spatial pattern is increasing similarly with the study area’s urban expansion, indicating the temperature rise due to urbanization. The study highlights the limitation of effective policies to regulate groundwater extraction in urban areas and the importance of proper planning to ensure the long-term sustainability of freshwater resources.

An improved rescaling algorithm for estimating groundwater depletion rates using the GRACE satellite

ABSTRACT The Gravity Recovery And Climate Experiments (GRACE) satellite mission has been instrumental in estimating large-scale groundwater storage changes across the globe. GRACE observations include significant errors, so pre-processing is normally required before the data are used. In particular, the terrestrial water storage anomalies (TWSA) are usually filtered to reduce the effects of measurement errors and then rescaled to reduce the unintended impacts of the filtering. The scaling is typically selected to maximize the Nash-Sutcliffe Efficiency (NSE) between the rescaled filtered TWSA and the original TWSA from large-scale hydrologic models that represent an incomplete water budget. The objectives of this study are as follows (1) to evaluate the use of NSE in the current GRACE rescaling methodology, (2) develop an improved methodology that incorporates a complete regional water budget, and (3) examine the impacts of the rescaling methodology on regional assessments of groundwater depletion. To evaluate the use of NSE as a performance metric, we compare it to an analytical solution that restores the relative variability between the rescaled filtered and original GRACE TWSA series. The relative variability approach produces more reliable estimates when comparing to TWSA estimates from global positioning systems (GPS) for the Sacramento and San Joaquin River basins in California. Rescaling the complete regional water budget results in a larger scale factor than the scale factor from the large-scale hydrologic model outputs, and the new TWSA results are more consistent with those from GPS. The large scale factor also suggests that regional groundwater depletion is more severe than previously estimated.

Analysis of Groundwater Storage Fluctuations Using GRACE and Remote Sensing Data in Wadi As-Sirhan, Northern Saudi Arabia

Human activity has led to a rise in the demand for water, prompting Saudi Arabia to search for alternative groundwater supplies. Wadi As-Sirhan is one area that has experienced extensive agricultural growth and the severe over-exploitation of its groundwater resources. The groundwater drawn from the wadi should be continuously monitored to determine the best management options for groundwater resources and economic growth. The most recent Gravity Recovery and Climate Experiment (GRACE) mission and outputs of land surface models were combined to estimate the depletion rate of the groundwater of the Wadi As-Sirhan drainage basin in the northern region of Saudi Arabia throughout the period of April 2002–December 2021. The findings are: (1) the average GRACE-derived terrestrial water storage variation (ΔTWS) was calculated at −13.82 ± 0.24 mm/yr; (2) the soil moisture storage variation was averaged at +0.008 ± 0.004 mm/yr; (3) the GRACE-derived groundwater depletion rate was estimated at −13.81 ± 0.24 mm/yr; (4) the annual precipitation data over the Wadi As-Sirhan was averaged at 60 mm/yr; (5) The wadi has a minimal recharge rate of +2.31 ± 0.24 mm/yr, which may partially compensate for a portion of the groundwater withdrawal; (6) the sediment thickness shows an increase from 0 m at the southern igneous and volcanic rocks to more than 3000 m close to the Saudi–Jordanian border; (7) The wadi’s eastern, southern, and western portions are the sources of its tributaries, which ultimately drain into its northwestern portion; (8) change detection from the Landsat photos reveals considerable agricultural expansions over recent decades. The integrated method is useful for analyzing changes to groundwater resources in large groundwater reservoirs and developing environmentally appropriate management programs for these resources.

Groundwater depletion in California’s Central Valley accelerates during megadrought

Groundwater provides nearly half of irrigation water supply, and it enables resilience during drought, but in many regions of the world, it remains poorly, if at all managed. In heavily agricultural regions like California’s Central Valley, where groundwater management is being slowly implemented over a 27-year period that began in 2015, groundwater provides two–thirds or more of irrigation water during drought, which has led to falling water tables, drying wells, subsiding land, and its long-term disappearance. Here we use nearly two decades of observations from NASA’s GRACE satellite missions and show that the rate of groundwater depletion in the Central Valley has been accelerating since 2003 (1.86 km3/yr, 1961–2021; 2.41 km3/yr, 2003–2021; 8.58 km3/yr, 2019–2021), a period of megadrought in southwestern North America. Results suggest the need for expedited implementation of groundwater management in the Central Valley to ensure its availability during the increasingly intense droughts of the future.

Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways

Climate change is one of the main sources of stress on the aquifers. However, few studies have appraised its impact on groundwater systems, especially in Iran with unprecedented groundwater depletion rates. Not influenced by anthropogenic influences, Pali aquifer, southwest Iran, has climate data scarcity. To determine the response of the aquifer, one approach is to utilize a pre-downscaled data set. To this end, WorldClim data set under shared socioeconomic pathways (SSPs), i.e. SSP245 and SSP585, was considered. Furthermore, nonlinear autoregressive exogenous (NARX) and artificial neural networks (ANNs) along with support vector regression (SVR) combined with the meta-heuristic algorithms of wavelet (WSVR) and innovative gunner (AIG-SVR) were applied, regarding 1970–2000 as the base time span, and 2021–2040 and 2041–2060 as the future time periods. Among observational data (2007–2018), 80 and 20% were considered for the network's training and testing, respectively. An increase in temperature, between +1.93 and + 3.7 °C, would occur in the future with the most increment in summer 2041–2060 under SSP585. Additionally, precipitation fluctuate from −0.43 to +1.17 mm/month with a probable rise in fall and most decline in winter 2041–2060 under SSP585. As regards the groundwater modeling, considering that NARX and ANNs were not able to simulate the aquifer altogether, the meta-heuristic approaches demonstrated high proficiencies. With R = 0.995, R2 = 0.989, mean absolute error (MAE) = 0.249 m and Nash-Sutcliffe efficiency (NSE) = 0.982 for the test data, WSVR illustrated the greatest efficacy, which disclosed that the groundwater level would face the utmost reduction from −5.4 to −10.9 m in observation wells, especially in spring 2041–2060. Indeed, this decline in groundwater level is exclusively induced by climate change, emphasizing on proposing groundwater adaptation strategies to address the groundwater issues, like land subsidence, in Iran. Moreover, it is proposed that different modeling approaches be evaluated, such that the outputs of the best model be considered.

Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions

The steady overexploitation of the Ogallala Aquifer underlying the U.S. High Plains Region has put irrigated crop production at risk, particularly in the Southern and Central High Plains. To manage this issue properly, a data-driven modeling framework is developed and tested that is fast to employ and yet provides reliable long-term groundwater level (GWL) forecasts as a function of climatological and anthropogenic factors. The modeling framework uses the random forests (RF) technique in combination with ordinary kriging, and is tested in Finney County in southwest Kansas. The introduction of groundwater withdrawal potential as a new surrogate for pumping intensity enables the RF model to capture decline in groundwater depletion rate as the system progresses towards aquifer depletion and/or as a result of well retirement policies. The RF model is executed from 2017 to 2099 for 20 different downscaled global climate models (GCMs) for the two representative concentration pathways (RCP) scenarios of 4.5 and 8.5. The results show the aquifer will cease to support irrigated agriculture in most of the county by 2060 under status quo management and average climate conditions. Moreover, climate will likely shift the aquifer’s depletion time frame by 15 years or less in most of the study area. The long-term combined impact of well retirement plans and climate conditions on groundwater depletion trends imply well retirement policies do not lead to sustained groundwater savings. This study demonstrates the capacity of machine learning models to serve as a rapid assessment tool, informing policymakers about future groundwater availability in intensively irrigated regions and under different climate and management conditions.

Improving the resolution of GRACE-based water storage estimates based on machine learning downscaling schemes

The applications of the Gravity Recovery and Climate Experiment (GRACE) on local scales are obstructed owing to the coarser spatial resolution of GRACE observations. Much attempts recently have been taken to improve the resolution of GRACE-based water storage estimates based on machine learning algorithms, focusing on new algorithm development. However, there are still two deficiencies in previous GRACE downscaling research, namely the selection of input variables and the scale of model construction. In this study, the partial least squares regression (PLSR) model firstly is employed to identify the representative predictors associated with GRACE observations. Then, the performance of two different downscaling schemes (namely pixel-scale and regional-scale models) are comprehensively investigated, based on a machine learning algorithm known as random forest, to enhance the resolution of GRACE-based water storage estimates to the grid resolution as small as 5 km. The downscaled results are validated against hydrological model simulations and a number of in-situ groundwater level measurements within one of most rapidly urbanized basin in China, Haihe River Basin. The PLSR model recognizes four variables (namely evapotranspiration, temperature, land surface temperature, and soil moisture) as the predominant factors, acting as the predictors of downscaling models. Starting with the GRACE observations, two kinds of pixel and regional downscaling schemes are developed. The downscaled results were consistent each other and with the original GRACE data at a broad scale with the correlation up to 0.98. It was found that there was 3.20 times deviation of the results from the model simulation in computation of groundwater depletion rates within plain areas. In-situ water level measurements highlight that the downscaling models are improved by 36.95 % and 23.25 % in correlation relative to the original GRACE data and the simulated groundwater storage anomalies, respectively. Generally, the pixel-scale model is slightly better than the regional-scale model with 69 % (171 out of 249 observation wells) of higher correlation and 53 % (131 out of 249 observation wells) of smaller root mean squared error. The high-resolution results presented in this study can lead to better understanding on regional water resources and provide quantitative information to water management considering irrigation water use and groundwater consumption.

Combining downscaled-GRACE data with SWAT to improve the estimation of groundwater storage and depletion variations in the Irrigated Indus Basin (IIB)

The growth of agricultural production systems is a major driver of groundwater depletion worldwide. Balancing groundwater supply and food production requires localized understanding of groundwater storage and depletion variations in response to diverse cropping systems and surface water availability for irrigation. While advances through Gravity Recovery and Climate Experiment (GRACE) have facilitated estimating the groundwater storage (GWS) changes in recent years, the coarse resolution of GRACE data hinders the characterization of GWS variation hotspots. Herein, we present a novel spatial water balance approach to improve the distributed estimation of groundwater storage and depletion changes at a spatial scale that can detect the hotspots of GWS variation. We used a mixed geographically weighted regression (MGWR) model to downscale GRACE Level-3 data from coarse resolution (1° × 1°) to fine scale (1 km × 1 km) based on high resolution environmental variables. We then combined the downscaled GRACE-based GWS variations with results from a calibrated Soil and Water Assessment Tool (SWAT) model. We demonstrate an application of the approach in the Irrigated Indus Basin (IIB). Between 2002 and 2019, total loss of groundwater reserves varied in the IIB's 55 canal command areas with the highest loss observed in Dehli Doab by >50 km3 followed by 7.8–49 km3 in the upstream, and 0.77–7.77 km3 in the downstream canal command areas. GWS declined by −325.55 mm/year at Dehli Doab, followed by −186.86 mm/year at BIST Doab, −119.20 mm/year at BARI Doab, and −100.82 mm/year at JECH Doab. The rate of groundwater depletion is increasing in the canal command areas of Delhi Doab and BIST Doab by 0.21–0.35 m/year. Larger groundwater depletion in some canal command areas (e.g., RACHNA, BIST Doab, and Delhi Doab) is associated with the rice-wheat cropping system, low rainfall, and low flows from tributaries.

Application of Time-Variable Gravity to Groundwater Storage Fluctuations in Saudi Arabia

In the Middle East, water shortage is becoming more and more serious due to the development of agriculture and industry and the increase in population. Saudi Arabia is one of the most water-consuming countries in the Middle East, and urgent measures are needed. Therefore, we integrated data from Gravity Recovery and Climate Experiment (GRACE), and other relevant data to estimate changes in groundwater storage in Saudi Arabia. The findings are as follows: 1) Average annual precipitation (AAP) was calculated to be 76.4, 90, and 72 mm for the entire period, Period I (April 2002 to March 2006) and Period II (April 2006 to July 2016), respectively. 2) The average TWS variation was estimated to be −7.94 ± 0.22, −1.39 ± 1.35, and −8.38 ± 0.34 mm/yr for the entire period, Period I and Period II, respectively. 3) The average groundwater storage was estimated to be +1.56 ± 1.35 mm/yr during Period I. 4) The higher average groundwater depletion rate was calculated to be −6.05 ± 0.34 mm/yr during Period II. 5) Both soil texture and surface streams in the study area promote lateral flow and carry surface water to the Arabian Gulf and the Red Sea. 6) During Period II, average annual recharge rates were estimated to be +9.48 ± 2.37 and +4.20 ± 0.15 km3 for Saudi Arabia and the Saq aquifer, respectively. 7) This integrated approach is an informative and cost-effective technique to assess the variability of groundwater resources in large areas more efficiently.

Large-scale sensitivities of groundwater and surface water to groundwater withdrawal

Abstract. 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.

Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage

Accurate and timely estimates of groundwater storage changes are critical to the sustainable management of aquifers worldwide, but are hindered by the lack of in-situ groundwater measurements in most regions. Hydrologic remote sensing measurements provide a potential pathway to quantify groundwater storage changes by closing the water balance, but the degree to which remote sensing data can accurately estimate groundwater storage changes is unclear. In this study, we quantified groundwater storage changes in California's Central Valley at two spatial scales for the period 2002 through 2020 using remote sensing data and an ensemble water balance method. To evaluate performance, we compared estimates of groundwater storage changes to three independent estimates: GRACE satellite data, groundwater wells and a groundwater flow model. Results suggest evapotranspiration has the highest uncertainty among water balance components, while precipitation has the lowest. We found that remote sensing-based groundwater storage estimates correlated well with independent estimates; annual trends during droughts fall within 15% of trends calculated using wells and groundwater models within the Central Valley. Remote sensing-based estimates also reliably estimated the long-term trend, seasonality, and rate of groundwater depletion during major drought events. Additionally, our study suggests that the proposed method estimate changes in groundwater at sub-annual latencies, which is not currently possible using other methods. The findings have implications for improving the understanding of aquifer dynamics and can inform regional water managers about the status of groundwater systems during droughts.

Anthropogenic depletion of Iran’s aquifers

Global groundwater assessments rank Iran among countries with the highest groundwater depletion rate using coarse spatial scales that hinder detection of regional imbalances between renewable groundwater supply and human withdrawals. Herein, we use in situ data from 12,230 piezometers, 14,856 observation wells, and groundwater extraction points to provide ground-based evidence about Iran's widespread groundwater depletion and salinity problems. While the number of groundwater extraction points increased by 84.9% from 546,000 in 2002 to over a million in 2015, the annual groundwater withdrawal decreased by 18% (from 74.6 to 61.3 km3/y) primarily due to physical limits to fresh groundwater resources (i.e., depletion and/or salinization). On average, withdrawing 5.4 km3/y of nonrenewable water caused groundwater tables to decline 10 to 100 cm/y in different regions, averaging 49 cm/y across the country. This caused elevated annual average electrical conductivity (EC) of groundwater in vast arid/semiarid areas of central and eastern Iran (16 out of 30 subbasins), indicating "very high salinity hazard" for irrigation water. The annual average EC values were generally lower in the wetter northern and western regions, where groundwater EC improvements were detected in rare cases. Our results based on high-resolution groundwater measurements reveal alarming water security threats associated with declining fresh groundwater quantity and quality due to many years of unsustainable use. Our analysis offers insights into the environmental implications and limitations of water-intensive development plans that other water-scarce countries might adopt.