Sustainable groundwater management requires accurate tools to assess changes in aquifer storage as climate extremes intensify and water demand grows. Currently, inadequate in-situ data and uncertainty in storativity estimates limit such assessments. We address these challenges by integrating in-situ observations with Interferometric Synthetic Aperture Radar (InSAR) to estimate aquifer properties and groundwater storage change in Colorado's San Luis Valley. We estimate storativity for management subdistricts based on the relationship between pumping and water levels, comparing a constant net inflow assumption against a refined time-varying net inflow regression that incorporates climate drivers. Both approaches yield consistent results across diverse hydrogeological settings, producing storativity estimates ranging from 0.03 in primarily confined aquifers to 0.21 in unconfined aquifers. Our results reveal a declining trend in groundwater storage, with a total storage loss of 6.3 & times; 108 m3 from coarse-grained layers for our studied subdistricts from 2010 to 2023, driven primarily by drought conditions. We further quantified the partitioning of storage loss, finding that in regions with significant pumping from confined aquifers, inelastic compaction accounts for a much higher portion of the total water withdrawn (39%) compared to regions where pumping is mainly from the unconfined aquifer (9%). In that case, gravity drainage is the dominant mechanism. Conversely, confined aquifers with no long-term depletion show elastic deformation patterns, with no storage loss in fine-grained units. This research offers a transferable framework for assessing groundwater storage loss and prospects for sustainability in data-limited regions and supports adaptive management in water-stressed basins worldwide.

A newly developed downscaling algorithm produces a 400-m resolution soil moisture (SM) product from the native 36-km Soil Moisture Active Passive (SMAP) observations. The objective of this research is to demonstrate that this downscaled SM product is beneficial for agricultural applications and produces better results than the existing 9-km SMAP product in the San Luis Valley in Colorado. We demonstrate that the 400-m product exhibits greater sensitivity to differences in irrigation type, crop type, evapotranspiration (ET) and planting/harvesting dates. The high-spatial resolution SM correlates well with Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and in situ SM observations. MODIS ET correlates better with the 400-m product than the 9-km product (0.422 versus 0.346) and better discerns differences between crop types. We also find that SM and precipitation have a stronger correlation in crops requiring less irrigation and that sprinkler-irrigated fields have lower ET and SM than flood-irrigated fields. Our rotated empirical orthogonal function (REOF) analysis shows that unlike the 9-km product, the 400-m soil moisture product can detect field-scale trends driven by agricultural irrigation. Both products detect basin-wide changes, including SM responses to snowmelt and increasing SM at higher elevations in recent years. Rotated principal component analysis of the 400-m SM product and MODIS ET revealed seasonal irrigation patterns, lower values during dry years, and increased runoff following winters with high snow water equivalent.

Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here we integrate multi-source Earth observation and environmental datasets and use machine learning to develop a medium-resolution (30 m) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. We subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management.

Surface displacement caused by natural and anthropogenic activities poses a significant risk to subsurface pipelines, particularly in areas experiencing subsidence. Stress and strain induced by deformation can lead to pipeline buckling and potential vulnerability. This study applies a multidisciplinary approach integrating interferometric synthetic aperture radar (InSAR) deformation analysis, well data, and geological context to assess the risk to oil and gas pipelines in Iran's Qazvin plain. The focus is on evaluating the impact of an unconfined aquifer, which has a lower risk of subsidence compared to a confined aquifer, on pipeline infrastructure. We analyzed multitemporal Sentinel-1 data collected between 2014 and 2021. Our findings reveal that 2,400 km2 of the study area is experiencing subsidence with vertical rates reaching up to 14 cm/year with an average S k {S}_{{\rm{k}}} , or skeletal storage value of 0.04. We explore the reasons for this unexpectedly high subsidence, finding that compressible unconfined aquifers with historically low water levels are also at risk for significant inelastic, or permanent, subsidence. We also evaluate pipeline profiles, which show spatial subsidence, much higher than typically seen in unconfined aquifers, with variations of up to 1 m, indicating long-term risks to the infrastructure. This study demonstrates the potential of InSAR techniques in assessing the risk to critical infrastructure, such as oil and gas pipelines, in regions with land deformation due to groundwater withdrawal. Our approach underscores the importance of continuous monitoring and offers valuable insights for addressing the challenges posed by subsidence on pipeline infrastructure.

Water scarcity is projected to affect half of the world's population, gradually exacerbated by climate change. This article elaborates from a panel discussion at the 2023 United Nations Water Conference on "Addressing Water Scarcity to Achieve Climate Resilience and Human Health." Understanding and addressing water scarcity goes beyond hydrological water balances to also include societal and economic measures. We consider five categories of health impacts resulting from deteriorating water qualities and quantities: (1) water-related diseases and water for hygiene, (2) malnutrition and water for food, (3) livelihoods, income, development, and water for energy, (4) adverse air quality from drought-induced dust and wildfire smoke, and (5) mental health effects from water scarcity-related factors. A discussion on the barriers and opportunities for resilient water systems begins by reframing water scarcity as a "pathway to water bankruptcy" and introducing Water Partnerships to empower local water leaders with the awareness, education, and resources to devise and implement locally appropriate water management strategies. Other barriers include the (1) lack of tools to consider the socioeconomic implications of water scarcity, (2) lack of water information being in actionable formats for decision-makers, (3) lack of clarity in the application of water scarcity modeling to gain policy-relevant findings, and (4) inadequate drought adaptation planning. The article includes recommendations for local governments, national governments, international actors, researchers, nongovernmental organizations, and local constituents in addressing these barriers. The predominant theme in these recommendations is collaborative, multidisciplinary Water Partnerships, knowledge-sharing in accessible formats, and empowering participation by all. This article's central thesis is that addressing water scarcity must focus on people and their ability to lead healthy and productive lives. Key points This article's central thesis is that addressing water scarcity must focus on people and their ability to lead healthy and productive lives.The predominant theme is to empower water users for decision-making roles in water management, made possible through Water Partnerships where trusted partners and stakeholders help inform water users' decision-making.A discussion on the barriers and opportunities for resilient water systems begins by reframing water scarcity as a "pathway to water bankruptcy," which allows for alternative decision-making approaches for water management.

The future of major aquifer systems supporting irrigated agriculture is threatened due to unsustainable groundwater pumping. Metering of pumping is key for implementing robust groundwater management, but metering is limited in most aquifers. Although machine learning methods have been used to estimate pumping over certain regions, these studies have not fully demonstrated the data quantity and input parameter requirements to accurately estimate regional groundwater pumping. This study determined the data quantity required and identified relevant features to develop Random Forests-based annual groundwater pumping estimates (2008-2020) over the Kansas High Plains aquifer. We predicted pumping at two spatial scales, i.e., point (well) and grid (2 km). We evaluated a combination of different training splits against a constant test set to understand the performance of the models. Summing predicted pumping over a 2 km grid was made possible with knowledge of crop irrigation area. This knowledge also decreased the uncertainty observed in linking individual wells with irrigated areas and further improved the spatial and temporal pumping estimates. At the 2 km scale, we observed that a model trained on 10 % of the total available data had coefficient of determination (R2) values of 0.98 and 0.75 for training and testing, respectively. These results show reasonable estimates of irrigation pumping are possible at the 2 km scale when 10 % of irrigation wells are metered and if the irrigated area is known. This finding has significant implications for groundwater management in many heavily stressed aquifers.

Estimates of groundwater abstraction volumes for irrigation are essential for water management and water supply forecasting. An integrated modeling approach is presented to quantify spatiotemporal field-level abstraction volumes. The approach is both demand-driven, using crop growth algorithms and soil moisture to trigger irrigation events, and supply-constrained, using simulated groundwater storage to constrain extraction. The approach uses the SWAT + hydrologic model, with the use of the gwflow subroutine for simulating spatially distributed groundwater storage and flow. The approach is demonstrated for the Mississippi Delta (northwest Mississippi, USA), a region of high groundwater irrigation and groundwater depletion, for the period 2000–2020. The model is corroborated using system responses that constrain both soil moisture and groundwater storage: streamflow, crop yield, crop evapotranspiration, groundwater level changes, and annual abstraction volumes. Results show good agreement for all system responses, with groundwater level changes matching groundwater depletion magnitudes in the central region of the Big Sunflower Watershed. Simulated annual abstraction volumes generally match measured volumes on an average and frequency basis, although an underestimation of 20

As groundwater depletion threatens future water availability in many regions of the world, improved monitoring is crucial. Interferometric synthetic aperture radar (InSAR) provides valuable deformation data that can be related to changes in groundwater storage, but interpreting these deformation data is challenging due to the delayed response of deformation to changes in the aquifer. Many studies have considered the delayed deformation induced by long-term depletion of groundwater, and found that permanent deformation can continue for decades after depletion has stopped. However, there have been relatively few studies that consider the delay in deformation in response to elastic, seasonal changes in aquifer groundwater levels, and an absence of studies that performed a formal modeling analysis to quantify the relative significance of various drivers of this delay. Recent studies leveraging seasonal InSAR-derived deformation data to estimate aquifer recharge pathways have highlighted the need to understand how quickly deformation responds to groundwater storage changes at the seasonal time scale. In this study, this analysis is conducted, and the findings demonstrate that under elastic conditions, seasonal delays can range from 0 to 60 days depending on the vertical hydraulic conductivity, fine-grained layer thickness, and fraction of the aquifer that is composed of coarse-grained material. Seasonal delays from field data are also explored in two regions of the western United States: the San Luis Valley, Colorado, and Central Valley, California. The seasonal delays in each of these areas were found to fall within the bounds determined in the modeling approach.

Groundwater quality is critical for safe drinking water and irrigation supplies but can be threatened by geogenic toxins that are difficult to predict. In the arid, high desert San Luis Valley (SLV), Colorado, a groundwater basin serves as the primary water supply with observed arsenic concentrations exceeding the maximum contaminant level (MCL) of 10 μg/L set by the U.S. EPA. However, the sources and processes responsible for As occurrence are unclear. Through a community-engaged sampling effort, we collected 244 groundwater samples and measured major/trace element concentrations. Long-term land subsidence and depth-resolved sediment texture were computed at the same locations. We tested three plausible geochemical processes responsible for As release: (1) overpumping-induced dewatering of As-bearing clays (proxied by land subsidence), (2) pH-promoted desorption as well as reductive dissolution of As(V)/Fe(III) (hydr)oxides, and (3) incursion of higher-As geothermal fluids (proxied by lithium, boron, tungsten, and molybdenum) into groundwater. We find that statistics, statistical/machine learning, and aqueous thermodynamics all agree that geothermal fluid mixing within the aquifer is the main source of dissolved As. Our findings suggest that overpumping draws higher-As thermal fluid from the bottom of the aquifer to pumping depth, leading to increased concentrations of As in drinking/irrigation water supplies at wells.

In the Western U.S., the combination of increased and projected droughts, rising irrigation water demands, and population growth is expected to intensify groundwater consumption leading to adverse consequences like land subsidence and aquifer depletion. Despite the urgent need to address these challenges, there is limited local-scale monitoring of groundwater withdrawals in most of the groundwater basins in this region. Understanding the volume of groundwater being withdrawn is indispensable for implementing sustainable solutions to tackle water security issues. Hence, developing reliable and efficient groundwater withdrawal monitoring solutions is critical to address the pressing water management concerns in this region. The existing methods for estimating groundwater withdrawals are either costly and time-consuming, or they cannot generate dependable predictions at the scales required for effective local management. While our earlier works on integrating remote sensing and machine learning techniques to estimate gridded (1-5 km) groundwater use in Kansas, Arizona, and the Mississippi Alluvial Plain have been successful, field-scale estimation is still a challenge. Here, we use statistical and machine learning-based approaches to relate field-scale groundwater withdrawals with remote sensing-derived datasets, e.g., Landsat evapotranspiration (ET), downscaled SMAP surface soil moisture, and other hydrometeorological datasets for several Western U.S. states. We apply and test our approach by estimating and comparing groundwater pumping measurements at field- and regional-scales for multiple groundwater basins. Preliminary results using linear regression and machine learning-based approaches in Nevada and Arizona show promise (R of 0.5 to 0.7), additional in-situ pumping data actively being compiled will likely improve the models. While there are clear opportunities for model improvements, modeled withdrawal estimates are likely more accurate than common water right duties and potential crop ET-based estimates. We aim to enable water resource and user communities better understand water use, water budgets and support field-scale management practices for metered and unmetered groundwater basins.

Parowan Valley, Utah is an agricultural area experiencing significant subsidence in recent decades due to extensive groundwater extraction. The subsidence occurs primarily due to consolidation in fine-grained units as groundwater heads decrease due to pumping. Efforts to predict future subsidence would be facilitated by an accurate understanding of the distribution of fine-grained materials in the subsurface. An analysis of drillers' logs across Parowan Valley from previous research indicates that significant fine-grained units are present, but the variable quality of these logs, as well as the limited spatial distribution of borings, makes the accurate determination of the location and extent of fine-grained units challenging. To overcome the limitations of drillers' logs analysis, ground-based and borehole geophysical data were acquired. The ground-based data were collected over large sections of the valley using a towed Time-Domain Electromagnetic (tTEM) system that measures electrical resistivity over different depth intervals. These data were used to characterize the distribution of fine- and coarsegrained sediments within the tTEM depth of investigation of similar to 60 m. Borehole gamma data were acquired in three boreholes near the tTEM traverses to compare with resistivity data and drillers' logs for further validation of tTEM resistivity data. In this study, we developed a methodology for rock physics transforms in regions with sparse geological information and variable saturation, as well as a scheme for using a variety of methods depending on the availability of lithology information, enabling us to produce robust rock physics transforms in an area with complex geological conditions.

The San Luis Valley (SLV), Colorado, is challenged with implementing sustainable groundwater management in the face of increasing surface water scarcity due to climate change. Groundwater extraction in unconsolidated aquifers such as the SLV can cause cm-scale subsidence and rebound. This study utilizes Interferometric Synthetic Aperture Radar (InSAR) data, validated by Global Navigation Satellite System (GNSS) measurements, to measure subsidence and analyze the groundwater dynamics that cause it. Addressing the challenges of phase decorrelation and data gaps, notably from September 2018 to April 2019, we adopted a modified DS-interpolation algorithm, alongside a Singular Spectrum Analysis (SSA)-based gap filling technique. Furthermore, we enhanced the temporal resolution of groundwater level data through the Theis curve interpolation. These methodologies enabled the integration of observational well data with satellite measurements to calibrate a one-dimensional deformation model, capturing both the elastic and inelastic responses of the aquifer system. Our investigation, spanning 2015 to 2021, reveals both seasonal and long-term subsidence, with the confined aquifer section experiencing up to 1 cm/year of subsidence alongside notable seasonal fluctuations. The methodology presented here provides a path to model subsidence in regions with sparse groundwater level and noisy InSAR data. It also provides valuable insights for developing effective water management strategies in the SLV.

Study region: The Mississippi Alluvial Plain (MAP) in the United States (US). Study focus: Understanding local-scale groundwater use, a critical component of the water budget, is necessary for implementing sustainable water management practices. The MAP is one of the most productive agricultural regions in the US and extracts more than 11 km3/year for irrigation activities. Consequently, groundwater-level declines in the MAP region pose a substantial challenge to water sustainability, and hence, we need reliable groundwater pumping monitoring solutions to manage this resource appropriately. New hydrological insights for the region: We incorporate remote sensing datasets and machine learning to improve an existing lookup table-based model of groundwater use previously developed by the U.S. Geological Survey (USGS). Here, we employ Distributed Random Forests, an ensemble machine learning algorithm to predict annual and monthly groundwater use (2014-2020) throughout this region at 1-km resolution, using pumping data from existing flowmeters in the Mississippi Delta. Our model compares favorably with the existing USGS model, with higher R2 (0.51 compared to 0.42 in the previous model), and lower root mean square error (RMSE) and mean absolute error (MAE)- 0.14 m and 0.09 m, respectively in our model, compared to 0.15 m and 0.1 m in the previous model. Therefore, this work advances our ability to predict groundwater use in regions with scarce or limited in-situ groundwater withdrawal data availability.

Groundwater overdraft gives rise to multiple adverse impacts including land subsidence and permanent groundwater storage loss. Existing methods are unable to characterize groundwater storage loss at the global scale with sufficient resolution to be relevant for local studies. Here we explore the interrelation between groundwater stress, aquifer depletion, and land subsidence using remote sensing and model-based datasets with a machine learning approach. The developed model predicts global land subsidence magnitude at high spatial resolution (~2 km), provides a first-order estimate of aquifer storage loss due to consolidation of ~17 km 3 /year globally, and quantifies key drivers of subsidence. Roughly 73% of the mapped subsidence occurs over cropland and urban areas, highlighting the need for sustainable groundwater management practices over these areas. The results of this study aid in assessing the spatial extents of subsidence in known subsiding areas, and in locating unknown groundwater stressed regions.

In the Parowan Valley of Utah, groundwater levels have declined by as much as 30 m over the past 50 years with accompanying subsidence rates of up to 5 cm/year. Traditional methods to estimate groundwater storage change use a combination of groundwater level and storativity estimates, but there is often considerable uncertainty in these. In this study, we demonstrate a new method that relies on a combination of geodetic data from InSAR, as well as groundwater level and pumping data, to estimate both the total groundwater storage loss and the percentages of storage loss in fine‐ and coarse‐grained layers within an aquifer system. We find that when aggregated over all of Parowan Valley, fine‐ and coarse‐grained layers account for roughly equal portions of the total groundwater storage loss. However, in confined aquifers, fine‐grained layers account for most of the storage loss. This has important implications on the source of groundwater in depleting aquifer systems, as many models do not account for fine‐grained layers as a source of water. We find that in the Parowan Valley, the aquifer depletion is roughly 12.5% of the volume of pumped groundwater, meaning that the remainder of pumped groundwater is sourced from net inflow. This study presents the first method that combines geodetic and in situ groundwater data to provide estimates of groundwater storage change that account for both coarse‐ and fine‐grained intervals, which are typically present in significant amounts in the major unconsolidated aquifer systems of the world.

Parowan Valley, Utah (USA), is an agricultural region experiencing rapid subsidence due to extensive groundwater extraction from aquifers with a significant portion of fine-grained sediments. To analyze the subsidence spatio-temporally, time-series Interferometric Synthetic Aperture Radar (InSAR) of 155 Sentinel-1 C-band scenes were processed. These data showed approximately 30 cm of ground subsidence in Parowan Valley from 2014 to 2020. Because of the high temporal sampling rate of the Sentinel-1 satellite (12-day cycle), it is possible to determine the seasonal changes of ground deformation and relate this to groundwater extraction. To better understand the relationship between ground deformation and groundwater extraction in the Parowan Valley, temporal changes in hydraulic head data from US Geological Survey observation wells were monitored. Additionally, well logs were analyzed and used to construct a map that showed the percentage of fine-grained material in the subsurface. The investigation of hydraulic head and geology, together with InSAR-derived ground displacement data, indicates that the most subsidence occurs where there is a co-occurrence of high groundwater demand and a high percentage of fine-grained sediments, but recharge likely plays a role in mitigating subsidence in some areas. The subsidence developed in Parowan Valley shows a long-term trend as well as seasonal variation and appears to be influenced by both agricultural activity and annual precipitation.

The San Joaquin Valley, California has experienced dramatic subsidence over the past 100 years, but the regions with the most subsidence have shifted dramatically over this time period, from west (Kettleman City/Los Banos) to south (Tulare/Pixley/Corcoran). To date, no study has done an in‐depth analysis of the mechanisms driving this shift in subsidence. We analyze head records, utilizing a novel approach that assimilates change in head data from multiple overlapping time periods, to produce an 80‐year record of change in head over both the historical and modern regions of greatest subsidence. We then calibrate a deformation model to fit both historical (measured with leveling surveys) and modern (measured with Interferometric Synthetic Aperture Radar, or InSAR) data sets. We find that the stress history of the Kettleman City/Los Banos region with historically high subsidence plays a large role in reducing modern subsidence in that region, while declining heads in both regions are likely to result in major subsidence over the next several decades. This study highlights the need for active groundwater management to mitigate ongoing and future subsidence. One key data set needed in this effort is accurate long‐term head histories to reconstruct the stress history of aquifers for accurate deformation modeling.

Purpose: To elucidate risk factors for meibomian gland disease (MGD) and understand associated changes in meibography and in relation to ocular surface disease.Patients and Methods: As part of the standard workup for ocular surface disease at a tertiary academic center, 203 patients received an ocular history and lifestyle questionnaire. The questionnaire included detailed inquiries about ocular health and lifestyle, including makeup use, cosmetic eyelid procedures, screen time, and contact lens habits. Subjects also took the standardized patient evaluation of eye dryness (SPEED) II questionnaire. Meibomian gland (MG) dropout and structural changes were evaluated on meibography and scored by three independent graders using meiboscores. Statistical analysis was conducted to identify significant risk factors associated with MG loss.Results: This retrospective, cross-sectional study included 189 patients (378 eyes) with high-quality images for grading, and the average age was 67 years (77% female). Patients older than 45 years had significantly more dropout than younger patients (p < 0.01). Self-reported eye makeup use did not significantly impact MG loss. Patients with a history of blepharoplasty trended toward higher meiboscores, but the difference was not statistically significant. Self-reported screen time did not affect meiboscores. Contact lens use over 20 years was associated with significant MG loss (p < 0.05). SPEED II scores had no relationship to meiboscores (p = 0.75).Conclusion: Older age is a significant risk factor for MG loss. Any contact lens use over 20 years also impacted MG dropout. Highlighting the incongruence of symptoms to signs, SPEED II scores showed no relationship to the structural integrity of MGs.