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Journal of Hydrology

Journal of Hydrology: explore 10 source-linked works published from 2024 to 2026, with original documents and citations.

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Sources: usgs. Collection updated 2026-09-15. Counts describe this index, not the complete source archives.

USGS2026

Groundwater drought in the United States: Spatial and temporal variability

Many communities and ecosystems in the United States that are dependent on groundwater are potentially adversely affected by groundwater drought. We computed yearly groundwater-drought metrics and mean groundwater levels at well locations across the conterminous United States (CONUS), using data from wells and remotely sensed and modeled Gravity Recovery and Climate Experiment Drought Monitor Data Assimilation (GRACE-DADM). We also modeled the probability of low or high human impact at each well location. The spatial distribution of groundwater-drought duration and severity from 2001 to 2020 for 1,510 wells shows longer maximum duration and higher maximum severity events in drier regions like the Southwest than in wetter regions like the Northeast. Based on 613 wells in CONUS from 1981 to 2020, there are many significant decreases in drought duration and severity in the Northeast and many significant increases in annual-mean groundwater levels. In contrast, there are many significant increases in drought metrics and decreases in mean water levels in parts of the Southeast. There are major differences in trends from 2001 to 2020 between well-based and GRACE-DADM-based groundwater metrics in some CONUS regions and a very low correlation between trends at individual locations across CONUS. A potential reason for this disparity is the low GRACE-DADM resolution (∼12 km) and the potential for a large amount of groundwater variation at the local scale. Also, GRACE-DADM represents shallow, unconfined aquifers which may not match the screened interval of the monitoring wells we evaluated. Large spatial gaps in long-term, high frequency, and quality-assured groundwater-well monitoring data present a challenge for understanding groundwater-drought variability across CONUS. Remote sensing tools such as GRACE can help but cannot fully replace well monitoring, as highlighted by our study results. Substantially more long-term monitoring wells would more accurately represent groundwater-drought trends and spatial variability across CONUS, particularly in western regions.

conterminous United States↗
USGS2026

Groundwater spatial variability within an atoll island: Assessing shallow aquifer heterogeneity with geophysical and physicochemical measurements

This study examines the spatial variability of shallow groundwater on Dhigelaabadhoo Island using electromagnetic induction surveys, groundwater monitoring, and sediment analyses. The research reveals how variations in island morphology—such as differences in elevation, reef flat width, and sediment composition—affect the spatial distribution of groundwater lenses and the overall aquifer dynamics. Saltwater intrusion is especially pronounced in low elevated areas, with narrow reef flat plate and areas where higher hydraulic conductivity—driven by the presence of coarser sediments—is observed, whereas regions characterized by finer sediments, higher elevation, and wider reef flat plates tend to support more symmetric and less saline groundwater lenses. The geophysical investigations reveal that tidal oscillations alter groundwater movement by markedly changing water levels and conductivity, thereby underscoring the critical need to account for temporal dynamics in atoll coastal aquifer systems and the importance of integrating tidal dynamics into the aquifer zone. The findings highlight the significant role of intrinsic morphological and external hydrodynamic factors in shaping groundwater distribution on atoll islands, offering critical insights for sustainable freshwater resource management.

Dhigelaabadhoo Island↗
USGS2026

Computational electromagnetic geophysics for groundwater system studies: A review on established practices and recent advances

Identifying effective solutions for locating groundwater resources and ensuring the quality of drinking water is increasingly urgent, given the challenges posed by climate change and population growth. This review investigates electromagnetic geophysical imaging techniques, in both time- and frequency-domain, that can provide valuable insights for groundwater assessment. We explore computational electromagnetic methods used to evaluate electromagnetic data and several recent hydrogeophysical case studies. As open-source frameworks for modeling electromagnetic geophysical problems become available, a broader range of researchers can interpret their data with computationally advanced software. We provide an overview of documented open-source codes for evaluating electromagnetic data and analyze various hydrological targets in relation to their electromagnetic surveying technique and the computational method applied. Furthermore, we evaluate the potential of advanced computational techniques, including three-dimensional modeling, non-deterministic inversion and machine learning, to couple geophysical with numerical groundwater modeling and apply it in groundwater system studies. Despite obstacles such as complexity and resource demands, our findings indicate that the quantification and integration of predictive uncertainties from both electromagnetic and hydrological data and simulations would significantly improve the reliability of hydrogeophysical models. This can lead to a deeper understanding of groundwater systems and improved management practices.

Journal of Hydrology↗
USGS2025

Hydrologic variability and groundwater age of springs in eastern Oregon and northern Nevada, USA

The ecological importance of springs in semiarid regions is far greater than their small size and sparse distribution, yet little is known about the hydrologic functioning of these systems. During 2016–22, 261 springs were visited in the volcanic terrane of eastern Oregon and northern Nevada. When conditions were suitable, measurements of discharge, water temperature, and specific conductance were made, and samples for the analysis of carbon-14, tritium, and water stable isotopes (WSI) were collected. A subset of 60 springs was revisited during different seasons in the same year and during the dry season in multiple years to evaluate variability in discharge, chemistry, and groundwater age. Specific conductance and WSI varied considerably among springs across the study area but were unexpectedly stable across seasons and years at individual springs. Seasonal and interannual variability in spring discharge was related to the residence time of the discharging groundwater. Springs discharging older groundwater (10 3 –10 4 years) had significantly less variability in their discharge compared to springs discharging younger groundwater (10 0 –10 1 years). Variability among springs discharging younger groundwater included cessation of late-summer discharge at 18 % of the repeat-visit springs. A logistic regression model predicted the age of discharging spring water with 89 % accuracy using only the spring latitude, longitude, elevation, and δ 2 H value. This study framework provides a simple, inexpensive, and robust method to provisionally assess the hydrologic behavior of springs having little or no prior information in understudied, semiarid regions across the globe.

Nevada, Oregon↗
USGS2025

A joint Gaussian process model of geochemistry, geophysics, and temperature for groundwater TDS in the San Ardo Oil Field, California, USA

Decline in availability of fresh groundwater has expanded interest in brackish groundwater resources; however, the distribution of brackish groundwater is poorly understood. Water resources in sedimentary basins across the United States often overlie oil and gas development. Mapping of groundwater total dissolved solids (TDS) using data from oil well geophysical logs has become an important technique for identifying fresh and brackish groundwater. Existing geophysical log analysis methods use porosity and temperature to relate formation resistivity to TDS. Typically, natural geothermal gradients are used to estimate temperature at the location of collected resistivity. However, in thermally enhanced oil fields, steam is injected into the subsurface to mobilize high viscosity oil, creating variable temperature distributions. Furthermore, TDS derived from resistivity also depends on the fractions of dominant ions. Typically, chloride and bicarbonate fractions must be determined. It is also necessary to model TDS across many geologic units with heterogenous porosity distributions. Collectively, each quantity used to estimate TDS (resistivity, porosity, temperature, bicarbonate fraction) varies in space and time, and available data points are rarely collocated. Here, we present a new method of mapping groundwater TDS that continuously models each quantity together with a joint Gaussian process. This method enables mapping fresh and brackish water with practically available data. We apply this method to the San Ardo Oil Field in Monterey County, California, where steam injection occurs. In some areas of the aquifer system overlying the oil zone, the temperature is ∼75 °C, roughly twice the natural background value. Groundwater TDS is typically <1,500 mg/L in the aquifer and increases with depth to ∼9,000 mg/L in the oil-producing zone. A low-permeability clay layer delineates the fresh and brackish water, likely by inhibiting surface recharge from penetrating the deeper zones, allowing higher-TDS connate water to remain in place. Weaker lateral TDS trends may be controlled by recharge patterns associated with the Salinas River. Our model reveals with high certainty that groundwater has freshened in one localized part of the oil-producing zone and suggests with less certainty that more widespread freshening has also occurred. The lowering of TDS was possibly from decades of low-TDS steam injection and the associated fluid production and disposal operations.

California↗
USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗
USGS2025

Ecohydrological response of a forested headwater catchment to a flash drought in the Southeastern U.S.

Flash droughts differ from traditionally defined droughts in their rapidity of intensification and often associated high vapor-pressure deficit. These droughts can lead to declines in streamflow and water table depth and induce water stress to vegetation at a greater rate than droughts that manifest over longer periods. However, little is known regarding the response of forested environments to flash drought because most studies of impacts have been conducted in agricultural settings. In this study we investigated water-use patterns of riparian trees using sap flow methods and examined the role of groundwater as a source of moisture over three periods that were delimited by antecedent soil moisture conditions. For a longer-term perspective we also examine monthly streamflow over the 35-year record. We observed that trees at only one monitoring plot showed a decrease in water use relative to evaporative demand during a flash drought. Total reverse sap flow (flow toward the roots rather than the canopy) greatly increased during the flash drought period, suggesting the likely occurrence of hydraulic redistribution to the excessively dry soils. Over the drought period groundwater became a more dominant source of moisture for sustaining forest water use. Monthly mean streamflow during the flash drought approached levels observed in past multiyear droughts. This is the first study, to our knowledge, to specifically investigate the response of multiple water budget components to flash drought in a humid forest. As more studies are conducted, a better understanding of the range of expected responses are likely to emerge.

Georgia↗
USGS2025

A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed

Seasonal shifts from runoff to groundwater dominance influence daily headwater stream temperatures, especially where local groundwater input is strong. This input buffers temperature during hot periods, supporting cold-water habitats. Recent studies use air–water temperature signal metrics to identify zones of strong stream–groundwater connectivity. While Previous studies used air–water signal ratios as proxies for groundwater influence but were limited to specific sites and periods, without dynamic forecasting. This study is the first to forecast daily A r as a spatiotemporal signal using a Graph Convolutional Network–Long Short-Term Memory (GCN-LSTM) model. The model was trained using hydroclimate data (air temperature, precipitation, shortwave radiation, streamflow) and watershed physical features (e.g., sand content, slope). Results showed high predictive skill, achieving R 2 (NSE, RMSE) of 0.86 (0.73, 0.0004) for one-day-ahead to 0.52 (0.50, 0.0009) for seven-days ahead forecasts. Prior studies often have not explicitly incorporated spatial hydrogeologic drivers, but this model explicitly incorporates them to assess their impact on A r forecasting and stream-groundwater connectivity. Feature analysis identified mean sand, elevation, slope, clay, and TWI as key predictors of A r . Stronger groundwater signals appeared in hillslopes, elevations, and tributaries, highlighting watershed influence on streamflow. However, limitations include reliance on historical air–water temperature patterns for training and limited representation of extreme climate conditions. Despite these limitations, unlike previous studies relying on measured in-situ stream and air temperature, this study forecasts A r directly from climate and physiographic features after training, avoiding in-situ data requirements. Findings aiding predictions of stream ecosystem resilience.

New York↗
USGS2025

Multi-lithofacies alluvial characterization via airborne electromagnetic-borehole fusion using ordinary interval kriging and geologic constraints

Airborne electromagnetic (AEM) survey provides extensive spatial coverage and detailed resolution at the near surface and can be used to develop hydrogeological models. However, utilization of AEM data is not straightforward because AEM resistivity is an indirect measurement for inferring sediment types. This study develops an ordinary interval kriging (OIK) algorithm and a resistivity-to-multi-lithofacies (R2ML) data fusion workflow for multi-lithofacies alluvial characterization. OIK utilizes irregular interval data to construct three-dimensional (3D) resistivity fields from one-dimensional inverted AEM resistivity models. The R2ML workflow maps the resistivity field generated from OIK into a multi-facies lithological model, incorporating geologic constraints derived from well logs and geological observations. The numerical and real-world cases demonstrate that OIK is computationally efficient, accounts for 3D anisotropy, and minimizes the smoothing effect, thereby preserving resistivity contrasts and reducing interpolation uncertainty. The methodology is applied to lithologic characterization of the Mississippi River Valley alluvial aquifer (MRVA) in the Shellmound area, Mississippi, U.S. A frequency-domain AEM survey was conducted to support groundwater studies for the managed aquifer recharge (MAR) to the MRVA. The resulting lithological model, including four types of lithofacies—clay, very fine sand, fine-medium sands, and graveliferous sands, illustrates the geomorphological processes of the MRVA and implies potential MAR. The alignment between the lithological model and existing geological and hydrogeological investigations demonstrates that OIK and R2ML workflow effectively capture the subsurface architecture of the MRVA. The methods have broad applicability for characterizing alluvial aquifers through AEM-borehole data fusion, supporting sustainable groundwater management.

Mississippi↗
USGS2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Colorado, Utah↗
Compare source metadata on this page
WorkPublishedSource identifierSource
Groundwater drought in the United States: Spatial and temporal variability2026-03-1170274216usgs
Groundwater spatial variability within an atoll island: Assessing shallow aquifer heterogeneity with geophysical and physicochemical measurements202670272657usgs
Computational electromagnetic geophysics for groundwater system studies: A review on established practices and recent advances202670276659usgs
Hydrologic variability and groundwater age of springs in eastern Oregon and northern Nevada, USA2025-07-2070269620usgs
A joint Gaussian process model of geochemistry, geophysics, and temperature for groundwater TDS in the San Ardo Oil Field, California, USA2025-05-1870267512usgs
Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA202570262495usgs
Ecohydrological response of a forested headwater catchment to a flash drought in the Southeastern U.S.202570264082usgs
A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed202570271905usgs
Multi-lithofacies alluvial characterization via airborne electromagnetic-borehole fusion using ordinary interval kriging and geologic constraints202570278844usgs
High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements202470257805usgs

These are bibliographic comparisons, not experimental rankings. Follow the original document for methods and conditions.