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Water Resources Research

Water Resources Research: explore 8 source-linked works published from 2023 to 2026, with original documents and citations.

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Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

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

USGS2026

A roadmap for identifying and interpreting physical processes and national water model prediction bias associated with baseflow index regimes across the contiguous United States

Understanding how groundwater–surface water interactions shape streamflow variability is critical for diagnosing low flow behavior and prediction bias in continental scale hydrologic models. We present a process informed framework that links observed baseflow (BF) dynamics, watershed attributes, and National Water Model (NWM) performance across the contiguous United States. Using daily observed streamflow from 797 reference quality streamgages, we developed monthly baseflow index (BFI) signatures using a streamgage specific, calibrated digital filter. Hierarchical clustering of these signatures identified seven distinct BFI regimes capturing regional and seasonal variability. We evaluated NWM v3.0 retrospective streamflow performance within each regime using multiple hydrograph and flow duration curve-based metrics. Model skill varied systematically across regimes: mixed flow systems were simulated most accurately, while predominantly BF dominated and quickflow dominated regimes exhibited substantially poorer performance. Across nearly all regimes, the NWM underestimated observed BFI magnitude and frequently failed to reproduce seasonal BF patterns, indicating systematic biases in simulated low flow contributions. To relate these regimes to potential process controls, we trained a Random Forest classifier using static watershed attributes and applied Shapley Additive Explanations to identify features most strongly associated with each regime. Results highlight regionally varying influences, including the dominant role of snow fraction and seasonal runoff timing in snow dominated basins and the importance of evapotranspiration and aridity in quickflow dominated systems. Collectively, these findings demonstrate how hydrologic signatures combined with interpretable machine learning can diagnose regime specific model biases and generate process-based hypotheses about limitations in large scale hydrologic prediction systems.

contiguous United States↗

USGS2026

Modeling legacy nitrogen transport under instantaneous, steady-state, and transient groundwater flow conditions

In hydrologic settings where groundwater discharge contributes substantially to surface waters, legacy nitrogen in groundwater can confound surface water nitrogen loads estimated exclusively from current terrestrial sources. Additionally, legacy nitrogen in groundwater can contribute to lagged responses to nitrogen management efforts. Some methods of estimating groundwater contributions to surface water nitrogen loads account for legacy nitrogen, while others do not. The resulting differences are rarely quantified. We used a numerical modeling framework to compare three methods of estimating time-varying annual groundwater nitrogen loads to surface water receptors on eastern Long Island, New York. The instantaneous load method used steady-state contributing areas and includes no temporal groundwater lag. The second method used numerical simulations of nitrogen loads under steady-state flow, which captures groundwater transport lags but omits the annual variability in transient hydrologic stresses. The third method numerically simulated both transient groundwater flow and nitrogen transport to explicitly capture the effects of legacy nitrogen in groundwater. Depending on antecedent nitrogen and hydrologic conditions, historical nitrogen loads estimated from the numerical simulations were sometimes similar (<10% difference) and other times substantially different (±100%) from the instantaneous load estimates. Additionally, simulated future surface water nitrogen loads responded asymptotically over several decades following reductions in terrestrial nitrogen sources, further highlighting the effect of groundwater transport lag times. The comparison of the three methods, quantification of historical interannual variability, and prediction of lagged responses to nitrogen source reductions provide important context for decision makers using estimated groundwater nitrogen loads to help evaluate nitrogen management efficacy.

New York↗

USGS2026

Modeling future groundwater depletion to evaluate sustainability goals set under the Sustainable Groundwater Management Act in the critically overdrafted basins of the Central Valley, California, USA (2020–2070)

In 2014, California's Sustainable Groundwater Management Act (SGMA) mandated local agencies to devise and implement groundwater sustainability plans to address critically overdrafted conditions throughout the state's aquifers. However, the feasibility of these agencies' sustainability goals has not previously been assessed through a regional-scale, integrative lens. Here, we develop and analyze a novel, basin-wide database of 936 sustainability indicator wells located within Central Valley subbasins designated as critically overdrafted, most of which lie in the San Joaquin Valley. Our database shows 2040 groundwater elevation goals vary widely from 60 m above to 80 m below 2020 levels, with variability within and between adjacent subbasins. To evaluate the feasibility of achieving these goals, we coupled the database with a regional hydrologic model (Central Valley Hydrologic Model version 2) and simulated multiple future pumping scenarios. Results show that under increased groundwater demand, 60%–70% of indicator wells may fail to meet their 2040 goals. Even a 50% reduction from 2020 demand levels leaves nearly 40% of wells failing to meet their sustainability thresholds by 2040. Baseline models show that by 2070, up to 70% of wells could fail to meet their goals due to large-scale, spatially connected regions of groundwater depletion. This integrated framework, linking the first region-wide compilation of SGMA indicator wells with a regional groundwater model, demonstrates that many local sustainability goals may be unattainable with substantial (up to 50%) reductions in pumping. Additional management interventions, such as expanded recharge or coordinated demand reductions, may help achieve sustainability goals.

California↗

USGS2025

Natural source zone depletion of crude oil in the subsurface: Processes controlling mass losses of individual compounds

At many petroleum hydrocarbon spill sites, residual spilled product forms a long-term source of groundwater contamination. The phrase source zone natural depletion is used to refer to the mass loss rates. Overall mass lost under environmental conditions was analyzed using conservative biomarker concentrations for a 1979 oil spill in northern Minnesota, USA. After 40–41 years, an average of 50% of the mass was lost with values ranging from 22% to 57% depending on location. It is also important to understand the composition changes in the source. To understand controls on the losses of individual compounds, concentrations of volatile hydrocarbons in oil samples were compared with aqueous solubilities, and pore-space oil saturations. The results of the comparison show that losses of the oil compounds were controlled by pore-space oil saturations, solubility, and susceptibility to degradation under methanogenic conditions. Compounds that degrade under methanogenic conditions, including toluene, o -xylene, and n -alkanes are more depleted compared to benzene, ethylbenzene, and m - and p -xylene for which losses are dominated by dissolution. These rates and compound-specific behaviors form a foundation for improved modeling approaches and risk analyses.

Minnesota↗

USGS2025

Quantifying groundwater response and uncertainty in beaver-influenced mountainous floodplains using machine learning-based model calibration

Beavers ( Castor canadensis ) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model-data integration workflow to address uncertainty in groundwater response to beaver-induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning-based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down-valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down-valley underflow, from 2% during dry pond periods to 20% during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down-valley underflow, minimizes the effect of beaver-induced inundation on water quality. We emphasize the need for field-scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response.

Colorado↗

USGS2025

Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration

Model criticism, discrimination, and selection methods often rely on calibrated model outputs. Because calibration can be computationally expensive, model criticism can first be undertaken by assessing model outputs obtained from limited prior parameter ensembles. However, such prior-based methods are often heuristic and do not formalize the notion of balancing model consistency with data and model complexity (i.e., model adequacy). We present a new framework to discriminate among candidate models prior to calibration that formalizes prior-to-calibration model adequacy into a metric to implicitly balance prior model output data coverage with model complexity represented by prior output (co)variance. The prior model adequacy metric “Mahalanobis distance deviation” quantifies the deviation of (a) the set of squared Mahalanobis distances of data from a prior model output distribution from (b) the set of squared Mahalanobis distances of data from their own distribution. A new data worth metric “discernment value” is also presented which quantifies the value of data for screening less-adequate models prior to calibration. Discernment value is calculated from the change in variance of a weighted average of prior model outputs from all candidate models due to less-adequate model outputs receiving lower weight. The framework is demonstrated using a one-dimensional groundwater flow model with eight possible configurations. A synthetic data network is used to test the framework. Results show the framework identifies the candidate models most similar to the true model used to create the synthetic data. Discernment values show variation in the value of different data types and locations for screening less-adequate models.

Water Resources Research↗

USGS2025

Over, under, and through: Hydrologic connectivity and the future of coastal landscape salinization

Seawater intrusion (SWI) affects coastal landscapes worldwide. Here we describe the hydrologic pathways through which SWI occurs - over land via storm surge or tidal flooding, under land via groundwater transport, and through watersheds via natural and artificial surface water channels—and how human modifications to those pathways alter patterns of SWI. We present an approach to advance understanding of spatiotemporal patterns of salinization that integrates these hydrologic pathways, their interactions, and how humans modify them. We use examples across the East Coast of the United States that exemplify mechanisms of salinization that have been reported around the planet to illustrate how hydrologic connectivity and human modifications alter patterns of SWI. Finally, we suggest a path for advancing SWI science that includes (a) deploying standardized and well-distributed sensor networks at local to global scales that intentionally track SWI fronts, (b) employing remote sensing and geospatial imaging techniques targeted at integrating above and belowground patterns of SWI, and (c) continuing to develop data analysis and model-data fusion techniques to measure the extent, understand the effects, and predict the future of coastal salinization.

Water Resources Research↗

USGS2023

WellSTIC: A cost-effective sensor for performing point dilution tests to measure groundwater velocity in shallow aquifers

Many individual measurement points are required to characterize groundwater velocity within an aquifer. Groundwater velocity is most commonly measured using a network of >5 cm diameter monitoring wells, which, if not already present at a site, are expensive and labor-intensive to install. Drive-point piezometers—simple, cost-effective wells that can be installed by hand—are a common tool for sampling groundwater in shallow, alluvial aquifers, but most groundwater velocity measurement techniques require equipment that is too large for these narrow (usually <2 cm inside diameter) piezometers. In this technical note, we introduce a low-cost sensor and well packer system (<$90 USD) for performing point dilution tests in narrow piezometers. Field data show that the magnitude of groundwater velocity measured with this technique agrees with velocities computed from natural gradient tracer tests. Additionally, with proper calibration, these sensors can be used to continuously monitor in-well specific conductance, either during inter-well tracer tests with saline tracers or for water quality monitoring. This system is a viable tool for rapid assessment of the magnitude of groundwater velocity in shallow aquifers.

Water Resources Research↗

Compare source metadata on this page

Work	Published	Source identifier	Source
A roadmap for identifying and interpreting physical processes and national water model prediction bias associated with baseflow index regimes across the contiguous United States	2026-08-17	70279220	usgs
Modeling legacy nitrogen transport under instantaneous, steady-state, and transient groundwater flow conditions	2026-07-09	70277249	usgs
Modeling future groundwater depletion to evaluate sustainability goals set under the Sustainable Groundwater Management Act in the critically overdrafted basins of the Central Valley, California, USA (2020–2070)	2026-04-21	70276244	usgs
Natural source zone depletion of crude oil in the subsurface: Processes controlling mass losses of individual compounds	2025-12-30	70273310	usgs
Quantifying groundwater response and uncertainty in beaver-influenced mountainous floodplains using machine learning-based model calibration	2025-09-25	70272041	usgs
Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration	2025-09-13	70273430	usgs
Over, under, and through: Hydrologic connectivity and the future of coastal landscape salinization	2025-06-27	70268695	usgs
WellSTIC: A cost-effective sensor for performing point dilution tests to measure groundwater velocity in shallow aquifers	2023-01-17	70279694	usgs

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