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Results for “Journal of Geophysical Research: Solid Earth”

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USGS2026

Software to support remote sensing of river discharge based on critical flow theory

Water resource management requires accurate observations of streamflow but standard field methods for measuring river discharge ( Q ) are costly and can be hazardous for equipment and personnel. Remote sensing has become a viable alternative, but many image-based techniques require field data for calibration and depth and velocity can seldom be mapped with a single sensor. A new approach based on critical flow theory, in contrast, allows both of these attributes to be inferred from readily available image data. This technique only pertains to sites with standing waves, called undular hydraulic jumps (UHJs), but a recent investigation demonstrated its potential to provide accurate discharge estimates. This paper introduces software designed to facilitate Inferring Q from UHJs Identified in River Images (InQUIRI). The package includes modules for retrieving data from image servers, making the measurements of wavelength and width required to calculate discharge, inferring a representative wavelength from a profile digitized along a wave train, combining multiple estimates to obtain an ensemble median discharge, and assessing accuracy via comparison to gage records from the U.S. Geological Survey. By making these steps easier to implement, InQUIRI enables users to apply the workflow to a variety of UHJ-containing images. Accumulating more case studies, some successful and others less so, would help constrain the range of applicability of the critical flow approach and foster development of refined guidelines for selecting and measuring waves. The software described herein could play an important role in promoting informed use of this new technique for non-contact streamflow measurement.

Arizona, Colorado, New Mexico, Utah↗
USGS2025

Updating regional‐scale geospatial liquefaction models with locally available geotechnical data

We present a method to update the geospatial liquefaction model used by the U.S. Geological Survey’s near‐real‐time ground failure product with subsurface geotechnical data. The geospatial model estimates liquefaction probability from peak ground velocity (via ShakeMap) and geospatial susceptibility proxies. In many regions, additional information relevant to constraining liquefaction likelihood is also available, including surface geology maps and subsurface geotechnical measurements. There is currently no mechanism to use these data in the ground failure product liquefaction model, even though these data could provide more precise constraints on spatial variations in the lithologic character of the soil (surface geology) and direct measurements of the subsurface mechanical properties that affect liquefaction occurrence and severity (geotechnical measurements). In this study, we develop a method to integrate these data with the geospatial model and assess how these data can improve regional‐scale predictions. We develop a Bayesian updating framework and apply it to the 1989 magnitude 6.9 Loma Prieta, California, earthquake, for which mapped observations are available to evaluate performance. We constrain the Bayesian framework with 373 Northern California cone penetration tests and liquefaction susceptibility classes based on the mapped surface geology. This Bayesian model incorporates geotechnical information into the geospatial model and more accurately predicts liquefaction occurrences than the geospatial model, while sacrificing less accuracy in terms of predicting the absence of liquefaction than the geotechnical model. In future applications, this approach could be adapted to update other geospatial models using locally available subsurface data.

California↗
USGS2024

Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning

We developed a suite of models using deep learning to make hindcast predictions of the 7‐day average backward‐looking nitrate concentration at 46 predominantly agricultural sites across the midwestern and eastern United States. The models used daily observations of discharge and meteorological variables and watershed attributes describing anthropogenic modification to hydrology, nitrogen application, climate, groundwater, land use, watershed physiographic attributes, and soils. Across all sites, discharge and watershed soil and physiographic attributes showed a strong influence on model performance. Analysis of drivers across sites revealed considerable regional differences related to controlling processes such as groundwater contributions. We tested several ways to pool data across sites to develop accurate models and make the most effective use of available data. Single‐site models, in which models are trained and tested at a single location, showed generally strong predictive performance (median Kling‐Gupta Efficiency = 0.66), and accuracy at poorly performing sites could be improved by grouping sites with similar characteristics. Developing a single model for all sites reduced performance at several locations with distinct characteristics, suggesting that there is a threshold of dissimilarity beyond which more data does not improve the model. While many deep learning studies have shown that national or even global models can outperform local models, it is not clear that this is true for water quality constituents. This study demonstrates how data can be combined effectively, using deep learning to develop accurate and interpretable models of instream nitrate at sites where varying processes are responsible for changes in nitrate concentration.

Water Resources Research↗
USGS2024

Are field observations of surface rupture useful? An example from the 2023 Mw 7.8 Pazarcık, Turkey (Türkiye), earthquake

Field investigations have long been an important component of the scientific response to surface‐faulting earthquakes. However, in light of advances in remote data and models, the question arises whether field‐based observations of surface rupture remain useful for understanding rupture processes and seismic hazards. We approach this question using a field‐based study of the central 2023 M w 7.8 Pazarcık, Turkey (Türkiye), earthquake rupture, at the intersection of the east Anatolian fault (EAF) and Narlı fault. Our field observations include the surface rupture expression and extent of the central EAF and northernmost Narlı fault in generally forested and steep terrain and 68 measurements of left‐lateral surface displacement. These data improve our understanding of the Pazarcık rupture complexity, resolve the surface geometry of the Narlı fault–EAF connection, and exhibit a clear (>2 m) change in surface displacement across this intersection zone that confirms remote‐based coseismic slip models. Our study shows that focusing field efforts in areas of obscured or low‐resolution remote data can yield essential data for refining rupture extent, documenting perishable on‐fault displacement, and improving postearthquake situational awareness. A comparison of similarly large‐magnitude continental surface‐rupturing earthquakes indicates that displacement uncertainties relate to a complex set of factors, including measurement methods, rupture complexity, and displacement magnitude. Our study validates the need for postearthquake field observations, which, when driven by clear motivating questions and knowledge of methodological strengths and limitations, provide high‐resolution rupture data that complement remote‐based models.

Pazarcık↗
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↗
USGS2026

Magnitude conversion relations create substantial differences in seismic hazard models

Earthquake catalogs are essential data inputs for seismic hazard modeling. Because earthquake magnitudes are reported in a variety of types (e.g., local magnitudes and moment magnitudes), magnitude conversion relationships must be used to convert the different magnitude types present in a catalog to a uniform magnitude type to avoid biases in the hazard computation. However, these conversion relationships are often uncertain and have been shown to sometimes perform poorly. Here, we investigate the sensitivity of the gridded seismicity component of the National Seismic Hazard Model (NSHM) to the catalog conversion equations in the Eastern United States. In the 2023 NSHM, magnitudes of various types were converted to moment magnitudes using equations developed by the Central and Eastern United States Seismic Source Characterization for Nuclear Facilities (CEUS‐SSCn), based on least‐squares (LS) regressions made using data from a catalog containing events up through 2008. We recompute these equations using events in the Advanced National Seismic System Comprehensive Earthquake Catalog with multiple magnitudes from 2000 to 2023. Although we prefer the use of orthogonal regressions for our datasets, LS regressions produce broadly similar results, with both approaches exhibiting large deviations from the CEUS‐SSCn conversions, especially at smaller magnitudes. We compare the spatial distribution of annual rates using three different models: (1) the 2023 NSHM conversions, (2) our updated conversions, and (3) no conversions. We find that the choice of conversions leads to substantial differences in the rate forecasts, which can greatly impact the seismic hazard model, particularly in regions with low‐seismicity rates such as the Eastern United States, where the hazard is dominated by gridded seismicity rather than a fault model.

Seismological Research Letters↗
USGS2025

Searching for seismic precursors - The Barry Landslide hazard clean up

The Barry Landslide, located in Barry Arm of Prince William Sound, Alaska, poses a major hazard due to its steep, unstable slopes and the potential for a massive landslide-generated tsunami. With an estimated volume of 500–700 million cubic meters, the Barry Landslide could trigger highly destructive waves. In this study, we focus on seismic signals from the Barry Landslide, which are critical for providing timely tsunami warnings. Since the summer of 2020, the region has been instrumented to monitor the landslide, but the seismic record is complicated by the presence of nearby glaciers and frequent regional earthquakes. Among these signals, we analyze a specific class of short-duration, high-frequency seismic events that exhibit strong seasonal variability, increasing in rate from late summer to mid-winter before ceasing abruptly in late winter or early spring. Our analysis suggests that the source of these signals is likely near or beneath Cascade Glacier, adjacent to the landslide, rather than within the landslide mass itself. We apply detection algorithms to construct a time history for this signal type, which we then compare with environmental factors like precipitation, temperature, and slope displacement data from ground-based radar and remote sensing. Correlations indicate that these seismic events may be driven by seasonal hydrological changes, particularly the freeze-up of subglacial water pathways. While these events are not directly linked to landslide motion, they serve as indirect markers of subsurface hydrological conditions that influence slope stability. Our findings highlight the complex interplay between glaciers, groundwater, and landslide dynamics, emphasizing the need for multi-parameter monitoring to assess evolving geohazards in the region.

Alaska↗
USGS2026

Making many out of one: Synthetic geologic deformation model distributions for use in USGS NSHM25‐PRVI Puerto Rico-U.S. Virgin Island update

A key use‐case of geologic slip rates is within deformation models used in probabilistic seismic hazard analyses. Field‐derived geologic slip rates have formed the cornerstone of deformation models in such applications for decades. Recent advancements in seismic hazard analyses have expanded the use of faults for which geologic slip rates are not well constrained using categorical slip rate estimates. Because of these advancements, application of a geologic deformation model for use in 2025 U.S. Geological Survey National Seismic Hazard Model Puerto Rico‐U.S. Virgin Islands (NSHM25‐PRVI) proved challenging due to: (1) a lack of field‐based geologic slip rates, and (2) a lack of epistemic uncertainty distributions within a broad range of estimated slip rates. Preliminary versions of the NSHM25‐PRVI model sampled these slip rate bins in a coincident manner along preferred and extreme value branches, which yielded untenable correlations in mean hazard results. To minimize the influence of correlated uncertainties amid these challenges, we develop a synthetic epistemic uncertainty distribution for deformation rate on each crustal fault. Each fault has a weighting schema across four possible distribution shapes: uniform, normal, triangular favoring local minima, and triangular favoring local maxima. The synthetic distributions are then sampled several times for each logic tree branch. The results provide a more realistic distribution of rates across the study region as compared with using correlated extrema sampling. This exploration of our method in a small region like PRVI can pave the way for larger‐scale, more complicated applications (e.g., western United States).

Puerto Rico, US Virgin Islands↗
USGS2024

Beyond the wedge: Impact of tidal streams on salinization of groundwater in a coastal aquifer stressed by pumping and sea-level rise

Saltwater intrusion (SWI) is a well-studied phenomenon that threatens the freshwater supplies of coastal communities around the world. The development and advancement of numerical models has led to improved assessment of the risk of salinization. However, these studies often fail to include the impact of surface waters as potential sources of aquifer salinity and how they may impact SWI. Based on field-collected data, we developed a regional, variable-density groundwater model using SEAWAT for east Dover, Delaware. In this location, major users of groundwater from the surficial aquifer are the City of Dover and irrigation for agriculture. Our model includes salinized marshland and tidal streams, along with irrigation and municipal pumping wells. Model scenarios were run for 100 years and included changes in pumping rates and sea-level rise (SLR). We examined how these drivers of SWI affect the extent and location of salinization in the surficial aquifer by evaluating differences in chloride concentration near surface waters and the subsurface freshwater-saltwater interface. We found the presence of the marsh inverts the typical freshwater-saltwater wedge interface and that the edge of the interface did not migrate farther inland. Additionally, we found that tidal streams are the dominant pathways of SWI at our site with salinization from streams being exacerbated by SLR. Our results also show that spatial distribution of pumping affects both the magnitude and extent of salinization, with an increase in concentrated pumping leading to more intensive salinization than a more widely distributed increase of the same total pumping volume.

Delaware↗
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

Summary of results from monitoring the Geysers with continuous passive seismic and repeat magnetotelluric measurements (2021-2023)

Understanding temporal variations in a geothermal field can support operators in decision making that pertains to optimizing production and mitigating hazards. Between 2021 and 2023, The Geysers geothermal field in northern California was monitored with an array of continuous passive seismic sensors and annual repeat magnetotelluric (MT) measurements. Each of these data sets were analyzed and modelled separately to understand the data, sensitivity, and any observable changes. Then, the data were inverted jointly using a crossgradient method to further constrain temporal changes in geophysical properties within the geothermal field. Multiple permutations of annual datasets were used as inputs to the joint inversion. Results demonstrate seismic data constrain smooth inversion of the MT data, and the MT data provide supplementary information about the location of temporal fluid changes. Estimating relative changes in steam saturation for various time intervals of the joint models shows compartmentalized changes in the field, and good spatial correlation with the location of injection wells. These results demonstrate that collecting both passive seismic and MT measurements then modeling them jointly provide complementary information and a relatively inexpensive method for monitoring temporal changes in an active geothermal field that provides results to support operators.

California↗
USGS2025

Invasion of perennial sagebrush steppe by shallow-rooted exotic cheatgrass reduces stable forms of soil carbon in a warmer but not cooler ecoregion

Soil organic carbon ('SOC') in drylands comprises nearly a third of the global SOC pool and has relatively rapid turnover and thus is a key driver of variability in the global carbon cycle. SOC is also a sensitive indicator of longer-term directional change and disturbance-responses of ecosystem C storage. Biome-scale disruption of the dryland carbon cycle by exotic annual grass invasions (mainly Bromus tectorum, 'Cheatgrass') threatens carbon storage and corresponding benefits to soil hydrology and nutrient retention. Past studies on cheatgrass impacts mainly focused on total C, and of the few that evaluated SOC, none compared the very different fractions of SOC, such as relatively unstable particulate organic carbon (POC) or relatively stable, mineral-associated organic carbon (MAOC). We measured SOC and its POC and MAOC constituents in the surface soils of sites that had sagebrush canopies but differed in whether their understories had been invaded by cheatgrass or not, in both warm and relatively colder ecoregions of the western USA. MAOC stocks were 36.1% less in the 0–10 cm depth and 46.1% less in the 10–20 cm depth in the cheatgrass-invaded stands compared to the uninvaded stands of the warmer Colorado Plateau, but not in the cooler and more carbon-rich Wyoming Basin ecoregion. In plots where cheatgrass increased SOC, it was via unstable POC. These findings indicate that cheatgrass effects on the distribution of soil carbon among POC and MAOC fractions may vary among ecoregions, and that cheatgrass can reduce forms of carbon that are otherwise considered stable and 'secure', i.e. sequestered.

Environmental Research Communications↗
USGS2026

Earthquake ground-motion model adjustments for the San Francisco Bay area

We develop adjustments to ergodic ground‐motion models (GMMs) to improve their performance in the San Francisco Bay Area (SFBA). GMMs are widely used in hazard assessments to estimate characteristics of ground shaking based on known properties of the source, path, and site. Such models are often developed using datasets containing records from various regions, resulting in models that represent median ground‐motion behavior, which may not adequately represent ground motions within subregions. This is true for the SFBA, where ground motions attenuate more rapidly with distance than in many other parts of California that dominate GMM databases. To support improved seismic hazard estimates in the SFBA, we calculate regional constants and anelastic attenuation coefficient adjustments relative to two commonly used ergodic GMMs: BSSA14 ( Boore et al. , 2014 ) and ASK14 ( Abrahamson et al. , 2014 ). These adjustments are obtained for a suite of ground‐motion intensity measures (peak ground acceleration, peak ground velocity, and 5%‐damped pseudospectral acceleration at oscillator periods ranging from 0.075 to 10 s) using mixed‐effects regression. Use of the regionally adjusted models reduces the overall bias by up to 0.5 natural log units for BSSA14 and up to 0.6 natural log units for ASK14. We demonstrate one application of our attenuation adjustments and their implications in an earthquake early warning case study of the 2014 M 6.0 South Napa earthquake. The predicted extent of shaking using the adjusted models better matches observed shaking at large source‐to‐site distances, especially for lower shaking intensities, thus potentially reducing overalerting. We encourage the use of our model adjustments when ergodic models are considered for seismic hazard studies in the SFBA.

California↗
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↗
USGS2025

Megafaunal community structure on ferromanganese and phosphorite hardgrounds in the Southern California Borderland

The Southern California Borderland (SCB) is a topographically complex region on the active continental margin that hosts varied hardground habitats, including ferromanganese (FeMn) crusts and phosphorites, marine minerals being considered for resource extraction. The SCB is influenced by seasonal upwelling and terrestrial inputs, and has a well-defined oxygen-minimum zone (OMZ). We analyzed megafaunal community composition, density, and diversity across 41 ROV video transects at 10 SCB hard substrate sites spanning depth (378–2765 m), temperature (1.79–7.96 °C), and oxygen (3.91–105.67 μM) gradients, with varying rock types (FeMn crusts, phosphorites, other rocks). We counted 32,426 individuals representing 146 unique taxa over a total area of 21,935 m 2 . Echinodermata contributed 48 % of the total abundance; Cnidaria 24 %; Porifera 13 %; Annelida 6 %; Arthropoda 6 %; Chordata 2 %; Mollusca, Foraminifera, and Hemichordata <1 %. Megafauna communities showed clear heterogeneity, with density, diversity, and community composition varying among transects within sites and across sites. Rock type recorded no influence on megafaunal density, but habitats that included FeMn crusts had higher diversity and distinct taxonomic composition. Location, depth, and oxygen were the most important variables explaining variance among megafaunal communities, with distinct communities formed at deeper depths and within the OMZ. This study provides baseline information about the megafauna inhabiting SCB hardgrounds, particularly those associated with minerals considered for their resource potential. It extends existing documentation of such communities to greater depths, reveals increased representation of sponges and corals below 1000 m, and highlights the variability within and among deep-sea megafaunal communities, offering context for informed policy decisions.

Southern California borderland↗
USGS2025

An unexplained tsunami: Was there megathrust slip during the 2020 Mw7.6 Sand Point, Alaska, earthquake?

On October 19, 2020, the M w 7.6 Sand Point earthquake struck south of the Shumagin Islands in Alaska. Moment tensors indicate the earthquake was primarily strike-slip, yet the event produced an enigmatic tsunami that was larger and more widespread than expected for an earthquake of that magnitude and mechanism. Using a suite of hydrodynamic, seismic, and geodetic modeling techniques, we explore plausible causes of the tsunami. We find that strike-slip models consistent with the moment tensor orientation cannot produce the observed tsunami. Hydrodynamic inversion of sea surface deformation from deep ocean and tide gauge data suggest seafloor deformation more closely matches a megathrust, rather than a strike-slip, source. Static slip inversions, using sea level and Global Navigation Satellite System data, allow for a portion of co-seismic megathrust slip that can explain tsunamigenesis. Combining all available geophysical datasets to model the kinematic rupture, we show that considerable, relatively slow, megathrust slip is allowable in the Shumagin segment, concurrent with strike-slip faulting. We hypothesize that the slow megathrust rupture does not contribute much seismic radiation allowing it to previously go unnoticed with traditional seismic monitoring.

Alaska↗
USGS2024

Simulation of groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado

Salinity, or total dissolved solids (TDS), of the Colorado River affects agricultural, municipal, and industrial water users and is an important concern in the Western United States. In the Paradox Valley of southwestern Colorado, natural discharge of sodium-chloride brine to the Dolores River from the underlying core of a salt-valley anticline accounts for about 6 percent of the salinity load to the Colorado River. Formation of the Paradox Valley began during the Miocene, and subsequent erosion exposed the Pennsylvania Paradox Formation in the core of the anticline where a cap rock, collapse features, breccia, and sodium-chloride saturated brine developed at the top of the exposed salt diapir. The discharge of brine to the Dolores River is affected by these dissolution features, along with seasonal hydrologic conditions and density-dependent flow between older dense brine and the younger fresh groundwater in the overlying alluvial aquifer. To reduce TDS concentrations in the Dolores River through the Paradox Valley, the Bureau of Reclamation has pumped brine from a series of shallow wells adjacent to the river since July 1996. The pumped brine is collected and piped to a deep disposal well where it is injected into the Mississippian Leadville Limestone at a depth of about 4,570-meters below land surface. The pumping and injection operation is collectively known as the Paradox Valley Unit (PVU), and by 2015, the PVU had substantially reduced TDS concentrations in the Dolores River by about 70 percent. Since 2019, injection-pressure limits and related seismic activity have constrained deep-well injection and thus brine pumping at the PVU. In cooperation with the Bureau of Reclamation, the U.S. Geological Survey developed a MODFLOW-6 three-dimensional, variable-density groundwater flow and TDS transport model of the Paradox Valley to evaluate the effects of PVU pumping operations on brine discharge to the Dolores River and to guide additional research. The finite-difference model grid consists of 76 rows and 48 columns oriented from northwest to southeast in alignment with valley topography and groundwater-flow directions in the near-surface freshwater alluvial aquifer. A 7-layer hydrogeologic framework was developed from existing datasets to represent the alluvial aquifer, cap rock, collapse breccia, and groundwater flow and TDS transport from the underlying Paradox Formation salt to the Dolores River. The model represents a 33-year transient calibration period from 1987 through 2020 that includes pre-PVU conditions from 1987 through June 1996 and post-PVU conditions from July 1996 through 2020. A 1,000-year simulation of groundwater flow and coupled TDS transport computed the initial conditions for the subsequent 33-year transient simulation. Observations of precipitation, streamflow, evaporation, agricultural land use, and PVU brine pumping rates were used to specify appropriate boundary conditions to the model representing time-varying recharge, tributary streamflow, groundwater underflow, evapotranspiration (ET), and PVU pumping. Values for average monthly streamflow and TDS concentration at the upstream streamgage, the Dolores River at Bedrock (USGS streamgage 09169500), were specified as model input where the Dolores River enters Paradox Valley. Observed pumping from the PVU, water levels and TDS concentrations in groundwater, and streamflow and estimated TDS concentrations at the downstream streamgage, the Dolores River near Bedrock (USGS streamgage 09171100), were calibration targets that constrained the manual calibration of model parameters representing aquifer hydraulic conductivity, storage, streambed conductance, recharge, and (ET). Two primary model-calibration targets were the match between observed and simulated TDS mass flux from PVU pumping wells and the match between estimated and simulated TDS mass flux to the Dolores River. The simulated TDS mass withdrawn by pumping wells is calculated by the model as the product of the assigned pumping rate and simulated groundwater TDS concentrations. Because actual pumping rates were assigned as simulated values, the total simulated PVU pumping for the 33-year calibration is within 0.5 percent of the observed values. However, simulated concentrations and thus mass flux of TDS withdrawn by the PVU pumping wells were consistently about 26 percent less than observed values for all the simulated time periods (33-year simulation, pre-PVU, and post-PVU). The representation of brine inflow was explored through additional modeling to evaluate the effect of the simulated brine source on groundwater TDS concentrations. Results indicated that a saturated-salt constant-flux brine source best replicated the magnitude and transient pattern observed for TDS mass flux from PVU pumping wells. The simulated TDS mass flux to the Dolores River is compared to estimates based on observed streamflow and specific conductance (SC) data for the downstream streamgage. The calibrated model provided a close fit of simulated to measured streamflow at the downstream streamgage, and the calibrated model fit to estimated TDS concentrations at the downstream streamgage was reasonable. The greatest differences between simulated and estimated values occurred during drought periods from June 2000 to March 2003, May 2012 to June 2013, and October 2013 to October 2014, when simulated TDS concentrations in the river were greater than estimated concentrations. In general, simulated TDS mass flux to the river for the pre-PVU period is in good agreement with estimated values (2-percent difference), but the model overestimated TDS mass flux to the river by about 41 percent during the post-PVU period. The model uncertainty with respect to TDS mass flux to the river indicates other processes or model parameters not well represented by the model are affecting the system, especially during drought. During model calibration, the most sensitive parameters were identified as vertical hydraulic conductivity of the alluvial aquifer, conductance of the Dolores River streambed, ET extinction depth and rate, and recharge rate. Five 5-year scenarios of conditions for 2021–25 were simulated to assist evaluation of alternative strategies to manage the discharge of brine into the Dolores River. The first scenario simulates no PVU pumping and serves as a base case for comparison to the other scenarios. Two scenarios simulate the effects of varying withdrawal timing at an annual rate about one-third less than during 2010 through 2018. During high-flow spring snowmelt runoff periods when brine discharge is naturally minimized, PVU pumping does not substantially affect salinity in the Dolores River, and comparison of these two scenarios indicates that scheduling brine withdrawals during times of low river stage is nearly as effective at reducing TDS mass flux to the river as pumping brine year-round. Cessation of pumping during periods of high river stage may be advantageous for system maintenance, brine injection, and seismic-risk reduction. The fourth scenario tested the effect of reducing irrigation-return flow on brine discharge and predicted a slight reduction of TDS mass flux to the Dolores River, but not as great a reduction as that of using the PVU to remove brine. The fifth scenario simulated 5 years of drought conditions without PVU pumping and indicates brine discharge during drought about 15 percent greater than during average hydrologic conditions. Results from scenario 5 are consistent with the calibrated model results and indicate that aquifer properties and ET processes and parameters may be affecting simulation results during drought. The Paradox Valley groundwater model provides a reasonable overall match to observed conditions in the Dolores River. The model is useful for evaluating relative differences between brine management scenarios to inform PVU operational decisions and to identify gaps in data and process understanding. Representation of the brine source, hydraulic-conductivity parameters, and recharge and ET processes were identified as potential areas for additional field and modeling research. Additional research in the Paradox Valley might include field-data collection that provides additional information on the hydrogeologic framework, groundwater levels, groundwater TDS concentrations, stream characteristics, and aquifer properties. Additional modeling efforts could benefit from applying advanced tools for model development, calibration, and visualization including parameter-estimation and sensitivity analysis. Statistical evaluation of known model uncertainties such as hydraulic conductivity, streambed conductance, representations of the brine source, recharge, and ET could improve the match between simulated and estimated TDS mass flux from PVU pumping wells and to the Dolores River further informing model predictions and system understanding for the Paradox Valley.

Colorado↗
USGS2024

Agricultural tile drains increase the susceptibility of streams to longer and more intense streamflow droughts

Streamflow droughts are receiving increased attention worldwide due to their impact on the environment and economy. One region of concern is the Midwestern United States, whose agricultural productivity depends on subsurface pipes known as tile drains to improve trafficability and soil conditions for crop growth. Tile drains accomplish this by rapidly transporting surplus soil moisture and shallow groundwater from fields, resulting in reduced watershed storage. However, no work has previously examined the connection between tile drainage and streamflow drought. Here, we pose the question: does the extent of watershed-level tile drainage lead to an increased susceptibly and magnitude of streamflow droughts? To answer this, we use daily streamflow data for 122 watersheds throughout the Midwestern United States to quantify streamflow drought duration, frequency, and intensity. Using spatial multiple regression models, we find that agricultural tile drainage generates statistically significant ( p < 0.05) increases in streamflow drought duration and intensity while significantly reducing drought frequency. The magnitude of the effect of tile drainage on streamflow drought characteristics is similar to that of water table depth and precipitation seasonality, both of which are known to influence streamflow droughts. Furthermore, projected changes in regional precipitation characteristics will likely drive the installation of additional tile drainage. We find that for each 10% increase in tile-drained watershed area, streamflow drought duration and intensity increase by 0.03 d and 12%, respectively, while frequency decreases by 0.10 events/year. Such increases in tile drainage may lead to more severe streamflow droughts and have a detrimental effect on the socio-environmental usage of streams throughout the Midwest.

Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio↗