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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

At least 73 records · Page 4Linked to original sources
USGS2026

From transcriptomic dysregulation to behavioral impairment: Mechanistic effects of weathered Brazilian oil on early life stage fish

The 2019–2020 oil spill along the northeastern coast of Brazil affected coastal reef ecosystems and the organisms that inhabit these environments. To characterize the toxicity of persistent weathered oil residues to early life stage fish, embryonic zebrafish ( Danio rerio ) were exposed to environmentally relevant concentrations of a water-accommodated fraction (WAF) prepared from weathered oil recovered following the spill. Embryos were exposed from 24 to 168 h postfertilization (hpf) to low (2.14 μg ∑PAH 60 L –1 ) and high (17.1 μg ∑PAH 60 L –1 ) concentrations representative of chronic postspill exposure conditions. Transcriptomic profiling revealed early alterations in pathways associated with connective tissue, cardiovascular, and respiratory development at 48 hpf, which corresponded with a significant reduction in heart rate at 72 hpf. By 96 and 168 hpf, molecular responses shifted toward visual system dysfunction, including pathways related to photoreceptor development and phototransduction. Histological evaluation confirmed significant thinning of retinal layers, consistent with impaired retinal development or photoreceptor degeneration. These structural changes translated to functional impairment, as larvae exhibited a 40% reduction in optokinetic response. These integrated analyses identify mechanistic targets of weathered oil toxicity and establish molecular to behavioral relationships that improve understanding of chronic oil spill impacts on early life stage fish.

Pernambuco↗
USGS2026

Long-term trends of selected pesticides in the Little Arkansas River, south-central Kansas, 1995–2023

The eastern part of the High Plains Aquifer, herein called the Equus beds aquifer, is currently a primary source of water (2026) for the city of Wichita, Kansas. The Equus Beds Aquifer Storage and Recovery Project was developed in the early 1990s to meet future water demands by using the Little Arkansas River as an artificial aquifer recharge water source during above-base-flow conditions in the river. Little Arkansas River water is diverted at an intake structure, treated following National Primary Drinking Water Regulations as a guideline, and delivered to the aquifer through recharge basins or injection wells for later use. The U.S. Geological Survey, in cooperation with the city of Wichita, completed the study described in this report to quantify and characterize Little Arkansas River water-quality data. Data in this report can be used to evaluate changing conditions in aquifer recharge source water, provide science-based information for decision making, and help meet regulatory monitoring requirements. Continuous streamflow, specific conductance, turbidity, and discrete atrazine data were collected from two Little Arkansas River study sites and discrete glyphosate and aminomethylphosphonic acid was collected from one of the two Little Arkansas River study sites from 1995 to 2023 to evaluate water-quality conditions and assess pesticide trends. Pesticide trends in the herbicides atrazine and glyphosate and the herbicide-associated compound aminomethylphosphonic acid (a glyphosate degradate) were evaluated using the R package seawaveQ. Pesticides modeled with the seawaveQ R package had increasing and decreasing trends during the study period, depending on the individual pesticide and if there were strong seasonal patterns; larger estimated concentrations occurring in late spring and early summer. Modeled atrazine concentrations exceeded the Federal maximum contaminant level of 3 micrograms per liter about 8 percent of the time at both study sites. Atrazine had a nonsignificant increasing trend (98 percent) at the Highway 50 site and a nonsignificant decreasing trend (31 percent) at the downstream Sedgwick site during the study period. Glyphosate and aminomethylphosphonic acid trends were not evident at the Sedgwick site during the study period.

Kansas↗
USGS2026

Authoritative knowledge infrastructure for GeoAI: Implications for scientific spatial data providers

Geospatial Artificial Intelligence (GeoAI) increasingly relies on automated analysis of authoritative geospatial data, exposing limitations in data infrastructures originally designed for simple dissemination rather than for knowledge-driven use. From the perspective of scientific spatial data providers, the challenge is enabling GeoAI responsibly while retaining semantic clarity, provenance, reproducibility, and governance. This short paper (i) examines how GeoAI exposed limitations within existing geospatial data infrastructures, and (ii) identifies provider-side requirements for an authoritative knowledge infrastructure. Drawing on GIScience, scientific workflow, and operational AI literature, the paper outlines implications for national mapping organizations that seek to support advanced analytics without jeopardizing scientific rigor or stewardship responsibilities.

Conference Paper↗
USGS2026

Probabilistic forecasting of coastal wetland elevation and soil carbon response to sea-level rise

Tidal wetlands build soil as a dynamic response to inundation, resulting in carbon sequestration and resilience to sea-level rise. Process models can be used to make site-specific forecasts, anticipate future changes, and plan management responses. But to be actionable, forecasts from these models need to be accompanied by descriptions of their confidence. In this study, we demonstrate the application of ecological forecasting principles to the unique case of tidal wetland vulnerability and carbon storage, given accelerating and uncertain sea-level rise. We present a case study using the Cohort Marsh Equilibrium Model at the Global Change Research Wetland in Maryland, USA. We used meta-analyses to quantify lab- and sample-level measurement uncertainties for common soil measurements and to develop Bayesian model priors. Reanalysis of previous literature supported predictive relationships between belowground turnover and mean annual temperature, as well as between soil erodibility and suspended sediment concentration that could be transferable to other locations. The model forecasted a likely conversion of low-elevation marsh to open water by the 2060s, accompanied by a decrease in marsh carbon storage capacity. Forecasted uncertainty in elevation was dominated by structural uncertainty in the model, while carbon accumulation rates were more influenced by parameter uncertainty. After 2050, and increasingly as the forecast approached 2100, uncertainty in sea-level rise projections contributed to uncertainty in both forecasts. Forecasts were most sensitive to processes related to organic matter preservation, plant productivity (maximum aboveground biomass and belowground turnover), and organic soil packing density. The soil formed was an organic-rich peat (>70% organic matter according to the data), and model predictions were insensitive to parameters associated with mineral sedimentation. We identify additional field data collection, such as on decomposition trajectories and plant productivity, and data-model coupling of near-term elevation records as the most impactful strategies for reducing uncertainties in future forecasting efforts. Future work can build on this forecasting framework in other locations, using it for adaptive management, identifying appropriate interventions to mitigate wetland loss, and projecting future carbon removal capacity in response to accelerated sea-level rise.

Maryland↗
USGS2026

An examination of the reasons for, and variability of, the differences between response spectra and pseudo response spectra

For many decades, it has been common practice to overlook the differences between response spectra and pseudo response spectra. This practice stems from analyses indicating that the average difference between response spectra and pseudo response spectra is negligible for a viscous damping ratio of 5%, which is near-universally adopted. As others have shown, the differences depend strongly on the damping ratio, earthquake magnitude, and oscillator period, with larger differences for larger damping, smaller magnitudes, and longer periods. To quantify the full statistical distribution of the ratio of the absolute acceleration response spectrum (SA) to the pseudo absolute acceleration response spectrum (PSA), we fit empirical ratios with the generalized gamma distribution. Data for magnitudes from 4.0 to 5.5 give 95% prediction intervals for the ratio of 1.00–1.02 at 1 s and 1.01–2.24 at 10 s for 5% damping. For 30% damping, these intervals increase to 1.14–2.01 at 1 s and 1.62–11.8 at 10 s, respectively. Further, we demonstrate how the shape of the ground-motion Fourier amplitude spectrum interacts with the natural period of the oscillator to control the differences in the response spectra type. Routinely treating the two measures of acceleration response spectra as interchangeable may lead to unacceptable inconsistencies when the response spectra of individual records are important or when larger damping ratios are used, such as in direct displacement-based design. If one must convert between them, the uncertainty of the conversion should be accounted for during analysis.

Earthquake Spectra↗
USGS2026

Characterizing metabolic responses of Eleutherodactylus frogs in Puerto Rico to different thermal treatments: Implications for conservation

Amphibians are increasingly vulnerable to extinction due to physiological stress induced by projected global warming and associated drying trends. Understanding the underlying mechanisms and consequences of these responses is crucial to developing effective conservation strategies. Puerto Rico hosts 15 endemic Eleutherodactylus frog species, which are particularly vulnerable to environmental changes due to poor vagility and narrow thermotolerance. In this study, we report routine metabolic rates (RMRs) of four species in response to four temperature treatments (15, 20, 25 and 30°C) at two temporal scales (24 and 72 hours) using indirect calorimetry. Results indicated that three species exhibited increased RMR with rising temperatures. The RMR at 25°C for E. wightmanae was spurious, and it was the only species that did not survive at 30°C. The Arrhenius breakpoint analysis indicated that E. juanariveroi RMR decreased between 25 and 30°C. In contrast, E. antillensis and E. coqui RMR increased with increasing assay temperatures. We discuss how diel metabolic demands decrease the scope at both thermal extremes for E. antillensis and E. coqui , and how habitat temperature may impinge energetic challenges to those coping with documented warming trends, such as E. wightmanae .

Puerto Rico↗
USGS2026

Water quality improvements and hydrogeomorphic change over 20 years contributed to macrophyte community recovery in the Upper Mississippi River

Several large rivers across the globe have recently undergone noticeable improvements to water quality. Yet, the cascading ecological consequences for aquatic life, including macrophytes, are largely unknown, owing to the lack of suitable long-term data and the difficulty of modelling community-level responses. Our study leveraged multiple big datasets from 1998 to 2019 within a 400-km stretch of the Upper Mississippi River in the United States, which experienced major changes to hydrogeomorphology, water quality, and climate during the 22-year study. These decadal changes resulted from both natural processes, such as increased discharge and hydrologic connectivity, and targeted management actions, including water quality improvements and restorations. Using joint species distribution models, we showed how changes to hydrogeomorphology and water quality increased macrophyte taxonomic and ecological (trait) diversity at a macroscale. We uncovered substantial macrophyte community recovery as evidenced by increased species prevalence and diversity, as well as favorable community trait shifts from undesirable free-floating plants towards desirable submersed plants. Submersed species were positively associated with water quality improvements (specifically, reduced concentrations of total phosphorus, chlorophyll a , and total suspended solids). However, total nitrogen concentration modestly increased over the 22-year period and nearly all macrophyte species were locally absent or negatively affected by high nitrogen values. Pollination mode and life form traits responded to environmental changes; as examples, submersed plants recovered with water quality improvements and epihydrophilous plants that pollinate on the water's surface increased with hydrogeomorphic connectivity. Water quality improvements explained approximately 25%–40% of the macrophyte community's temporal variation. Other major environmental drivers of macrophyte distribution included spatial variations of water depth and velocity. Macrophyte community assembly was shaped partially by strong temporal changes in hydrogeomorphology and water quality, and to a greater extent the spatial habitat heterogeneity within the large riverscape.

Iowa, Illinois, Minnesota, Wisconsin↗
USGS2026

Movement mechanisms explain current animal density patterns and predict their reorganization under global change

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

BioRxiv↗
USGS2026

Application of close-kin mark–recapture to an American black bear population using harvest samples

Close-kin mark–recapture (CKMR) offers a promising alternative to traditional mark–recapture methods for estimating wildlife population parameters. CKMR estimates demographic parameters by identifying genetic relationships between individuals, effectively “recapturing” parental genotypes through their offspring's genetic profiles. One key advantage of CKMR over traditional methods is the ability to estimate abundance without recapture, facilitating the use of lethally collected biological samples. Here, we present an application of CKMR to a terrestrial species using samples collected from harvested individuals. We analyzed genetic data from 1767 American black bears ( Ursus americanus ) harvested over two consecutive years in Michigan's Upper Peninsula. Using Genotyping-in-Thousands by sequencing (GT-seq), we identified parent-offspring pairs and developed sex-specific models tailored to black bear reproductive biology and management strategies. Female population estimates aligned with our expectations, based on prior knowledge of the population. In contrast, male population estimates deviated from expectations, potentially due to an unmodeled combination of factors pertaining to male reproductive biology and sampling bias. Our study not only demonstrates the viability of using harvest-based CKMR for monitoring of terrestrial wildlife populations, but also highlights the need for careful model specification. By providing a novel method to estimate demographic parameters and population size, CKMR using harvest samples can inform management decisions and harvest quotas. This approach leverages existing management practices involving collection of genetic samples, such as mandatory harvest registrations and health monitoring programs. Our findings contribute to the growing body of CKMR applications and expand its use to terrestrial species management.

Michigan↗
USGS2026

Human factors used to estimate and forecast water supply and demand in the Delaware River Basin

The objective of this report by the U.S. Geological Survey is to identify cultural and economic determinants of water demand, supply, and access, and to locate associated datasets of those determinants. Although climate and its influence on hydrology are important components of water availability, this study focused only on the components of the human system. The Delaware River Basin (the area including Delaware Bay, and parts of four States including Delaware, New Jersey, New York, and Pennsylvania) was used as a proxy. The basin covers approximately 13,600 square miles, supplies agricultural, municipal, and industrial water, and provides jobs for millions of people in the region. The most salient social and economic determinants affecting current water availability, as well as existing data sources on those determinants, were identified through a literature review. Regional water availability results from complex interactions of water supply and demand, as well as hydrological and climatological interactions between institutions and economics. Following the definition of water availability from Herman-Mercer and others (2023), “access to a specific quantity and quality of water at a point in time and space, for a specific use, recognizing the social and economic value of water across uses and institutions that facilitate or hinder its impartial and efficient provisioning,” we identify human determinants influencing water supply and demand and provide examples from the extensive datasets available to estimate these determinants in the agricultural, municipal, and industrial water-use sectors, using the Delaware River Basin as a case study. We also reference determinants related to recreation and ecosystem services that influence water availability in the Delaware River Basin. Gaps were found pertaining to best practices for prioritization of human determinants within water-availability assessments and a lack of consistent data collection across different scales. A major focus of the work identified a need to prioritize collection and refinement of the human determinants to improve water availability estimation and forecasting. We ascertained that predictions of water use in these sectors often fall short. Predictions may use approximations for changes in demand, such as population or economic activity. Changes in demand also may be influenced by political or other strategic behavior, which are more difficult to incorporate into predictions. Approximations and other assumptions about water supply and demand can be improved to better predict water availability. Therefore, there is an opportunity to further understand and use the fundamental human determinants of water supply and demand along with changing climate variables to predict water availability in the future.

Delaware, New Jersey, New York, Pennsylvania↗
USGS2026

Methods for the computation of streamflow statistics for selected streamgages in or near Nevada through water year 2021

Streamflow statistics are used by Federal, State, and local agencies to protect and manage the water resources of Nevada. Peak-streamflow, low-streamflow, streamflow-duration, mean, minimum, and maximum streamflow statistics were computed for 310 streamgages in Nevada and surrounding States through water year 2021 (the period from October 1, 2020, to September 30, 2021) by the U.S. Geological Survey. Trend analyses of a subset of 54 active streamgages with at least 30 years of data that were minimally affected by human activities indicated that most sites had “somewhat likely downward” or “likely downward” trends for peak, mean, and low streamflow. Statistically significant (probability value less than 0.05) negative trends were associated with peak streamflow at 12 percent of sites, annual mean streamflow at 31 percent of sites, and annual 7-day average low streamflow at 28 percent of sites. Statistically significant positive trends were associated with peak streamflow at 2 percent of sites, annual mean streamflow at 3 percent of sites, and annual 7-day average low streamflow at 9 percent of sites. Comparisons with previously computed statistics determined that changes in computational methods, the length of the streamgage record, and instance of wetter than average periods were the most influential on the computed statistics.

Arizona, California, Idaho, Nevada, Oregon, Utah↗
USGS2026

Evaluating seedling traits, maintenance and effective costs to support tropical forest restoration practices

While large-scale restoration is essential, tropical forest restoration often depends on small-scale practitioners and landowners who produce, plant and maintain trees. Their collective impact on restoring forest diversity and function is substantial, making it critical to address the constraints they face through efficient, effective techniques. We used generalized linear mixed models to evaluate how seedling traits, weeding maintenance and a fertilizer application influenced the 5-year survival of 1796 seedlings from 11 tree species planted in Monteverde, Costa Rica. Estimates of annual survival increased with seedling age (time after planting) and leaf count at planting. Weeding effects varied by species, improving survival in 7 of 10 species and reducing survival in one. All species reached >80% annual survival after 2 years regardless of maintenance, and seedlings planted with ≥25 leaves showed increased survival that decreased the need for weeding. Weeding is costly: all effective costs per 4-year surviving seedlings were higher with weeding than without and varied by species. Costs were calculated by species, treatment and seedling leaf counts when planted (5, 10, 15, 25, 35, 45). Beyond the 2-year threshold, where mortality is highest, weeding effort and costs no longer appear justified. A single fertilizer application did not improve survival in either of the species tested. Practical implications . These findings support landowner restoration by giving restoration technicians accessible tools to improve seedling survival—increasing leaf counts before planting—and reducing post-planting weeding costs by targeting only species for which it benefits and limiting weeding to 2 years.

Los Llanos, Monteverde↗
USGS2026

Cognitive and institutional barriers to effective natural resource management under global change

Many of the most challenging decisions facing natural resource managers today can be understood as time dependent. The concept of time-dependent decisions is useful when considering natural resource management decisions that are characterized by nonstationarity in the underlying ecological system, uncertainty surrounding key ecological relationships, efficacy of management interventions, and difficult trade-offs between the present and the future. In a time-dependent decision, the optimal action changes depending on when the decision is made. We surveyed literature from the fields of cognitive psychology, environmental decision-making, and natural resource management to identify a key set of cognitive and institutional barriers that prevent decision makers from recognizing their decisions as time dependent. This failure obscures the complexity of nonstationarity, uncertainty, and intertemporal choice in natural resource management and facilitates assumptions of stationarity and time independence. We used land acquisition in the context of climate change as an example to explore the barriers to effective time-dependent decision-making at both individual and institutional scales. Using a theoretical model of land acquisition, we quantified the value of accounting for time dependence by comparing the performance of time-dependent, nonstationary decision models with models that assume time independence and stationarity. We found that the conservation value of land acquisition decisions was significantly improved through the use of time-dependent optimization models and that this improvement increased as nonstationarity increased. Finally, explicitly recognizing the time dependence of many natural resource management decisions will help improve the utility of existing decision tools and frameworks.

Conservation Biology↗
USGS2026

Evaluating bank restoration and stabilization in the Yellow River in northern Indiana: Mitigation of sediment and nutrient transport

The U.S. Geological Survey, in cooperation with the Kankakee River Basin and Yellow River Basin Development Commission, evaluated the effects of streambank remediation on the Yellow River in northwest Indiana. Two phases of remediation were completed on the Yellow River between 2021 and 2023, after assessments indicated it was a large contributor of sediment to the Kankakee River. Temporal effects were assessed using pre‑remediation (April 2019 –October 2021) and post‑remediation (February – October 2024) data from two long‑term monitoring sites, USGS 05516665 Yellow River near Oak Grove (Oak Grove) and USGS 05517000 Yellow River at Knox (Knox). Spatial patterns were evaluated among sites from upstream to downstream during the post-remediation period. Monitoring included the pre-existing Oak Grove and Knox sites, an additional monitoring site at USGS streamgage 05516500 Yellow River at Plymouth (Plymouth), and discrete sampling at two additional sites. Discrete sites include USGS 05516663 Yellow River at Upas Rd near Oak Grove (Upas Rd), located upstream of the remediation reach, and USGS 05516667 Yellow River at CR11100E near Oak Grove (CR1100E), located within the remediation reach. Generalized least squares and linear mixed‑effects regression models were used to evaluate temporal differences in turbidity-to-discharge relations for slope and turbidity units, discharge, and suspended‑sediment in before and after remediation data. Similar analysis using generalized least squares and linear mixed‑effects regression models were conducted for spatial analysis of discharge and sediment and nutrient concentrations and fluxes at each site post-remediation. Temporal comparisons showed that turbidity increased less with discharge increases after remediation, indicated by slope of relation, at both Oak Grove and Knox, indicating improved water clarity during higher flows. Mean turbidity and suspended‑sediment concentrations were not statistically different between remediation periods. Comparison of the post-remediation continuous water quality data from upstream to downstream sites through the remediated reaches indicated that downstream sites exhibited steeper turbidity‑to‑discharge slopes than the upstream control site at Plymouth. A statistically significant difference was detected between the slopes at Oak Grove and Plymouth, whereas Knox did not differ significantly from either Plymouth or Oak Grove. Downstream sites had significantly lower mean turbidity, suggesting improved water clarity within and downstream of the remediation reach. Nutrient concentrations generally decreased moving downstream and suspended‑sediment concentrations showed no consistent trend. Nutrient and sediment fluxes increased downstream, likely reflecting the influence of greater discharge volumes. Interpretation of remediation effects was limited by the absence of pre‑remediation data at upstream sites and by the short post‑remediation monitoring period. Continued monitoring could allow for assessment of longer‑term responses to streambank remediation on the Yellow River, as effects may become more pronounced over time.

EarthArXiv↗
USGS2026

Updates to the Lake Washington Ship Canal and Ballard Locks model, Seattle, Washington, 2016–20

The Hiram M. Chittenden Locks (Ballard Locks or Locks) and Lake Washington Ship Canal connect freshwater Lake Washington with saline Shilshole Bay of Puget Sound. The Locks are operated by the U.S. Army Corps of Engineers (USACE) and serve commercial and recreational vessels. Anadromous salmonids migrate through the canal by making use of a fish ladder at the Locks. To help understand canal hydrodynamics, water temperature, and salinity, a two-dimensional model (CE-QUAL-W2) of the Locks and Lake Washington Ship Canal was constructed by WEST Consultants, Inc. In cooperation with USACE, the U.S. Geological Survey reviewed and updated this model and documentation. The review, published previously, recognized the strengths of the model and suggested some modifications and updates. This report documents these modifications, reports on sensitivity testing, describes model recalibration, and reports model results for calendar years 2016–20. Modeled water velocity was less than 0.2 meter per second at most sites and depths; velocity was greater nearer the Locks. Water age, which estimates residence time, was greatest during September and October. Water temperature was greater near-surface and cooler near the channel bottom at some times and locations. The model also simulated the inflow of lower-salinity waters from Lake Washington and the import of higher-salinity waters from the Locks.

Washington↗