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At least 1,279 records · Page 71Linked to original sources
USGS2017

Prediction and visualization of redox conditions in the groundwater of Central Valley, California

Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.

California↗
USGS2007

Hierarchical spatiotemporal matrix models for characterizing invasions

The growth and dispersal of biotic organisms is an important subject in ecology. Ecologists are able to accurately describe survival and fecundity in plant and animal populations and have developed quantitative approaches to study the dynamics of dispersal and population size. Of particular interest are the dynamics of invasive species. Such nonindigenous animals and plants can levy significant impacts on native biotic communities. Effective models for relative abundance have been developed; however, a better understanding of the dynamics of actual population size (as opposed to relative abundance) in an invasion would be beneficial to all branches of ecology. In this article, we adopt a hierarchical Bayesian framework for modeling the invasion of such species while addressing the discrete nature of the data and uncertainty associated with the probability of detection. The nonlinear dynamics between discrete time points are intuitively modeled through an embedded deterministic population model with density-dependent growth and dispersal components. Additionally, we illustrate the importance of accommodating spatially varying dispersal rates. The method is applied to the specific case of the Eurasian Collared-Dove, an invasive species at mid-invasion in the United States at the time of this writing. ?? 2006, The International Biometric Society.

Biometrics↗
USGS2022

Database of biodiversity, habitat, and aquatic-resource quantification tools used in market-based conservation — 2022 update

Market-based conservation makes use of economic incentives to promote actions that avoid, minimize, or compensate for detrimental effects on natural resources and the environment. Examples of market-based conservation mechanisms include aquatic-resource (such as, streams, wetlands, and estuaries) compensatory mitigation, conservation banking, habitat exchanges, and payments for ecosystem services. A critical component in the operation of these market-based conservation mechanisms is the methods (hereafter referred to as “quantification tools”) used to assess existing (sometimes referred to as “baseline”) or potential site conditions. Quantification tools are used to assign values to the benefits provided by preservation, restoration, or enhancement actions, as well as the negative effects of human activities (for example, infrastructure development, energy extraction, and anthropogenic disasters). In 2018, the U.S. Geological Survey (USGS) published a database describing the attributes of 69 quantification tools developed for United States conservation markets. The database focused on tools used for species-based mitigation, payments for ecosystem services, and ecolabel programs (Chiavacci and Pindilli, 2020). Recently, the USGS, in collaboration with the U.S. Environmental Protection Agency, revised the original database by updating the existing tool information, adding newly developed tools, and broadening the scope to include tools developed for compensatory mitigation under the Clean Water Act Section 404 Regulatory Program.

Fact Sheet↗
USGS2010

Strontium isotope detection of brine contamination in the East Poplar oil field, Montana

Brine contamination of groundwater in the East Poplar oil field was first documented in the mid-1980s by the U.S. Geological Survey by using hydrochemistry, with an emphasis on chloride (Cl) and total dissolved solids concentrations. Supply wells for the City of Poplar are located downgradient from the oil field, are completed in the same shallow aquifers that are documented as contaminated, and therefore are potentially at risk of being contaminated. In cooperation with the Office of Environmental Protection of the Fort Peck Tribes, groundwater samples were collected in 2009 and 2010 from supply wells, monitor wells, and the Poplar River for analyses of major and trace elements, including strontium (Sr) concentrations and isotopic compositions. The ratio of strontium-87 to strontium-86 ( 87 Sr/ 86 Sr) is used extensively as a natural tracer in groundwater to detect mixing among waters from different sources and to study the effects of water/rock interaction. On a plot of the reciprocal strontium concentration against the 87 Sr/ 86 Sr ratio, mixtures of two end members will produce a linear array. Using this plotting method, data for samples from most of the wells, including the City of Poplar wells, define an array with reciprocal strontium values ranging from 0.08 to 4.15 and 87 Sr/ 86 Sr ratios ranging from 0.70811 to 0.70828. This array is composed of a brine end member with an average 87 Sr/ 86 Sr of 0.70822, strontium concentrations in excess of 12.5 milligrams per liter (mg/L), and chloride concentrations exceeding 8,000 mg/L mixing with uncontaminated water similar to that in USGS06-08 with 18.0 mg/L chloride, 0.24 mg/L strontium, and a 87 Sr/ 86 Sr ratio of 0.70811. The position of samples from the City of Poplar public-water supply wells within this array indicates that brine contamination has reached all three wells. Outliers from this array are EPU-4G (groundwater from the Cretaceous Judith River Formation), brine samples from disposal wells (Huber 5-D and EPU 1-D), USGS92-11 (a well with water that was considerably contaminated in 1992 and becoming less saline with time), and PNR-27 (only slightly below the defined trend with an 87 Sr/ 86 Sr ratio of 0.70793). Water samples from the City of Poplar wells are also enriched in anions and cations that are abundant in oil-field brine.

Open-File Report↗
USGS2016

Abstract volume for the 2016 biennial meeting of the Yellowstone Volcano Observatory

Introduction Every two years, scientists, natural resource managers, outreach specialists, and a variety of other interested parties get together for the biennial meeting of the Yellowstone Volcano Observatory (YVO). Each time, the theme varies. In past years, we have focused the meeting around topics including monitoring plans, emergency response, geodesy, and outreach. This year, we spent the first half-day devoted to recent research results, plans for upcoming studies, and geothermal monitoring. On the second day, our focus switched to eruption precursors, particularly as they apply to large caldera systems. Very few large explosive eruptions from caldera systems have taken place in recorded history. Therefore, there are few empirical data with which to characterize the nature of volcanic unrest that might precede eruptions with volcano explosivity index (VEI) of six or greater. For this reason, we set up a series of talks that explore what we know and don’t know about large eruptions. We performed an informal expert elicitation (a frequently used method to characterize expert opinion) with a small number of our colleagues, which served as the basis for a productive discussion session. This short volume of abstracts and extended abstracts provides a summary of the presentations made at the YVO meeting held in Mammoth Hot Springs, Wyoming, on May 10–11, 2016.

Open-File Report↗
USGS2022

Risk assessment for bull trout introduction into Sullivan Lake and Harvey Creek, northeastern Washington

The Kalispel Tribe of Indians (KT), U. S. Fish and Wildlife Service, and Washington Department of Fish and Wildlife are engaged in conservation of bull trout ( Salvelinus confluentus ) in the Lake Pend Oreille (LPO) Core Area. The LPO is a complex habitat core area which falls within three states (Montana, Idaho, and Washington) and a tribal entity. As part of the conservation process, KT worked in cooperation with the U. S. Geological Survey (USGS) to complete a risk assessment for introduction of bull trout into Sullivan Lake/Harvey Creek, northeastern, Washington. The risk assessment was designed to evaluate potential risks to resident fish species, to bull trout introduced into Sullivan Lake, and to bull trout donor source populations. This risk assessment describes the potential risks associated with pathogens (introduction of pathogens and increased pathogen burden), genetics (such as risk to donor sources, straying and breeding with native bull trout, and introduction of bull-brook hybrids), and ecological interactions (such as predation and competition). Potential donor source populations were identified and evaluated using a qualitative approach based on expert opinion and a decision framework. Literature reviews were completed for fish species composition and abundance in Sullivan Lake basin to assess potential ecological interactions and risks to these populations and to the introduced bull trout. The USGS assessed pathogen risks through two major questions: (1) whether introduced bull trout might bring pathogens into the Sullivan Lake basin that were not previously present and (2) whether the health of introduced bull trout could be adversely affected by pathogens already present in the basin. Assessment of genetic risks included demographic risks to donor source populations, potential for hybridization with native bull trout, and the risk of introducing bull-brook hybrids. Literature reviews were used in conjunction with discussions among regional biologists to identify potential donor source populations and their population attributes. A decision framework was developed by USGS in collaboration with KT biologists that identified desirable population attributes (life history behavior, abundance, population viability, feasibility of collection, and environmental match) associated with donor source populations and established ranking criteria. The population attribute information was used with the (1) decision framework, (2) established ranking criteria, and (3) expert opinion of regional biologists, to assign scores for overall ranking of donor source populations. The LPO source population was the highest ranked and is considered a robust and stable population. The risk of introducing pathogens from LPO into Sullivan Lake via a bull trout introduction program seems low, and indirect pathogen burden risks to resident species can be mitigated using established pathogen surveillance methods. The likelihood that bull trout, introduced into Sullivan Lake, stray and spawn with native bull trout is low. Nearest-neighbor donor source populations, such as LPO, could minimize negative fitness impacts that might occur from straying and interbreeding of individuals that become entrained and help maintain natural patterns of genetic diversity in native populations. The ecological risk that a bull trout introduction presents to resident species seems to be low but with some uncertainty. Pygmy whitefish, a Washington State Sensitive species, is likely most vulnerable to extirpation with increased predation pressure with introduction of an additional piscivore into the ecosystem. The status of the pygmy whitefish in Sullivan Lake is unknown. The ecological risks most likely to reduce the viability of introduced bull trout are predation by burbot and an adequate forage base in Sullivan Lake. Prior fish surveys provided data on resident species abundance, provided an established baseline for effective monitoring, and identifying ecosystem changes post-bull trout introduction to inform future adaptive management decisions.

Washington↗
USGS2026

Colocating artificial intelligence data centers with energy infrastructure on Federal public lands—A science synthesis and spatial analysis to inform decision making

Executive Summary Artificial intelligence (AI) is rapidly transforming industries and economies, creating an urgent need to strategically plan for the energy and infrastructure required to support increasing AI use. U.S. Federal agencies and bureaus have been directed to explore ways to accelerate permitting, development, and deployment of energy resources and AI technologies, including encouraging the colocation of energy infrastructure and data centers. To inform these initiatives, this report synthesizes relevant scientific information and presents a spatial analysis of existing energy infrastructure and data centers on or near U.S. Federal public lands managed by the Bureau of Land Management (BLM). The purpose of this science synthesis and spatial analysis is to provide the BLM with foundational information for considering potential colocation of data centers with energy infrastructure on Federal public lands to support evidence-based decisions. Additionally, this report provides insight into current (2025) and potential future energy demands by providing projections of a range of potential future environmental conditions relevant to maintaining industry-recommended cooling temperature standards necessary for efficient data center operations. As a part of this effort, a rapid response literature review was conducted of the best available science on the topic of data center development and energy infrastructure in July–August 2025, supplemented by additional resources recommended by U.S. Federal agency and bureau subject matter experts (hereafter experts; including the U.S. Department of Energy National Laboratory of the Rockies) and peer reviewers. To better understand current conditions relevant to AI data center development, a spatial analysis was conducted across Alaska and 11 States in the Western United States, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming, all of which contain extensive BLM-managed surface lands (hereafter referred to as “BLM lands”) that could be considered for the colocation of energy infrastructure and AI data centers. This effort identified BLM lands within 10 miles of existing transmission lines, consistent with methods used in previous BLM programmatic environmental impact statements. This report describes the types of data centers operating within the United States, which vary in ownership, size, technology, and proximity to end users. This report then outlines the primary considerations of data center development, including reliable energy supply, natural resources (such as water availability to support cooling requirements), and relevant policy and regulatory considerations. Energy supply considerations are pivotal for data center operation. Between 2014 and 2018, data centers in the United States accounted for nearly 2 percent of the Nation’s total electricity consumption, and data center energy consumption is projected to increase from 2 to 6.7–12 percent of total U.S. electricity use by 2028. These energy requirements necessitate careful consideration of energy supply when considering potentially suitable locations for data center development. Experts anticipate that an increase in renewable energy generation will likely support most potential future power demand needs, including for data centers, followed by increases in natural gas, nuclear, and geothermal energy production. Additional capacity in the form of battery storage will likely not generate electricity, but may improve the reliability and flexibility of supply, helping to ensure that growing data center loads can be met. However, the U.S. Department of Energy estimates that the United States will need, on average, 57 percent more energy transmission infrastructure by 2035 to account for the growing power demand introduced by development such as data centers. Cooling server equipment in data centers requires large amounts of electricity and water, and this demand can be exacerbated by hot and humid conditions. Energy efficient water-based cooling technologies may reduce electricity consumption onsite but require more water consumption. This additional water demand has the potential to increase water stress and competition with other users. As such, developing data centers will likely need a thorough assessment of current and potential future water availability, as well as consideration of how water demand may change across other sectors. Data center development involves policy and regulatory considerations, as projects must undergo environmental review and authorization processes that can take 18–24 months. Coordinating these environmental reviews and authorizations with other energy development projects, such as building new transmission lines, may cause additional delays. Recent efforts by the U.S. Department of Energy and U.S. Department of the Interior aim to expedite environmental reviews and authorizations and improve coordination across agencies. The spatial analysis identified 771 existing AI data centers and more than 3,300 power plants. The spatial analysis found that 6 percent of AI data centers and 22 percent of power plants in the Western United States were on or within 1 mile of BLM lands, and California had the largest number of facilities. Most existing AI data centers were near high-voltage transmission lines and close to power plants, supporting efficient energy delivery. More than 90,000,000 acres of BLM lands were within 10 miles of existing high-voltage transmission lines, representing 38 percent of BLM lands in the study area. Available transmission infrastructure and the overlap with BLM lands varied by State, and Alaska had limited overlap compared to the rest of the Western United States. To operate most efficiently, data center temperatures must be at or below 80.6 degrees Fahrenheit. This analysis of future temperature and precipitation projections indicated increasing cooling demands for data centers, particularly in Arizona, California, and Nevada, where rising temperatures are expected to increase energy and operational costs while potentially stressing current regional electrical grid infrastructure. This report highlights relevant energy supply, natural resources, and regulatory considerations for data center development on BLM lands. This report does not provide a comprehensive ecological, regulatory, land suitability, or permitting analysis. The factors described here are contextual considerations only and are not intended to identify, rank, quantify, or recommend optimal areas for data center colocation. This spatial analysis focused solely on energy considerations relevant to data centers and did not consider water availability, critical habitats, BLM National Conservation Lands, areas of cultural or historical significance, and other sensitive resources. These topics are recognized as critical but were not within the scope of this science synthesis and spatial analysis.

Scientific Investigations Report↗
USGS2024

An evaluation of fin ray microchemistry to describe movement of White Sturgeon in the Kootenai River basin: Insights and limitations

Introduction: White Sturgeon Acipenser transmontanus in the Kootenai River basin is listed as endangered in the United States and Canada. Declines have been mainly attributed to poor recruitment exacerbated by the environmental effects of Libby Dam in Montana. Reduced primary production downstream of Libby Dam has been identified as one factor limiting growth of White Sturgeon, thereby limiting natural reproduction through delayed sexual maturity. However, estimating changes in growth over time without knowledge of fish location (i.e., Kootenai River vs. Kootenay Lake) is difficult. The objective of this project was to use microchemistry analysis to describe the movement of White Sturgeon within the Kootenai River basin. Methods: Water samples ( n = 27) and White Sturgeon fin rays ( n = 162) collected in the Kootenai River basin were measured for strontium isotope ( 87 Sr: 86 Sr) ratios using laser ablation multicollector inductively coupled plasma mass spectrometry. All water samples and a subset of fin rays ( n = 8) were also analyzed for trace elements (e.g., Sr, Ba, Mg). Fin ray annuli were measured and tested using knowledge of known age; known locations from physical capture events were assigned to age at capture. Results: Strontium isotope analysis was unable to detect differences in the Kootenai River and Kootenay Lake using water or fin ray samples. The Kootenai River and Kootenay Lake were distinguishable using trace element data from water samples, but not fin rays. The discrepancy with trace elements appears to be associated with the physiology of fin ray growth. Discussion: Although this study did not meet its original objective of describing the movement of White Sturgeon in the Kootenai River basin from fin ray microchemistry, our results provide insight into the potential influence of physiology on microchemistry analysis. In particular, fin ray microchemistry of slow-growing fishes may be possible in freshwater systems with further investigation into the physiological processes associated with growth and the incorporation of elements into calcified structures.

British Columbia, Idaho, Montana↗
USGS2013

Improving predictive power of physically based rainfall-induced shallow landslide models: a probablistic approach

Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS) code. The new code (TRIGRS-P) adopts a stochastic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying the input parameters are analyzed statistically, and compared to the original (deterministic) model output. The comparison suggests an improvement of the predictive power of the model of about 10% and 16% in two small test areas, i.e. the Frontignano (Italy) and the Mukilteo (USA) areas, respectively. We discuss the computational requirements of TRIGRS-P to determine the potential use of the numerical model to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides in very large areas, extending for several hundreds or thousands of square kilometers. Parallel execution of the code using a simple process distribution and the Message Passing Interface (MPI) on multi-processor machines was successful, opening the possibly of testing the use of TRIGRS-P for the operational forecasting of rainfall-induced shallow landslides over large regions.

Geoscientific Model Development and Discussions↗
USGS2009

Environmental effects of hydrothermal alteration and historical mining on water and sediment quality in Central Colorado

The U.S. Geological Survey conducted an environmental assessment of 198 catchments in a 54,000-km2 area of central Colorado, much of which is on Federal land. The Colorado Mineral Belt, a northeast-trending zone of historical base- and precious-metal mining, cuts diagonally across the study area. The investigation was intended to test the hypothesis that degraded water and sediment quality are restricted to catchments in which historical mining has occurred. Water, streambed sediment, and aquatic insects were collected from (1) catchments underlain by single lithogeochemical units, some of which were hydrothermally altered, that had not been prospected or mined; (2) catchments that contained evidence of prospecting, most of which contain hydrothermally altered rock, but no historical mining; and (3) catchments, all of which contain hydrothermally altered rock, where historical but now inactive mines occur. Geochemical data determined from catchments that did not contain hydrothermal alteration or historical mines met water quality criteria and sediment quality guidelines. Base-metal concentrations from these types of catchments showed small geochemical variations that reflect host lithology. Hydrothermal alteration and mineralization typically are associated with igneous rocks that have intruded older bedrock in a catchment. This alteration was regionally mapped and characterized primarily through the analysis of remote sensing data acquired by the ASTER satellite sensor. Base-metal concentrations among unaltered rock types showed small geochemical variations that reflect host lithology. Base-metal concentrations were elevated in sediment from catchments underlain by hydrothermally altered rock. Classification of catchments on the basis of mineral deposit types proved to be an efficient and accurate method for discriminating catchments that have degraded water and sediment quality. Only about 4.5 percent of the study area has been affected by historical mining, whereas a larger part of the study area is underlain by hydrothermally altered rock that has weathered to produce water and sediment with naturally elevated geochemical baselines.

Colorado↗
USGS2018

Methods used to estimate daily streamflow and water availability in the Massachusetts Sustainable-Yield Estimator version 2.0

The Massachusetts Sustainable-Yield Estimator is a decision support tool that provides estimates of daily unaltered streamflow, water-use-adjusted streamflow, and water availability for ungaged, user-defined basins in Massachusetts. Daily streamflow at the ungaged site is estimated for unaltered (no water use) and water-use scenarios. The procedure for estimating streamflow was developed previously and has been implemented with minor changes and updated water-use data in version 2.0 of the Massachusetts Sustainable-Yield Estimator. Unaltered streamflow at selected exceedance probabilities is estimated by previously published regression equations. Streamflow is interpolated between the regressed quantiles to produce a continuous flow duration curve. A daily streamflow time series is produced for the ungaged site by relating the estimated flow duration curve at the ungaged site to a flow duration curve at a gaged reference site and then transferring the dates from the reference site to the ungaged site. Minor refinements were made to the previously published methods to estimate unaltered and water-use-adjusted streamflow, including a procedure to enforce the monotonic structure of the regression-based unaltered flow quantiles, improvements to the interpolation method used for computing the estimated flow duration curve, and updates to the methods used to compute time-lagged stream alterations from groundwater pumping or discharges. Additionally, a procedure was developed to estimate prediction intervals for daily and monthly unregulated streamflow time series at an ungaged site. The Massachusetts Sustainable-Yield Estimator computes water-use-adjusted streamflow using water-use data provided by the Massachusetts Department of Environmental Protection. Available water-use data included monthly withdrawal and wastewater discharge volumes from 2010 to 2014 for surface-water and groundwater sources. Water-use-adjusted streamflow represents the potential effect of current water use on natural streamflow in the basin over the range of historical hydrologic conditions. Georeferenced water withdrawal and discharge volumes were incorporated into the Massachusetts StreamStats web application for use in version 2.0 of the Massachusetts Sustainable-Yield Estimator. To compute water-use-adjusted streamflow, mean daily withdrawals and discharges within a user-defined basin are subtracted and added to the unaltered time series, respectively. Surface-water volumes are applied directly to the equation. Time-lagged streamflow alterations from groundwater withdrawal or wastewater discharge sources are estimated by using a response-coefficient method developed from results of previously published, calibrated groundwater models in Massachusetts. The Massachusetts Sustainable-Yield Estimator was updated to version 2.0 to improve software stability and usability. The version 2.0 software application was developed in Microsoft Access with a graphical user interface. All geoprocessing steps, including basin delineation and compilation of basin characteristics and water use within the basin, were completed in the Massachusetts StreamStats web application and exported for use by the Massachusetts Sustainable-Yield Estimator version 2.0.

Massachusetts↗
USGS2018

Field evaluation of carbon dioxide as a fish deterrent at a water management structure along the Illinois River

Construction of a water management structure (WMS) in the levee surrounding The Nature Conservancy’s Emiquon Preserve (Havana, Illinois, USA) created a new hydrological connection and potential aquatic invasive species pathway between the Illinois River and a large conservation wetland complex. Site managers need a control tool that deters the upstream passage of non-native fishes into the wetland lakes, but does not interfere with normal gate operation and water discharge. This short field study evaluated carbon dioxide (CO 2 ) injected into water as a non-obstructive method to reduce fish abundance near the WMS culverts. We quantified relative fish abundance using underwater sonar with and without injection of CO 2 into culverts during three discharge events: no flow (0 m 3 /s), restricted flow (0.9 m 3 /s), and unrestricted flow (3.2 m 3 /s). Overall, CO 2 reached or exceeded our target concentration of 100 mg/L during no flow and restricted flow, and fish abundance was 70–95% lower at culvert entrances relative to untreated control days. The target CO 2 level was not reached during unrestricted flow and fish abundance was not reduced during CO 2 injection. Atmospheric CO 2 concentrations were inconsequential and unaffected by CO 2 treatments throughout testing. Results from this initial field study provide several considerations for CO 2 as a fish deterrent in natural environments.

Illinois↗
USGS2023

Conservation decision support for Silver Chub habitat in Lake Erie

Objective Conservation and restoration of aquatic species is difficult, especially for rare species, because their habitats are typically disturbed, obscuring the natural ability of the habitat to support each species. The Lake Erie population of Silver Chub Macrhybopsis storeriana struggles to sustain itself in a habitat disturbed by a wide spectrum of anthropogenic factors. Application of multiple model predictions can provide indications of conservation or restoration opportunities for this species. Methods A combination of models that predict the best potential for Lake Erie habitat to support Silver Chub and the effects of anthropogenic disturbances on that population were used to identify habitat conditions throughout the western aquatic lake unit. Result As many as 76 combinations of best habitat potential and disturbance conditions were present, but the best opportunities occurred in <12% of the study area. Some of the best protection opportunities were farthest offshore, and extensive areas of least disturbed habitat for restoration were near the southern and western shores. The location-specific model predictions provide fine-scale decision support for Silver Chub habitat protection or restoration. Conclusion The approach applied here may help identify compatibilities among species to achieve the desirable fish community for Lake Erie and reconcile conflicting management actions.

Michigan, Ohio, Ontario↗
USGS1970

Distribution of rubidium between sodic sanidine and natural silicic liquid

Phenocrysts of sodic sanidine from twelve upper Cenozoic units of silicic ash-flow tuff and lava from the Western United States contain from 0.25 to 0.45 the Rb present in the associated groundmass materials. The ratios of potassium to rubidium in the sanidines are, on the average, about four times greater than those of the groundmass. Separation of phenocrystic sanidine from salic melts provides an efficient method for raising the Rb content and lowering the K/Rb ratio of the melts, although the amount of differentiation probably is limited by continuous reequilibration of the alkalis between crystal and liquid phases through ion exchange. Syenites of cumulate origin will have appreciably lower Rb contents and higher K/Rb ratios than the melts from which they precipitated. Available data on the distribution of Rb between synthetic biotite and K-sanidine demonstrate that the separation of biotite probably will not deplete salic melts in Rb relative to K.

Contributions to Mineralogy and Petrology↗
USGS2002

Determination of small quantities of fluoride in water: A modified zirconium-alizarin method

The zirconium-alizarin method has been modified to facilitate the convenient and accurate determination of small amounts of fluoride in a large number of water samples. Sulfuric acid is used to acidify the samples to reduce the interference of sulfate. The pH is accurately controlled to give the most sensitive comparisons. Most natural waters can be analyzed by the modified procedure without resorting to correction curves. The fluoride content of waters containing less than 500 parts per million of sulfate, 500 parts per million of bicarbonate, and 1000 parts per million of chloride may be determined within a limit of about 0.1 part per million when a 100-ml. sample is used.

Industrial And Engineering Chemistry Analytical Ed↗
USGSDate not supplied

Characterizing the interface between wild ducks and poultry to evaluate the potential of transmission of avian pathogens

Background Characterizing the interface between wild and domestic animal populations is increasingly recognized as essential in the context of emerging infectious diseases (EIDs) that are transmitted by wildlife. More specifically, the spatial and temporal distribution of contact rates between wild and domestic hosts is a key parameter for modeling EIDs transmission dynamics. We integrated satellite telemetry, remote sensing and ground-based surveys to evaluate the spatio-temporal dynamics of indirect contacts between wild and domestic birds to estimate the risk that avian pathogens such as avian influenza and Newcastle viruses will be transmitted between wildlife to poultry. We monitored comb ducks ( Sarkidiornis melanotos melanotos ) with satellite transmitters for seven months in an extensive Afro-tropical wetland (the Inner Niger Delta) in Mali and characterise the spatial distribution of backyard poultry in villages. We modelled the spatial distribution of wild ducks using 250-meter spatial resolution and 8-days temporal resolution remotely-sensed environmental indicators based on a Maxent niche modelling method. Results Our results show a strong seasonal variation in potential contact rate between wild ducks and poultry. We found that the exposure of poultry to wild birds was greatest at the end of the dry season and the beginning of the rainy season, when comb ducks disperse from natural water bodies to irrigated areas near villages. Conclusions Our study provides at a local scale a quantitative evidence of the seasonal variability of contact rate between wild and domestic bird populations. It illustrates a GIS-based methodology for estimating epidemiological contact rates at the wildlife and livestock interface integrating high-resolution satellite telemetry and remote sensing data.

Inner Niger Delta↗
USGS2022

Physics-guided recurrent neural networks for predicting lake water temperature

This chapter presents a physics-guided recurrent neural network model (PGRNN) for predicting water temperature in lake systems. Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. ML models have found tremendous success in several commercial applications, e.g., computer vision and natural language processing. The chapter presents PGRNN as a general framework for modeling physical processes in engineering and environmental systems. The proposed PGRNN explicitly incorporates physical laws such as energy conservation or mass conservation. In particular, researchers started pursing this direction by using residual modeling, where an ML model is learned to predict the errors, or residuals, made by a physics-based model. Advanced ML models, especially deep learning models, often require a large amount of training data for tuning model parameters.

Book chapter↗
USGS2004

Estimating population trends with a linear model: Technical comments

Controversy has sometimes arisen over whether there is a need to accommodate the limitations of survey design in estimating population change from the count data collected in bird surveys. Analyses of surveys such as the North American Breeding Bird Survey (BBS) can be quite complex; it is natural to ask if the complexity is necessary, or whether the statisticians have run amok. Bart et al. (2003) propose a very simple analysis involving nothing more complicated than simple linear regression, and contrast their approach with model-based procedures. We review the assumptions implicit to their proposed method, and document that these assumptions are unlikely to be valid for surveys such as the BBS. One fundamental limitation of a purely design-based approach is the absence of controls for factors that influence detection of birds at survey sites. We show that failure to model observer effects in survey data leads to substantial bias in estimation of population trends from BBS data for the 20 species that Bart et al. (2003) used as the basis of their simulations. Finally, we note that the simulations presented in Bart et al. (2003) do not provide a useful evaluation of their proposed method, nor do they provide a valid comparison to the estimating- equations alternative they consider.

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