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At least 19 recordsLinked to original sources
USGS1983

Results of an adaptive environmental assessment modeling workshop concerning potential impacts of drilling muds and cuttings on the marine environment

Drilling fluids or "muds" are essential components of modern drilling operations. They provide integrity for the well bore, a medium for removal of formation cuttings, and lubrication and cooling of the drill bit and pipe. The modeling workshop described in this report was conducted September 14-18, 1981 in Gulf Breeze, Florida to consider potential impacts of discharged drilling muds and cuttings on the marine environment. The broad goals of the workshop were synthesis of information on fate and effects, identification of general relationships between drilling fluids and the marine environment, and identification of site-specific variables likely to determine impacts of drilling muds and cuttings in various marine sites. The workshop was structured around construction of a model simulating fate and effects of discharges from a single rig into open water areas of the Gulf of Mexico, and discussion of factors that might produce different fate and effects in enclosed areas such as bays and estuaries. The simulation model was composed of four connected submodels. A Discharge/Fate submodel dealt with the discharge characteristics of the rig and the subsequent fate of discharged material. Three effects submodels then calculated biological responses at distances away from the rig for the water column, soft bottom benthos (assuming the rig was located over a soft bottom environment), and hard bottom benthos (assuming the rig was located over a hard bottom environment). The model focused on direct linkages between the discharge and various organisms rather than on how the marine ecosystem itself is interconnected. Behavior of the simulation model indicated relatively localized effects of drilling muds and cuttings discharged from a single platform into open water areas. Water column fate and effects were dominated by rapid dilution. Effects from deposition of spent mud and cuttings were spatially limited with relatively rapid recovery, especially in soft bottom benthic communities which were conceptualized as being adapted to frequent storms. This behavior was generated by the set of assumptions about linkages and functional relationships used to construct the model. Areas of uncertainty included methods for extrapolating 96-hr LC50 so results to exposures of varying lengths and concentrations; recovery rates of benthic communities; responses to various depths and rates of burial; fate and effects of the plume in relationship to stratification layers; and long-term and sub-lethal effects of slightly elevated concentrations of discharged materials. Evaluation of the assumptions of the Soft Bottom Submodel suggest that the assumptions used may have been relatively liberal estimates of resiliency of these communities. Discussion of "closed" water bodies such as bays and estuaries indicated several reasons to expect different and more complex fate and effects behavior in these areas. These factors included different species and communities (such as aquatic macrophytes and oyster beds), more complex circulation and stratification patterns, and potentially more active resuspension processes. Much of the possible difference in behavior in these areas centers around the extent to which they are “closed” or in the relative residence times of water and sediments in these areas as they determine the long-term dispersion of discharged material. Despite the complexity and variability of these areas, a large body of knowledge (such as that concerning fate and physical effects of dredge spoil) that could be effectively employed in analysis of potential fate and physical effects in enclosed areas was identified.

Report↗
USGS2012

Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m 2 cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r 2 . For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r 2 values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.

Wyoming↗
USGS2005

Environmental exposure modeling and monitoring of human pharmaceutical concentrations in the environment

Human pharmaceuticals are receiving increased attention as environmental contaminants. This is due to their biological activity and the number of monitoring programs focusing on analysis of these compounds in various environmental media and compartments. Risk assessments are needed to understand the implications of reported concentrations; a fundamental part of the risk assessment is an assessment of environmental exposures. The purpose of this chapter is to provide guidance on the use of predictive tools (e.g., models) and monitoring data in exposure assessments for pharmaceuticals in the environment. Methods to predict environmental concentrations from equations based on first principles are presented. These equations form the basis of existing GIS (geographic information systems)-based systems for understanding the spatial distribution of pharmaceuticals in the environment. The pharmaceutical assessment and transport (P h ATE), georeferenced regional exposure assessment tool for European rivers (GREAT-ER), and geographical information system (GIS)-ROUT models are reviewed and recommendations are provided concerning the design and execution of monitoring studies. Model predictions and monitoring data are compared to evaluate the relative utility of each approach in environmental exposure assessments. In summary, both models and monitoring data can be used to define representative exposure concentrations of pharmaceuticals in the environment in support of environmental risk assessments.

Book chapter↗
USGS2010

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

International Journal of Remote Sensing↗
USGS2020

Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey

The US Geological Survey (USGS) is currently (2020) integrating its water science programs to better address the nation’s greatest water resource challenges now and into the future. This integration will rely, in part, on data from 10 or more intensively monitored river basins from across the USA. A team of USGS scientists was convened to develop a systematic, quantitative approach to prioritize candidate basins for this monitoring investment to ensure that, as a group, the 10 basins will support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC04) with some of the smaller HUC04s being combined; median candidate-basin area is 46,600 km 2 . Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Ten geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were selected to rank candidate basins within each of the 18 hydrologic regions. The two highest ranking candidate basins in each of the 18 regions were identified as finalists for selection as “Integrated Water Science Basins”; final selection will consider input from a variety of stakeholders. The regional framework, with only one basin selected per region, ensures that as a group, the basins represent the range in major drivers of the hydrologic cycle. Ranking within each region, primarily based on anthropogenic stressors of water resources, ensures that settings representing important water-resource challenges for the nation will be studied.

contiguous United States↗
USGS2012

An alternative process model of preferential contaminant travel times in the unsaturated zone: Application to Rainier Mesa and Shoshone Mountain, Nevada

Simulating contaminant transport in unsaturated zones with sparse hydraulic property information is a difficult, yet common, problem. When contaminant transport may occur via preferential flow, simple modeling approaches can provide predictions of interest, such as the first arrival of contaminant, with minimal site characterization. The conceptual model for unsaturated zone flow at Rainier Mesa and Shoshone Mountain, Nevada National Security Site, establishes the possibility of preferential flow through lithologies between potential radionuclide sources and the saturated zone. After identifying preferential flow as a possible contaminant transport process, we apply a simple model to estimate first arrival times for conservatively transported radionuclides to reach the saturated zone. Simulated preferential flow travel times at Rainier Mesa are tens to hundreds of years for non-ponded water sources and 1 to 2 months for continuously ponded water sources; first arrival times are approximately twice as long at Shoshone Mountain. These first arrival time results should then be viewed as a worst-case scenario but not necessarily as a timescale for a groundwater-contamination hazard, because concentrations may be very low. The alternative approach demonstrated here for estimating travel times can be useful in situations where predictions are needed by managers for the fastest arrival of contaminants, yet budgetary or time constraints preclude more rigorous analysis, and when additional model estimates are needed for comparison (i.e., model abstraction).

Nevada↗
USGS2005

A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.

Remote Sensing of Environment↗
USGS2024

Using models of local environmental conditions for biological assessment

A common approach for biological assessment is to compare current observations of biota at a site to predictions of the biota that would occur if the site were in reference condition. To estimate these reference expectations, predictive bioassessment models use only data collected at reference sites and estimate relationships between biota and environmental variables that are unaffected by human activities (i.e., immutable variables). However, in some areas where human activities are pervasive, few reference sites are available. Here, we introduce a new approach for bioassessment, in which we first estimate relationships between widely available measurements of both immutable and human-influenced landscape variables and local environmental conditions (conductivity, dissolved total P, total suspended solids, percentage of sand and fines in the substrate, and dissolved organic C), and then we estimate the relationship between these local environmental variables and total macroinvertebrate richness. We then calculate the values of the local environmental variables under reference conditions by adjusting predictors that are strongly influenced by human activities to levels that are consistent with those observed at reference sites. Reference values of local environmental variables are then combined with the model for taxon richness to calculate taxon richness expected under reference conditions. Predictions of total taxon richness in validation data using the new approach are similar in accuracy and precision as predictions calculated using a traditional approach that focuses only on reference site data. The new approach complements existing bioassessment methods by highlighting types of sites in which estimates of reference expectations are uncertain and by improving our understanding of how instream stressors affect expected total taxon richness.

conterminous United States↗
USGS2020

Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu

Ecological risk assessments play an important role in environmental management and decision-making. Although empirical measurements of the effects of habitat changes and chemical exposure are often made at molecular and individual levels, environmental decision-making often requires the quantification of management-relevant, population-level outcomes. In this study, we generalized a modeling framework to evaluate population-level ecological risk of environmental stress and bioactive chemicals. The modeling framework includes (1) a biological model module that incorporates complex and interacting biological and ecological processes, and environmental stochasticity, (2) an effect module that links the impacts of environmental changes and chemical exposure to individual characteristics, and (3) a population module that makes decisions on the choice of population-level properties to best capture the effects and thus to track in the model based on the target species and the research and management interest. This framework is a 3-module procedure that provides an alternative way for researchers to organize, present and communicate the risk assessment modeling studies. To demonstrate this framework, we used a socioeconomically important riverine fish species, smallmouth bass Micropterus dolomieu , as the model species. We developed an individual-based model as the biological model module. We evaluated the impacts of changing water temperature and flow regimes, and the impacts of exposure to estrogenic endocrine disrupting compounds (EEDC) on smallmouth bass populations in the Chesapeake Bay Watershed, USA. Warm summer water temperatures and year-round high flows had the most severe impacts on the smallmouth bass population. An increase in exposure level to EEDC, both year-round and in summer months, substantially reduced population size, spawner and recruit abundance, and the proportion of quality-length individuals. Acute exposure to EEDC was more detrimental to the population than chronic exposure. Acute exposure during spawning season had the most severe impacts. This modeling framework can be extended to other species, environmental factors and chemicals, and can be used to inform management and conservation decisions.

Delaware, Maryland, New York, Pennsylvania, Virgin↗
USGS2025

Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus

Black Carp Mylopharyngodon piceus were imported into the United States in the 1970s and 1980s for use in aquaculture; escape occurred and reported wild captures increased. Lacking species-specific capture methods, we assessed fisheries dependent incidental Black Carp catches for a common method, hoop nets, by kernel density analysis to identify an area of increased reporting and compare frequency of reports for water temperature, river stage, and capture date to identify seasonality. We then used fisheries independent effort to identify co-occurrence of species via non-metric multi-dimensional scaling and fit Black Carp catch and environmental covariates by generalized linear models to assess site-specific environmental covariates facilitating capture. The best approximating distribution was refitted for predictions and inference. The greatest density of fisheries dependent hoop net captures (39 %) was near the confluence of the Missouri and Mississippi rivers, primarily from July-September. Captures were characterized by median water temperature 26.7°C, river stage 5.02 m, and 223 day-of-year (DOY; mid-August). Ordination of fisheries independent catch identified similarity in environmental covariates of Smallmouth Buffalo Ictiobus bubalus and Black Carp. The probability of capturing ≥ 1 Black Carp increased with DOY, decreased with increasing current velocity, and increased with depth. Most captures occurred in outside bends (87 %) or side channels (12 %). Probability of Black Carp capture was low but increased in summer and early fall when stage is lower, facilitating reduced current velocity and access to deeper areas. Results may be validated beyond this river segment to test if site-specific hydrology or habitat characteristics facilitated increased commercial and biologist capture and for replication.

Illinois, Missouri↗
USGS1989

Animal population dynamics: Identification of critical components

There is a growing interest in the use of population dynamics models in environmental risk assessment and the promulgation of environmental regulatory policies. Unfortunately, because of species and areal differences in the physical and biotic influences on population dynamics, such models must almost inevitably be both complex and species- or site-specific. Given the emormous variety of species and sites of potential concern, this fact presents a problem; it simply is not possible to construct models for all species and circumstances. Therefore, it is useful, before building predictive population models, to discover what input parameters are of critical importance to the desired output. This information should enable the construction of simpler and more generalizable models. As a first step, it is useful to consider population models as composed to two, partly separable classes, one comprising the purely mechanical descriptors of dynamics from given demographic parameter values, and the other describing the modulation of the demographic parameters by environmental factors (changes in physical environment, species interactions, pathogens, xenobiotic chemicals). This division permits sensitivity analyses to be run on the first of these classes, providing guidance for subsequent model simplification. We here apply such a sensitivity analysis to network models of mammalian and avian population dynamics.

Ecological Modelling↗
USGS2013

Challenge theme 5: Current and future needs of energy and mineral resources in the Borderlands and the effects of their development: Chapter 7 in United States-Mexican Borderlands: Facing tomorrow's challenges through USGS science

Exploration and extraction activities related to energy and mineral resources in the Borderlands—such as coal-fired power plants, offshore drilling, and mining—can create issues that have potentially major economic and environmental implications. Resource assessments and development projects, environmental studies, and other related evaluations help to understand some of these issues, such as power plant emissions and the erosion/denudation of abandoned mine lands. Information from predictive modeling, monitoring, and environmental assessments are necessary to understand the full effects of energy and mineral exploration, development, and utilization. The exploitation of these resources can negatively affect human health and the environment, its natural resources, and its ecological services (air, water, soil, recreation, wildlife, etc.). This chapter describes the major energy and mineral issues of the Borderlands and how geologic frameworks, integrated interdisciplinary (geobiologic) investigations, and other related studies can address the anticipated increases in demands on natural resources in the region.

United States-Mexico Borderlands↗
USGS2026

Identifying headwater streams across the conterminous United States

Headwater streams play critical roles in hydrologic and biogeochemical processes and functions, yet their spatial distribution and land cover context remain poorly understood at continental scales, and no dedicated geospatial dataset exists. Building from a high-resolution conterminous United States (CONUS) hydrography network dataset, we quantified the spatial extent, density, and upstream catchment characteristics of headwater stream segments across the CONUS. We identified approximately 8.4 million kilometers of headwater streams, finding that 77% of the total stream network consists of headwaters, nearly double the total length represented in prior estimates. Stream density varied fivefold across regions, from < 1 km·km −2 in arid basins to > 5 km·km −2 in humid, forested areas. Over 73% of the CONUS landmass drains from headwater streams. The majority of headwater stream length occurred in forested and cultivated catchments across the CONUS, while substantial regional differences were evident for headwater stream distribution in other land cover classes (for example, wetlands, urban areas, shrublands, and herbaceous-dominated catchments). The dedicated and novel geospatial dataset, HELiOS (HEadwater streams and Low-Order Systems) is introduced for management and research use. The HELiOS dataset provides the first continental-scale, high-resolution characterization of headwater streams, offering new insights and opportunities for hydrologic modeling, ecological assessments, and environmental policy.

conterminous United States↗
USGS2014

Comparing species distribution models constructed with different subsets of environmental predictors

Aim To assess the usefulness of combining climate predictors with additional types of environmental predictors in species distribution models for range-restricted species, using common correlative species distribution modelling approaches. Location Florida, USA Methods We used five different algorithms to create distribution models for 14 vertebrate species, using seven different predictor sets: two with bioclimate predictors only, and five &lsquo;combination&rsquo; models using bioclimate predictors plus &lsquo;additional&rsquo; predictors from groups representing: human influence, land cover, extreme weather or noise (spatially random data).We use a linear mixed-model approach to analyse the effects of predictor set and algorithm on model accuracy, variable importance scores and spatial predictions. Results Regardless of modelling algorithm, no one predictor set produced significantly more accurate models than all others, though models including human influence predictors were the only ones with significantly higher accuracy than climate-only models. Climate predictors had consistently higher variable importance scores than additional predictors in combination models, though there was variation related to predictor type and algorithm. While spatial predictions varied moderately between predictor sets, discrepancies were significantly greater between modelling algorithms than between predictor sets. Furthermore, there were no differences in the level of agreement between binary &lsquo;presence&ndash;absence&rsquo; maps and independent species range maps related to the predictor set used. Main conclusions Our results indicate that additional predictors have relatively minor effects on the accuracy of climate-based species distribution models and minor to moderate effects on spatial predictions. We suggest that implementing species distribution models with only climate predictors may provide an effective and efficient approach for initial assessments of environmental suitability.

Florida↗
USGS2009

Predictive Models of the Hydrological Regime of Unregulated Streams in Arizona

Three statistical models were developed by the U.S. Geological Survey in cooperation with the Arizona Department of Environmental Quality to improve the predictability of flow occurrence in unregulated streams throughout Arizona. The models can be used to predict the probabilities of the hydrological regime being one of four categories developed by this investigation: perennial, which has streamflow year-round; nearly perennial, which has streamflow 90 to 99.9 percent of the year; weakly perennial, which has streamflow 80 to 90 percent of the year; or nonperennial, which has streamflow less than 80 percent of the year. The models were developed to assist the Arizona Department of Environmental Quality in selecting sites for participation in the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program. One model was developed for each of the three hydrologic provinces in Arizona - the Plateau Uplands, the Central Highlands, and the Basin and Range Lowlands. The models for predicting the hydrological regime were calibrated using statistical methods and explanatory variables of discharge, drainage-area, altitude, and location data for selected U.S. Geological Survey streamflow-gaging stations and a climate index derived from annual precipitation data. Models were calibrated on the basis of streamflow data from 46 stations for the Plateau Uplands province, 82 stations for the Central Highlands province, and 90 stations for the Basin and Range Lowlands province. The models were developed using classification trees that facilitated the analysis of mixed numeric and factor variables. In all three models, a threshold stream discharge was the initial variable to be considered within the classification tree and was the single most important explanatory variable. If a stream discharge value at a station was below the threshold, then the station record was determined as being nonperennial. If, however, the stream discharge was above the threshold, subsequent decisions were made according to the classification tree and explanatory variables to determine the hydrological regime of the reach as being perennial, nearly perennial, weakly perennial, or nonperennial. Using model calibration data, misclassification rates for each model were 17 percent for the Plateau Uplands, 15 percent for the Central Highlands, and 14 percent for the Basin and Range Lowlands models. The actual misclassification rate may be higher; however, the model has not been field verified for a full error assessment. The calibrated models were used to classify stream reaches for which the Arizona Department of Environmental Quality had collected miscellaneous discharge measurements. A total of 5,080 measurements at 696 sites were routed through the appropriate classification tree to predict the hydrological regime of the reaches in which the measurements were made. The predictions resulted in classification of all stream reaches as perennial or nonperennial; no reaches were predicted as nearly perennial or weakly perennial. The percentages of sites predicted as being perennial and nonperennial, respectively, were 77 and 23 for the Plateau Uplands, 87 and 13 for the Central Highlands, and 76 and 24 for the Basin and Range Lowlands.

Open-File Report↗