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USGS2004

Estimating the Magnitude and Frequency of Floods in Small Urban Streams in South Carolina, 2001

The magnitude and frequency of floods at 20 streamflowgaging stations on small, unregulated urban streams in or near South Carolina were estimated by fitting the measured wateryear peak flows to a log-Pearson Type-III distribution. The period of record (through September 30, 2001) for the measured water-year peak flows ranged from 11 to 25 years with a mean and median length of 16 years. The drainage areas of the streamflow-gaging stations ranged from 0.18 to 41 square miles. Based on the flood-frequency estimates from the 20 streamflow-gaging stations (13 in South Carolina; 4 in North Carolina; and 3 in Georgia), generalized least-squares regression was used to develop regional regression equations. These equations can be used to estimate the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence-interval flows for small urban streams in the Piedmont, upper Coastal Plain, and lower Coastal Plain physiographic provinces of South Carolina. The most significant explanatory variables from this analysis were mainchannel length, percent impervious area, and basin development factor. Mean standard errors of prediction for the regression equations ranged from -25 to 33 percent for the 10-year recurrence-interval flows and from -35 to 54 percent for the 100-year recurrence-interval flows. The U.S. Geological Survey has developed a Geographic Information System application called StreamStats that makes the process of computing streamflow statistics at ungaged sites faster and more consistent than manual methods. This application was developed in the Massachusetts District and ongoing work is being done in other districts to develop a similar application using streamflow statistics relative to those respective States. Considering the future possibility of implementing StreamStats in South Carolina, an alternative set of regional regression equations was developed using only main channel length and impervious area. This was done because no digital coverages are currently available for basin development factor and, therefore, it could not be included in the StreamStats application. The average mean standard error of prediction for the alternative equations was 2 to 5 percent larger than the standard errors for the equations that contained basin development factor. For the urban streamflow-gaging stations in South Carolina, measured water-year peak flows were compared with those from an earlier urban flood-frequency investigation. The peak flows from the earlier investigation were computed using a rainfall-runoff model. At many of the sites, graphical comparisons indicated that the variance of the measured data was much less than the variance of the simulated data. Several statistical tests were applied to compare the variances and the means of the measured and simulated data for each site. The results indicated that the variances were significantly different for 11 of the 13 South Carolina streamflow-gaging stations. For one streamflow-gaging station, the test for normality, which is one of the assumptions of the data when comparing variances, indicated that neither the measured data nor the simulated data were distributed normally; therefore, the test for differences in the variances was not used for that streamflow-gaging station. Another statistical test was used to test for statistically significant differences in the means of the measured and simulated data. The results indicated that for 5 of the 13 urban streamflowgaging stations in South Carolina there was a statistically significant difference in the means of the two data sets. For comparison purposes and to test the hypothesis that there may have been climatic differences between the period in which the measured peak-flow data were measured and the period for which historic rainfall data were used to compute the simulated peak flows, 16 rural streamflow-gaging stations with long-term records were reviewed using similar techniques as those used for the measured an

South Carolina↗

USGS2021

A watershed moment: Analysis of sub-basins refocuses the geography of turtle conservation across the globe

Conservation planners use a variety of decision-making tools, many of which require identifying and prioritizing spatial units based on their biodiversity and levels of imperilment. Turtles are highly imperiled, but present schemes for determining global priority areas are focused mostly on broad regional scales. We conduct the first global evaluation of turtle biodiversity and imperilment at a sub-basin level to identify geographically smaller areas of high conservation value, and compare with these existing prioritizations. We employed two spatial analyses—bivariate maps and local indicator of spatial association (LISA)—to identify and prioritize sub-basin clusters based on multiple biodiversity and conservation metrics in addition to species richness. Most high-priority sub-basin clusters were located along tropical and subtropical coastlines. A new area of global significance for turtle conservation was identified in southwest India. Many sub-basins of the Indomalayan Realm were clustered as high or intermediate priority, with large clusters of high-priority sub-basins also in tropical Australasia. Other high and intermediate priority sub-basin clusters were found in the Afrotropical, Neotropical, and Nearctic realms, often in previously recognized turtle biodiversity hotspots. Many conservation-priority sub-basins with high turtle-species richness and endemism are in lowland and coastal areas where endemics (some from ancient lineages) are imperiled in association with a high human footprint. Our findings reiterate the global significance of Asia as a key area of chelonian conservation need, while identifying focal areas across the globe where the need for targeted turtle conservation is especially great.

Biological Conservation↗

USGS2012

Producing fractional rangeland component predictions in a sagebrush ecosystem, a Wyoming sensitivity analysis

Remote sensing information has been widely used to monitor vegetation condition and variations in a variety of ecosystems, including shrublands. Careful application of remotely sensed imagery can provide additional spatially explicit, continuous, and extensive data on the composition and condition of shrubland ecosystems. Historically, the most widely available remote sensing information has been collected by Landsat, which has offered large spatial coverage and moderate spatial resolution data globally for nearly three decades. Such medium-resolution satellite remote sensing information can quantify the distribution and variation of terrestrial ecosystems. Landsat imagery has been frequently used with other high-resolution remote sensing data to classify sagebrush components and quantify their spatial distributions (Ramsey and others, 2004; Seefeldt and Booth, 2004; Stow and others, 2008; Underwood and others, 2007). Modeling algorithms have been developed to use field measurements and satellite remote sensing data to quantify the extent and evaluate the quality of shrub ecosystem components in large geographic areas (Homer and others, 2009). The percent cover of sagebrush ecosystem components, including bare-ground, herbaceous, litter, sagebrush, and shrub, have been quantified for entire western states (Homer and others, 2012). Furthermore, research has demonstrated the use of current measurements with historical archives of Landsat imagery to quantify the variations of these components for the last two decades (Xian and others, 2012). The modeling method used to quantify the extent and spatial distribution of sagebrush components over a large area also has required considerable amounts of training data to meet targeted accuracy requirements. These training data have maintained product accuracy by ensuring that they are derived from good quality field measurements collected during appropriate ecosystem phenology and subsequently maximized by extrapolation on high-resolution remote sensing data (Homer and others, 2012). This method has proven its utility; however, to develop these products across even larger areas will require additional cost efficiencies to ensure that an adequate product can be developed for the lowest cost possible. Given the vast geographic extent of shrubland ecosystems in the western United States, identifying cost efficiencies with optimal training data development and subsequent application to medium resolution satellite imagery provide the most likely areas for methodological efficiency gains. The primary objective of this research was to conduct a series of sensitivity tests to evaluate the most optimal and practical way to develop Landsat scale information for estimating the extent and distribution of sagebrush ecosystem components over large areas in the conterminous United States. An existing dataset of sagebrush components developed from extensive field measurements, high-resolution satellite imagery, and medium resolution Landsat imagery in Wyoming was used as the reference database (Homer and others, 2012). Statistical analysis was performed to analyze the relation between the accuracy of sagebrush components and the amount and distribution of training data on Landsat scenes needed to obtain accurate predictions.

Wyoming↗

USGS2022

Community established best practice recommendations for tephra studies— From collection through analysis

Tephra is a unique volcanic product with an unparalleled role in understanding past eruptions, long-term behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, high-resolution time-stratigraphic markers across a range of sedimentary settings and thus are used in numerous disciplines (e.g., volcanology, climate science, archaeology). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that inhibits data integration across geographic regions and disciplines. We present comprehensive recommendations for tephra data gathering and reporting that were developed by the tephra science community to guide future investigators and to ensure that sufficient data are gathered for interoperability. Recommendations include standardized field and laboratory data collection, reporting and correlation guidance. These are organized as tabulated lists of key metadata with their definition and purpose. They are system independent and usable for template, tool, and database development. This standardized framework promotes consistent documentation and archiving, fosters interdisciplinary communication, and improves effectiveness of data sharing among diverse communities of researchers.

Scientific Data↗

USGS2010

Revisions of rump fat and body scoring indices for deer, elk, and moose

Because they do not require sacrificing animals, body condition scores (BCS), thickness of rump fat (MAXFAT), and other similar predictors of body fat have advanced estimating nutritional condition of ungulates and their use has proliferated in North America in the last decade. However, initial testing of these predictors was too limited to assess their reliability among diverse habitats, ecotypes, subspecies, and populations across the continent. With data collected from mule deer ( Odocoileus hemionus ), elk ( Cervus elaphus ), and moose ( Alces alces ) during initial model development and data collected subsequently from free-ranging mule deer and elk herds across much of the western United States, we evaluated reliability across a broader range of conditions than were initially available. First, to more rigorously test reliability of the MAXFAT index, we evaluated its robustness across the 3 species, using an allometric scaling function to adjust for differences in animal size. We then evaluated MAXFAT, rump body condition score (rBCS), rLIVINDEX (an arithmetic combination of MAXFAT and rBCS), and our new allometrically scaled rump-fat thickness index using data from 815 free-ranging female Roosevelt and Rocky Mountain elk ( C. e. roosevelti and C. e. nelsoni ) from 19 populations encompassing 4 geographic regions and 250 free-ranging female mule deer from 7 populations and 2 regions. We tested for effects of subspecies, geographic region, and captive versus free-ranging existence. Rump-fat thickness, when scaled allometrically with body mass, was related to ingesta-free body fat over a 38–522-kg range of body mass ( r 2 = 0.87; P < 0.001), indicating the technique is remarkably robust among at least the 3 cervid species of our analysis. However, we found an underscoring bias with the rBCS for elk that had >12% body fat. This bias translated into a difference between subspecies, because Rocky Mountain elk tended to be fatter than Roosevelt elk in our sample. Effects of observer error with the rBCS also existed for mule deer with moderate to high levels of body fat, and deer body size significantly affected accuracy of the MAXFAT predictor. Our analyses confirm robustness of the rump-fat index for these 3 species but highlight the potential for bias due to differences in body size and to observer error with BCS scoring. We present alternative LIVINDEX equations where potential bias from rBCS and bias due to body size are eliminated or reduced. These modifications improve the accuracy of estimating body fat for projects intended to monitor nutritional status of herds or to evaluate nutrition's influence on population demographics.

Journal of Wildlife Diseases↗

USGS2015

Estuarine bed-sediment-quality data collected in New Jersey and New York after Hurricane Sandy, 2013

This report describes a reconnaissance study of estuarine bed-sediment quality conducted June–October 2013 in New Jersey and New York after Hurricane Sandy in October 2012 to assess the extent of contamination and the potential long-term human and ecological impacts of the storm. The study, funded through the Disaster Relief Appropriations Act of 2013 (PL 113-2), was conducted by the U.S. Geological Survey in cooperation with the U.S. Environmental Protection Agency and the National Oceanographic and Atmospheric Administration. In addition to presenting the bed-sediment-quality data, the report describes the study design, documents the methods of sample collection and analysis, and discusses the steps taken to assure the quality of the data. Bed-sediment samples were collected from June to October 2013 from 167 estuarine sites extending from Cape May, New Jersey, to the New York Harbor and the eastern end of Long Island. Each sampling location and study region was characterized by using geographic information to identify potential contaminant sources. Characterizations included land cover, locations and types of businesses (industrial, financial, and others), spills (sewage, chemical, and others), bulk storage facilities, effluent discharges within 2 kilometers of the sampling point, and discharges within inundated and non-inundated regions near the sampling location. Samples were analyzed for particle size, total organic carbon, metals and trace elements, semivolatile organic compounds, wastewater compounds, hormones, and sediment toxicity. Samples were also screened using x-ray fluorescence, Fourier transform infrared spectroscopy, and x-ray diffraction. In addition, bioassays for endocrine disruptors and protein phosphatase 2A inhibition were conducted. The study was designed to provide the data needed to understand the extent and sources of contamination resulting from Hurricane Sandy, to compare the chemistry and toxicity of estuarine bed sediments before and after the storm, and to evaluate the usefulness of rapid screening and bioassay approaches in disaster settings.

New Jersey, New York↗

USGS2000

Statistical identification of hydrochemical response units for hydrologic monitoring and modeling in Maryland

In support of Maryland's efforts to develop statewide water-quality management plans, a statistical analysis was performed to identify a set of representative and relatively homogeneous areas referred to as Hydrochemical Response Units (HRUs). The State intends to select representative areas within each hydrochemical response unit for monitoring and model development, and then apply the model to the entire unit. To identify hydrochemical response units, cluster analysis was applied to 1,136 digitally defined watershed units. Basin characteristics including land cover, soil type, slope, and geology were determined for each watershed unit and a clustering algorithm was applied to the data sets. A total of 11 hydrochemical response units were identified by the analysis. Major features that were important in distinguishing different areas of the state include: 1) upland and lowland Coastal Plain settings, 2) igneous, shale, and carbonate geology; and 3) urban land cover. The hydrochemical response units described in this report are considered to be an initial classification of watersheds in Maryland that can be refined as geographic data sets are improved and additional hydrologic data are collected.

Water-Resources Investigations Report↗

USGS2008

Trichinella murrelli in scavenging mammals from south-central Wisconsin, USA

Tissues and serum from 59 raccoons (Procyon lotor), 42 coyotes (Canis latrans), and seven Striped Skunks (Mephitis mephitis) collected in Dane and Iowa Counties, Wisconsin, USA, between October 2005 and March 2006 were microscopically and serologically examined for the presence of Trichinella spp. Encapsulated larvae were found on compression slides prepared from tongue tissues from a few animals. Complete tissue digestion of tongues revealed that 19% of the raccoons, 26% of the coyotes, and none of the seven skunks tested were infected with Trichinella spp. Cats were subsequently experimentally infected by feeding them the raccoon tissues containing muscle larvae, and muscle larvae isolated from the collected tongues were experimentally transmitted to mice. Multiplex polymerase chain reaction analysis of the isolated muscle larvae demonstrated two distinct bands migrating at 127 base pairs (bp) and 316 bp in all samples, which together are diagnostic for Trichinella murrelli; the isolates were assigned Istituto Superiore di Sanita (ISS) codes ISS1656 through ISS1667, and ISS1708 through ISS1710 by the International Trichinella Reference Centre. These findings extend the geographic range of T. murrelli into Wisconsin, USA. ?? Wildlife Disease Association 2008.

Journal of Wildlife Diseases↗

USGS2021

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Methods in Ecology and Evolution↗

USGS2019

Relatedness within and among Myotis septentrionalis colonies at a local scale

Abstract: We assessed parentage within and among maternity colonies of the northern long-eared bat (Myotis septentrionalis Troessart 1897) in north-central Kentucky from 2011–2013 to better understand colony social structure, formation, and membership dynamics. We intensively sampled colonies in close and remote (> 10 km) spatial proximity both before and after targeted day-roost removal. Colonies were not necessarily comprised of closely related individuals, but natal philopatry was common. Adjacent colonies often contained maternally related individuals, indicating that some pups did disperse, albeit not far from their natal home range. Lack of apparent overlap among maternity colonies, along with no observed individual movements between colonies, suggests that colonies may be relatively closed once established in the maternity season. Whereas some young on site had been sired by males collected on site that by chance had dispersed to the same summering grounds, most had not, as would be expected since the species mates in the fall swarms near hibernacula. The number of parentages that we inferred among colonies, however, suggests that outside the maternity season, social groups may be relatively flexible and open, with individuals moving among groups close to their natal area. Analysis of microsatellite DNA data showed a low FST (= 0.011) and best fit to a model of one multilocus genotypic cluster across the study area. We observed high turnover in colony membership between years in all colonies, regardless of roost removal treatment. Our results suggest that female northern long-eared bats exhibit fidelity to a general geographic area rather than individual colonies between years, and indicate presence of a complex and dynamic social-genetic structure. Greater understanding of colony dynamics, including formation, dissolution, and dispersal patterns, may contribute to conservation and management of this threatened species.

Kentucky↗

USGS2023

Simulation of regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018

A three-dimensional numerical model of groundwater flow was developed and calibrated for the unconsolidated New Jersey Coastal Plain aquifers underlying Joint Base McGuire-Dix-Lakehurst (JBMDL) and vicinity, New Jersey, to evaluate groundwater flow pathways of per- and polyfluoroalkyl substances (PFAS) contamination associated with use of aqueous film forming foam (AFFF) at the base. The regional subsurface flow model spans an area of approximately 518 square miles around JBMDL and is based on a previously developed hydrogeologic framework of the area. Steady-state flow in the unconsolidated aquifers was simulated using the MODFLOW 6 groundwater flow model, which is able to account for hydrostratigraphic pinchouts and discontinuities in the Coastal Plain aquifers underlying JBMDL. To account for local patterns of fluid flow driving advective subsurface migration of PFAS, the grid was refined using quadtree meshes spanning 21 areas where historical AFFF use was identified, five off-site reconnaissance areas identified by AFCEC as areas in which the occurrence of PFAS is most likely to pose a potential danger to local drinking water supplies, and along streams that behave as drains in the base-flow-dominated Coastal Plain. Following grid refinement, four physical processes known to govern subsurface flow were introduced to the model. These included effective precipitation recharge, discharge to streams and stream-connected wetlands, regional inflows and outflows along the model bottom, and withdrawals from wells, each of which were incorporated into the model as either external or internal boundary conditions. To account for effective precipitation recharge, a specified-flow boundary was assigned along the top of the model. Similarly, regional flows predicted using the modified U.S Geological Survey’s New Jersey Coastal Plain Regional Aquifer System Analysis model were treated as specified-flow boundary conditions along the bottom of the model. Base-flow losses were treated as drains along streams delineated using a 10-foot LiDAR dataset. Drains were also assigned to cells falling within stream-connected National Hydrologic Database wetlands. Finally, well-pumpage data mined from the New Jersey Water Transfer database were added to the model to account for extraction of groundwater through pumping from industrial-supply and drinking-water-supply wells. Along model edges established at groundwater divides, where the net flux of water across the boundary is equal to zero, natural no-flow boundary conditions were imposed. The refined flow model was calibrated using the parameter-estimation (PEST) program, which adjusts model parameters by performing a gradient search over the sum-of-squared-error objective function until the parameter set that produces simulated water levels and base flows most closely matches 544 water levels and 20 estimated base flows and closely adheres to initial parameter estimates. Based on the analysis of calibration residuals, the model did not appear to be affected by significant model structural error. The MODPATH particle-tracking algorithm was used to estimate advective transport paths of PFAS in the vicinity of JBMDL. Forward tracking was used to determine paths of PFAS away from AFFF source areas to streams, wetlands, pumping wells, and geographic areas that PFAS may contaminate. Additionally, reverse tracking was used to determine particle pathlines away from off-site PFAS reconnaissance areas, or areas within which all sources of PFAS might be advectively transported into subsurface drinking-water supplies, to locations at land surface that may indicate a source of PFAS. The coupled and calibrated groundwater flow and particle-tracking transport model provide valuable tools for predicting the relative extent of PFAS contamination from onsite legacy source areas. The calibrated model also provides measures of water-level and base-flow observation influence that can help guide future data-collection efforts related to groundwater and surface water sampling for PFAS.

New Jersey↗

USGS2024

Peak streamflow trends in Missouri and their relation to changes in climate, water years 1921–2020

This report characterizes changes in peak streamflow in Missouri and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-streamflow frequency analysis in Missouri. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend periods (100 years, water years 1921–2020; 75 years, 1946–2020; 50 years, 1971–2020; and 30 years, 1991–2020) for trends, change points (abrupt changes in the streamflow time series), and other statistical properties indicative of changing conditions. Peak streamflow magnitudes generally exhibit upward trends across the State for the 100-, 75-, and 50-year trend periods and only in southern Missouri for the 30-year trend period. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 10-percent increase during the 30-year trend period to a 40-percent increase during the 100-year trend period. Changes in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, are not as substantial or widespread, showing consistent increases mainly in the southern part of the State in the 50- and 30-year trend periods. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. Change points in peak streamflows generally follow that of the peak streamflow trends, with upward change points throughout most of the State at the 100- and 75-year trend periods and in southern Missouri at the 30-year trend period. Temporally, clusters upward of change points are observed in the 1970s through 1980s for the 100-, 75-, and 50-year trend periods and around 2006 and 2007 for the 50- and 30-year trend periods. A peaks-over-threshold analysis, which evaluates changes in the frequency of peak streamflows over a certain threshold, indicates that high flows have increased in frequency at 50 to 64 percent of streamgages in the 100- and 75-year trend periods. Most streamgages in the 50- and 30-year trend periods exhibit no change. Although the frequency of high flows has increased at some streamgages and trend periods in Missouri, these increases are not as widespread as the increases in the magnitude of peak streamflow. Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with no downward trends in precipitation and only two somewhat likely downward trends in temperature for the 100-year trend period. Increases in annual precipitation mostly are limited to southern Missouri for the 30-year trend period. The proportion of precipitation falling as snow has largely decreased in the study basins across the State, which is expected in response to increasing temperature. Upward trends in modeled annual runoff, which in this study incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation. When peak streamflow and climatic trends are considered together, widespread upward trends in peak streamflows for the 100-, 75-, and 50-year trend periods and for the 30-year trend period mainly in southern Missouri (encompassing both trends and abrupt change) appear to be driven largely by increases in precipitation based on spatial patterns and statistical relations. The prevalence of nonstationarity in peak streamflow in Missouri has important implications for peak-flow frequency analysis. Winter and spring precipitation and the occurrence of extreme precipitation events are expected to increase across the State. If precipitation continues to increase as expected, peak-flow frequency estimates based on older records may no longer represent the hydrologic regime of today, and methods for nonstationary peak-flow frequency analysis may be needed.

Missouri↗

USGS1997

Recovery strategies for the California clapper rail (Rallus longirostris obsoletus) in the heavily-urbanized San Francisco estuarine ecosystem

The California clapper rail ( Rallus longirostris obsoletus ), a Federal- and State-listed endangered marsh bird, has a geographic range restricted to one of the most heavily-urbanized estuaries in the world. The rail population has long been in a state of decline, although the exact contribution of each of the many contributing causes remains unclear. The rail is one of the key targets of emerging plans to conserve and restore tidal marshlands. Reduction of tidal marsh habitat, estimated at 85–95%, has been the major historical cause of rail decline. Increased predation intensity may be the more important present problem, because habitat fragmentation and alteration coupled with the invasion of the red fox have made the remaining populations more vulnerable to predators. Population viability analysis shows that adult survivorship is the key demographic variable; reversals in population fate occur over a narrow range of ecologically realistic values. Analysis of habitat requirements and population dynamics of the clapper rail in the San Francisco Estuary shows that decreased within-marsh habitat quality, particularly reduction of tidal flows and alteration of drainage, is an important barrier to population recovery. Management and restoration activities should emphasize the development of well-channelized high tidal marsh, because this is the key requirement of rail habitat. Developing effective restoration programs depends upon having information that field research will not provide. The effect of spatial pattern of reserves requires accurate estimation of the effects of prédation and inter-marsh movement, both of which are practically impossible to measure adequately. It will be necessary to develop and use simulation models that can be applied to geographic data to accomplish this task.

Landscape and Urban Planning↗

USGS1996

Development of the temporal transportation database for the analysis of urban development in the Baltimore-Washington region

The U.S. Geological Survey is participating in a joint effort with the University of Maryland Baltimore County and other government agencies to construct a dynamic database of urban development for the Baltimore-Washington region from the late-1700's to the 1990's. The multi-theme temporal database includes a principal transportation data layer that documents the primary roads, railroads, and other transportation features that provided the infrastructure for urban development. A geographic information system was used to collect from various maps at various scales. Compilation criteria, such as connectivity, mobility, lineage, and alignment, were developed to accommodate limitations in the source materials. Visualization techniques such as animation and flight simulation are used to portray the changing transportation infrastructure and its effect on urbanization. The database will be used to study the evolution of the Baltimore-Washington metropolitan area. The principal transportation data layer is essential to illustrate and analyze the regional and temporal aspects of human-induced land transformations. Modeling applications will explore the urban growth process and its correlation to key transportation development.

Maryland↗

USGS2008

Interannual streamflow variability in the United States based on principal components

Interannual modes of streamflow variation at 106 locations across the United States during the period 1931–1978 are defined by using principal components. Five statistically significant components are found to account for more than 56% of the total streamflow variance. The first principal component represents a nationwide tendency for either above- or below-mean streamflow. The second component represents a north-south opposition in departures from mean flow, and the third, an east-west opposition. Higher-order components (fourth and fifth) geographically depict regional patterns of opposition in the sign of streamflow departures between coastal-continental areas and between the northern and southern plains, respectively. Analyses using spatially and temporally modified data sets indicate that the first three components (which explain 45% of the variance) are quite stable spatially, while only the first component is stable temporally. Time series analysis of principal component scores indicates that all but the fourth component are first-order autoregressive processes, as is mean annual nationwide streamflow. The fourth component is an autoregressive (AR)(2) process. In general, the principal components of streamflow are found to exhibit more persistence over annual time scales than the mean annual flow data themselves.

Water Resources Research↗

USGS2010

Fish habitat degradation in U.S. reservoirs

As the median age of the thousands of large reservoirs (> 200 ha) in the United States tops 50, many are showing various signs of fish habitat degradation. Our goal was to identify major factors degrading fish habitat in reservoirs across the country, and to explore regional degradation patterns. An online survey including 14 metrics was scored on a 0 (no degradation) to 5 (high degradation) point scale by 221 fisheries scientists (92% response rate) to describe degradation in 482 reservoirs randomly distributed throughout the continental United States. The highest scored sources of degradation were lack of aquatic macrophytes (41% of the reservoirs scored as 4–5), lack or loss of woody debris (35% scored 4–5), mistimed water level fluctuations (34% scored 4–5), and sedimentation (31% scored 4–5). Factor analysis identified five primary degradation factors that accounted for most of the variability in the 14 degradation metrics. The factors reflected siltation, structural habitat, eutrophication, water regime, and aquatic plants. Three degradation factors were driven principally by in-reservoir processes, whereas the other two were driven by inputs from the watershed. A comparison across U.S. regions indicated significant geographical differences in degradation relative to the factors emphasized by each region. Reservoirs sometimes have been dismissed as unnatural and disruptive, but they are a product of public policy, a critical feature of landscapes, and they cannot be overlooked if managers are to effectively conserve river systems. Protection and restoration of reservoir habitats may be enhanced with a broader perspective that includes watershed management, in addition to in reservoir activities.

Fisheries↗

USGS2006

Geologic Map of Loudoun County, Virginia

Introduction The geology of Loudoun County, Va., was mapped from 1988 through 1991 under a cooperative agreement between the U.S. Geological Survey (USGS) and the Loudoun County Office of Mapping and Geographic Information. This geologic map was compiled in 1993 from a series of detailed published and unpublished field investigations at scales of 1:12,000 and 1:24,000. Some of these same data were compiled as a digital geologic map at 1:100,000 scale (Burton and others, 1992a) and were the basis for a cost-benefit analysis of the societal value of geologic maps (Bernknopf and others, 1993).

IMAP↗

USGS2013

Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE; Version 1.0): web-based tools to assess the impact of sea level rise in south Florida

South Florida's Greater Everglades area is particularly vulnerable to sea level rise, due to its rich endowment of animal and plant species and its heavily populated urban areas along the coast. Rising sea levels are expected to have substantial impacts on inland flooding, the depth and extent of surge from coastal storms, the degradation of water supplies by saltwater intrusion, and the integrity of plant and animal habitats. Planners and managers responsible for mitigating these impacts require advanced tools to help them more effectively identify areas at risk. The U.S. Geological Survey's (USGS) Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE) Web site has been developed to address these needs by providing more convenient access to projections from models that forecast the effects of sea level rise on surface water and groundwater, the extent of surge and resulting economic losses from coastal storms, and the distribution of habitats. IMMAGE not only provides an advanced geographic information system (GIS) interface to support decision making, but also includes topic-based modules that explain and illustrate key concepts for nontechnical users. The purpose of this report is to familiarize both technical and nontechnical users with the IMMAGE Web site and its various applications.

Florida↗