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1,271 records · Page 71Linked to original sources
USGS2018

Preliminary groundwater salinity mapping near selected oil fields using historical water-sample data, central and southern California

The distribution of groundwater salinity was mapped for 31 oil fields and adjacent aquifers and summarized by 8 subregions across major oil-producing areas of central and southern California. The objectives of this study were to describe the distribution of groundwater near oil fields having total dissolved solids less than 10,000 milligrams per liter (mg/L) based on available data and to document where data gaps exist. Salinity was represented by the measured or calculated concentration of total dissolved solids (TDS) in samples of produced water obtained from petroleum wells and groundwater obtained from water wells. The water chemistry data were used to estimate the minimum depths of TDS greater than 3,000 mg/L and greater than 10,000 mg/L in areas near selected oil fields using historical water-chemistry data coupled with available well-location and construction information. The 10,000 mg/L threshold, representing the highest level of TDS concentration of water that could be considered as a potential source of drinking water, was present in all but 4 (Jasmin, Kern Bluff, Kern Front, and Mount Poso) of the 31 individual oil fields. Among petroleum wells, the median TDS concentration of produced water ranged from 500 mg/L for the Jasmin field to 32,636 mg/L for the Elk Hills field. Among water wells, median TDS concentrations, either reported or calculated from specific conductance, ranged from 151 mg/L for wells within 2 miles of the Ten Section field to 9,750 mg/L for wells within 2 miles of the combined North and South Belridge fields. In general, TDS across the eight geographic subregions increased with depth, but the relation of TDS with depth varied regionally. The most pronounced increases in TDS with depth were across the West Kern Valley Floor and West Kern Valley Margin subregions on the west side of the San Joaquin Valley, and in the vicinity of the Wilmington field in the Los Angeles Basin subregion; in these areas, relatively high TDS concentrations greater than 10,000 mg/L were present within the upper few hundred to several thousand feet of land surface. Total dissolved solids concentrations increased more gradually with depth in the Middle Kern Valley Floor subregion, in the South Kern Valley Margin subregion, in the vicinity of the Montebello and Santa Fe Springs fields in the Los Angeles Basin subregion, and in the Central Coast Basin subregion. The Kern Sierran Foothills and East Kern Valley Floor subregions, on the east side of the San Joaquin Valley, had the most gradual increases in TDS with depth. Fields in the East Kern Valley Floor subregion generally had groundwater and produced water with TDS less than 10,000 mg/L that extended to a large depth compared to most other subregions. Overall, the west side of the San Joaquin Valley in Kern County and the Wilmington field in Los Angeles County generally have the highest TDS values and the shallowest depths to high TDS. High TDS at relatively shallow depths on the west side of the San Joaquin Valley may be because of a combination of natural conditions and anthropogenic factors. In the vicinity of the Wilmington field in the Los Angeles Basin subregion, high TDS at relatively shallow depths is attributable at least in part to seawater intrusion. Fields on the east side of the San Joaquin Valley in Kern County have the lowest TDS and greatest depths to TDS greater than 10,000 mg/L because of their geologic setting adjacent to Sierra Nevada recharge areas. Reconnaissance salinity mapping was limited by several factors. The primary limitation was the lack of well-construction data for a significant number of water wells. Bottom perforation, well depth, or hole depth were not available for 35 percent of wells used for salinity mapping. A second limitation was variability in data quality. Total dissolved solids and specific conductance data were compiled from different data sources with varying degrees of documentation that ranged from comprehensive to very little or none. As a result, it was not always possible to assess the quality of the provided data with respect to either conditions at each well during sampling or the methodology used for sample collection and analysis. A third limitation was the lack of wells, either petroleum or water, and associated TDS data over large vertical intervals for some fields. As a result, the distribution of salinity and the depths at which TDS concentration exceeds the 3,000 and 10,000 mg/L thresholds could not always be precisely determined. This analysis highlights key gaps that need to be filled with additional analysis of other sources of information, such as borehole geophysical logs and new water sample or geophysical data collection.

California↗
USGS2025

North American Waterfowl Management Plan survey regional profile—Southeast region

Executive Summary Gaining a better understanding of the human dimensions of waterfowl management to inform the North American Waterfowl Management Plan is a valuable but challenging goal for the future success of waterfowl management. Increasing engagement with key stakeholder groups will lead to more support and effective waterfowl management. Social systems are complex because individual values and preferences may vary across geographic and cultural dimensions, so it is valuable to describe those differences rather than only looking at national-scale trends. Therefore, using broad engagement strategies that do not consider the differences among regional groups may do more harm than good. This study analyzed a subset of responses from waterfowl hunters (hereafter respondents) in the Southeast region of the United States from a national-scale survey (Patton, 2018). This study compared how respondents’ opinions differed among two subsections of the Atlantic and Mississippi flyways—the Atlantic subflyway and Mississippi subflyway—and responses from the national survey. Respondents in the Atlantic subflyway had a primary home ZIP Code in Florida, Georgia, North Carolina, South Carolina, and Tennessee. Respondents in the Mississippi subflyway had a primary home ZIP Code in Alabama, Arkansas, Kentucky, Louisiana, Missouri, and Mississippi. Investigating these differences provides waterfowl managers decision-making support and a better understanding of how perceptions may differ among respondents in the Southeast region and the Nation. Responses from each group are presented for each for each survey topic, and statistical tests of homogeneity are included to inform how the differences may be considered when managing for waterfowl and waterfowl hunting. Respondents from the Mississippi and Atlantic subflyways did not differ substantially except in their perceptions and preferences of waterfowl harvest and harvest regulations. Respondents from the Mississippi subflyway consistently reported a higher average harvest of ducks and geese, emphasized the importance of higher harvest for their satisfaction with waterfowl hunting, and typically placed a greater emphasis on regulatory decisions that facilitated increased harvest opportunity than respondents from the Atlantic subflyway. This emphasis was especially true when preferring species-specific limits more than simpler aggregate limits. Respondents in the Mississippi subflyway were in direct opposition to respondents in the Atlantic subflyway and preferred the opportunity for increased harvest that species-specific bag limits provide. Respondents in the Mississippi subflyway placed greater emphasis on harvest and larger bag limits compared with respondents in the Atlantic subflyway and the national survey. Respondents in the Atlantic subflyway often aligned with the national survey respondents’ perceptions of harvest that placed a lower emphasis on the number of ducks or geese harvested in comparison to Mississippi subflyway respondents. The Atlantic and Mississippi subflyway respondents reported hunting ducks and geese to a much lower degree than the national survey respondents, who favored only hunting ducks or hunting neither ducks nor geese. Similarly, respondents in the Mississippi and Atlantic subflyways reported that overcrowding, high hunting pressure, and interference from other respondents limited their participation to a higher degree than respondents from the national survey. The trip-specific preferences for waterfowl hunting in the Southeast region were calculated using latent class analysis and three groups were determined based on individual estimates of attribute importance: generalist, seclusionist, and harvest oriented. The generalist group did not place a high degree of importance on any one attribute and was most likely to choose to not participate given suboptimal conditions. The seclusionist group placed a high degree of importance on lower levels of competition from other groups and felt their well-being was most affected by higher levels of competition. The harvest-oriented group placed a higher degree of importance on harvesting more than three birds and felt their well-being was most negatively affected if they only expected to harvest a single bird. These groups existed uniformly between the Mississippi and Atlantic subflyways, had a slightly higher membership of each group in rural areas, and an overall higher membership in the seclusionist group. Subsetting national survey data to profile regional differences provides key information to waterfowl managers seeking to make tailored decisions in their region or flyway. This investigation provides an important resource for informed management decisions in the Southeast region and will assist waterfowl managers by supporting engagement and communication with respondents in the Southeastern United States.

Scientific Investigations Report↗
USGS2021

Detrital zircon record of magmatism and sediment dispersal across the North American Cordilleran arc system (28-48°N)

As zircon U-Pb geochronology has become a leading method in sediment provenance studies and basin analysis over the past 20 years, the volume of detrital zircon data made available in published literature has enabled researchers to go beyond source-to-sink provenance studies to explore increasingly complex geologic problems. In this review, we utilize the growing body of detrital zircon data acquired from Jurassic-Paleocene forearc and foreland basin strata of the North American Cordillera to investigate the Mesozoic to earliest Cenozoic evolution of the arc and its associated basins between 28°N and 48°N. Our compilation includes 830 detrital zircon samples (101,898 individual ages) from 70 studies published between 2000 and 2020. For comparative purposes, we also compile 1307 igneous zircon U-Pb ages that characterize the magmatic history of the arc. We place primary emphasis on detrital zircon ages between 251 and 56 Ma that we infer to be uniquely derived from magmatic sources in the arc. Informed by existing knowledge of magmatic, structural, and sedimentological processes that acted on the orogen, we investigate spatial and temporal trends in these “arc-derived zircon” to establish a detrital record of arc magmatism, investigate source-to-sink relationships between the arc and adjacent basins, and discuss controls on sediment dispersal across the orogen. Our review shows that compilations of detrital zircon data from the Cordilleran forearc and foreland basin systems are excellent proxies for arc magmatism because the basins are enriched in arc-derived zircon and compilations provide space- and time-integrated records of crystallization ages. The compiled detrital zircon data support a history of continuous arc magmatism throughout Mesozoic and earliest Cenozoic time, characterized by low-volume magmatism from Triassic-Early Jurassic time (~251–174 Ma) and episodic higher-volume magmatism from Middle Jurassic-Late Cretaceous time (~174–66 Ma). These trends elucidate the initiation and timing of magmatic events at the orogen-scale and corroborate our understanding of cyclic arc behavior. Detrital zircon distributions are spatially and temporally variable both within and across basins, which we discuss relative to topographic development of the orogen and attendant responses of sediment dispersal systems. Detrital zircon distributions in the forearc signal rapid transfer of sediment from the arc to basins dominantly via fluvial processes. In contrast, detrital zircon distributions across the foreland reflect the presence of topographic barriers in the hinterland region of the arc that effectively isolated parts of the foreland. The presence of hinterland topography in turn highlights the important role of ash-fall events in delivering arc-derived zircon to the foreland, underscoring the need to consider ash-fall processes in paleodrainage reconstructions. These broad regional trends, and in general the close linkage between orogenic process and sediment dispersal, emerge from our compilation because it averages out much of the local variability observed in studies of more limited geographic or temporal extent.

North American Cordilleran arc system↗
USGS2011

Data compilation and assessment for water resources in Pennsylvania state forest and park lands

As a result of a cooperative study between the U.S. Geological Survey and the Pennsylvania Department of Conservation and Natural Resources (PaDCNR), available electronic data were compiled for Pennsylvania state lands (state forests and parks) to allow PaDCNR to initially determine if data exist to make an objective evaluation of water resources for specific basins. The data compiled included water-quantity and water-quality data and sample locations for benthic macroinvertebrates within state-owned lands (including a 100-meter buffer around each land parcel) in Pennsylvania. In addition, internet links or contacts for geographic information system coverages pertinent to water-resources studies also were compiled. Water-quantity and water-quality data primarily available through January 2007 were compiled and summarized for site types that included streams, lakes, ground-water wells, springs, and precipitation. Data were categorized relative to 35 watershed boundaries defined by the Pennsylvania Department of Environmental Protection for resource-management purposes. The primary sources of continuous water-quantity data for Pennsylvania state lands were the U.S. Geological Survey (USGS) and the National Weather Service (NWS). The USGS has streamflow data for 93 surface-water sites located in state lands; 38 of these sites have continuous-recording data available. As of January 2007, 22 of these 38 streamflow-gaging stations were active; the majority of active gaging stations have over 40 years of continuous record. The USGS database also contains continuous ground-water elevation data for 32 wells in Pennsylvania state lands, 18 of which were active as of January 2007. Sixty-eight active precipitation stations (primarily from the NWS network) are located in state lands. The four sources of available water-quality data for Pennsylvania state lands were the USGS, U.S. Environmental Protection Agency, Pennsylvania Department of Environmental Protection (PaDEP), and the Susquehanna River Basin Commission. The water-quality data, which were primarily collected after 1970, were summarized by categorizing the analytical data for each site into major groups (for example, trace metals, pesticides, major ions, etc.) for each type (streams, lakes, ground-water wells, and springs) of data compiled. The number of samples and number of detections for each analyte within each group also were summarized. A total of 410 stream sites and 205 ground-water wells in state lands had water-quality data from the available data sets, and these sites were well-distributed across the state. A total of 107 lakes and 47 springs in state lands had water-quality data from the available data sets, but these data types were not well-distributed across the state; the majority of water-quality data for lakes was in the western or eastern sections of the state and water-quality data for springs was primarily located in the central part of the Lower Susquehanna River Valley. The most common types of water-quality data collected were major ions, trace elements, and nutrients. Physical parameters, such as water temperature, stream discharge, or water level, typically were collected for most water-quality samples. Given the large database available from PaDEP for benthic macroinvertebrates, along with some data from other agencies, there is very good distribution of benthic-macroinvertebrate data for state lands. Benthic macroinvertebrate samples were collected at 1,077 locations in state lands from 1973 to 2006. Most (980 samples) of the benthic-macroinvertebrate samples were collected by PaDEP as part of the state assessment of stream conditions required by the Clean Water Act. Data compiled in this report can be used for various water-resource issues, such as basin-wide water-budget analysis, studies of ecological or instream flow, or water-quality assessments. The determination of an annual water budget in selected basins is best supported by the availab

Pennsylvania↗
USGS2023

Climate change and the global redistribution of biodiversity: Substantial variation in empirical support for expected range shifts

Background Among the most widely predicted climate change-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution to track their climate niches. A series of commonly articulated hypotheses have emerged in the scientific literature suggesting species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to rising temperatures associated with climate change. Yet, many species are not demonstrating range shifts consistent with these expectations. Here, we evaluate the impact of anthropogenic climate change (specifically, changes in temperature and precipitation) on species’ ranges, and assess whether expected range shifts are supported by the body of empirical evidence. Methods We conducted a Systematic Review, searching online databases and search engines in English. Studies were screened in a two-stage process (title/abstract review, followed by full-text review) to evaluate whether they met a list of eligibility criteria. Data coding, extraction, and study validity assessment was completed by a team of trained reviewers and each entry was validated by at least one secondary reviewer. We used logistic regression models to assess whether the direction of shift supported common range-shift expectations (i.e., shifts to higher latitudes and elevations, and deeper depths). We also estimated the magnitude of shifts for the subset of available range-shift data expressed in distance per time (i.e., km/decade). We accounted for methodological attributes at the study level as potential sources of variation. This allowed us to answer two questions: (1) are most species shifting in the direction we expect (i.e., each observation is assessed as support/fail to support our expectation); and (2) what is the average speed of range shifts? Review findings We found that less than half of all range-shift observations (46.60%) documented shifts towards higher latitudes, higher elevations, and greater marine depths, demonstrating significant variation in the empirical evidence for general range shift expectations. For the subset of studies looking at range shift rates, we found that species demonstrated significant average shifts towards higher latitudes (average = 11.8 km/dec) and higher elevations (average = 9 m/dec), although we failed to find significant evidence for shifts to greater marine depths. We found that methodological factors in individual range-shift studies had a significant impact on the reported direction and magnitude of shifts. Finally, we identified important variation across dimensions of range shifts (e.g., greater support for latitude and elevation shifts than depth), parameters (e.g., leading edge shifts faster than trailing edge for latitude), and taxonomic groups (e.g., faster latitudinal shifts for insects than plants). Conclusions Despite growing evidence that species are shifting their ranges in response to climate change, substantial variation exists in the extent to which definitively empirical observations confirm these expectations. Even though on average, rates of shift show significant movement to higher elevations and latitudes for many taxa, most species are not shifting in expected directions. Variation across dimensions and parameters of range shifts, as well as differences across taxonomic groups and variation driven by methodological factors, should be considered when assessing overall confidence in range-shift hypotheses. In order for managers to effectively plan for species redistribution, we need to better account for and predict which species will shift and by how much. The dataset produced for this analysis can be used for future research to explore additional hypotheses to better understand species range shifts.

Journal of Environmental Evidence↗
USGS2014

An initial abstraction and constant loss model, and methods for estimating unit hydrographs, peak streamflows, and flood volumes for urban basins in Missouri

Streamflow data, basin characteristics, and rainfall data from 39 streamflow-gaging stations for urban areas in and adjacent to Missouri were used by the U.S. Geological Survey in cooperation with the Metropolitan Sewer District of St. Louis to develop an initial abstraction and constant loss model (a time-distributed basin-loss model) and a gamma unit hydrograph (GUH) for urban areas in Missouri. Study-specific methods to determine peak streamflow and flood volume for a given rainfall event also were developed. Distinct basin characteristics were evaluated and selected for use on the basis of their theoretical relation to flow, results from previous studies, and the ability to reliably measure the basin characteristic using digital datasets and geographic information system (GIS) technology. The key basin characteristics determined or computed for each of the 39 basins upstream from the streamflow-gaging stations were drainage area, percent impervious area, main-channel slope based on the 10- and 85-percent length method, percentage of the basin area in storage (lakes, ponds, reservoirs, wetlands), the composite Natural Resources Conservation Service curve number estimated from a combination of the soil type data and land-use characteristics, and the streamflow variability index developed for the recently completed study of low-flow regression in Missouri. Characteristics of spatial and temporal rainfall distribution came from the next generation weather radar (NEXRAD) network. Procedures were developed for this study to convert the variable radar sweep rate into a 5-minute total rainfall hyetograph using data from the radar bin at the centroid of a given basin. Additional characteristics determined for each storm on the basin included the 5-day and 14-day antecedent rainfall, estimated from the mean of daily rainfall values from various rain gages in the area. The database of observed rainfall and runoff events for the 39 basins upstream from the streamflow-gaging stations was analyzed to compute the optimal storm-specific initial abstraction and constant loss values, as well as the time to peak, peak streamflow, and shape factor values of the GUH. The optimal storm-specific values were used to develop a regional regression equation for initial abstraction; conversely, the constant loss was estimated not by regression but from either a generalized or specific regional mean value. The optimal storm-specific values of GUH time to peak, GUH peak streamflow, and GUH shape factor were used to develop regression equations for the GUH. The regression equations for the GUH initially were tested alone, and then were combined with the appropriate regional regression equation for initial abstraction and both the generalized regional and specific regional mean constant loss values. For the GUH regression equations, the interquartile range was substantially smaller than the range spanned by the minimum and maximum values, which indicates most of the errors have much smaller variation, and the minimum and maximum values may be extreme outliers. The central tendency of the regressed errors for peak streamflow and runoff hydrograph volume were both approximately zero, which implies a generally unbiased estimation of these values. The mean and median of the regressed errors for time to peak streamflow were both small but greater than zero, which implies the GUH regression equations create a hydrograph that has a peak that is later in time than observed. Specifically, the regressed times indicate an offset of about 10 minutes, on average, from observed. The mean and median of the regressed errors for widths of the runoff hydrograph at 50 and 75 percent were less than zero, which implies the GUH tends to slightly underestimate these widths compared to the observed. The appropriate regional initial abstraction regression equation was combined with both the generalized and the specific regional mean constant loss values and the GUH regression equations. Both the generalized regional mean constant loss and specific regional mean constant loss forms of the basin-loss model worked equally well to model the observed runoff hydrograph based on the error analysis, and neither model seems to make a consistently better approximation. Both initial abstraction and constant loss models combined with the GUH regression equations were further validated using several storms available after the start of the project in early 2011 with similar but consistently higher error results. If these methods are used in an urban area in Missouri other than those examined in this study, advice to the user is given to consider using the generalized regional mean values. If these methods are used in an urban area that is a subbasin of one of the basins in this study, advice to the user is given to consider using the specific regional mean values. The rainfall-runoff pairs from the storm-specific GUH analysis were further analyzed against various basin and rainfall characteristics to develop equations to estimate the peak streamflow and flood volume based on a quantity of rainfall on the basin.

Missouri↗
USGS1996

Use of a ground-water flow model with particle tracking to evaluate ground-water vulnerability, Clark County, Washington

A ground-water flow model was used in conjunction with particle tracking to evaluate ground-water vulnerability in Clark County, Washington. Using the particle-tracking program, particles were placed in every cell of the flow model (about 60,000 particles) and tracked backwards in time and space upgradient along flow paths to their recharge points. A new computer program was developed that interfaces the results from a particle-tracking program with a geographic information system (GIS). The GIS was used to display and analyze the particle-tracking results. Ground-water vulnerability was evaluated by selecting parts of the ground-water flow system and combining the results with ancillary information stored in the GIS to determine recharge areas, characteristics of recharge areas, downgradient impact of land use at recharge areas, and age of ground water. Maps of the recharge areas for each hydrogeologic unit illustrate the presence of local, intermediate, or regional ground-water flow systems and emphasize the three-dimensional nature of the ground-water flow system in Clark County. Maps of the recharge points for each hydrogeologic unit were overlaid with maps depicting aquifer sensitivity as determined by DRASTIC (a measure of the pollution potential of ground water, based on the intrinsic characteristics of the near-surface unsaturated and saturated zones) and recharge from on-site waste-disposal systems. A large number of recharge areas were identified, particularly in southern Clark County, that have a high aquifer sensitivity, coincide with areas of recharge from on-site waste-disposal systems, or both. Using the GIS, the characteristics of the recharge areas were related to the downgradient parts of the ground-water system that will eventually receive flow that has recharged through these areas. The aquifer sensitivity, as indicated by DRASTIC, of the recharge areas for downgradient parts of the flow system was mapped for each hydrogeologic unit. A number of public-supply wells in Clark County may be receiving a component of water that recharged in areas that are more conducive to contaminant entry. The aquifer sensitivity maps illustrate a critical deficiency in the DRASTIC methodology: the failure to account for the dynamics of the ground-water flow system. DRASTIC indices calculated for a particular location thus do not necessarily reflect the conditions of the ground-water resources at the recharge areas to that particular location. Each hydrogeologic unit was also mapped to highlight those areas that will eventually receive flow from recharge areas with on-site waste-disposal systems. Most public-supply wells in southern Clark County may eventually receive a component of water that was recharged from on-site waste-disposal systems.Traveltimes from particle tracking were used to estimate the minimum and maximum age of ground water within each model-grid cell. Chlorofluorocarbon (CFC)-age dating of ground water from 51 wells was used to calibrate effective porosity values used for the particle- tracking program by comparison of ground-water ages determined through the use of the CFC-age dating with those calculated by the particle- tracking program. There was a 76 percent agreement in predicting the presence of modern water in the 51 wells as determined using CFCs and calculated by the particle-tracking program. Maps showing the age of ground water were prepared for all the hydrogeologic units. Areas with the youngest ground-water ages are expected to be at greatest risk for contamination from anthropogenic sources. Comparison of these maps with maps of public- supply wells in Clark County indicates that most of these wells may withdraw ground water that is, in part, less than 100 years old, and in many instances less than 10 years old. Results of the analysis showed that a single particle-tracking analysis simulating advective transport can be used to evaluate ground-water vulnerability for any part of a ground-wate

Open-File Report↗
USGS2023

Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska

Freshwater ecosystems of the Alaskan Arctic and Subarctic provide resources that are culturally, ecologically, and economically invaluable. Presently, these regions are relatively free of the impacts from invasive species compared to southern latitudes. To date, there have been relatively few verified introductions of aquatic invasive species (AIS) to freshwater ecosystems in Alaska. The expanding list and distribution of AIS has led to significant negative ecological and economic impacts (e.g., waterweed Elodea nuttalli ; E. canadensis and northern pike Esox Lucius introduced outside its native range in Alaska). Escalating human activity across Alaskan lands and waters, coupled with rapidly shifting environmental conditions, increases the potential for new species introductions and subsequent establishment. Creating a proactive framework for well-informed decision-making and action can improve the effectiveness of prevention efforts and bolster decision support tools that help resource managers direct limited resources. Prioritizing AIS that may be introduced and become established, as well as the locations at highest risk of invasion, is foundational to building a proactive invasive species management framework in Alaska. This project sought to identify and prioritize AIS known to be invasive in the contiguous United States, evaluate current and future habitat suitability for AIS in Alaska, and assess potential for AIS to be transported to habitats across Alaska, utilizing similar assessment methods as implemented for Bering Sea marine invasive species and non-native plants in Alaska. To accomplish this goal, the objectives of the project were to: 1) develop a formal ranked list of potential AIS to freshwater systems of Alaska; 2) assess the level of establishment risk for potential AIS by developing habitat suitability models for waterbodies across Alaska; and 3), identify potential pathways and specific vectors for high-risk AIS to invade Alaska and develop a framework for how vector analysis will be completed to understand transport risk. Overall, our goal is horizon scanning which is defined by Roy et al. (2019) as “a systematic examination of potential threats and opportunities, within a given context, and likely future developments, which are at the margin of current thinking and planning.” The scans include pathway analyses and risk screening of species present at pathway origin points, with a focus on identifying species at high risk of being introduced, becoming established, spreading, and causing harm. We refined a list of 28 AIS from a list of hundreds based on characterizations of species’ invasiveness and species’ proximity to Alaska (USGS 2020; GBIF 2022). Next, we evaluated the relative invasiveness of individual species to create an initial AIS ranking. We sought to characterize habitat suitability of AIS by selecting variables that were continental in scale, covering North America to include Alaska as well as the lower 48 states comparing natural discharge, sub-basin average terrain slope (degrees), average silt fraction, average organic carbon, lithological class, and human footprint in sub-basin in 2009. We estimated AIS habitat suitability across the entire state of Alaska using the physiological tolerances of the AIS (Appendix 2). We also evaluated pathways and vectors for the introduction of AIS (Appendix 2). Many pathways and vectors considered did not meet the criteria for Alaska or freshwater systems. Of the 28 ranked species that we categorized as very high, high, and moderate levels of invasiveness; all three risk groups included fish and mollusks (Appendix 2). One commonality of the very high-invasiveness-ranked species was the availability of Ecological Risk Screening Summary documents (USFWS, 2022) produced by U.S. Fish and Wildlife Service (USFWS), except for the goldfish ( Carassius auratus ) and the New Zealand mudsnail ( Potamopyrgus antipodarum ). The Ecological Risk Screening Summary is now available for New Zealand mudsnails. In general, fish species often ranked very high or high in invasiveness and included sportfish and aquarium fish, suggesting the importance of pathways such as aquarium trade, fishing industry, intentional (but illegal) introductions of sportfishes and aquarium fishes for establishment. The technique we used for habitat suitability models necessitated aquatic environmental datasets that were continental in scale, which was often interpolated from very coarse resolution source data layers, particularly in Alaska. Better spatial data representing aquatic environments would likely improve this approach. While the lack of introductions in Alaska and nearby provinces and states is encouraging, the lack of occurrence data for the focal species also created complications for habitat suitability modeling. Despite the challenges, the habitat suitability models indicated limited suitability for warmwater species while some species, such as Brook trout (S alvelinus fontinalis ), have high habitat suitability across Alaska no matter what threshold approach is taken. Some environmental predictors were more important than others. Specifically, the most important predictor variable, ‘frost free days,’ was critical for 15 out of 28 species as expected due to harsh winter conditions in Arctic and Subarctic regions. The second most important predictor was ‘subbasin land surface runoff’, a variable that indicates the amount of discharge and runoff, while the third most important predictor was ‘snow cover’ another indication of winter conditions. Overall, the ability to understand the effect of future climate scenarios on the establishment of AIS was challenging. A detailed dataset of freshwater temperatures and water chemistry (e.g., pH, calcium) would greatly improve the ability to predict invasiveness of freshwater species to Alaska’s ecosystems on a regional basis. Future studies may benefit from a more focused geographic scope examining a group of subbasins or a regional basin rather than the entire state. These drainages could be selected based upon the mostly likely locations of introduction pathways. The two most prevalent pathway risks for AIS are in-state transfer and stowaways/contaminants. Although there are examples of introductions from other pathways, the risk is somewhat mitigated by Alaska’s climate and regulations. However, variable application of protocols for inspection and cleaning of fishing gear, watercraft, and other similar items while traveling into Alaska as well as transferring from waterbody to waterbody within the state creates a substantial risk in introducing invasive species. We plot cumulative invasive vulnerability for all subbasins and for the top 10% of subbasins (Appendix 3).

Alaska↗
USGS2013

The use of process models to inform and improve statistical models of nitrate occurrence, Great Miami River Basin, southwestern Ohio

Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables. A source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers. The degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas. Area-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range. Differences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models. The use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.

Ohio↗
USGS2021

Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud

Executive Summary Global food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often subjective, leading to substantial uncertainties in cropland-area estimates, as well as their locations; and (3) extracted from one or more classes of a land use–land cover product in which cropland classes are not the focus of mapping, leading to their mixing with other classes and creating significant errors of omission and commission. These limitations can be overcome by producing high-resolution cropland-extent maps using satellite-sensor data, such as Landsat 30-m resolution or higher. The most fundamental cropland product is the high-resolution cropland-extent map because all higher level cropland products, such as crop-watering method (that is, whether crops are irrigated or rainfed), crop types, cropping intensities, cropland fallows, crop productivity, and crop-water productivity, are dependent on a precise and accurate cropland-extent product. Given these realities, the overarching goal of this study was to produce a Landsat satellite-derived global cropland-extent product at 30-m resolution. The work, which involved a paradigm shift in how global cropland-extent maps are produced, involved the following five key steps: (1) petabyte-scale computing that involved multiyear, 8- to 16-day, time-series Landsat 30-m resolution data for the global land surface; (2) composition of analysis-ready data (ARD) cubes; (3) creation of a large global-reference data hub for machine learning; (4) use of multiple machine-learning algorithms (MLAs) by writing software and computing in the cloud; and (5) Google Earth Engine (GEE) cloud computing. The five key steps involved nine distinct phases. First, the world was segmented into 74 agroecological zones (AEZs). Second, Landsat 8- to 16-day data were used to time-composite 10-band (blue, green, red, near-infrared, short-wave infrared band 1, short-wave infrared band 2, thermal infrared, enhanced vegetation index, normalized difference water index, and normalized difference vegetation index) Landsat 30-m resolution data cubes for every 2- to 4-month time period during 3- to 4-year periods (stated as nominal-year 2015 or, simply, 2015), along with two additional 30-m resolution bands (Shuttle Radar Topography Mission elevation, and slope) in each of the 74 AEZs. Third, more than 100,000 reference-training data samples were collected using ground data (some of which were collected using a mobile application), as well as submeter- to 5-m-resolution, very high-resolution imagery sourced from other reliable sources. Fourth, reference-training data were used to create a knowledge base for separating cropland from noncropland. Fifth, MLAs such as the pixel-based supervised random forest and support-vector machines were written on the GEE using Python and JavaScript. Sixth, object-based recursive hierarchical segmentation algorithm was used, in addition to MLAs, to overcome uncertainties. Seventh, MLAs used the knowledge base to classify and separate cropland from noncropland. Eighth, accuracy assessment was conducted by generating error matrices for each of the 74 AEZs using 19,171 independent validation-data samples. Ninth, cropland areas were computed for all countries of the world and compared with United Nation’s (UN’s) Food and Agricultural Organization (FAO) and other national statistics. The outcome was a Landsat-derived global cropland-extent product at 30-m resolution (GCEP30), which has an overall accuracy of 91.7 percent. For the cropland class, producer’s accuracy was 83.4 percent, and user’s accuracy was 78.3 percent. GCEP30 calculated (using direct pixel count) the global net-cropland area (GNCA) for the year 2015 as 1.873 billion hectares (~12.6 percent of the Earth’s terrestrial area). The continental cropland distribution as a percentage of GNCA was Asia, 33 percent; Europe, 25.5 percent; Africa, 16.7 percent; North America, 14.4 percent; South America, 8.1 percent; and Australia and Oceania, 2.4 percent. The worldwide cropland areas in GCEP30 for 2015 were higher by 236 to 299 million hectares (Mha) compared to national statistics reported elsewhere for the same year (for example, in Food and Agriculture Organization’s corporate statistical database [FAOSTAT] and in the monthly irrigated and rainfed crop areas [MIRCA] database). The global cropland area reported for 2015 increased by 344 Mha (22.5 percent), compared to the year 2000. During the same period (2000–2015), the world’s population increased by 20 percent. Whereas some of these areal increases are real increases in cropland areas, others are due to the types of data, methods, and approaches used. Using the highest known resolution (compared to previous coarse-resolution global products) enabled this study to capture fragmented croplands. Coarse-resolution data compute areas on the basis of subpixels, which, for a large proportion of certain land use–land cover classes, will show only a certain percentage of the total pixel area as actual area. Subpixel areas can lead to substantial uncertainties in area computation, as determining the exact fraction of cropland areas within a coarse-resolution pixel is resource intensive and subject to errors. Other innovations in GCEP30 include reference-data hubs, machine learning, and cloud computing. Cropland areas in 214 countries, territories, departments, and regions were calculated for the year 2015 using GCEP30, on the basis of UN’s global administrative unit layers (GAUL) boundaries. The 10 leading countries in terms of cropland area (as a percentage of the GNCA) were India (9.6 percent), United States (8.95 percent), China (8.82 percent), Russia (8.32 percent), Brazil (3.42 percent), Ukraine (2.32 percent), Canada (2.29 percent), Argentina (2.05 percent), Indonesia (2 percent), and Nigeria (1.91 percent). Together, these 10 countries occupy 50 percent of the global cropland, and they have 52 percent of the global population. Their combined cropland area increased by 2 percent between 2000 and 2015, compared to the substantial increase in population of 517 million (15.5 percent). Together, India, United States, China, and Russia encompass 36 percent of the total area. In the United States and Canada, from 2000 to 2015, cropland decreased by about 2 percent, whereas their populations increased by 14 and 13 percent, respectively. The additional food requirements in these 10 countries, which are caused by increased populations, as well as increasing nutritional demands, are met by production increases in existing cropland or through virtual food trade, or both. More than 18 countries, territories, departments, or regions had 60 percent or more of their geographic area as cropland: Republic of Moldova, San Marino, and Hungary had more than 80 percent of the country’s area as cropland; Denmark, Ukraine, Ireland, and Bangladesh, 70 to 80 percent; and Uruguay, Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czechia, Italy, India, and Azerbaijan, 60 to 70 percent. Europe and South Asia can be considered agricultural capitals of the world, on the basis of their percentages of geographic area as cropland. United States, China, and Russia, which all have high cropland areas, are ranked second, third, and fourth in the world; India is ranked first. However, the amount of cropland as a percentage of the country’s geographic area is relatively very low for United States (18.3 percent), China (17.7 percent), and Russia (9.5 percent), whereas it is 60.5 percent for India. Most African and South American countries, territories, departments, or regions have less than 15 percent of their geographic area as cropland. China and India together house 36 percent of the world’s population; however, between 2000 and 2015, the amount of China’s cropland area fell by 18.9 percent, owing to urban expansion and the abandonment of farmlands caused by demographic changes (that is, the movement of population from villages to cities). In contrast, China’s population grew by 10 percent. The amount of India’s cropland increased by 8.5 percent, whereas its population grew by 20 percent. This study showed that, out of the 10 leading cropland countries, Ukraine, Nigeria, Russia, and Indonesia showed an 18 to 31 percent increase in cropland areas, on the basis of GCEP30 by the year 2015, compared to 2000. Nigeria’s cropland area increased by 25 percent, and its population increased by 31 percent in the same period. In these countries, food security is maintained by cropland expansion, productivity increases, and virtual food trade. Nevertheless, this trend of increasing net-cropland area and productivity will likely become difficult to maintain, owing to diminishing arable lands and plateauing of 50 years of continual yield increases, requiring policymakers to explore novel and data-supported approaches to solving future food security issues. The GCEP30 product, which can be browsed at full resolution at www.croplands.org , has been released for public download and use through U.S. Geological Survey (USGS)–National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (see https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/ ).

Professional Paper↗
USGS2005

Occurrence of fecal-indicator bacteria and protocols for identification of fecal-contamination sources in selected reaches of the West Branch Brandywine Creek, Chester County, Pennsylvania

The presence of fecal-indicator bacteria indicates the potential presence of pathogens originating from the fecal matter of warm-blooded animals. These pathogens are responsible for numerous human diseases ranging from common diarrhea to meningitis and polio. The detection of fecal-indicator bacteria and interpretation of the resultant data are, therefore, of great importance to water-resource managers. Current (2005) techniques used to assess fecal contamination within the fluvial environment primarily assess samples collected from the water column, either as grab samples or as depth- and (or) width-integrated samples. However, current research indicates approximately 99 percent of all bacteria within nature exist as attached, or sessile, bacteria. Because of this condition, most current techniques for the detection of fecal contamination, which utilize bacteria, assess only about 1 percent of the total bacteria within the fluvial system and are, therefore, problematic. Evaluation of the environmental factors affecting the occurrence and distribution of bacteria within the fluvial system, as well as the evaluation and modification of alternative approaches that effectively quantify the larger population of sessile bacteria within fluvial sediments, will present water-resource managers with more effective tools to assess, prevent, and (or) eliminate sources of fecal contamination within pristine and impaired watersheds. Two stream reaches on the West Branch Brandywine Creek in the Coatesville, Pa., region were studied between September 2002 and August 2003. The effects of sediment particle size, climatic conditions, aquatic growth, environmental chemistry, impervious surfaces, sediment and soil filtration, and dams on observed bacteria concentrations were evaluated. Alternative approaches were assessed to better detect geographic sources of fecal contamination including the use of turbidity as a surrogate for bacteria, the modification and implementation of sandbag bacteria samplers, and the use of optical brighteners. For the purposes of this report, sources of bacteria were defined as geographic locations where elevated concentrations of bacteria are observed within, or expected to enter, the main branch of the West Branch Brandywine Creek. Biologic sources (for example, waterfowl) were noted where applicable; however, no specific study of biologic sources (such as bacterial source tracking) was conducted. Data indicated that specific bacterial populations within fluvial sediments could be related to specific particle-size ranges. This relation is likely the result of the reduced porosity and permeability associated with finer sediments and the ability of specific bacteria to tolerate particular environments. Escherichia coli (E. coli) showed a higher median concentration (2,160 colonies per gram of saturated sediment) in the 0.125 to 0.5-millimeter size range of natural sediments than in other ranges, and enterococcus bacteria showed a higher median concentration (61,830 colonies per gram of saturated sediment) in the 0.062 to 0.25-millimeter size range of natural sediments than in other ranges. There were insufficient data to assess the particle-size relation to fecal coliform bacteria and (or) fecal streptococcus bacteria. Climatic conditions were shown to affect bacteria concentrations in both the water column and fluvial sediments. Drought conditions in 2002 resulted in lower overall bacteria concentrations than the more typically wet year of 2003. E. coli concentrations in fluvial sediment along the Coatesville study reach in 2002 had a median concentration of 92 colonies per gram of saturated sediment; in 2003, the median concentration had risen to 4,752 colonies per gram of saturated sediment. Symbiotic relations between bacteria and aquatic growth were likely responsible for increased bacteria concentrations observed within an impoundment area on the Coatesville study reach. This reach showed evidence of elevated aquatic growth and sharp increases in E. coli concentrations from upstream to downstream through the impoundment area in both 2002 and 2003. In 2003, E. coli concentrations within the waters column increased from 940 colonies per 100 milliliters upstream to 6,000 colonies per 100 milliliters at the dam crest. Given that these bacteria likely resulted from natural bacterial regrowth, the use of E. coli as an indicator of fecal contamination was severely impaired. Variable environmental conditions along the West Branch Brandywine Creek made the common field-chemical parameters of specific conductance, temperature, pH, and dissolved oxygen ineffective and (or) impossible to use for the determination of inputs of fecal contamination. Extreme variations in chemical gradients commonly were related to the urban/industrial signature of the watershed. For example, during base-flow sampling in 2002, specific-conductance values exceeding 1,000 microsiemens per centimeter observed in effluent from a local steel mill. This effluent raised the specific conductance within the West Branch Brandywine from just above 200 microsiemens per centimeter upstream from the outfall to just below 500 microsiemens per centimeter downstream from the outfall. These chemical gradients also, likely, had an effect on the initial colonization of bacteria, the formation of biofilms, and the persistence of certain types of bacteria along the study reach. Data collected in 2003 indicated that nutrients increased during both base-flow and stormflow conditions along the Coatesville study reach. For example, during base-flow sampling in 2003, 20 pounds of phosphorus was shown to enter the West Branch Brandywine Creek along the Coatesville study reach. The largest contributors to this base-flow nutrient load were likely two wastewater-treatment facilities adjacent to the study reach. During stormflow sampling in 2003, 480 pounds of phosphorus was shown to enter the West Branch Brandywine Creek along the Coatesville study reach. Data, along with other research, indicated the largest contributor to this stormflow nutrient load was likely remobilized sediment originating from a large dam impoundment. These elevated nutrient concentrations were considered sufficient to promote accelerated aquatic growth along the reach. Data collected in 2003 showed that wastewater constituents entered the West Branch Brandywine Creek largely from urban storm-sewer systems. Samples from the primary storm sewer for the city of Coatesville had detections for 20 of 69 wastewater constituents. These constituents included both strong and weak indicators of fecal contamination and generally indicated the storm-sewer system along the Coatesville study reach was a likely source of fecal-indicator bacteria and fecal contamination under base-flow conditions. By comparison, 5 constituents were detected in samples from the upstream end of the reach, and 10 constituents were detected in samples from the downstream end of the reach. During stormflow, numbers of detections were similar along the entire length of the study reach-five in samples from the upstream end, eight in samples from the center of the reach, and seven in samples from the downstream end of the reach. These data indicate that point sources (such as culverts and pipes, septic systems, and wastewater-treatment facilities) are not likely the origin of bacteria contamination during stormflow. The bacteria concentrations observed during stormflow events probably result from remobilized sessile bacteria stored within fluvial sediments. In this case, these bacteria should not be considered indicators of current fecal contamination. Impervious surfaces were found to increase bacteria concentrations along the West Branch Brandywine Creek because contaminated runoff from impervious areas generally flows into, and is concentrated within, the confines of the local storm-sewer system. During 2002, storm-sewer outfalls draining impervious areas were associated with all major locations of elevated bacterial concentrations (greater than 1,200 colonies per gram of saturated sediment) in fluvial sediments. During 2003, wetter conditions and overall bacteria concentrations higher than in 2002 resulted in point sources of bacterial contamination becoming less pronounced; however, the storm-sewer system, draining adjacent impervious areas, was still observed to be the primary source of bacteria along the reach. Where stormwater and (or) other runoff from these areas was allowed to infiltrate and (or) flow through wetland and riparian buffers, bacteria concentrations were not observed to be elevated above background levels commonly observed throughout similar areas of the same reach. Two run-of-the-river dams along the Coatesville study reach were evaluated for their effects on observed bacterial concentrations. These dams were shown to have greater or lesser effects on bacterial concentrations depending on the size of the structure and the capacity of the structure to impede flows. The smaller upstream dam had an approximate height of 3 feet and showed little observed effect on measured turbidity values; these data indicated that the dam did not effectively impede the flow of water or sediment within the West Branch Brandywine Creek. Consequently, this small dam did not show any observed effect on bacterial concentrations either upstream or downstream of the structure. The larger dam, near the middle of the reach, had an approximate height of 20 feet and showed greater effects on both turbidity and bacteria concentrations. The capacity of the larger dam to impede flows, combined with nutrients entering the reach, resulted in increased biologic activity throughout the impoundment area. Within this larger impoundment, enterococcus bacteria populations were observed to decrease sharply and E. coli bacteria populations were observed to increase sharply as flow approached the dam crest. All bacteria levels were then observed to drop to background levels, in both the water column and fluvial sediment, immediately downstream from the dam crest. Additional study is required to determine the cause for this rapid die off. Turbidity was assessed as a potential surrogate for E. coli bacteria. Regression analysis indicated higher turbidity levels usually can indicate higher concentrations of bacteria (R2 = 0.67), but the relation was too sporadic on the West Branch Brandywine Creek to use turbidity as a surrogate for estimated bacteria concentrations. Evaluation of data from individual base-flow and stormflow events resulted in variable and generally poor statistical relations between E. coli bacteria and turbidity (R2 values ranged from 0.02 to 0.94). Sandbag samplers were used in 2003 to determine their suitability for the assessment of fecal contamination. Sandbag samplers rely on the ability of bacteria to attach to surfaces and use the larger sessile bacteria populations instead of the more commonly used planktonic bacteria populations. E. coli bacteria concentrations observed in the sandbag samplers, after 1 week in place, were similar to those found within natural sediments collected concurrently. Enterococcus bacteria concentrations within the same sandbag samplers were not similar, and were generally lower, than those observed within the natural sediments. This discrepancy was likely because sand within the samplers was sieved to a size that was likely too coarse for enterococcus bacteria to persist. Optical-brightener samplers were installed along with each sandbag sampler. Optical brighteners are additives used in common household detergents; therefore, detection of optical brighteners, along with elevated fecal-indicator bacteria concentrations, strongly indicates a link to humans. Positive results for optical brighteners were detected only at the outfalls of two sewage-treatment facilities; because of treatment of the effluent from these facilities, these samples did not have elevated bacteria concentrations. The lack of additional positive results was largely because this method is not sensitive to low concentrations of optical brighteners.

Pennsylvania↗