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At least 667 records · Page 37Linked to original sources
USGS1999

Water-quality and lake-stage data for Wisconsin lakes, water year 1998

The U.S. Geological Survey (USGS), in cooperation with local and other agencies, collects data at selected lakes throughout Wisconsin. These data, accumulated over many years, provide a data base for developing an improved understanding of the water quality of lakes. To make these data available to interested parties outside the USGS, the data are published annually in this report series. The location of water-quality and lake-stage stations in Wisconsin for water year 1998 are shown in figure 1. A water year is the 12-month period from October 1 through September 30. It is designated by the calendar year in which it ends. Thus, the period October 1, 1997 through September 30, 1998 is called "water year 1998." The purpose of this report is to provide information about the chemical and physical characteristics of Wisconsin lakes. Data that have been collected at specific lakes, and information to aid in the interpretation of those data, are included in this report. Data collected include measurements of in-lake water quality and lake stage. Time series of Secchi depths, surface total phosphorus and chlorophyll a concentrations collected during non-frozen periods are usually included for lakes with two or more years of data. Graphs of vertical profiles of temperature, dissolved oxygen, pH, and specific conductance are included for sites where these parameters were measured. Descriptive information for each lake includes: location of the lake, area of the lake's watershed, period for which data are available, revisions to previously published records, and pertinent remarks. Additional data, such as streamflow and water quality in tributary and outlet streams of some of the lakes, are published in another volume: "Water Resources Data- Wisconsin, 1998."

Wisconsin↗
USGS2003

Irrigation-induced contamination of water, sediment, and biota in the western United States-synthesis of data from the National Irrigation Water Quality Program

In October 1985 the U.S. Department of the Interior (DOI), through the National Irrigation Water Quality Program (NIWQP), began a series of field investigations at 26 areas in the Western United States to determine whether irrigation drainage has had harmful effects on fish, wildlife, and humans or has reduced beneficial uses of water. In 1992 NIWQP initiated the Data Synthesis Project to evaluate data collected during the field investigations. Geologic, climatologic, and hydrologic data were evaluated and water, sediment, and biota from the 26 areas were analyzed to identify commonalities and dominant factors that result in irrigation-induced contamination of water and biota. Data collected for the 26 area investigations have been compiled and merged into a common data base. The structure of the data base is designed to enable assessment of relations between contaminant concentrations in water, sediment, and biota. The data base is available to the scientific community through the World Wide Web at URL http://www.usbr.gov/niwqp. Analysis of the data base for the Data Synthesis included use of summary statistics, factor analysis, and logistic regression. A Geographic Information System was used to store and analyze spatially oriented digital data such as land use, geology and evaporation rates. In the U.S. Department of the Interior (DOI) study areas, samples of water, bottom sediment, and biota were collected for trace-element and pesticide analysis. Contaminants most commonly associated with irrigation drainage were identified by comparing concentrations in water with established criteria. For surface water, the criteria used were typically chronic criteria for the protection of freshwater aquatic life. Because ground water can discharge to the surface where wildlife can be exposed to it, the criteria used for ground water were both the maximum contaminant levels (MCL's) for drinking water and the chronic criteria for the protection of freshwater aquatic life. Data collected by the NIWQP studies indicated that, in surface water, filtered and unfiltered samples had nearly the same concentrations of arsenic, boron, molybdenum, and selenium for concentrations greater than about 10 micrograms per liter. Therefore, in this concentration range, filtered concentrations can be directly compared to biological-effect levels developed for unfiltered samples. In the range of 1 to 10 micrograms per liter there may be a tendency for unfiltered arsenic concentrations to be greater than filtered concentrations. For selenium, however, the data suggest differences from equality in that range result from analytical imprecision and not a general tendency for unfiltered concentrations to be greater than filtered concentrations. This relation may not be true in lentic, nutrient-rich waters because in such settings algae can bioaccumulate large amounts of selenium and other trace elements. Selenium was the trace element in surface water that most commonly exceeded chronic criteria for the protection of freshwater aquatic life; more than 40 percent of the selenium concentrations in surface-water samples exceeded the U.S. Environmental Protection Agency (USEPA) aquatic-life chronic criterion (5 micrograms per liter). In 12 of the 26 areas at least 25 percent of the surface water-samples had selenium concentrations that either equaled or exceeded the chronic criterion (5 micrograms per liter). More than 28 percent of boron concentrations and almost 17 percent of the molybdenum concentrations exceeded the aquatic life criteria established by the State of California (550 and 19 micrograms per liter, respectively). In ground water, more than 22 percent of the arsenic concentrations and more than 35 percent of the selenium concentrations exceeded the MCL (10 and 50 micrograms per liter, respectively). Few samples of uranium in surface water exceeded a criterion for the protection of aquatic life (300 micrograms per liter), but 44 percent

Professional Paper↗
USGS2015

Reducing cross-sectional data using a genetic algorithm method and effects on cross-section geometry and steady-flow profiles

Reduction of cross-sectional data using a genetic algorithm method, and the effects of data reduction on channel geometry and steady-flow profiles, were analyzed. Two reduction methods─standard and genetic algorithms─were used to reduce cross-sectional data from the Kootenai River in northern Idaho. Cross sections that are representative of meander, straight, braided, and canyon reaches were used to evalutate the reduction methods. Visual and hydraulic analyses were used to assess the methods. The genetic algorithm-reduced cross sections approximated the shape of the original cross sections better than the standard-reduced cross sections. A greater number of cross-sectional data points were needed for reduced cross sections in the straight reach, and even more in the braided reach, because a greater amount of data points are needed to adequately define cross sections that have greater topographic varability. For the genetic algorithm-reduction method, about 40 data points were needed to adequately define the shape of a reduced cross section in the braided reach compared to 10 to 20 data points in the meander and canyon reaches. The standard-reduction method needed about 70 data points for the braided reach and more than 30 points for the meander and canyon reaches. The genetic algorithm can effectively reduce data while staying within the threshold set by the maximum number of points to be included in the reduced dataset. The effects of reduced cross-sectional data points on steady-flow profiles were also determined. Thirty-five cross sections of the original steady-flow model of the Kootenai River were used. These two methods were tested for all cross sections with each cross section resolution reduced to 10, 20 and 30 data points, that is, six tests were completed for each of the thirty-five cross sections. Generally, differences from the original water-surface elevation were smaller as the number of data points in reduced cross sections increased, but this was not always the case, especially in the braided reach. Differences were smaller for reduced cross sections developed by the genetic algorithm method than the standard algorithm method.

Scientific Investigations Report↗
USGS2017

The U.S. Geological Survey Peak-Flow File Data Verification Project, 2008–16

Annual peak streamflow (peak flow) at a streamgage is defined as the maximum instantaneous flow in a water year. A water year begins on October 1 and continues through September 30 of the following year; for example, water year 2015 extends from October 1, 2014, through September 30, 2015. The accuracy, characterization, and completeness of the peak streamflow data are critical in determining flood-frequency estimates that are used daily to design water and transportation infrastructure, delineate flood-plain boundaries, and regulate development and utilization of lands throughout the United States and are essential to understanding the implications of climate and land-use change on flooding and high-flow conditions. As of November 14, 2016, peak-flow data existed for 27,240 unique streamgages in the United States and its territories. The data, collectively referred to as the “peak-flow file,” are available as part of the U.S. Geological Survey (USGS) public web interface, the National Water Information System, at https://nwis.waterdata.usgs.gov/usa/nwis/peak . Although the data have been routinely subjected to periodic review by the USGS Office of Surface Water and screening at the USGS Water Science Center level, these data were not reviewed in a national, systematic manner until 2008 when automated scripts were developed and applied to detect potential errors in peak-flow values and their associated dates, gage heights, and peak-flow qualification codes, as well as qualification codes associated with the gage heights. USGS scientists and hydrographers studied the resulting output, accessed basic records and field notes, and corrected observed errors or, more commonly, confirmed existing data as correct. This report summarizes the changes in peak-flow file data at a national level, illustrates their nature and causation, and identifies the streamgages affected by these changes. Specifically, the peak-flow data were compared for streamgages with peak flow measured as of November 19, 2008 (before the automated scripts were widely applied) and on November 14, 2016 (after several rounds of corrections). There were 659,332 peak-flow values in the 2008 dataset and 731,965 peak-flow values in the 2016 dataset. When compared to the 2016 dataset, 5,179 (0.79 percent) peak-flow values had changed; 36,506 (5.54 percent) of the peak-flow qualification codes had changed; 1,938 (0.29 percent) peak-flow dates had changed; 18,599 (2.82 percent) of the peak-flow gage heights had changed; and 20,683 (3.14 percent) of the gage-height qualification codes had changed—most as a direct result of the peak-flow file data verification effort led by USGS personnel. The various types of changes are summarized and mapped in this report. In addition to this report, a corresponding USGS data release is provided to identify changes in peak flows at individual streamgages. The data release and the procedures to access the data release are described in this report.

Scientific Investigations Report↗
USGS2020

Interpretation of hydrogeologic data to support groundwater management, Bazile Groundwater Management Area, northeast Nebraska, 2019—A case demonstration of the Nebraska Geocloud

Nitrate, age tracer, and continuous groundwater-level data were interpreted in conjunction with airborne electromagnetic (AEM) survey data to understand the movement of nitrate within the Bazile Groundwater Management Area (BGMA) in northeastern Nebraska. Previously published age tracer data and nitrate data indicated vertical stratification of groundwater quality. Younger groundwater sampled within shallow parts of the aquifer had higher concentrations of nitrate, with 70 percent exceeding the U.S. Environmental Protection Agency maximum contaminant level of 10 milligrams per liter. In contrast, groundwater sampled from deeper parts of the aquifer indicated that nitrate concentrations were less than 2 milligrams per liter and that groundwater likely recharged prior to widespread use of commercial fertilizer. The hydrostratigraphic interpretation of AEM profiles indicated that shallow and deep monitoring wells were often screened within the same homogenous zone of aquifer material. In contrast, test-hole logs indicated that there often are fine-grained layers within these homogenous zones that separate the shallow and deep monitoring well screens, but these fine-grained layers are not detected by the AEM technique because of decreased resolution of the AEM technique with depth. The stratification of groundwater ages and nitrate concentrations likely was caused by groundwater-flow paths of different length, location and time of recharge, and denitrification. Within paleochannels interpreted from AEM and test-hole data, pesticides detected in groundwater generally coincide with elevated nitrate concentrations. Continuous groundwater-level data from four monitoring well nests indicated that groundwater pumping can impose or increase downward hydraulic gradients and facilitate the downward movement of nitrate into deeper parts of the High Plains aquifer. Given the density of irrigation wells within the BGMA, this effect on the hydraulic gradient is likely prevalent in other areas of the BGMA. Understanding seasonal water-level changes can allow water managers to better predict and assess the hydraulic gradient and the vulnerability of groundwater in deeper parts of the High Plains aquifer. Nitrate, age tracer, and continuous groundwater-level data within the BGMA were interpreted in conjunction with AEM data as a case demonstration of the Nebraska Geocloud. The Nebraska Geocloud was initiated to protect taxpayer investments in AEM data collection and realize maximum benefit of these data by creating a publicly available, online digital database for long-term data storage. The Lower Platte North, Lower Platte South, Papio-Missouri River, Nemaha, Lower Loup, Central Platte, Upper Elkhorn, Lower Elkhorn, Lower Niobrara, and Lewis and Clark Natural Resources Districts; the University of Nebraska-Lincoln Conservation and Survey Division, Nebraska Natural Resources Commission, Nebraska Department of Natural Resources; and the U.S. Geological Survey entered a cooperative agreement to begin a program of data management and research aimed at understanding the best use of AEM for groundwater sustainability and management. Resulting case-study interpretations are provided to guide use of the Nebraska Geocloud to assess water-quality conditions and can be used by water managers and staff to address applicable water resource problems.

Nebraska↗
USGS2015

Design, analysis, and interpretation of field quality-control data for water-sampling projects

The process of obtaining and analyzing water samples from the environment includes a number of steps that can affect the reported result. The equipment used to collect and filter samples, the bottles used for specific subsamples, any added preservatives, sample storage in the field, and shipment to the laboratory have the potential to affect how accurately samples represent the environment from which they were collected. During the early 1990s, the U.S. Geological Survey implemented policies to include the routine collection of quality-control samples in order to evaluate these effects and to ensure that water-quality data were adequately representing environmental conditions. Since that time, the U.S. Geological Survey Office of Water Quality has provided training in how to design effective field quality-control sampling programs and how to evaluate the resultant quality-control data. This report documents that training material and provides a reference for methods used to analyze quality-control data. Quality-control data are those generated from the collection and analysis of quality-control samples, and are used to estimate the magnitude of errors in the process of obtaining environmental data. “Bias” and “variability” are the terms used in this report for the two types of errors in environmental data that are quantified by the data from quality-control samples. Bias is the systematic error inherent in a method or measurement system. Variability is the random error that occurs in independent measurements. The types of field quality-control samples discussed in this report include blanks, spikes, and replicates. Blanks are samples prepared with water that is intended to be free of measurable constituents that will be analyzed by the laboratory; blanks are used to estimate bias caused by contamination. Spiked samples are modified by addition of specific analytes; spikes are used to determine the performance of analytical methods and to estimate the potential bias due to matrix interference or analyte degradation. Replicate samples are two or more samples that are considered to be essentially identical in composition. Replicates are used to evaluate variability in analytical results. Various sub-types of these quality-control samples are defined and discussed in this report, and guidance is provided for incorporating the proper samples into the design for a project. The concept of inference space is introduced to help determine where and when quality-control samples should be collected as well as which environmental samples are related to a set of quality-control samples. The recommended basic quality-control design incorporates project-specific considerations, such as the objectives and scale of the study, and hydrologic and chemical conditions within the study area. The report provides extensive information about statistical methods used to analyze quality-control data in order to estimate potential bias and variability in environmental data. These methods include construction of confidence intervals on various statistical measures, such as the mean, percentiles and percentages, and standard deviation. The methods are used to compare quality-control results with the larger set of environmental data in order to determine whether the effects of bias and variability might interfere with interpretation of these data. Examples from published reports are presented to illustrate how the methods are applied, how bias and variability are reported, and how the interpretation of environmental data can be qualified based on the quality-control analysis.

Techniques and Methods↗
USGS2004

Summary of available state ambient stream-water-quality data, 1990-98, and limitations for national assessment

The investigation described in this report summarized data from State ambient stream-water-quality monitoring sites for 10 water-quality constituents or measurements (suspended solids, fecal coliform bacteria, ammonia as nitrogen, nitrite plus nitrate as nitrogen, total phosphorus, total arsenic, dissolved solids, chloride, sulfate, and pH). These 10 water-quality constituents or measurements commonly are listed nationally as major contributors to degradation of surface water. Water-quality data were limited to that electronically accessible from the U.S. Environmental Protection Agency Storage and Retrieval System (STORET), the U.S. Geological Survey National Water Information System (NWIS), or individual State databases. Forty-two States had ambient stream-water-quality data electronically accessible for some or all of the constituents or measurements summarized during this investigation. Ambient in this report refers to data collected for the purpose of evaluating stream ecosystems in relation to human health, environmental and ecological conditions, and designated uses. Generally, data were from monitoring sites assessed for State 305(b) reports. Comparisons of monitoring data among States are problematic for several reasons, including differences in the basic spatial design of monitoring networks; water-quality constituents for which samples are analyzed; water-quality criteria to which constituent concentrations are compared; quantity and comprehensiveness of water-quality data; sample collection, processing, and handling; analytical methods; temporal variability in sample collection; and quality-assurance practices. Large differences among the States in number of monitoring sites precluded a general assumption that statewide water-quality conditions were represented by data from these sites. Furthermore, data from individual monitoring sites may not represent water-quality conditions at the sites because sampling conditions and protocols are unknown. Because of these factors, a high level of uncertainty exists in a national assessment of water quality. The purpose of this report is to present a summary of electronically available State ambient stream-water-quality data for 10 selected constituents and measurements from monitoring sites with nine or more analyses for 199098 and to discuss limitations for use of the data for national assessment. These analyses were statistiscally summarized by monitoring site and State, and the results presented in tabular format. Most of the selected constituents or measurements have U.S. Environmental Protection Agency criteria or guidelines for aquatic-life or drinking-water purposes. A significant finding of this investigation is that for a large percentage of monitoring sites in the Nation, there are insufficient data to meet U.S. Environmental Protection Agency recommendations for determining if water-quality conditions are degraded and for making informed decisions regarding total maximum daily loads.

Water-Resources Investigations Report↗
USGS1986

Sediment-data sources and estimated annual suspended-sediment loads of rivers and streams in Colorado

Sources of sediment data collected by several government agencies through water year 1984 are summarized for Colorado. The U.S. Geological Survey has collected suspended-sediment data at 243 sites; these data are stored in the U.S. Geological Survey 's water data storage and retrieval system. The U.S. Forest Service has collected suspended-sediment and bedload data at an additional 225 sites, and most of these data are stored in the U.S. Environmental Protection Agency 's water-quality-control information system. Additional unpublished sediment data are in the possession of the collecting entities. Annual suspended-sediment loads were computed for 133 U.S. Geological Survey sediment-data-collection sites using the daily mean water-discharge/sediment-transport-curve method. Sediment-transport curves were derived for each site by one of three techniques: (1) Least-squares linear regression of all pairs of suspended-sediment and corresponding water-discharge data, (2) least-squares linear regression of data sets subdivided on the basis of hydrograph season; and (3) graphical fit to a logarithm-logarithm plot of data. The curve-fitting technique used for each site depended on site-specific characteristics. Sediment-data sources and estimates of annual loads of suspended, bed, and total sediment from several other reports also are summarized. (USGS)

Colorado↗
USGS1995

Water-quality assessment of the Rio Grande Valley study unit, Colorado, New Mexico, and Texas -- Analysis of selected nutrient, suspended-sediment, and pesticide data

This report contains a summary of data compiled from sources throughout the Rio Grande Valley study unit of the National Water-Quality Assessment program. Information presented includes the sources and types of water-quality data available, the utility of water-quality data for statistical analysis, and a description of recent water-quality conditions and trends and their relation to natural and human factors. Water-quality data are limited to concentrations of selected nutrient species in surface water and ground water, concentrations of suspended sediment and suspended solids in surface water, and pesticides in surface water, ground water, and biota. The Rio Grande Valley study unit includes about 45,900 square miles in Colorado, New Mexico, and Texas upstream from the streamflow-monitoring station Rio Grande at El Paso, Texas. The area also includes the San Luis Closed Basin and the surface-water closed basins east of the Continental Divide and north of the United States-Mexico international border. The Rio Grande drains about 29,300 square miles in these States; the remainder of the study unit area is in closed basins. Concentrations of all nutrients found in surface-water samples collected from the Rio Grande, with the exception of phosphorus, generally remained nearly constant from the northernmost station in the study unit to Rio Grande near Isleta, where concentrations were larger by an order of magnitude. Total nitrogen and total phosphorus loads increased downstream between Lobatos, Colorado, and Albuquerque, New Mexico. Nutrient concentrations remained elevated with slight variations until downstream from Elephant Butte Reservoir, where nutrient concentrations were lower. Nutrient concentrations then increased downstream from the reservoir, as evidenced by elevated concentrations at Rio Grande at El Paso, Texas. Suspended-sediment concentrations were similar at stations upstream from Otowi Bridge near San Ildefonso, New Mexico. The concentration and estimated load were nearly two orders of magnitude larger at this station relative to upstream stations. Cochiti Lake allows suspended sediment to settle, thus the resulting concentration is substantially lower downstream from the reservoir. Downstream from Cochiti Lake, concentrations again increased due to inflow from tributaries, other ephemeral streams and arroyos, and agricultural and urban areas. Two ephemeral tributaries (Rio Puerco and Rio Salado, which are south of Albuquerque) contribute substantial amounts of suspended sediment to the Rio Grande. Suspended-sediment concentrations in the Rio Grande just downstream from Elephant Butte Dam decreased by nearly three orders of magnitude due to settling in the reservoir. Concentrations then increased due to agricultural and urban impacts downstream from the reservoir. Nutrients in ground water in the study unit do not appear to be a widespread problem. However, localized areas that have elevated nitrate concentrations have been documented. The largest median nitrate concentration was found in water from wells located in the Basin and Range-mountains-urban data stratum (3.0 milligrams per liter) and the smallest median nitrate concentration was found in water from wells located in the Southern Rocky Mountainsmountains-forest data stratum (0.08 milligram per liter). Few (3 percent) nitrate concentrations in water from wells in all data strata were greater than 10 milligrams per liter, and most (82 percent) were less than 2 milligrams per liter. Comparison of nitrate concentrations in water from wells located in specific land-use settings across all hydrogeologic settings, with the exception of the Colorado Plateau, indicated that the largest median nitrate concentration was associated with rangeland land use and that larger nitrate concentrations were found in water from shallow wells. Water from wells located in areas of rangeland land use consistently had larger median nutrient concentrations than water from wells in areas of other land uses. The largest median ammonia concentration was in water from wells located in the Colorado Plateau-San Juan Basin-rangeland data stratum (0.27 milligram per liter). Most median ammonia concentrations were less than 0.03 milligram per liter, indicating that elevated ammonia concentrations are not a major issue in the study unit. The largest median orthophosphate concentration was found in water from wells located in the Southern Rocky Mountains-mountains-forest data stratum (0.15 milligram per liter) and the smallest was found in water from wells located in the Basin and Range-mountains-urban data stratum (0.02 milligram per liter). Most orthophosphate concentrations (85 percent) sampled were less than 0.2 milligram per liter, indicating that elevated orthophosphate concentrations are not a major issue in the study unit. Pesticide analyses were available for only 38 ground-water sampling sites in the Rio Grande Valley study unit. Diazinon, at a concentration of 0.01 microgram per liter, was the only pesticide detected and it was detected at only one site. More study is needed to determine if pesticides are affecting ground-water quality in the Rio Grande Valley study unit. Surface-water biological pesticide data were inadequate for in-depth analysis. The primary sources of data were the U.S. Fish and Wildlife Service and the U.S. Geological Survey. In the U.S. Fish and Wildlife Service study p,p'-DDE, a degradation product of DDT, was detected most frequently; highest concentrations were found at Stahman Farms in carp (6.3 micrograms per gram wet-weight) and at Hatch in Western kingbird (5.1 micrograms per gram wet-weight). In the U.S. Geological Survey study of Bosque del Apache National Wildlife Refuge no detectable organochlorine concentrations were found in plants, but detectable levels of p,p'-DDE were found in coot and carp, with a maximum concentration of 0.12 microgram per gram wet-weight found in coot.

Colorado, New Mexico, Texas↗
USGS1997

Water-quality assessment of the Lower Susquehanna River Basin, Pennsylvania and Maryland: sources, characteristics, analysis and limitations of nutrient and suspended-sediment data, 1975-90

This report describes analyses of available information on nutrients and suspended sediment collected in the Lower Susquehanna River Basin during water years 1975-90. Most of the analyses were applied to data collected during water years 1980-89. The report describes the spatial and temporal availability of nutrient and suspended-sediment data and presents a preliminary concept of the spatial and temporal patterns of concentrations and loads within the basin. Where data were available, total and dissolved forms of nitrogen and phosphorus species from precipitation, surface water, ground water, and springwater, and bottom material from streams and reservoirs were evaluated. Suspended-sediment data from streams also were evaluated. The U.S. Geological Survey National Water Information System (NWIS) database was selected as the primary database for the analyses. Precipitation-quality data from the National Atmospheric Deposition Program (NADP) and bottom-material-quality data from the National Uranium Resource Evaluation (NURE) were used to supplement the water-quality data from NWIS. Concentrations of nutrients were available from 3 precipitation sites established for longterm monitoring purposes, 883 wells (854 synoptic areal survey sites and 29 project and research sites), 23 springs (17 synoptic areal survey sites and 6 project and research sites), and 894 bottom-material sites (840 synoptic areal survey sites and 54 project and research sites). Concentrations of nutrients and (or) suspended sediment were available from 128 streams (36 long-term monitoring sites, 51 synoptic areal survey sites, and 41 project and research sites). Concentrations of nutrients and suspended sediment in streams varied temporally and spatially and were related to land use, agricultural practices, and streamflow. A general north-to-south pattern of increasing median nitrate concentrations, from 2 to 5 mg/L, was detected in samples collected in study unit streams. In streams that drain areas dominated by agriculture, concentrations of nutrients and suspended sediment tend to be elevated with respect to those found in areas of other land-use types and are related to the amount of commercial fertilizer and animal manure applied to the area drained by the streams. Animal manure is the dominant source of nitrogen for the streams in the lower, agricultural part of the basin. Concentrations of nutrients in samples from wells varied with season and well depth and were related to hydrogeologic setting. Median concentrations of nitrate were 2.5 and 3.5 mg/L for wells drawing water at depths of 0 to 100 ft and 101 to 200 ft, respectively. The lowest median concentrations for nitrate in ground water from wells were generally found in siliciclastic-bedrock, forested settings of the Ridge and Valley Physiographic Province, and the highest were found in carbonate-bedrock agricultural settings of the Piedmont Physiographic Province. Twenty-five percent of the measurements from wells in carbonate rocks in the Piedmont Physiographic Province exceeded the Pennsylvania drinking-water standard. An estimate of mass balance of nutrient loads within the Lower Susquehanna River Basin was produced by combining the available information on stream loads, atmosphericdeposition loads, commercial-fertilizer applications, animal-manure production, privateseptic-system nonpoint-source loads, and municipal and industrial point-source loads. The percentage of the average annual nitrate load carried in base flow of streams in the study unit ranged from 45 to 76 percent, and the average annual phosphorus load carried in base flow ranged from 20 to 33 percent. Average annual yields of nutrients and suspended sediment from tributary basins are directly related to percentage of drainage area in agriculture and inversely to drainage area. Information required to compute loads of nitrogen and phosphorus were available for all sources except atmospheric deposition, for which only nitrogen data were available. Atmospheric deposition is the dominant source of nitrogen for the mostly forested basins draining the upper half of the study unit. The estimate of total annual nitrogen load to the study unit from precipitation is 98.8 million pounds. Nonpoint and point sources of nutrients were estimated. Nonpoint and point sources combined, including atmospheric deposition, provide a potential annual load of 390 million pounds of nitrogen and 79.5 million pounds of phosphorus. The range of percentages of the estimated nonpoint and point sources that were measured in the stream was 20 to 47 percent for nitrogen and 6 to 14 percent for phosphorus. On the average, the Susquehanna River discharges 141,000 pounds of nitrogen and 7,920 pounds of phosphorus to the Lower Susquehanna River reservoir system each year. About 98 percent of the nitrogen and 60 percent of the phosphorus passes through the reservoir system. Interpretations of available water-quality data and conclusions about the water quality of the Lower Susquehanna River Basin were limited by the scarcity of certain types of water-quality data and current ancillary data. A more complete assessment of the water quality of the basin with respect to nutrients and suspended sediment would be enhanced by the availability of additional data for multiple samples over time from all water environments; samples from streams in the northern and western part of the basin; samples from streams and springs throughout the basin during high base-flow or stormflow conditions; and information on current land-use, and nutrient loading from all types of land-use settings.

Maryland, Pennsylvania↗
USGS2022

GSPy: A new toolbox and data standard for Geophysical Datasets

The diversity of geophysical methods and datatypes, as well as the isolated nature of various specialties (e.g., electromagnetic, seismic, potential fields) leads to a profusion of separate data file formats and documentation conventions. This can hinder cooperation and reduce the impact of datasets researchers have invested in heavily to collect and prepare. An open, portable, and well-supported community data standard could greatly improve the interoperability, transferability, and long-term archival of geophysical data. Airborne geophysical methods particularly need an open and accessible data standard, and they exemplify the complexity that is common in geophysical datasets where critical auxiliary information on the survey and system parameters are required to fully utilize and understand the data. Here, we propose a new Geophysical Standard, termed the GS convention, that leverages the well-established and widely used NetCDF file format and builds on the Climate and Forecasts (CF) metadata convention. We also present an accompanying open-source Python package, GSPy, to provide methods and workflows for building the GS-standardized NetCDF files, importing and exporting between common data formats, preparing input files for geophysical inversion software, and visualizing data and inverted models. By using the NetCDF format, handled through the Xarray Python package, and following the CF conventions, we standardize how metadata is recorded and directly stored with the data, from general survey and system information down to specific variable attributes. Utilizing the hierarchical nature of NetCDF, GS-formatted files are organized with a root Survey group that contains global metadata about the geophysical survey. Data are then organized into subgroups beneath Survey and are categorized as Tabular or Raster depending on the geometry and point of origin for the data. Lastly, the standard ensures consistency in constructing and tracking coordinate reference systems, which is vital for accurate portability and analysis. Development and adoption of a NetCDF-based data standard for geophysical surveys can greatly improve how these complex datasets are shared and utilized, making the data more accessible to a broader science community. The architecture of GSPy can be easily transferred to additional geophysical datatypes and methods in future releases.

Frontiers in Earth Science - Environmental Informa↗
USGS2021

Citizen science data collection for integrated wildlife population analyses

Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear ( Ursus americanus ), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.

Frontiers in Ecology and Evolution↗
USGS2024

Standard data management practices

Accomplishing data management in a standardized and practical way begins with an understanding of what data management is. The Data Management Association defines data management as “the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles” (Earley 2017). In this chapter, we present the tenets of data management in the context of fisheries for our target audience: fisheries biologists who may have limited formal training in data science. Experienced data managers may also benefit from the contents of this chapter, but the authors’ goal is to enhance and improve those activities that produce data, from project planning to acquiring data through field sampling, to cataloging and analyzing data, to sharing and archiving data.

Book chapter↗
USGS2017

The evolution, approval and implementation of the U.S. Geological Survey Science Data Lifecycle Model

This paper details how the United States Geological Survey (USGS) Community for Data Integration (CDI) Data Management Working Group developed a Science Data Lifecycle Model, and the role the Model plays in shaping agency-wide policies. Starting with an extensive literature review of existing data Lifecycle models, representatives from various backgrounds in USGS attended a two-day meeting where the basic elements for the Science Data Lifecycle Model were determined. Refinements and reviews spanned two years, leading to finalization of the model and documentation in a formal agency publication . The Model serves as a critical framework for data management policy, instructional resources, and tools. The Model helps the USGS address both the Office of Science and Technology Policy (OSTP) for increased public access to federally funded research, and the Office of Management and Budget (OMB) 2013 Open Data directives, as the foundation for a series of agency policies related to data management planning, metadata development, data release procedures, and the long-term preservation of data. Additionally, the agency website devoted to data management instruction and best practices (www2.usgs.gov/datamanagement) is designed around the Model’s structure and concepts. This paper also illustrates how the Model is being used to develop tools for supporting USGS research and data management processes.

Journal of eScience Librarianship↗
USGS2013

LANDFIRE 2010 - updated data to support wildfire and ecological management

Wildfire is a global phenomenon that affects human populations and ecosystems. Wildfire effects occur at local to global scales impacting many people in different ways (Figure 1). Ecological concerns due to land use, fragmentation, and climate change impact natural resource use, allocation, and conservation. Access to consistent and current environmental data is a constant challenge, yet necessary for understanding the complexities of wildfire and ecological management. Data products and tools from the LANDFIRE Program help decision-makers to clarify problems and identify possible solutions when managing fires and natural resources. LANDFIRE supports the reduction of risk from wildfire to human lives and property, monitoring of fire danger, prediction of fire behavior on active incidents, and assessment of fire severity and impacts on natural systems [1] [2] [3]. LANDFIRE products are unique in that they are nationally consistent and provide the only complete geospatial dataset describing vegetation and wildland fuel information for the entire U.S. As such, LANDFIRE data are useful for many ecological applications [3]. For example, LANDFIRE data were recently integrated into a decision-support system for resource management and conservation decision-making along the Appalachian Trail. LANDFIRE is a joint effort between the U.S. Department of the Interior Office of Wildland Fire, U.S. Department of Agriculture Forest Service Fire & Aviation Management, and The Nature Conservancy. To date, seven versions of LANDFIRE data have been released, with each successive version improving the quality of the data, adding additional features, and/or updating the time period represented by the data. The latest version, LANDFIRE 2010 (LF 2010), released mid-2013, represents circa 2010 landscape conditions and succeeds LANDFIRE 2008 (LF 2008), which represented circa 2008 landscape conditions. LF 2010 used many of the same processes developed for the LF 2008 effort [3]. Ongoing refinement of the LANDFIRE vegetation and fuel data is necessary to improve the quality and usability of the data and to capture landscape disturbance. LANDFIRE relies on Landsat multi-spectral imagery to produce and update vegetation and fuel data. The deep Landsat archive provides data needed for vegetation classification, change analysis, and historical disturbance characterization, for which LANDFIRE has used more than 24,000 image scenes since the program’s inception. In addition, LF 2010 used airborne and spaceborne lidar, and spaceborne synthetic aperture radar (SAR) to map vegetation structure in areas where ground-based field information was lacking, including Alaska and U.S.-affiliated islands in the Caribbean and the Pacific. The mapping of insular areas is new for the 2010 data release; previous versions of LANDFIRE were limited to the conterminous U.S., Alaska, and Hawaii.

Earthzine↗
USGS1983

Evaluation of thematic mapper data for natural resource assessment

The U.S. Geological Survey EROS Data Center evaluated the utility of Landsat Thematic Mapper (TM) date for natural resource assessment, emphasizing manual interpretation and digital classification of the data for U.S. Department of the Interior applications. Substantially more information was derived from TM data than from Landsat Multispectral Scanner (MSS) data. Greater resolution of TM data aided in locating roads, small stock ponds, and many other land features that could be used as landmarks. The improved spatial resolution of TM data also permitted more efficient visual interpretations of land use, better identification of resource types, and improved assessment of ecological status of natural vegetation. TM data also provided a new source of spectral information that was useful for natural resource assessment. New mid-infrared spectral bands, TM band 5 and band 7, aided in distinguishing water resources, wetland vegetation resources, and other important terrain features. The added information was useful for both manual interpretation and digital data classification of vegetation resources and land features. Results from the analyses of both TM and TM simulator (TMS) spectral data suggest that the coefficient of variation for major land cover types is generally less for TM data than for MSS data taken from the same area. This reduction in variance should contribute to an improved multispectral analysis, contributing new information about vegetation in natural ecosystems. Although the amount of new information in TM bands 5 and 7 is mall, it is unique in that the same information cannot be derived from four-band Landsat MSS spectral data.

Conference Paper↗
USGS1993

CD-ROM technology at the EROS data center

The vast amount of digital spatial data often required by a single user has created a demand for media alternatives to 1/2" magnetic tape. One such medium that has been recently adopted at the U.S. Geological Survey's EROS Data Center is the compact disc (CD). CD's are a versatile, dynamic, and low-cost method for providing a variety of data on a single media device and are compatible with various computer platforms. CD drives are available for personal computers, UNIX workstations, and mainframe systems, either directly connected, or through a network. This medium furnishes a quick method of reproducing and distributing large amounts of data on a single CD. Several data sets are already available on CD's, including collections of historical Landsat multispectral scanner data and biweekly composites of Advanced Very High Resolution Radiometer data for the conterminous United States. The EROS Data Center intends to provide even more data sets on CD's. Plans include specific data sets on a customized disc to fulfill individual requests, and mass production of unique data sets for large-scale distribution. Requests for a single compact disc-read only memory (CD-ROM) containing a large volume of data either for archiving or for one-time distribution can be addressed with a CD-write once (CD-WO) unit. Mass production and large-scale distribution will require CD-ROM replication and mastering.

Pecora 12 Symposium↗
USGS1989

Regional lists of plant species that occur in wetlands: data base user's guide

The Data Base List of Plant Species that Occur in Wetlands (LIST) currently contains records for 6,728 plant species. Each record provides information on nomenclature, plant characteristics and lifeforms, distribution, and frequency of occurrence in wetlands. The List of Plant Species that Occur in Wetlands, developed to supplement the U.S. Fish and Wildlife Service's Classification of Wetlands and Deepwater Habitats of the United States (Cowardin et al. 1979), underwent an intensive review by field botanists across the country. This review was coordinated by national and regional interagency wetland plant list review panels composed of representatives from the U. S. Fish and Wildlife Service, U. S. Army Corps of Engineers, Soil Conservation Service, and the Environmental Protection Agency. Initial and updated versions of the Data Base List of Plant Species that Occur in Wetlands are available in hardcopy (Reed 1986, 1988). Regional lists are available as U.S. Fish and Wildlife Service Biological Report Series 88(26.126.13). State lists are available as National Ecology Research Center Report Series 88(18.01-18.50). The computerized data base tracks and documents indicator assignments made by regional interagency review panels and facilitates generation of reports. This user's guide describes the format and contents of the LIST Data Base. The Data Base is available on 5-1/4" floppy disks in ASCII format for use with a data base management system on an IBM PC/XT/AT compatible computer. The LIST Data Base was developed using the QUICKTEXT Data Base Management System (Osborn and Strong 1984). Use of QUICKTEXT with the LIST Data Base is strongly recommended. Instructions for loading LIST into QUICKTEXT are included in this user's guide. Other data base management systems capable of handling variable length fields can be used by individuals familiar with these software packages. LIST distribution disks are available for 13 regions (Table 1). QUICKTEXT (course QT100--Data Base Management Techniques) and regional subsets of the LIST Data Base (distributed as self-tutorial courses, Table 1) are available through the Office of Conference Services, Colorado State University.

Report↗