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At least 703 records · Page 39Linked to original sources
USGS2022

Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat

Highly mobile species, such as migratory birds, respond to seasonal and inter-annual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally-resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16-days), and 17-year long-term average, surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. While modeling with both real-time and long-term average data provided good fit to withheld validation data (0.79 < AUC < 0.89 across taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g. perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration.

Ecological Applications↗
USGS2017

Integrating count and detection–nondetection data to model population dynamics

There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture–recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection–nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection–nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection–nondetection data (1995–2014) with newly collected count data (2015–2016) from a growing population of Barred Owl ( Strix varia ) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.

Ecology↗
USGS2005

Retrospective ecotoxicological data and current information needs for terrestrial vertebrates residing in coastal habitat of the United States

The Contaminant Exposure and Effects—Terrestrial Vertebrates (CEE-TV) database was developed to conduct simple searches for ecotoxicological information, examine exposure trends, and identify significant data gaps. The CEE-TV database contains 16,696 data records on free-ranging amphibians, reptiles, birds, and mammals residing in estuarine and coastal habitats of the Atlantic, Gulf, and Pacific coasts, Alaska, Hawaii, and the Great Lakes. Information in the database was derived from over 1800 source documents, representing 483 unique species (about 252,000 individuals), with sample collection dates spanning from 1884 to 2003. The majority of the records contain exposure data (generally contaminant concentrations) on a limited number ( n = 209) of chlorinated and brominated compounds, cholinesterase-inhibiting pesticides, economic poisons, metals, and petroleum hydrocarbons, whereas only 9.3% of the records contain biomarker or bioindicator effects data. Temporal examination of exposure data provides evidence of declining concentrations of certain organochlorine pesticides in some avian species ( e.g. , ospreys, Pandion haliaetus ), and an apparent increase in the detection and possibly the incidence of avian die-offs related to cholinesterase-inhibiting pesticides. To identify spatial data gaps, 11,360 database records with specific sampling locations were combined with the boundaries of coastal watersheds, and National Wildlife Refuge and National Park units. Terrestrial vertebrate ecotoxicological data were lacking in 41.9% of 464 coastal watersheds in the continental United States. Recent (1990–2003) terrestrial vertebrate contaminant exposure or effects data were available for only about half of the National Wildlife Refuge and National Park units in the geographic area encompassed by the database. When these data gaps were overlaid on watersheds exhibiting serious water quality problems and/or high vulnerability to pollution, 72 coastal watersheds, and 76 National Wildlife Refuge and 59 National Park units in the continental United States were found to lack recent terrestrial vertebrate ecotoxicology data. Delineation of data gaps in watersheds of concern can help prioritize monitoring in areas with impaired water quality and emphasize the need for comprehensive monitoring to gain a more complete understanding of coastal ecosystem health.

Archives of Environmental Contamination and Toxico↗
USGS2022

Asking nicely: Best practices for requesting data

Compiling disparate datasets into publicly available composite databases helps natural resource communities explore ecological trends and effectively manage across spatiotemporal scales. Though some studies have reported on the database construction phase, fewer have evaluated the data acquisition and distribution process. To facilitate future data sharing collaborations, Louisiana State University surveyed data providers and requestors to understand the characteristics of effective data requests and sharing. Data providers were largely U.S. natural resource agency personnel, and they reported that unclear data requests, privacy issues, and rigid timelines and formats were the greatest barriers toward providing data, but that they were motivated by improving science and collaboration. Data requestors identified challenges such as evolving needs, standardization issues, and insufficient resources (time and funding) as barriers to compiling data for these types of efforts. In a time of big data, open access, and collaboration, significant scientific advances can be made with effective requests and inclusion of data sets into larger and more powerful databases.

Ecological Indicators↗
USGS2005

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

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

Remote Sensing of Environment↗
USGS2012

The next Landsat satellite: The Landsat Data Continuity Mission

The National Aeronautics and Space Administration (NASA) and the Department of Interior United States Geological Survey (USGS) are developing the successor mission to Landsat 7 that is currently known as the Landsat Data Continuity Mission (LDCM). NASA is responsible for building and launching the LDCM satellite observatory. USGS is building the ground system and will assume responsibility for satellite operations and for collecting, archiving, and distributing data following launch. The observatory will consist of a spacecraft in low-Earth orbit with a two-sensor payload. One sensor, the Operational Land Imager (OLI), will collect image data for nine shortwave spectral bands over a 185 km swath with a 30 m spatial resolution for all bands except a 15 m panchromatic band. The other instrument, the Thermal Infrared Sensor (TIRS), will collect image data for two thermal bands with a 100 m resolution over a 185 km swath. Both sensors offer technical advancements over earlier Landsat instruments. OLI and TIRS will coincidently collect data and the observatory will transmit the data to the ground system where it will be archived, processed to Level 1 data products containing well calibrated and co-registered OLI and TIRS data, and made available for free distribution to the general public. The LDCM development is on schedule for a December 2012 launch. The USGS intends to rename the satellite "Landsat 8" following launch. By either name a successful mission will fulfill a mandate for Landsat data continuity. The mission will extend the almost 40-year Landsat data archive with images sufficiently consistent with data from the earlier missions to allow long-term studies of regional and global land cover change.

Remote Sensing of Environment↗
USGS2020

Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as "duplicate interpretations"). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time; and 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30 year period. Protocols to quantify and enhance interpreter consistency are critical elements to document and ensure quality of these reference data.

Remote Sensing of Environment↗
USGS2016

Do we need demographic data to forecast plant population dynamics?

Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts. Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction. In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types. In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist for many plant species. Modelers should exploit these data to predict the impacts of environmental change.

Methods in Ecology and Evolution↗
USGS2013

NCWin — A Component Object Model (COM) for processing and visualizing NetCDF data

NetCDF (Network Common Data Form) is a data sharing protocol and library that is commonly used in large-scale atmospheric and environmental data archiving and modeling. The NetCDF tool described here, named NCWin and coded with Borland C + + Builder, was built as a standard executable as well as a COM (component object model) for the Microsoft Windows environment. COM is a powerful technology that enhances the reuse of applications (as components). Environmental model developers from different modeling environments, such as Python, JAVA, VISUAL FORTRAN, VISUAL BASIC, VISUAL C + +, and DELPHI, can reuse NCWin in their models to read, write and visualize NetCDF data. Some Windows applications, such as ArcGIS and Microsoft PowerPoint, can also call NCWin within the application. NCWin has three major components: 1) The data conversion part is designed to convert binary raw data to and from NetCDF data. It can process six data types (unsigned char, signed char, short, int, float, double) and three spatial data formats (BIP, BIL, BSQ); 2) The visualization part is designed for displaying grid map series (playing forward or backward) with simple map legend, and displaying temporal trend curves for data on individual map pixels; and 3) The modeling interface is designed for environmental model development by which a set of integrated NetCDF functions is provided for processing NetCDF data. To demonstrate that the NCWin can easily extend the functions of some current GIS software and the Office applications, examples of calling NCWin within ArcGIS and MS PowerPoint for showing NetCDF map animations are given.

Cartographic Journal↗
USGS2011

Recovery and reprocessing of legacy geophysical data from the archives of the State Company of Geology and Mining (GEOSURV) of Iraq and Iraq Petroleum Company (IPC)

Aeromagnetic data belonging to the State Company of Geology and Mining of Iraq (GEOSURV) have been recovered from magnetic tapes and early paper maps. In 1974 a national airborne survey was flown by the French firm Compagnie General de Geophysique (CGG). Following the survey the magnetic data were stored on magnetic tapes within an air conditioned archive run by GEOSURV. In 1990, the power supply to the archive was cut resulting in the present‐day poor condition of the tapes. Frontier Processing Company and the U.S. Geological Survey (USGS) have been able to recover over 99 percent of the original digital data from the CGG tapes. Preliminary reprocessing of the data yielded a total magnetic field anomaly map that reveals fine structures not evident in available published maps. Successful restoration of these comprehensive, high quality digital datasets obviates the need to resurvey the entire country, thereby saving considerable time and money. These data were delivered to GEOSURV in a standard format for further analysis and interpretation. A parallel effort by GETECH concentrated on recovering the legacy gravity data from the original field data sheets archived by IPC (Iraq Petroleum Company). These data have been compiled with more recent GEOSURV sponsored surveys thus allowing for the first time a comprehensive digital and unified national gravity database to be constructed with full principal facts. Figure 1 shows the final aeromagnetic and gravity data coverage of Iraq. The only part of Iraq lacking gravity and aeromagnetic data coverage is the mountainous areas of the Kurdish region of northeastern Iraq. Joint interpretation of the magnetic and gravity data will help guide future geophysical investigations by GEOSURV, whose ultimate aim is to discover economical mineral and energy resources.

SEG Technical Program Expanded Abstracts↗
USGS2021

Hyperspectral narrowband data propel gigantic leap in the earth remote sensing

Hyperspectral narrowbands (HNBs) capture data as nearly continuous “spectral signatures” rather than a “few spectral data points” along the electromagnetic spectrum as with multispectral broadbands (MBBs). Almost all of satellite remote sensing of the Earth in the twentieth century was conducted using MBB data from sensors such as the Landsat-series, Advanced Very High-Resolution Radiometer (AVHRR), SPOT (Système Pour l’Observation de la Terre), and the Indian Remote Sensing (IRS) satellites. These systems typically provide 4 to 9 broad spectral wavebands spread from 400 to 2500 nm, often with one or two additional bands in the thermal range. Significant advances in the study of the Earth have been made based on these data [Thenkabail et al., 2018a,b,c,d; Thenkabail et al., 2015a,b,c]. Possibilities of great advances that can be made using HNB data over MBB data are well established based on studies conducted using hyperspectral sensors such as the hand-held spectroradiometers, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and spaceborne Earth Observing -1 (EO-1) Hyperion [Thenkabail 2018a,b,c,d]. The twenty-first century is already seeing the dawn of hyperspectral imaging data from sensors such as the German Aerospace Center’s (DLR’s) DESIS (DLR Earth Sensing Imaging Spectrometer) onboard the MUSES (Multi-User System for Earth Sensing) platform on the International Space Station (ISS), the polar-orbiting Italian Space Agency’s (ASI) PRISMA (PRecursore IperSpettrale della Missione Applicativa), and many other upcoming sensors such as the NASA Surface Biology and Geology (SBG) [Thenkabail et al., 2018a,b,c,d]. These satellites acquire data in hundreds of narrow spectral bands of 1 to 10 nm width, typically between 400 to 2500 nm; also future planned missions will be extending HNBs to the thermal (9,000 to 14,000 nm) electromagnetic spectrum. This expansion creates a quantum leap in new data, new information, and myriad possible new applications in the study of the Earth in addition to great advances in existing applications. Given the above, the objective of this article is to provide insights on the gigantic leap in our understanding, modeling, mapping, and monitoring of the Earth that can be made using HNB relative to MBB by focusing on agricultural and vegetation applications. We will address this in four aspects: 1. Comparison between HNB and MBB data; 2. Spectral libraries of agricultural crops; 3. HNB data analysis in general; and 4. HNB analysis using machine learning (ML) and cloud computing.

Photogrammetric Engineering and Remote Sensing.↗
USGS2013

Developing standards for a national spatial data infrastructure

The concept of a framework for data and information linkages among producers and users, known as a National Spatial Data Infrastructure (NSDI), is built upon four corners: data, technology, institutions, and standards. Standards are paramount to increase the efficiency and effectiveness of the NSDI. Historically, data standards and specifications have been developed with a very limited scope - they were parochial, and even competitive in nature, and promoted the sharing of data and information within only a small community at the expense of more open sharing across many communities. Today, an approach is needed to grow and evolve standards to support open systems and provide consistency and uniformity among data producers. There are several significant ongoing activities in geospatial data standards: transfer or exchange, metadata, and data content. In addition, standards in other areas are under discussion, including data quality, data models, and data collection.

Cartography and Geographic Information Systems↗
USGS1968

Maps showing locations of surface water stations: catalog of information on water data

United States Bureau of the Budget Circular A-67, issued in 1964, calls upon the Department of the Interior to coordinate certain water-data-acquisition activities by Federal agencies. The Circular contains guidelines for coordinating Federal water-data collection from streams, lakes, reservoirs, estuaries, and ground water; it also contains guidelines for developing a national network to acquire the data. Specific responsibilities are: (1) design and operation of a national network for acquiring water data, including organizing the network data; (2) coordination of national network and specialized water-data acquisition activities, and preparation of an annual Federal plan for water-data acquisition; and (3) maintenance of a central catalog of information on water data and on Federal activities being planned or conducted to acquire such data. The Office of Water Data Coordination (OWOC) was established within the Interior Department's Geological Survey to serve as a focal point for activities in conjunction with implementation of the Circular. The "Catalog of Information on Water Data" was developed in response to item 3.

Report↗
USGS2002

Data-quality measures for stakeholder-implemented watershed-monitoring programs

Community-based watershed groups, many of which collect environmental data, have steadily increased in number over the last decade. The data generated by these programs are often underutilized due to uncertainty in the quality of data produced. The incorporation of data-quality measures into stakeholder monitoring programs lends statistical validity to data. Data-quality measures are divided into three steps: quality assurance, quality control, and quality assessment. The quality-assurance step attempts to control sources of error that cannot be directly quantified. This step is part of the design phase of a monitoring program and includes clearly defined, quantifiable objectives, sampling sites that meet the objectives, standardized protocols for sample collection, and standardized laboratory methods. Quality control (QC) is the collection of samples to assess the magnitude of error in a data set due to sampling, processing, transport, and analysis. In order to design a QC sampling program, a series of issues needs to be considered: (1) potential sources of error, (2) the type of QC samples, (3) inference space, (4) the number of QC samples, and (5) the distribution of the QC samples. Quality assessment is the process of evaluating quality-assurance measures and analyzing the QC data in order to interpret the environmental data. Quality assessment has two parts: one that is conducted on an ongoing basis as the monitoring program is running, and one that is conducted during the analysis of environmental data. The discussion of the data-quality measures is followed by an example of their application to a monitoring program in the Big Thompson River watershed of northern Colorado.

Open-File Report↗
USGS2005

New York Water-Use Program and data, 2000

The U.S. Geological Survey (USGS) has been publishing estimates of water use every five years since 1950 in the Estimated use of water in the United States circular series. In 1978, the Congress expanded the water-use activities of the USGS by establishing the National Water-Use Information Program (NWUIP). The water-use program in New York is part of the NWUIP and is based on a cooperative agreement between the USGS and the New York State Department of Environmental Conservation (NYSDEC). Together, the NYSDEC and USGS collect, compile, and store water-use data to provide a data base that is useful for water-resources management. The New York State Department of Health (NYSDOH) collects a wide variety of data elements relating to public-water supplies such as the name and location of the suppliers and the amount of water withdrawn. This valuable information is provided to the water-use program. The information summarized in this fact sheet has been published in U.S. Geological Survey Circular 1268, Estimated use of water in the United States in 2000 which can be accessed at http://water.usgs.gov/watuse . The 2000 data (by county) as well as previous years data can also be found at that site. Water withdrawal refers to the removal of water from the ground or its diversion from a surface-water source for use. Withdrawals of fresh and saline surface water and fresh ground water during 2000 were included in this study. The categories considered in this data compilation are public-water supply; deliveries to domestic from public-water suppliers and domestic self-supplied; industrial; thermoelectric-power generation; and irrigation. Saline withdrawals were included for the categories of industrial, mining, and thermoelectric-power generation. Saline water is a significant percentage of total withdrawals for the category of thermoelectric-power generation. The categories of withdrawal for which data were compiled for the 2000 calendar year do not include some of the categories that were addressed in 1995 and published in USGS Circular 1200, Estimated use of water in the United States in 1995. For the 2000 compilation, emphasis was placed on ensuring the quality of data that were collected, rather than attempting to address all categories and data elements on a national scale. Categories that were included in 1995 but not in 2000 are commercial; livestock; mining; the non-withdrawal categories of hydroelectric-power generation, wastewater treatment and public-water supply deliveries to commercial and industrial users. Any comparison made between 1995 and 2000 data need to be made with these category differences in mind. In 1995, the categories of commercial, livestock, and mining were about 280 Mgal/d (million gallons per day) of freshwater and 2.7 percent of total freshwater withdrawals. In addition, the report, New York water-use program and data, 1995, U. S. Geological Survey Fact Sheet 014-02, concentrated on freshwater and did not include saline-surface water withdrawals in the figures. Any comparison between the 1995 and 2000 figures need to be made with this in mind. In 2000, about 12,100 Mgal/d (million gallons per day) of fresh surface and ground water and saline surface water were withdrawn from New York's rivers, streams, lakes, estuaries, bays, and aquifers for the categories addressed. Freshwater withdrawals comprised about 7,080 Mgal/d of this total. With a total population of 18,980,000 people in New York State, the total freshwater withdrawals represent an average of more than 370 gal/d (gallons per day) per capita. The amounts of fresh surface water, ground water, saline surface water, and total amounts of water withdrawn by categories of water use in New York during 2000 are shown. Many of New York's large population centers have developed along major rivers and lakes; as a result, more than 87 percent of the freshwater withdrawals in 2000 were from surface-water bodies (6,190 Mgal/d). More than 65 percent of fresh surface-water withdrawals were for thermoelectric-power generation, and about 32 percent were by public-water suppliers. Of the 890 Mgal/d of ground water withdrawn in 2000 statewide, 65 percent was withdrawn by public-water suppliers, and about 16 percent each by industrial and domestic users. More freshwater is withdrawn by thermoelectric plants than for any other water-use category. Of the freshwater withdrawals within New York, about 57 percent were made by fossil-fuel and nuclear powerplants; about 36 percent were for public-water supply; about 4.2 percent was by industrial users, 2.0 percent by domestic users, and about 0.5 percent was for irrigation. Total withdrawals, total surface-water and fresh ground-water withdrawals, in New York are plotted, by county. The categories of public-water supply and thermoelectric power account for the withdrawals that exceed 100 Mgal/d per county. The counties that have public-water supply withdrawals that exceed 100 Mgal/d are: Delaware (453 Mgal/d), Ulster, Nassau, Erie, Sullivan, Westchester, Suffolk, Putnam, and Schoharie (115 Mgal/d). These large withdrawals are from surface water except in Nassau and Suffolk Counties on Long Island, where groundwater is the sole source of freshwater. Delaware, Putnam, Schoharie, Sullivan, Ulster, and Westchester Counties, in the southeastern part of the State, provide surface water to the aqueducts that supply drinking water to New York City. In 2000, the average amount of water delivered to New York City from these counties averaged 1,260 Mgal/d. Erie County, in western New York, had withdrawals totaling 176 Mgal/d of fresh surface water for public-water supply. More freshwater and more total water is withdrawn for the generation of thermoelectric power than for any other water-use category. All of the withdrawals are of surface water. Seven counties have total thermoelectric withdrawals that exceed 500 Mgal/d: Queens (1,690 Mgal/d), Westchester, Oswego, Suffolk, Erie, Orange, and Rockland (560 Mgal/d). The counties of Oswego, Erie, and Orange withdrew only fresh surface water for thermoelectric plants. Their sources of water were Lake Ontario (Oswego County), Niagara River (Erie County), and the Hudson River (Orange County). Queens, Westchester, Suffolk, and Rockland withdraw only saline surface water for the production of thermoelectric power. The sources of saline surface water for the thermoelectric plants in the counties of Queens, Westchester, Suffolk, and Rockland are the estuaries of the Hudson River and East River and bays of the Atlantic Ocean (Long Island Sound and the eastern shore of Jamaica Bay). New York ranked eighth in the United States in 2000 in total withdrawals (fresh and saline water); the States that exceeded New York in total withdrawals are those that have large populations (such as California and Texas) and (or) use large quantities of water for irrigation (such as Florida and Idaho). Of the categories considered for the 2000 compilation, New York did not have nationally significant withdrawals for either irrigation or industrial water use. New York ranked third after California and Texas in withdrawals of freshwater for public supply, in the withdrawal of fresh surface water for public-water supply, in total population, and in number of people served by public-water supplies. New York ranked sixth in total withdrawals for the generation of thermoelectric power and total surface-water withdrawals. Finally, New York ranked fourth in withdrawals of ground water for public supply.

Open-File Report↗
USGS2006

Quality-Assurance Data for Routine Water Analyses by the U.S. Geological Survey Laboratory in Troy, New York--July 1999 through June 2001

The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) Water Science Center in Troy, N.Y., analyzes samples collected by USGS projects throughout the Northeast. The laboratory's quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures that were developed to ensure proper sample collection, processing, and analysis. The quality-assurance and quality-control data were stored in the laboratory's LabMaster data-management system, which provides efficient review, compilation, and plotting of data. This report presents and discusses results of quality-assurance and quality-control samples analyzed from July 1999 through June 2001. Results for the quality-control samples for 18 analytical procedures were evaluated for bias and precision. Control charts indicate that data for eight of the analytical procedures were occasionally biased for either high-concentration or low-concentration samples but were within control limits; these procedures were: acid-neutralizing capacity, total monomeric aluminum, total aluminum, calcium, chloride and nitrate (ion chromatography and colormetric method) and sulfate. The total aluminum and dissolved organic carbon procedures were biased throughout the analysis period for the high-concentration sample, but were within control limits. The calcium and specific conductance procedures were biased throughout the analysis period for the low-concentration sample, but were within control limits. The magnesium procedure was biased for the high-concentration and low concentration samples, but was within control limits. Results from the filter-blank and analytical-blank analyses indicate that the procedures for 14 of 15 analytes were within control limits, although the concentrations for blanks were occasionally outside the control limits. The data-quality objective was not met for dissolved organic carbon. Sampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in the procedures for 17 of the 18 analytes. At least 90 percent of the samples met data-quality objectives for all analytes except ammonium (81 percent of samples met objectives), chloride (75 percent of samples met objectives), and sodium (86 percent of samples met objectives). Results of the USGS interlaboratory Standard Reference Sample (SRS) Project indicated good data quality over the time period, with most ratings for each sample in the good to excellent range. The P-sample (low-ionic-strength constituents) analysis had one satisfactory rating for the specific conductance procedure in one study. The T-sample (trace constituents) analysis had one satisfactory rating for the aluminum procedure in one study and one unsatisfactory rating for the sodium procedure in another. The remainder of the samples had good or excellent ratings for each study. Results of Environment Canada's National Water Research Institute (NWRI) program indicated that at least 89 percent of the samples met data-quality objectives for 10 of the 14 analytes; the exceptions were ammonium, total aluminum, dissolved organic carbon, and sodium. Results indicate a positive bias for the ammonium procedure in all studies. Data-quality objectives were not met in 50 percent of samples analyzed for total aluminum, 38 percent of samples analyzed for dissolved organic carbon, and 27 percent of samples analyzed for sodium. Results from blind reference-sample analyses indicated that data-quality objectives were met by at least 91 percent of the samples analyzed for calcium, chloride, fluoride, magnesium, pH, potassium, and sulfate. Data-quality objectives were met by 75 percent of the samples analyzed for sodium and 58 percent of the samples analyzed for specific conductance.

Open-File Report↗
USGS2008

Water-Quality and Lake-Stage Data for Wisconsin Lakes, Water Year 2007

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 locations of water-quality and lake-stage stations in Wisconsin for water year 2007 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, 2005 through September 30, 2007 is called 'water year 2007.' 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 included for all lakes. 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, 2007.'

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USGS2011

Derived crop management data for the LandCarbon Project

The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop products are used as input to the LandCarbon models to represent the historic and the future scenario management data. The overall algorithm to generate each of the gridded management products is based on the land cover and the derived crop type. For each year in the land cover dataset, the algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that crop type. The following is the overall algorithm used for the set of derived grids. The specific algorithm to generate each management dataset is discussed in the respective section for that dataset, along with special data handling and a description of the output product.

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