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At least 1,369 records · Page 76Linked to original sources
USGS2018

General introduction for the “National Field Manual for the Collection of Water-Quality Data”

Background As part of its mission, the U.S. Geological Survey (USGS) collects data to assess the quality of our Nation’s water resources. A high degree of reliability and standardization of these data are paramount to fulfilling this mission. Documentation of nationally accepted methods used by USGS personnel serves to maintain consistency and technical quality in data-collection activities. “The National Field Manual for the Collection of Water-Quality Data” (NFM) provides documented guidelines and protocols for USGS field personnel who collect water-quality data. The NFM provides detailed, comprehensive, and citable procedures for monitoring the quality of surface water and groundwater. Topics in the NFM include (1) methods and protocols for sampling water resources, (2) methods for processing samples for analysis of water quality, (3) methods for measuring field parameters, and (4) specialized procedures, such as sampling water for low levels of mercury and organic wastewater chemicals, measuring biological indicators, and sampling bottom sediment for chemistry. Personnel who collect water-quality data for national USGS programs and projects, including projects supported by USGS cooperative programs, are mandated to use protocols provided in the NFM per USGS Office of Water Quality Technical Memorandum 2002.13. Formal training, for example, as provided in the USGS class, “Field Water-Quality Methods for Groundwater and Surface Water,” and field apprenticeships supplement the guidance provided in the NFM and ensure that the data collected are high quality, accurate, and scientifically defensible.

Techniques and Methods↗
USGS1980

Evaluation of remote hydrologic data-acquisition systems, west-central Florida

The study provides an evaluation of the hydrologic applications of a land-line and two satellite data-relay systems operated during 1977-78 in the Southwest Florida Water Management District. These systems were tested to evaluate operational and reliability characteristics. Telephone lines were used to relay data in the land-line system, and the Geostationary Operational Environmental Satellite (GOES) and Land satellite (Landsat) were used in the satellite system. The land-line system was tested for 15 months at a streamflow site. Accurate data were obtained 94% of the time during the test period. Data losses were attributed to telephone-line interference, low-battery voltage, and vandalism. The GOES system was tested at a rainfall site for 17 months. During this period, 79% of the transmissions received from the station were relayed by the GOES system to the U.S. Geological Survey computer, resulting in successful processing of 88% of all possible rainfall observations. On the average, seven data transmissions were completed each day. The Landsat system was tested at a rainfall site for about 17 months and for about 8 months at a streamflow site. During these periods of operation, only about 2% of all data observations for the stations were successfully relayed by the Landsat system to the U.S. Geological Survey computer. An average of about three data transmissions was completed each day for each site. (USGS).

Florida↗
USGS1979

Urban stormwater data management system with applications to south Florida studies

A data management system was developed to store and retrieve urban stormwater data collected from four small urban basins in south Florida. The system is event oriented in that all data from one storm are stored together on the computer file. The data include descriptive information about the storm and data on rainfall, stage, discharge, and water quality. The data management system, composed of about 20 Fortran programs, was developed to store data, retrieve tables for publication, calculate flow discharges and constituent loads, and provide for interfacing with statistical and deterministic model application programs. (Kosco-USGS)

Water-Resources Investigations Report↗
USGS1986

Analysis of fixed-station water-quality data in the Umpqua River basin, Oregon

An appraisal of surface water quality in the Umpqua River basin was made using existing monthly data collected by the Oregon Department of Environmental Quality and the U.S. Geological Survey in cooperation with the Douglas County Water Resources Survey. This appraisal was limited to interpretation of instantaneous monthly water quality data collected in the Umpqua River basin from water years 1974 to 1983. These data were used to compare water quality conditions throughout the basin and to determine if data collected from the NASQAN (National Stream Quality Accounting network) station are representative of upstream basin conditions. In general, data collected at the NASQAN station represent a composite of water quality from the North and South Umpqua Rivers. These river basins account for 82 % of the NASQAN station drainage. Water quality concentrations, loads, yields, and trends were statistically described and related to point source effluent loads and basin characteristics including geohydrology, hydrology, population, land use, and water use. Available point-and nonpoint-source data provided minimal information for determining cause-effect relations and for explaining observed trends in water quality; however, the data did indicate that the largest effluent discharges are located in the South Umpqua River basin in the Roseburg-Winston area. Instantaneous and annual flow weighted levels of specific conductance, phosphorus, organic plus ammonia nitrogen, nitrite plus nitrate, and fecal coliform bacteria are generally highest in the South Umpqua River near Roseburg. These high levels generally occur during the summer months when river flow is extremely low relative to flow in the North Umpqua River. The North Umpqua River has among the lowest constituent concentrations observed in the basin. (Lantz-PTT)

Water-Resources Investigations Report↗
USGS1987

Quality-assurance data for routine water analysis in the laboratories of the US Geological Survey for water-year 1984

The U.S. Geological Survey maintains a quality-assurance program based on the analysis of reference samples for its two water-analysis laboratories located in Atlanta, Georgia, and Denver, Colorado. Reference samples containing selected inorganic constituents are prepared at the U.S. Geological Survey's Ocala, Florida, office, disguised as routine samples, and sent daily or weekly, as appropriate, to each laboratory through other U.S. Geological Survey offices. The results are permanently stored in the National Water Data Storage and Retrieval System (WATSTORE), the U.S. Geological Survey's data base for all water data. These data are analyzed statistically for precision, bias, and comparability. The results of these statistical analyses are presented for data collected during the 1984 water year. Nutrient samples, simulated-precipitation (low-concentration level) samples, and selected pesticide samples also were submitted as samples of unknown concentrations. The results of these determinations were statistically analyzed for comparability and these data are presented. An overall evaluation of the data for water year 1984 indicated a lack of precision in the Atlanta laboratory for the determination of five constituents and in the Denver laboratory for seven constituents. A biased condition existed in the determination of twenty common constituents at both laboratories. Finally, if any constituent determined by ICP indicated bias, it tended to be positive.

Water-Resources Investigations Report↗
USGS1991

Computer software for converting ground-water and water-quality data from the National Water Information System for use in a geographic information system

A geographic information system (GIS) is computer software that provides the capabilities to rapidly display and analyze spatially referenced data. To use a GIS efficiently, software is needed that provides a link between existing spatially referenced data-base systems and the GIS. The U.S. Geological Survey (USGS) stores large amounts of spatially referenced hydrologic data in computerized data-base systems. Currently, these data-base systems are physically located on computers that are installed in many USGS offices nationwide. This report describes software that can be used quickly and easily to convert site-description and water-quality data from these data-base systems to the GIS that is used by the USGS. The software described in this report is designed to be used with the same hardware and software that is available to most USGS personnel.

Water-Resources Investigations Report↗
USGS1995

Water-quality assessment of the South Platte River basin, Colorado, Nebraska, and Wyoming: Analysis of available nutrient, suspended-sediment, and pesticide data, water years 1980-92

Nutrient, suspended-sediment, and pesticide data from surface- and ground-water sites in the South Platte River Basin for water years 1980-92 were compiled, screened, and interpreted. This activity is part of the U.S. Geological Survey's National Water Quality Assessment (NAWQA) program. The analysis of existing water-quality data provides a perspective on recent water-quality conditions in the South Platte River Basin, evaluations of the strengths and weaknesses of available data, and implications for water-quality issues and future study priorities and design. Most data analyzed were collected by the U.S. Geological Survey. Additionally, data from three local agencies were used in the analysis. A total of 3,484 samples from 54 surface-water sites and 107 wells were used in the analysis. The areal distribution of nutrient samples collected from surface-water and ground-water sites were sufficient in number and areal distribution to describe current water- quality conditions throughout the basin, but data were not sufficient to analyze factors and processes affecting water quality. However, suspended- sediment and pesticide data were sparse in their distribution with respect to time, space, and flow regime, and were sufficient only to provide a preliminary description of conditions in the basin.

Colorado, Nebraska, Wyoming↗
USGS1995

Verification and adjustment of regional regression models for urban storm-runoff quality using data collected in Little Rock, Arkansas

Storm-runoff water-quality data were used to verify and, when appropriate, adjust regional regression models previously developed to estimate urban storm- runoff loads and mean concentrations in Little Rock, Arkansas. Data collected at 5 representative sites during 22 storms from June 1992 through January 1994 compose the Little Rock data base. Comparison of observed values (0) of storm-runoff loads and mean concentrations to the predicted values (Pu) from the regional regression models for nine constituents (chemical oxygen demand, suspended solids, total nitrogen, total ammonia plus organic nitrogen as nitrogen, total phosphorus, dissolved phosphorus, total recoverable copper, total recoverable lead, and total recoverable zinc) shows large prediction errors ranging from 63 to several thousand percent. Prediction errors for six of the regional regression models are less than 100 percent, and can be considered reasonable for water-quality models. Differences between 0 and Pu are due to variability in the Little Rock data base and error in the regional models. Where applicable, a model adjustment procedure (termed MAP-R-P) based upon regression with 0 against Pu was applied to improve predictive accuracy. For 11 of the 18 regional water-quality models, 0 and Pu are significantly correlated, that is much of the variation in 0 is explained by the regional models. Five of these 11 regional models consistently overestimate O; therefore, MAP-R-P can be used to provide a better estimate. For the remaining seven regional models, 0 and Pu are not significanfly correlated, thus neither the unadjusted regional models nor the MAP-R-P is appropriate. A simple estimator, such as the mean of the observed values may be used if the regression models are not appropriate. Standard error of estimate of the adjusted models ranges from 48 to 130 percent. Calibration results may be biased due to the limited data set sizes in the Little Rock data base. The relatively large values of standard error of estimate for some of the constituent models may be unacceptable for some applications. The user may need to collect additional local data for these constituents and repeat the model adjustment procedure analysis or calibrate an independent local regression model.

Water-Resources Investigations Report↗
USGS1984

Appraisal of data for ground-water quality in Nebraska

This report summarizes existing data for groundwater quality in Nebraska and indicates their adequacy as a data base. Analyses have been made of water from nearly 10,000 wells by 8 agencies. Those analyses that meet reliability criteria have been aggregated by geologic source of water into four principal aquifer groupings--Holocene-Pleistocene aquifers, Tertiary aquifers, Mesozoic aquifers, and Paleozoic aquifers. For each aquifer grouping, data for specific conductance and 24 constituents in the water are summarized statistically. Also, diagrams are presented showing differences in statistical parameters, or in chemical composition, of water from the different aquifer groupings. Additionally, for each grouping except Paleozoic aquifers, maps show ranges in concentration of dissolved solids, calcium, alkalinity, and sulfate. In areas where data are insufficient to delimit, ranges in concentration also are shown on the maps. Point-source contamination has been identified at 41 locations and nonpoint-source contamination in 3 areas, namely, the central Platte Valley, Holt County, and Boyd County. Potential for nonpoint-source contamination exists in 10 major areas, which together comprise more than one-third of the State. Existing data are mostly from specific projects having limited areas and objectives. Consequently, a lack of data exists for other areas and for certain geologic units, particularly the Mesozoic and Paleozoic aquifers. Specific data needs for each of the four principal aquifer groupings are indicated in a matrix table.

Water Supply Paper↗
USGS1997

Pesticides in surface and ground water of the San Joaquin-Tulare basins, California: Analysis of available data, 1966 Through 1992

Available pesticide data (1966-92) for surface and ground water were analyzed for the San Joaquin-Tulare Basins, California, one of 60 large hydrologic systems being studied as part of the National Water-Quality Assessment Program of the U.S. Geological Survey. Most of the pesticide data were for the San Joaquin Valley, one of the most intensively farmed and irrigated areas of the United States. Data were obtained from the Storage and Retrieval data base of the U.S. Environmental Protection Agency, the water-quality data base of the U.S. Geological Survey, and from data files of State agencies. Pesticides detected in surface water include organochlorine pesticides, organophosphate pesticides, carbamate pesticides, and triazine herbicides. Pesticides detected in ground water include triazine and other organonitrogen herbicides and soil fumi gants. Surface-water data indicate seasonal patterns for the detection of organophosphate and carbamate pesticides, which are attributed to their use on almond orchards and alfafa fields. Organochlorine pesticides were detected primarily in river-bed sediments. Concentrations detected in bed sediments of the San Joaquin River near Vernalis are among the highest of any major river system in the United States. Patterns and timing of pesticide use indicate that pesticides might be present in surface-water systems during most months of a year. The most commonly detected pesticide in ground water is the soil fumigant, dibromochloropropane. Dibromochloropropane, used primarily on vineyards and orchards, was detected in ground water near the city of Fresno. Triazine and other organonitrogen herbicides were detected near vineyards and orchards in the same general locations as the detections of dibromochloropropane. Pesticides were detected in ground water of the east side of the valley floor, where the soils are sandy or coarsegrained, and water-soluble pesticides with long environmental half-lives were used. In contrast, fewer pesticides were detected in ground water of the west side of the valley, where soils generally are finer grained.

California↗
USGS2008

Strong-motion data from the two Pingtung, Taiwan, earthquakes of 26 December 2006

1016 strong-motion records at 527 free-field stations and 131 records at 42 strong-motion arrays at buildings and bridges were obtained for the Pingtung earthquake doublet from the Taiwan Central Weather Bureau's dense, digital strong-motion network. We carried out standard processing of these strong-motion records at free-field stations. A data set, including the originally recorded files, processed data files, and supporting software and information, is archived online http:// tecdc.earth.sinica.edu.tw/data/EQ2006Pingtung/. We have not yet completed the processing of the strong-motion array data at buildings and bridges. However, some preliminary results and the strong-motion array data recorded at the second nearest instrumented building to the Pingtung earthquake doublet are shown. This paper is intended to document our data processing procedures and the online archived data files, so that researchers can efficiently use the data. We also include two preliminary analyses: (1) a comparison of ground motions recorded by multiple accelerographs at a common site, the TAP 117 station in Taipei, and (2) attenuation of the horizontal ground motions (peak acceleration and response spectra at periods of 0.2, 1.0, and 3.0 s) with respect to distance. Our comparison study of multiple recordings at TAP 117 indicates that waveform coherence among 20- and 24-bit accelerograph records is much higher as compared to records from 16-bit or 12-bit accelerographs, suggesting that the former are of better quality. For the 20- and 24-bit accelerographs, waveform coherence is nearly 1 over the frequency range 1 to 8 Hz for all components, and is greater than about 0.9 from 8 to 20 Hz for the horizontal component, but only from 8 to 12 Hz for the vertical component. Plots of pseudo-acceleration response spectra (PSA) as a function of distance, however, show no clear indication for a difference related to the performance level of the accelerographs. The ground-motions of the first event (Mw = 7.0) are comparable, or even somewhat lower, than those from the smaller second event (Mw = 6.9), consistent with the relative difference of the local magnitudes (ML = 6.96 and 6.99 for the first and second events, respectively). The ground motions from the first event are generally lower than those predicted from equations based on other in-slab subduction earthquakes, whereas the ground motions from the second event are closer to the predictions. Ground-motions for soil sites are generally larger than those from rock sites.

Terrestrial, Atmospheric and Oceanic Sciences↗
USGS2020

Good practices for species distribution modeling of deep-sea corals and sponges for resource management: Data collection, analysis, validation, and communication

Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, we suggest analysts: 1) integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys); and 2) include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty. When possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.

Frontiers in Marine Science↗
USGS2020

Training data selection for annual land cover classification for the LCMAP initiative

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative characterizes changes in land cover, use, and condition with the goal of producing land change information that improves understanding of the earth system and provides insight into the impacts of land change on society. For LCMAP, all available high-quality data from the Landsat archive is used in a time series approach to detect land surface change. Annual thematic land cover maps are produced by classifying time series models. In this paper, we describe optimization of the classification method used to derive the thematic land cover product. We investigated the influences of auxiliary data, sample size, and training from different sources such as the U.S. Geological Survey’s Land Cover Trends project and National Land Cover Database (NLCD 2001 and NLCD 2011). Results were evaluated and validated based on independent data from the training dataset. We found that refining auxiliary data effectively reduced artifacts in the thematic land cover map that are related to data availability (i.e., SLC-off). The classification accuracy and stability were improved considerably by using a total of 20 million training pixels with a minimum of 600,000 and a maximum of 8 million training pixels per class. Finally, the NLCD 2001 training data delivered the best classification accuracy. Comparing to the original LCMAP classification strategy (Trends training data, 20,000 samples), the optimized classification strategy considerably improved the annual land cover map accuracy.

Remote Sensing↗
USGS2022

Giant planet observations in NASA's Planetary Data System

While there have been far fewer missions to the outer Solar System than to the inner Solar System, spacecraft destined for the giant planets have conducted a wide range of fundamental investigations, returning data that continues to reshape our understanding of these complex systems, sometimes decades after the data were acquired. These data are preserved and accessible from national and international planetary science archives. For all NASA planetary missions and instruments the data are available from the science discipline nodes of the NASA Planetary Data System (PDS). Looking ahead, the PDS will be the primary repository for giant planets data from several upcoming missions and derived datasets, as well as supporting research conducted to aid in the interpretation of the remotely sensed giant planets data already archived in the PDS.

Remote Sensing↗
USGS2024

Mitigating disparate elevation differences between adjacent topobathymetric data models using binary code

Integrating coastal topographic and bathymetric data for creating regional seamless topobathymetric digital elevation models of the land/water interface presents a complex challenge due to the spatial and temporal gaps in data acquisitions. The Coastal National Elevation Database (CoNED) Applications Project develops topographic (land elevation) and bathymetric (water depth) regional scale digital elevation models by integrating multiple sourced disparate topographic and bathymetric data models. These integrated regional models are broadly used in coastal and climate science applications, such as sediment transport, storm impact, and sea-level rise modeling. However, CoNED’s current integration method does not address the occurrence of measurable vertical discrepancies between adjacent near-shore topographic and bathymetric data sources, which often create artificial barriers and sinks along their intersections. To tackle this issue, the CoNED project has developed an additional step in its integration process that collectively assesses the input data to define how to transition between these disparate datasets. This new step defines two zones: a micro blending zone for near-shore transitions and a macro blending zone for the transition between high-resolution (3 m or less) to moderate-resolution (between 3 m and 10 m) bathymetric datasets. These zones and input data sources are reduced to a multidimensional array of zeros and ones. This array is compiled into a 16-bit integer representing a vertical assessment for each pixel. This assessed value provides the means for dynamic pixel-level blending between disparate datasets by leveraging the 16-bit binary notation. Sample site RMSE assessments demonstrate improved accuracy, with values decreasing from 0.203–0.241 using the previous method to 0.126–0.147 using the new method. This paper introduces CoNED’s unique approach of using binary code to improve the integration of coastal topobathymetric data.

Remote Sensing↗
USGS2013

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Minnesota↗
USGS2023

Recharge estimation approach in a data-scarce semi-arid region, Northern Ethiopian Rift Valley

Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.

Kobo Valley, Riya Valley↗
USGS2021

Beyond streamflow: Call for a national data repository of streamflow presence for streams and rivers in the United States

Observations of the presence or absence of surface water in streams are useful for characterizing streamflow permanence, which includes the frequency, duration, and spatial extent of surface flow in streams and rivers. Such data are particularly valuable for headwater streams, which comprise the vast majority of channel length in stream networks, are often non-perennial, and are frequently the most data deficient. Datasets of surface water presence exist across multiple data collection groups in the United States but are not well aligned for easy integration. Given the value of these data, a unified approach for organizing information on surface water presence and absence collected by diverse surveys would facilitate more effective and broad application of these data and address the gap in streamflow data in headwaters. In this paper, we highlight the numerous existing datasets on surface water presence in headwater streams, including recently developed crowdsourcing approaches. We identify the challenges of integrating multiple surface water presence/absence datasets that include differences in the definitions and categories of streamflow status, data collection method, spatial and temporal resolution, and accuracy of geographic location. Finally, we provide a list of critical and useful components that could be used to integrate different streamflow permanence datasets.

Water↗