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At least 37 records · Page 2Linked to original sources

Assessment of relative accuracy in the determination of organic matter concentrations in aquatic systems

Accurate determinations of total (TOC), dissolved (DOC) and particulate (POC) organic carbon concentrations are critical for understanding the geochemical, environmental, and ecological roles of aquatic organic matter. Of particular significance for the drinking water industry, TOC measurements are the basis for compliance with US EPA regulations. The results of an interlaboratory comparison designed to identify problems associated with the determination of organic matter concentrations in drinking water supplies are presented. The study involved 31 laboratories and a variety of commercially available analytical instruments. All participating laboratories performed well on samples of potassium hydrogen phthalate (KHP), a compound commonly used as a standard in carbon analysis. However, problems associated with the oxidation of difficult to oxidize compounds, such as dodecylbenzene sulfonic acid and caffeine, were noted. Humic substances posed fewer problems for analysts. Particulate organic matter (POM) in the form of polystyrene beads, freeze-dried bacteria and pulverized leaf material were the most difficult for all analysts, with a wide range of performances reported. The POM results indicate that the methods surveyed in this study are inappropriate for the accurate determination of POC and TOC concentration. Finally, several analysts had difficulty in efficiently separating inorganic carbon from KHP solutions, thereby biasing DOC results.

Journal of Environmental Monitoring↗

Regression equations for estimation of annual peak-streamflow frequency for undeveloped watersheds in Texas using an L-moment-based, PRESS-minimized, residual-adjusted approach

Annual peak-streamflow frequency estimates are needed for flood-plain management; for objective assessment of flood risk; for cost-effective design of dams, levees, and other flood-control structures; and for design of roads, bridges, and culverts. Annual peak-streamflow frequency represents the peak streamflow for nine recurrence intervals of 2, 5, 10, 25, 50, 100, 200, 250, and 500 years. Common methods for estimation of peak-streamflow frequency for ungaged or unmonitored watersheds are regression equations for each recurrence interval developed for one or more regions; such regional equations are the subject of this report. The method is based on analysis of annual peak-streamflow data from U.S. Geological Survey streamflow-gaging stations (stations). Beginning in 2007, the U.S. Geological Survey, in cooperation with the Texas Department of Transportation and in partnership with Texas Tech University, began a 3-year investigation concerning the development of regional equations to estimate annual peak-streamflow frequency for undeveloped watersheds in Texas. The investigation focuses primarily on 638 stations with 8 or more years of data from undeveloped watersheds and other criteria. The general approach is explicitly limited to the use of L-moment statistics, which are used in conjunction with a technique of multi-linear regression referred to as PRESS minimization. The approach used to develop the regional equations, which was refined during the investigation, is referred to as the 'L-moment-based, PRESS-minimized, residual-adjusted approach'. For the approach, seven unique distributions are fit to the sample L-moments of the data for each of 638 stations and trimmed means of the seven results of the distributions for each recurrence interval are used to define the station specific, peak-streamflow frequency. As a first iteration of regression, nine weighted-least-squares, PRESS-minimized, multi-linear regression equations are computed using the watershed characteristics of drainage area, dimensionless main-channel slope, and mean annual precipitation. The residuals of the nine equations are spatially mapped, and residuals for the 10-year recurrence interval are selected for generalization to 1-degree latitude and longitude quadrangles. The generalized residual is referred to as the OmegaEM parameter and represents a generalized terrain and climate index that expresses peak-streamflow potential not otherwise represented in the three watershed characteristics. The OmegaEM parameter was assigned to each station, and using OmegaEM, nine additional regression equations are computed. Because of favorable diagnostics, the OmegaEM equations are expected to be generally reliable estimators of peak-streamflow frequency for undeveloped and ungaged stream locations in Texas. The mean residual standard error, adjusted R-squared, and percentage reduction of PRESS by use of OmegaEM are 0.30log 10 , 0.86, and -21 percent, respectively. Inclusion of the OmegaEM parameter provides a substantial reduction in the PRESS statistic of the regression equations and removes considerable spatial dependency in regression residuals. Although the OmegaEM parameter requires interpretation on the part of analysts and the potential exists that different analysts could estimate different values for a given watershed, the authors suggest that typical uncertainty in the OmegaEM estimate might be about +or-0.10 10 . Finally, given the two ensembles of equations reported herein and those in previous reports, hydrologic design engineers and other analysts have several different methods, which represent different analytical tracks, to make comparisons of peak-streamflow frequency estimates for ungaged watersheds in the study area.

Texas↗

U.S. Geological Survey standards-II. First compilation of data for the new U.S.G.S. rocks

Rock analyses, determinations of major and minor constituents, and determinations of 57 trace elements reported by analysts throughout the world are presented for the new series of U.S. Geological Survey rock samples. Averages for both the constituents generally reported in a rock analysis and the trace elements are given. Variances of the distributions of data by rock analysts for several oxides in G-2 are compared with similar variances for G-l.

Geochimica et Cosmochimica Acta↗

Locating CVBEM collocation points for steady state heat transfer problems

The Complex Variable Boundary Element Method or CVBEM provides a highly accurate means of developing numerical solutions to steady state two-dimensional heat transfer problems. The numerical approach exactly solves the Laplace equation and satisfies the boundary conditions at specified points on the boundary by means of collocation. The accuracy of the approximation depends upon the nodal point distribution specified by the numerical analyst. In order to develop subsequent, refined approximation functions, four techniques for selecting additional collocation points are presented. The techniques are compared as to the governing theory, representation of the error of approximation on the problem boundary, the computational costs, and the ease of use by the numerical analyst.

Engineering Analysis↗

Microscopical characterization of carbon materials derived from coal and petroleum and their interaction phenomena in making steel electrodes, anodes and cathode blocks for the Microscopy of Carbon Materials Working Group of the ICCP

This paper describes the evaluation of petrographic textures representing the structural organization of the organic matter derived from coal and petroleum and their interaction phenomena in the making of steel electrodes, anodes and cathode blocks. This work represents the results of the Microscopy of Carbon Materials Working Group in Commission III of the International Committee for Coal and Organic Petrology between the years 2009 and 2013. The round robin exercises were run on photomicrograph samples. For textural characterization of carbon materials the existing ASTM classification system for metallurgical coke was applied. These round robin exercises involved 15 active participants from 12 laboratories who were asked to assess the coal and petroleum based carbons and to identify the morphological differences, as optical texture (isotropic/anisotropic), optical type (punctiform, mosaic, fibre, ribbon, domain), and size. Four sets of digital black and white microphotographs comprising 151 photos containing 372 fields of different types of organic matter were examined. Based on the unique ability of carbon to form a wide range of textures, the results showed an increased number of carbon occurrences which have crucial role in the chosen industrial applications. The statistical method used to evaluate the results was based on the “raw agreement indices”. It gave a new and original view on the analysts' opinion by not only counting the correct answers, but also all of the knowledge and experience of the participants. Comparative analyses of the average values of the level of overall agreement performed by each analyst in the exercises during 2009–2013 showed a great homogeneity in the results, the mean value being 90.36%, with a minimum value of 83% and a maximum value of 95%.

International Journal of Coal Geology↗

Markov decision processes in natural resources management: observability and uncertainty

The breadth and complexity of stochastic decision processes in natural resources presents a challenge to analysts who need to understand and use these approaches. The objective of this paper is to describe a class of decision processes that are germane to natural resources conservation and management, namely Markov decision processes, and to discuss applications and computing algorithms under different conditions of observability and uncertainty. A number of important similarities are developed in the framing and evaluation of different decision processes, which can be useful in their applications in natural resources management. The challenges attendant to partial observability are highlighted, and possible approaches for dealing with it are discussed.

Ecological Modelling↗

Quantitative evaluation of vitrinite reflectance and atomic O/C in coal using Raman spectroscopy and multivariate analysis

Vitrinite reflectance (VRo) is a standard petrographic method for assessing thermal maturity (rank) of coal. The vitrinite reflectance technique, however, requires significant petrographic experience, can be time-consuming, and may be biased by analyst subjectivity. Correlations between coal rank and Raman spectral properties are a promising alternative that can supplant some of the limitations inherent in the VRo protocol. The traditional peak-fitting methodologies for quantifying metrics from Raman spectra, however, also suffer from analyst subjectivity that can affect correlations between analyte and spectral properties. This research combines high-throughput Raman spectroscopy with multivariate analysis (MVA) to create calibration models for the prediction of coal rank though VRo and atomic O/C ratio. MVA techniques eliminate the ambiguous subjectivity prevalent in peak-fitting methods by evaluating the full Raman spectrum, then identifying the integral vibrational modes for constructing accurate models. Partial least squares (PLS) regression models were developed using Raman spectra and VRo values (0.23–5.23%) for 68 geographically diverse coal samples. The calibration set was validated using one-half of the samples to rigorously assess the model’s predictive accuracy. The root mean standard error of prediction was 0.19 for the VRo model and 0.014 for the atomic O/C model. Both models exhibited linear correlations, with coefficients of determination ( R 2 ) for the validation set of 0.99 (VRo) and 0.93 (atomic O/C), despite the geographic and rank diversity of the samples. This study demonstrates the applicability and power of using PLS models for the prediction of both the VRo and atomic O/C ratio from Raman spectra. The quantitative MVA protocol contained herein provides a Raman alternative to the VRo industry benchmark for coal rank that is not subject to the limitations and subjectivity of peak-fitting methods.

Fuel↗

A comparison of E. coli concentration estimates quantified by the EPA and a Michigan laboratory network using EPA Draft Method C

We evaluated data from 10 laboratories that analyzed water samples from 82 recreational water sites across the state of Michigan between 2016 and 2018. Water sample replicates were analyzed by experienced U.S. Environmental Protection Agency (EPA) analysts and Michigan laboratories personnel, many of whom were newly trained, using EPA Draft Method C—a rapid quantitative polymerase chain reaction (qPCR) technique that provides same day Escherichia coli ( E. coli ) concentration results. Beach management decisions (i.e. remain open or issue an advisory or closure) based on E. coli concentration estimates obtained by Michigan labs and by the EPA were compared; the beach management decision agreed in 94% of the samples analyzed. We used the Wilcoxon one-sample signed rank test and nonparametric quantile regression to assess (1) the degree of agreement between E. coli concentrations quantified by Michigan labs versus the EPA and (2) Michigan lab E. coli measurement precision, relative to EPA results, in different years and water body types. The median quantile regression curve for Michigan labs versus EPA approximated the 1:1 line of perfect agreement more closely as years progressed. Similarly, Michigan lab E. coli estimates precision also demonstrated yearly improvements. No meaningful difference was observed in the degree of association between Michigan lab and EPA E. coli concentration estimates for inland lake and Great Lakes samples (median regression curve average slopes 0.93 and 0.95, respectively). Overall, our study shows that properly trained laboratory personnel can perform Draft Method C to a degree comparable with experienced EPA analysts. This allows health departments that oversee recreational water quality monitoring to be confident in qPCR results generated by the local laboratories responsible for analyzing the water samples.

Journal of Microbiological Methods↗

IKONOS geometric characterization

The IKONOS spacecraft acquired images on July 3, 17, and 25, and August 13, 2001 of Brookings SD, a small city in east central South Dakota, and on May 22, June 30, and July 30, 2000, of the rural area around the EROS Data Center. South Dakota State University (SDSU) evaluated the Brookings scenes and the USGS EROS Data Center (EDC) evaluated the other scenes. The images evaluated by SDSU utilized various natural objects and man-made features as identifiable targets randomly distribution throughout the scenes, while the images evaluated by EDC utilized pre-marked artificial points (panel points) to provide the best possible targets distributed in a grid pattern. Space Imaging provided products at different processing levels to each institution. For each scene, the pixel (line, sample) locations of the various targets were compared to field observed, survey-grade Global Positioning System locations. Patterns of error distribution for each product were plotted, and a variety of statistical statements of accuracy are made. The IKONOS sensor also acquired 12 pairs of stereo images of globally distributed scenes between April 2000 and April 2001. For each scene, analysts at the National Imagery and Mapping Agency (NIMA) compared derived photogrammetric coordinates to their corresponding NIMA field-surveyed ground control point (GCPs). NIMA analysts determined horizontal and vertical accuracies by averaging the differences between the derived photogrammetric points and the field-surveyed GCPs for all 12 stereo pairs. Patterns of error distribution for each scene are presented.

Remote Sensing of Environment↗

A new amphibious earthquake catalog for the Alaska Peninsula: Training, testing, and applying machine learning earthquake picking in offshore and amphibious environments

The dual seismological developments of large-scale offshore observational networks and high-quality machine-learning earthquake detection and picking allow unprecedented seismicity detection in high hazard subduction zones. We develop a high resolution machine learning enhanced seismicity catalog for the Alaska Peninsula stretch of the Alaska-Aleutian Subduction Zone, which was instrumented with both land and ocean bottom seismometers during the Alaska Amphibious Community Seismic Experiment (AACSE) in 2018–2019. We first use an analyst-checked AACSE catalog to develop new local land and ocean-bottom earthquake waveform training data sets, which we then use to fine-tune the EQTransformer machine learning picking model. In the process, we test multiple training strategies for best picking performance on ocean-bottom waveform data. We evaluate the effect of choice of pre-trained starting model, addition of waveform bandpass filtering, and choice of random metadata split. We find no training strategy to be substantially better than the others. Instead, simply having a local waveform data set to fine-tune the picking model substantially improves performance on both the ocean-bottom and land seismic data from AACSE. We finally apply the separately trained land and ocean-bottom models to continuous seismic data for AACSE and use an iterative catalog refinement method to detect new small-magnitude seismicity. The machine learning enhanced catalog has roughly 3x more earthquakes than the analyst-checked catalog, and the magnitude of completeness is regionally variable but decreases 0.1–0.4 magnitude units. With more small seismicity detected, seismicity patterns mirror prior observations in the region. Detection is especially improved far offshore and in the outer rise.

Alaska↗

Offsetting the noise: A framework for applying phenological offset corrections in remotely sensed burn severity assessments

Background Phenological correction of pre- and post-fire imagery is used to improve remotely sensed burn severity evaluations. Unburned offset values standardize greenness between image pairs; however, efficacy across diverse scenarios remains underexplored. Aims We evaluated the impact of phenological offset correction methods to support analyst decision-making across fire-prone environments. Methods We generated burn severity spectral index values for a dataset of Composite Burn Index (CBI) field plots across the conterminous US. The effectiveness of offset corrections was tested across image selection techniques, spectral indices, offset generation methods and burn perimeter sources. We assessed the influence of offset corrections on the modeled relationship with CBI, agreement between burn severity thresholds and potential bias. Key results Applying offset corrections consistently improved the modeled relationship with CBI by addressing extreme outlier severity values. However, automated offset corrections had the potential to introduce bias, systematically lowering severity values and reducing correspondence with observed burn severity categories. Conclusions Offset corrections offer benefits but also present trade-offs to accurately representing remotely sensed burn severity. Implications The utility of offset corrections depends on the environment, methods and scale of analysis. We propose a decision-tree framework for analysts to consider when employing offset corrections given their study scope.

conterminous United States↗

The use of historical imagery in the remediation of an urban hazardous waste site

The information derived from the interpretation of historical aerial photographs is perhaps the most basic multitemporal application of remote-sensing data. Aerial photographs dating back to the early 20th century can be extremely valuable sources of historical landscape activity. In this application, imagery from 1918 to 1927 provided a wealth of information about chemical weapons testing, storage, handling, and disposal of these hazardous materials. When analyzed by a trained photo-analyst, the 1918 aerial photographs resulted in 42 features of potential interest. When compared with current remedial activities and known areas of contamination, 33 of 42 or 78.5% of the features were spatially correlated with areas of known contamination or other remedial hazardous waste cleanup activity.

IEEE Journal of Selected Topics in Applied Earth O↗

Stakeholder engagement to guide decision-relevant water data delivery

Water resources management and policy making require access to reliable scientific data. However, water managers may need to overcome various obstacles to accessing data. For example, insufficient technological infrastructures, low data literacy, and data format complexities often inhibit data user access. Thus, it is imperative to include stakeholders in the design of data delivery systems. The United States Geological Survey's Water Resources Mission Area is currently developing Integrated Water Availability Assessments (IWAAs) — multi-extent, stakeholder driven, near real-time water availability census and prediction for human and ecological uses. To provide appropriate user accessibility to data delivery systems developed for IWAAs, a user-centered design process including stakeholder focus groups was used to determine potential water data user needs and preferences. Focus groups identified five types of potential users: Public sector water resources managers, Public sector water resources manager data analysts, Industry and private companies, Tribal Nations, and Nonprofit organizations. Different water data user types depended on diverse spatial and temporal scale data. Public sector water resources managers benefitted most from data synthesized into user-friendly platforms and Public sector water resources data analysts preferred easy access to raw data. These findings can support the development of a water data delivery platform that meets a variety of user needs.

Journal of the American Water Resources Associatio↗

Second round of an interlaboratory comparison of SARS-CoV2 molecular detection assays used by 45 veterinary diagnostic laboratories in the United States

The COVID-19 pandemic presents a continued public health challenge. Veterinary diagnostic laboratories in the United States use RT-rtPCR for animal testing, and many laboratories are certified for testing human samples; hence, ensuring that laboratories have sensitive and specific SARS-CoV2 testing methods is a critical component of the pandemic response. In 2020, the FDA Veterinary Laboratory Investigation and Response Network (Vet-LIRN) led an interlaboratory comparison (ILC1) to help laboratories evaluate their existing RT-rtPCR methods for detecting SARS-CoV2. All participating laboratories were able to detect the viral RNA spiked in buffer and PrimeStore molecular transport medium (MTM). With ILC2, Vet-LIRN extended ILC1 by evaluating analytical sensitivity and specificity of the methods used by participating laboratories to detect 3 SARS-CoV2 variants (B.1; B.1.1.7 [Alpha]; B.1.351 [Beta]) at various copy levels. We analyzed 57 sets of results from 45 laboratories qualitatively and quantitatively according to the principles of ISO 16140-2:2016. More than 95% of analysts detected the SARS-CoV2 RNA in MTM at ≥500 copies for all 3 variants. In addition, for nucleocapsid markers N1 and N2, 81% and 92% of the analysts detected ≤20 copies in the assays, respectively. The analytical specificity of the evaluated methods was >99%. Participating laboratories were able to assess their current method performance, identify possible limitations, and recognize method strengths as part of a continuous learning environment to support the critical need for the reliable diagnosis of COVID-19 in potentially infected animals and humans.

Journal of Veterinary Diagnostic Investigation↗

Small-mammal density estimation: A field comparison of grid-based vs. web-based density estimators

Statistical models for estimating absolute densities of field populations of animals have been widely used over the last century in both scientific studies and wildlife management programs. To date, two general classes of density estimation models have been developed: models that use data sets from capture–recapture or removal sampling techniques (often derived from trapping grids) from which separate estimates of population size ( NÌ‚ ) and effective sampling area ( AÌ‚ ) are used to calculate density ( DÌ‚ = NÌ‚ / AÌ‚ ); and models applicable to sampling regimes using distance-sampling theory (typically transect lines or trapping webs) to estimate detection functions and densities directly from the distance data. However, few studies have evaluated these respective models for accuracy, precision, and bias on known field populations, and no studies have been conducted that compare the two approaches under controlled field conditions. In this study, we evaluated both classes of density estimators on known densities of enclosed rodent populations. Test data sets ( n = 11) were developed using nine rodent species from capture–recapture live-trapping on both trapping grids and trapping webs in four replicate 4.2-ha enclosures on the Sevilleta National Wildlife Refuge in central New Mexico, USA. Additional “saturation” trapping efforts resulted in an enumeration of the rodent populations in each enclosure, allowing the computation of true densities. Density estimates ( DÌ‚ ) were calculated using program CAPTURE for the grid data sets and program DISTANCE for the web data sets, and these results were compared to the known true densities ( D ) to evaluate each model's relative mean square error, accuracy, precision, and bias. In addition, we evaluated a variety of approaches to each data set's analysis by having a group of independent expert analysts calculate their best density estimates without a priori knowledge of the true densities; this “blind” test allowed us to evaluate the influence of expertise and experience in calculating density estimates in comparison to simply using default values in programs CAPTURE and DISTANCE. While the rodent sample sizes were considerably smaller than the recommended minimum for good model results, we found that several models performed well empirically, including the web-based uniform and half-normal models in program DISTANCE, and the grid-based models M b and M bh in program CAPTURE (with AÌ‚ adjusted by species-specific full mean maximum distance moved (MMDM) values). These models produced accurate DÌ‚ values (with 95% confidence intervals that included the true D values) and exhibited acceptable bias but poor precision. However, in linear regression analyses comparing each model's DÌ‚ values to the true D values over the range of observed test densities, only the web-based uniform model exhibited a regression slope near 1.0; all other models showed substantial slope deviations, indicating biased estimates at higher or lower density values. In addition, the grid-based DÌ‚ analyses using full MMDM values for WÌ‚ area adjustments required a number of theoretical assumptions of uncertain validity, and we therefore viewed their empirical successes with caution. Finally, density estimates from the independent analysts were highly variable, but estimates from web-based approaches had smaller mean square errors and better achieved confidence-interval coverage of D than did grid-based approaches. Our results support the contention that web-based approaches for density estimation of small-mammal populations are both theoretically and empirically superior to grid-based approaches, even when sample size is far less than often recommended. In view of the increasing need for standardized environmental measures for comparisons among ecosystems and through time, analytical models based on distance sampling appear to offer accurate density estimation approaches for research studies involving small-mammal abundances.

Ecological Monographs↗

Guidelines for determining flood flow frequency: Bulletin #17B of the Hydrology Subcommittee

In December 1967, Bulletin No. 15, "A Uniform Technique for Determining Flood Flow Frequencies," was issued by the Hydrology Committee of the Water Resources Council. The report recommended use of the Pearson Type III distribution with log transformation of the data (log-Pearson Type III distribution) as a base method for flood flow frequency studies. As pointed out in that report, further studies were needed covering various aspects of flow frequency determinations. In March 1976, Bulletin 17, "Guidelines for Determining Flood Flow Frequency" was issued by the Water Resources Council. The guide was an extension and update of Bulletin No. 15. It provided a more complete guide for flood flow frequency analysis incorporating currently accepted technical methods with sufficient detail to promote uniform application. It was limited to defining flood potentials in terms of peak discharge and exceedance probability at locations where a systematic record of peak flood flows is available. The recommended set of procedures was selected from those used or described in the literature prior to 1976, based on studies conducted for this purpose at the Center for Research in Water Resources of the University of Texas at Austin (summarized in Appendix 14) and on studies by the Work Group on Flood Flow Frequency. The "Guidelines" were revised and reissued in June 1977 as Bulletin 17A. Bulletin 17B is the latest effort to improve and expand upon the earlier publications. Bulletin 17B provides revised procedures for weighting a station skew value with the results from a generalized skew study, detecting and treating outliers, making two station comparisons, and computing confidence limits about a frequency curve. The Work Group that prepared this revision did not address the suitability of the original distribution or the generalized skew map. Major problems are encountered when developing guides for flood flow frequency determinations. There is no procedure or set of procedures that can be adopted which, when rigidly applied to the available data, will accurately define the flood potential of any given watershed. Statistical analysis alone will not resolve all flood frequency problems. As discussed in subsequent sections of this guide, elements of risk and uncertainty are inherent in any flood frequency analysis. User decisions must be based on properly applied procedures and proper interpretation of results considering risk and uncertainty. Therefore, the judgment of a professional experienced in hydrologic analysis will enhance the usefulness of a flood frequency analysis and promote appropriate application. It is possible to standarize many elements of flood frequency analysis. This guide describes each major element of the process of defining the flood potential at a specific location in terms of peak discharge and exceedance probability. Use is confined to stations where available records are adequate to warrant statistical analysis of the data. Special situations may require other approaches. In those cases where the procedures of this guide are not followed, deviations must be supported by appropriate study and accompanied by a comparison of results using the recommended procedures. As a further means of achieving consistency and improving results, the Work Group recommends that studies be coordinated when more than one analyst is working currently on data for the same location. This recommendation holds particularly when defining exceedance probabilities for rare events, where this guide allows more latitude. Flood records are limited. As more years of record become available at each location, the determination of flood potential may change. Thus, an estimate may be outdated a few years after it is made. Additional flood data alone may be sufficient reason for a fresh assessment of the flood potential. When making a new assessment, the analyst should incorporate in his study a review of earlier estimates. Where differences appear, they should be acknowledged and explained.

Bulletin↗

Summary of percentages of zero daily mean streamflow for 712 U.S. Geological Survey streamflow-gaging stations in Texas through 2003

Analysts and managers of surface-water resources might have interest in the zero-flow potential for U.S.Geological Survey (USGS) streamflow-gaging stations in Texas. The USGS, in cooperation with the Texas Commission on Environmental Quality, initiated a data and reporting process to generate summaries of percentages of zero daily mean streamflow for 712 USGS streamflow-gaging stations in Texas. A summary of the percentages of zero daily mean streamflow for most active and inactive, continuous-record gaging stations in Texas provides valuable information by conveying the historical perspective for zero-flow potential for the watershed. The summaries of percentages of zero daily mean streamflow for each station are graphically depicted using two thematic perspectives: annual and monthly. The annual perspective consists of graphs of annual percentages of zero streamflow by year with the addition of lines depicting the mean and median annual percentage of zero streamflow. Monotonic trends in the percentages of zero streamflow also are identified using Kendall's T. The monthly perspective consists of graphs of the percentage of zero streamflow by month with lines added to indicate the mean and median monthly percentage of zero streamflow. One or more summaries could be used in a watershed, river basin, or other regional context by analysts and managers of surface-water resources to guide scientific, regulatory, or other inquiries of zero-flow or other low-flow conditions in Texas.

Data Series↗

Summary of annual mean, maximum, minimum, and L-scale statistics of daily mean streamflow for 712 U.S. Geological Survey streamflow-gaging Stations in Texas Through 2003

Analysts and managers of surface-water resources might have interest in selected statistics of daily mean streamflow for U.S. Geological Survey (USGS) streamflow-gaging stations in Texas. The selected statistics are the annual mean, maximum, minimum, and L-scale of daily meanstreamflow. Annual L-scale of streamflow is a robust measure of the variability of the daily mean streamflow for a given year. The USGS, in cooperation with the Texas Commission on Environmental Quality, initiated in 2006a data and reporting process to generate annual statistics for 712 USGS streamflow-gaging stations in Texas. A graphical depiction of the history of the annual statistics for most active and inactive, continuous-record gaging stations in Texas provides valuable information by conveying the historical perspective of streamflow for the watershed. Each figure consists off our time-series plots of the annual statistics of daily mean streamflow for each streamflow-gaging station. Each of the four plots is augmented with horizontal lines that depict the mean and median annual values of the corresponding statistic for the period of record. Monotonic trends for each of the four annual statistics also are identified using Kendall's T. The history of one or more streamflow-gaging stations could be used in a watershed, river basin, or other regional context by analysts and managers of surface-water resources to guide scientific, regulatory, or other inquiries of streamflow conditions in Texas.

Data Series↗