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USGS2021

Toward improved decision-support tools for Delta Smelt management actions

The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level. Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions. Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance. Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement. • DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions. • DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth. • DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt. There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address. For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models. Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time. Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires: - Engagement of a committed group of technical experts with appropriate expertise. - The development of a coordinated workplan including appropriate project management and tracking. - Dialog between potential users (i.e., managers and policy makers) and technical experts. - Resources to pursue DST development including personnel and computational resources. This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.

White Paper↗
USGS2011

Low-flow characteristics of Virginia streams

Low-flow annual non-exceedance probabilities (ANEP), called probability-percent chance (P-percent chance) flow estimates, regional regression equations, and transfer methods are provided describing the low-flow characteristics of Virginia streams. Statistical methods are used to evaluate streamflow data. Analysis of Virginia streamflow data collected from 1895 through 2007 is summarized. Methods are provided for estimating low-flow characteristics of gaged and ungaged streams. The 1-, 4-, 7-, and 30-day average streamgaging station low-flow characteristics for 290 long-term, continuous-record, streamgaging stations are determined, adjusted for instances of zero flow using a conditional probability adjustment method, and presented for non-exceedance probabilities of 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.02, 0.01, and 0.005. Stream basin characteristics computed using spatial data and a geographic information system are used as explanatory variables in regional regression equations to estimate annual non-exceedance probabilities at gaged and ungaged sites and are summarized for 290 long-term, continuous-record streamgaging stations, 136 short-term, continuous-record streamgaging stations, and 613 partial-record streamgaging stations. Regional regression equations for six physiographic regions use basin characteristics to estimate 1-, 4-, 7-, and 30-day average low-flow annual non-exceedance probabilities at gaged and ungaged sites. Weighted low-flow values that combine computed streamgaging station low-flow characteristics and annual non-exceedance probabilities from regional regression equations provide improved low-flow estimates. Regression equations developed using the Maintenance of Variance with Extension (MOVE.1) method describe the line of organic correlation (LOC) with an appropriate index site for low-flow characteristics at 136 short-term, continuous-record streamgaging stations and 613 partial-record streamgaging stations. Monthly streamflow statistics computed on the individual daily mean streamflows of selected continuous-record streamgaging stations and curves describing flow-duration are presented. Text, figures, and lists are provided summarizing low-flow estimates, selected low-flow sites, delineated physiographic regions, basin characteristics, regression equations, error estimates, definitions, and data sources. This study supersedes previous studies of low flows in Virginia.

Virginia↗
USGS2007

Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2002-07

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is particularly useful when analyzing a wide variety of spatial data such as with remote sensing and spatial analysis. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This document presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup from 2002 through 2007.

Fact Sheet↗
USGS2002

User's manual for computer program BASEPLOT

The checking and reviewing of daily records of streamflow within the U.S. Geological Survey is traditionally accomplished by hand-plotting and mentally collating tables of data. The process is time consuming, difficult to standardize, and subject to errors in computation, data entry, and logic. In addition, the presentation of flow data on the internet requires more timely and accurate computation of daily flow records. BASEPLOT was developed for checking and review of primary streamflow records within the U.S. Geological Survey. Use of BASEPLOT enables users to (1) provide efficiencies during the record checking and review process, (2) improve quality control, (3) achieve uniformity of checking and review techniques of simple stage-discharge relations, and (4) provide a tool for teaching streamflow computation techniques. The BASEPLOT program produces tables of quality control checks and produces plots of rating curves and discharge measurements; variable shift (V-shift) diagrams; and V-shifts converted to stage-discharge plots, using data stored in the U.S. Geological Survey Automatic Data Processing System database. In addition, the program plots unit-value hydrographs that show unit-value stages, shifts, and datum corrections; input shifts, datum corrections, and effective dates; discharge measurements; effective dates for rating tables; and numeric quality control checks. Checklist/tutorial forms are provided for reviewers to ensure completeness of review and standardize the review process. The program was written for the U.S. Geological Survey SUN computer using the Statistical Analysis System (SAS) software produced by SAS Institute, Incorporated.

Open-File Report↗
USGS2007

Flood of June 22-24, 2006, in North-Central Ohio, With Emphasis on the Cuyahoga River Near Independence

Heavy rains caused severe flooding on June 22-24, 2006, and damaged approximately 4,580 homes and 48 businesses in Cuyahoga County. Damage estimates in Cuyahoga County for the two days of flooding exceed $47 million; statewide damage estimates exceed $150 million. Six counties (Cuyahoga, Erie, Huron, Lucas, Sandusky, and Stark) in northeast Ohio were declared Federal disaster areas. One death, in Lorain County, was attributed to the flooding. The peak streamflow of 25,400 cubic feet per second and corresponding peak gage height of 23.29 feet were the highest recorded at the U.S. Geological Survey (USGS) streamflow-gaging station Cuyahoga River at Independence (04208000) since the gaging station began operation in 1922, exceeding the previous peak streamflow of 24,800 cubic feet per second that occurred on January 22, 1959. An indirect calculation of the peak streamflow was made by use of a step-backwater model because all roads leading to the gaging station were inundated during the flood and field crews could not reach the station to make a direct measurement. Because of a statistically significant and persistent positive trend in the annual-peak-streamflow time series for the Cuyahoga River at Independence, a method was developed and applied to detrend the annual-peak-streamflow time series prior to the traditional log-Pearson Type III flood-frequency analysis. Based on this analysis, the recurrence interval of the computed peak streamflow was estimated to be slightly less than 100 years. Peak-gage-height data, peak-streamflow data, and recurrence-interval estimates for the June 22-24, 2006, flood are tabulated for the Cuyahoga River at Independence and 10 other USGS gaging stations in north-central Ohio. Because flooding along the Cuyahoga River near Independence and Valley View was particularly severe, a study was done to document the peak water-surface profile during the flood from approximately 2 miles downstream from the USGS streamflow-gaging station at Independence to approximately 2 miles upstream from the gaging station. High-water marks were identified and flagged in the field. Third-order-accuracy surveys were used to determine elevations of the high-water marks, and the data were tabulated and plotted.

Scientific Investigations Report↗
USGS2012

Variance of discharge estimates sampled using acoustic Doppler current profilers from moving boats

This paper presents a model for quantifying the random errors (i.e., variance) of acoustic Doppler current profiler (ADCP) discharge measurements from moving boats for different sampling times. The model focuses on the random processes in the sampled flow field and has been developed using statistical methods currently available for uncertainty analysis of velocity time series. Analysis of field data collected using ADCP from moving boats from three natural rivers of varying sizes and flow conditions shows that, even though the estimate of the integral time scale of the actual turbulent flow field is larger than the sampling interval, the integral time scale of the sampled flow field is on the order of the sampling interval. Thus, an equation for computing the variance error in discharge measurements associated with different sampling times, assuming uncorrelated flow fields is appropriate. The approach is used to help define optimal sampling strategies by choosing the exposure time required for ADCPs to accurately measure flow discharge.

Journal of Hydraulic Engineering↗
USGS2018

A guide to Bayesian model checking for ecologists

Checking that models adequately represent data is an essential component of applied statistical inference. Ecologists increasingly use hierarchical Bayesian statistical models in their research. The appeal of this modeling paradigm is undeniable, as researchers can build and fit models that embody complex ecological processes while simultaneously accounting for observation error. However, ecologists tend to be less focused on checking model assumptions and assessing potential lack of fit when applying Bayesian methods than when applying more traditional modes of inference such as maximum likelihood. There are also multiple ways of assessing the fit of Bayesian models, each of which has strengths and weaknesses. For instance, Bayesian P values are relatively easy to compute, but are well known to be conservative, producing P values biased toward 0.5. Alternatively, lesser known approaches to model checking, such as prior predictive checks, cross‐validation probability integral transforms, and pivot discrepancy measures may produce more accurate characterizations of goodness‐of‐fit but are not as well known to ecologists. In addition, a suite of visual and targeted diagnostics can be used to examine violations of different model assumptions and lack of fit at different levels of the modeling hierarchy, and to check for residual temporal or spatial autocorrelation. In this review, we synthesize existing literature to guide ecologists through the many available options for Bayesian model checking. We illustrate methods and procedures with several ecological case studies including (1) analysis of simulated spatiotemporal count data, (2) N‐mixture models for estimating abundance of sea otters from an aircraft, and (3) hidden Markov modeling to describe attendance patterns of California sea lion mothers on a rookery. We find that commonly used procedures based on posterior predictive P values detect extreme model inadequacy, but often do not detect more subtle cases of lack of fit. Tests based on cross‐validation and pivot discrepancy measures (including the “sampled predictive P value”) appear to be better suited to model checking and to have better overall statistical performance. We conclude that model checking is necessary to ensure that scientific inference is well founded. As an essential component of scientific discovery, it should accompany most Bayesian analyses presented in the literature.

Ecological Monographs↗
USGS2009

On selecting a prior for the precision parameter of Dirichlet process mixture models

In hierarchical mixture models the Dirichlet process is used to specify latent patterns of heterogeneity, particularly when the distribution of latent parameters is thought to be clustered (multimodal). The parameters of a Dirichlet process include a precision parameter ?? and a base probability measure G 0 . In problems where ?? is unknown and must be estimated, inferences about the level of clustering can be sensitive to the choice of prior assumed for ??. In this paper an approach is developed for computing a prior for the precision parameter ?? that can be used in the presence or absence of prior information about the level of clustering. This approach is illustrated in an analysis of counts of stream fishes. The results of this fully Bayesian analysis are compared with an empirical Bayes analysis of the same data and with a Bayesian analysis based on an alternative commonly used prior.

Journal of Statistical Planning and Inference↗
USGS2001

Catalog of earthquake hypocenters at Alaskan volcanoes: January 1, 1994 through December 31, 1999

The Alaska Volcano Observatory (AVO), a cooperative program of the U.S. Geological Survey, the Geophysical Institute of the University of Alaska - Fairbanks, and the Alaska Division of Geological and Geophysical Surveys, has maintained a seismic monitoring program at potentially active volcanoes in Alaska since 1988 (Power and others, 1993; Jolly and others, 1996). The primary objectives of this program are the seismic surveillance of active, potentially hazardous, Alaskan volcanoes and the investigation of seismic processes associated with active volcanism. Between 1994 and 1999, the AVO seismic monitoring program underwent significant changes with networks added at new volcanoes during each summer from 1995 through 1999. The existing network at Katmai –Valley of Ten Thousand Smokes (VTTS) was repaired in 1995, and new networks were installed at Makushin (1996), Akutan (1996), Pavlof (1996), Katmai - south (1996), Aniakchak (1997), Shishaldin (1997), Katmai - north (1998), Westdahl, (1998), Great Sitkin (1999) and Kanaga (1999). These networks added to AVO's existing seismograph networks in the Cook Inlet area and increased the number of AVO seismograph stations from 46 sites and 57 components in 1994 to 121 sites and 155 components in 1999. The 1995–1999 seismic network expansion increased the number of volcanoes monitored in real-time from 4 to 22, including Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Mount Snowy, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount Mageik, Mount Martin, Aniakchak Crater, Pavlof Volcano, Mount Dutton, Isanotski volcano, Shisaldin Volcano, Fisher Caldera, Westdahl volcano, Akutan volcano, Makushin Volcano, Great Sitkin volcano, and Kanaga Volcano (see Figures 1-15). The network expansion also increased the number of earthquakes located from about 600 per year in1994 and 1995 to about 3000 per year between 1997 and 1999. Highlights of the catalog period include: 1) a large volcanogenic seismic swarm at Akutan volcano in March and April 1996 (Lu and others, 2000); 2) an eruption at Pavlof Volcano in fall 1996 (Garces and others, 2000; McNutt and others, 2000); 3) an earthquake swarm at Iliamna volcano between September and December 1996; 4) an earthquake swarm at Mount Mageik in October 1996 (Jolly and McNutt, 1999); 5) an earthquake swarm located at shallow depth near Strandline Lake; 6) a strong swarm of earthquakes near Becharof Lake; 7) precursory seismicity and an eruption at Shishaldin Volcano in April 1999 that included a 5.2 ML earthquake and aftershock sequence (Moran and others, in press; Thompson and others, in press). The 1996 calendar year is also notable as the seismicity rate was very high, especially in the fall when 3 separate areas (Strandline Lake, Iliamna Volcano, and several of the Katmai volcanoes) experienced high rates of located earthquakes. This catalog covers the period from January 1, 1994, through December 31,1999, and includes: 1) earthquake origin times, hypocenters, and magnitudes with summary statistics describing the earthquake location quality; 2) a description of instruments deployed in the field and their locations and magnifications; 3) a description of earthquake detection, recording, analysis, and data archival; 4) velocity models used for earthquake locations; 5) phase arrival times recorded at individual stations; and 6) a summary of daily station usage from throughout the report period. We have made calculated hypocenters, station locations, system magnifications, velocity models, and phase arrival information available for download via computer network as a compressed Unix tar file.

Alaska↗
USGS2016

Uncertainty in biological monitoring: a framework for data collection and analysis to account for multiple sources of sampling bias

Biological monitoring programmes are increasingly relying upon large volumes of citizen-science data to improve the scope and spatial coverage of information, challenging the scientific community to develop design and model-based approaches to improve inference. Recent statistical models in ecology have been developed to accommodate false-negative errors, although current work points to false-positive errors as equally important sources of bias. This is of particular concern for the success of any monitoring programme given that rates as small as 3% could lead to the overestimation of the occurrence of rare events by as much as 50%, and even small false-positive rates can severely bias estimates of occurrence dynamics. We present an integrated, computationally efficient Bayesian hierarchical model to correct for false-positive and false-negative errors in detection/non-detection data. Our model combines independent, auxiliary data sources with field observations to improve the estimation of false-positive rates, when a subset of field observations cannot be validated a posteriori or assumed as perfect. We evaluated the performance of the model across a range of occurrence rates, false-positive and false-negative errors, and quantity of auxiliary data. The model performed well under all simulated scenarios, and we were able to identify critical auxiliary data characteristics which resulted in improved inference. We applied our false-positive model to a large-scale, citizen-science monitoring programme for anurans in the north-eastern United States, using auxiliary data from an experiment designed to estimate false-positive error rates. Not correcting for false-positive rates resulted in biased estimates of occupancy in 4 of the 10 anuran species we analysed, leading to an overestimation of the average number of occupied survey routes by as much as 70%. The framework we present for data collection and analysis is able to efficiently provide reliable inference for occurrence patterns using data from a citizen-science monitoring programme. However, our approach is applicable to data generated by any type of research and monitoring programme, independent of skill level or scale, when effort is placed on obtaining auxiliary information on false-positive rates.

Methods in Ecology and Evolution↗
USGS2015

Low-flow characteristics for selected streams in Indiana

The management and availability of Indiana’s water resources increase in importance every year. Specifically, information on low-flow characteristics of streams is essential to State water-management agencies. These agencies need low-flow information when working with issues related to irrigation, municipal and industrial water supplies, fish and wildlife protection, and the dilution of waste. Industrial, municipal, and other facilities must obtain National Pollutant Discharge Elimination System (NPDES) permits if their discharges go directly to surface waters. The Indiana Department of Environmental Management (IDEM) requires low-flow statistics in order to administer the NPDES permit program. Low-flow-frequency characteristics were computed for 272 continuous-record stations. The information includes low-flow-frequency analysis, flow-duration analysis, and harmonic mean for the continuous-record stations. For those stations affected by some form of regulation, low-flow frequency curves are based on the longest period of homogeneous record under current conditions. Low-flow-frequency values and harmonic mean flow (if sufficient data were available) were estimated for the 166 partial-record stations. Partial-record stations are ungaged sites where streamflow measurements were made at base flow.

Indiana↗
USGS2001

Surface-water, water-quality, and ground-water assessment of the Municipio of Carolina, Puerto Rico, 1997-99

To meet the increasing need for a safe and adequate supply of water in the municipio of Carolina, an integrated surface-water, water-quality, and ground-water assessment of the area was conducted. The major results of this study and other important hydrologic and water-quality features were compiled in a Geographic Information System and are presented in two 1:30,000-scale map plates to facilitate interpretation and use of the diverse water-resources data. Because the supply of safe drinking water was a critical issue during recent dry periods, the surface-water assessment portion of this study focused on analysis of low-flow characteristics in local streams and rivers. Low-flow characteristics were evaluated for one continuous-record gaging station, based on graphical curve-fitting techniques and log-Pearson Type III frequency analysis. Estimates of low-flow characteristics for seven partial-record stations were generated using graphical-correlation techniques. Flow-duration characteristics were computed for the one continuous-record gaging station and were estimated for the partial-record stations using the relation curves developed from the low-flow study. Stream low-flow statistics document the general hydrology under current land and water use. Low-flow statistics may substantially change as a result of streamflow diversions for public supply, and an increase in ground-water development, waste-water discharges, and flood-control measures; the current analysis provides baseline information to evaluate these impacts and develop water budgets. A sanitary quality survey of streams utilized 29 sampling stations to evaluate the sanitary quality of about 87 miles of stream channels. River and stream samples were collected on two occasions during base-flow conditions and were analyzed for fecal coliform and fecal streptococcus. Bacteriological analyses indicate that a significant portion of the stream reaches within the municipio of Carolina may have fecal coliform concentrations above the water-quality goal established by the Puerto Rico Environmental Quality Board (Junta de Calidad Ambiental de Puerto Rico) for inland surface waters. Sources of fecal contamination may include: illegal discharge of sewage to storm-water drains, malfunctioning sanitary sewer ejectors, clogged and leaking sewage pipes, septic tank leakage, unfenced livestock, and runoff from livestock pens. Long-term fecal coliform data at two sampling stations, Quebrada Blasina in Carolina and the Rio Grande de Loiza, downstream from the town of Trujillo Alto, indicate that the sanitary quality of Quebrada Blasina is and has generally been poor for more than a decade. The sanitary quality of the Rio Grande de Loiza has generally been in compliance with the water-quality goal standard fecal coliform concentrations established in July 1990 by the Puerto Rico Environmental Quality Board. Geologic, topographic, soil, hydrogeologic, and streamflow data were used to divide the municipio of Carolina into five hydrogeologic terranes. This integrated database was then used to evaluate the ground-water potential of each hydrogeologic terrane. Analysis suggests that areas with slopes greater than 15 degrees have relatively low ground-water development potential. Fractures may be locally important in enhancing the water-bearing properties in the hydrogeologic terranes containing igneous rocks. Potentiometric-surface elevations recorded in piezometers installed in the coastal area during this study were used to define ground-water flow directions in the hydrogeologic terranes composed of coastal plain clastic and limestone units. The resultant potentiometric map indicates that the coastal plain aquifer and streams in the lowland parts of the municipio of Carolina are hydraulically connected. The potentiometric map also indicates that ground-water discharge to the Rio Grande de Loiza, downstream from highway PR-3, has been enhanced by dredging of the streambed for

Water-Resources Investigations Report↗
USGS1977

Image analysis as a method for determining grain-size distributions in sandstones

A Quantimet 720 image analyzing computer is useful for A Quantimet 720 image analyzing computer is useful for determination of grain-size distributions of sand-size material. Results including histograms and statistical measures of size can be produced in approximately 5 to 10 minutes per sample. Comparison with sieve results shows only small differences in the mean grain-sizes obtained. Research into operating methods for the image analyzing computer and into mathematical methods for combining data is continuing so that material coarser and finer than sand can be measured. The instrument, which is attached to a microscope via a black and white television scanner, uses the gray shades of features in the field of view as the means of detecting those features. The image analyzing computer can then measure the features including area, perimeter, and several types of diameters. These data are then analyzed by a computer and the results are plotted. Sample preparation for this technique is conventional. Samples can be run as loose disaggregated grains, grain mounts, or thin sections.

Open-File Report↗
USGS2011

Peak-flow characteristics of Virginia streams

Peak-flow annual exceedance probabilities, also called probability-percent chance flow estimates, and regional regression equations are provided describing the peak-flow characteristics of Virginia streams. Statistical methods are used to evaluate peak-flow data. Analysis of Virginia peak-flow data collected from 1895 through 2007 is summarized. Methods are provided for estimating unregulated peak flow of gaged and ungaged streams. Station peak-flow characteristics identified by fitting the logarithms of annual peak flows to a Log Pearson Type III frequency distribution yield annual exceedance probabilities of 0.5, 0.4292, 0.2, 0.1, 0.04, 0.02, 0.01, 0.005, and 0.002 for 476 streamgaging stations. Stream basin characteristics computed using spatial data and a geographic information system are used as explanatory variables in regional regression model equations for six physiographic regions to estimate regional annual exceedance probabilities at gaged and ungaged sites. Weighted peak-flow values that combine annual exceedance probabilities computed from gaging station data and from regional regression equations provide improved peak-flow estimates. Text, figures, and lists are provided summarizing selected peak-flow sites, delineated physiographic regions, peak-flow estimates, basin characteristics, regional regression model equations, error estimates, definitions, data sources, and candidate regression model equations. This study supersedes previous studies of peak flows in Virginia.

Virginia↗
USGS2015

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

This report presents the results of a cooperative study by the U.S. Geological Survey and the Oklahoma Department of Transportation to estimate the magnitude and frequency of peak streamflows from regional regression equations for ungaged stream sites in and near the Oklahoma Panhandle. These methods relate basin characteristics (physiographic and climatic attributes) to selected peak streamflow frequency statistics with the 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities. These relations were developed based on data from 32 selected streamflow-gaging stations in the Oklahoma Panhandle and in neighboring parts of Colorado, Kansas, New Mexico, and Texas. The basin characteristics for the selected streamflow-gaging stations were determined by using a geographic information system and the Oklahoma StreamStats application. Peak-streamflow frequency statistics were computed from annual peak-streamflow records from the irrigated period of record from water year 1978 through water year 2014. Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

Colorado, Kansas, New Mexico, Oklahoma, Texas↗
USGS2010

Methods for development of planning-level estimates of stormflow at unmonitored stream sites in the conterminous United States

This report documents methods for data compilation and analysis of statistics for stormflows that meet data-quality objectives for order-of-magnitude planning-level water-quality estimates at unmonitored sites in the conterminous United States. Statistics for prestorm streamflow, precipitation, and runoff coefficients are used to model stormflows for use with the Stochastic Empirical Loading and Dilution Model (SELDM), which is a highway-runoff model. SELDM is designed to better quantify the risk of exceeding water-quality criteria as precipitation, discharge, ambient water quality, and highway-runoff quality vary from storm to storm. Summary statistics also may be used to help estimate annual-average water-quality loads. Streamflow statistics are used to estimate prestorm flows. Streamflow statistics are estimated by analysis of data from 2,873 U.S. Geological Survey streamgages in the conterminous United States with drainage areas ranging from 10 to 500 square miles and at least 24 years of record during the period 1960−2004. Streamflow statistics are regionalized using U.S. Environmental Protection Agency Level III nutrient ecoregions. Storm-event precipitation statistics are estimated by analysis of data from 2,610 National Oceanic and Atmospheric Administration hourly-precipitation data stations in the conterminous United States with at least 25 years of data during the 1965−2006 period. Storm-event precipitation statistics are regionalized using U.S. Environmental Protection Agency rain zones. Statistics to characterize volumetric runoff coefficients are estimated using data from 6,142 storm events at 306 study sites. Runoff coefficient statistics are not regionalized, but are organized by total impervious area. All of the geographic information system files, computer programs, data files, and regression results developed for this study are included on the CD−ROM accompanying this report.

Report↗
USGS2015

Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts

Ecologists often fit models to survey data to estimate and explain variation in animal abundance. Such models typically require that animal density remains constant across the landscape where sampling is being conducted, a potentially problematic assumption for animals inhabiting dynamic landscapes or otherwise exhibiting considerable spatiotemporal variation in density. We review several concepts from the burgeoning literature on spatiotemporal statistical models, including the nature of the temporal structure (i.e., descriptive or dynamical) and strategies for dimension reduction to promote computational tractability. We also review several features as they specifically relate to abundance estimation, including boundary conditions, population closure, choice of link function, and extrapolation of predicted relationships to unsampled areas. We then compare a suite of novel and existing spatiotemporal hierarchical models for animal count data that permit animal density to vary over space and time, including formulations motivated by resource selection and allowing for closed populations. We gauge the relative performance (bias, precision, computational demands) of alternative spatiotemporal models when confronted with simulated and real data sets from dynamic animal populations. For the latter, we analyze spotted seal ( Phoca largha ) counts from an aerial survey of the Bering Sea where the quantity and quality of suitable habitat (sea ice) changed dramatically while surveys were being conducted. Simulation analyses suggested that multiple types of spatiotemporal models provide reasonable inference (low positive bias, high precision) about animal abundance, but have potential for overestimating precision. Analysis of spotted seal data indicated that several model formulations, including those based on a log-Gaussian Cox process, had a tendency to overestimate abundance. By contrast, a model that included a population closure assumption and a scale prior on total abundance produced estimates that largely conformed to our a priori expectation. Although care must be taken to tailor models to match the study population and survey data available, we argue that hierarchical spatiotemporal statistical models represent a powerful way forward for estimating abundance and explaining variation in the distribution of dynamical populations.

Ecological Monographs↗
USGS1969

The Breeding Bird Survey, 1967 and 1968

In the Breeding Bird Survey of North America, cooperators ran 982 survey routes in 1967 and 1,174 in 1968. All States except Hawaii and all Canadian Provinces except Newfoundland were included. Roadside routes are selected at random within 1-degree blocks of latitude and longitude. Each 24 1/2-mile route, with 3-minute stops spaced half a mile apart, is driven by automobile. All birds heard or seen at the stops are recorded on special forms, and the data are transferred to magnetic tape for analysis. The average number of birds of each species per route is tabulated by State and Province, presenting for the first time a record of the comparative abundance of each species across the continent. The sample size is given for each species recorded. A sophisticated analysis program, here employed for the first time, is used to compute weighted mean values of the survey results for selected species at the State, stratum, regional, and continental level. The statistical significance of year-to-year changes at the 80, 90, 95, and 99 percent levels of probability are part of the computer output. An index for comparing populations of each species from year to year is established, with 1968 as the base year. Maps show the breeding range and comparative abundance of selected species.

Special Scientific Report - Wildlife↗