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At least 19 recordsLinked to original sources
USGS2014

Application of statistical methods to the analysis of ground‐water levels

Valuable hydrologic information can be obtained from statistical analysis of water‐level trends. The time‐series and the functional‐equation approaches are applied to New Jersey well records representing different hydrologic conditions. The results are valuable as concise summaries of the records, for extrapolating observed data, for interpolating between measurements, and for estimating hydrologic factors such as coefficients of transrnissibility and storage, evapotranspiration, and ground‐water discharge.

Eos, Transactions, American Geophysical Union↗
USGS2012

Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

Introduction Evaluating the environmental impacts of climate change on water resources and biological components of the landscape is an integral part of hydrologic and ecological investigations, and the resultant land and resource management in the twenty-first century. Impacts of both climate and simulated hydrologic parameters on ecological processes are relevant at scales that reflect the heterogeneity and complexity of landscapes. At present, simulations of climate change available from global climate models [GCMs] require downscaling for hydrologic or ecological applications. Methods Using statistically downscaled future climate projections developed using constructed analogues, a methodology was developed to further downscale the projections spatially using a gradient-inverse-distance-squared approach for application to hydrologic modeling at 270-m spatial resolution. Results This paper illustrates a methodology to downscale and bias-correct national GCMs to subkilometer scales that are applicable to fine-scale environmental processes. Four scenarios were chosen to bracket the range of future emissions put forth by the Intergovernmental Panel on Climate Change. Fine-scale applications of downscaled datasets of ecological and hydrologic correlations to variation in climate are illustrated. Conclusions The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.

Ecological Processes↗
USGS2015

Estimation of river and stream temperature trends under haphazard sampling

Long-term temporal trends in water temperature in rivers and streams are typically estimated under the assumption of evenly-spaced space-time measurements. However, sampling times and dates associated with historical water temperature datasets and some sampling designs may be haphazard. As a result, trends in temperature may be confounded with trends in time or space of sampling which, in turn, may yield biased trend estimators and thus unreliable conclusions. We address this concern using multilevel (hierarchical) linear models, where time effects are allowed to vary randomly by day and date effects by year. We evaluate the proposed approach by Monte Carlo simulations with imbalance, sparse data and confounding by trend in time and date of sampling. Simulation results indicate unbiased trend estimators while results from a case study of temperature data from the Illinois River, USA conform to river thermal assumptions. We also propose a new nonparametric bootstrap inference on multilevel models that allows for a relatively flexible and distribution-free quantification of uncertainties. The proposed multilevel modeling approach may be elaborated to accommodate nonlinearities within days and years when sampling times or dates typically span temperature extremes.

Illinois River↗
USGS2015

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↗
USGS2021

Clustering supported classification of ChemCam data from Gale crater, Mars

The Chemistry and Camera (ChemCam) instrument on board the MSL rover Curiosity has collected a very large and unique dataset of in-situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot LIBS (laser-induced breakdown spectroscopy) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a dataset is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method non-negative matrix factorization (NMF) and the repetition of k-means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756.

Earth and Space Science↗
USGS2000

Statistical Approaches to Interpretation of Local, Regional, and National Highway-Runoff and Urban-Stormwater Data

Decision makers need viable methods for the interpretation of local, regional, and national-highway runoff and urban-stormwater data including flows, concentrations and loads of chemical constituents and sediment, potential effects on receiving waters, and the potential effectiveness of various best management practices (BMPs). Valid (useful for intended purposes), current, and technically defensible stormwater-runoff models are needed to interpret data collected in field studies, to support existing highway and urban-runoffplanning processes, to meet National Pollutant Discharge Elimination System (NPDES) requirements, and to provide methods for computation of Total Maximum Daily Loads (TMDLs) systematically and economically. Historically, conceptual, simulation, empirical, and statistical models of varying levels of detail, complexity, and uncertainty have been used to meet various data-quality objectives in the decision-making processes necessary for the planning, design, construction, and maintenance of highways and for other land-use applications. Water-quality simulation models attempt a detailed representation of the physical processes and mechanisms at a given site. Empirical and statistical regional water-quality assessment models provide a more general picture of water quality or changes in water quality over a region. All these modeling techniques share one common aspect-their predictive ability is poor without suitable site-specific data for calibration. To properly apply the correct model, one must understand the classification of variables, the unique characteristics of water-resources data, and the concept of population structure and analysis. Classifying variables being used to analyze data may determine which statistical methods are appropriate for data analysis. An understanding of the characteristics of water-resources data is necessary to evaluate the applicability of different statistical methods, to interpret the results of these techniques, and to use tools and techniques that account for the unique nature of water-resources data sets. Populations of data on stormwater-runoff quantity and quality are often best modeled as logarithmic transformations. Therefore, these factors need to be considered to form valid, current, and technically defensible stormwater-runoff models. Regression analysis is an accepted method for interpretation of water-resources data and for prediction of current or future conditions at sites that fit the input data model. Regression analysis is designed to provide an estimate of the average response of a system as it relates to variation in one or more known variables. To produce valid models, however, regression analysis should include visual analysis of scatterplots, an examination of the regression equation, evaluation of the method design assumptions, and regression diagnostics. A number of statistical techniques are described in the text and in the appendixes to provide information necessary to interpret data by use of appropriate methods. Uncertainty is an important part of any decisionmaking process. In order to deal with uncertainty problems, the analyst needs to know the severity of the statistical uncertainty of the methods used to predict water quality. Statistical models need to be based on information that is meaningful, representative, complete, precise, accurate, and comparable to be deemed valid, up to date, and technically supportable. To assess uncertainty in the analytical tools, the modeling methods, and the underlying data set, all of these components need be documented and communicated in an accessible format within project publications.

Open-File Report↗
USGS2010

CORSSA: The Community Online Resource for Statistical Seismicity Analysis

Statistical seismology is the application of rigorous statistical methods to earthquake science with the goal of improving our knowledge of how the earth works. Within statistical seismology there is a strong emphasis on the analysis of seismicity data in order to improve our scientific understanding of earthquakes and to improve the evaluation and testing of earthquake forecasts, earthquake early warning, and seismic hazards assessments. Given the societal importance of these applications, statistical seismology must be done well. Unfortunately, a lack of educational resources and available software tools make it difficult for students and new practitioners to learn about this discipline. The goal of the Community Online Resource for Statistical Seismicity Analysis (CORSSA) is to promote excellence in statistical seismology by providing the knowledge and resources necessary to understand and implement the best practices, so that the reader can apply these methods to their own research. This introduction describes the motivation for and vision of CORRSA. It also describes its structure and contents.

Community Online Resource for Statistical Seismici↗
USGS2022

Introduction to the Python Hyperspectral Analysis Tool (PyHAT)

Spectroscopic data are rich in information and are commonly used in planetary research. Many mission teams, research labs, and individual research scientists derive thematic products from multi- and hyperspectral data sets and apply spectroscopic analysis techniques to derive new understanding. The PyHAT is a powerful and versatile, free, and open-source Python library designed to support exploratory spectral data analysis, the derivation of mission generated thematic products, and the application of statistical learning methods to spectral data. We present a general overview of the software architecture and identify the classes of users we seek to support. Four case studies demonstrate the use for both orbital and in-situ (landed) hyperspectral data.

Book chapter↗
USGS2009

Trends in pesticide concentrations in corn-belt streams, 1996-2006

Trends in the concentrations of commonly occurring pesticides in the Corn Belt of the United States were assessed, and the performance and application of several statistical methods for trend analysis were evaluated. Trends in the concentrations of 11 pesticides with sufficient data for trend assessment were assessed at up to 31 stream sites for two time periods: 1996–2002 and 2000–2006. Pesticides included in the trend analyses were atrazine, acetochlor, metolachlor, alachlor, cyanazine, EPTC, simazine, metribuzin, prometon, chlorpyrifos, and diazinon. The statistical methods applied and compared were (1) a modified version of the nonparametric seasonal Kendall test (SEAKEN), (2) a modified version of the Regional Kendall test, (3) a parametric regression model with seasonal wave (SEAWAVE), and (4) a version of SEAWAVE with adjustment for streamflow (SEAWAVE-Q). The SEAKEN test is a statistical hypothesis test for detecting monotonic trends in seasonal time-series data such as pesticide concentrations at a particular site. Trends across a region, represented by multiple sites, were evaluated using the regional seasonal Kendall test, which computes a test for an overall trend within a region by computing a score for each season at each site and adding the scores to compute the total for the region. The SEAWAVE model is a parametric regression model specifically designed for analyzing seasonal variability and trends in pesticide concentrations. The SEAWAVE-Q model accounts for the effect of changing flow conditions in order to separate changes caused by hydrologic trends from changes caused by other factors, such as pesticide use. There was broad, general agreement between unadjusted trends (no adjustment for streamflow effects) identified by the SEAKEN and SEAWAVE methods, including the regional seasonal Kendall test. Only about 10 percent of the paired comparisons between SEAKEN and SEAWAVE indicated a difference in the direction of trend, and none of these had differences significant at the 10-percent significance level. This consistency of results supports the validity and robustness of all three approaches as trend analysis tools. The SEAWAVE method is favored, however, because it has less restrictive data requirements, enabling analysis for more site/pesticide combinations, and can incorporate adjustment for streamflow (SEAWAVE-Q) with substantially fewer measurements than the flow-adjustment procedure used with SEAKEN. Analysis of flow-adjusted trends is preferable to analysis of non-adjusted trends for evaluating potential effects of changes in pesticide use or management practices because flow-adjusted trends account for the influence of flow-related variability. Analysis of flow-adjusted trends by SEAWAVE-Q showed that all of the pesticides assessed, except simazine and acetochlor, were dominated by varying degrees of concentration downtrends in one or both analysis periods. Atrazine, metolachlor, alachlor, cyanazine, EPTC, and metribuzin—all major corn herbicides, as well as prometon and chlorpyrifos, showed more prevalent concentration downtrends during 1996–2002 compared to 2000–2006. Diazinon had no clear trends during 1996–2002, but had predominantly downward trends during 2000–2006. Acetochlor trends were mixed during 1996–2002 and slightly upward during 2000–2006, but most of the trends were not statistically significant. Simazine concentrations trended upward at most sites during both 1996–2002 and 2000–2006. Comparison of concentration trends to agricultural-use trends indicated similarity in direction and magnitude for acetochlor, metolachlor, alachlor, cyanazine, EPTC, and metribuzin. Concentration downtrends for atrazine, chlorpyrifos, and diazinon were steeper than agricultural-use downtrends at some sites, indicating the possibility that agricultural management practices may have increasingly reduced transport to streams (particularly atrazine) or, for chlorpyrifos and diazinon, that nonagricultural uses declined substantially. Concentration uptrends for simazine generally were steeper than agricultural-use uptrends, indicating the possibility that nonagricultural uses of this herbicide increased during the study period.

Scientific Investigations Report↗
USGS2016

Statistical analysis of lake levels and field study of groundwater and surface-water exchanges in the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015: Chapter A of Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015

Water levels declined from 2003 to 2011 in many lakes in Ramsey and Washington Counties in the northeast Twin Cities Metropolitan Area, Minnesota; however, water levels in other northeast Twin Cities Metropolitan Area lakes increased during the same period. Groundwater and surface-water exchanges can be important in determining lake levels where these exchanges are an important component of the water budget of a lake. An understanding of groundwater and surface-water exchanges in the northeast Twin Cities Metropolitan Area has been limited by the lack of hydrologic data. The U.S. Geological Survey, in cooperation with the Metropolitan Council and Minnesota Department of Health, completed a field and statistical study assessing lake-water levels and regional and local groundwater and surface-water exchanges near northeast Twin Cities Metropolitan Area lakes. This report documents the analysis of collected hydrologic, water-quality, and geophysical data; and existing hydrologic and geologic data to (1) assess the effect of physical setting and climate on lake-level fluctuations of selected lakes, (2) estimate potential percentages of surface-water contributions to well water across the northeast Twin Cities Metropolitan Area, (3) estimate general ages for waters extracted from the wells, and (4) assess groundwater inflow to lakes and lake-water outflow to aquifers downgradient from White Bear Lake. Statistical analyses of lake levels during short-term (2002–10) and long-term (1925–2014) periods were completed to help understand lake-level changes across the northeast Twin Cities Metropolitan Area. Comparison of 2002–10 lake levels to several landscape and geologic characteristics explained variability in lake-level changes for 96 northeast Twin Cities Metropolitan Area lakes. Application of several statistical methods determined that (1) closed-basin lakes (without an active outlet) had larger lake-level declines than flow-through lakes with an outlet; (2) closed-basin lake-level changes reflected groundwater-level changes in the Quaternary, Prairie du Chien, and Jordan aquifers; (3) the installation of outlet-control structures, such as culverts and weirs, resulted in smaller multiyear lake-level changes than lakes without outlet-control structures; (4) water levels in lakes primarily overlying Superior Lobe deposits were significantly more variable than lakes primarily overlying Des Moines Lobe deposits; (5) lake-level declines were larger with increasing mean lake-level elevation; and (6) the frequency of some of these characteristics varies by landscape position. Flow-through lakes and lakes with outlet-control structures were more common in watersheds with more than 50 percent urban development compared to watersheds with less than 50 percent urban development. A comparison of two 35-year periods during 1925–2014 revealed that variability of annual mean lake levels in flow-through lakes increased when annual precipitation totals were more variable, whereas variability of annual mean lake levels in closed-basin lakes had the opposite pattern, being more variable when annual precipitation totals were less variable. Oxygen-18/oxygen-16 and hydrogen-2/hydrogen-1 ratios for water samples from 40 wells indicated the well water was a mixture of surface water and groundwater in 31 wells, whereas ratios from water sampled from 9 other wells indicated that water from these wells receive no surface-water contribution. Of the 31 wells with a mixture of surface water and groundwater, 11 were downgradient from White Bear Lake, likely receiving water from deeper parts of the lake. Age dating of water samples from wells indicated that the age of water in the Prairie du Chien and Jordan aquifers can vary widely across the northeast Twin Cities Metropolitan Area. Estimated ages of recharge for 9 of the 40 wells sampled for chlorofluorocarbon concentrations ranged widely from the early 1940s to mid-1970s. The wide range in estimated ages of recharge may have resulted from the wide range in the open-interval lengths and depths for the wells. Results from stable isotope analyses of water samples, lake-sediment coring, continuous seismic-reflection profiling, and water-level and flow monitoring indicated that there is groundwater inflow from nearshore sites and lake-water outflow from deep-water sites in White Bear Lake. Continuous seismic-reflection profiling indicated that deep sections of White Bear, Pleasant, Turtle, and Big Marine Lakes have few trapped gases and little organic material, which indicates where groundwater and lake-water exchanges are more likely. Water-level differences between White Bear Lake and piezometer and seepage measurements in deep waters of the lake indicate that groundwater and lake-water exchange is happening in deep waters, predominantly downgradient from the lake and into the lake sediment. Seepage fluxes measured in the nearshore sites of White Bear Lake generally were higher than seepage fluxes measured in the deep-water sites, which indicates that groundwater-inflow rates at most of the nearshore sites are higher than lake-water outflow from the deep-water sites.

Minnesota↗
USGS1998

Methods and guidelines for effective model calibration; with application to UCODE, a computer code for universal inverse modeling, and MODFLOWP, a computer code for inverse modeling with MODFLOW

This report documents methods and guidelines for model calibration using inverse modeling. The inverse modeling and statistical methods discussed are broadly applicable, but are presented as implemented in the computer programs UCODE, a universal inverse code that can be used with any application model, and MODFLOWP, an inverse code limited to one application model. UCODE and MODFLOWP perform inverse modeling, posed as a parameter-estimation problem, by calculating parameter values that minimize a weighted least-squares objective function using nonlinear regression. Minimization is accomplished using a modified Gauss-Newton method, and prior, or direct, information on estimated parameters can be included in the regression. Inverse modeling in many fields is plagued by problems of instability and nonuniqueness, and obtaining useful results depends on (1) defining a tractable inverse problem using simplifications appropriate to the system under investigation and (2) wise use of statistics generated using calculated sensitivities and the match between observed and simulated values, and associated graphical analyses. Fourteen guidelines presented in this work suggest ways of constructing and calibrating models of complex systems such that the resulting model is as accurate and useful as possible.

Water-Resources Investigations Report↗
USGS2013

Reserve growth of oil and gas fields—Investigations and applications

The reserve growth of fields has been a topic for ongoing discussion for over half a century and will continue to be studied well into the future. This is due to the expected size of the volumetric contribution of reserve growth to the future supply of oil and natural gas. Understanding past methods of estimating future volumes based on the data assembly methods that have been used can lead to a better understanding of their applicability. The statistical nature of past methods and the (1) possible high level of dependency on a limited number of fields, (2) assumption of an age-based correlation with effective reserve growth, and (3) assumption of long-lived and more common than not reserve growth, may be improved by employing a more geologically based approach.

Scientific Investigations Report↗
USGS2007

Estimation of long-term discharge statistics by regional adjustment

A regional adjustment relationship was developed to estimate long-term (30-year) monthly median discharges from short term (three-year) records. This method differs from traditional approaches in that it is based on site-specific discharge data but does not require correlation of these data with discharges from a single hydrologically similar long-term gage. The method is shown to be statistically robust, and applicable to statistics other than the median.A regional adjustment relationship was developed to estimate long-term (30-year) monthly discharges from short term (three-year) records. This method differs from traditional approaches in that it is based on site-specific discharge data but does not require correlation of these data with discharges from a single hydrologically similar long-term gage. The method is shown to be statistically robust, and applicable to statistics other than the median.

Journal of the American Water Resources Associatio↗
USGS2004

Estimating the Magnitude and Frequency of Floods in Small Urban Streams in South Carolina, 2001

The magnitude and frequency of floods at 20 streamflowgaging stations on small, unregulated urban streams in or near South Carolina were estimated by fitting the measured wateryear peak flows to a log-Pearson Type-III distribution. The period of record (through September 30, 2001) for the measured water-year peak flows ranged from 11 to 25 years with a mean and median length of 16 years. The drainage areas of the streamflow-gaging stations ranged from 0.18 to 41 square miles. Based on the flood-frequency estimates from the 20 streamflow-gaging stations (13 in South Carolina; 4 in North Carolina; and 3 in Georgia), generalized least-squares regression was used to develop regional regression equations. These equations can be used to estimate the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence-interval flows for small urban streams in the Piedmont, upper Coastal Plain, and lower Coastal Plain physiographic provinces of South Carolina. The most significant explanatory variables from this analysis were mainchannel length, percent impervious area, and basin development factor. Mean standard errors of prediction for the regression equations ranged from -25 to 33 percent for the 10-year recurrence-interval flows and from -35 to 54 percent for the 100-year recurrence-interval flows. The U.S. Geological Survey has developed a Geographic Information System application called StreamStats that makes the process of computing streamflow statistics at ungaged sites faster and more consistent than manual methods. This application was developed in the Massachusetts District and ongoing work is being done in other districts to develop a similar application using streamflow statistics relative to those respective States. Considering the future possibility of implementing StreamStats in South Carolina, an alternative set of regional regression equations was developed using only main channel length and impervious area. This was done because no digital coverages are currently available for basin development factor and, therefore, it could not be included in the StreamStats application. The average mean standard error of prediction for the alternative equations was 2 to 5 percent larger than the standard errors for the equations that contained basin development factor. For the urban streamflow-gaging stations in South Carolina, measured water-year peak flows were compared with those from an earlier urban flood-frequency investigation. The peak flows from the earlier investigation were computed using a rainfall-runoff model. At many of the sites, graphical comparisons indicated that the variance of the measured data was much less than the variance of the simulated data. Several statistical tests were applied to compare the variances and the means of the measured and simulated data for each site. The results indicated that the variances were significantly different for 11 of the 13 South Carolina streamflow-gaging stations. For one streamflow-gaging station, the test for normality, which is one of the assumptions of the data when comparing variances, indicated that neither the measured data nor the simulated data were distributed normally; therefore, the test for differences in the variances was not used for that streamflow-gaging station. Another statistical test was used to test for statistically significant differences in the means of the measured and simulated data. The results indicated that for 5 of the 13 urban streamflowgaging stations in South Carolina there was a statistically significant difference in the means of the two data sets. For comparison purposes and to test the hypothesis that there may have been climatic differences between the period in which the measured peak-flow data were measured and the period for which historic rainfall data were used to compute the simulated peak flows, 16 rural streamflow-gaging stations with long-term records were reviewed using similar techniques as those used for the measured an

South Carolina↗
USGS2024

Realizing the potential of eDNA biodiversity monitoring tools in the marine environment with application to offshore renewable energy

The U.S. Geological Survey (USGS) researches the biological diversity and distribution of species to support management, conservation, and resource use decisions. USGS scientists advance detection and monitoring technologies to assess changes in fish and wildlife populations, biodiversity, and the health of ecosystems. The United States is planning to install 30 gigawatts of offshore marine and wind energy by 2030. However, the effects on fish and wildlife and their habitats are not well understood. The USGS is a leader in the field of eDNA technologies and has helped advance robotic eDNA samplers, has extensive experience working in the offshore environment, and has developed novel and actionable statistical methods and standards for eDNA monitoring applications. This fact sheet presents key eDNA research and development advances needed for realizing the potential of eDNA biodiversity monitoring tools in the marine environment and applying eDNA monitoring to offshore renewable energy development. New and cost-effective tools for measuring changes in biodiversity in response to offshore renewable energy development can help to inform natural resource management and project planning and permitting decisions.

Fact Sheet↗
USGS2003

Hierarchical models and the analysis of bird survey information

Management of birds often requires analysis of collections of estimates. We describe a hierarchical modeling approach to the analysis of these data, in which parameters associated with the individual species estimates are treated as random variables, and probability statements are made about the species parameters conditioned on the data. A Markov-Chain Monte Carlo (MCMC) procedure is used to fit the hierarchical model. This approach is computer intensive, and is based upon simulation. MCMC allows for estimation both of parameters and of derived statistics. To illustrate the application of this method, we use the case in which we are interested in attributes of a collection of estimates of population change. Using data for 28 species of grassland-breeding birds from the North American Breeding Bird Survey, we estimate the number of species with increasing populations, provide precision-adjusted rankings of species trends, and describe a measure of population stability as the probability that the trend for a species is within a certain interval. Hierarchical models can be applied to a variety of bird survey applications, and we are investigating their use in estimation of population change from survey data.

Ornis Hungarica↗
USGS1974

Palynological applications of principal component and cluster analyses

Two multivariate statistical methods are suggested to help describe patterns in pollen data that result from changes in the relative frequencies of pollen types produced by past climatic and environmental variations. These methods, based on a geometric model, compare samples by use of the product-moment correlation coefficient computed from data subjected to a centering transformation. If there are m samples and n pollen types, then the data can be regarded as a set of m points in an n -dimensional space. The first method, cluster analysis, produces a dendrograph or clustering tree in which samples are grouped with other samples on the basis of their similarity to each other. The second method, principal component analysis, produces a set of variates that arc linear combinations of the pollen samples, are uncorrelated with each other, and best describe the data using a minimum number of dimensions. This method is useful in reducing the dimensionality of data sets. A further transformation known as varimax rotation acts on a subset of the principal components to make them easier to interpret. Both methods offer the advantages of reproducibility of results and speed in pattern description. Once the patterns in the data have been described, however, they must be interpreted by the palynologist. An application of the methods in palynology is shown by using data from Osgood Swamp, Calif.

Journal of Research of the U.S. Geological Survey↗
USGS2003

Airflows and turbulent flux measurements in mountainous terrain: Part 1. Canopy and local effects

We have studied the effects of local topography and canopy structure on turbulent flux measurements at a site located in mountainous terrain within a subalpine, coniferous forest. Our primary aim was to determine whether the complex terrain of the site affects the accuracy of eddy flux measurements from a practical perspective. We observed displacement heights, roughness lengths, spectral peaks, turbulent length scales, and profiles of turbulent intensities that were comparable in magnitude and pattern to those reported for forest canopies in simpler terrain. We conclude that in many of these statistical measures, the local canopy exerts considerably more influence than does topographical complexity. Lack of vertical flux divergence and modeling suggests that the flux footprints for the site are within the standards acceptable for the application of flux statistics. We investigated three different methods of coordinate rotation: double rotation (DR), triple rotation (TR), and planar-fit rotation (PF). Significant variability in rotation angles at low wind speeds was encountered with the commonly used DR and TR methods, as opposed to the PF method, causing some overestimation of the fluxes. However, these differences in fluxes were small when applied to large datasets involving sensible heat and CO 2 fluxes. We observed evidence of frequent drainage flows near the ground during stable, stratified conditions at night. Concurrent with the appearance of these flows, we observed a positive bias in the mean vertical wind speed, presumably due to subtle topographic variations inducing a flow convergence below the measurement sensors. In the presence of such drainage flows, advection of scalars and non-zero bias in the mean vertical wind speed can complicate closure of the mass conservation budget at the site.

Agricultural and Forest Meteorology↗