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At least 235 records · Page 13Linked to original sources
USGS2011

Methodology for quantifying uncertainty in coal assessments with an application to a Texas lignite deposit

A common practice for characterizing uncertainty in coal resource assessments has been the itemization of tonnage at the mining unit level and the classification of such units according to distance to drilling holes. Distance criteria, such as those used in U.S. Geological Survey Circular 891, are still widely used for public disclosure. A major deficiency of distance methods is that they do not provide a quantitative measure of uncertainty. Additionally, relying on distance between data points alone does not take into consideration other factors known to have an influence on uncertainty, such as spatial correlation, type of probability distribution followed by the data, geological discontinuities, and boundary of the deposit. Several geostatistical methods have been combined to formulate a quantitative characterization for appraising uncertainty. Drill hole datasets ranging from widespread exploration drilling to detailed development drilling from a lignite deposit in Texas were used to illustrate the modeling. The results show that distance to the nearest drill hole is almost completely unrelated to uncertainty, which confirms the inadequacy of characterizing uncertainty based solely on a simple classification of resources by distance classes. The more complex statistical methods used in this study quantify uncertainty and show good agreement between confidence intervals in the uncertainty predictions and data from additional drilling.

International Journal of Coal Geology↗
USGS2015

Linking biophysical models and public preferences for ecosystem service assessments: a case study for the Southern Rocky Mountains

Through extensive research, ecosystem services have been mapped using both survey-based and biophysical approaches, but comparative mapping of public values and those quantified using models has been lacking. In this paper, we mapped hot and cold spots for perceived and modeled ecosystem services by synthesizing results from a social-values mapping study of residents living near the Pike–San Isabel National Forest (PSI), located in the Southern Rocky Mountains, with corresponding biophysically modeled ecosystem services. Social-value maps for the PSI were developed using the Social Values for Ecosystem Services tool, providing statistically modeled continuous value surfaces for 12 value types, including aesthetic, biodiversity, and life-sustaining values. Biophysically modeled maps of carbon sequestration and storage, scenic viewsheds, sediment regulation, and water yield were generated using the Artificial Intelligence for Ecosystem Services tool. Hotspots for both perceived and modeled services were disproportionately located within the PSI’s wilderness areas. Additionally, we used regression analysis to evaluate spatial relationships between perceived biodiversity and cultural ecosystem services and corresponding biophysical model outputs. Our goal was to determine whether publicly valued locations for aesthetic, biodiversity, and life-sustaining values relate meaningfully to results from corresponding biophysical ecosystem service models. We found weak relationships between perceived and biophysically modeled services, indicating that public perception of ecosystem service provisioning regions is limited. We believe that biophysical and social approaches to ecosystem service mapping can serve as methodological complements that can advance ecosystem services-based resource management, benefitting resource managers by showing potential locations of synergy or conflict between areas supplying ecosystem services and those valued by the public.

Regional Environmental Change↗
USGS2005

Significance of stress transfer in time-dependent earthquake probability calculations

A sudden change in stress is seen to modify earthquake rates, but should it also revise earthquake probability? Data used to derive input parameters permits an array of forecasts; so how large a static stress change is require to cause a statistically significant earthquake probability change? To answer that question, effects of parameter and philosophical choices are examined through all phases of sample calculations, Drawing at random from distributions of recurrence-aperiodicity pairs identifies many that recreate long paleoseismic and historic earthquake catalogs. Probability density funtions built from the recurrence-aperiodicity pairs give the range of possible earthquake forecasts under a point process renewal model. Consequences of choices made in stress transfer calculations, such as different slip models, fault rake, dip, and friction are, tracked. For interactions among large faults, calculated peak stress changes may be localized, with most of the receiving fault area changed less than the mean. Thus, to avoid overstating probability change on segments, stress change values should be drawn from a distribution reflecting the spatial pattern rather than using the segment mean. Disparity resulting from interaction probability methodology is also examined. For a fault with a well-understood earthquake history, a minimum stress change to stressing rate ratio of 10:1 to 20:1 is required to significantly skew probabilities with >80-85% confidence. That ratio must be closer to 50:1 to exceed 90-95% confidence levels. Thus revision to earthquake probability is achievable when a perturbing event is very close to the fault in question or the tectonic stressing rate is low.

Journal of Geophysical Research B: Solid Earth↗
USGS2016

Detecting sea-level hazards: Simple regression-based methods for calculating the acceleration of sea level

This report documents the development of statistical tools used to quantify the hazard presented by the response of sea-level elevation to natural or anthropogenic changes in climate and ocean circulation. A hazard is a physical process (or processes) that, when combined with vulnerability (or susceptibility to the hazard), results in risk. This study presents the development and comparison of new and existing sea-level analysis methods, exploration of the strengths and weaknesses of the methods using synthetic time series, and when appropriate, synthesis of the application of the method to observed sea-level time series. These reports are intended to enhance material presented in peer-reviewed journal articles where it is not always possible to provide the level of detail that might be necessary to fully support or recreate published results. The purpose of this report is to document and compare three simple methodologies that have previously been used to provide estimates with associated errors of the acceleration of sea-level elevation. These techniques have been used by coastal scientists and planners in assessing coastal risk over a wide range of spatial and temporal scales. Because relative sea-level (SL) elevation time series contain energetic fluctuations at many time scales, extracting what can be relatively small rate and acceleration signals (along with estimates of the error) from much larger “noise” has proven to be both difficult and controversial. Acceleration is a preferred measure of SL response to recent changes in the Earth’s climate because over time scales of 100 years or less slow vertical land motions (such as glacial isostatic adjustment) contribute only to the linear signal and not to acceleration, thus reducing the complexity of the analysis. Hence acceleration is useful if the goal of a study is to characterize and quantify the hazard associated with the changing relative elevation of water with respect to land on decadal time scales. Although in some cases it may be necessary to determine the cause of relative sea level rise, as a first step, it is important to accurately estimate the magnitude of the threat. Most researchers agree that global sea level (GSL) rose persistently through much of the 20th century at about 1.5–2.0 millimeters per year (mm/yr). There is far less agreement about whether the rate of sea-level rise (SLR) is increasing (that is, an acceleration in SL). Recent studies, and most of their predecessors, use tide gage data to quantify SL acceleration, A SL (t). In the current study, three techniques were used to calculate acceleration from tide gage data, and of those examined, it was determined that the two techniques based on sliding a regression window through the time series are more robust compared to the technique that fits a single quadratic form to the entire time series, particularly if there is temporal variation in the magnitude of the acceleration. The single-fit quadratic regression method has been the most commonly used technique in determining acceleration in tide gage data. The inability of the single-fit method to account for time-varying acceleration may explain some of the inconsistent findings between investigators. Properly quantifying A SL (t) from field measurements is of particular importance in evaluating numerical models of past, present, and future SLR resulting from anticipated climate change.

Open-File Report↗
USGS1990

Reference manual for generation and analysis of Habitat Time Series: version II

The selection of an instream flow requirement for water resource management often requires the review of how the physical habitat changes through time. This review is referred to as 'Time Series Analysis." The Tune Series Library (fSLIB) is a group of programs to enter, transform, analyze, and display time series data for use in stream habitat assessment. A time series may be defined as a sequence of data recorded or calculated over time. Examples might be historical monthly flow, predicted monthly weighted usable area, daily electrical power generation, annual irrigation diversion, and so forth. The time series can be analyzed, both descriptively and analytically, to understand the importance of the variation in the events over time. This is especially useful in the development of instream flow needs based on habitat availability. The TSLIB group of programs assumes that you have an adequate study plan to guide you in your analysis. You need to already have knowledge about such things as time period and time step, species and life stages to consider, and appropriate comparisons or statistics to be produced and displayed or tabulated. Knowing your destination, you must first evaluate whether TSLIB can get you there. Remember, data are not answers . This publication is a reference manual to TSLIB and is intended to be a guide to the process of using the various programs in TSLIB. This manual is essentially limited to the hands-on use of the various programs. a TSLIB use interface program (called RTSM) has been developed to provide an integrated working environment where the use has a brief on-line description of each TSLIB program with the capability to run the TSLIB program while in the user interface. For information on the RTSM program, refer to Appendix F. Before applying the computer models described herein, it is recommended that the user enroll in the short course "Problem Solving with the Instream Flow Incremental Methodology (IFIM)." This course is offered by the Aquatic Systems Branch of the National Ecology Research Center. For more information about the TSLIB software, refer to the Memorandum of Understanding. Chapter 1 provides a brief introduction to the Instream Flow Incremental Methodology and TSLIB. Other chapters in this manual provide information on the different aspects of using the models. The information contained in the other chapters includes (2) acquisition, entry, manipulation, and listing of streamflow data; (3) entry, manipulation, and listing of the habitat-versus-streamflow function; (4) transferring streamflow data; (5) water resources systems analysis; (6) generation and analysis of daily streamflow and habitat values; (7) generation of the time series of monthly habitats; (8) manipulation, analysis, and display of month time series data; and (9) generation, analysis, and display of annual time series data. Each section includes documentation for the programs therein with at least one page of information for each program, including a program description, instructions for running the program, and sample output. The Appendixes contain the following: (A) sample file formats; (B) descriptions of default filenames; (C) alphabetical summary of batch-procedure files; (D) installing and running TSLIB on a microcomputer; (E) running TSLIB on a CDC Cyber computer; (F) using the TSLIB user interface program (RTSM); and (G) running WATSTORE on the USGS Amdahl mainframe computer. The number for this version of TSLIB--Version II-- is somewhat arbitrary, as the TSLIB programs were collected into a library some time ago; but operators tended to use and manage them as individual programs. Therefore, we will consider the group of programs from the past that were only on the CDC Cyber computer as Version 0; the programs from the past that were on both the Cyber and the IBM-compatible microcomputer as Version I; and the programs contained in this reference manual as Version II.

Report↗
USGS2019

Monitoring drought impact on annual forage production in semi-arid grasslands: A case study of Nebraska sandhills

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha −1 *yr −1 , was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R 2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses. View Full-Text

Nebraska↗
USGS2009

Population trends of native Hawaiian forest birds, 1976–2008: the data and statistical analyses

The Hawaii Forest Bird Interagency Database Project has produced a centralized database of forest bird survey data collected in Hawai`i since the mid-1970s. The database contains over 1.1 million bird observation records of 90 species from almost 600 surveys on the main Hawaiian Islands—a dataset including nearly all surveys from that period. The primary objective has been to determine the status and trends of native Hawaiian forest birds derived from this comprehensive dataset. We generated species-specific density estimates from each survey and tested for changes in population densities over the longest possible temporal period. Although this cumulative data set seems enormous and represents the best available information on status of Hawaiian forest birds, detecting meaningful population distribution, density, and trends for forest birds in Hawai`i has been difficult. These population parameters are best derived from long-term, large-scale, standardized monitoring programs. The basis for long-term population monitoring in Hawai`i was established by the Hawaii Forest Bird Survey of 1976-1983 (Scott et al. 1986). Since then, however, only key areas have been resurveyed, primarily to monitor rare species. The majority of surveys since the early 1980s have been conducted by numerous, independent programs, resulting in some inconsistencies in methodology and sampling that in some cases has been intermittent and usually at limited scale (temporally or spatially). Thus, despite the consolidation of data into a centralized database, our understanding of population patterns is rather limited, especially at the regional and landscape scales. To rectify their deficiency, we present a framework to improve the understanding of forest bird trends in Hawai`i through an overarching monitoring design that allocates sampling at appropriate regional and temporal scales. Despite the limitations of the current monitoring effort, important generalities stand out vividly from the multiplicity of species-specific trends. Overall, in marginal habitats the Hawaiian passerine fauna continues to decline, with populations of most species shrinking in size and distribution. Since the early 1980s, 10 species that were rare at the time may now be extinct, although one, the `Alalā (Corvus hawaiiensis), survives in captivity. Dedicated search effort for the remaining nine species has been inadequate. Of the 22 species remaining, eight have declined, five appear to be stable, two are increasing, and the trend for seven species is unclear. On the bright side, native passerines, including endangered species, appear to be stable or increasing in areas with large tracts of native forest above 1,500 m elevation, even while decreasing in more fragmented or disturbed habitats, particularly at lower elevation. For example, all eight native species resident at Hakalau Forest National Wildlife Refuge have shown stable trends or significant increases in density over the long-term. Thus, native birds are ever more restricted to high-elevation forest and woodland refugia. It is these upland habitats that require sustained and all-out restoration to prevent further extinctions of Hawaiian forest birds.

Technical Report↗
USGS2008

Watershed Regressions for Pesticides (WARP) for Predicting Annual Maximum and Annual Maximum Moving-Average Concentrations of Atrazine in Streams

Regression models were developed for predicting annual maximum and selected annual maximum moving-average concentrations of atrazine in streams using the Watershed Regressions for Pesticides (WARP) methodology developed by the National Water-Quality Assessment Program (NAWQA) of the U.S. Geological Survey (USGS). The current effort builds on the original WARP models, which were based on the annual mean and selected percentiles of the annual frequency distribution of atrazine concentrations. Estimates of annual maximum and annual maximum moving-average concentrations for selected durations are needed to characterize the levels of atrazine and other pesticides for comparison to specific water-quality benchmarks for evaluation of potential concerns regarding human health or aquatic life. Separate regression models were derived for the annual maximum and annual maximum 21-day, 60-day, and 90-day moving-average concentrations. Development of the regression models used the same explanatory variables, transformations, model development data, model validation data, and regression methods as those used in the original development of WARP. The models accounted for 72 to 75 percent of the variability in the concentration statistics among the 112 sampling sites used for model development. Predicted concentration statistics from the four models were within a factor of 10 of the observed concentration statistics for most of the model development and validation sites. Overall, performance of the models for the development and validation sites supports the application of the WARP models for predicting annual maximum and selected annual maximum moving-average atrazine concentration in streams and provides a framework to interpret the predictions in terms of uncertainty. For streams with inadequate direct measurements of atrazine concentrations, the WARP model predictions for the annual maximum and the annual maximum moving-average atrazine concentrations can be used to characterize the probable levels of atrazine for comparison to specific water-quality benchmarks. Sites with a high probability of exceeding a benchmark for human health or aquatic life can be prioritized for monitoring.

Open-File Report↗
USGS2021

Performance of the GenEst Mortality Estimator Compared to The Huso and Shoenfeld Estimators

The impacts of wind power development on bat and bird populations are commonly assessed by estimating the number of fatalities at wind power facilities through post-construction monitoring (PCM) studies. Standard methodology involves periodic carcass searches on plots beneath turbines (Strickland et al. 2011, US Fish and Wildlife Service 2012). The resulting counts are adjusted to compensate for bias due to imperfect carcass detection by searchers, removal of carcasses by scavengers or other processes (Korner-Nievergelt et al. 2011), and carcasses that may have fallen outside of searched areas. To account for the bias in counts due to imperfect detection and carcass removal, investigators typically conduct bias trial experiments to inform models of carcass detection probability. Many different estimators have been proposed that combine information about the bias trial experiments to estimate a detection probability for carcasses (g) and ultimately obtain an estimate of total mortality (M). The two estimators that have seen the most widespread use in North America recently are the Huso (Huso 2011, Huso et al. 2012) and Shoenfeld (Shoenfeld 2004; also called the Erickson estimator) estimators. GenEst (Dalthorp et al. 2018a, 2018b, 2018c) is the newest statistical estimator to become available and was designed to improve upon the Huso and Shoenfeld estimators by generalizing the key assumptions in both, and to improve comparability among new PCM studies. In addition to relaxing some of the assumptions inherent to the Huso and Shoenfeld estimators, GenEst uses a parametric bootstrap applied to a novel approach to variance estimation (Madsen et al. 2019). The current study was undertaken to document the performance of GenEst relative to the Huso and Shoenfeld estimators. We took a simulation approach to the study because simulation data provides the basis to compare mortality estimators under conditions where the “truth” is known. The estimators were compared on three metrics: 1) bias—the tendency of an estimator to over- or under-estimate actual mortality, 2) precision—the ability of an estimator to constrain an estimate to a narrow range (measured here as the width of a 90% confidence interval [CI] around the point estimate divided by the true, known mortality), and 3) CI coverage—the probability a CI with a specified level of confidence actually includes the true level of mortality. Although our simulations were conceived and designed—and are discussed—with respect to wind power facilities, it is important to note that the estimators and results discussed here are relevant to any post-construction fatality monitoring study that may occur (such as at solar facilities) where detection is imperfect. Although our study treats the problem of mortality estimation when detection is imperfect, it is also important to note that all of the estimators considered here are Horvitz-Thompson (Horvitz and Thompson 1952) style estimators, that is, none are designed to estimate the mortality of rare species as might be necessary under an Incidental Take Permit. The Evidence of Absence estimator (Dalthorp et al. 2017) is still the most appropriate statistical tool for rare event estimation. The simulations cover a broad range of conditions that may occur in field studies and complete results are presented without commentary in the appendix. The main body of this report does not provide a comprehensive treatment of our results; rather, we try to identify some of the more important differences among the estimators and some conditions under which reliable mortality estimates are especially challenging.

AWWI Technical Report↗
USGS2014

Modeling uncertainty in coal resource assessments, with an application to a central area of the Gillette coal field, Wyoming

Standards for the public disclosure of mineral resources and reserves do not require the use of any specific methodology when it comes to estimating the reliability of the resources. Unbeknownst to most intended recipients of resource appraisals, such freedom commonly results in subjective opinions or estimations based on suboptimal approaches, such as use of distance methods. This report presents the results of a study of the third of three coal deposits in which drilling density has been increased one order of magnitude in three stages. Applying geostatistical simulation, the densest dataset was used to check the results obtained by modeling the sparser drillings. We have come up with two summary displays of results based on the same simulations, which individually and combined provide a better assessment of uncertainty than traditional qualitative resource classifications: (a) a display of cell 90 percent confidence interval versus cumulative cell tonnage, and (b) a histogram of total resources. The first graph allows classification of data into any number of bins with dividers to be decided by the assessor on the basis of a discriminating variable that is statistically accepted as a measure of uncertainty, thereby improving the quality and flexibility of the modeling. The second display expands the scope of the modeling by providing a quantitative measure of uncertainty for total tonnage, which is a fundamental concern for stockholders, geologists, and decision makers. Our approach allows us to correctly model uncertainty issues not possible to predict with distance methods, such as (a) different levels of uncertainty for individual beds with the same pattern and density of drill holes, (b) different local degrees of reduction of uncertainty with drilling densification reflecting fluctuation in the complexity of the geology, (c) average reduction in uncertainty at a disproportionately lesser rate than the reduction in area per drill hole, (d) the proportional effect of higher uncertainty in areas of higher tonnages, despite a regular drilling pattern, (e) the possibility of a local increase in uncertainty despite drilling densification to reflect a more complex geology as the deposit is known in more detail, and (f) for exactly the same drilling pattern, tonnage per individual beds with different uncertainty than the aggregated tonnage. These results should be considered realistic improvements over distance methods used for quantitative classification of uncertainty in coal resource, such as U.S. Geological Survey Circular 891. 1 The approach should be a welcome addition to the toolkit of Competent Persons preparing public disclosures according to international mineral codes such as those promoted by the Combined Reserves International Reporting Standards Committee 2 and the Joint Ore Reserve Committee. 3 1 Wood, G.H., Jr., Kehn, T.M., Carter, M.D., and Culbertson, W.C., 1983, Coal resources classification system of the U.S. Geological Survey: U.S. Geological Survey Circular 891, 65 p. 2 CRIRSCO (Combined Reserves International Reporting Standards Committee), 2013, International reporting template for the public reporting of exploration results, mineral resources and mineral reserves: Accessed February 2014 at http://www.crirsco.com/crirsco_template_may2013.pdf. 3 JORC (Joint Ore Reserves Committee), 2012, Australasian code for reporting of exploration results, mineral resources and ore reserves: Accessed September 2014 at http://www.jorc.org/docs/jorc_code2012.pdf.

Wyoming↗
USGS2002

New shoreline change data and analysis for the Massachusetts shore with emphasis on Cape Cod and the islands: Mid-1800s to 1994

That shorelines change, oftentimes dramatically in short periods of time, is an accepted fact for those who live along the shore. However, when two-thirds or approximately 512 miles of a state's ocean-facing shore exhibits a long-term erosional trend, in some locations eroding at an average annual rate of 12 feet per year, as is the case in Massachusetts, shoreline property owners, prospective shorefront property owners, and coastal managers need to pay particular attention to the future location of the shoreline to avoid physical and economic disasters. The Woods Hole Oceanographic Institution, Sea Grant Program, the U.S. Geological Survey, and the Cape Cod Cooperative Extension recently completed an update and statistical analysis of historical shoreline change along approximately 1,000 miles of Massachusetts' ocean-facing shore, of which 754 miles were statistically analyzed (Thieler, O'Connell and Schupp, 2001; Schupp, Thieler & O'Connell, 2001). The project was funded by the Massachusetts Office of Coastal Zone Management. In general, four to five shoreline positions mapped between the mid-1800s to 1994 were used to analyze changes along the Massachusetts shore. Seventy-six shoreline change maps with accompanying data tables and a Technical Report were produced. The results of this study reveal that approximately two-thirds of the Massachusetts shore is eroding, with 68% of the shore exhibiting a long-term erosional trend, 30% showing long-term accretion, and 2% showing no net change. ln some areas, erosion rates have accelerated based on a comparison study of previous data that was conducted in 1997 (O'Connell, 1997). Ironically, coastal property that commands some of the highest real estate values in the Commonwealth also exhibits the highest consistent long-term average annual erosion rates. This paper describes the data sources used to map historic shorelines in Massachusetts, the methodology used to both plot a new shoreline and analyze the long-term historical data, and describes cautions necessary when interpreting and applying shoreline change data, with site-specific examples along the Massachusetts shore.

Massachusetts↗
USGS2000

Soil moisture tendencies into the next century for the conterminous United States

A monthly snow-pack and soil- moisture accounting model is formulated for application to each of the climate divisions of the conterminous United States for use in climate impacts-assessment studies. Statistical downscaling and bias-adjustment components complement the model for the assimilation of large-scale global climate model data. Simulations of the formulated model driven by precipitation and temperature for the period 1931-1998 produce streamflows that are broadly consistent with observed data from several drainage basins in the US. Simulated historical soil moisture fields reproduce several features of the available observed soil moisture in the Midwest. The simulations produce large-scale coherent seasonal patterns of soil moisture field- moments over the conterminous US, with high soil moisture means over divisions in the Ohio Valley, the northeastern US and the Pacific Northwest, and with pronounced low means in most of the western US climate divisions. Characteristically low field-standard- deviations are produced for the Ohio Valley and northeastern US, and the Pacific Northwest in winter, and the southwestern US in summer. Differences in extreme standardized anomalies of soil moisture over the historical record range possess high values (2.5 - 3) in the central US where the available water capacity of the soils is high. An application of the model to exemplify the methodology for determining projected US monthly soil moisture fields under control and greenhouse gas forcing is also documented. Climate simulations of the coupled global climate model from the Canadian Centre for Climate Modeling and Analysis were used for these sensitivity examples. The climatology of the control-run soil moisture fields reproduces several characteristic features of the historical soil moisture climatology. Simulations with forcing by a 1% greenhouse-gas- increase scenario show that for at least the first few decades of the 21 st Century somewhat drier-than-present soil conditions are projected, with highest drying trends found in the southeastern US. The soil moisture deficits in most areas are of the same order of magnitude as the soil moisture field-standard- deviations aris ing from historical natural variability. In a companion paper (Brumbelow and A. Georgakakos, 2000), the monthly soil moisture fields for the historical, control and greenhouse-gas-increase runs are used to initialize a site-specific daily crop yield model at the start of the growing season. Assessments of potential impacts of climate variability and trends on irrigation requirements and crop yield across the conterminous US are made.

Open-File Report↗
USGS2015

The U.S. Geological Survey coal quality (COALQUAL) database version 3.0

Since the mid-1970s, the U.S. Geological Survey (USGS) has maintained a coal quality database of national scope named USCHEM, which currently contains data for over 13,000 samples. A subset of the USCHEM database called COALQUAL Version 1.3 was initially published in 1994 and was followed by Version 2.0 in 1997. Version 3.0 of the COALQUAL database represents a major editing effort to resolve some of the DOS software limitations used by earlier versions of the database. Because of database size limits during the development of COALQUAL Version 1.3, many analyses of individual bench samples were merged into whole coal bed averages. The methodology for making these composite intervals was not consistent. Size limits also restricted the amount of georeferencing information and forced removal of qualifier notations such as "less than detection limit" (<) information, which can cause problems when using the data. A review of the original data sheets revealed that COALQUAL Version 2.0 was missing information that was needed for a complete understanding of a coal section. Another important database issue to resolve was the USGS "remnant moisture" problem. Prior to 1998, tests for remnant moisture (as-determined moisture in the sample at the time of analysis) were not performed on any USGS major, minor, or trace element coal analyses. Without the remnant moisture, it is impossible to convert the analyses to a usable basis (as-received, dry, etc.). Based on remnant moisture analyses of hundreds of samples of different ranks (and known residual moisture) reported after 1998, it was possible to develop a method to provide reasonable estimates of remnant moisture for older data to make it more useful in COALQUAL Version 3.0. In addition, COALQUAL Version 3.0 is improved by (1) adding qualifiers, including statistical programming to deal with the qualifiers; (2) clarifying the sample compositing problems; and (3) adding associated samples. Version 3.0 of COALQUAL also represents the first attempt to incorporate data verification by mathematically crosschecking certain analytical parameters. Finally, a new database system was designed and implemented to replace the outdated DOS program used in earlier versions of the database.

Data Series↗
USGS2010

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma

Peak-streamflow regression equations were determined for estimating flows with exceedance probabilities from 50 to 0.2 percent for the state of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the state by use of a generalized least squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for watershed basins with drainage areas less than 2,510 square miles that are not affected by regulation. The resulting regression equations had a standard model error ranging from 31 to 46 percent. Annual-maximum peak flows observed at 231 streamflow-gaging stations through water year 2008 were used for the regression analysis. Gage peak-streamflow estimates were used from previous work unless 2008 gaging-station data were available, in which new peak-streamflow estimates were calculated. The U.S. Geological Survey StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 231 gaging stations in and near Oklahoma are listed. Methodologies are presented to estimate peak streamflows at ungaged sites by using estimates from gaging stations on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Scientific Investigations Report↗
USGS2013

A GIS and statistical approach to identify variables that control water quality in hydrothermally altered and mineralized watersheds, Silverton, Colorado, USA

Hydrothermally altered bedrock in the Silverton mining area, southwest Colorado, USA, contains sulfide minerals that weather to produce acidic and metal-rich leachate that is toxic to aquatic life. This study utilized a geographic information system (GIS) and statistical approach to identify watershed-scale geologic variables in the Silverton area that influence water quality. GIS analysis of mineral maps produced using remote sensing datasets including Landsat Thematic Mapper, advanced spaceborne thermal emission and reflection radiometer, and a hybrid airborne visible infrared imaging spectrometer and field-based product enabled areas of alteration to be quantified. Correlations between water quality signatures determined at watershed outlets, and alteration types intersecting both total watershed areas and GIS-buffered areas along streams were tested using linear regression analysis. Despite remote sensing datasets having varying watershed area coverage due to vegetation cover and differing mineral mapping capabilities, each dataset was useful for delineating acid-generating bedrock. Areas of quartz–sericite–pyrite mapped by AVIRIS have the highest correlations with acidic surface water and elevated iron and aluminum concentrations. Alkalinity was only correlated with area of acid neutralizing, propylitically altered bedrock containing calcite and chlorite mapped by AVIRIS. Total watershed area of acid-generating bedrock is more significantly correlated with acidic and metal-rich surface water when compared with acid-generating bedrock intersected by GIS-buffered areas along streams. This methodology could be useful in assessing the possible effects that alteration type area has in either generating or neutralizing acidity in unmined watersheds and in areas where new mining is planned.

Colorado↗
USGS2010

On the resolution of shallow mantle viscosity structure using post-earthquake relaxation data: Application to the 1999 Hector Mine, California, earthquake

Most models of lower crust/mantle viscosity inferred from postearthquake relaxation assume one or two uniform-viscosity layers. A few existing models possess apparently significant radially variable viscosity structure in the shallow mantle (e.g., the upper 200 km), but the resolution of such variations is not clear. We use a geophysical inverse procedure to address the resolving power of inferred shallow mantle viscosity structure using postearthquake relaxation data. We apply this methodology to 9 years of GPS-constrained crustal motions after the 16 October 1999 M = 7.1 Hector Mine earthquake. After application of a differencing method to isolate the postearthquake signal from the “background” crustal velocity field, we find that surface velocities diminish from ∼20 mm/yr in the first few months to ≲2 mm/yr after 2 years. Viscoelastic relaxation of the mantle, with a time-dependent effective viscosity prescribed by a Burgers body, provides a good explanation for the postseismic crustal deformation, capturing both the spatial and temporal pattern. In the context of the Burgers body model (which involves a transient viscosity and steady state viscosity), a resolution analysis based on the singular value decomposition reveals that at most, two constraints on depth-dependent steady state mantle viscosity are provided by the present data set. Uppermost mantle viscosity (depth ≲ 60 km) is moderately resolved, but deeper viscosity structure is poorly resolved. The simplest model that explains the data better than that of uniform steady state mantle viscosity involves a linear gradient in logarithmic viscosity with depth, with a small increase from the Moho to 220 km depth. However, the viscosity increase is not statistically significant. This suggests that the depth-dependent steady state viscosity is not resolvably different from uniformity in the uppermost mantle.

California↗
USGS2021

Linking modern pollen accumulation rates to biomass: Quantitative vegetation reconstruction in the western Klamath Mountains, NW California, USA

Quantitative reconstructions of vegetation abundance from sediment-derived pollen systems provide unique insights into past ecological conditions. Recently, the use of pollen accumulation rates (PAR, grains cm −2 year −1 ) has shown promise as a bioproxy for plant abundance. However, successfully reconstructing region-specific vegetation dynamics using PAR requires that accurate assessments of pollen deposition processes be quantitatively linked to spatially-explicit measures of plant abundance. Our study addressed these methodological challenges. Modern PAR and vegetation data were obtained from seven lakes in the western Klamath Mountains, California. To determine how to best calibrate our PAR-biomass model, we first calculated the spatial area of vegetation where vegetation composition and patterning is recorded by changes in the pollen signal using two metrics. These metrics were an assemblage-level relevant source area of pollen (aRSAP) derived from extended R-value analysis ( sensu Sugita, 1993) and a taxon-specific relevant source area of pollen (tRSAP) derived from PAR regression ( sensu Jackson, 1990). To the best of our knowledge, aRSAP and tRSAP have not been directly compared. We found that the tRSAP estimated a smaller area for some taxa (e.g. a circular area with a 225 m radius for Pinus ) than the aRSAP (a circular area with a 625 m radius). We fit linear models to relate PAR values from modern lake sediments with empirical, distance-weighted estimates of aboveground live biomass (AGL dw ) for both the aRSAP and tRSAP distances. In both cases, we found that the PARs of major tree taxa – Pseudotsuga, Pinus, Notholithocarpus , and TCT (Taxodiaceae, Cupressaceae, and Taxaceae families) – were statistically significant and reasonably precise estimators of contemporary AGL dw . However, predictions weighted by the distance defined by aRSAP tended to be more precise. The relative root-mean squared error for the aRSAP biomass estimates was 9% compared to 12% for tRSAP. Our results demonstrate that calibrated PAR-biomass relationships provide a robust method to infer changes in past plant biomass.

California, Oregon↗
USGS2025

Effect of passive integrated transponder tag size on survival, tag loss, and growth of Santa Ana Sucker

Objective The Santa Ana Sucker Pantosteus santaanae is endemic to southern California and is listed as threatened under the U.S. Endangered Species Act. Seasonal limitations on conventional sampling and inconsistencies in survey methodologies have led to an incomplete understanding of population dynamics. Alternative sampling methods have the potential to fill important knowledge gaps in biology and life history. One option is to use passive integrated transponder (PIT) tags to identify individuals and track their movements. The objective of this study was to test the effect of PIT tag size on survival, tag loss, and growth of Santa Ana Suckers. Methods Sixty-one Santa Ana Suckers were randomly assigned to one of three treatments (control: N = 20; 8-mm PIT tag: N = 21; or 12-mm PIT tag: N = 20). A full-duplex PIT tag was injected into each fish, and lengths (standard, fork, and total length) and weight were recorded. Fish remained in raceways for 36 d, after which they were retrieved, scanned, and remeasured. Results There were five fish mortalities within 48 h of tagging, resulting in an 87.8% overall survival rate. There were two fish mortalities from the 8-mm tag treatment and three mortalities from the 12-mm tag treatment. One fish from each tagging treatment lost their tag, resulting in a 5.6% overall tag loss rate. The Fisher’s exact test indicated no statistically significant difference in survival or tag loss between treatments. The ordinary least squares regression detected no effect of tag size on growth for Santa Ana Suckers. Conclusions Results suggest that 8- and 12-mm PIT tags are suitable options for tagging Santa Ana Suckers measuring at least 62 and 70 mm fork length, respectively. Supplementing the current sampling strategy with PIT tags could help monitoring efforts to expand spatially and temporally while providing expanded population data to inform adaptive management actions for the Santa Ana Sucker.

North American Journal of Fisheries Management↗