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USGS2013

Digital-image processing and image analysis of glacier ice

This document provides a methodology for extracting grain statistics from 8-bit color and grayscale images of thin sections of glacier ice—a subset of physical properties measurements typically performed on ice cores. This type of analysis is most commonly used to characterize the evolution of ice-crystal size, shape, and intercrystalline spatial relations within a large body of ice sampled by deep ice-coring projects from which paleoclimate records will be developed. However, such information is equally useful for investigating the stress state and physical responses of ice to stresses within a glacier. The methods of analysis presented here go hand-in-hand with the analysis of ice fabrics (aggregate crystal orientations) and, when combined with fabric analysis, provide a powerful method for investigating the dynamic recrystallization and deformation behaviors of bodies of ice in motion. The procedures described in this document compose a step-by-step handbook for a specific image acquisition and data reduction system built in support of U.S. Geological Survey ice analysis projects, but the general methodology can be used with any combination of image processing and analysis software. The specific approaches in this document use the FoveaPro 4 plug-in toolset to Adobe Photoshop CS5 Extended but it can be carried out equally well, though somewhat less conveniently, with software such as the image processing toolbox in MATLAB, Image-Pro Plus, or ImageJ.

Techniques and Methods↗

USGS2020

Operational Bayesian GLS regression for regional hydrologic analyses

This paper develops the quasi-analytic Bayesian analysis of the generalized least squares (GLS) (B-GLS) model introduced by Reis et al. (2005, https://doi.org/10.1029/2004WR003445 ) into an operational and statistically comprehensive GLS regional hydrologic regression methodology to estimate flood quantiles, regional shape parameters, low flows, and other statistics with spatially correlated flow records. New GLS regression diagnostic statistics include a Bayesian plausibility value, pseudo adjusted R 2 , pseudo analysis of variance table, and two diagnostic error variance ratios. Traditional leverage and influence are extended to identify rogue observations, address lack of fit, and support gauge network design and region-of-influence regression. Formulas are derived for the Bayesian computation of estimators, standard errors, and diagnostic statistics. The use of B-GLS and the new diagnostic statistics are illustrated with a regional log-space skew regression analysis for the Piedmont region in the Southeastern U.S. A comparison of ordinary, weighted, and GLS analyses documents the advantages of the Bayesian estimator over the method-of-moment estimator of model error variance introduced by Stedinger and Tasker (1985, https://doi.org/10.1029/WR021i009p01421 ). Of the three types of analyses, only GLS considers the covariance among concurrent flows. The example demonstrates that GLS regional skewness models can be highly accurate when correctly analyzed: The B-GLS average variance of prediction is 0.090 for South Carolina (92 stations), whereas a traditional ordinary least squares analysis published by the U.S. Geological Survey yielded 0.193 (Feaster & Tasker, 2002, https://doi.org/10.3133/wri024140 ). B-GLS provides a statistical valid framework for the rigorous analysis of spatially correlated hydrologic data, allowing for the estimation of parameters and their actual precision and computation of several diagnostic statistics, as well as correctly attributing variability to the three key sources: time sampling error, model error, and signal.

Water Resources Research↗

USGS1993

Estimation of lipids and lean mass of migrating sandpipers

Estimation of lean mass and lipid levels in birds involves the derivation of predictive equations that relate morphological measurements and, more recently, total body electrical conductivity (TOBEC) indices to known lean and lipid masses. Using cross-validation techniques, we evaluated the ability of several published and new predictive equations to estimate lean and lipid mass of Semipalmated Sandpipers (Calidris pusilla) and White-rumped Sandpipers ( C. fuscicollis ). We also tested ideas of Morton et al. (1991), who stated that current statistical approaches to TOBEC methodology misrepresent precision in estimating body fat. Three published interspecific equations using TOBEC indices predicted lean and lipid masses of our sample of birds with average errors of 8-28% and 53-155%, respectively. A new two-species equation relating lean mass and TOBEC indices revealed average errors of 4.6% and 23.2% in predicting lean and lipid mass, respectively. New intraspecific equations that estimate lipid mass directly from body mass, morphological measurements, and TOBEC indices yielded about a 13% error in lipid estimates. Body mass and morphological measurements explained a substantial portion of the variance (about 90%) in fat mass of both species. Addition of TOBEC indices improved the predictive model more for the smaller than for the larger sandpiper. TOBEC indices explained an additional 7.8% and 2.6% of the variance in fat mass and reduced the minimum breadth of prediction intervals by 0.95 g (32%) and 0.39 g (13%) for Semipalmated and White-rumped Sandpipers, respectively. The breadth of prediction intervals for models used to predict fat levels of individual birds must be considered when interpreting the resultant lipid estimates.

The Condor↗

USGS2012

Geologic framework for the national assessment of carbon dioxide storage resources

The 2007 Energy Independence and Security Act (Public Law 110–140) directs the U.S. Geological Survey (USGS) to conduct a national assessment of potential geologic storage resources for carbon dioxide (CO 2 ) and to consult with other Federal and State agencies to locate the pertinent geological data needed for the assessment. The geologic sequestration of CO 2 is one possible way to mitigate its effects on climate change. The methodology used for the national CO 2 assessment (Open-File Report 2010-1127; http://pubs.usgs.gov/of/2010/1127/) is based on previous USGS probabilistic oil and gas assessment methodologies. The methodology is non-economic and intended to be used at regional to subbasinal scales. The operational unit of the assessment is a storage assessment unit (SAU), composed of a porous storage formation with fluid flow and an overlying sealing unit with low permeability. Assessments are conducted at the SAU level and are aggregated to basinal and regional results. This report identifies and contains geologic descriptions of SAUs in separate packages of sedimentary rocks within the assessed basin and focuses on the particular characteristics, specified in the methodology, that influence the potential CO 2 storage resource in those SAUs. Specific descriptions of the SAU boundaries as well as their sealing and reservoir units are included. Properties for each SAU such as depth to top, gross thickness, net porous thickness, porosity, permeability, groundwater quality, and structural reservoir traps are provided to illustrate geologic factors critical to the assessment. Although assessment results are not contained in this report, the geologic information included here will be employed, as specified in the methodology, to calculate a statistical Monte Carlo-based distribution of potential storage space in the various SAUs. Figures in this report show SAU boundaries and cell maps of well penetrations through the sealing unit into the top of the storage formation. Wells sharing the same well borehole are treated as a single penetration. Cell maps show the number of penetrating wells within one square mile and are derived from interpretations of incompletely attributed well data, a digital compilation that is known not to include all drilling. The USGS does not expect to know the location of all wells and cannot guarantee the amount of drilling through specific formations in any given cell shown on cell maps.

Open-File Report↗

USGS2012

Geologic framework for the national assessment of carbon dioxide storage resources: Hanna, Laramie, and Shirley Basins, Wyoming: Chapter C in Geologic framework for the national assessment of carbon dioxide storage resources

The 2007 Energy Independence and Security Act (Public Law 110-140) directs the U.S. Geological Survey (USGS) to conduct a national assessment of potential geologic storage resources for carbon dioxide (CO 2 ). The methodology used for the national CO 2 assessment is non-economic and intended to be used at regional to subbasinal scales. This report identifies and contains geologic descriptions of twelve storage assessment units (SAUs) in six separate packages of sedimentary rock within the Hanna, Laramie, and Shirley Basins of Wyoming. It focuses on the particular characteristics, specified in the methodology, that influence the potential CO 2 storage resource in those SAUs. Specific descriptions of SAU boundaries as well as their sealing and reservoir units are included. Properties for each SAU, such as depth to top, gross thickness, net porous thickness, porosity, permeability, groundwater quality, and structural reservoir traps are provided to illustrate geologic factors critical to the assessment. Although assessment results are not contained in this report, the geologic information included herein will be employed, as specified in the methodology, to calculate a statistical Monte Carlo-based distribution of potential storage space in the various SAUs. Figures in this report show SAU boundaries and cell maps of well penetrations through the sealing unit into the top of the storage formation. Cell maps show the number of penetrating wells within one square mile and are derived from interpretations of incompletely attributed well data in a digital compilation that is known not to include all drilling. The USGS does not expect to know the location of all wells and cannot guarantee the amount of drilling through specific formations in any given cell shown on cell maps.

Wyoming↗

USGS2006

Selected Streamflow Statistics and Regression Equations for Predicting Statistics at Stream Locations in Monroe County, Pennsylvania

A suite of 28 streamflow statistics, ranging from extreme low to high flows, was computed for 17 continuous-record streamflow-gaging stations and predicted for 20 partial-record stations in Monroe County and contiguous counties in north-eastern Pennsylvania. The predicted statistics for the partial-record stations were based on regression analyses relating inter-mittent flow measurements made at the partial-record stations indexed to concurrent daily mean flows at continuous-record stations during base-flow conditions. The same statistics also were predicted for 134 ungaged stream locations in Monroe County on the basis of regression analyses relating the statistics to GIS-determined basin characteristics for the continuous-record station drainage areas. The prediction methodology for developing the regression equations used to estimate statistics was developed for estimating low-flow frequencies. This study and a companion study found that the methodology also has application potential for predicting intermediate- and high-flow statistics. The statistics included mean monthly flows, mean annual flow, 7-day low flows for three recurrence intervals, nine flow durations, mean annual base flow, and annual mean base flows for two recurrence intervals. Low standard errors of prediction and high coefficients of determination (R2) indicated good results in using the regression equations to predict the statistics. Regression equations for the larger flow statistics tended to have lower standard errors of prediction and higher coefficients of determination (R2) than equations for the smaller flow statistics. The report discusses the methodologies used in determining the statistics and the limitations of the statistics and the equations used to predict the statistics. Caution is indicated in using the predicted statistics for small drainage area situations. Study results constitute input needed by water-resource managers in Monroe County for planning purposes and evaluation of water-resources availability.

Scientific Investigations Report↗

USGS2010

Development and analysis of a meteorological database, Argonne National Laboratory, Illinois

A database of hourly values of air temperature, dewpoint temperature, wind speed, and solar radiation from January 1, 1948, to September 30, 2003, primarily using data collected at the Argonne National Laboratory station, was developed for use in continuous-time hydrologic modeling in northeastern Illinois. Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as 'backup'. Temporal variations in the statistical properties of the data resulting from changes in measurement and data-storage methodologies were adjusted to match the statistical properties resulting from the data-collection procedures that have been in place since January 1, 1989. The adjustments were computed based on the regressions between the primary data series from Argonne National Laboratory and the backup series using data obtained during common periods; the statistical properties of the regressions were used to assign estimated standard errors to values that were adjusted or filled from other series. Each hourly value was assigned a corresponding data-source flag that indicates the source of the value and its transformations. An analysis of the data-source flags indicates that all the series in the database except dewpoint have a similar fraction of Argonne National Laboratory data, with about 89 percent for the entire period, about 86 percent from 1949 through 1988, and about 98 percent from 1989 through 2003. The dewpoint series, for which observations at Argonne National Laboratory did not begin until 1958, has only about 71 percent Argonne National Laboratory data for the entire period, about 63 percent from 1948 through 1988, and about 93 percent from 1989 through 2003, indicating a lower reliability of the dewpoint sensor. A basic statistical analysis of the filled and adjusted data series in the database, and a series of potential evapotranspiration computed from them using the computer program LXPET (Lamoreux Potential Evapotranspiration) also was carried out. This analysis indicates annual cycles in solar radiation and potential evapotranspiration that follow the annual cycle of extraterrestrial solar radiation, whereas temperature and dewpoint annual cycles are lagged by about 1 month relative to the solar cycle. The annual cycle of wind has a late summer minimum, and spring and fall maximums. At the annual time scale, the filled and adjusted data series and computed potential evapotranspiration have significant serial correlation and possibly have significant temporal trends. The inter-annual fluctuations of temperature and dewpoint are weakest, whereas those of wind and potential evapotranspiration are strongest.

Illinois↗

USGS2000

Quantification and regionalization of groundwater recharge in South-Central Kansas: Integrating field characterization, statistical analysis, and GIS

A practical methodology for recharge characterization was developed based on several years of field-oriented research at 10 sites in the Great Bend Prairie of south-central Kansas. This methodology combines the soil-water budget on a storm-by-storm year-round basis with the resulting watertable rises. The estimated 1985-1992 average annual recharge was less than 50mm/year with a range from 15 mm/year (during the 1998 drought) to 178 mm/year (during the 1993 flood year). Most of this recharge occurs during the spring months. To regionalize these site-specific estimates, an additional methodology based on multiple (forward) regression analysis combined with classification and GIS overlay analyses was developed and implemented. The multiple regression analysis showed that the most influential variables were, in order of decreasing importance, total annual precipitation, average maximum springtime soil-profile water storage, average shallowest springtime depth to watertable, and average springtime precipitation rate. Therefore, four GIS (ARC/INFO) data "layers" or coverages were constructed for the study region based on these four variables, and each such coverage was classified into the same number of data classes to avoid biasing the results. The normalized regression coefficients were employed to weigh the class rankings of each recharge-affecting variable. This approach resulted in recharge zonations that agreed well with the site recharge estimates. During the "Great Flood of 1993," when rainfall totals exceeded normal levels by -200% in the northern portion of the study region, the developed regionalization methodology was tested against such extreme conditions, and proved to be both practical, based on readily available or easily measurable data, and robust. It was concluded that the combination of multiple regression and GIS overlay analyses is a powerful and practical approach to regionalizing small samples of recharge estimates.

The Compass: Earth Science Journal of Sigma Gamma ↗

USGS1975

In search of a statistical probability model for petroleum-resource assessment : a critique of the probabilistic significance of certain concepts and methods used in petroleum-resource assessment : to that end, a probabilistic model is sketched

Exploratory drilling is still in incipient or youthful stages in those areas of the world where the bulk of the potential petroleum resources is yet to be discovered. Methods of assessing resources from projections based on historical production and reserve data are limited to mature areas. For most of the world's petroleum-prospective areas, a more speculative situation calls for a critical review of resource-assessment methodology. The language of mathematical statistics is required to define more rigorously the appraisal of petroleum resources. Basically, two approaches have been used to appraise the amounts of undiscovered mineral resources in a geologic province: (1) projection models, which use statistical data on the past outcome of exploration and development in the province; and (2) estimation models of the overall resources of the province, which use certain known parameters of the province together with the outcome of exploration and development in analogous provinces. These two approaches often lead to widely different estimates. Some of the controversy that arises results from a confusion of the probabilistic significance of the quantities yielded by each of the two approaches. Also, inherent limitations of analytic projection models-such as those using the logistic and Gomperts functions --have often been ignored. The resource-assessment problem should be recast in terms that provide for consideration of the probability of existence of the resource and of the probability of discovery of a deposit. Then the two above-mentioned models occupy the two ends of the probability range. The new approach accounts for (1) what can be expected with reasonably high certainty by mere projections of what has been accomplished in the past; (2) the inherent biases of decision-makers and resource estimators; (3) upper bounds that can be set up as goals for exploration; and (4) the uncertainties in geologic conditions in a search for minerals. Actual outcomes can then be viewed as phenomena subject to statistical uncertainty and responsive to changes in economic and technologic factors.

Circular↗

USGS2020

Aligning climate models with stakeholder needs: Advances in communicating future rainfall uncertainties for south Florida decision makers

Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. Geological Survey workshop, “Increasing Confidence in Precipitation Projections for Everglades Restoration,” highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate‐model simulations based on user‐defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high‐resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.

South Florida↗

USGS2017

Factors associated with bat mortality at wind energy facilities in the United States

Hundreds of thousands of bats are killed annually by colliding with wind turbines in the U.S., yet little is known about factors causing variation in mortality across wind energy facilities. We conducted a quantitative synthesis of bat collision mortality with wind turbines by reviewing 218 North American studies representing 100 wind energy facilities. This data set, the largest compiled for bats to date, provides further evidence that collision mortality is greatest for migratory tree-roosting species (Hoary Bat [ Lasiurus cinereus ], Eastern Red Bat [ Lasiurus borealis ], Silver-haired Bat [ Lasionycteris noctivagans ]) and from July to October. Based on 40 U.S. studies meeting inclusion criteria and analyzed under a common statistical framework to account for methodological variation, we found support for an inverse relationship between bat mortality and percent grassland cover surrounding wind energy facilities. At a national scale, grassland cover may best reflect openness of the landscape, a factor generally associated with reduced activity and abundance of tree-roosting species that may also reduce turbine collisions. Further representative sampling of wind energy facilities is required to validate this pattern. Ecologically informed placement of wind energy facilities involves multiple considerations, including not only factors associated with bat mortality, but also factors associated with bird collision mortality, indirect habitat-related impacts to all species, and overall ecosystem impacts.

Biological Conservation↗

USGS2016

Establishing a pre-mining geochemical baseline at a uranium mine near Grand Canyon National Park, USA

During 2012, approximately 404,000 ha of Federal Land in northern Arizona was withdrawn from consideration of mineral extraction for a 20-year period to protect the Grand Canyon watershed from potentially adverse effects of U mineral exploration and development. The development, operation, and reclamation of the Canyon Mine during the withdrawal period provide an excellent field site to understand and document off-site migration of radionuclides within the withdrawal area. As part of the Department of Interior's (DOI's) study plan for the exclusion area, the objective of our study is to utilize pre-defined decision units (DUs) in areas within and surrounding the Canyon Mine to demonstrate how newly established incremental sampling methodologies (ISM) combined with multivariate statistical methods can be used to document a repeatable and statistically defensible measure of pre-mining baseline conditions in surface soils and stream sediment samples prior to ore extraction. During the survey in June 2013, the highest pre-mining 95% upper confidence level (UCL) concentrations with respect to As, Mo, U, and V were found in the triplicate samples collected from surface soils in the mine site DU designated as M1. Gamma activities were slightly elevated in soils within the M1 DU (up to 28 μR/h); however, off-site gamma activities in soil and stream-sediment samples were lower (< 6 to 12 μR/h). Hierarchical cluster analysis (HCA) was applied to 33 chemical constituents contained in the multivariate data generated from the analysis of triplicate samples collected in the soil and stream sediment DUs within and surrounding Canyon Mine. Most of the triplicate samples from individual DUs were grouped in the same dendrogram cluster when using a similarity value (SV) of 0.70 (unitless). Different group membership of triplicate samples from two of the four haul road DUs was likely the result of heterogeneity induced by non-native soil material introduced from the gravel road base or from vehicular traffic. Application of HCA and ISM will provide critical metrics to meet DOI's long-term goals for assessing off-site migration of radionuclides resulting from mining and reclamation in the current (2015) exclusion area associated within the Grand Canyon watershed and the associated national park.

Arizona↗

USGS2015

Desert tortoises ( Gopherus agassizii ) are selective herbivores that track the flowering phenology of their preferred food plants

Previous studies of desert tortoise foraging ecology in the western Mojave Desert suggest that these animals are selective herbivores, which alter their diet according to the temporal availability of preferred food plants. These studies, however, did not estimate availability of potential food plants by taking into account the spatial and temporal variability in ephemeral plant abundance that occurs within the spring season. In this study, we observed 18 free-ranging adult tortoises take 35,388 bites during the spring foraging season. We also estimated the relative abundance of potential food plants by stratifying our sampling across different phenological periods of the 3-month long spring season and by different habitats and microhabitats. This methodology allowed us to conduct statistical tests comparing tortoise diet against plant abundance. Our results show that tortoises choose food plants non-randomly throughout the foraging season, a finding that corroborates the hypothesis that desert tortoises rely on key plants during different phenological periods of spring. Moreover, tortoises only consumed plants in a succulent state until the last few weeks of spring, at which time most annuals and herbaceous perennials had dried and most tortoises had ceased foraging. Many species of food plants—including several frequently eaten species—were not detected in our plant surveys, yet tortoises located these rare plants in their home ranges. Over 50% of bites consumed were in the group of undetected species. Interestingly, tortoises focused heavily on several leguminous species, which could be nutritious foods owing to their presumably high nitrogen contents. We suggest that herbaceous perennials, which were rare on our study area but represented ~30% of tortoise diet, may be important in sustaining tortoise populations during droughts when native annuals are absent. These findings highlight the vulnerability of desert tortoises to climate change if such changes alter the availability of their preferred food plants.

California↗

USGS2016

Book review: Mineral resource estimation

Mineral Resource Estimation is about estimating mineral resources at the scale of an ore deposit and is not to be mistaken with mineral resource assessment, which is undertaken at a significantly broader scale, even if similar data and geospatial/geostatistical methods are used. The book describes geological, statistical, and geostatistical tools and methodologies used in resource estimation and modeling, and presents case studies for illustration. The target audience is the expert, which includes professional mining geologists and engineers, as well as graduate-level and advanced undergraduate students. Review info: Mineral Resource Estimation . By Mario E. Rossi, Clayton V. Deutsch . 2014 . ISBN 978-1-4020-5716-8 332 pp.

Economic Geology↗

USGS2018

Model methodology for estimating pesticide concentration extremes based on sparse monitoring data

This report describes a new methodology for using sparse (weekly or less frequent observations) and potentially highly censored pesticide monitoring data to simulate daily pesticide concentrations and associated quantities used for acute and chronic exposure assessments, such as the annual maximum daily concentration. The new methodology is based on a statistical model that expresses log-transformed daily pesticide concentration in terms of a seasonal wave, flow-related variability, long-term trend, and serially correlated errors. Methods are described for estimating the model parameters, generating conditional simulations of daily pesticide concentration given sparse (weekly or less frequent) and potentially highly censored observations, and estimating concentration extremes based on the conditional simulations. The model can be applied to datasets with as few as 3 years of record, as few as 30 total observations, and as few as 10 uncensored observations. The model was applied to atrazine, carbaryl, chlorpyrifos, and fipronil data for U.S. Geological Survey pesticide sampling sites with sufficient data for applying the model. A total of 112 sites were analyzed for atrazine, 38 for carbaryl, 34 for chlorpyrifos, and 33 for fipronil. The results are summarized in this report; and, R functions, described in this report and provided in an accompanying model archive, can be used to fit the model parameters and generate conditional simulations of daily concentrations for use in investigations involving pesticide exposure risk and uncertainty.

Scientific Investigations Report↗

USGS2015

Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

Journal of Hydrometeorology↗

USGS2012

A methodology for conducting national-scale alluvial diamond resource potential and production capacity assessments, with a focus on artisanal mining

In May of 2000, a meeting was convened in Kimberley, South Africa, by representatives of the diamond industry and leaders of African governments to develop a certification process intended to assure that export shipments of rough diamonds were free of conflict concerns. Outcomes of the meeting were formally supported later in December of 2000 by the United Nations in a resolution adopted by the General Assembly. By 2002, the Kimberley Process Certification Scheme was ratified and signed by diamond-producing and diamond-importing countries. As of August 2012, the Kimberley Process (KP) had 51 participants representing 77 countries. It is often difficult to obtain independent verification of the diamond production statistics that are provided to the KP. However, some degree of independent verification can be obtained through an understanding of a country’s naturally occurring endowment of diamonds and the intensity of mining activities. Studies that integrate these two components can produce a range of estimated values for a country’s diamond production, and these estimates can then be compared to the production statistics released by that country. This methodology is used to calculate (1) the diamond resource potential of a country, which refers to the total number of carats estimated to be remaining in the country, and (2) the diamond production capacity of a country, which is the current volume of diamonds that may realistically be produced per year utilizing current human and physical resources. The following sections outline the methodology used by the U.S. Geological Survey (USGS) to perform diamond assessments in Mali, the Central African Republic, Ghana, and Guinea.

Fact Sheet↗

USGS2005

Streamflow statistics for the Paradise and Pocono Creek watersheds and selected streamflow-gaging stations in Monroe County, Pennsylvania

suite of 36 observed streamflow statistics, ranging from high to low flows, were computed for 7 continuous-record and predicted for 12 partial-record streamflow-gaging stations in Monroe County, Pa. The predicted statistics for the partial-record stations were determined from regression analyses of intermittent streamflow measurements made at the partial-record stations and concurrent daily mean flows at index continuous-record stations. The prediction methodology has been previously used only for estimating low-flow statistics. Results from this study indicate the methodology may have applicability for predicting high- and intermediate-flow statistics as well. Three sets of base-flow measurements were made at 40 sites in the Paradise and Pocono Creek watersheds to determine subbasin yields and stream reaches gaining or losing flow. Subbasin yields, the base-flow measurements normalized to respective drainage areas, were consistent for each measurement period.

Scientific Investigations Report↗