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At least 91 records · Page 5Linked to original sources
USGS1985

An investigation of hydraulic conductivity estimation in a ground-water flow study of Northern Long Valley, New Jersey

The purpose of this study was to develop a methodology to be used to investigate the aquifer characteristics and water supply potential of an aquifer system. In particular, the geohydrology of northern Long Valley, New Jersey, was investigated. Geohydrologic data were collected and analyzed to characterize the site. Analysis was accomplished by interpreting the available data and by using a numerical simulation of the watertable aquifer. Special attention was given to the estimation of hydraulic conductivity values and hydraulic conductivity structure which together define the hydraulic conductivity of the modeled aquifer. Hydraulic conductivity and all other aspects of the system were first estimated using the trial-and-error method of calibration. The estimation of hydraulic conductivity was improved using a least squares method to estimate hydraulic conductivity values and by improvements in the parameter structure. These efforts improved the calibration of the model far more than a preceding period of similar effort using the trial-and-error method of calibration. In addition, the proposed method provides statistical information on the reliability of estimated hydraulic conductivity values, calculated heads, and calculated flows. The methodology developed and applied in this work proved to be of substantial value in the evaluation of the aquifer considered.

Thesis↗
USGS2025

The influence of scale in modeling social vulnerability and disaster assistance

Understanding how social vulnerability relates to disaster impacts is critical for addressing social equity, yet the role of spatial scale in this relationship is often overlooked. Most studies use aggregated data, risking ecological fallacy—misinterpreting individual outcomes from group-level data. This study examines how spatial scale influences the relationship between social vulnerability and federal disaster assistance after Hurricane Harvey. Using spatial econometric models at both household and census tract levels, we assessed the strength of key vulnerability indicators in explaining disaster assistance. Results show that disability, housing tenure, household size, and income predict assistance at the household level, but their influence shifts across scales. Income and disability remain strong predictors at the tract level, whereas housing factors weaken or reverse. These findings suggest that using aggregated data to model household-level relationships between social vulnerability and disaster assistance can distort understanding of vulnerability processes, potentially leading to misinformed disaster policies and inequitable outcomes. Our findings have implications for disaster management, but the primary contribution of this study is methodological, in providing a critical evaluation of how spatial scale and data aggregation shape the statistical interpretation of social vulnerability and aid distribution.

Texas↗
USGS2024

An integrative paradigm for building causal knowledge

A core aspiration of the ecological sciences is to determine how systems work, which implies the challenge of developing a causal understanding. Causal inference has long been approached from a statistical perspective, which can be limited and restrictive for a variety of reasons. Ecologists and other natural scientists have historically pursued mechanistic knowledge as an alternative approach to causal understanding, though without explicit reference to the requirements of causal statistics. In this paper, I describe the premises of an expanded paradigm for causal studies, the Integrative Causal Investigation Paradigm, that subsumes causal statistics and mechanistic investigation into a multi-evidence approach. This paradigm is distinct from the one articulated by causal statistics in that it (1) focuses its attention on the long-term goal of building causal knowledge across multiple studies and (2) recognizes the essential role of mechanistic investigations in establishing a causal understanding. The Integrative Paradigm, consequentially, proposes that there are multiple methodological routes to building causal knowledge and thus represents a pluralistic perspective. This paper begins by describing the crux of the problem faced by causal statistics. To understand this problem, it should be recognized that the word causal has multiple meanings and a variety of evidential standards. An expanded vocabulary is developed so as to reduce ambiguities and clarify critical issues. I further show by example that there is an important ingredient typically omitted from consideration in causal statistics, which is the known information related to the mechanisms underlying relationships being evaluated. To address this issue, I describe a procedure, Causal Knowledge Analysis, that involves an evaluation and compilation of existing evidence indicative of causal content and the features of mechanisms. Causal Knowledge Analysis is applied to three example situations to illustrate the process and its potential for contributing to the development of causal knowledge. The implications of adopting the proposed paradigm and associated procedures are discussed and include the potential for advancing ecology, the potential for clarifying causal methodology, and the potential for contributing to predictive forecasting.

Ecological Monographs↗
USGS1984

GIRAS TO MOSS INTERFACE.

An analysis of land use change for an area in Boulder County, Colorado, was conducted using digital cartographic data. The authors selected data in the Geographic Information Retrieval and Analysis System (GIRAS) format which is digitized from the 1:250,000-scale land use and land cover map series. The Map Overlay and Statistical System (MOSS) was used as an analytical tool for the study. The authors describe the methodology used in converting the GIRAS file into a MOSS format and the activities associated with the conversion.

Conference Paper↗
USGS1984

ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.

Conference Paper↗
USGS2017

Baseline and projected future carbon storage and carbon fluxes in ecosystems of Hawai‘i

This assessment was conducted to fulfill the requirements of section 712 of the Energy Independence and Security Act of 2007 and to improve understanding of factors influencing carbon balance in ecosystems of Hawai‘i. Ecosystem carbon storage, carbon fluxes, and carbon balance were examined for major terrestrial ecosystems on the seven main Hawaiian islands in two time periods: baseline (from 2007 through 2012) and future (projections from 2012 through 2061). The assessment incorporated observed data, remote sensing, statistical methods, and simulation models. The national assessment has been completed for the conterminous United States, using methodology described in SIR 2010-5233, with results provided in three regional reports (PP 1804, PP 1797, and PP 1897), and for Alaska, with results provided in PP 1826.

Hawai'i↗
USGS1990

Wood duck population trends from the North American Breeding Bird Survey

The North American Breeding Bird Survey (BBS) has been conducted yearly since 1966, and can be used to describe relative population density and trends of birds in North America north of Mexico. Wood ducks (Air sponsa) are difficult to survey using conventional waterfowl monitoring techniques, but a large portion of their range is surveyed by the BBS. Wood ducks are detected at low densities on many BBS routes, and population trends can be estimated for most regions. Populations have been increasing throughout North America since 1966, but statistically significant increases generally occurred only in the early (1966-78) part of the period. Roadside survey methodology used in the BBS has limitations that make it inefficient as a survey technique for wood ducks, and we recommend using modified methodologies that specifically survey wood duck habitat.

Book chapter↗
USGS2019

Introduction: Defining and interpreting ecological disturbances

Within the field of ecology, disturbance can be defined as a physical force, agent, or process, either abiotic or biotic, causing a perturbation or stress, to an ecological component or system, relative to a specified reference state and/or system. Disturbance drive ecosystems, and our understanding of how disturbances interact with biological diversity and scales of space, time, and ecological complexity, have matured over a century of advancement in ecology since early ideas of perturbations and community organization were first formalized. Throughout this book, we approach a set of unifying framing questions for disturbance ecology, including: How can disturbances be categorized in meaningful ways? How do we address scale in disturbance ecology? How does geographic context influence ecological consequences of disturbance, in the near and longer terms? In this introductory chapter, we provide an overview of disturbance ecology and the related topics of diversity and scale that are fundamental to understanding the dynamics of perturbed ecosystems. Subsequently, we outline recent advances in disturbance ecology, which have facilitated greater understanding about dynamic systems and context dependencies. These, in turn, have provided richer insights into the complex manner in which ecosystems change under stress. We survey analytical and methodological advances that are expanding the data flows available to inform disturbance ecology as well as the statistical tools available to investigate disturbance dynamics and ecosystem structure and function. Finally, we lay out four core themes threaded through the remainder of the book: (1) fundamental mechanisms related to ecological theory drive complex system behaviors, including the existence of thresholds; (2) dynamics of ecological disturbance are context-dependent and can be unpredictable; (3) antecedent conditions and the legacies of past disturbances influence contemporary ecosystem dynamics; and (4) natural and anthropogenic disturbances interact in complex ways. Summaries are provided for each of the book’s remaining chapters, highlighting how that material relates to these four core themes. In sum, in this introductory chapter we seek to set a foundation for concepts to ground the remainder of the book. By highlighting constraints in past research and identifying research frontiers, we hope to provide a path forward for advancements in disturbance ecology.

Book chapter↗
USGS2022

Streamlined approach for assessing embedded consumption of lithium and cobalt in the United States

In today's complex global supply chains, time and data intensive analyses are required to understand global flows of mineral commodities from mine to consumer, particularly for mineral commodities in products (electronics, automobiles, etc.) that contain multiple parts with many mineral commodities. National and regional analyses require additional time and data to incorporate international trade flows. However, data limitations and time constraints often prohibit global and national material flow analyses for minor metals. Here we present a methodological approach to circumvent these constraints by utilizing readily available industry-level global data from the United Nations Statistics Division and national industrial data to estimate total requirements for a mineral commodity. We apply this approach to lithium and cobalt use in the United States for the year 2018 and distinguish between apparent raw material consumption versus inferred embedded consumption of lithium and cobalt materials in all forms. The results show that more than half of the United States’ total requirements for both lithium and cobalt is in parts and products that were manufactured outside the United States. In large part, this is due to limited US manufacturing capability for lithium-ion battery materials and cells and the United States’ high import reliance for electronics that use those batteries.

Journal of Industrial Ecology↗
USGS2024

Bayesian approaches to proxy uncertainty quantification in paleoecology: A mathematical justification and practical integration

Paleoenvironmental data are essential for reconstructing environmental conditions in the distant past, and these reconstructions strongly depend on proxies and age–depth models. Proxies are indirect measurements that substitute for variables that cannot be directly measured, such as past precipitation. Conversely, an age–depth model is a tool that correlates the observed proxy with a specific moment in time. Bayesian age–depth modelling has proved to be a powerful method for estimating sediment ages and their associated uncertainties. However, there remains considerable potential for further integration into proxy analysis. In this paper, we explore a mathematical justification and a computational approach that integrates uncertainty at the age–depth level and propagates it to the proxy scale in the form of a posterior predictive distribution. This method mitigates potential biases and errors by removing the need to assign a single age to a given proxy measurement. It allows for quantifying the likelihood that proxy data values correspond to modelled ages, thus enabling the quantification of uncertainty in both the temporal and proxy value domains. The use of Bayesian statistics in proxy analysis represents a relatively recent advancement. We aim to mathematically justify incorporating the Markov chain Monte Carlo output from age–depth models into proxy analysis and to present a novel methodology for constructing environmental reconstructions using this approach.

Journal of Agricultural, Biological and Environmen↗
USGS2021

Probabilistic methodology for the assessment of original and recoverable coal resources, illustrated with an application to a coal bed in the Fort Union Formation, Wyoming

Executive Summary The U.S. Geological Survey (USGS) has been using its Circular 891 for evaluating uncertainty in coal resource assessments for more than 35 years. Calculated cell tonnages are assigned to four qualitative reliability classes depending exclusively on distance to the nearest drill hole. The main appeal of this methodology, simplicity, is also its main drawback. Reliability may depend so marginally on distance to the nearest drill hole that, over time, it has become evident that Circular 891 is inadequate for modeling reliability and is limited by other shortcomings. The present publication describes the use of geostatistics as an approach allowing a more satisfactory performance than that which is achieved following Circular 891. Geostatistics takes advantage of partly random and partly organized fluctuations in attributes such as coal thickness, coal density, and elevation of the top of a coal bed, borrowing concepts and tools that have been standard features in statistics and risk analysis for decades. Considering that readers interested in this study may not have the background to go directly into the details of the methodology, we start by explaining geostatistical concepts and modeling techniques. The remainder of the publication is devoted to formulating the assessment methodology, applying it to data from the Fillmore Ranch coal bed in the Fort Union Formation in Wyoming, and explaining the computer software applied for performing calculations and displays. The assessment methodology has been designed to report three different forms of resources: coal in place, coal mineable by surface mining methods, and coal mineable by underground mining methods. These three types of resources are reported graphically by displaying both the magnitude and the reliability of total coal resources and resources at the cell scale. In the case of the Fillmore Ranch coal bed example, there is a 90-percent probability that the resources in place are 9.687 ± 0.383 billion short tons (bst), while the coal available for underground mining is 2.279 ± 0.160 bst, and that available for surface mining is only 0.240 ± 0.025 bst because of the steep dip to the west away from the outcrop. These magnitudes are derived from numerical probability distributions not following any specific form.

Wyoming↗
USGS2026

Potential flood events in the lower Missouri River basin over multiple centuries identified using tree-ring based multi-model streamflow reconstructions

The Missouri River basin (MRB), the largest river basin in the United States, presents major water management challenges due to its complex topology and extensive infrastructure designed to manage high annual flows. Severe hydroclimatic events, particularly floods in 1993, 2011, and 2019, have highlighted vulnerabilities, leading the U.S. Army Corps of Engineers to enhance flood risk assessments in 2023. This study focuses on the full MRB, using tree-ring based streamflow reconstructions to extend the Basin's historic streamflow perspective. We developed annual streamflow reconstructions for the Hermann, Missouri streamgage from water years 1360–1900 using K-nearest neighbor and principal component regression methods. These reconstructions incorporate 183 tree-ring chronologies (1360–2020) and modeled natural streamflow (1901–‍2020). Reconstructions were evaluated for consistency using statistical measures and wavelet analysis. Both methods produced similar results, confirming their reliability in reconstructing multi-century streamflows. This novel methodology, guided by wavelet analysis, was developed to identify potential flood events in the lower Missouri River basin (LMRB), resulting in the identification of 172 potential flood events from water years 1360–2020, with 126 events common to the two versions of the MRB reconstructions. The reconstructed streamflow records showed an increase in the number of events until the late-18th- or mid-19th-century. These findings provide insights into evolving flood characteristics in the LMRB. The novel methodology developed in this study can be used to identify potential flood events from tree-ring based annual streamflow reconstructions and generally be applicable to other river basins.

Colorado, Iowa, Kansas, Missouri, Nebraska, Wyomin↗
USGS2009

A less field-intensive robust design for estimating demographic parameters with Mark-resight data

The robust design has become popular among animal ecologists as a means for estimating population abundance and related demographic parameters with mark-recapture data. However, two drawbacks of traditional mark-recapture are financial cost and repeated disturbance to animals. Mark-resight methodology may in many circumstances be a less expensive and less invasive alternative to mark-recapture, but the models developed to date for these data have overwhelmingly concentrated only on the estimation of abundance. Here we introduce a mark-resight model analogous to that used in mark-recapture for the simultaneous estimation of abundance, apparent survival, and transition probabilities between observable and unobservable states. The model may be implemented using standard statistical computing software, but it has also been incorporated into the freeware package Program MARK. We illustrate the use of our model with mainland New Zealand Robin (Petroica australis) data collected to ascertain whether this methodology may be a reliable alternative for monitoring endangered populations of a closely related species inhabiting the Chatham Islands. We found this method to be a viable alternative to traditional mark-recapture when cost or disturbance to species is of particular concern in long-term population monitoring programs. ?? 2009 by the Ecological Society of America.

Ecology↗
USGS2014

Status and understanding of groundwater quality in the Klamath Mountains study unit, 2010: California GAMA Priority Basin Project

Groundwater quality in the Klamath Mountains (KLAM) study unit was investigated as part of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is located in Del Norte, Humboldt, Shasta, Siskiyou, Tehama, and Trinity Counties. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory. The GAMA Priority Basin Project was designed to provide a spatially unbiased, statistically robust assessment of the quality of untreated (raw) groundwater in the primary aquifer system. The assessment is based on water-quality data and explanatory factors for groundwater samples collected in 2010 by the USGS from 39 sites and on water-quality data from the California Department of Public Health (CDPH) water-quality database. The primary aquifer system was defined by the depth intervals of the wells listed in the CDPH water-quality database for the KLAM study unit. The quality of groundwater in the primary aquifer system may be different from that in the shallower or deeper water-bearing zones; shallow groundwater may be more vulnerable to surficial contamination. This study included two types of assessments: (1) a status assessment , which characterized the status of the current quality of the groundwater resource by using data from samples analyzed for volatile organic compounds, pesticides, and naturally occurring inorganic constituents, such as major ions and trace elements, and (2) an understanding assessment , which evaluated the natural and human factors potentially affecting the groundwater quality. The assessments were intended to characterize the quality of groundwater resources in the primary aquifer system of the KLAM study unit, not the quality of treated drinking water delivered to consumers by water purveyors. Relative-concentrations (sample concentrations divided by the health- or aesthetic-based benchmark concentrations) were used for evaluating groundwater quality for those constituents that have Federal or California regulatory or non-regulatory benchmarks for drinking-water quality. A relative-concentration greater than (>) 1.0 indicates a concentration greater than a benchmark, and a relative-concentration less than or equal to (≤) 1.0 indicates a concentration less than or equal to a benchmark. Relative-concentrations of organic constituents were classified as “high” (relative-concentration > 1.0), “moderate” (0.1 < relative-concentration ≤ 1.0), or “low” (relative-concentration ≤ 0.1). For inorganic constituents, the boundary between low and moderate relative-concentration was set at 0.5. Aquifer-scale proportion was used in the status assessment as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the percentage of the area of the primary aquifer system with a relative-concentration greater than 1.0 for a particular constituent or class of constituents; percentage is based on an areal rather than a volumetric basis. Moderate and low aquifer-scale proportions were defined as the percentages of the primary aquifer system with moderate and low relative-concentrations, respectively. The KLAM study unit includes more than 8,800 square miles (mi 2 ), but only those areas near the sampling sites, about 920 mi 2 , are included in the areal assessment of the study unit. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. To confirm this methodology, 90 percent confidence intervals were calculated for the grid-based high aquifer-scale proportions and were compared to the spatially weighted results, which were found to be within these confidence intervals in all cases. Grid-based results were selected for use in the status assessment unless, as was observed in a few cases, a grid-based result was zero and the spatially weighted result was not zero, in which case, the spatially weighted result was used. The status assessment showed that inorganic constituents with human-health benchmarks were detected at high relative-concentrations in 2.6 percent of the primary aquifer system and at moderate relative-concentrations in 10 percent of the system. The high aquifer-scale proportion for inorganic constituents mainly reflected the high aquifer-scale proportions of boron. Inorganic constituents with secondary maximum contaminant levels were detected at high relative-concentrations in 13 percent of the primary aquifer system and at moderate relative-concentrations in 10 percent of the system. The constituents present at high relative-concentrations included iron and manganese. Organic constituents with human-health benchmarks were not detected at high relative-concentrations, but were detected at moderate relative-concentrations in 1.9 percent of the primary aquifer system. The 1.9 percent reflected a spatially weighted moderate aquifer-scale proportion for the gasoline additive methyl tert-butyl ether. Of the 148 organic constituents analyzed, 14 constituents were detected. Only one organic constituent had a detection frequency of greater than 10 percent—the trihalomethane, chloroform. The second component of this study, the understanding assessment , identified the natural and human factors that may have affected the groundwater quality in the KLAM study unit by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were aquifer lithology, land use, hydrologic conditions, depth, groundwater age, and geochemical conditions. Results of the statistical evaluations were used to explain the occurrence and distribution of constituents in the KLAM study unit. Groundwater age distribution (modern, mixed, or pre-modern), redox class (oxic, mixed, or anoxic), and dissolved oxygen concentration were the explanatory factors that best explained occurrence patterns of the inorganic constituents. High concentrations of boron were found to be associated with groundwater classified as mixed or pre-modern with respect to groundwater age. Boron was also negatively correlated to dissolved oxygen and positively correlated to specific conductance. Iron and manganese concentrations were strongly associated with low dissolved oxygen concentrations, anoxic and mixed redox classifications, and pre-modern groundwater. Specific conductance concentrations were found to be related to pre-modern groundwater, low dissolved oxygen concentrations, and high pH. Chloroform was selected for additional evaluation in the understanding assessment because it was detected in more than 10 percent of wells sampled in the KLAM study unit. Septic tank density was the only explanatory factor that was found to relate to chloroform concentrations.

California↗
USGS2021

Bankfull shear velocity predicts embeddedness and silt cover in gravel streambeds

Excess fine sediment (<2 mm) deposition on gravel streambeds can degrade habitat quality for stream biota. Two measures of fine sediment deposition include embeddedness and silt cover (<62.5 μm). Embeddedness measures fine sediment in interstitial pore spaces, whereas silt cover, primarily deposited during low flows, measures fine sediment draped on the streambed's surface. Here, we demonstrate that a baseline level of embeddedness and a maximum value of silt cover can be predicted from bankfull shear velocity, which can be estimated from river channel and streamflow characteristics, independently of knowing the sediment supply. We derive an equation for bankfull shear velocity that only requires knowing bankfull flow, channel width, and channel slope, which can be readily obtained in the United States from freely available, remotely sensed data. We apply this methodology to data collected at 30 sites in the Piedmont region of Virginia and North Carolina. This work is an important step in developing statistical models of stream ecosystems in which geophysical variables can predict embeddedness and silt cover, which commonly limit biotic assemblages.

North Carolina, Virginia↗
USGS2020

Validation of laboratory tests for infectious diseases in wild mammals: Review and recommendations

Evaluation of the diagnostic sensitivity (DSe) and specificity (DSp) of tests for infectious diseases in wild animals is challenging, and some of the limitations may affect compliance with the OIE-recommended test validation pathway. We conducted a methodologic review of test validation studies for OIE-listed diseases in wild mammals published between 2008 and 2017 and focused on study design, statistical analysis, and reporting of results. Most published papers addressed Mycobacterium bovis infection in one or more wildlife species. Our review revealed limitations or missing information about sampled animals, identification criteria for positive and negative samples (case definition), representativeness of source and target populations, and species in the study, as well as information identifying animals sampled for calculations of DSe and DSp as naturally infected captive, free-ranging, or experimentally challenged animals. The deficiencies may have reflected omissions in reporting rather than design flaws, although lack of random sampling might have induced bias in estimates of DSe and DSp. We used case studies of validation of tests for hemorrhagic diseases in deer and white-nose syndrome in hibernating bats to demonstrate approaches for validation when new pathogen serotypes or genotypes are detected and diagnostic algorithms are changed, and how purposes of tests evolve together with the evolution of the pathogen after identification. We describe potential benefits of experimental challenge studies for obtaining DSe and DSp estimates, methods to maintain sample integrity, and Bayesian latent class models for statistical analysis. We make recommendations for improvements in future studies of detection test accuracy in wild mammals.

Journal of Veterinary Diagnostic Investigation↗
USGS2009

Uniform California earthquake rupture forecast, version 2 (UCERF 2)

The 2007 Working Group on California Earthquake Probabilities (WGCEP, 2007) presents the Uniform California Earthquake Rupture Forecast, Version 2 (UCERF 2). This model comprises a time-independent (Poisson-process) earthquake rate model, developed jointly with the National Seismic Hazard Mapping Program and a time-dependent earthquake-probability model, based on recent earthquake rates and stress-renewal statistics conditioned on the date of last event. The models were developed from updated statewide earthquake catalogs and fault deformation databases using a uniform methodology across all regions and implemented in the modular, extensible Open Seismic Hazard Analysis framework. The rate model satisfies integrating measures of deformation across the plate-boundary zone and is consistent with historical seismicity data. An overprediction of earthquake rates found at intermediate magnitudes (6.5 ??? M ???7.0) in previous models has been reduced to within the 95% confidence bounds of the historical earthquake catalog. A logic tree with 480 branches represents the epistemic uncertainties of the full time-dependent model. The mean UCERF 2 time-dependent probability of one or more M ???6.7 earthquakes in the California region during the next 30 yr is 99.7%; this probability decreases to 46% for M ???7.5 and to 4.5% for M ???8.0. These probabilities do not include the Cascadia subduction zone, largely north of California, for which the estimated 30 yr, M ???8.0 time-dependent probability is 10%. The M ???6.7 probabilities on major strike-slip faults are consistent with the WGCEP (2003) study in the San Francisco Bay Area and the WGCEP (1995) study in southern California, except for significantly lower estimates along the San Jacinto and Elsinore faults, owing to provisions for larger multisegment ruptures. Important model limitations are discussed.

Bulletin of the Seismological Society of America↗
USGS2007

Geostatistical Modeling of Sediment Abundance in a Heterogeneous Basalt Aquifer at the Idaho National Laboratory, Idaho

The spatial distribution of sediment in the eastern Snake River Plain aquifer was evaluated and modeled to improve the parameterization of hydraulic conductivity (K) for a subregional-scale ground-water flow model being developed by the U.S. Geological Survey. The aquifer is hosted within a layered series of permeable basalts within which intercalated beds of fine-grained sediment constitute local confining units. These sediments have K values as much as six orders of magnitude lower than the most permeable basalt, and previous flow-model calibrations have shown that hydraulic conductivity is sensitive to the proportion of intercalated sediment. Stratigraphic data in the form of sediment thicknesses from 333 boreholes in and around the Idaho National Laboratory were evaluated as grouped subsets of lithologic units (composite units) corresponding to their relative time-stratigraphic position. The results indicate that median sediment abundances of the stratigraphic units below the water table are statistically invariant (stationary) in a spatial sense and provide evidence of stationarity across geologic time, as well. Based on these results, the borehole data were kriged as two-dimensional spatial data sets representing the sediment content of the layers that discretize the ground-water flow model in the uppermost 300 feet of the aquifer. Multiple indicator kriging (mIK) was used to model the geographic distribution of median sediment abundance within each layer by defining the local cumulative frequency distribution (CFD) of sediment via indicator variograms defined at multiple thresholds. The mIK approach is superior to ordinary kriging because it provides a statistically best estimate of sediment abundance (the local median) drawn from the distribution of local borehole data, independent of any assumption of normality. A methodology is proposed for delineating and constraining the assignment of hydraulic conductivity zones for parameter estimation, based on the locally estimated CFDs and relative kriging uncertainty. A kriging-based methodology improves the spatial resolution of hydraulic property zones that can be considered during parameter estimation and should improve calibration performance and sensitivity by more accurately reflecting the nuances of sediment distribution within the aquifer.

Scientific Investigations Report↗