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At least 775 records · Page 43Linked to original sources
USGS2013

Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models

Natural climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.

Geophysical Research Letters↗
USGS2020

Modelling pinniped abundance and distribution by combining counts at terrestrial sites and in-water sightings

Pinnipeds are commonly monitored using aerial photographic surveys at land- or ice-based sites, where animals come ashore for resting, pupping, molting, and to avoid predators. Although these counts form the basis for monitoring population change over time, they do not provide information regarding where animals occur in the water, which is often of management and conservation interest. In this study, we developed a hierarchical model that links counts of pinnipeds at terrestrial sites to sightings-at-sea and estimates abundance, spatial distribution, and the proportion of time spent on land (attendance probability). The structure of the model also allows for the inclusion of predictors that may explain variation in ecological and observation processes. We applied the model to Steller sea lions ( Eumetopias jubatus ) in Glacier Bay, Alaska using counts of sea lions from aerial photographic surveys and opportunistic in-water sightings from vessel surveys. Glacier Bay provided an ideal test and application of the model because data are available on attendance probability based on long-term monitoring. We found that occurrence in the water was positively related to proximity to terrestrial sites, as would be expected for a species that engages in central-place foraging. The proportion of sea lions in attendance at terrestrial sites and overall abundance estimates were consistent with reports from the literature and monitoring programs. The model we describe has benefit and utility for park managers who wish to better understand the overlap between pinnipeds and visitors, and the framework that we present has potential for application across a variety of study systems and taxa.

Alaska↗
USGS2013

Descriptive models, grade-tonnage relations, and databases for the assessment of sediment-hosted copper deposits: with emphasis on deposits in the Central Africa Copperbelt, Democratic Republic of the Congo and Zambia: Chapter J in Global mineral resource assessment

The Central African Copperbelt (CACB) is one of the most important copper-producing regions of the world. The majority of copper produced in Africa comes from this region defined by the Neoproterozoic Katanga sedimentary basin of the southern Democratic Republic of the Congo (DRC) and northern Zambia. Copper in the CACB is mined from sediment-hosted stratabound copper deposits associated with red beds and includes the giant deposits in the Kolwezi and Tenge-Fungurume districts in the DRC and the Konkola-Musoshi and Nchanga-Chingola districts in Zambia. In recent years, sediment-hosted structurally controlled replacement and vein (SCRV) copper deposits, such as the giant Kansanshi deposit in Zambia have become important exploration targets in the CACB region. In 2011, the CACB accounted for 7.2 percent of the estimated global mine production of copper. Global production of copper is principally derived from porphyry and sediment-hosted copper deposits (57 and 23 percent, respectively). Almost 50 percent of the copper known to exist in sediment-hosted deposits (past production plus identified resources) is contained in the CACB, 25 percent is contained in the Zechstein Basin of northern Europe, and the remainder is contained in an additional 29 sedimentary basins distributed around the globe. The U.S. Geological Survey (USGS) led an assessment of undiscovered copper resources in the CACB as part of a global mineral resource assessment for undiscovered resources of potash, copper, and platinum-group elements in selected mineral deposit types. As part of the assessment process, available data for the CACB were compiled and evaluated. This report describes the results of that work, including new descriptive mineral-deposit and grade and tonnage models and spatial databases for deposits and occurrences, ore bodies and open pits. Chapter 1 of this report summarizes a descriptive model of sediment-hosted stratabound copper deposits. General characteristics and subtypes of sediment-hosted stratabound copper deposits are described based upon worldwide examples. Chapter 2 provides a global database of 170 sediment-hosted copper deposits, along with a statistical evaluation of grade and tonnage data for stratabound deposits, a comparison of stratabound deposits in the CACB with those found elsewhere, a discussion of the distinctive characteristics of the subtypes of sediment-hosted copper deposits that occur within the CACB, and guidelines for using grade and tonnage distributions for assessment of undiscovered resources in sediment-hosted stratabound deposits in the CACB. Chapter 3 presents a new descriptive model of sediment-hosted structurally controlled replacement and vein (SCRV) copper deposits with descriptions of individual deposits of this type in the CACB and elsewhere. Appendix A describes a relational database of tonnage, grade, and other information for more than 100 sediment-hosted copper deposits in the CACB. These data are used to calculate the pre-mining mineral endowment for individual deposits in the CACB and serve as the basis for the grade and tonnage models presented in chapter 2. Appendix B describes three spatial databases (Esri shapefiles) for (1) point locations of more than 500 sediment-hosted copper deposits and prospects, (2) projected surface extent of 86 selected copper ore bodies, and (3) areal extent of 77 open pits, all within the CACB.

Scientific Investigations Report↗
USGS2023

Predicting large hydrothermal systems

We train five models using two machine learning (ML) regression algorithms (i.e., linear regression and XGBoost) to predict hydrothermal upflow in the Great Basin. Feature data are extracted from datasets supporting the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems project (INGENIOUS). The label data (the reported convective signals) are extracted from measured thermal gradients in wells by comparing the total estimated heat flow at the wells to the modeled background conductive heat flow. That is, the reported convective signal is the difference between the background conductive heat flow and the well heat flow. The reported convective signals contain outliers that may affect upflow prediction, so the influence of outliers is tested by constructing models for two cases: 1) using all the data (i.e., -91 to 11,105 mW/m2), and 2) truncating the range of labels to include only reported convective signals between -25 and 200 mW/m2. Because hydrothermal systems are sparse, models that predict high convective signal in smaller areas better match the natural frequency of hydrothermal systems. Early results demonstrate that XGBoost outperforms linear regression. For XGBoost using the truncated range of labels, half of the high reported signals are within < 3 % of the highest predictions. For XGBoost using the entire range of labels, half of the high reported signals are in < 13 % of the highest predictions. While this implies that the truncated regression is superior, the all-data model better predicts the locations of power-producing systems (i.e., the operating power plants are in a smaller fraction of the study area given by the highest predictions). Even though the models generally predict greater hydrothermal upflow for higher reported convective signals than for lower reported convective signals, both XGBoost models consistently underpredict the magnitude of higher signals. This behavior is attributed to low resolution/granularity of input features compared with the scale of a hydrothermal upflow zone (a few km or less across). Trouble estimating exact values while still reliably predicting high versus low convective signals suggests that a future strategy such as ranked ordinal regression (e.g., classifying into ordered bins for low, medium, high, and very high convective signal) might fit better models, since doing so reduces problems introduced by outliers while preserving the property of larger versus smaller signals.

Geothermal Resources Council Transactions↗
USGS2006

Exhumation of Greater Himalayan rock along the main central thrust in Nepal: Implications for channel flow

South-vergent channel flow from beneath the Tibetan Plateau may have played an important role in forming the Himalaya. The possibility that Greater Himalayan rocks currently exposed in the Himalayan Fold-Thrust Belt flowed at mid-crustal depths before being exhumed is intriguing, and may suggest a natural link between orogenic processes operating under the Tibetan Plateau and in the fold-thrust belt. Conceptual and numeric models for the Himalayan-Tibetan Orogen currently reported in the literature do an admirable job of replicating many of the observable primary geological features and relationships. However, detailed observations from Greater Himalayan rocks exposed in the fold-thrust belt's external klippen, and from Lesser Himalayan rocks in the proximal footwall of the Main Central Thrust, suggest that since Early Miocene time, it may be more appropriate to model the evolution of the fold-thrust belt using the critical taper paradigm. This does not exclude the possibility that channel flow and linked extrusion of Greater Himalayan rocks may have occurred, but it places important boundaries on a permissible time frame during which these processes may have operated. ?? The Geological Society of London 2006.

Conference Paper↗
USGS2001

Resource materials for a GIS spatial analysis course

This report consists of materials prepared for a GIS spatial analysis course offered as part of the Geography curriculum at the University of Nevada, Reno and the University of California at Santa Barbara in the spring of 2000. The report is intended to share information with instructors preparing spatial-modeling training and scientists with advanced GIS expertise. The students taking this class had completed each universities GIS curriculum and had a foundation in statistics as part of a science major. This report is organized into chapters that contain the following: Slides used during lectures, Guidance on the use of Arcview, Introduction to filtering in Arcview, Conventional and spatial correlation in Arcview, Tools for fuzzification in Arcview, Data and instructions for creating using ArcSDM for simple weights-of-evidence, fuzzy logic, and neural network models for Carlin-type gold deposits in central Nevada, Reading list on spatial modeling, and Selected student spatial-modeling posters from the laboratory exercises.

Open-File Report↗
USGS1974

Presentation and interpretation of chemical data for igneous rocks

Arguments are made in favor of using variation diagrams to plot analyses of igneous rocks and their derivatives and modeling differentiation processes by least-squares mixing procedures. These methods permit study of magmatic differentiation and related processes in terms of all of the chemical data available. Data are presented as they are reported by the chemist and specific processes may be modeled and either quantitatively described or rejected as inappropriate or too simple. Examples are given of the differing interpretations that can arise when data are plotted on an AEM ternary vs. the same data on a full set of MgO variation diagrams. Mixing procedures are illustrated with reference to basaltic lavas from the Columbia Plateau.

Idaho, Washington, Oregon↗
USGS2025

Mount Spurr volcano, August 18, 1992: The eruption heard around Alaska

The August 18, 1992, eruption of Mount Spurr volcano, Alaska, produced an impressive Vulcanian to Subplinian eruption column reaching up to 40 km above sea level that blanketed the nearby city of Anchorage with ash. At the time of the eruption, the Alaska Volcano Observatory received reports of audible sound hundreds of kilometers from the source from a variety of azimuths from the volcano and found the pattern that did not solely depend on distance. Explosions are generally inaudible to the human ear at these distances due to the attenuation of sound in the atmosphere as the energy propagates, but low-frequency sound waves (infrasound) persist and can be used to detect and characterize eruptions. Similar reports of zones of audible sounds at great distances have been observed from notable eruptions such as Krakatau 1883, Mount St. Helens 1980, Mount Pinatubo 1991, and Hunga volcano 2022. We perform infrasound propagation modeling to help understand the unique distribution of audible reports, finding that areas with favorable infrasound propagation may help explain areas in which nonlinear propagation, including energy cascading effects into higher frequencies that are audible to the human ear, may have occurred.

Alaska↗
USGS1987

Review of literature on the finite-element solution of the equations of two-dimensional surface-water flow in the horizontal plane

Published literature on the application of the finite-element method to solving the equations of two-dimensional surface-water flow in the horizontal plane is reviewed in this report. The finite-element method is ideally suited to modeling two-dimensional flow over complex topography with spatially variable resistance. A two-dimensional finite-element surface-water flow model with depth and vertically averaged velocity components as dependent variables allows the user great flexibility in defining geometric features such as the boundaries of a water body, channels, islands, dikes, and embankments. The following topics are reviewed in this report: alternative formulations of the equations of two-dimensional surface-water flow in the horizontal plane; basic concepts of the finite-element method; discretization of the flow domain and representation of the dependent flow variables; treatment of boundary conditions; discretization of the time domain; methods for modeling bottom, surface, and lateral stresses; approaches to solving systems of nonlinear equations; techniques for solving systems of linear equations; finite-element alternatives to Galerkin's method of weighted residuals; techniques of model validation; and preparation of model input data. References are listed in the final chapter.

Circular↗
USGS2016

Algorithms used in the Airborne Lidar Processing System (ALPS)

The Airborne Lidar Processing System (ALPS) analyzes Experimental Advanced Airborne Research Lidar (EAARL) data—digitized laser-return waveforms, position, and attitude data—to derive point clouds of target surfaces. A full-waveform airborne lidar system, the EAARL seamlessly and simultaneously collects mixed environment data, including submerged, sub-aerial bare earth, and vegetation-covered topographies. ALPS uses three waveform target-detection algorithms to determine target positions within a given waveform: centroid analysis, leading edge detection, and bottom detection using water-column backscatter modeling. The centroid analysis algorithm detects opaque hard surfaces. The leading edge algorithm detects topography beneath vegetation and shallow, submerged topography. The bottom detection algorithm uses water-column backscatter modeling for deeper submerged topography in turbid water. The report describes slant range calculations and explains how ALPS uses laser range and orientation measurements to project measurement points into the Universal Transverse Mercator coordinate system. Parameters used for coordinate transformations in ALPS are described, as are Interactive Data Language-based methods for gridding EAARL point cloud data to derive digital elevation models. Noise reduction in point clouds through use of a random consensus filter is explained, and detailed pseudocode, mathematical equations, and Yorick source code accompany the report.

Open-File Report↗
USGS2018

Conceptual model to assess water use associated with the life cycle of unconventional oil and gas development

As the demand for energy increases in the United States, so does the demand for water used to produce many forms of that energy. Technological advances, limited access to conventional oil and gas accumulations, and the rise of oil and gas prices resulted in increased development of unconventional oil and gas (UOG) accumulations. Unconventional oil and gas is developed using a method that combines directional drilling and hydraulic fracturing techniques, allowing for greater oil and gas production from previously unrecoverable reservoirs. Quantification of the water resources required for UOG development and production is difficult because of disparate data sources, variable reporting requirements across boundaries (local, State, and national), and incomplete or proprietary datasets. A topical study was started in 2015 under the U.S. Geological Survey’s Water Availability and Use Science Program, as part of the directive in the Secure Water Act for the U.S. Geological Survey to conduct a National Water Census, to better understand the relation between production of UOG resources for energy and the amount of water needed to produce and sustain this type of energy development in the United States. The Water Availability and Use Science Program goal for this topical study is to develop and apply a statistical model to better estimate the water use associated with UOG development, regardless of the location and target geologic formation. As a first step, a conceptual model has been developed to characterize the life cycle of water use in areas of UOG development. Categories of water use and the way water-use data are collected might change over time; therefore, a generic approach was used in developing the conceptual model to allow for greater flexibility in adapting to future changes or newly available data. UOG development can be summarized into four stages: predrilling construction, drilling, hydraulic fracturing, and ongoing production. The water used in UOG production can be categorized further as direct, indirect, or ancillary water use. Direct water use is defined as the water used for drilling and hydraulic fracturing a well and for maintaining the well during ongoing production. Indirect water use is defined as the water used at or near a well pad. The water used for dust abatement also is considered an indirect use but may be applied away from the well pad. Ancillary water use is defined as the additional local or regional water use resulting from a change (for example, population) directly related to UOG development throughout the life cycle that is not used directly in the well or indirectly for any other purpose at the well pad. The conceptual model presented in this report consists of five elements: (1) input data, (2) processes, (3) decisions, (4) output data, and (5) outcomes. The input data requirements for estimating water use associated with UOG development are somewhat onerous, and obtaining suitable datasets can be challenging because local, State, and Federal agencies do not collect data similarly. The quality of a water-use assessment that uses the conceptual model presented in this report is dependent on the quality and quantity of data that are available for a UOG play. The conceptual model can be used for an assessment with sparse data; however, having sparse data likely will result in greater uncertainty in the water-use estimates. The conceptual model presented in this report is designed to be robust to characterize and simulate the data processing to estimate water use associated with UOG development. Although the results of an analysis that includes missing data have greater uncertainty, the analysis still can be insightful because it can establish a baseline estimate of UOG water use that may be refined further as more data become available. Analysis of models that include missing data also could aid in identifying the data most needed for future water-use estimates. Characterizing individual model limitations is important because the conceptual model can be used in future water-use studies to facilitate data compiling, data processing, estimating, and assessing UOG activities regardless of location.

Scientific Investigations Report↗
USGS2019

Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data

Fine scale maps of soil properties enable efficient land management and inform earth system models. Recent efforts to create soil property maps from field observations tend to use similar tree-based machine learning interpolation approaches, but often deal with depth of predictions, validation, and uncertainty differently. One of the main differences in approaches is whether to model individual depths of interest separately as ‘2D’ models, or to create models that incorporate depth as a predictor variable creating a ‘3D’ model that can make pre-dictions for all depths. It is unclear how choice of 2D or 3D approach influences model accuracy and uncertainty due to lack of direct comparison and inconsistent presentation of results in past studies. This study compares 2D and 3D methods for mapping soil electrical conductivity (salinity), pH, sum of fine and very fine sands, and organic carbon at 30 m resolution for the upper 432,000 km 2 of the Colorado River Watershed of the United States of America. A new, simple, model-agnostic relative prediction interval (RPI) approach to report un-certainty is presented that scales prediction interval width to the 95% interquantile width of the original training sample distribution. The RPI approach enables direct comparison of uncertainty between properties and depths and is easily interpretable by end users. Results indicate that 3D mapping of soil properties with strong variation with depth can result in substantial areas with much higher uncertainty that coincide with unrealistic predictions relative to 2D models, even though 3D models had slightly better global cross-validation scores. Maps and global model summaries of RPI proved helpful in identifying these issues with 3D models. These results suggest that the use of RPI or similar approaches to evaluate models can identify accuracy problems not evident in global va-lidation diagnostics.

Geoderma↗
USGS2022

Incorporating snowmelt into daily estimates of recharge using a state-space model of infiltration

A state-space model (SSM) of infiltration estimates daily groundwater recharge using time-series of groundwater-level altitude and meteorological inputs (liquid precipitation, snowmelt, and evapotranspiration). The model includes diffuse and preferential flow through the unsaturated zone, where preferential flow is a function of liquid precipitation and snowmelt rates and a threshold rate, above which there is direct recharge to the water table. Model parameters are estimated over seasonal periods and the SSM is coupled with the Kalman Filter (KF) to assimilate recent observations (hydraulic head) and meteorological inputs into recharge estimates. The approach can take advantage of real-time hydrologic and meteorological data to deliver real-time recharge estimates. The model is demonstrated on daily observations from two bedrock wells in carbonate aquifers of northwestern New York (USA) between 2013 and 2018. Meteorological inputs for liquid precipitation and snowmelt are compiled from SNODAS (2021). Results for recharge during winter and spring seasons show preferential flow events to the water table from liquid precipitation, snowmelt, or a combination of the two. Recharge estimates summed annually are consistent with previous estimates of recharge reported from groundwater flow and surface-process models. Results from the SSM and KF point to errors in meteorological inputs, such as the snowmelt rate, that are not compatible with hydraulic head observations. Whereas liquid and solid precipitation are measured at discrete stations and extrapolated to 1-km 2 grid cells, snowmelt is a meteorological modeled outcome that may not represent conditions in the vicinity of monitoring well locations.

Groundwater↗