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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 829 records · Page 46Linked to original sources
USGS1999

Seismic hazard map of North and Central America and the Caribbean

Minimization of the loss of life, property damage, and social and economic disruption due to earthquakes depends on reliable estimates of seismic hazard. National, state, and local government, decision makers, engineers, planners, emergency response organizations, builders, universities, and the general public require seismic hazard estimates for land use planning, improved building design and construction (including adoption of building construction codes), emergency response preparedness plans, economic forecasts, housing and employment decisions, and many more types of risk mitigation. The seismic hazard map of North and Central America and the Caribbean is the concatenation of various national and regional maps, involving a suite of approaches. The combined maps and documentation provide a useful regional seismic hazard framework and serve as a resource for any national or regional agency for further detailed studies applicable to their needs. This seismic hazard map depicts Peak Ground Acceleration (PGA) with a 10% chance of exceedance in 50 years. PGA, a short-period ground motion parameter that is proportional to force, is the most commonly mapped ground motion parameter because current building codes that include seismic provisions specify the horizontal force a building should be able to withstand during an earthquake. This seismic hazard map of North and Central America and the Caribbean depicts the likely level of short-period ground motion from earthquakes in a fifty-year window. Short-period ground motions effect short-period structures (e.g., one-to-two story buildings). The highest seismic hazard values in the region generally occur in areas that have been, or are likely to be, the sites of the largest plate boundary earthquakes.

Annals of Geophysics↗
USGS2015

The Everglades Depth Estimation Network (EDEN) surface-water model, version 2

The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models that generate daily water-level and water-depth data, and applications that compute derived hydrologic data across the freshwater part of the greater Everglades landscape. The U.S. Geological Survey Greater Everglades Priority Ecosystems Science provides support for EDEN in order for EDEN to provide quality-assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. The EDEN surface-water model, version 2 (V2), interpolates water-level data from a network of 240 gages to generate gridded daily water-level surfaces for the freshwater domain of the Everglades. When these spatiotemporal continuous surfaces are combined with EDEN’s digital elevation model of ground surface, derived hydrologic data provide scientists and water managers working in the Everglades with data necessary to analyze ecological and biotic responses to hydrologic changes in the Everglades. Derived datasets include water depth, recession rates, days since last dry, water-surface slopes, and hydroperiod. The V2 model includes enhancements from the previous model (version 1; V1) to accommodate changes in the water-level gage network, adjustments to water-level data, improved understanding of the flow dynamics (particularly near canals), and installation of an elevation benchmark network. Enhancements to the V2 model included Expansion of the EDEN domain: The model domain was expanded to include a part of southern Big Cypress National Preserve and northwestern Everglades National Park upstream of the marsh mangrove wetlands, thus completing the coastal connection along the southwestern boundary of the model; and Development of subdomain models: To account for insufficient water-control structure gage data at some subbasin boundaries, subdomain models were developed for five subdomains, and the resulting water-level surfaces were merged to generate the final water-level surface. Model performance statistics show a general improvement in the V2 model as compared to the V1 model. Overall, the root mean squared error (RMSE) was reduced by 2.42 centimeters (cm) to 4.68 cm. In Water Conservation Area 3A North and Water Conservation Area 3B, the RMSE was reduced by 10.88 and 9.15 cm, respectively. In addition to evaluating model performance statistics, 2-cm water-level maps were generated and evaluated for irregular contours that would indicate a potential problem either with data input or water-level estimates. Three applications of the EDEN-modeled water surfaces and other EDEN datasets are presented in the report to show how scientists and resource managers are using EDEN datasets to analyze biological and ecological responses to hydrologic changes in the Everglades. The biological responses of two important Everglades species, alligators and wading birds, to changes in hydrology are described. The effects of hydrology on fire dynamics in the Everglades are also discussed.

Florida↗
USGS2019

Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2017

The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project. An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. The network currently (2017) consists of 122 wells and piezometers. (A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers open to different depths.) The USGS, in cooperation with the ABCWUA and the New Mexico Office of the State Engineer, currently (2017) measures and reports water levels from the 122 wells and piezometers in the network; this report presents water-level data collected by USGS personnel at those 122 sites through water years 2016 and 2017 (October 1, 2015, through September 30, 2017). Water levels that were collected from wells in previous water years were published in previous USGS reports.

New Mexico↗
USGS2019

Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2018

The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project. An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of 2018, the network consisted of 120 wells and piezometers. (A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers open to different depths.) The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels from the 120 wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at those 120 sites through water year 2018 (October 1, 2017, through September 30, 2018). Water levels that were collected from wells in previous water years were published in previous USGS reports.

New Mexico↗
USGS2020

Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2019

The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project. An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of 2019, the network consisted of 120 wells and piezometers. (A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers screened at different depths.) The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels from the 120 wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at those 120 sites through water year 2019 (October 1, 2018, through September 30, 2019). Water levels that were collected from those discontinued wells in previous water years were published in previous USGS reports.

New Mexico↗
USGS2021

Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2020

The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project. An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of 2020, the network consisted of 120 wells and piezometers. A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers screened at different depths. The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels from the wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at the sites through water year 2020 (October 1, 2019, through September 30, 2020). Water levels that were collected from discontinued wells in previous water years were published in previous USGS reports.

New Mexico↗
USGS2008

Remote sensing and GIS approach for water-well site selection, southwest Iran

The Pabdeh-Lali Anticline of northern Khuzestan province is located in southwestern Iran and occupies 790 km2. This structure is situated in the Zagros folded belt. As a result of well-developed karst systems in the anticlinal axis, the water supply potential is high and is drained by many peripheral springs. However, there is a scarcity of water for agriculture and population centers on the anticlinal flanks, which imposes a severe problem in terms of area development. This study combines remotely sensed (RS) data and a geographical information system (GIS) into a RSGIS technique to delineate new areas for groundwater development and specific sites for drilling productive water wells. Toward these goals, RS data were used to develop GIS layers for lithology, structural geology, topographic slope, elevation, and drainage density. Field measurements were made to create spring-location and groundwater-quality GIS layers. Subsequently, expert choice and relational methods were used in a GIS environment to conjunctively analyze all layers to delineate preferable regions and 43 individual sites in which to drill water wells. Results indicate that the most preferred areas are, in preferential order, within recent alluvial deposits, the Bakhtiyari Conglomerates, and the Aghajari Sandstone. The Asmari Limestone and other units have much lower potential for groundwater supplies. Potential usefulness of the RSGIS method was indicated when six out of nine producing wells recently drilled by the Khozestan Water and Power Authority (which had no knowledge of this study) were located in areas preferentially selected by this technique.

Environmental & Engineering Geoscience↗
USGS2005

Southern California — Wildfires and debris flows

Wildland fires are inevitable in the western United States. Expansion of man-made developments into fire-prone wildlands has created situations where wildfires can destroy lives and property, as can the flooding and debris flows that are common in the aftermath of the fires. Fast-moving, highly destructive debris flows triggered by intense rainfall are one of the most dangerous post-fire hazards. Such debris flows are particularly dangerous because they tend to occur with little warning. Their mass and speed make them particularly destructive: debris flows can strip vegetation, block drainages, damage structures, and endanger human life. The U.S. Geological Survey’s Landslide Hazards Program is participating in a multi-agency cooperative effort to investigate debris flows in burned areas of southern California and other parts of the western United States. Participating agencies are the USDA Forest Service, the Natural Resources Conservation Service, and the California, Colorado, and Montana Geological Surveys. The objective of this project is to develop methods needed to estimate the locations, probability of occurrence, and size of potentially destructive debris flows. Public officials can use this information to plan and execute emergency response and post-fire rehabilitation. Analysis of data collected from studies of debris flows following wildfires can answer many of the questions fundamental to post-fire hazard assessments— what and why, where, when, how big, and how often?

California↗
USGS2025

Review of the Lake Washington Ship Canal and Ballard Locks model, Seattle, Washington, 2014–20

Executive Summary The Hiram M. Chittenden (Ballard) Locks and Lake Washington Ship Canal connect freshwater Lake Washington and saline Shilshole Bay of Puget Sound in Seattle, Washington. The locks and canal allow for ships to traverse this reach. Anadromous salmonids also migrate through, transitioning between saline and freshwater environments, and making use of a fish ladder at the locks when traveling upstream. WEST Consultants, Inc., constructed a two-dimensional hydrodynamic and water-quality model (CE-QUAL-W2) simulating flow, water temperature, and salinity for the Ballard Locks and the Lake Washington Ship Canal. An initial model was built for calendar years 2014–15, and the model was updated using a more recent and modern dataset for calendar years 2016–20. The U.S. Army Corps of Engineers requested that the U.S. Geological Survey review this model and its documentation to evaluate the technical aspects of its development and calibration. Findings from this review include the following: Overall, the Lake Washington Ship Canal CE-QUAL-W2 model was well-documented and constructed largely following typical model-development methods. The Lake Washington Ship Canal model was built with CE-QUAL-W2 model version 4.5, compiled and released by Portland State University in April 2021. CE-QUAL-W2 updates and improvements are regularly released with bug fixes and new features, so any model updates would benefit from the use of the most-recent software release. The model grid that represents the Lake Washington Ship Canal bathymetry was 9.2 kilometers (5.7 miles) long, matching the expected length of the waterway. The deepest model segments were near sampling site LLLW (Large Locks site) near the locks. Lake Union is reported to constitute most of the volume of the Lake Washington Ship Canal and is depicted as such in the model grid. The model includes several water outflows at Ballard Locks, including the large and small locks, a saltwater drain, a spillway, smolt flumes, and a fish ladder. Flows from the spillway, smolt flumes, and fish ladder were combined into one structure outflow in the model and assigned one withdrawal elevation from the Lake Washington Ship Canal. The smolt flume and spillway withdraw from the same elevation, but the fish ladder flow withdraws from a higher elevation in Lake Washington Ship Canal, and that flow could be separated into its own withdrawal. The model input files were created using the Coordinated Universal Time standard instead of the more typical choice of using local standard time. This is not incorrect, but sub-daily results would need to be converted to local time for science-communication purposes. The meteorological dataset had some unexpected anomalies, such as a baseline shift in the wind-speed dataset. Other nearby meteorological datasets could be used instead or used to correct the current meteorological inputs. The upstream boundary was configured with water-temperature data from a continuous monitor buoy in Lake Washington. The boundary salinity was set at 0 parts per thousand for the duration of the model simulation. A more realistic estimate of salinity at the upstream boundary could be constructed using data from the same buoy. Saline inflow at the downstream boundary of the Lake Washington Ship Canal model through lock exchanges at the large lock was included as a tributary in the model. Salinity and temperature inputs in this tributary at the large locks were set as constant values for the entire simulation. Saline inflow through the small lock was not included in the model because few data were available, and the input was likely to be small because of the smaller surface area and volume of the small lock relative to the large lock. The model did not include any flow, water temperature, or salinity inputs to the Lake Washington Ship Canal other than at the locks and at the upstream boundary. Any point sources, small tributaries, or stormwater inputs were omitted from the model. It is unclear whether this is a substantial omission relative to model results. Most model parameters were set as defaults or to reasonable values. However, the value of the WINDH parameter, the height of the wind speed measurement, was different than the height of the meteorological site. Compared to measured data, the model simulated water-surface elevations and water temperatures with reasonable accuracy. Differences in the modeled and measured salinities revealed some opportunities to improve the simulation of salinity, both baseline salinity and the salinity maxima in summer and autumn.

Washington↗
USGS2011

Project Planning for Cougar Dam during 2010

Cougar Dam is a 158 m-tall, rock fill dam located about 63 km east of Springfield, Oregon. Completed in 1963, the dam is owned and operated by the U.S. Army Corps of Engineers (USACE). It impounds Cougar Reservoir, which is 9.7 km long, has a surface area of 518 ha, and is predominately used for flood control. The pool elevation typically ranges from a maximum conservation pool of 515 m (1,690 ft) National Geodetic Vertical Datum (NGVD) in summer to a minimum flood control elevation of 467 m (1,532 ft NGVD) in winter. The reservoir thermally stratifies in the summer, has an average depth of 37 m, and holds 153,500 acre-feet when full. Cougar Dam is located on the South Fork of the McKenzie River 7 km upstream from the mainstem McKenzie River, a tributary of the Willamette River. The McKenzie River Basin basin supports the largest remaining population of wild spawning spring Chinook salmon in the Willamette River Basin (National Oceanic and Atmospheric Administration; NOAA, 2008). Cougar Dam and others were collectively deemed to cause jeopardy to the sustainability of anadromous fish stocks in the Willamette River Basin (NOAA, 2008). Prior to dam construction, as many as 805 redds were observed in the South Fork of the McKenzie River (Willis and others, 1960) and it is estimated that 40 km of spawning habitat were lost when access was blocked after dam construction. The 2008 Willamette Biological Opinion (BIOP) requires improvements to operations and structures to reduce impacts on Upper Willamette River (UWR) Chinook salmon (Oncorhynchus tshawytscha) and UWR steelhead (O. mykiss; NOAA, 2008). In 2010, an adult fish collection facility was completed below Cougar Dam to collect returning adult salmon for transport to spawning habitats above the dam. Before that time, returning adult spring Chinook salmon were transported to upstream spawning areas as part of a trap-and-haul program with adults passed ranging annually from 0 to 1,038 (Taylor, 2000). The progeny of adult fish that are allowed to spawn above Cougar Dam move downstream into Cougar Reservoir in the spring. Under the BIOP, the USACE is required to provide downstream fish passage or operational alternatives at Cougar Dam by 2014. Currently, there is little information about the seasonal timing of reservoir entry of juvenile Chinook salmon and what habitats they and other fishes use in the reservoir. However, rotary screw traps placed in the outlet channel below the dam indicate peak juvenile passage coinciding with seasonally low pool elevation in mid December and late January. It is unknown whether juveniles upstream of Cougar Dam can be captured in large enough numbers for tagging and subsequent survival studies to proceed. These studies are needed to examine the feasibility of installing downstream fish passage structures at Cougar Dam to meet BIOP requirements. Therefore, the USACE contracted with the U.S. Geological Survey (USGS) to test the efficacy of using a mid-water trawl and lampara seine to capture fish in Cougar Reservoir on three consecutive days in the fall of 2010. These collection methods could potentially provide fish for feasibility and subsequent survival studies and as verification of fish targets in future active hydroacoustic surveys.

Oregon↗
USGS2009

The effect of bathymetric filtering on nearshore process model results

Nearshore wave and flow model results are shown to exhibit a strong sensitivity to the resolution of the input bathymetry. In this analysis, bathymetric resolution was varied by applying smoothing filters to high-resolution survey data to produce a number of bathymetric grid surfaces. We demonstrate that the sensitivity of model-predicted wave height and flow to variations in bathymetric resolution had different characteristics. Wave height predictions were most sensitive to resolution of cross-shore variability associated with the structure of nearshore sandbars. Flow predictions were most sensitive to the resolution of intermediate scale alongshore variability associated with the prominent sandbar rhythmicity. Flow sensitivity increased in cases where a sandbar was closer to shore and shallower. Perhaps the most surprising implication of these results is that the interpolation and smoothing of bathymetric data could be optimized differently for the wave and flow models. We show that errors between observed and modeled flow and wave heights are well predicted by comparing model simulation results using progressively filtered bathymetry to results from the highest resolution simulation. The damage done by over smoothing or inadequate sampling can therefore be estimated using model simulations. We conclude that the ability to quantify prediction errors will be useful for supporting future data assimilation efforts that require this information.

Coastal Engineering↗
USGS2002

Visualization of Flow Alternatives, Lower Missouri River

Background The U.S. Army Corps of Engineers (COE) 'Missouri River Master Water Control Manual' (Master Manual) review has resulted in consideration of many flow alternatives for managing the water in the river (COE, 2001; 1998a). The purpose of this report is to present flow-management alternative model results in a way that can be easily visualized and understood. This report was updated in October 2001 to focus on the specific flow-management alternatives presented by the COE in the 'Master Manual Revised Draft Environmental Impact Statement' (RDEIS; COE, 2001). The original version (February 2000) is available by clicking here. The COE, U.S. Fish and Wildlife Service (FWS), Missouri River states, and Missouri River basin tribes have been participating in discussions concerning water management of the Missouri River mainstem reservoir system (MRMRS), the Missouri River Bank Stabilization and Navigation Project, and the Kansas River reservoir system since 1986. These discussions include general input to the revision of the Master Manual as well as formal consultation under Section 7 of the Endangered Species Act. In 2000, the FWS issued a Biological Opinion that prescribed changes to reservoir management on the Missouri River that were believed to be necessary to preclude jeopardy to three endangered species, the pallid sturgeon, piping plover, and interior least tern (USFWS, 2000). The combined Missouri River system is large and complex, including many reservoirs, control structures, and free-flowing reaches extending over a broad region. The ability to assess future impacts of altered management scenarios necessarily involves complex, computational models that attempt to integrate physical, chemical, biological, and economic effects. Graphical visualization of the model output is intended to improve understanding of the differences among flow-management alternatives.

Colorado, Iowa, Kansas, Missouri, Montana, Nebrask↗
USGS2006

Reserve Growth in Oil Fields of West Siberian Basin, Russia

Although reserve (or field) growth has proven to be an important factor contributing to new reserves in mature petroleum basins, it is still a poorly understood phenomenon. Limited studies show that the magnitude of reserve growth is controlled by several major factors, including (1) the reserve booking and reporting requirements in each country, (2) improvements in reservoir characterization and simulation, (3) application of enhanced oil recovery techniques, and (4) the discovery of new and extensions of known pools in discovered fields. Various combinations of these factors can affect the estimates of proven reserves in particular fields and may dictate repeated estimations of reserves during a field's life. This study explores the reserve growth in the 42 largest oil fields in the West Siberian Basin, which contain about 55 percent of the basin's total oil reserves. The West Siberian Basin occupies a vast swampy plain between the Ural Mountains and the Yenisey River, and extends offshore into the Kara Sea; it is the richest petroleum province in Russia. About 600 oil and gas fields with original reserves of 144 billion barrels of oil (BBO) and more than 1,200 trillion cubic feet of gas (TCFG) have been discovered. The principal oil reserves and most of the oil fields are in the southern half of the basin, whereas the northern half contains mainly gas reserves. Sedimentary strata in the basin consist of Upper Triassic through Tertiary clastic rocks. Most oil is produced from Neocomian (Lower Cretaceous) marine to deltaic sandstone reservoirs, although substantial oil reserves are also in the marine Upper Jurassic and continental to paralic Lower to Middle Jurassic sequences. The majority of oil fields are in structural traps, which are gentle, platform-type anticlines with closures ranging from several tens of meters to as much as 150 meters (490 feet). Fields producing from stratigraphic traps are generally smaller except for the giant Talin field which contains oil in Jurassic river-valley sandstones. Principal source rocks are organic-rich marine shales of the Volgian (uppermost Jurassic) Bazhenov Formation, which is 30-50 m (98- 164 feet) thick. Bazhenov-derived oils are mostly of medium gravity, and contain 0.8-1.3 percent sulfur and 2-5 percent paraffin. Oils in the Lower to Middle Jurassic clastics were sourced from lacustrine and estuarine shales of the Toarcian Togur Bed. These oils are medium to low gravity, with low sulfur (less than 0.25 percent) and high paraffin (commonly to 10 percent) contents. Among the 42 fields analyzed for reserve growth, 30 fields are located in the Middle Ob region, which includes the Samotlor field with reserves of more than 25 BBO and the Fedorov field with reserves of about 5 BBO. Data used in the study include year of discovery, year of first production, annual and cumulative production, and remaining reserves reported by Russian reserve categories (A+B+C1 and C2) in January of each year. Correlation of these Russian resource categories to U.S. categories of the Society of Petroleum Engineers classification is complex and somewhat uncertain. Reserve growth in oil fields of West Siberia was calculated using a newly developed Group Growth method, which requires that the total reserve (proven reserve plus cumulative production) of individual fields with an equal length of reserve record be added together starting with discovery year or the first production year. Then the annual growth factor (AGF), which is the ratio of total reserves of two consecutive years, is calculated for all years. Once AGFs have been calculated, the cumulative growth factor (CGF) is calculated by multiplying the AGFs of all the previous years. The CGF data are used to develop reserve growth models. The West Siberian oil fields show a 13-fold reserve growth 20 years after the discovery year and only about a 2-fold growth after the first production year. This difference is attributed to extensive exploration and field delineation activities between the discovery and the first production years. Because of uncertainty in the length of evaluation time and in reported reserves during this initial period, reserve growth based on the first production year is more reliable for model development. However, reserve growth models based both on discovery year and first production year show rapid growth in the first few years and slower growth in the following years. In contrast, the reserve growth patterns for the conterminous United States and offshore Gulf of Mexico show a steady reserve increase throughout the productive lives of the fields. The different reserve booking requirements and the lack of capital investment for improved reservoir management and production technologies in West Siberian fields relative to U.S. fields are the probable causes for the difference in growth patterns. Reserve growth models based on the first production year predict that the reserve growth potential in the 42 largest oil fields of West Siberia over a five-year period (1998-2003) ranges from 270-330 million barrels or 0.34-0.42 percent per year. For a similar five-year period (1996-2001), models for the conterminous United States predict a growth of 0.54-0.75 percent per year. This abstract presents the contents of a poster prepared for the AAPG Hedberg Research Conference on Understanding World Oil Resources, November 12-17, 2006 - Colorado Springs, Colorado. A paper 'Reserve Growth in Oil Fields of West Siberian Basin, Russia' was published in Natural Resources Research, v. 12, no. 2, June, 2003.

Open-File Report↗
USGS2014

Mineral commodity summaries 2014

Each chapter of the 2014 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production and resources. The MCS is the earliest comprehensive source of 2013 mineral production data for the world. More than 90 individual minerals and materials are covered by two-page synopses. For mineral commodities for which there is a Government stockpile, detailed information concerning the stockpile status is included in the two-page synopsis. Abbreviations and units of measure, and definitions of selected terms used in the report, are in Appendix A and Appendix B, respectively. “Appendix C—Reserves and Resources” includes “Part A—Resource/Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is Appendix D. The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2014 are welcomed.

Mineral Commodity Summaries↗
USGS2015

Mineral commodity summaries 2015

Each chapter of the 2015 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production and resources. The MCS is the earliest comprehensive source of 2014 mineral production data for the world. More than 90 individual minerals and materials are covered by two-page synopses. For mineral commodities for which there is a Government stockpile, detailed information concerning the stockpile status is included in the two-page synopsis. Abbreviations and units of measure, and definitions of selected terms used in the report, are in Appendix A and Appendix B, respectively. "Appendix C—Reserves and Resources” includes “Part A—Resource/Reserve Classification for Minerals” and “Part B—Sources of Reserves Data." A directory of USGS minerals information country specialists and their responsibilities is Appendix D. The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2015 are welcomed.

Mineral Commodity Summaries↗
USGS2013

Mineral commodity summaries 2013

Each chapter of the 2013 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production and resources. The MCS is the earliest comprehensive source of 2012 mineral production data for the world. More than 90 individual minerals and materials are covered by two-page synopses. For mineral commodities for which there is a Government stockpile, detailed information concerning the stockpile status is included in the two-page synopsis. Abbreviations and units of measure, and definitions of selected terms used in the report, are in Appendix A and Appendix B, respectively. “Appendix C—Reserves and Resources” includes “Part A—Resource/Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is Appendix D. The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2013 are welcomed.

Mineral Commodity Summaries↗
USGS2002

3D near-surface soil response from H/V ambient-noise ratios

The applicability of the horizontal-to-vertical (H/V) ambient-noise spectral ratio for characterizing earthquake site effects caused by nearsurface topography and velocity structures was evaluated at sites underlain by thick (i.e. >100 m) sediment deposits near the southern-end of the New Madrid seismic zone in the central United States. Three-component ambient-noise and velocity models derived from seismic (shearwave) refraction/reflection surveys showed that a relatively horizontal, sharp shear-wave velocity interface in the soil column resulted in an H/V spectral ratio with a single well-defined peak. Observations at sites with more than one sharp shear-wave velocity contrast and horizontally arranged soil layers resulted in at least two well-defined H/V spectral ratio peaks. Furthermore, at sites where there were sharp shear-wave velocity contrasts in nonhorizontal, near-surface soil layers, the H/V spectra exhibited a broad-bandwidth, relatively low amplitude signal instead of a single well-defined peak.

Soil Dynamics and Earthquake Engineering↗
USGS2016

Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow

The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba and the State of North Dakota. In May and June of 2011, record-setting rains were seen in the headwater areas of the basin. Emergency spillways of major reservoirs were discharging at full or nearly full capacity, and extensive flooding was seen in numerous downstream communities. To determine the probability of future extreme floods and droughts, the U.S. Geological Survey, in cooperation with the North Dakota State Water Commission, developed a stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural (unregulated) streamflow. Simulations from the model can be used in future studies to simulate regulated streamflow, design levees, and other structures; and to complete economic cost/benefit analyses. Long-term climatic variability was analyzed using tree-ring chronologies to hindcast precipitation to the early 1700s and compare recent wet and dry conditions to earlier extreme conditions. The extended precipitation record was consistent with findings from the Devils Lake and Red River of the North Basins (southeast of the Souris River Basin), supporting the idea that regional climatic patterns for many centuries have consisted of alternating wet and dry climate states. A stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration for the Souris River Basin was developed using recorded meteorological data and extended precipitation records provided through tree-ring analysis. A significant climate transition was seen around1970, with 1912–69 representing a dry climate state and 1970–2011 representing a wet climate state. Although there were some distinct subpatterns within the basin, the predominant differences between the two states were higher spring through early fall precipitation and higher spring potential evapotranspiration for the wet compared to the dry state. A water-balance model was developed for simulating monthly natural (unregulated) mean streamflow based on precipitation, temperature, and potential evapotranspiration at select streamflow-gaging stations. The model was calibrated using streamflow data from the U.S. Geological Survey and Environment Canada, along with natural (unregulated) streamflow data from the U.S. Army Corps of Engineers. Correlation coefficients between simulated and natural (unregulated) flows generally were high (greater than 0.8), and the seasonal means and standard deviations of the simulated flows closely matched the means and standard deviations of the natural (unregulated) flows. After calibrating the model for a monthly time step, monthly streamflow for each subbasin was disaggregated into three values per month, or an approximately 10-day time step, and a separate routing model was developed for simulating 10-day streamflow for downstream gages. The stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration was combined with the water-balance model to simulate potential future sequences of 10-day mean streamflow for each of the streamflow-gaging station locations. Flood risk, as determined by equilibrium flow-frequency distributions for the dry (1912–69) and wet (1970–2011) climate states, was considerably higher for the wet state compared to the dry state. Future flood risk will remain high until the wet climate state ends, and for several years after that, because there may be a long lag-time between the return of drier conditions and the onset of a lower soil-moisture storage equilibrium.

Manitoba, North Dakota, Saskatchewan↗