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USGS2013

Shifts in controls on the temporal coherence of throughfall chemical flux in Acadia National Park, Maine, USA

Major ion and mercury (Hg) inputs to terrestrial ecosystems include both wet and dry deposition (total deposition). Estimating total deposition to sensitive receptor sites is hampered by limited information regarding its spatial heterogeneity and seasonality. We used measurements of throughfall flux, which includes atmospheric inputs to forests and the net effects of canopy leaching or uptake, for ten major ions and Hg collected during 35 time periods in 1999–2005 at over 70 sites within Acadia National Park, Maine to (1) quantify coherence in temporal dynamics of seasonal throughfall deposition and (2) examine controls on these patterns at multiple scales. We quantified temporal coherence as the correlation between all possible site pairs for each solute on a seasonal basis. In the summer growing season and autumn, coherence among pairs of sites with similar vegetation was stronger than for site-pairs that differed in vegetation suggesting that interaction with the canopy and leaching of solutes differed in coniferous, deciduous, mixed, and shrub or open canopy sites. The spatial pattern in throughfall hydrologic inputs across Acadia National Park was more variable during the winter snow season, suggesting that snow re-distribution affects net hydrologic input, which consequently affects chemical flux. Sea-salt corrected calcium concentrations identified a shift in air mass sources from maritime in winter to the continental industrial corridor in summer. Our results suggest that the spatial pattern of throughfall hydrologic flux, dominant seasonal air mass source, and relationship with vegetation in winter differ from the spatial pattern of throughfall flux in these solutes in summer and autumn. The coherence approach applied here made clear the strong influence of spatial heterogeneity in throughfall hydrologic inputs and a maritime air mass source on winter patterns of throughfall flux. By contrast, vegetation type was the most important influence on throughfall chemical flux in summer and autumn.

Maine↗

USGS2004

The springs of Lake Pátzcuaro: chemistry, salt-balance, and implications for the water balance of the lake

Lake Pa??tzcuaro, the center of the ancient Tarascan civilization located in the Mexican altiplano west of the city of Morelia, has neither river input nor outflow. The relatively constant lake-salinity over the past centuries indicates the lake is in chemical steady state. Springs of the south shore constitute the primary visible input to the lake, so influx and discharge must be via sub-lacustrine ground water. The authors report on the chemistry and stable isotope composition of the springs, deeming them representative of ground-water input. The springs are dominated by Ca, Mg and Na, whereas the lake is dominated by Na. Combining these results with previously published precipitation/rainfall measurements on the lake, the authors calculate the chemical evolution from spring water to lake water, and also calculate a salt balance of the ground-water-lake system. Comparing Cl and ??18O compositions in the springs and lake water indicates that 75-80% of the spring water is lost evaporatively during evolution toward lake composition. During evaporation Ca and Mg are lost from the water by carbonate precipitation. Each liter of spring water discharging into the lake precipitates about 18.7 mg of CaCO3. Salt balance calculations indicate that ground water input to the lake is 85.9??106 m3/a and ground water discharge from the lake is 23.0??106 m3/a. Thus, the discharge is about 27% of the input, with the rest balanced by evaporation. A calculation of time to reach steady-state ab initio indicates that the Cl concentration of the present day lake would be reached in about 150 a. ?? 2004 Elsevier Ltd. All rights reserved.

Applied Geochemistry↗

USGS2019

A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States

"Recovery factor (RF) is one of the most fundamental parameters that define engineering and economical success of any operational phase in oil and gas production. The effectiveness of the operation, e.g. CO2-EOR (enhanced oil recovery with carbon dioxide injection), is usually defined by multiplying the resultant recovery factor by the original oil in place. Moreover, investment decisions for such engineering projects are also performed based on predicted recovery factors. Despite its importance, though, it is not easy to predict recovery factors as they are affected by many factors including the type of the recovery process, reservoir type, fluid properties, reservoir heterogeneity, depth, thickness, to name a few. The usual method of estimating recovery factors is laboratory experiments or numerical modeling, each of which has their own limitations due to data requirements, boundary conditions and scale effects. In this work, a fuzzy inference system approach has been adopted to predict miscible CO2-EOR recovery factors of the major field applications in the United States with the premise that it can be used as a guidance tool for making decisions based on different inputs. The fuzzy system was build using a Mamdani-type fuzzy logic inference engine, and by using reservoir data compiled from different sources as inputs and recovery factors gathered from a literature survey. Due to the limited number of field cases that could be used for this purpose, 24 sets of applications were included in the study. Selected input variables were water saturation after waterflood (Sorw), well spacing, porosity, permeability, depth, net pay thickness, initial pressure, API gravity of oil, hydrocarbon pore volume CO2 injected, and reservoir lithology. The type of membership functions were decided based on the system’s predictive performance. The model showed reasonable predictive capability for the field observations of recovery factor despite the complexity of this parameter. In addition, since the fuzzy solution was multi-dimensional due to multiple inputs, system behavior was used to demonstrate response of miscible CO2-EOR recovery factor to different inputs. "

Journal of Petroleum Science and Engineering↗

USGS1998

Effect of chronic nitrogen additions on soil nitrogen fractions in red spruce stands

The responses of temperate and boreal forest ecosystems to increased nitrogen (N) inputs have been varied, and the responses of soil N pools have been difficult to measure. In this study, fractions and pool sizes of N were determined in the forest floor of red spruce stands at four sites in the northeastern U.S. to evaluate the effect of increased N inputs on forest floor N. Two of the stands received 100 kg N ha-1 yr-1 for three years, one stand received 34 kg N ha-1 yr-1 for six years, and the remaining stand received only ambient N inputs. No differences in total N content or N fractions were measured in samples of the Oie and Oa horizons between treated and control plots in the three sites that received N amendments. The predominant N fraction in these samples was amino acid N (31-45 % of total N), followed by hydrolyzable unidentified N (16-31% of total N), acid- soluble N (18-22 % of total N), and NH4/+-N (9-13 % of total N). Rates of atmospheric deposition varied greatly among the four stands. Ammonium N and amino acid N concentrations in the Oie horizon were positively related to wet N deposition, with respective r2 values of 0.92 and 0.94 (n = 4, p < 0.05). These relationships were somewhat stronger than that observed between atmospheric wet N deposition and total N content of the forest floor, suggesting that these pools retain atmospherically deposited N. The NH4/+- N pool may represent atmospherically deposited N that is incorporated into organic matter, whereas the amino acid N pool could result from microbial immobilization of atmospheric N inputs. The response of forest floor N pools to applications of N may be masked, possibly by the large soil N pool, which has been increased by the long-term input of N from atmospheric deposition, thereby overwhelming the short-term treatments.

Water, Air, & Soil Pollution↗

USGS1997

Accumulation and turnover of carbon in organic and mineral soils of the BOREAS northern study area

Rates of input, accumulation, and turnover of C differ markedly within soil profiles and in soils with different drainage in the BOREAS northern study area. Soil C storage increases from ∼3 kg C m −2 in well-drained, sandy soils to greater than 100 kg C m −2 in wetlands. Two modes of C accumulation were observed in upland soil profiles. Large annual C inputs (0.06–0.1 kg C m −2 yr −1 ) and slow decomposition (turnover times of 6–250 years) lead to rapid C accumulation in regrowing surface moss and detrital layers following fire. Deep organic layers that have accumulated over the millennia since the initiation of soil development, and are located below the most recent charred horizon, show slower rates of input (0.015–0.03 kg C m −2 yr −1 ) and turnover (100–1600 years) and accumulate C about 10 times slower than surface detrital layers. Rates of C input to soils derived from C and 14 C data were in accord with net primary production estimates, with highest rates of input (0.14–0.6 kg C m −2 yr −1 ) in wetlands. Turnover times for C in surface detrital layers were 6–15 years for well-drained sand soils that showed highest soil temperatures in summer, 30–40 years for wetlands, and 36–250 years for uplands with thick moss cover and black spruce trees. Long (>100 years) turnover times in upland black spruce/clay soils most likely reflect the influence of woody debris incorporated into detrital layers. Turnover times for deep organic and mineral layer C were controlled by drainage, with fastest turnover (80–130 years) in well-drained sand soils and slowest turnover (>3000 years) in wetlands. Total C accumulation rates, which account for C losses from both deep organic and surface detrital layers, are close to zero for sand/jack pine soils, 0.003–0.01 kg C m −2 yr −1 for moderately to poorly drained sites in mature forest stands, and 0.03 kg C m −2 yr −1 for a productive fen. Decomposition of organic matter more than several decades old accounts for 9–22% of total heterotrophic respiration at these sites. The rates of C accumulation derived here are decadal averages for specific stands and will vary as stands age or undergo disturbance. Extrapolation to larger regions and longer timescales, where burning offsets C gains in moss layers, will yield smaller rates of C storage.

Journal of Geophysical Research D: Atmospheres↗

USGS2001

Ecosystem processes and nitrogen export in northern U.S. watersheds.

There is much interest in the relationship of atmospheric nitrogen (N) inputs to ecosystem outputs as an indicator of possible "nitrogen saturation" by human activity. Longer-term, ecosystem-level mass balance studies suggest that the relationship is not clear and that other ecosystem processes may dominate variation in N outputs. We have been studying small, forested watershed ecosystems in five northern watersheds for periods up to 35 years. Here I summarize the research on ecosystem processes and the N budget. During the past 2 decades, average wet-precipitation N inputs ranged from about 0.1 to 6 kg N ha(-1) year(-1) among sites. In general, sites with the lowest N inputs had the highest output-to-input ratios. In the Alaska watersheds, streamwater N output exceeded inputs by 70 to 250%. The ratio of mean monthly headwater nitrate (NO3-) concentration to precipitation NO3- concentration declined with increased precipitation concentration. A series of ecosystem processes have been studied and related to N outputs. The most important appear to be seasonal change in hydrologic flowpath, soil freezing, seasonal forest-floor inorganic N pools resulting from over-winter mineralization beneath the snowpack, spatial variation in watershed forest-floor inorganic N pools, the degree to which snowmelt percolates soils, and gross soil N mineralization rates.

Scientific World Journal↗

USGS2005

MODFLOW 2000 Head Uncertainty, a First-Order Second Moment Method

A computationally efficient method to estimate the variance and covariance in piezometric head results computed through MODFLOW 2000 using a first-order second moment (FOSM) approach is presented. This methodology employs a first-order Taylor series expansion to combine model sensitivity with uncertainty in geologic data. MODFLOW 2000 is used to calculate both the ground water head and the sensitivity of head to changes in input data. From a limited number of samples, geologic data are extrapolated and their associated uncertainties are computed through a conditional probability calculation. Combining the spatially related sensitivity and input uncertainty produces the variance-covariance matrix, the diagonal of which is used to yield the standard deviation in MODFLOW 2000 head. The variance in piezometric head can be used for calibrating the model, estimating confidence intervals, directing exploration, and evaluating the reliability of a design. A case study illustrates the approach, where aquifer transmissivity is the spatially related uncertain geologic input data. The FOSM methodology is shown to be applicable for calculating output uncertainty for (1) spatially related input and output data, and (2) multiple input parameters (transmissivity and recharge).

Ground Water↗

USGS2007

Wetland ecosystems studies from a hydrologic perspective

Selected studies from the literature were reviewed to determine the extent of knowledge about the relationship between hydrology and wetland ecosystem studies. Wetland studies of chemical input-output relationships have been the most dependent on hydrologic data of all wetland investigations; yet, very few of these studies have attempted to measure all components of a wetland's water balance. Usually, unmeasured components were calculated as the difference between measured inputs and outputs. Ground water frequently was overlooked. Chemical input-output investigations primarily were concerned with determining the amount of input retained in the wetlands. Few studies also included direct measurement of biogeochemical processes within wetlands of elements that were part of simultaneous input-output investigations. The importance of uncertainties in chemical budgets that are due to uncertainties in hydrologic budgets has been addressed in very few wetland investigations. Although many studies have emphasized the importance of hydrology to wetland ecosystem research, few studies have documented this, so that hydrology remains one of the least understood components of wetland ecosystems.

Journal of the American Water Resources Associatio↗

USGS2005

Nitrogen retention across a gradient of 15N additions to an unpolluted temperate forest soil in Chile

Accelerated nitrogen (N) inputs can drive nonlinear changes in N cycling, retention, and loss in forest ecosystems. Nitrogen processing in soils is critical to understanding these changes, since soils typically are the largest N sink in forests. To elucidate soil mechanisms that underlie shifts in N cycling across a wide gradient of N supply, we added 15 NH 4 15 NO 3 at nine treatment levels ranging in geometric sequence from 0.2 kg to 640 kg N·ha −1 ·yr −1 to an unpolluted old-growth temperate forest in southern Chile. We recovered roughly half of 15 N tracers in 0–25 cm of soil, primarily in the surface 10 cm. Low to moderate rates of N supply failed to stimulate N leaching, which suggests that most unrecovered 15 N was transferred from soils to unmeasured sinks above ground. However, soil solution losses of nitrate increased sharply at inputs >160 kg N·ha −1 ·yr −1 , corresponding to a threshold of elevated soil N availability and declining 15 N retention in soil. Soil organic matter (<5.6 mm) dominated tracer retention at low rates of N input, but coarse roots and particulate organic matter became increasingly important at higher N supply. Coarse roots and particulate organic matter together accounted for 38% of recovered 15 N in soils at the highest N inputs and may explain a substantial fraction of the “missing N” often reported in studies of fates of N inputs to forests. Contrary to expectations, N additions did not stimulate gross N cycling, potential nitrification, or ammonium oxidizer populations. Our results indicate that the nonlinearity in N retention and loss resulted directly from excessive N supply relative to sinks, independent of plant–soil–microbial feedbacks. However, N additions did induce a sharp decrease in microbial biomass C:N that is predicted by N saturation theory, and which could increase long-term N storage in soil organic matter by lowering the critical C:N ratio for net N mineralization. All measured sinks accumulated 15 N tracers across the full gradient of N supply, suggesting that short-term nonlinearity in N retention resulted from saturation of uptake kinetics, not uptake capacity, in plant, soil, and microbial pools.

Parque Nacional Chiloé↗

USGS2006

Quantitative methods to direct exploration based on hydrogeologic information

Quantitatively Directed Exploration (QDE) approaches based on information such as model sensitivity, input data covariance and model output covariance are presented. Seven approaches for directing exploration are developed, applied, and evaluated on a synthetic hydrogeologic site. The QDE approaches evaluate input information uncertainty, subsurface model sensitivity and, most importantly, output covariance to identify the next location to sample. Spatial input parameter values and covariances are calculated with the multivariate conditional probability calculation from a limited number of samples. A variogram structure is used during data extrapolation to describe the spatial continuity, or correlation, of subsurface information. Model sensitivity can be determined by perturbing input data and evaluating output response or, as in this work, sensitivities can be programmed directly into an analysis model. Output covariance is calculated by the First-Order Second Moment (FOSM) method, which combines the covariance of input information with model sensitivity. A groundwater flow example, modeled in MODFLOW-2000, is chosen to demonstrate the seven QDE approaches. MODFLOW-2000 is used to obtain the piezometric head and the model sensitivity simultaneously. The seven QDE approaches are evaluated based on the accuracy of the modeled piezometric head after information from a QDE sample is added. For the synthetic site used in this study, the QDE approach that identifies the location of hydraulic conductivity that contributes the most to the overall piezometric head variance proved to be the best method to quantitatively direct exploration. ?? IWA Publishing 2006.

Journal of Hydroinformatics↗

USGS2013

Topobathymetric model of Mobile Bay, Alabama

Topobathymetric Digital Elevation Models (DEMs) are a merged rendering of both topography (land elevation) and bathymetry (water depth) that provides a seamless elevation product useful for inundation mapping, as well as for other earth science applications, such as the development of sediment-transport, sea-level rise, and storm-surge models. This 1/9-arc-second (approximately 3 meters) resolution model of Mobile Bay, Alabama was developed using multiple topographic and bathymetric datasets, collected on different dates. The topographic data were obtained primarily from the U.S. Geological Survey (USGS) National Elevation Dataset (NED) (http://ned.usgs.gov/) at 1/9-arc-second resolution; USGS Experimental Advanced Airborne Research Lidar (EAARL) data (2 meters) (http://pubs.usgs.gov/ds/400/); and topographic lidar data (2 meters) and Compact Hydrographic Airborne Rapid Total Survey (CHARTS) lidar data (2 meters) from the U.S. Army Corps of Engineers (USACE) (http://www.csc.noaa.gov/digitalcoast/data/coastallidar/). Bathymetry was derived from digital soundings obtained from the National Oceanic and Atmospheric Administration’s (NOAA) National Geophysical Data Center (NGDC) (http://www.ngdc.noaa.gov/mgg/geodas/geodas.html) and from water-penetrating lidar sources, such as EAARL and CHARTS. Mobile Bay is ecologically important as it is the fourth largest estuary in the United States. The Mobile and Tensaw Rivers drain into the bay at the northern end with the bay emptying into the Gulf of Mexico at the southern end. Dauphin Island (a barrier island) and the Fort Morgan Peninsula form the mouth of Mobile Bay. Mobile Bay is 31 miles (50 kilometers) long by a maximum width of 24 miles (39 kilometers) with a total area of 413 square miles (1,070 square kilometers). The vertical datum of the Mobile Bay topobathymetric model is the North American Vertical Datum of 1988 (NAVD 88). All the topographic datasets were originally referenced to NAVD 88 and no transformations were made to these input data. The NGDC hydrographic, multibeam, and trackline surveys were transformed from mean low water (MLW) or mean lower low water (MLLW) to NAVD 88 using VDatum (http://vdatum.noaa.gov). VDatum is a tool developed by the National Geodetic Survey (NGS) that performs transformations among tidal, ellipsoid-based, geoid-based, and orthometric datums using calibrated hydrodynamic models. The vertical accuracy of the input topographic data varied depending on the input source. Because the input elevation data were derived primarily from lidar, the vertical accuracy ranges from 6 to 20 centimeters in root mean square error (RMSE). he horizontal datum of the Mobile Bay topobathymetric model is the North American Datum of 1983 (NAD 83), geographic coordinates. All the topographic and bathymetric datasets were originally referenced to NAD 83, and no transformations were made to the input data. The bathymetric surveys were downloaded referenced to NAD 83 geographic, and therefore no horizontal transformations were required. The topbathymetric model of Mobile Bay and detailed metadata can be obtained from the USGS Web sites: http://nationalmap.gov/.

Alabama↗

USGS2012

Slope-Area Computation Program Graphical User Interface 1.0—A Preprocessing and Postprocessing Tool for Estimating Peak Flood Discharge Using the Slope-Area Method

The slope-area method is a technique for estimating the peak discharge of a flood after the water has receded (Dalrymple and Benson, 1967). This type of discharge estimate is called an “indirect measurement” because it relies on evidence left behind by the flood, such as high-water marks (HWMs) on trees or buildings. These indicators of flood stage are combined with measurements of the cross-sectional geometry of the stream, estimates of channel roughness, and a mathematical model that balances the total energy of the flow between cross sections. This is in contrast to a “direct” measurement of discharge during the flood where cross-sectional area is measured and a current meter or acoustic equipment is used to measure the water velocity. When a direct discharge measurement cannot be made at a gage during high flows because of logistics or safety reasons, an indirect measurement of a peak discharge is useful for defining the high-flow section of the stage-discharge relation (rating curve) at the stream gage, resulting in more accurate computation of high flows. The Slope-Area Computation program (SAC; Fulford, 1994) is an implementation of the slope-area method that computes a peak-discharge estimate from inputs of water-surface slope (from surveyed HWMs), channel geometry, and estimated channel roughness. SAC is a command line program written in Fortran that reads input data from a formatted text file and prints results to another formatted text file. Preparing the input file can be time-consuming and prone to errors. This document describes the SAC graphical user interface (GUI), a crossplatform “wrapper” application that prepares the SAC input file, executes the program, and helps the user interpret the output. The SAC GUI is an update and enhancement of the slope-area method (SAM; Hortness, 2004; Berenbrock, 1996), an earlier spreadsheet tool used to aid field personnel in the completion of a slope-area measurement. The SAC GUI reads survey data, develops a plan-view plot, water-surface profile, cross-section plots, and develops the SAC input file. The SAC GUI also develops HEC-2 files that can be imported into HEC–RAS.

Fact Sheet↗

USGS2007

Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions in a Subbasin of the Red River of the North

Executive Summary Concern over flooding along rivers in the Prairie Pothole Region has stimulated interest in developing spatially distributed hydrologic models to simulate the effects of wetland water storage on peak river flows. Such models require spatial data on the storage volume and interception area of existing and restorable wetlands in the watershed of interest. In most cases, information on these model inputs is lacking because resolution of existing topographic maps is inadequate to estimate volume and areas of existing and restorable wetlands. Consequently, most studies have relied on wetland area to volume or interception area relationships to estimate wetland basin storage characteristics by using available surface area data obtained as a product from remotely sensed data (e.g., National Wetlands Inventory). Though application of areal input data to estimate volume and interception areas is widely used, a drawback is that there is little information available to provide guidance regarding the application, limitations, and biases associated with such approaches. Another limitation of previous modeling efforts is that water stored by wetlands within a watershed is treated as a simple lump storage component that is filled prior to routing overflow to a pour point or gaging station. This approach does not account for dynamic wetland processes that influence water stored in prairie wetlands. Further, most models have not considered the influence of human-induced hydrologic changes, such as land use, that greatly influence quantity of surface water inputs and, ultimately, the rate that a wetland basin fills and spills. The goals of this study were to (1) develop and improve methodologies for estimating and spatially depicting wetland storage volumes and interceptions areas and (2) develop models and approaches for estimating/simulating the water storage capacity of potentially restorable and existing wetlands under various restoration, land use, and climatic scenarios. To address these goals, we developed models and approaches to spatially represent storage volumes and interception areas of existing and potentially restorable wetlands in the upper Mustinka subbasin within Grant County, Minn. We then developed and applied a model to simulate wetland water storage increases that would result from restoring 25 and 50 percent of the farmed and drained wetlands in the upper Mustinka subbasin. The model simulations were performed during the growing season (May-October) for relatively wet (1993; 0.79 m of precipitation) and dry (1987; 0.40 m of precipitation) years. Results from the simulations indicated that the 25 percent restoration scenario would increase water storage by 21-24 percent and that a 50 percent scenario would increase storage by 34-38 percent. Additionally, we estimated that wetlands in the subbasin have potential to store 11.57-20.98 percent of the total precipitation that fell over the entire subbasin area (52,758 ha). Our simulation results indicated that there is considerable potential to enhance water storage in the subbasin; however, evaluation and calibration of the model is necessary before simulation results can be applied to management and planning decisions. In this report we present guidance for the development and application of models (e.g., surface area-volume predictive models, hydrology simulation model) to simulate wetland water storage to provide a basis from which to understand and predict the effects of natural or human-induced hydrologic alterations. In developing these approaches, we tried to use simple and widely available input data to simulate wetland hydrology and predict wetland water storage for a specific precipitation event or a series of events. Further, the hydrology simulation model accounted for land use and soil type, which influence surface water inputs to wetlands. Although information presented in this report is specific to the Mustinka subbasin, the approaches

Open-File Report↗

USGS2012

User's guide for mapIMG 3--Map image re-projection software package

Version 0.0 (1995), Dan Steinwand, U.S. Geological Survey (USGS)/Earth Resources Observation Systems (EROS) Data Center (EDC)--Version 0.0 was a command line version for UNIX that required four arguments: the input metadata, the output metadata, the input data file, and the output destination path. Version 1.0 (2003), Stephen Posch and Michael P. Finn, USGS/Mid-Continent Mapping Center (MCMC--Version 1.0 added a GUI interface that was built using the Qt library for cross platform development. Version 1.01 (2004), Jason Trent and Michael P. Finn, USGS/MCMC--Version 1.01 suggested bounds for the parameters of each projection. Support was added for larger input files, storage of the last used input and output folders, and for TIFF/ GeoTIFF input images. Version 2.0 (2005), Robert Buehler, Jason Trent, and Michael P. Finn, USGS/National Geospatial Technical Operations Center (NGTOC)--Version 2.0 added Resampling Methods (Mean, Mode, Min, Max, and Sum), updated the GUI design, and added the viewer/pre-viewer. The metadata style was changed to XML and was switched to a new naming convention. Version 3.0 (2009), David Mattli and Michael P. Finn, USGS/Center of Excellence for Geospatial Information Science (CEGIS)--Version 3.0 brings optimized resampling methods, an updated GUI, support for less than global datasets, UTM support and the whole codebase was ported to Qt4.

Open-File Report↗

USGS2014

Response of Global Navigation Satellite System receivers to known shaking between 0.2 and 20 Hertz

Over the past decade, several technological advances have allowed Global Navigation Satellite Systems (GNSS) receivers to have the capability to record displacements at high frequencies, with sampling rates approaching 100 samples per second (sps). In addition, communication and computer hardware and software have allowed various institutions, including the U.S. Geological Survey (USGS), to retrieve, process, and display position changes recorded by a network of GNSS sites with small, less than 1-s delays between the time that the GNSS receiver records signals from a constellation of satellites and the time that the position is estimated (a method known as “real-time”). These improvements in hardware and software have allowed the USGS to process GNSS (or a subset of the GNSS, the Global Positioning System, GPS) data in real-time at 1 sps with the goal of determining displacements from earthquakes and volcanoes in real-time. However, the current set of GNSS equipment can record at rates of 100 sps, which allows the possibility of using this equipment to record earthquake displacements over the full range of frequencies that typically are recorded by acceleration and velocity transducers. The advantage of using GNSS to record earthquakes is that the displacement, rather than acceleration or velocity, is recorded, and for large earthquakes, the GNSS sensor stays on scale and will not distort the observations due to clipping of the signal at its highest amplitude. The direct observation of displacement is advantageous in estimating the size and spatial extent of the earthquake rupture. Otherwise, when using velocity or acceleration sensors, the displacements are determined by numerical integration of the observations, which can introduce significant uncertainty in the estimated displacements. However, GNSS technology can, at best, resolve displacements of a few millimeters, and for most earthquakes, their displacements are less than 1 mm. Consequently, to be useful, GNSS data are only relevant for the large earthquakes with magnitudes (M) exceeding M5.5 at best. With the capability to record GNSS data at high-rate, at sampling rates typical for seismological applications, experiments are needed to quantify the response of GNSS to shaking from earthquakes. There have been a few studies that examine the response of GNSS to strong shaking. One of the first was Elosegui and others (2006), where they simulated surface waves from a distant earthquake and mechanically applied the shaking to a GPS antenna. They processed the 1 sps observations and compared the estimated displacements with the simulated displacements. They determined that the GPS could accurately track the simulated surface wave whose primary frequency spans from 0.01 to 0.1 Hertz (Hz), which spanned the frequency band of the simulation. To test GNSS equipment due to shaking from a large earthquake in the near-field, Wang and others (2012) used a mechanical simulator or shake table with 6 degrees of freedom and studied two different inputs to the simulator—(1) the accelerometer record from one station that was located near the 2010 M8.8 Maule, Chile earthquake, and (2) a 2-Hz sinusoid. Wang and others (2012) analyzed the 2-Hz data with spectral analysis and determined that the displacements observed by the GPS included higher harmonics along with the 2-Hz signal. In addition, the background spectral amplitude was greater during periods of 2-Hz shaking than when at rest. With the simulated M 8.8 earthquake, Wang and others (2012) observed decreased signal to noise for L1 and L2 carrier frequencies of the GPS signal, at times corresponding to high acceleration and jerk (first derivative of acceleration). One of the principal limitations of these experiments was that the displacements of the shake table itself could not be measured independently. Although with the 2-Hz sinusoidal measurements, the input displacements were purely translational, Wang and others (2012) analysis of the data showed that the shake table also included rotational motions which affect horizontal inertial sensors like accelerometers and seismometers at first order. More recently, Ebinuma and Kato (2012) used a GPS simulator to electronically test several GNSS receivers and obtain the receiver characteristics at three frequencies: 1, 2, and 5 Hz. The results showed that the amplitude of 5-Hz displacements recorded by the GPS was, depending on the receiver model, between 30 and 125 percent more than the displacement input to the simulator. At low frequencies, the GPS displacement was nearly equal to the input displacement. In addition, Ebinuma and Kato (2012) examined how each receiver model amplified an earthquake displacement record in the 2–8 Hz band. The simulated earthquake was the 2008 moment magnitude (Mw) 6.8 Iwate-Miyagi earthquake where, for the simulated record, acceleration peaked at 1 G. The study discussed here builds on the tests by Ebinuma and Kato (2012), but rather than using electronic simulation, the tests are setup outdoors and closer to actual field installations of GNSS equipment. We used a one-dimensional shake table capable of 400 mm of displacement and high acceleration; the shake table also is constrained by a precision linear slider to have very low tilt that would affect inertial sensors. In addition, the stage position can be accurately monitored independent of the GNSS hardware and, importantly, provides a reference to compare with the estimated displacements from the GNSS data. Our tests spanned a greater frequency range from 0.2 to 20 Hz and we used equipment from three different manufacturers covering five different combinations of receivers and antennas. In addition, we have been able to simulate the frequency response of the GNSS equipment using a simple, causal filter. The quality of the filter was tested using additional test data where a step function in displacement was applied to the shake table. The observed displacements from the GNSS data show an overshoot in displacement at the time of the step or transition of the stage. That overshoot was accurately predicted using the filter design derived from our sinusoidal displacement tests. Similar to Wang and others (2012), we also examined the GPS displacement records using standard spectral techniques. However, we extended their work by evaluating several models of GNSS receivers using a variety of input frequencies. Because our shake table was limited on acceleration and displacement, we did not attempt to duplicate the high shaking associated with high magnitude earthquakes. However, because our shake table could measure the table displacement, we could directly compare the measured GPS displacements with the true displacements.

Open-File Report↗

USGS2015

Incorporating induced seismicity in the 2014 United States National Seismic Hazard Model: results of the 2014 workshop and sensitivity studies

The U.S. Geological Survey National Seismic Hazard Model for the conterminous United States was updated in 2014 to account for new methods, input models, and data necessary for assessing the seismic ground shaking hazard from natural (tectonic) earthquakes. The U.S. Geological Survey National Seismic Hazard Model project uses probabilistic seismic hazard analysis to quantify the rate of exceedance for earthquake ground shaking (ground motion). For the 2014 National Seismic Hazard Model assessment, the seismic hazard from potentially induced earthquakes was intentionally not considered because we had not determined how to properly treat these earthquakes for the seismic hazard analysis. The phrases “potentially induced” and “induced” are used interchangeably in this report, however it is acknowledged that this classification is based on circumstantial evidence and scientific judgment. For the 2014 National Seismic Hazard Model update, the potentially induced earthquakes were removed from the NSHM’s earthquake catalog, and the documentation states that we would consider alternative models for including induced seismicity in a future version of the National Seismic Hazard Model. As part of the process of incorporating induced seismicity into the seismic hazard model, we evaluate the sensitivity of the seismic hazard from induced seismicity to five parts of the hazard model: (1) the earthquake catalog, (2) earthquake rates, (3) earthquake locations, (4) earthquake Mmax (maximum magnitude), and (5) earthquake ground motions. We describe alternative input models for each of the five parts that represent differences in scientific opinions on induced seismicity characteristics. In this report, however, we do not weight these input models to come up with a preferred final model. Instead, we present a sensitivity study showing uniform seismic hazard maps obtained by applying the alternative input models for induced seismicity. The final model will be released after further consideration of the reliability and scientific acceptability of each alternative input model. Forecasting the seismic hazard from induced earthquakes is fundamentally different from forecasting the seismic hazard for natural, tectonic earthquakes. This is because the spatio-temporal patterns of induced earthquakes are reliant on economic forces and public policy decisions regarding extraction and injection of fluids. As such, the rates of induced earthquakes are inherently variable and nonstationary. Therefore, we only make maps based on an annual rate of exceedance rather than the 50-year rates calculated for previous U.S. Geological Survey hazard maps.

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USGS2015

csa2sac—A program for computing discharge from continuous slope-area stage data

Introduction Continuous Slope-Area (CSA) gages were developed by the Arizona Water Science Center to enable the estimation of hydrographs when direct measurements of discharge cannot be made (Smith and others, 2010). CSA gages extend standard U.S. Geological Survey (USGS) methods for determining peak discharges to mid and high flows over a hydrograph computed at regular intervals with indirect measurement methods (Benson and Dalrymple, 1967; Dalrymple and Benson, 1967). CSA gages combine continuous stage records at two or more (typically three or four) cross sections with crosssection surveys and estimates of channel roughness to compute discharge over a range of flows. With standard indirect methods of determining peak discharge, water-surface elevation in the study reach at the peak flow is estimated from surveys of debris associated with the peak-flow water line. With CSA gages, stages are continuously measured at the cross sections, at regular and synchronized intervals (typically 5 minutes) over a flow event, and discharge can be calculated at each interval. Calculation of discharge using indirect methods has been automated with the slope-area computation (SAC) program (Fulford, 1994). SAC is a widely used program within the USGS; it is easily run and displays output in a clear and convenient format, which includes flags that alert the user to shortcomings in the calculation. Use of SAC has been facilitated by SACGUI (Bradley, 2012; SACGUI uses a version of SAC called SAC7), a user interface that directly reads and displays survey data, allows for specification of water-surface slope and channel roughness, writes the input file for SAC7, runs SAC7, and displays SAC7 output. csa2sac is a program (appendix 1) that repeatedly runs SAC7 using stage data and a SAC7 input template file to compute the discharge at CSA gages. It is written in the C programming language, and is compatible with 64-bit Windows operating systems. The program reads a SAC7 input file and a file containing stage-data time series. It writes a new version of the SAC7 input file with the stage data for one time step, runs SAC7, then extracts computed discharges from the SAC7 output file and collates the discharges and stages to a separate file. It repeats these steps for each time interval in the stage file to produce a discharge time series from the stage data. csa2sac has been tested with two, three, four, and six cross sections and found to operate successfully. By running SAC7, csa2sac maintains consistency and comparability of both discharges calculated from CSA gages and of standard USGS methods for computing discharges indirectly. Brown and Metcalfe (2014) have made available alternative software for producing CSA discharges. In addition to csa2sac, the SAC7 program is required. It is the same as the original SAC program, except that it is compiled for 64-bit Windows operating systems and has a slightly different command line input. It is available online (http://water.usgs.gov/software/SAC/) as part of the SACGUI installation program. The program name, “SAC7.exe,” is coded into csa2sac, and must not be changed.

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USGS1978

Least-squares refinement of powder diffraction data for unit cell parameters: Program listing for DEC 10 computer

This FORTRAN computer program is a time-sharing version of the classic program of Appleman and Evans (1973) and Evans, Appleman, and Handwerker (1963), which indexes powder patterns and refines unit-cell dimensions. The program, originally written to be processed in batch mode for an IBM computer, was modified to run in time-sharing mode on the DEC 10 computer. This version does not depart philosophically from the original program; however, the input and output formats have been adapted to the time-sharing terminal. These modifications and the actual running of the program are not discussed in detail here, as this report is intended only to make the program itself available. Examples of the input and output formats are shown on pages 3-8. Input can be entered through the terminal by two methods. An input file may either be independently created and then entered into the program later, or created directly through the interactive mode of the program. With the latter method a file of the input information may also be saved for later use. Both methods allow for the data to be edited after each refinement run. The output format has been changed to an 80-column width, common to many terminals. Only the last refinement cycle is printed and some of the statistical computations have been surpressed. Using the "Unit Cell" program from the terminal in a "real time" situation emphasizes the versatility of the program. This ability to interact directly with the computer gives the user a better appreciation of the program, its options, the data, and the problem being solved.

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