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USGS2012

Estimating landscape carrying capacity through maximum clique analysis

Habitat suitability (HS) maps are widely used tools in wildlife science and establish a link between wildlife populations and landscape pattern. Although HS maps spatially depict the distribution of optimal resources for a species, they do not reveal the population size a landscape is capable of supporting--information that is often crucial for decision makers and managers. We used a new approach, "maximum clique analysis," to demonstrate how HS maps for territorial species can be used to estimate the carrying capacity, N(k), of a given landscape. We estimated the N(k) of Ovenbirds (Seiurus aurocapillus) and bobcats (Lynx rufus) in an 1153-km2 study area in Vermont, USA. These two species were selected to highlight different approaches in building an HS map as well as computational challenges that can arise in a maximum clique analysis. We derived 30-m2 HS maps for each species via occupancy modeling (Ovenbird) and by resource utilization modeling (bobcats). For each species, we then identified all pixel locations on the map (points) that had sufficient resources in the surrounding area to maintain a home range (termed a "pseudo-home range"). These locations were converted to a mathematical graph, where any two points were linked if two pseudo-home ranges could exist on the landscape without violating territory boundaries. We used the program Cliquer to find the maximum clique of each graph. The resulting estimates of N(k) = 236 Ovenbirds and N(k) = 42 female bobcats were sensitive to different assumptions and model inputs. Estimates of N(k) via alternative, ad hoc methods were 1.4 to > 30 times greater than the maximum clique estimate, suggesting that the alternative results may be upwardly biased. The maximum clique analysis was computationally intensive but could handle problems with < 1500 total pseudo-home ranges (points). Given present computational constraints, it is best suited for species that occur in clustered distributions (where the problem can be broken into several, smaller problems), or for species with large home ranges relative to grid scale where resampling the points to a coarser resolution can reduce the problem to manageable proportions.

Ecological Applications↗
USGS2007

OPR-PPR, a computer program for assessing data importance to model predictions using linear statistics

The OPR-PPR program calculates the Observation-Prediction (OPR) and Parameter-Prediction (PPR) statistics that can be used to evaluate the relative importance of various kinds of data to simulated predictions. The data considered fall into three categories: (1) existing observations, (2) potential observations, and (3) potential information about parameters. The first two are addressed by the OPR statistic; the third is addressed by the PPR statistic. The statistics are based on linear theory and measure the leverage of the data, which depends on the location, the type, and possibly the time of the data being considered. For example, in a ground-water system the type of data might be a head measurement at a particular location and time. As a measure of leverage, the statistics do not take into account the value of the measurement. As linear measures, the OPR and PPR statistics require minimal computational effort once sensitivities have been calculated. Sensitivities need to be calculated for only one set of parameter values; commonly these are the values estimated through model calibration. OPR-PPR can calculate the OPR and PPR statistics for any mathematical model that produces the necessary OPR-PPR input files. In this report, OPR-PPR capabilities are presented in the context of using the ground-water model MODFLOW-2000 and the universal inverse program UCODE_2005. The method used to calculate the OPR and PPR statistics is based on the linear equation for prediction standard deviation. Using sensitivities and other information, OPR-PPR calculates (a) the percent increase in the prediction standard deviation that results when one or more existing observations are omitted from the calibration data set; (b) the percent decrease in the prediction standard deviation that results when one or more potential observations are added to the calibration data set; or (c) the percent decrease in the prediction standard deviation that results when potential information on one or more parameters is added.

Techniques and Methods↗
USGS2017

A general modeling framework for describing spatially structured population dynamics

Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles

Ecology and Evolution↗
USGS2017

USGS Spectral Library Version 7

We have assembled a library of spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns [μm]). Laboratory samples of specific minerals, plants, chemical compounds, and manmade materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically constructed as well as mathematically computed mixtures. Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of Analytical Spectral Devices (ASD) field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Measurements of rocks, soils, and natural mixtures of minerals were made in laboratory and field settings. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. This report describes the instruments used, the organization of materials into chapters, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements. To facilitate greater application of the spectra, the library has also been convolved to selected spectrometer and imaging spectrometers sampling and bandpasses, and resampled to selected broadband multispectral sensors. The native file format of the library is the SPECtrum Processing Routines (SPECPR) data format. This report describes how to access freely available software to read the SPECPR format. To facilitate broader access to the library, we produced generic formats of the spectra and metadata in text files. The library is provided on digital media and online at https://speclab.cr.usgs.gov/spectral-lib.html . A long-term archive of these data are stored on the USGS ScienceBase data server ( https://dx.doi.org/10.5066/F7RR1WDJ ).

Data Series↗
USGS1989

A vertically averaged spectral model for tidal circulation in estuaries

A frequency dependent computer model based on the two-dimensional vertically averaged shallow-water equations is described for general purpose application in tidally dominated embayments. This model simulates the response of both tides and tidal currents to user-specified geometries and boundary conditions. The mathematical formulation and practical application of the model are discussed in detail. Salient features of the model include the ability to specify: (1) stage at the open boundaries as well as within the model grid, (2) velocities on open boundaries (river inflows and so forth), (3) spatially variable wind stress, and (4) spatially variable bottom friction. Using harmonically analyzed field data as boundary conditions, this model can be used to make real time predictions of tides and tidal currents. (USGS)

Water-Resources Investigations Report↗
USGS2013

Conditional spectrum computation incorporating multiple causal earthquakes and ground-motion prediction models

The conditional spectrum (CS) is a target spectrum (with conditional mean and conditional standard deviation) that links seismic hazard information with ground-motion selection for nonlinear dynamic analysis. Probabilistic seismic hazard analysis (PSHA) estimates the ground-motion hazard by incorporating the aleatory uncertainties in all earthquake scenarios and resulting ground motions, as well as the epistemic uncertainties in ground-motion prediction models (GMPMs) and seismic source models. Typical CS calculations to date are produced for a single earthquake scenario using a single GMPM, but more precise use requires consideration of at least multiple causal earthquakes and multiple GMPMs that are often considered in a PSHA computation. This paper presents the mathematics underlying these more precise CS calculations. Despite requiring more effort to compute than approximate calculations using a single causal earthquake and GMPM, the proposed approach produces an exact output that has a theoretical basis. To demonstrate the results of this approach and compare the exact and approximate calculations, several example calculations are performed for real sites in the western United States. The results also provide some insights regarding the circumstances under which approximate results are likely to closely match more exact results. To facilitate these more precise calculations for real applications, the exact CS calculations can now be performed for real sites in the United States using new deaggregation features in the U.S. Geological Survey hazard mapping tools. Details regarding this implementation are discussed in this paper.

Bulletin of the Seismological Society of America↗
USGS2011

A two-phase debris-flow model that includes coupled evolution of volume fractions, granular dilatancy, and pore-fluid pressure

Pore-fluid pressure plays a crucial role in debris flows because it counteracts normal stresses at grain contacts and thereby reduces intergranular friction. Pore-pressure feedback accompanying debris deformation is particularly important during the onset of debrisflow motion, when it can dramatically influence the balance of forces governing downslope acceleration. We consider further effects of this feedback by formulating a new, depth-averaged mathematical model that simulates coupled evolution of granular dilatancy, solid and fluid volume fractions, pore-fluid pressure, and flow depth and velocity during all stages of debris-flow motion. To illustrate implications of the model, we use a finite-volume method to compute one-dimensional motion of a debris flow descending a rigid, uniformly inclined slope, and we compare model predictions with data obtained in large-scale experiments at the USGS debris-flow flume. Predictions for the first 1 s of motion show that increasing pore pressures (due to debris contraction) cause liquefaction that enhances flow acceleration. As acceleration continues, however, debris dilation causes dissipation of pore pressures, and this dissipation helps stabilize debris-flow motion. Our numerical predictions of this process match experimental data reasonably well, but predictions might be improved by accounting for the effects of grain-size segregation.

Conference Paper↗
USGS2006

Suspended-sediment yields and stream-channel processes on Judy's Branch watershed in the St. Louis Metro East region in Illinois

Judy's Branch watershed, a small basin (8.64 square miles) in the St. Louis Metro East region in Illinois, was selected as a pilot site to determine suspended-sediment yields and stream-channel processes in the bluffs and American Bottoms (expansive low-lying valley floor in the region). Suspended-sediment and stream-chan-nel data collected and analyzed for Judy's Branch watershed are presented in this report to establish a baseline of data for water-resource managers to evaluate future stream rehabilitation and manage-ment alternatives. The sediment yield analysis determines the amount of sediment being delivered from the watershed and two subwatersheds: an urban tributary and an undeveloped headwater (pri-marily agricultural). The analysis of the subwater-sheds is used to compare the effects of urbanization on sediment yield to the river. The stream-channel contribution to sediment yield was determined by evaluation of the stream-channel processes operat-ing on the streambed and banks of Judy's Branch watershed. Bank stability was related to hydrologic events, bank stratigraphy, and channel geometry through model development and simulation. The average suspended-sediment yield from two upland subwatersheds (drainage areas of 0.23 and 0.40 sq.mi. was 1,163 tons per square mile per year (tons/sq.mi.-year) between July 2000 and June 2004. The suspended-sediment yield at the Route 157 station was 2,523 tons/sq.mi.-year, near the outlet of Judy's Branch watershed (drainage area = 8.33 sq.mi.). This is approximately 1,360 tons/sq.mi.-year greater than the average at the upland stations for the same time period. This result is unexpected in that, generally, the suspended-sediment yield decreases as the watershed area increases because of sediment stored in the channel and flood plain. The difference indicates a possible increase in yield from a source, such as bank retreat, and supports the concept that land-use changes increase stream-flows that may in turn result in higher rates of bank retreat. Utilizing both bank-rod data and resurveyed cross-section data, it was determined that approxi-mately half of the suspended- sediment yield at Route 157 during July 2000-June 2004 came from bank retreat. Given that bank retreat can be a substantial portion of the sediment yield, understanding bank stability processes is important. Bank stability can be assessed mathematically by computing the factor of safety, which is defined by the ratio of the shear strength (resisting force) along the failure surface and the shear stress (driving gravitational force). Once the factor of safety falls below one, the bank theoretically becomes unstable. Bank-stability conditions were related to hydrologic events, bank type, and channel geometry through model develop-ment and simulation. The most common type of bank in the watershed consists of cohesive alluvial soil deposits overlying a stiff glacial till. A stabil-ity chart for different bank types was developed using a bank-stability analysis. Banks steeper than 70 degrees and higher than from 10 to 11.5 feet (depending on bank type) become at risk for mass failure in the watershed under conditions that pro-mote saturation of the bank and a sudden drop in the river level.

Scientific Investigations Report↗
USGS2021

The application of ensemble wave forcing to quantify uncertainty of shoreline change predictions

Reliable predictions and accompanying uncertainty estimates of coastal evolution on decadal to centennial time scales are increasingly sought. So far, most coastal change projections rely on a single, deterministic realization of the unknown future wave climate, often derived from a global climate model. Yet, deterministic projections do not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, we present an ensemble Kalman filter shoreline change model to predict coastal erosion and uncertainty due to waves at a variety of time scales. We compare shoreline change projections, simulated with and without ensemble wave forcing conditions by applying ensemble wave time series produced by a computationally efficient statistical downscaling method. We demonstrate a sizable (site-dependent) increase in model uncertainty compared with the unrealistic case of model projections based on a single, deterministic realization (e.g., a single time series) of the wave forcing. We support model-derived uncertainty estimates with a novel mathematical analysis of ensembles of idealized process models. Here, the developed ensemble modeling approach is applied to a well-monitored beach in Tairua, New Zealand. However, the model and uncertainty quantification techniques derived here are generally applicable to a variety of coastal settings around the world.

JGR Earth Surface↗
USGS1987

Estimating threshold values for the land disposal of organic solvent-contaminated wastes

This paper describes conceptual and mathematical modeling approaches to evaluate environmentally suitable waste management practices that prevent ground water pollution from landfill disposal of solid wastes contaminated with organic solvents. Threshold values for disposal of solid wastes contaminated with acetone, nitrobenzene, chlorobenzene, and carbon tetrachloride were estimated using a disposal site model using a two-dimensional advection-,dispersion solute transport computer code. For example, the threshold value for disposal of carbon tetrachloride at the site model was approximately 600 g/acre, while the value for acetone was limited only by the characteristics of the waste to prevent free drainage of liquid solvent. The study indicated that the toxicity, mass loading rate, and the mass of solvent initially disposed are critical parameters determining the relative success of a given site to attenuate contaminants to environmentally acceptable levels.

Journal of Hazardous Materials↗
USGS2011

Partnership of Environmental Education and Research-A compilation of student research, 1999-2008

The U.S. Geological Survey (USGS) Tennessee Water Science Center and the College of Engineering and Technology at Tennessee State University developed a Partnership in Environmental Education and Research (PEER) to support environmental research at TSU and to expand the environmental research capabilities of the USGS in Tennessee. The PEER program is driven by the research needs to better define the occurrence, fate, and transport of contaminants in groundwater and surface water. Research in the PEER program has primarily focused on the transport and remediation of organic contamination in karst settings. Research conducted through the program has also expanded to a variety of media and settings. Research areas include contaminant occurrence and transport, natural and enhanced bioremediation, geochemical conditions in karst aquifers, mathematical modeling for contaminant transport and degradation, new methods to evaluate groundwater contamination, the resuspension of bacteria from sediment in streams, the use of bioluminescence and chemiluminescence to identify the presence of contaminants, and contaminant remediation in wetlands. The PEER program has increased research and education opportunities for students in the College of Engineering, Technology, and Computer Science and has provided students with experience in presenting the results of their research. Students in the program have participated in state, regional, national and international conferences with more than 140 presentations since 1998 and more than 40 student awards. The PEER program also supports TSU outreach activities and efforts to increase minority participation in environmental and earth science programs at the undergraduate and graduate levels. TSU students and USGS staff participate in the TSU summer programs for elementary and high school students to promote earth sciences. The 2007 summer camps included more than 130 students from 20 different States and Washington DC.

Tennessee↗
USGS2020

Dimensional effects of inter-phase mass transfer on attenuation of structurally trapped gaseous carbon dioxide in shallow aquifers

Based on experimental evidence and using mathematical modeling, inter-phase mass transfer processes of CO 2 exsolving from and dissolving into water in heterogeneous porous media are investigated under two fundamentally different flow conditions: in a quasi one dimensional vertical column and in a two-dimensional tank with a lateral background water flow, both at laboratory scale. In both cases, the CO 2 dissolved in water under a given overpressure is injected for a certain period at the bottom of the tank, exsolves, and migrates upwards. A layer of fine sand is present in the tanks designed to mimic geological scenarios of accumulation and trapping of exsolved CO 2 in shallow aquifers. Then, clean water is injected and the accumulated CO 2 is dissolved back into the flowing water. The study aims to point out the differences in the mass transfer processes between the quasi-1D and 2D cases using a mathematical model of two-phase compositional flow in heterogeneous porous media calibrated to the experimental datasets, and expose strategies that should be explored in future research. Additionally, temperature variations observed during the 2D experiments allow for analysis of isothermal versus non-isothermal effects on the processes of multiphase CO 2 evolution. The mathematical model is discretized and solved using the mixed hybrid finite element method in 2D that allows for the simulation of both advection- and diffusion-dominated processes accurately.

Journal of Computational Physics↗
USGS2020

Partly cloudy with a chance of lava flows: Forecasting volcanic eruptions in the 21st century

A primary goal of volcanology is forecasting hazardous eruptive activity. Despite much progress over the last century, however, volcanoes still erupt with no detected precursors, lives and livelihoods are lost to eruptive activity, and forecasting the onsets of eruptions remains fraught with uncertainty. Long‐term forecasts are generally derived from the geological and historical records, from which recurrence intervals and styles of activity can be inferred, while shorter‐term forecasts are derived from patterns in monitoring data. Information from geology and monitoring data can be evaluated and combined using statistical analysis, expert elicitation, and conceptual and or mathematical models. Integrative frameworks, such as event trees, combine this diversity of information to produce probabilistic forecasts that can inform the style and scale of the societal response to a potential future eruption. Several developments show promise to revolutionize the utility and accuracy of these forecasts. These include growth in the quantity and quality of multidisciplinary monitoring data, coupled with increases in computing power; machine learning algorithms, which will allow far better utilization of this growing volume of data; and new physiochemical volcano models and data assimilation algorithms, which take advantage of a wide range of monitoring data and realistic physics to better predict the evolution of a given physical state. Although eruption forecasts may never be as generally reliable as weather forecasts, and great caution must be exercised when attempting to predict highly complex volcanic behavior, these and other innovations—particularly when combined in integrative, fully probabilistic forecasting frameworks—should help volcanologists to better issue warnings of volcanic activity on societally relevant time frames.

Hawaii↗
USGS2010

Bayesian Inference: with ecological applications

This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

Book↗
USGS1986

Evaluation of six methods for estimating magnitude and frequency of peak discharges on urban streams in New York

Six methods of estimating peak discharges of urban streams were compared and evaluated for applicability to urban streams in New York. Discharge and frequency values developed from a series of synthesized annual flood records were compared with values obtained from the six methods. The synthesized flood records were computed from rainfall-runoff models of 11 urban basins in three counties across the State. Four of these basins had a sufficient period of record to enable rainfall-runoff modeling of two different 5-year periods so that increases in peak flow due to increased urbanization could also be used for comparison of the six methods. A graphical analysis and three types of mathematical analyses were made to evaluate the closeness of fit and bias of the methods. All methods showed a tendency to overestimate synthetic urban flood-magnitude values, but the two adjust rural flood-frequency estimates on a nationwide basis showed smallest standard errors of estimate and bias. The standard errors for these two methods ranged from 44 to 57 percent over the six recurrence intervals (2, 5, 10, 25, 50, and 100 year), and the bias ranged from +28 to +53 percent. The bias , however, is probably due to errors inherent in using synthetic records and in applying the New York rural flood-frequency equations to urban basins with small drainage areas. (USGS)

Water-Resources Investigations Report↗
USGS1981

Modeling natural gas reservoirs: A simple model

A mathematical model is developed and tested for the production of natural gas with water encroachment and gas entrapment. The model is built on the material and volumetric balance relations, the Schilthuis water drive model, and a gas entrapment mechanism which assumes that the rate of gas entrapment is proportional to the volumetric rate of water influx. This model represents an alternative to the large grid models because of its low computer, maintenance, and manpower costs.

Society of Petroleum Engineers journal↗
USGS2000

Uncertainty estimation for resource assessment-an application to coal

The U.S. Geological Survey is conducting a national assessment of coal resources. As part of that assessment, a geostatistical procedure has been developed to estimate the uncertainty of coal resources for the historical categories of geological assurance: measured, indicated, inferred, and hypothetical coal. Data consist of spatially clustered coal thickness measurements from coal beds and/or zones that cover, in some cases, several thousand square kilometers. Our procedure involved trend removal, an examination of spatial correlation, computation of a sample semivariogram, and fitting a semivariogram model. This model provided standard deviations for the uncertainty estimates. The number of sample points (drill holes) in each historical category also was estimated. Measurement error in the thickness of the coal bed/zone was obtained from the fitted model or supplied exogenously. From this information approximate estimates of uncertainty on the historical categories were computed. We illustrate the methodology using drill hole data from the Harmon coal bed located in southwestern North Dakota. The methodology will be applied to approximately 50 coal data sets.

Mathematical Geology↗