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At least 217 records · Page 12Linked to original sources
USGS2026

Estimating the magnitude and frequency of floods at ungaged locations on urban streams in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, using data through the 2022 water year

In 2024, the U.S. Geological Survey, in cooperation with the Tennessee Department of Transportation, updated the methods for predicting the magnitude and frequency of floods at ungaged locations on streams in urban areas in Tennessee. The study area included 136 streamgages in urban areas in Tennessee, Mississippi, Alabama, Georgia, South Carolina, and North Carolina that had at least 10 percent developed imperviousness in their basins as indicated by data from the 2011 National Land Cover Database. Regression equations were developed to predict streamflows corresponding to the 50-​, 20-​, 10-​, 4-​, 2-​, 1-​, 0.5-​, and 0.2-​percent annual exceedance probabilities (AEPs) and were incorporated into the StreamStats application. In generalized least-​squares regression, the base-​10 logarithm of drainage area, the percentages of the streamgage basins in developed land use, and the percentages of the streamgage basins in the Piedmont and Ridge and Valley Level 3 ecoregions were statistically significant in explaining the variability in annual peak streamflows in the study area. Drainage areas ranged from 0.164 to 93.4 square miles, the percentage of the streamgage basins in developed land use ranged from 26 to 100 percent, and the percentage of the streamgage basins in Piedmont and Ridge and Valley Level 3 ecoregions ranged from 0 to 100 percent. Pseudo R-​squared values for the regression equations ranged from 0.86, or 86 percent, for the 50-​ and 20-​percent AEPs (2-​ and 5-​year floods) to 0.71, or 71 percent, for the 0.2-​percent AEP (500-​year flood). The average variance of prediction (in log base-​10 units) ranged from 0.023 for the 20-​ and 10-​percent AEPs to 0.05 for the 0.2-​percent AEP. The average variance of prediction can be reported as a percentage of the predicted value, known as the standard error of prediction, which ranged from 35.8 percent for the 20-​percent AEP (5-​year flood) to 55.4 percent for the 0.2-​percent AEP (500-​year flood). Methods are presented for estimating annual peak streamflows for gaged locations, ungaged locations on gaged streams, and locations on ungaged streams.

Alabama, Georgia, Mississippi, North Carolina, Sou↗
USGS2005

The inverse problem of refraction travel times, part II: Quantifying refraction nonuniqueness using a three-layer model

This paper is the second of a set of two papers in which we study the inverse refraction problem. The first paper, "Types of Geophysical Nonuniqueness through Minimization," studies and classifies the types of nonuniqueness that exist when solving inverse problems depending on the participation of a priori information required to obtain reliable solutions of inverse geophysical problems. In view of the classification developed, in this paper we study the type of nonuniqueness associated with the inverse refraction problem. An approach for obtaining a realistic solution to the inverse refraction problem is offered in a third paper that is in preparation. The nonuniqueness of the inverse refraction problem is examined by using a simple three-layer model. Like many other inverse geophysical problems, the inverse refraction problem does not have a unique solution. Conventionally, nonuniqueness is considered to be a result of insufficient data and/or error in the data, for any fixed number of model parameters. This study illustrates that even for overdetermined and error free data, nonlinear inverse refraction problems exhibit exact-data nonuniqueness, which further complicates the problem of nonuniqueness. By evaluating the nonuniqueness of the inverse refraction problem, this paper targets the improvement of refraction inversion algorithms, and as a result, the achievement of more realistic solutions. The nonuniqueness of the inverse refraction problem is examined initially by using a simple three-layer model. The observations and conclusions of the three-layer model nonuniqueness study are used to evaluate the nonuniqueness of more complicated n-layer models and multi-parameter cell models such as in refraction tomography. For any fixed number of model parameters, the inverse refraction problem exhibits continuous ranges of exact-data nonuniqueness. Such an unfavorable type of nonuniqueness can be uniquely solved only by providing abundant a priori information. Insufficient a priori information during the inversion is the reason why refraction methods often may not produce desired results or even fail. This work also demonstrates that the application of the smoothing constraints, typical when solving ill-posed inverse problems, has a dual and contradictory role when applied to the ill-posed inverse problem of refraction travel times. This observation indicates that smoothing constraints may play such a two-fold role when applied to other inverse problems. Other factors that contribute to inverse-refraction-problem nonuniqueness are also considered, including indeterminacy, statistical data-error distribution, numerical error and instability, finite data, and model parameters. ?? Birkha??user Verlag, Basel, 2005.

Pure and Applied Geophysics↗
USGS2008

Analysis of a spatial point pattern: Examining the damage to pavement and pipes in Santa Clara Valley resulting from the Loma Prieta earthquake

This report describes some simple spatial statistical methods to explore the relationships of scattered points to geologic or other features, represented by points, lines, or areas. It also describes statistical methods to search for linear trends and clustered patterns within the scattered point data. Scattered points are often contained within irregularly shaped study areas, necessitating the use of methods largely unexplored in the point pattern literature. The methods take advantage of the power of modern GIS toolkits to numerically approximate the null hypothesis of randomly located data within an irregular study area. Observed distributions can then be compared with the null distribution of a set of randomly located points. The methods are non-parametric and are applicable to irregularly shaped study areas. Patterns within the point data are examined by comparing the distribution of the orientation of the set of vectors defined by each pair of points within the data with the equivalent distribution for a random set of points within the study area. A simple model is proposed to describe linear or clustered structure within scattered data. A scattered data set of damage to pavement and pipes, recorded after the 1989 Loma Prieta earthquake, is used as an example to demonstrate the analytical techniques. The damage is found to be preferentially located nearer a set of mapped lineaments than randomly scattered damage, suggesting range-front faulting along the base of the Santa Cruz Mountains is related to both the earthquake damage and the mapped lineaments. The damage also exhibit two non-random patterns: a single cluster of damage centered in the town of Los Gatos, California, and a linear alignment of damage along the range front of the Santa Cruz Mountains, California. The linear alignment of damage is strongest between 45° and 50° northwest. This agrees well with the mean trend of the mapped lineaments, measured as 49? northwest.

California↗
USGS2023

Assessment and characterization of ephemeral stream channel stability in the Grand Valley, Colorado, 2018-22

The purpose of this study is to provide information regarding the stability of ephemeral streams on the north side of the Grand Valley, Colorado. The ungaged ephemeral streams in this semiarid region are of particular interest because (1) the underlying bedrock geology, Mancos Shale, is a sedimentary rock deposit that has been identified as a major contributor of salinity to the Colorado River and (2) despite infrequent flows of short duration, monsoon derived floods in these ephemeral streams can carry substantial amounts of sediment downstream, affecting up and downstream banks and channel cross sections. The study area is of interest as salinity, or the total dissolved solids concentration, in the Colorado River causes an estimated $300 to $400 million per year in economic damages in the United States and it is estimated that 62% of Upper Colorado River Basin dissolved-solid loads originate from geologic sources. In an effort to minimize salt contributions to the Colorado River from public lands administered by the Bureau of Land Management (BLM) a comprehensive three-pronged salinity control approach is being used which incorporates (1) controlling point sources of salinity; (2) controlling nonpoint sources of salinity; and (3) preventing nonpoint sources of salinity from persisting. In 2018, the U.S. Geological Survey, in cooperation with BLM, began an assessment of ephemeral streams located in the north side of the Grand Valley, Colorado, to characterize stream channel stability. The USGS developed a method for automatically extracting channel cross-section geometry from existing remotely sensed terrain models. Based on estimated flood stage and surrogate streamflows, hydraulic characteristics were calculated. Furthermore, the channel geometries and hydraulic characteristics were used to estimate channel stability utilizing a statistical model. In this ongoing study, cross-section stabilities were determined from a stream channel stability assessment for a subset of 1,406 visited locations out of a desired 13,415 cross sections which were delineated from remotely sensed terrain models. The application of Manning’s resistance equation in combination with multiple Logistic Regression models demonstrated that channel stability can be estimated with an 0.85 goodness of fit for a validation dataset when using a combination of drainage area, width to depth ratio, sinuosity, and shear stress as the explanatory variables. Stream channel stability was extrapolated for the remaining 13,415 unvisited cross sections using the multiple Logistic Regression model and defined explanatory variables. Mapping the ephemeral streams and their associated stabilities could be used to prioritize areas for BLM remediation or changes in management strategies to reduce sediment and salinity loading to the Colorado River.

Colorado↗
USGS2006

Analysis of sensitivity of simulated recharge to selected parameters for seven watersheds modeled using the precipitation-runoff modeling system

Recharge is a vital component of the ground-water budget and methods for estimating it range from extremely complex to relatively simple. The most commonly used techniques, however, are limited by the scale of application. One method that can be used to estimate ground-water recharge includes process-based models that compute distributed water budgets on a watershed scale. These models should be evaluated to determine which model parameters are the dominant controls in determining ground-water recharge. Seven existing watershed models from different humid regions of the United States were chosen to analyze the sensitivity of simulated recharge to model parameters. Parameter sensitivities were determined using a nonlinear regression computer program to generate a suite of diagnostic statistics. The statistics identify model parameters that have the greatest effect on simulated ground-water recharge and that compare and contrast the hydrologic system responses to those parameters. Simulated recharge in the Lost River and Big Creek watersheds in Washington State was sensitive to small changes in air temperature. The Hamden watershed model in west-central Minnesota was developed to investigate the relations that wetlands and other landscape features have with runoff processes. Excess soil moisture in the Hamden watershed simulation was preferentially routed to wetlands, instead of to the ground-water system, resulting in little sensitivity of any parameters to recharge. Simulated recharge in the North Fork Pheasant Branch watershed, Wisconsin, demonstrated the greatest sensitivity to parameters related to evapotranspiration. Three watersheds were simulated as part of the Model Parameter Estimation Experiment (MOPEX). Parameter sensitivities for the MOPEX watersheds, Amite River, Louisiana and Mississippi, English River, Iowa, and South Branch Potomac River, West Virginia, were similar and most sensitive to small changes in air temperature and a user-defined flow routing parameter. Although the primary objective of this study was to identify, by geographic region, the importance of the parameter value to the simulation of ground-water recharge, the secondary objectives proved valuable for future modeling efforts. The value of a rigorous sensitivity analysis can (1) make the calibration process more efficient, (2) guide additional data collection, (3) identify model limitations, and (4) explain simulated results.

Scientific Investigations Report↗
USGS2026

Flood-frequency estimates for Kentucky streamgages based on data through water year 2021 and results of updating the fundamental layers in Kentucky StreamStats

The U.S. Geological Survey, in cooperation with the Kentucky Transportation Cabinet, analyzed flood-frequency statistics for streamgages in Kentucky. Using annual peak-flow data through water year 2021, flood-frequency estimates were computed for 261 streamgages, including unregulated and regulated sites as well as sites with mixed regulation records. Methods followed those outlined in “Guidelines for Determining Flood Flow Frequency—Bulletin 17C” (U.S. Geological Survey Techniques and Methods 4–B5). These estimates included flows corresponding to annual exceedance probabilities of 50, 20, 10, 4, 2, 1, and 0.2 percent. Temporal trend analyses using the Mann-Kendall test indicated that 18 percent of unregulated streamgages with (1) at least 30 years of peak-flow record and (2) peak-flow record at least as recent as water year 2000 showed statistically significant trends, most of which were weak to moderate increases in peak flows. Concurrently, the fundamental geospatial datasets that support the Kentucky StreamStats application were updated by using high-resolution digital elevation models and hydrography datasets to derive flow direction, flow accumulation, and stream definition rasters. Comparisons of regression-based flood-frequency models using the old and new layers demonstrated consistent results, with a statewide root-mean-square error of 0.019, in the base-10 logarithm of cubic feet per second. Furthermore, to assess model performance, flood-frequency estimates made by using the updated layers and previously published regression-based models were compared to flood-frequency estimates newly computed by following Bulletin 17C. This analysis showed the models performed adequately for most Kentucky stream locations. The updated statistics and geospatial layers provide stakeholders with more accurate, current data for flood-risk assessment, infrastructure design, and water-resource management.

Kentucky↗
USGS2007

Tennessee StreamStats: A Web-Enabled Geographic Information System Application for Automating the Retrieval and Calculation of Streamflow Statistics

The U.S. Geological Survey (USGS) provides streamflow and other stream-related information needed to protect people and property from floods, to plan and manage water resources, and to protect water quality in the streams. Streamflow statistics provided by the USGS, such as the 100-year flood and the 7-day 10-year low flow, frequently are used by engineers, land managers, biologists, and many others to help guide decisions in their everyday work. In addition to streamflow statistics, resource managers often need to know the physical and climatic characteristics (basin characteristics) of the drainage basins for locations of interest to help them understand the mechanisms that control water availability and water quality at these locations. StreamStats is a Web-enabled geographic information system (GIS) application that makes it easy for users to obtain streamflow statistics, basin characteristics, and other information for USGS data-collection stations and for ungaged sites of interest. If a user selects the location of a data-collection station, StreamStats will provide previously published information for the station from a database. If a user selects a location where no data are available (an ungaged site), StreamStats will run a GIS program to delineate a drainage basin boundary, measure basin characteristics, and estimate streamflow statistics based on USGS streamflow prediction methods. A user can download a GIS feature class of the drainage basin boundary with attributes including the measured basin characteristics and streamflow estimates.

Fact Sheet↗
USGS2006

Hierarchical models of animal abundance and occurrence

Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we provide a Bayesian analysis of the models using the software WinBUGS.

Journal of Agricultural, Biological, and Environme↗
USGS2018

Convergent validity between willingness to pay elicitation methods: An application to Grand Canyon whitewater boaters

We directly compare trip willingness to pay (WTP) values between dichotomous choice contingent valuation (DCCV) and discrete choice experiment (DCE) stated preference surveys of private party Grand Canyon whitewater boaters. The consistency of DCCV and DCE estimates is debated in the literature, and this study contributes to the body of work comparing the methods. Comparisons were made of mean WTP estimates for four hypothetical Colorado River flow-level scenarios. Boaters were found to most highly value mid-range flows, with very low and very high flows eliciting lower WTP estimates across both DCE and DCCV surveys. Mean WTP precision was estimated through simulation. No statistically significant differences were detected between the two methods at three of the four hypothetical flow levels.

Journal of Environmental Planning and Management↗
USGS2003

Quantifying migratory delay: A new application of survival analysis

Statistical techniques commonly used in fish passage research fail to adequately quantify delays incurred at obstacles, or the effects of modifications to those obstacles on passage rates. Analyses of telemetry data describing these effects can be misleading, particularly when passage route of some individuals is not established (e.g., because of mortality, tag failure, passage through unmonitored or alternate routes, etc.). Here, we demonstrate how event-time analysis, better known as survival analysis, can be used to quantify passage rates for any study that allows tracking of individuals through time, even when some individuals fail to pass the route or obstacle in question. We review two of the primary methods of event-time analysis (parametric and Cox's proportional hazards regression analyses) and use them in combination with logistic regression to provide unbiased estimates of delay incurred at a hydroelectric facility, as well as insights on factors affecting both rates of passage and route selection. Passage rate increased with increased depth of a surface bypass sluice gate and, among fish that passed through the turbines, with turbine flow. The data further indicate that risk of turbine passage increased with both delay and turbine flow.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2007

MMA, A Computer Code for Multi-Model Analysis

This report documents the Multi-Model Analysis (MMA) computer code. MMA can be used to evaluate results from alternative models of a single system using the same set of observations for all models. As long as the observations, the observation weighting, and system being represented are the same, the models can differ in nearly any way imaginable. For example, they may include different processes, different simulation software, different temporal definitions (for example, steady-state and transient models could be considered), and so on. The multiple models need to be calibrated by nonlinear regression. Calibration of the individual models needs to be completed before application of MMA. MMA can be used to rank models and calculate posterior model probabilities. These can be used to (1) determine the relative importance of the characteristics embodied in the alternative models, (2) calculate model-averaged parameter estimates and predictions, and (3) quantify the uncertainty of parameter estimates and predictions in a way that integrates the variations represented by the alternative models. There is a lack of consensus on what model analysis methods are best, so MMA provides four default methods. Two are based on Kullback-Leibler information, and use the AIC (Akaike Information Criterion) or AICc (second-order-bias-corrected AIC) model discrimination criteria. The other two default methods are the BIC (Bayesian Information Criterion) and the KIC (Kashyap Information Criterion) model discrimination criteria. Use of the KIC criterion is equivalent to using the maximum-likelihood Bayesian model averaging (MLBMA) method. AIC, AICc, and BIC can be derived from Frequentist or Bayesian arguments. The default methods based on Kullback-Leibler information have a number of theoretical advantages, including that they tend to favor more complicated models as more data become available than do the other methods, which makes sense in many situations. Many applications of MMA will be well served by the default methods provided. To use the default methods, the only required input for MMA is a list of directories where the files for the alternate models are located. Evaluation and development of model-analysis methods are active areas of research. To facilitate exploration and innovation, MMA allows the user broad discretion to define alternatives to the default procedures. For example, MMA allows the user to (a) rank models based on model criteria defined using a wide range of provided and user-defined statistics in addition to the default AIC, AICc, BIC, and KIC criteria, (b) create their own criteria using model measures available from the code, and (c) define how each model criterion is used to calculate related posterior model probabilities. The default model criteria rate models are based on model fit to observations, the number of observations and estimated parameters, and, for KIC, the Fisher information matrix. In addition, MMA allows the analysis to include an evaluation of estimated parameter values. This is accomplished by allowing the user to define unreasonable estimated parameter values or relative estimated parameter values. An example of the latter is that it may be expected that one parameter value will be less than another, as might be the case if two parameters represented the hydraulic conductivity of distinct materials such as fine and coarse sand. Models with parameter values that violate the user-defined conditions are excluded from further consideration by MMA. Ground-water models are used as examples in this report, but MMA can be used to evaluate any set of models for which the required files have been produced. MMA needs to read files from a separate directory for each alternative model considered. The needed files are produced when using the Sensitivity-Analysis or Parameter-Estimation mode of UCODE_2005, or, possibly, the equivalent capability of another program. MMA is constructed using

Techniques and Methods↗
USGS2018

Evaluating and monitoring forest fuel treatments using remote sensing applications in Arizona, U.S.A.

The practice of fire suppression across the western United States over the past century has led to dense forests, and when coupled with drought has contributed to an increase in large and destructive wildfires. Forest management efforts aimed at reducing flammable fuels through various fuel treatments can help to restore frequent fire regimes and increase forest resilience. Our research examines how different fuel treatments influenced burn severity and post-fire vegetative stand dynamics on the San Carlos Apache Reservation, in east-central Arizona, U.S.A. Our methods included the use of multitemporal remote sensing data and cloud computing to evaluate burn severity and post-fire vegetation conditions as well as statistical analyses. We investigated how forest thinning, commercial harvesting, prescribed burning, and resource benefit burning (managed wildfire) related to satellite measured burn severity (the difference Normalized Burn Ratio – dNBR) following the 2013 Creek Fire and used spectral measures of post-fire stand dynamics to track changes in land surface characteristics (i.e., brightness, greenness and wetness). We found strong negative relationships between dNBR and post-fire greenness and wetness, and a positive non-linear relationship between dNBR and brightness, with greater variability at higher severities. Fire severity and post-fire surface changes also differed by treatment type. Our results showed harvested and thinned sites that were not treated with prescribed fire had the highest severity fire. When harvesting was followed by a prescribed burn, the sites experienced lower burn severity and reduced post-fire changes in vegetation greenness and wetness. Areas that had previously experienced resource benefit burns had the lowest burn severities and the highest post-fire greenness measurements compared to all other treatments, except for where the prescribed burn had occurred. These results suggest that fire treatments may be most effective at reducing the probability of hazardous fire and increasing post-fire recovery. This research demonstrates the utility of remote sensing and spatial data t o inform forest management, and how various fuel treatments can influence burn severity and post-fire vegetation response within ponderosa pine forests across the southwestern U.S.

Arizona↗
USGS2008

GIS technology and models for assessing landslide hazard and risk

Advances in Geographical Information Systems (GIS) technology and the mathematical/statistical tools for modelling and simulation have led to the growing application of quantitative techniques in many areas of the earth sciences. Few disciplines have embraced these developments more enthusiastically than the study of landslide hazards. Because the shape of the land surface plays a fundamental role in landslide processes, much of this new work has been driven by the increasing availability of digital elevation models (DEMs) and the software to manipulate them. As a result, investigators worldwide are devising spatial models to forecast slope instability in efforts to keep landslide hazards from becoming landslide disasters. The methods range from empirical and heuristic to statistical and physically-based. Despite nearly a quarter-century of progress, e.g. since Carrara (1983) , and a veritable “cottage industry” of current DEM-based hazard mapping, many of these spatial predictions either are based on unsuitable data or are not properly modelled.

Geomorphology↗
USGS2003

Methods for quality-assurance review of water-quality data in New Jersey

This report is an instructional and reference manual that describes methods developed and used by the U.S. Geological Survey (USGS), New Jersey District, to assure the accuracy and precision of the results of analyses of surface- and ground-water samples received from analyzing laboratories and, ultimately, to ensure the integrity of water-quality data in USGS databases and published reports. A statistical-analysis computer program, COMP.PPL, is used to determine whether the values reported by the laboratories are internally consistent, whether they are reasonable when compared with values for samples previously collected at the same site, and whether they exceed applicable drinking-water regulations. The program output consists of three files -- QWREVIEW, QWOUTLIERS, and QWCALC. QWREVIEW presents the results of tests of chemical logic and shows values that exceed drinking-water regulations. QWOUTLIERS identifies values that fall outside the historical range of values for the site sampled. QWCALC shows values and calculations used for reference purposes. Because values that are identified by the program as questionable may or may not be in error, the reviewer looks at both qualitative and quantitative relations between analytes during the period of record and then uses technical judgement to decide whether to accept a questionable value or investigate further. Guidelines for, and the use of regression analysis in, making this decision are described. Instructions are given for requesting that the analyzing laboratory reanalyze a constituent or otherwise verify the reported value. If, upon reanalysis or verification, a value is still questionable, consideration must be given to deleting the value or marking the value in the USGS National Water Information System database as having been reviewed and rejected.

New Jersey↗
USGS2006

Genetic analyses of captive Alala (Corvus hawaiiensis) using AFLP analyses

Population level studies of genetic diversity can provide information about population structure, individual genetic distinctiveness and former population size. They are especially important for rare and threatened species like the Alala, where they can be used to assess extinction risks and evolutionary potential. In an ideal situation multiple methods should be used to detect variation, and these methods should be comparable across studies. In this report, we discuss AFLP (Amplified Fragment Length Polymorphism) as a genetic approach for detecting variation in the Alala , describe our findings, and discuss these in relation to mtDNA and microsatellite data reported elsewhere in this same population. AFLP is a technique for DNA fingerprinting that has wide applications. Because little or no prior knowledge of the particular species is required to carry out this method of analysis, AFLP can be used universally across varied taxonomic groups. Within individuals, estimates of diversity or heterozygosity across genomes may be complex because levels of diversity differ between and among genes. One of the more traditional methods of estimating diversity employs the use of codominant markers such as microsatellites. Codominant markers detect each allele at a locus independently. Hence, one can readily distinguish heterozygotes from homozygotes, directly assess allele frequencies and calculate other population level statistics. Dominant markers (for example, AFLP) are scored as either present or absent (null) so heterozygotes cannot be directly distinguished from homozygotes. However, the presence or absence data can be converted to expected heterozygosity estimates which are comparable to those determined by codominant markers. High allelic diversity and heterozygosity inherent in microsatellites make them excellent tools for studies of wild populations and they have been used extensively. One limitation to the use of microsatellites is that heterozygosity estimates are affected by the mutation rate at microsatellite loci, thus introducing a bias. Also, the number of loci that can be studied is frequently limited to fewer than 10. This theoretically represents a maximum of one marker for each of 10 chromosomes. Dominant markers like AFLP allow a larger fraction of the genome to be screened. Large numbers of loci can be screened by AFLP to resolve very small individual differences that can be used for identification of individuals, estimates of pairwise relatedness and, in some cases, for parentage analyses. Since AFLP is a dominant marker (can not distinguish between +/+ homozygote versus +/- heterozygote), it has limitations for parentage analyses. Only when both parents are homozygous for the absence of alleles (-/-) and offspring show a presence (+/+ or +/-) can the parents be excluded. In this case, microsatellites become preferable as they have the potential to exclude individual parents when the other parent is unknown. Another limitation of AFLP is that the loci are generally less polymorphic (only two alleles/locus) than microsatellite loci (often >10 alleles/locus). While generally fewer than 10 highly polymorphic microsatellite loci are enough to exclude and assign parentage, it might require up to 100 or more AFLP loci. While there are pros and cons to different methodologies, the total number of loci evaluated by AFLP generally offsets the limitations imposed due to the dominant nature of this approach and end results between methods are generally comparable. Overall objectives of this study were to evaluate the level of genetic diversity in the captive population of Alala, to compare genetic data with currently available pedigree information, and to determine the extent of relatedness of mating pairs and among founding individuals.

Open-File Report↗
USGS2008

Influence of the Houma Navigation Canal on Salinity Patterns and Landscape Configuration in Coastal Louisiana

Coastal Louisiana is a dynamic and ever changing landscape. From 1956 to 2004, over 297,000 ha of Louisiana's coastal wetlands were lost because of the effects of natural and human-induced activities. Studies show that, in 2005, Hurricanes Katrina and Rita transformed over 56,200 ha of wetlands to open water in various parts of coastal Louisiana. Besides the catastrophic hurricanes, factors such as subsidence, sea-level rise, freshwater and sediment deprivation, saltwater intrusion, the dredging of oil and gas canals, navigation canals, shoreline erosion, and herbivory are all contributors to wetland loss in Louisiana. Various scientific literatures have well described the direct impacts associated with an immediate physical conversion of habitat in coastal Louisiana; however, the indirect impacts that are subtle and operate over longer time horizons (such as salinity intrusion) have been difficult to discern. In this report, long-term influences on salinity patterns and landscape configuration are evaluated for pre- and postconstruction periods of the Houma Navigation Canal (HNC), which is located in the coastal region of southeastern Louisiana. Analysis of daily and hourly salinity data from long-term data collection stations within the areas surrounding the HNC indicated that there were no obvious patterns in increasing salinity levels following the completion of the canal, except for the immediate increase in salinity spikes that occurred toward the completion of its construction in 1961. Increases in salinity spikes were also observed during a severe drought in 1999-2000. Data from Bayou Grand Caillou at Dulac, however, show a longer term trend of increasing salinity levels, which is similar to the pattern observed at the Houma Water Treatment Plant. A potential explanation for these patterns is based on the dredging history of the HNC, where dates of maintenance dredging correspond fairly closely to the salinity peaks in Bayou Grand Caillou and the canal. It appears that the dredging events opened up a deeper route from the canal to Crozier and into Grand Bayou Caillou, but it also may be a result of the general breakup of the marsh in the adjacent area, which resulted in greater exchange of bay water and subsequently higher salinity levels. Although the available salinity data were insufficient to conduct statistical correlations, there was close agreement between salinity changes and specific dredging events of the HNC. A procedure for analyzing marsh landscapes, which utilizes the FRAGSTATS landscape statistical application and a two-part marsh classification system, was developed as a means of determining the connectivity and configuration of marsh and water patches within the study area. Individual landscape metrics were used to determine the percentage and rate of land change and the shifts in density, shape, and cohesiveness of water within the marsh. Wetland loss rates for coastal Louisiana and Terrebonne basin were compared to the long- and short-term loss rates of the Houma Navigation Canal study area that were quantified by using the FRAGSTATS landscape analysis method. These results suggest that the canal study area was losing land at a significantly faster rate than both the marshes of coastal Louisiana (over all periods) and the other highly degraded neighboring marshes within Terrebonne basin. Overall, 37 percent (17,625 ha) of the project area marsh was lost between 1958 and 1998. As a means of quantifying the distance and degree of influence that the HNC had on marsh degradation, a 3-km interval buffer array and comparable years of vegetation data were used to describe the changes in primary metric values across the three project dates (1958, 1968/69, and 1998). The patterns across landscape metrics varied, and it was difficult to discern direct relationships based on proximity to the canal. Even though the canal may have an influence on marsh degradation, these analyses show that the degree and d

Open-File Report↗
USGS2018

Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R 2 = 0.77, RMSE = 7%), annual grasses (R 2 = 0.70, RMSE = 21%), perennial grasses (R 2 = 0.36, RMSE = 12%), forbs (R 2 = 0.52, RMSE = 6%), bare earth or litter (R 2 = 0.49, RMSE = 19%), and the biomass of shrubs (R 2 = 0.71, RMSE = 175 g) and herbaceous vegetation (R 2 = 0.61, RMSE = 99 g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.

Idaho↗
USGS2019

User’s guide for Assessment Tract Aggregation GUI (ATA GUI)—A graphical user interface for the AggtEx.fn R script

The U.S. Geological Survey three-part method for mineral resource assessments estimates numbers of undiscovered mineral deposits as probability distributions in geologically defined regions termed “permissive tracts.” This report describes a graphical user interface (GUI) script developed in open-source statistical software (R) that aggregates estimated undiscovered deposits of a given type from two or more permissive tracts using the AggtEx.fn R script. The AggtEx.fn R script aggregates undiscovered deposit estimates assuming independence, total dependence, or some degree of correlation among aggregated areas, given a user-specified correlation matrix. The script outputs three sets of aggregated estimates based on those three assumptions. The GUI script described in this report, Assessment Tract Aggregation GUI (ATA GUI), provides an easy-to-use tool that supports implementation of the AggtEx.fn R script, installation of the R packages needed to run the application, and creation of a combined input file from individual files generated by the MapMark4GUI software. Users can also use EMINERS output information by creating a file of output values following the MapMark4GUI output file format. The probabilistic estimates of aggregated undiscovered deposits produced by ATA GUI can be used as input for MapMark4GUI to estimate contained resources for the aggregated tracts. MapMark4GUI uses Monte Carlo simulation to combine undiscovered deposit estimates with tonnage and grade models to simulate undiscovered mineral resources for a region of interest. This simulation includes the amounts of commodities and rock that could be present within a permissive tract. This report includes instructions on installing and running the ATA GUI script and describes the input and output files used and created during the aggregation process.

Techniques and Methods↗