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At least 37 records · Page 2Linked to original sources
USGS2003

Development and calibration of a ground-water flow model for the Sparta Aquifer of southeastern Arkansas and north-central Louisiana and simulated response to withdrawals, 1998-2027

The Sparta aquifer, which consists of the Sparta Sand, in southeastern Arkansas and north-central Louisiana is a major water resource and provides water for municipal, industrial, and agricultural uses. In recent years, the demand in some areas has resulted in withdrawals from the Sparta aquifer that substantially exceed replenishment of the aquifer. Considerable drawdown has occurred in the potentiometric surface forming regional cones of depression as water is removed from storage by withdrawals. These cones of depression are centered beneath the Grand Prairie area and the cities of Pine Bluff and El Dorado in Arkansas, and Monroe in Louisiana. The rate of decline for hydraulic heads in the aquifer has been greater than 1 foot per year for more than a decade in much of southern Arkansas and northern Louisiana where hydraulic heads are now below the top of the Sparta Sand. Continued hydraulic-head declines have caused water users and managers alike to question the ability of the aquifer to supply water for the long term. Concern over protecting the Sparta aquifer as a sustainable resource has resulted in a continued, cooperative effort by the Arkansas Soil and Water Conservation Commission, U.S. Army Corps of Engineers, and the U.S. Geological Survey to develop, maintain, and utilize numerical ground-water flow models to manage and further analyze the ground-water system. The work presented in this report describes the development and calibration of a ground-water flow model representing the Sparta aquifer to simulate observed hydraulic heads, documents major differences in the current Sparta model compared to the previous Sparta model calibrated in the mid-1980's, and presents the results of three hypothetical future withdrawal scenarios. The current Sparta model-a regional scale, three-dimensional numerical ground-water flow model-was constructed and calibrated using available hydrogeologic, hydraulic, and water-use data from 1898 to 1997. Significant changes from the previous model include grid rediscretization of the aquifer, extension of the active model area northward beyond the Cane River Formation facies change, and representation of model boundaries. The current model was calibrated with the aid of parameter estimation, a nonlinear regression technique, combined with trial and error parameter adjustment using a total of 795 observations from 316 wells over 4 different years-1970, 1985, 1990, and 1997. The calibration data set provides broad spatial and temporal coverage of aquifer conditions. Analysis of the residual statistics, spatial distribution of residuals, simulated compared to observed hydrographs, and simulated compared to observed potentiometric surfaces were used to analyze the ability of the calibrated model to simulate aquifer conditions within acceptable error. The calibrated model has a root mean square error of 18 feet for all observations, an improvement of more than 12 feet from the previous model. The current Sparta model was used to predict the effects of three hypothetical withdrawal scenarios on hydraulic heads over the period 1998-2027 with one of those extended indefinitely until equilibrium conditions were attained, or steady state. In scenario 1a, withdrawals representing the time period from 1990 to 1997 was held constant for 30 years from 1998 to 2027. Hydraulic heads in the middle of the cone of depression centered on El Dorado decreased by 10 feet from the 1997 simulation to 222 feet below NGVD of 1929 in 2027. Hydraulic heads in the Pine Bluff cone of depression showed a greater decline from 61 feet below NGVD of 1929 to 78 feet below NGVD of 1929 in the center of the cone. With these same withdrawals extended to steady state (scenario 1b), hydraulic heads in the Pine Bluff cone of depression center declined an 2 Development and Calibration of a Ground-Water Flow Model for the Sparta Aquifer of Southeastern Arkansas and North-Central Louisiana and Simulated Response to Withdrawa

Water-Resources Investigations Report↗
USGS2013

Effects of sampling interval on spatial patterns and statistics of watershed nitrogen concentration

This study investigates how spatial patterns and statistics of a 30 m resolution, model-simulated, watershed nitrogen concentration surface change with sampling intervals from 30 m to 600 m for every 30 m increase for the Little River Watershed (Georgia, USA). The results indicate that the mean, standard deviation, and variogram sills do not have consistent trends with increasing sampling intervals, whereas the variogram ranges remain constant. A sampling interval smaller than or equal to 90 m is necessary to build a representative variogram. The interpolation accuracy, clustering level, and total hot spot areas show decreasing trends approximating a logarithmic function. The trends correspond to the nitrogen variogram and start to level at a sampling interval of 360 m, which is therefore regarded as a critical spatial scale of the Little River Watershed. Copyright ?? 2009 by Bellwether Publishing, Ltd. All right reserved.

GIScience and Remote Sensing↗
USGS2007

Hierarchical spatial models of abundance and occurrence from imperfect survey data

Many estimation and inference problems arising from large-scale animal surveys are focused on developing an understanding of patterns in abundance or occurrence of a species based on spatially referenced count data. One fundamental challenge, then, is that it is generally not feasible to completely enumerate ('census') all individuals present in each sample unit. This observation bias may consist of several components, including spatial coverage bias (not all individuals in the Population are exposed to sampling) and detection bias (exposed individuals may go undetected). Thus, observations are biased for the state variable (abundance, occupancy) that is the object of inference. Moreover, data are often sparse for most observation locations, requiring consideration of methods for spatially aggregating or otherwise combining sparse data among sample units. The development of methods that unify spatial statistical models with models accommodating non-detection is necessary to resolve important spatial inference problems based on animal survey data. In this paper, we develop a novel hierarchical spatial model for estimation of abundance and occurrence from survey data wherein detection is imperfect. Our application is focused on spatial inference problems in the Swiss Survey of Common Breeding Birds. The observation model for the survey data is specified conditional on the unknown quadrat population size, N(s). We augment the observation model with a spatial process model for N(s), describing the spatial variation in abundance of the species. The model includes explicit sources of variation in habitat structure (forest, elevation) and latent variation in the form of a correlated spatial process. This provides a model-based framework for combining the spatially referenced samples while at the same time yielding a unified treatment of estimation problems involving both abundance and occurrence. We provide a Bayesian framework for analysis and prediction based on the integrated likelihood, and we use the model to obtain estimates of abundance and occurrence maps for the European Jay (Garrulus glandarius), a widespread, elusive, forest bird. The naive national abundance estimate ignoring imperfect detection and incomplete quadrat coverage was 77 766 territories. Accounting for imperfect detection added approximately 18 000 territories, and adjusting for coverage bias added another 131 000 territories to yield a fully corrected estimate of the national total of about 227 000 territories. This is approximately three times as high as previous estimates that assume every territory is detected in each quadrat.

Ecological Monographs↗
USGS2016

A goodness-of-fit test for occupancy models with correlated within-season revisits

Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.

Ecology and Evolution↗
USGS2012

The walk is never random: subtle landscape effects shape gene flow in a continuous white-tailed deer population in the Midwestern United States

One of the pervasive challenges in landscape genetics is detecting gene flow patterns within continuous populations of highly mobile wildlife. Understanding population genetic structure within a continuous population can give insights into social structure, movement across the landscape and contact between populations, which influence ecological interactions, reproductive dynamics or pathogen transmission. We investigated the genetic structure of a large population of deer spanning the area of Wisconsin and Illinois, USA, affected by chronic wasting disease. We combined multiscale investigation, landscape genetic techniques and spatial statistical modelling to address the complex questions of landscape factors influencing population structure. We sampled over 2000 deer and used spatial autocorrelation and a spatial principal components analysis to describe the population genetic structure. We evaluated landscape effects on this pattern using a spatial autoregressive model within a model selection framework to test alternative hypotheses about gene flow. We found high levels of genetic connectivity, with gradients of variation across the large continuous population of white-tailed deer. At the fine scale, spatial clustering of related animals was correlated with the amount and arrangement of forested habitat. At the broader scale, impediments to dispersal were important to shaping genetic connectivity within the population. We found significant barrier effects of individual state and interstate highways and rivers. Our results offer an important understanding of deer biology and movement that will help inform the management of this species in an area where overabundance and disease spread are primary concerns.

Illinois, Wisconsin↗
USGS2007

Using simulated historical time series to prioritize fuel treatments on landscapes across the United States: The LANDFIRE prototype project

Canopy and surface fuels in many fire-prone forests of the United States have increased over the last 70 years as a result of modern fire exclusion policies, grazing, and other land management activities. The Healthy Forest Restoration Act and National Fire Plan establish a national commitment to reduce fire hazard and restore fire-adapted ecosystems across the USA. The primary index used to prioritize treatment areas across the nation is Fire Regime Condition Class (FRCC) computed as departures of current conditions from the historical fire and landscape conditions. This paper describes a process that uses an extensive set of ecological models to map FRCC from a departure statistic computed from simulated time series of historical landscape composition. This mapping process uses a data-driven, biophysical approach where georeferenced field data, biogeochemical simulation models, and spatial data libraries are integrated using spatial statistical modeling to map environmental gradients that are then used to predict vegetation and fuels characteristics over space. These characteristics are then fed into a landscape fire and succession simulation model to simulate a time series of historical landscape compositions that are then compared to the composition of current landscapes to compute departure, and the FRCC values. Intermediate products from this process are then used to create ancillary vegetation, fuels, and fire regime layers that are useful in the eventual planning and implementation of fuel and restoration treatments at local scales. The complex integration of varied ecological models at different scales is described and problems encountered during the implementation of this process in the LANDFIRE prototype project are addressed.

Ecological Modelling↗
USGS2009

Geospatial data to support analysis of water-quality conditions in basin-fill aquifers in the southwestern United States

The Southwest Principal Aquifers study area consists of most of California and Nevada and parts of Utah, Arizona, New Mexico, and Colorado; it is about 409,000 square miles. The Basin-fill aquifers extend through about 201,000 square miles of the study area and are the primary source of water for cities and agricultural communities in basins in the arid and semiarid southwestern United States (Southwest). The demand on limited ground-water resources in areas in the southwestern United States has increased significantly. This increased demand underscores the importance of understanding factors that affect the water quality in basin-fill aquifers in the region, which are being studied through the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) program. As a part of this study, spatial datasets of natural and anthropogenic factors that may affect ground-water quality of the basin-fill aquifers in the southwestern United States were developed. These data include physical characteristics of the region, such as geology, elevation, and precipitation, as well as anthropogenic factors, including population, land use, and water use. Spatial statistics for the alluvial basins in the Southwest have been calculated using the datasets. This information provides a foundation for the development of conceptual and statistical models that relate natural and anthropogenic factors to ground-water quality across the Southwest. A geographic information system (GIS) was used to determine and illustrate the spatial distribution of these basin-fill variables across the region. One hundred-meter resolution raster data layers that represent the spatial characteristics of the basins' boundaries, drainage areas, population densities, land use, and water use were developed for the entire Southwest.

Arizona, California, Colorado, Nevada, New Mexico,↗
USGS2007

Spatial distribution of juvenile and adult female Tanner crabs (Chionoecetes bairdi) in a glacial fjord ecosystem: Implications for recruitment processes

A systematic pot survey in Glacier Bay, Alaska, was conducted to characterize the spatial distribution of juvenile and adult female Tanner crabs, and their association with depth and temperature. The information was used to infer important recruitment processes for Tanner crabs in glaciated ecosystems. High-catch areas for juvenile and adult female Tanner crabs were identified using local autocorrelation statistics. Spatial segregation by size class corresponded to features in the glacial landscape: high-catch areas for juveniles were located at the distal ends of two narrow glacial fjords, and high-catch areas for adults were located in the open waters of the central Bay. Juvenile female Tanner crabs were found at nearly all sampled depths (15–439 m) and temperatures (4–8°C), but the biggest catches were at depths <150 m where adults were scarce. Because adults may prey on or compete with juveniles, the distribution of juveniles could be influenced by the distribution of adults. Areas where adults or predators are scarce, such as glacially influenced fjords, could serve as refuges for juvenile Tanner crabs.

ICES Journal of Marine Science↗
USGS2021

Retention and dimensional changes of evergreen brush piles within a flood control reservoir

Brush piles (i.e., trees and large woody debris) are often installed in reservoirs to supplement fish habitat. The retention and dimensional change of brush piles after installation is important information that can be used to maximize the effectiveness of this management action. We evaluated the retention and dimensional change of 70 eastern red cedar Juniperus virginiana and bald cypress Taxodium distichum brush piles in an embayment of a drawdown reservoir up to four annual cycles of submergence and exposure. We used satellite imagery to supplement our onsite measurements of retention. We also examined spatial patterns of brush pile retention and dimensional change. Brush piles were lost at 10% per year, and their volume was lost at 14% per year. We compared our rates of brush pile retention and dimensional change with those from a holdout data set of 50 brush piles. Estimates between data sets did not differ statistically. Spatial patterns of retention and dimensional change coincided with morphological features in our study area, suggesting that retention and dimensional change is influenced by variable physical forces (e.g., wave action and flow) at installation locations. Our estimates of brush pile retention and dimensional change can be used to generally sustain desirable brush densities. For example, to maintain a fixed total volume of brush in our study embayment, roughly 23% of the total brush volume installed would need to be replaced annually. Similar research in reservoirs managed for other purposes is needed, as length and cycle of inundation could lead to variable rates of retention and dimensional change. Additionally, advancements into computer-assisted detection and volume estimation could reduce the time and effort needed to monitor brush piles.

Mississippi↗
USGS2003

Atmospheric deposition maps for the Rocky Mountains

Variability in atmospheric deposition across the Rocky Mountains is influenced by elevation, slope, aspect, and precipitation amount and by regional and local sources of air pollution. To improve estimates of deposition in mountainous regions, maps of average annual atmospheric deposition loadings of nitrate, sulfate, and acidity were developed for the Rocky Mountains by using spatial statistics. A parameter-elevation regressions on independent slopes model (PRISM) was incorporated to account for variations in precipitation amount over mountainous regions. Chemical data were obtained from the National Atmospheric Deposition Program/National Trends Network and from annual snowpack surveys conducted by the US Geological Survey and National Park Service, in cooperation with other Federal, State and local agencies. Surface concentration maps were created by ordinary kriging in a geographic information system, using a local trend and mathematical model to estimate the spatial variance. Atmospheric-deposition maps were constructed at 1-km resolution by multiplying surface concentrations from the kriged grid and estimates of precipitation amount from the PRISM model. Maps indicate an increasing spatial trend in concentration and deposition of the modeled constituents, particularly nitrate and sulfate, from north to south throughout the Rocky Mountains and identify hot-spots of atmospheric deposition that result from combined local and regional sources of air pollution. Highest nitrate (2.5-3.0kg/ha N) and sulfate (10.0-12.0kg/ha SO4) deposition is found in northern Colorado.

Atmospheric Environment↗
USGS2013

Landscape-level estimation of nitrogen removal in coastal Louisiana wetlands: potential sinks under different restoration scenarios

Coastal eutrophication in the northern Gulf of Mexico (GOM) is the primary anthropogenic contributor to the largest zone of hypoxic bottom waters in North America. Although biologically mediated processes such as denitrification (Dn) are known to act as sinks for inorganic nitrogen, it is unknown what contribution denitrification makes to landscape-scale nitrogen budgets along the coast. As the State of Louisiana plans the implementation of a 2012 Coastal Master Plan (MP) to help restore its wetlands and protect its coast, it is critical to understand what effect potential restoration projects may have in altering nutrient budgets. As part of the MP, a spatial statistical approach was developed to estimate nitrogen removal under varying scenarios of future conditions and coastal restoration project implementation. In every scenario of future conditions under which MP implementation was modeled, more nitrogen ( ) was removed from coastal waters when compared with conditions under which no action is taken. Overall, the MP increased coast-wide average nitrogen removal capacity (NRC) rates by up to 0.55 g N m −2 y −1 compared with the “future without action” (FWOA) scenario, resulting in a conservative estimate of up to 25% removal of the annual + load of the Mississippi-Atchafalaya rivers (956,480 t y −1 ). These results are spatially correlated, with the lower Mississippi River and Chenier Plain exhibiting the greatest change in NRC. Since the implementation of the MP can maintain, and in some regions increase the NRC, our results show the need to preserve the functionality of wetland habitats and use this ecosystem service ( i.e . Dn) to decrease eutrophication of the GOM.

Louisiana↗
USGS2017

Migratory connectivity of american woodcock using band return data

American woodcock ( Scolopax minor ) are managed as a Central and an Eastern population in the United States and Canada based on band return data showing little crossover between populations or management regions. The observed proportion of crossover between management regions, however, depends on the criteria used to subset the band return data. We analyzed the amount of crossover between management regions using only band return records that represent complete migrations between the breeding and wintering grounds by using only band return records in which the capture took place during the breeding season and the band recovery took place during the wintering season or vice versa ( n = 224). Additionally, we applied spatial statistics and a clustering algorithm to investigate woodcock migratory connectivity using this subset of migratory woodcock band return records. Using raw counts, 17.9% of records showed crossover between management regions, a higher proportion than the <5% crossover reported in studies that did not use only migratory band returns. Our results showed woodcock from the breeding grounds in the Central Region largely migrate to destinations within the Central Region, whereas woodcock from the breeding grounds in the Eastern Region migrate to destinations across the entire wintering range and mix with individuals from the Central Region. Using the division coefficient, we estimated that 54% of woodcock from the breeding grounds of the Eastern Region migrate to the Central Region wintering grounds. Our result that many woodcock from separate regions of the breeding grounds mix on the wintering grounds has implications for the 2-region basis for woodcock management. Elucidating finer scale movement patterns among regions provides a basis for reassessing the need for separate management regions to ensure optimal conservation and management of the species.

Journal of Wildlife Management↗
USGS2011

Building on crossvalidation for increasing the quality of geostatistical modeling

The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria.

Stochastic Environmental Research and Risk Assessm↗
USGS2014

A landscape-based reconnaissance survey of estrogenic activity in streams of the upper Potomac, upper James,and Shenandoah Rivers, USA

Endocrine-disrupting compounds (EDCs) are becoming of increasing concern in waterways of the USA and worldwide. What remains poorly understood, however, is how prevalent these emerging contaminants are in the environment and what methods are best able to determine landscape sources of EDCs. We describe the development of a spatially structured sampling design and a reconnaissance survey of estrogenic activity along gradients of land use within sub-watersheds. We present this example as a useful approach for state and federal agencies with an interest in identifying locations potentially impacted by EDCs that warrant more intensive, focused research. Our study confirms the importance of agricultural activities on levels of a measured estrogenic equivalent (E2Eq) and also highlights the importance of other potential sources of E2Eq in areas where intensive agriculture is not the dominant land use. Through application of readily available geographic information system (GIS) data, coupled with spatial statistical analysis, we demonstrate the correlation of specific land use types to levels of estrogenic activity across a large area in a consistent and unbiased manner.

Environmental Monitoring and Assessment↗
USGS2025

Applications of unoccupied aerial systems (UAS) in landscape ecology: A review of recent research, challenges and emerging opportunities

Context Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications. Objectives The review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research. Methods We conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function. Results We summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software. Conclusion We observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics.

Landscape Ecology↗
USGS2022

Factors Affecting Groundwater Quality Used for Domestic Supply in Marcellus Shale Region of North-Central and North-East Pennsylvania, USA

Factors affecting groundwater quality used for domestic supply within the Marcellus Shale footprint in north-central and north-east Pennsylvania are identified using a combination of spatial, statistical, and geochemical modeling. Untreated groundwater, sampled during 2011–2017 from 472 domestic wells within the study area, exhibited wide ranges in pH (4.5–9.3), total dissolved solids (TDS, 22–1960 mg/L), sodium (0.3–760 mg/L), chloride (0.3–1020 mg/L), bromide (<0.01–8.6 mg/L), and methane (<0.001–77 mg/L). The wells had depths ranging from 10 to 394 m; 69.5 percent were completed in sandstone bedrock, 19.3 percent in shale, 4.2 percent in siltstone, 4 percent in carbonate, and 3 percent in unconsolidated alluvial or glacial deposits. Groundwater quality in the Delaware River watershed, in the eastern part of the study area where Marcellus gas has not been developed, was similar to that in the Susquehanna, Allegheny, and Genesee River watersheds in the western part of the study area where natural gas production from Marcellus Shale has been ongoing since 2008. Most groundwaters were calcium/bicarbonate type with near-neutral pH; approximately 10 percent were sodium/bicarbonate and 1 percent were sodium/chloride types. Sodium-enriched waters, which were mostly from shale and siltstone aquifers, had the greatest frequency of elevated pH (>8.5) and elevated concentrations of TDS (>250 mg/L), bromide (>0.15 mg/L), methane (>7.0 mg/L), and lithium (>60 μg/L). Geochemical models indicate these characteristics could result from progressive mineral dissolution combined with cation exchange, plus mixing with locally important salinity sources, including as much as 0.7 percent Appalachian Basin brine and/or road-deicing salt. Multivariate correlation models suggest the observed variability in methane concentrations may be attributed to several environmental factors, such as geochemical evolution along groundwater flow paths, redox conditions, and/or mixing with saline groundwater or brine. Most samples having elevated methane were from shale aquifers, which were mainly in the Susquehanna River basin and had the greatest density of gas wells compared to other lithologies . Samples having elevated methane were also observed in the Delaware River watershed and other areas outside gas development. Isotopic compositions of methane for a subset of 39 samples (selected because of elevated methane) and relatively high ratios of methane to ethane in those samples indicated methane could be derived from microbial gas mixed with thermogenic gas that may have undergone degradation and/or fractionation during migration. The methods used in this study could be broadly applicable to understanding major factors affecting groundwater quality, particularly for explaining variations in ionic composition with pH and identifying sources of salinity and associated constituents (e.g. sodium, chloride, bromide, lithium, methane) that may have geogenic or anthropogenic origins.

Pennsylvania↗
USGS2010

Automated feature extraction and spatial organization of seafloor pockmarks, Belfast Bay, Maine, USA

Seafloor pockmarks occur worldwide and may represent millions of m 3 of continental shelf erosion, but few numerical analyses of their morphology and spatial distribution of pockmarks exist. We introduce a quantitative definition of pockmark morphology and, based on this definition, propose a three-step geomorphometric method to identify and extract pockmarks from high-resolution swath bathymetry. We apply this GIS-implemented approach to 25 km 2 of bathymetry collected in the Belfast Bay, Maine USA pockmark field. Our model extracted 1767 pockmarks and found a linear pockmark depth-to-diameter ratio for pockmarks field-wide. Mean pockmark depth is 7.6 m and mean diameter is 84.8 m. Pockmark distribution is non-random, and nearly half of the field's pockmarks occur in chains. The most prominent chains are oriented semi-normal to the steepest gradient in Holocene sediment thickness. A descriptive model yields field-wide spatial statistics indicating that pockmarks are distributed in non-random clusters. Results enable quantitative comparison of pockmarks in fields worldwide as well as similar concave features, such as impact craters, dolines, or salt pools.

Geomorphology↗
USGS2004

Assessing the resolution-dependent utility of tomograms for geostatistics

Geophysical tomograms are used increasingly as auxiliary data for geostatistical modeling of aquifer and reservoir properties. The correlation between tomographic estimates and hydrogeologic properties is commonly based on laboratory measurements, co-located measurements at boreholes, or petrophysical models. The inferred correlation is assumed uniform throughout the interwell region; however, tomographic resolution varies spatially due to acquisition geometry, regularization, data error, and the physics underlying the geophysical measurements. Blurring and inversion artifacts are expected in regions traversed by few or only low-angle raypaths. In the context of radar traveltime tomography, we derive analytical models for (1) the variance of tomographic estimates, (2) the spatially variable correlation with a hydrologic parameter of interest, and (3) the spatial covariance of tomographic estimates. Synthetic examples demonstrate that tomograms of qualitative value may have limited utility for geostatistics; moreover, the imprint of regularization may preclude inference of meaningful spatial statistics from tomograms.

Geophysical Research Letters↗