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USGS2009

Regional variations in water quality and relationships to soil and bedrock weathering in the southern Sacramento Valley, California, USA

Regional patterns in ground- and surface-water chemistry of the southern Sacramento Valley in California were evaluated using publicly available geochemical data from the US Geological Survey's National Water Information System (NWIS). Within the boundaries of the study area, more than 2300 ground-water analyses and more than 20,000 surface-water analyses were available. Ground-waters from the west side of the Sacramento Valley contain greater concentrations of Na, Ca, Mg, B, Cl and SO 4 , while the east-side ground-waters contain greater concentrations of silica and K. These differences result from variations in surface-water chemistry as well as from chemical reactions between water and aquifer materials. Sediments that fill the Sacramento Valley were derived from highlands to the west (the Coast Ranges) and east (the Sierra Nevada Mountains), the former having an oceanic provenance and the latter continental. These geologic differences are at least in part responsible for the observed patterns in ground-water chemistry. Thermal springs that are common along the west side of the Sacramento Valley appear to have an effect on surface-water chemistry, which in turn may affect the ground-water chemistry.

Applied Geochemistry↗

USGS2009

Exploratory and spatial data analysis (EDA-SDA) for determining regional background levels and anomalies of potentially toxic elements in soils from Catorce-Matehuala, Mexico

The threshold between geochemical background and anomalies can be influenced by the methodology selected for its estimation. Environmental evaluations, particularly those conducted in mineralized areas, must consider this when trying to determinate the natural geochemical status of a study area, quantifying human impacts, or establishing soil restoration values for contaminated sites. Some methods in environmental geochemistry incorporate the premise that anomalies (natural or anthropogenic) and background data are characterized by their own probabilistic distributions. One of these methods uses exploratory data analysis (EDA) on regional geochemical data sets coupled with a geographic information system (GIS) to spatially understand the processes that influence the geochemical landscape in a technique that can be called a spatial data analysis (SDA). This EDA-SDA methodology was used to establish the regional background range from the area of Catorce-Matehuala in north-central Mexico. Probability plots of the data, particularly for those areas affected by human activities, show that the regional geochemical background population is composed of smaller subpopulations associated with factors such as soil type and parent material. This paper demonstrates that the EDA-SDA method offers more certainty in defining thresholds between geochemical background and anomaly than a numeric technique, making it a useful tool for regional geochemical landscape analysis and environmental geochemistry studies.

Applied Geochemistry↗

USGS2016

Use of mineral/solution equilibrium calculations to assess the potential for carnotite precipitation from groundwater in the Texas Panhandle, USA

This study investigated the potential for the uranium mineral carnotite (K 2 (UO 2 ) 2 (VO 4 ) 2 ·3H 2 O) to precipitate from evaporating groundwater in the Texas Panhandle region of the United States. The evolution of groundwater chemistry during evaporation was modeled with the USGS geochemical code PHREEQC using water-quality data from 100 groundwater wells downloaded from the USGS National Water Information System (NWIS) database. While most modeled groundwater compositions precipitated calcite upon evaporation, not all groundwater became saturated with respect to carnotite with the system open to CO 2 . Thus, the formation of calcite is not a necessary condition for carnotite to form. Rather, the determining factor in achieving carnotite saturation was the evolution of groundwater chemistry during evaporation following calcite precipitation. Modeling in this study showed that if the initial major-ion groundwater composition was dominated by calcium-magnesium-sulfate (>70 precent Ca + Mg and >50 percent SO 4 + Cl) or calcium-magnesium-bicarbonate (>70 percent Ca + Mg and <70 percent HCO 3 + CO 3 ) and following the precipitation of calcite, the concentration of calcium was greater than the carbonate alkalinity (2 m Ca +2 > m HCO 3 − + 2 m CO 3 −2 ) carnotite saturation was achieved. If, however, the initial major-ion groundwater composition is sodium-bicarbonate (varying amounts of Na, 40–100 percent Na), calcium-sodium-sulfate, or calcium-magnesium-bicarbonate composition (>70 percent HCO 3 + CO 3 ) and following the precipitation of calcite, the concentration of calcium was less than the carbonate alkalinity (2 m Ca +2 < m HCO 3 - + 2 m CO 3 −2 ) carnotite saturation was not achieved. In systems open to CO 2, carnotite saturation occurred in most samples in evaporation amounts ranging from 95 percent to 99 percent with the partial pressure of CO 2 ranging from 10 −3.5 to 10 −2.5 atm. Carnotite saturation occurred in a few samples in evaporation amounts ranging from 98 percent to 99 percent with the partial pressure of CO 2 equal to 10 −2.0 atm. Carnotite saturation did not occur in any groundwater with the system closed to CO 2 .

Texas↗

USGS2026

Long lives, short futures: Freshwater turtle and tortoise imports to the United States highlight global trade and conservation challenges

Global trade of freshwater turtles and tortoises presents substantial conservation, biosecurity, and regulatory challenges, particularly for long-lived species that are already highly imperiled. While the United States has historically played a key role in supplying the international chelonian trade, investigations into its role as an importer remain limited. We used import data from the U.S. Fish and Wildlife Service Law Enforcement Management Information System (LEMIS) between 2000 and 2022 to evaluate the trade of freshwater turtles and tortoises. Over 2.5 million individuals were imported into the United States during this period, comprising 172 species that are likely imported for the pet trade, food, and traditional medicine markets. Reported annual imports for freshwater turtles increased until approximately 2013 and decreased thereafter, whereas reported annual imports for tortoises have remained relatively constant. Two species, Chinese softshell turtles ( Pelodiscus sinensis ) and Horsfield's tortoises ( Testudo horsfieldii ) represent 62.9% of all imports. Imported species were largely classified as Vulnerable, Endangered, or Critically Endangered by the IUCN Red List, yet the conservation status of a concerning number of species ( n = 45; 26.2%) has not been evaluated. Many imports appear to be sourced from wild populations (28.3% of freshwater turtles, 71.3% of tortoises); however, captive-sourced imports increased significantly over time across both groups. Import volumes raise concerns regarding the sustainability of freshwater turtle and tortoise trade, the potential for invasive species establishment, and disease spread. Our results highlight the need for updated IUCN assessments, verification of trade records, and strengthening of biosecurity screening at U.S. ports of entry.

Biological Conservation↗

USGS2009

A mosaic of diverse ideas: The ecological legacy of J. Frederick Grassle

During the 40 years (and counting) of his scientific career, J. Frederick Grassle has made fundamental contributions to our understanding of marine ecosystems from coral reefs to deep-sea sediments. His advocacy and passion for marine biodiversity in the form of myriad groundbreaking studies and influential reviews, his generosity of ideas and capacity to catalyze and inspire those working with him as well as the science community in general, his breakthroughs in improved ocean observation, his marine science infrastructure initiatives, together with his tireless persistence, have helped lead to major shifts in approaches to marine science and the shape of modern ocean studies to one that favours multidisciplinary research, teamwork, continuous, long-term observation, in situ experimentation, recognition of the importance of marine biodiversity, and global cooperation on research and data sharing. In shallow-water ecology, he co-discovered sibling species of Capitella spp., important not only because it is a key pollution indicator but also because the work helped to pave the way for the discovery of numerous sibling species in other taxa with major ramifications for ecological understanding. He was also a key player in the West Falmouth oil spill study which, along with complementary mesocosm experiments, remains one of the most important and detailed studies of its kind. He was also a lead player in the first biological expedition to hydrothermal vents and wrote the seminal articles that helped to inspire the flurry of vent research that followed. He is perhaps best known for his deep-sea work, where he brought submersibles to the forefront as a sampling tool, brought experimental manipulative studies to the primarily descriptive discipline of deep-sea benthic ecology, and generated tremendous excitement, debate, and rekindled interest in marine biodiversity with the first quantitative estimate of global deep-sea diversity. His efforts to document marine biodiversity resulted in the international Census of Marine Life, and his emphasis on the need for continuous, long-term ocean observation has led to breakthroughs in international cooperation in cabled observatories such as LEO-15. These efforts have also enhanced efforts to integrate ocean data on a global scale in platforms such as the Ocean Biogeographic Information System (OBIS). The diversity of his contributions to marine science mirror the immense marine diversity he has recognized, documented, and championed so effectively over the last four decades.

Deep-Sea Research Part II: Topical Studies in Ocea↗

USGS2014

Mapping and monitoring Mount Graham red squirrel habitat with Lidar and Landsat imagery

The Mount Graham red squirrel ( Tamiasciurus hudsonicus grahamensis ) is an endemic subspecies located in the Pinaleño Mountains of southeast Arizona. Living in a conifer forest on a sky-island surrounded by desert, the Mount Graham red squirrel is one of the rarest mammals in North America. Over the last two decades, drought, insect infestations, and fire destroyed much of its habitat. A federal recovery team is working on a plan to recover the squirrel and detailed information is necessary on its habitat requirements and population dynamics. Toward that goal I developed and compared three probabilistic models of Mount Graham red squirrel habitat with a geographic information system and logistic regression. Each model contained the same topographic variables (slope, aspect, elevation), but the Landsat model contained a greenness variable (Normalized Difference Vegetation Index) extracted from Landsat, the Lidar model contained three forest-inventory variables extracted from lidar, while the Hybrid model contained Landsat and lidar variables. The Hybrid model produced the best habitat classification accuracy, followed by the Landsat and Lidar models, respectively. Landsat-derived forest greenness was the best predictor of habitat, followed by topographic (elevation, slope, aspect) and lidar (tree height, canopy bulk density, and live basal area) variables, respectively. The Landsat model's probabilities were significantly correlated with all 12 lidar variables, indicating its utility for habitat mapping. While the Hybrid model produced the best classification results, only the Landsat model was suitable for creating a habitat time series or habitat–population function between 1986 and 2013. The techniques I highlight should prove valuable in the development of Landsat- or lidar-based habitat models range wide.

Arizona↗

USGS2005

Tree species and size structure of old-growth Douglas-fir forests in central western Oregon, USA

We characterized the structure of 91 old-growth forests dominated by Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco), using inventory data from recent (1985–1991) old-growth timber sales in western Oregon. The data were complete counts (i.e., censuses) of all live trees >20 cm diameter at breast height (dbh, measured at 1.4 m above the ground) over a mean area of 17.1 ha at each site. Across all sites, Douglas-fir accounted for 79% of the total basal area (m 2 /ha) of all species. The average density of trees >100 cm dbh was 19 trees/ha and 90% of these trees were Douglas-fir. Species other than Douglas-fir constituted only about 20% of the total basal area at each old-growth site, on average, but largely accounted for the structural variation between sites. We used multivariate techniques such as cluster analysis, indicator species analysis, and ordination with non-metric multidimensional scaling (NMS) to identify and characterize six structural groups in terms of basal area in different species–diameter classes. Almost 97% of the structural information was captured by the first ( r 2 = 0.841) and second ( r 2 = 0.128) NMS ordination axis. Geographic information systems (GIS) analysis and NMS indicated that the structural differences among groups of sites were associated with moisture, temperature, and elevation gradients within the study area. This type of analysis can be used to help define differences among old-growth forests and to set local structural goals for growing forests with old-growth characteristics.

Oregon↗

USGS2016

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~ 2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.

Geoderma↗

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↗

USGS2011

Hillslope chemical weathering across Paraná, Brazil: a data mining-GIS hybrid approach

Self-organizing map (SOM) and geographic information system (GIS) models were used to investigate the nonlinear relationships associated with geochemical weathering processes at local (~100 km 2 ) and regional (~50,000 km 2 ) scales. The data set consisted of 1) 22 B-horizon soil variables: P, C, pH, Al, total acidity, Ca, Mg, K, total cation exchange capacity, sum of exchangeable bases, base saturation, Cu, Zn, Fe, B, S, Mn, gammaspectrometry (total count, potassium, thorium, and uranium) and magnetic susceptibility measures; and 2) six topographic variables: elevation, slope, aspect, hydrological accumulated flux, horizontal curvature and vertical curvature. It is characterized at 304 locations from a quasi-regular grid spaced about 24 km across the state of Paraná. This data base was split into two subsets: one for analysis and modeling (274 samples) and the other for validation (30 samples) purposes. The self-organizing map and clustering methods were used to identify and classify the relations among solid-phase chemical element concentrations and GIS derived topographic models. The correlation between elevation and k-means clusters related the relative position inside hydrologic macro basins, which was interpreted as an expression of the weathering process reaching a steady-state condition at the regional scale. Locally, the chemical element concentrations were related to the vertical curvature representing concave–convex hillslope features, where concave hillslopes with convergent flux tends to be a reducing environment and convex hillslopes with divergent flux, oxidizing environments. Stochastic cross validation demonstrated that the SOM produced unbiased classifications and quantified the relative amount of uncertainty in predictions. This work strengthens the hypothesis that, at B-horizon steady-state conditions, the terrain morphometry were linked with the soil geochemical weathering in a two-way dependent process: the topographic relief was a factor on environmental geochemistry while chemical weathering was for terrain feature delineation.

Parana↗

USGS2020

Global physical controls on estuarine habitat distribution during sea levelchange: Consequences for genetic diversification through time

Determining the extrinsic (physical) factors controlling speciation and diversification of species through time is of key interest in paleontology and evolutionary biology. The role of sea-level change in shaping species richness patterns of marginal marine species has received much attention, but with variable conclusions. Recent work combining genetic data and Geographical Information Systems (GIS)-based habitat modeling yielded a framework for how geomorphology of continental margins mediates genetic connectivity of populations during sealevel change. This approach may ultimately yield insights on how distinct lineages, species, and biodiversity accumulate in coastal settings. Here, we expand this GIS work globally to different geomorphic settings to model estuarine habitat in a larger geographic framework and test how tectonic setting, oceanographic setting, climate, and margin age affect habitat distribution during sea-level change. In addition, independent of estuaries we explore paleobiologic (e.g. Olsson, 1961) and neontolologic effects of sea-level change on evolution, and test the relation between overall shelf area and species richness using data of 1721 fish species. We find 82% global reduction of estuarine habitat abundance at lowstand relative to highstand, and find large habitats change in size much more than small habitats. Consistent with prior work, narrow continental margins have significantly less habitat at highstand and lowstand than wide margins, and narrow margins significantly associate with fore-arc settings, effectively linking tectonic setting to habitat abundance. Surprisingly, narrow margins host greater species richness, a finding which violates the canonical species-area relation. This finding can be explained if: 1) the physical isolation imposed by narrow margins facilitates the formation of new species over time; 2) the sizestability of small habitats, which disproportionately occur on narrow margins, accumulate and retain species extirpated in the more variable habitats on wide margins; or 3) the smaller habitats on narrow margins facilitate greater species richness through greater habitat heterogeneity. These results are generally at odds with prior interpretations, but the combination of richness data and population genetic principles offer a different perspective on these long-studied questions. Finally, we emphasize that the nuance of Pleistocene-Holocene sea level oscillations should be more explicitly considered in genetic studies.

Global and Planetary Change↗

USGS2022

Multi-hazard risk analysis for the U.S. Department of the Interior: An integration of expert elicitation, planning priorities, and geospatial analysis

An integral part of disaster risk management is identifying and prioritizing hazards and their potential impacts in a meaningful way to support risk-reduction planning. There has been considerable use and subsequent criticism of threat prioritization efforts that simply compare likelihoods and consequences of plausible threats. This article summarizes a new mixed-methods and scalable approach for prioritizing risks in a multi-hazard, multi-objective, and multi-criteria organizational context. This approach integrates (1) hazard characterizations using subject-matter-expert (SME) elicitation, (2) expressed preferences in planning priorities provided by emergency managers, and (3) quantitative estimates of asset exposure to hazards using geospatial data and geographic-information-systems (GIS) software. We demonstrate this approach with a case study designed to support multi-hazard mitigation and response planning done by the U.S. Department of the Interior (DOI) Office of Emergency Management, which required a national understanding of the risks posed by 75 natural, technological, and adversarial hazards to DOI managed and administered lands, facilities, people, revenues, and resources. Results demonstrate that hazard priorities vary depending on the asset, scale, and risk-management context, thereby making the case that “one-size-fits-all” hazard rankings have limited utility or relevance to real-world, risk mitigation and response planning. Our results suggest that recognizing the risk-management context provides greater transparency, flexibility, and relevance in comparing threats than traditional likelihood-threat matrices or the use of hazard SMEs to decide for planners which hazard scenarios are emphasized in risk planning.

International Journal of Disaster Risk Reduction↗

USGS2018

Landscape-scale processes influence riparian plant composition along a regulated river

Hierarchical frameworks are useful constructs when exploring landscape- and local-scale factors affecting patterns of vegetation in riparian areas. In drylands, which have steep environmental gradients and high habitat heterogeneity, landscape-scale variables, such as climate, can change rapidly along a river's course, affecting the relative influence of environmental variables at different scales. To assess how landscape-scale factors change the structure of riparian vegetation, we measured riparian vegetation composition along the Colorado River through Grand Canyon, determined which factors best explain observed changes, identified how richness and functional diversity vary, and described the implications of our results for river management. Cluster analysis identified three divergent floristic groups that are distributed longitudinally along the river. These groups were distributed along gradients of elevation, temperature and seasonal precipitation, but were not associated with annual precipitation or local-scale factors. Species richness and functional diversity decreased as a function of distance downstream showing that changing landscape-scale factors result in changes to ecosystem characteristics. Species composition and distribution remain closely linked to seasonal precipitation and temperature. These patterns in floristic composition in a semiarid system inform management and provide insights into potential future changes as a result of shifts in climate and changes in flow management.

Arizona↗

USGS2014

Habitat used by juvenile lake sturgeon ( Acipenser fulvescens ) in the North Channel of the St. Clair River (Michigan, USA)

Lake sturgeon ( Acipenser fulvescens ) occupy the St. Clair River, part of a channel connecting lakes Huron and Erie in the Laurentian Great Lakes. In the North Channel of the St. Clair River, juvenile lake sturgeon (3–7 years old and 582–793 mm in length) were studied to determine movement patterns and habitat usage. Fourteen juveniles were implanted with ultrasonic transmitters and tracked June–August of 2004, 2005 and 2006. Telemetry data, Geographic Information System software, side-scan sonar, video images of the river bottom, scuba diving, and benthic substrate samples were used to determine the extent and composition of habitats they occupied. Juvenile lake sturgeon habitat selection was strongly related to water depth. No fish were found in <6 m of water and over 97% of the relocations were found at depths greater than 9 m. Available water depths exceeding 18 m only represented 3.5% of the available habitat, however 34.9% of the relocations were found at depths exceeding 18 m. Juvenile lake sturgeon did not use most areas in proportion to their availability. Sturgeon avoided clay ledges and shallow areas with silt or soft clay, which comprised approximately 39% of the benthic habitat in the North Channel. A total of 300 out of 351 documented locations were on sand and gravel habitat types mixed with clay. Lake sturgeon > 700 mm in length selected sand and gravel areas mixed with zebra mussels and areas dominated by zebra mussels, while fish < 700 mm used these habitat types in proportion to their availability.

Michigan↗

USGS2018

Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA

Nitrate contamination of groundwater in agricultural areas poses a major challenge to the sustainability of water resources. Aquifer vulnerability models are useful tools that can help resource managers identify areas of concern, but quantifying nitrogen (N) inputs in such models is challenging, especially at large spatial scales. We sought to improve regional nitrate (NO 3 − ) input functions by characterizing unsaturated zone NO 3 − transport to groundwater through use of surrogate, machine-learning metamodels of a process-based N flux model. The metamodels used boosted regression trees (BRTs) to relate mappable landscape variables to parameters and outputs of a previous “vertical flux method” (VFM) applied at sampled wells in the Fox, Wolf, and Peshtigo (FWP) river basins in northeastern Wisconsin. In this context, the metamodels upscaled the VFM results throughout the region, and the VFM parameters and outputs are the metamodel response variables. The study area encompassed the domain of a detailed numerical model that provided additional predictor variables, including groundwater recharge, to the metamodels. We used a statistical learning framework to test a range of model complexities to identify suitable hyperparameters of the six BRT metamodels corresponding to each response variable of interest: NO 3 − source concentration factor (which determines the local NO 3 − input concentration); unsaturated zone travel time; NO 3 − concentration at the water table in 1980, 2000, and 2020 (three separate metamodels); and NO 3 − “extinction depth”, the eventual steady state depth of the NO 3 − front. The final metamodels were trained to 129 wells within the active numerical flow model area, and considered 58 mappable predictor variables compiled in a geographic information system (GIS). These metamodels had training and cross-validation testing R 2 values of 0.52 – 0.86 and 0.22 – 0.38, respectively, and predictions were compiled as maps of the above response variables. Testing performance was reasonable, considering that we limited the metamodel predictor variables to mappable factors as opposed to using all available VFM input variables. Relationships between metamodel predictor variables and mapped outputs were generally consistent with expectations, e.g. with greater source concentrations and NO 3 − at the groundwater table in areas of intensive crop use and well drained soils. Shorter unsaturated zone travel times in poorly drained areas likely indicated preferential flow through clay soils, and a tendency for fine grained deposits to collocate with areas of shallower water table. Numerical estimates of groundwater recharge were important in the metamodels and may have been a proxy for N input and redox conditions in the northern FWP, which had shallow predicted NO 3 − extinction depth. The metamodel results provide proof-of-concept for regional characterization of unsaturated zone NO 3 − transport processes in a statistical framework based on readily mappable GIS input variables.

Wisconsin↗

USGS2019

An ANCOVA model for porosity and its uncertainty for oil reservoirs based on TORIS dataset

Porosity is one of the most important parameters to assess in-place oil or gas in reservoirs, and to evaluate recovery from enhanced production operations. Since it is relatively well-established to determine porosity using different laboratory and field methods, its value is usually determined at many locations across a reservoir as part of the common practice to capture reservoir heterogeneity and the variability in values. This suite of measurements and the distribution of values are most valuable for probabilistic reservoir assessments, and for spatial modeling if the exact data locations are known. Despite the importance of individual measurements to set the range of values for probabilistic studies, it is not always possible to access these data due to confidentiality. In most cases, commercial or publicly available databases that assessments may rely on usually report only mean values of porosity, like any other reservoir data, or they may not report a value at all. This makes both quantifying the mean value and the uncertainty around it difficult for probabilistic assessments. In this study, the TORIS (Tertiary Oil Recovery Information System) dataset of the National Petroleum Council and the U.S. Department of Energy was used to model porosity and the uncertainty around predicted values. TORIS is an integrated dataset of production data, reservoir properties, and project databases of crude oil reservoirs in the United States. The model presented in the paper was based on ANCOVA (Analysis of Co-Variance) of data from 1038 reservoirs from the TORIS dataset for porosity prediction, validation and testing for quantitative and qualitative parameters that may be readily available in most cases, and to estimate uncertainty around the mean values. This model also explored association of porosity values to different parameters, and to different depositional systems and diagenetic overprint conditions. Furthermore, an ANN (Artificial Neural Network) model was created to compare the predicted values of both models. Results showed that the ANN model was able to represent more of the variability, however it lacked the insights that might be gained from the ANCOVA model.

Journal of Petroleum Science and Engineering↗

USGS2014

The digital global geologic map of Mars: Chronostratigraphic ages, topographic and crater morphologic characteristics, and updated resurfacing history

A new global geologic map of Mars has been completed in a digital, geographic information system (GIS) format using geospatially controlled altimetry and image data sets. The map reconstructs the geologic history of Mars, which includes many new findings collated in the quarter century since the previous, Viking-based global maps were published, as well as other discoveries that were made during the course of the mapping using new data sets. The technical approach enabled consistent and regulated mapping that is appropriate not only for the map's 1:20,000,000 scale but also for its widespread use by diverse audiences. Each geologic unit outcrop includes basic attributes regarding identity, location, area, crater densities, and chronostratigraphic age. In turn, units are grouped by geographic and lithologic types, which provide synoptic global views of material ages and resurfacing character for the Noachian, Hesperian, and Amazonian periods. As a consequence of more precise and better quality topographic and morphologic data and more complete crater-density dating, our statistical comparisons identify significant refinements for how Martian geologic terrains are characterized. Unit groups show trends in mean elevation and slope that relate to geographic occurrence and geologic origin. In comparison with the previous global geologic map series based on Viking data, the new mapping consists of half the number of units due to simpler, more conservative and globally based approaches to discriminating units. In particular, Noachian highland surfaces overall have high percentages of their areas now dated as an epoch older than in the Viking mapping. Minimally eroded (i.e., pristine) impact craters ≥3 km in diameter occur in greater proportion on Hesperian surfaces. This observation contrasts with a deficit of similarly sized craters on heavily cratered and otherwise degraded Noachian terrain as well as on young Amazonian surfaces. We interpret these as reflecting the relatively stronger, lava-rich, yet less-impacted materials making up much of the younger units. Reconstructions of resurfacing of Mars by its eight geologic epochs using the Hartmann and Neukum chronology models indicate high rates of highland resurfacing during the Noachian (peaking at 0.3 km 2 /yr during the Middle Noachian), modest rates of volcanism and transition zone and lowland resurfacing during the Hesperian (∼0.1 km 2 /yr), and low rates of mainly volcanic and polar resurfacing (∼0.01 km 2 /yr) for most of the Amazonian. Apparent resurfacing increased in the Late Amazonian (∼0.03 km 2 /yr), perhaps due to better preservation of this latest record.

Planetary and Space Science↗

USGS2012

Potential pollutant sources in a Choptank River (USA) subwatershed and the influence of land use and watershed characteristics

Row-crop and poultry production have been implicated as sources of water pollution along the Choptank River, an estuary and tributary of the Chesapeake Bay. This study examined the effects of land use, subwatershed characteristics, and climatic conditions on the water quality parameters of a subwatershed in the Choptank River watershed. The catchments within the subwatershed were defined using advanced remotely-sensed data and current geographic information system processing techniques. Water and sediment samples were collected in May–October 2009 and April–June 2010 under mostly baseflow conditions and analyzed for select bacteria, nitrate-N, ammonium-N, total arsenic, total phosphorus (TP), orthophosphate (ortho-P), and particle-phase phosphorus (PP); n = 96 for all analytes except for arsenic, n = 136, and for bacteria, n = 89 (aqueous) and 62 (sediment). Detections of Enterococci and Escherichia coli concentrations were ubiquitous in this subwatershed and showed no correlation to location or land use, however larger bacterial counts were observed shortly after precipitation. Nitrate-N concentrations were not correlated with agricultural lands, which may reflect the small change in percent agriculture and/or the similarity of agronomic practices and crops produced between catchments. Concentration data suggested that ammonia emission and possible deposition to surface waters occurred and that these processes may be influenced by local agronomic practices and climatic conditions. The negative correlation of PP and arsenic concentrations with percent forest was explained by the stronger signal of the head waters and overland flow of particulate phase analytes versus dissolved phase inputs from groundwater. Service roadways at some poultry production facilities were found to redirect runoff from the facilities to neighboring catchment areas, which affected water quality parameters. Results suggest that in this subwatershed, catchments with poultry production facilities are possible sources for arsenic and PP as compared to catchment areas where these facilities were not present.

Choptank River↗