Geology topics

Research about Eastern United States

Source-linked reports with geographic coverage including Eastern United States.

5 recordsLinked to original sources

USGS2022

Multiple dimensions of functional diversity affect stream fish β-diversity

When investigating metacommunity dynamics, functional differences among species are often assumed to be as important as environmental differences between sites in determining β-diversity. However, few studies have examined the influence of functional diversity on β-diversity. We examine the relative importance of regional functional diversity partitioned by niche dimensions and environmental variation in structuring taxonomic β-diversity of stream fishes using a large dataset of stream fish assemblages (hereafter, simply β-diversity). We predicted that both functional diversity and environmental variation play a role in determining β-diversity. We tested this prediction by modelling the patterns of stream fish β-diversity as a function of environmental variation, functional diversity and γ-richness across 10,220 sites for 329 fish species using a series of conceptual path models. Environmental variation consistently affected β-diversity across all models, whereas functional diversity and γ-richness influenced β-diversity only in some models. We show that including relevant trait differences among species in path models can improve their ability to explain β-diversity, suggesting that functional traits influence β-diversity. The ability of path models to explain β-diversity varied depending on the trait grouping included in the model, demonstrating that specific path models representing different niche dimensions can improve the ability of a model to explain β-diversity. In addition, parsing traits into different niche dimensions revealed alternative patterns of functional diversity–β-diversity relationships that otherwise would have been missed. The selection of relevant traits and linked niche dimensions is critical for detecting relationships between functional diversity and β-diversity. Using traits associated with different niche dimensions allows for the identification of niche dimensions most strongly associated with species sorting and the detection of patterns missed by focusing on a single niche dimension. Determining the niche dimensions that influence β-diversity could provide insights into the processes driving biodiversity and metacommunity dynamics, improving our ability to conserve or restore aquatic communities.

Eastern United States↗

USGS2014

Nutrient load summaries for major lakes and estuaries of the Eastern United States, 2002

Nutrient enrichment of lakes and estuaries across the Nation is widespread. Nutrient enrichment can stimulate excessive plant and algal growth and cause a number of undesirable effects that impair aquatic life and recreational activities and can also result in economic effects. Understanding the amount of nutrients entering lakes and estuaries, the physical characteristics affecting the nutrient processing within these receiving waterbodies, and the natural and manmade sources of nutrients is fundamental to the development of effective nutrient reduction strategies. To improve this understanding, sources and stream transport of nutrients to 255 major lakes and 64 estuaries in the Eastern United States were estimated using Spatially Referenced Regression on Watershed attributes (SPARROW) nutrient models.

Eastern United States↗

USGS2013

Estimating irrigation water use in the humid eastern United States

Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to develop the models as well as two independent validation datasets from Georgia and Virginia that were not used in model development. Irrigation water-use estimates from the logistic regression method more closely matched mean reported irrigation rates than estimates from the crop-water-demand model when compared to the irrigation data used to develop the equations. The root mean squared errors (RMSEs) for the logistic regression estimates of mean annual irrigation ranged from 0.3 to 2.0 inches (in.) for the five crop types; RMSEs for the crop-water-demand models ranged from 1.4 to 3.9 in. However, when the models were applied and compared to the independent validation datasets from southwest Georgia from 2010, and from Virginia from 1999 to 2007, the crop-water-demand model estimates were as good as or better at predicting the mean irrigation volume than the logistic regression models for most crop types. RMSEs for logistic regression estimates of mean annual irrigation ranged from 1.0 to 7.0 in. for validation data from Georgia and from 1.8 to 4.9 in. for validation data from Virginia; RMSEs for crop-water-demand model estimates ranged from 2.1 to 5.8 in. for Georgia data and from 2.0 to 3.9 in. for Virginia data. In general, regression-based models performed better in areas that had quality daily or weekly irrigation data from which the regression equations were developed; however, the regression models were less reliable than the crop-water-demand models when applied outside the area for which they were developed. In most eastern coastal states that do not have quality irrigation data, the crop-water-demand model can be used more reliably. The development of predictive models of irrigation water use in this study was hindered by a lack of quality irrigation data. Many mid-Atlantic and New England states do not require irrigation water use to be reported. A survey of irrigation data from 14 eastern coastal states from Maine to Georgia indicated that, with the exception of the data in Georgia, irrigation data in the states that do require reporting commonly did not contain requisite ancillary information such as irrigated area or crop type, lacked precision, or were at an aggregated temporal scale making them unsuitable for use in the development of predictive models. Confidence in the reliability of either modeling method is affected by uncertainty in the reported data from which the models were developed or validated. Only through additional collection of quality data and further study can the accuracy and uncertainty of irrigation water-use estimates be improved in the humid eastern United States.

Eastern United States↗

USGS1979

Chemical analysis of 617 coal samples from the Eastern United States

This report includes all the analytical data on 617 coal samples from 8 states east of the Mississippi River. The samples from each state are, Pennsylvania 71, Ohio 40, West Virginia 252, Virginia 72, Kentucky 27, Tennessee 27, Alabama 20, and Indiana 108. The U.S. Geological Survey has quantitatively determined the amounts of 35 major, minor and trace elements in each sample. It has also searched for 35 other trace elements using semi-quantitative spectrographic methods. In addition, the Coal Analysis Section of the Department of Energy has provided proximate and ultimate analyses, Btu, forms of sulfur, free swelling index, and ash fusion temperatures on 491 samples. Comparison of the geometric means of these samples with 331 bituminous coal samples of the Appalachian region reported by Swanson and others (1976) are as follows. As shown by the means for ultimate and proximate analyses small differences exist between the two sets of data, only the moisture content and oxygen are significantly different. The forms of sulfur and heat of combustion are also similar. The means for the major and minor oxides in ash are similar for SiO 2 , Al 2 O 3 , CaO, MgO, K 2 O 3 and TiO 2 . Na 2 O is significantly lower and Fe 2 O 3 and MnO higher in the analyses of the 617 samples of this report. Most means for the trace elements in the coals studied for this report are lower. Only Be is significantly higher in these coals.

Alabama, Indiana, Kentucky, Ohio, Pennsylvania, Te↗

USGS1968

Geologic implications of aeromagnetic data for the eastern continental margin of the United States

An aeromagnetic survey extending from the Gulf of Maine to the tip of Florida was conducted by the U. S. Naval Oceanographic Office between 1964 and 1966. Flight traverses were flown in a northwesterly direction at right angles to the geologic grain. The flight lines were approximately 800 km long and had an 8-km separation. The survey traversed part of the New England, Piedmont, and Coastal Plain provinces and extended some 320 km beyond the continental shelf into the Atlantic Ocean. Despite the wide flight-line spacing, numerous geological and structural features became apparent from this survey. Interpretation of these features was aided by using the available gravity and seismic data in addition to the State and Provincial geologic maps. The residual aeromagnetic map shows a continuous magnetic high on or near the continental slope as far south as the 31st parallel. At about the 36th parallel, this east-coast magnetic anomaly splits into two branches, and both of them parallel the 850-fathom contour. At the 31st parallel, the outer branch of the anomaly swings westward and crosses the coastline near Brunswick, Georgia. This continuous magnetic anomaly may result from an igneous intrusive body that parallels the edge of the pre-Paleozoic continental landmass. These magnetic data suggest that Florida and part of Georgia were added to the paleo-continent in pre-Paleozoic time. Landward from the east-coast anomaly, the magnetic field is quite variable, whereas oceanward it has an extremely small gradient. The absence of magnetic anomalies east of the continental slope suggests that in this region layer 2 may be composed of metamorphosed basalt. The characteristic magnetic patterns observed over the Piedmont and New England provinces extend oceanward to the east-coast anomaly.

Eastern United States↗