Geology topics

Jennifer C. Murphy

Publications and source records attributed to Jennifer C. Murphy.

40 records · Page 3Linked to original sources

USGS2013

Ecological limit functions relating fish community response to hydrologic departures of the ecological flow regime in the Tennessee River basin, United States

Ecological limit functions relating streamflow and aquatic ecosystems remain elusive despite decades of research. We investigated functional relationships between species richness and changes in streamflow characteristics at 662 fish sampling sites in the Tennessee River basin. Our approach included the following: (1) a brief summary of relevant literature on functional relations between fish and streamflow, (2) the development of ecological limit functions that describe the strongest discernible relationships between fish species richness and streamflow characteristics, (3) the evaluation of proposed definitions of hydrologic reference conditions, and (4) an investigation of the internal structures of wedge-shaped distributions underlying ecological limit functions. Twenty-one ecological limit functions were developed across three ecoregions that relate the species richness of 11 fish groups and departures from hydrologic reference conditions using multivariate and quantile regression methods. Each negatively sloped function is described using up to four streamflow characteristics expressed in terms of cumulative departure from hydrologic reference conditions. Negative slopes indicate increased departure results in decreased species richness. Sites with the highest measured fish species richness generally had near-reference hydrologic conditions for a given ecoregion. Hydrology did not generally differ between sites with the highest and lowest fish species richness, indicating that other environmental factors likely limit species richness at sites with reference hydrology. Use of ecological limit functions to make decisions regarding proposed hydrologic regime changes, although commonly presented as a management tool, is not as straightforward or informative as often assumed. We contend that statistical evaluation of the internal wedge structure below limit functions may provide a probabilistic understanding of how aquatic ecology is influenced by altered hydrology and may serve as the basis for evaluating the potential effect of proposed hydrologic changes.

Tennessee River basin↗

USGS2013

Nitrate in the Mississippi River and its tributaries, 1980-2010: an update

Nitrate concentration and flux were estimated from 1980 through 2010 at eight sites in the Mississippi River Basin as part of the National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS). These estimates extend the results from a previous investigation that provided nitrate estimates from 1980 through 2008 at the same sites. From 1980 through 2010, annual flow-normalized (FN) nitrate concentration and flux in the Iowa and Illinois Rivers decreased by 11 to 15 percent. These two rivers had the highest FN nitrate concentration in 1980 (5.3 milligrams per liter [mg/L] and 3.9 mg/L, respectively) of any of the study sites. Nitrate increased in the Missouri River (79 and 45 percent increase in FN concentration and flux, respectively), and at the four sites on the Mississippi River (17 to 70 percent increase in FN concentration and 8 to 55 percent increase in FN flux) from 1980 through 2010. Nitrate in the Ohio River was generally stable during this time. Historically, nitrate was high and changed little in the Iowa and Illinois Rivers; however, nitrate concentrations began to decrease around 2000, and this decrease continued through 2010. Also during this time, near-flat nitrate trends in lower sections of the Mississippi River began increasing, likely reflecting the acceleration of already increasing nitrate trends in the upper Mississippi and Missouri Rivers, in addition to increases in inputs from other tributaries in the lower part of the Mississippi River Basin. Spring trends (April through June) generally parallel annual trends at all sites from 1980 through 2010, except in the Iowa River where decreasing nitrate during the spring was not observed. In general, most sites had increases in nitrate concentration at low streamflows, which suggests increases in legacy nitrate from groundwater or point source contributions. In aggregate, the decreases in nitrate concentrations from the Iowa and Illinois Rivers, which largely occurred during high flows, appear to be overshadowed by increasing nitrate concentrations across much of the Mississippi River Basin.

Gulf Of Mexico;Illinois River;Iowa River;Mississip↗

USGS2013

Methods for estimating water consumption for thermoelectric power plants in the United States

Water consumption at thermoelectric power plants represents a small but substantial share of total water consumption in the U.S. However, currently available thermoelectric water consumption data are inconsistent and incomplete, and coefficients used to estimate consumption are contradictory. The U.S. Geological Survey (USGS) has resumed the estimation of thermoelectric water consumption, last done in 1995, based on the use of linked heat and water budgets to complement reported water consumption. This report presents the methods used to estimate freshwater consumption at a study set of 1,284 power plants based on 2010 plant characteristics and operations data. Power plants were categorized for estimation of water consumption in two tiers. First, generating units were assigned to categories based on the technology used to generate electricity. These generation-type categories are combustion steam, combined-cycle, nuclear, geothermal, and solar thermal. Second, cooling systems were separately categorized as either wet cooling towers or surface-water cooling systems, and the surface-water cooling systems were subcategorized as cooling ponds, lakes, and rivers. Heat budgets were constructed for the first four generation-type categories; data at solar thermal plants were insufficient for heat budgets. These heat budgets yielded estimates of the amount of heat transferred to the condenser. The ratio of evaporation to the heat discharged through the condenser was estimated using existing heat balance models that are sensitive to environmental data; this feature allows estimation of consumption under different climatic conditions. These two estimates were multiplied to yield an estimate of consumption at each power plant.

Scientific Investigations Report↗

USGS2012

Predicting ecological flow regime at ungaged sites: A comparison of methods

Nineteen ecologically relevant streamflow characteristics were estimated using published rainfall–runoff and regional regression models for six sites with observed daily streamflow records in Kentucky. The regional regression model produced median estimates closer to the observed median for all but two characteristics. The variability of predictions from both models was generally less than the observed variability. The variability of the predictions from the rainfall–runoff model was greater than that from the regional regression model for all but three characteristics. Eight characteristics predicted by the rainfall–runoff model display positive or negative bias across all six sites; biases are not as pronounced for the regional regression model. Results suggest that a rainfall–runoff model calibrated on a single characteristic is less likely to perform well as a predictor of a range of other characteristics (flow regime) when compared with a regional regression model calibrated individually on multiple characteristics used to represent the flow regime. Poor model performance may misrepresent hydrologic conditions, potentially distorting the perceived risk of ecological degradation. Without prior selection of streamflow characteristics, targeted calibration, and error quantification, the widespread application of general hydrologic models to ecological flow studies is problematic. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Kentucky, Tennessee↗