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Jeffrey D. Martin

Publications and source records attributed to Jeffrey D. Martin.

34 records · Page 2Linked to original sources
USGS2009

Adjustment of pesticide concentrations for temporal changes in analytical recovery, 1992-2006

Recovery is the proportion of a target analyte that is quantified by an analytical method and is a primary indicator of the analytical bias of a measurement. Recovery is measured by analysis of quality-control (QC) water samples that have known amounts of target analytes added ('spiked' QC samples). For pesticides, recovery is the measured amount of pesticide in the spiked QC sample expressed as percentage of the amount spiked, ideally 100 percent. Temporal changes in recovery have the potential to adversely affect time-trend analysis of pesticide concentrations by introducing trends in environmental concentrations that are caused by trends in performance of the analytical method rather than by trends in pesticide use or other environmental conditions. This report examines temporal changes in the recovery of 44 pesticides and 8 pesticide degradates (hereafter referred to as 'pesticides') that were selected for a national analysis of time trends in pesticide concentrations in streams. Water samples were analyzed for these pesticides from 1992 to 2006 by gas chromatography/mass spectrometry. Recovery was measured by analysis of pesticide-spiked QC water samples. Temporal changes in pesticide recovery were investigated by calculating robust, locally weighted scatterplot smooths (lowess smooths) for the time series of pesticide recoveries in 5,132 laboratory reagent spikes; 1,234 stream-water matrix spikes; and 863 groundwater matrix spikes. A 10-percent smoothing window was selected to show broad, 6- to 12-month time scale changes in recovery for most of the 52 pesticides. Temporal patterns in recovery were similar (in phase) for laboratory reagent spikes and for matrix spikes for most pesticides. In-phase temporal changes among spike types support the hypothesis that temporal change in method performance is the primary cause of temporal change in recovery. Although temporal patterns of recovery were in phase for most pesticides, recovery in matrix spikes was greater than recovery in reagent spikes for nearly every pesticide. Models of recovery based on matrix spikes are deemed more appropriate for adjusting concentrations of pesticides measured in groundwater and stream-water samples than models based on laboratory reagent spikes because (1) matrix spikes are expected to more closely match the matrix of environmental water samples than are reagent spikes and (2) method performance is often matrix dependent, as was shown by higher recovery in matrix spikes for most of the pesticides. Models of recovery, based on lowess smooths of matrix spikes, were developed separately for groundwater and stream-water samples. The models of recovery can be used to adjust concentrations of pesticides measured in groundwater or stream-water samples to 100 percent recovery to compensate for temporal changes in the performance (bias) of the analytical method.

Scientific Investigations Report↗
USGS2008

Modeling variability and trends in pesticide concentrations in streams

A parametric regression model was developed for assessing the variability and long-term trends in pesticide concentrations in streams. The dependent variable is the logarithm of pesticide concentration and the explanatory variables are a seasonal wave, which represents the seasonal variability of concentration in response to seasonal application rates; a streamflow anomaly, which is the deviation of concurrent daily streamflow from average conditions for the previous 30 days; and a trend, which represents long-term (inter-annual) changes in concentration. Application of the model to selected herbicides and insecticides in four diverse streams indicated the model is robust with respect to pesticide type, stream location, and the degree of censoring (proportion of nondetections). An automatic model fitting and selection procedure for the seasonal wave and trend components was found to perform well for the datasets analyzed. Artificial censoring scenarios were used in a Monte Carlo simulation analysis to show that the fitted trends were unbiased and the approximate p-values were accurate for as few as 10 uncensored concentrations during a three-year period, assuming a sampling frequency of 15 samples per year. Trend estimates for the full model were compared with a model without the streamflow anomaly and a model in which the seasonality was modeled using standard trigonometric functions, rather than seasonal application rates. Exclusion of the streamflow anomaly resulted in substantial increases in the mean-squared error and decreases in power for detecting trends. Incorrectly modeling the seasonal structure of the concentration data resulted in substantial estimation bias and moderate increases in mean-squared error and decreases in power. ?? 2008 American Water Resources Association.

Journal of the American Water Resources Associatio↗
USGS2006

Pesticides in the Nation's Streams and Ground Water, 1992–2001

This report is one of a series of publications, The Quality of Our Nation's Waters, that describe major findings of the NAWQA Program on water-quality issues of regional and national concern. This report presents evaluations of pesticides in streams and ground water based on findings for the first decadal cycle of NAWQA. 'Pesticides in the Nation's Streams and Ground Water, 1992-2001' greatly expands the analysis of pesticides presented in 'Nutrients and Pesticides,' which was the first report in the series and was based on early results from 1992 to 1995. Other reports in this series cover additional water-quality constituents of concern, such as volatile organic compounds and trace elements, as well as physical and chemical effects on aquatic ecosystems. Each report builds toward a more comprehensive understanding of regional and national water resources. The information in this series is intended primarily for those interested or involved in resource management, conservation, regulation, and policymaking at regional and national levels. In addition, the information might interest those at a local level who wish to know more about the general quality of streams and ground water in areas near where they live and how that quality compares with other areas across the Nation.

Circular↗
USGS2002

Variability of pesticide detections and concentrations in field replicate water samples collected for the National Water-Quality Assessment Program, 1992-97

Field replicate water samples (“field replicates”) collected for the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Program during 1992 to 1997 were used to assess the variability of pesticide detections and concentrations in environmental water samples collected from the surface-and ground-water-quality networks of the NAWQA Program. Field replicates are two or more identically collected, processed, and analyzed environmental water samples that are used to assess the overall variability of field and laboratory procedures. Variability is the degree of random error in independent measurements of the same quantity and is the opposite of precision—the degree of mutual agreement. Information on variability can be used to estimate the reproducibility of individual measurements, the concentration needed to be assured of exceeding a water-quality standard, and the likelihood that two measurements of water quality are different. Variability of pesticide detections was assessed by calculating the mean percentage detection of a pesticide and the percentage of inconsistent replicate sets. Variability of pesticide concentrations was assessed by pooling estimates of the standard deviation and relative standard deviation in replicate sets. Variability of pesticide detections and concentrations was a function of concentration, and estimates of variability were developed for discrete ranges of concentration. Reliability of estimates of variability was assessed by calculating 90-percent upper confidence bounds for the percentage of inconsistent replicate sets and for the pooled estimates. The variability of detection for most pesticides is high at concentrations less than the minimum reporting level, but the variability of detection decreases dramatically at higher concentrations. In view of the highly diverse sources of water submitted as field replicates for the NAWQA Program and the generally low concentrations (concentrations in 79 percent of replicate sets were less than 0.1 microgram per liter) of pesticides in most replicates, inconsistent detections in replicate sets likely were caused by variability in the analytical method and by water-matrix interferences (or other loss processes) that result in false-negative errors. Consequently, estimates of the frequency of detection of pesticides in environmental water samples collected for the NAWQA Program probably are biased low because of false-negative errors at concentrations near the minimum reporting level. Correlation analysis indicates that for most pesticides and concentrations, pooled estimates of relative standard deviation rather than pooled estimates of standard deviation should be used to estimate variability because pooled estimates of relative standard deviation are less affected by heteroscedasticity. The 2 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 median pooled relative standard deviation was calculated for all pesticides to summarize the typical variability for pesticide data collected for the NAWQA Program. The median pooled relative standard deviation was 15 percent at concentrations less than 0.01 micrograms per liter (µg/L), 13 percent at concentrations near 0.01 µg/L, 12 percent at concentrations near 0.1 µg/L, 7.9 percent at concentrations near 1 µg/L, and 2.7 percent at concentrations greater than 5 µg/L. Pooled estimates of standard deviation or relative standard deviation presented in this report are larger than estimates based on averages, medians, smooths, or regression of the individual measurements of standard deviation or relative standard deviation from field replicates. Pooled estimates, however, are the preferred method for characterizing variability because they provide unbiased estimates of the variability of the population. Assessments of variability based on standard deviation (rather than variance) underestimate the true variability of the population. Because pooled estimates of variability are larger than estimates based on other approaches, users of estimates of variability must be cognizant of the approach used to obtain the estimate and must use caution in the comparison of estimates based on different approaches.

Water-Resources Investigations Report↗
USGS1999

Summary and evaluation of pesticides in field blanks collected for the National Water-Quality Assessment Program, 1992-95

Field blanks are quality-control samples used to assess contamination in environmental water samples. Contamination is the unintentional introduction of a chemical (pesticides in this instance) into an environmental water sample from sources such as inadequately cleaned equipment, dirty hands, dust, rain, or fumes. Contamination causes a positive bias in analytical measurements that may need to be considered in the analysis and interpretation of the environmental data. Estimates of pesticide contamination in environmental water samples collected for the National Water-Quality Assessment (NAWQA) Program are used to qualify, where needed, interpretations of the occurrence and distribution of pesticides in the surface and ground waters of the United States. Field blanks collected from 1992 to 1995 as part of the NAWAQA Program were analyzed for 88 pesticides and pesticide metabolites. Of 47 pesticides determined by gas chromatography/mass spectrometry, 23 were detected at least once in 175 surface-water field blanks and 15 were detected at least once in 145 ground-water field blanks. The most frequently detected pesticides in surface-water field blanks were atrazine (in 10.9 percent of blanks), simazine (9.1 percent), and metolachlor (4.6 percent). The most frequently detected pesticides in ground-water field blanks were p,p' -DDE (4.1 percent) and atrazine (2.8 percent). The maximum pesticide concentration detected by gas chromatography/mass spectrometry in a surface-water field blank was 0.120 microgram per liter (µg/L) for pronamide; the maximum concentration detected in a ground-water field blank was 0.013 µg /L for chlorpyrifos and prometon. Of 41 pesticides determined by high-performance liquid chromatography, diuron and 2,4-D were detected once in 109 surface-water field blanks and bromacil, diuron, and fenuron were detected once in 104 ground-water field blanks. Except for a detection of 2,4-D at 0.230 µg /L, the detectable concentrations of these pesticides were less than or equal to 0.020 µg /L. Field blanks showed no evidence of contamination by most pesticides. Of the 88 pesticides for which the field blanks were analyzed, 63 were not detected in field blanks from surface-water sites and 70 were not detected in field blanks from ground-water sites. Therefore, environmental data for the pesticides not detected in field blanks can be interpreted without qualification for contamination. Field blanks did show evidence of contamination by some pesticides. Most of the pesticides detected in field blanks, however, were detected more frequently and at higher concentrations in environmental water samples. Two criteria were used to evaluate the need to consider contamination in water-quality assessments: (1) a ratio of the frequency of pesticide detection in environmental water samples to the frequency of detection in field blanks of 5.0 or less and (2) a ratio of the median concentration detected in environmental water samples to the maximum concentration detected in field blanks of 2.0 or less. These criteria indicate that contamination, for the majority of the pesticide data collected for the NAWQA Program, probably does not need to be considered in the analysis and interpretation of (1) the frequency of pesticide detection or (2) the median concentration of pesticides detected. Contamination must be considered, however, in detection frequency for cispermethrin, pronamide, p,p' -DDE, pebulate, propargite, ethalfluralin, and triallate in surface water and fenuron, benfluralin, pronamide, cis-permethrin, triallate, chlorpyrifos, trifluralin, propanil, p,p' -DDE, bromacil, dacthal, diazinon, and diuron in ground water. Contamination also must be considered in median concentrations detected for pronamide, p,p' -DDE, propargite, napropamide, and triallate in surface water and benfluralin, cis-permethrin, triallate, chlorpyrifos, trifluralin, p,p' -DDE, dacthal, and diazinon in ground water.

Open-File Report↗
USGS1999

Environmental setting and natural factors and human influences affecting water quality in the White River Basin, Indiana

The White River Basin drains 11,349 square miles of central and southern Indiana and is one of 59 Study Units selected for water-quality assessment as part of the U.S. Geological Survey's National WaterQuality Assessment Program. Defining the environmental setting of the basin and identifying the natural factors and human influences that affect water quality are important parts of the assessment. Interrelated natural factors help determine the quality of surface and ground water in a river basin. The White River Basin has a humid continental climate, characterized by well-defined winter and summer seasons. Geologic features in the basin include glaciated and nonglaciated areas; a region of karst geomorphology that is characterized by caves and sinkholes; and a thick, sedimentary bedrock sequence underlying the entire basin. Unconsolidated glacial deposits of clay, silt, sand and gravel cover more than 60 percent of the basin. Soils developed in unconsolidated glacial deposits are typically fertile, naturally or artificially well drained, and farmed. Soils in the unglaciated south-central part of the basin are thin, have low fertility, and are best suited for forest or pasture. Agriculture is the principal land use in the White River Basin. Approximately 70 percent of the basin is used for agriculture, and about 50 percent of the basin is cropland. Corn and soybeans are the major crops. Other significant land uses are forest (22 percent) and urban and residential (7 percent). The population of the basin was 2.1 million in 1990. Water use in the White River Basin totaled 1,284 million gallons per day in 1995, of which 84.5 percent was surface water and 15.5 percent was ground water. Despite the predominant use of surface water, ground water was the primary source of drinking water for approximately 56 percent of the population. The general water chemistry in the White River Basin is determined by natural factors such as soils and geologic materials that water contacts as it moves through the hydrologic system. In the southern part of the basin, bedrock upland areas are dominated by non-carbonate bedrock, thin soils, and high runoff-rainfall ratios. These areas have small chemical concentrations in streamwater. Conversely, in the northern part of the basin where glacial deposits are thick and in the southwestern part of the basin where loess deposits are thick, water has longer periods of time to react with soils and aquifers and to acquire substantial quantities of dissolved constituents. As a result, streams in the till plain and glacial lowland have higher concentrations of most constituents than streams in the unglaciated parts of the basin. Water quality is significantly modified by human influences. Water quality is affected locally by point sources of contamination that include combined-sewer overflows, power-generation-plant cooling stations, and wastewater-treatment-plant effluents that are generally associated with densely populated areas. Water quality is additionally affected by non-point sources of contamination related to agriculture, urban runoff, and mining. Six hydrogeomorphic regions of the White River Basin are delineated on the basis of distinct and relatively homogeneous natural characteristics. These six regions are used in the White River Basin study as a framework for examining the effects of natural factors on water quality in the basin. Bedrock is exposed or near the surface in three hydrogeomorphic regions the bedrock uplands, bedrock lowland and plain, and karst plain; streams and shallow aquifers in these regions are susceptible to contamination, especially in the karst plain, and show rapid response to rainfall. The other three hydrogeomorphic regions the fluvial deposits, till plain, and glacial lowland are in the glaciated part of the basin. Where thick fine-grained unconsolidated sediments are present, primarily in the till plain, ground-water supplies are protected from contamination, and extreme high and low streamflows are moderated.

Indiana↗
USGS1997

Quality-control design for surface-water sampling in the National Water-Quality Assessment Program

The data-quality objectives of the National Water-Quality Assessment Program include estimating the extent to which contamination, matrix effects, and measurement variability affect interpretation of chemical analyses of surface-water samples. The quality-control samples used to make these estimates include field blanks, field matrix spikes, and replicates. This report describes the design for collection of these quality-control samples in National Water-Quality Assessment Program studies and the data management needed to properly identify these samples in the U.S. Geological Survey's national data base.

Open-File Report↗
USGS1996

Water-quality assessment of the White River Basin, Indiana: Analysis of selected information on nutrients, 1980-92

Water-quality data from 23 surfacewater-quality monitoring sites operated by the Indiana Department of Environmental Management and streamflow data from 11 U.S. Geological Survey streamflow-gaging stations in the White River Basin were analyzed to determine recent (1981 90 water years) water-quality conditions, trends, and river loads for ammonia, nitrate, total nitrogen, and total phosphorus. The White River Basin drains 11,349 square miles of central and south-central Indiana and is divided into two nearly equal subbasins the East Fork White River and the White River upstream from its confluence with the East Fork (called the "west fork" of the White River by the State's water-management agencies). Nutrient concentrations generally were higher in the more urbanized west fork than in the more rural east fork because of the much larger volumes of treated municipal sewage, combined-sewer overflows, and urban runoff discharged to the west fork. Concentrations of nutrients, especially ammonia and total phosphorus, were higher downstream from Muncie, Anderson, and Indianapolis than they were upstream from these cities. Nutrient concentrations decreased downstream from Indianapolis in the White River and in the downstream reach of the East Fork White River because of dilution, nitrification, adsorption to stream-bottom sediments, and uptake by aquatic vegetation. Seasonal variations in nutrient concentrations and the relations of nutrient concentrations to streamflow depended on the relative contributions of point and nonpoint sources of the nutrients. Total phosphorus increased with increasing streamflow at monitoring sites on the east fork but decreased with increasing streamflow at sites on the west fork. Increasing concentrations of phosphorus with increasing streamflow were consistent with nonpoint sources of phosphorus that wash off land surfaces, whereas decreasing concentrations of phosphorus with increasing streamflow were consistent with dilution of point sources of phosphorus. Median concentrations of total phosphorus were highest during summer and fall downstream from urban areas on the White River because streamflows that dilute point sources of phosphorus are lowest during summer and fall. Median concentrations of ammonia in the White River were highest in winter because of reduced biological uptake and nitrification of ammonia during cold temperatures.

Indiana↗
USGS1995

Effects of combined-sewer overflows and urban runoff on the water quality of Fall Creek, Indianapolis, Indiana

In 1986, the U.S. Geological Survey and the Indianapolis Department of Public Works began a study to evaluate the effects of combined-sewer overflows and urban runoff discharging to Fall Geek on the White River. This report describes the effects of combined-sewer overflows and urban runoff on the water quality of Fall Creek during summer 1987 by comparing the water quality during base flow with that during storm runoff and by comparing water quality in the urbanized area with that in the less urbanized area upstream from the combined-sewer overflows. Data were collected at three streamflow-gaging stations located upstream from, downstream from, and in the middle of 27 combined-sewer overflows on Fall Creek. The most downstream station also was immediately downstream from the discharge of filter backwash from a water-treatment plant for public supply. Specific conductance and concentrations of major ions and dissolved solids in base flow increased downstream in response to surface-water withdrawn for public supply, ground-water inflow, and the discharge of filter backwash. Concentrations of dissolved oxygen were least in the reach of Fall Creek in the middle of the combined- sewer overflows where black sludge deposits covered the stream bottom. Concentrations of nitrate plus nitrite and ammonia steadily increased downstream, whereas concentrations of organic nitrogen, phosphorus, and orthophosphate only increased at the most downstream station. Nearly all concentrations of chromium, copper, lead, nickel, and zinc at the upstream and middle stations were less than the detection limit of 10 micrograms per liter. Detectable concentrations of these metals and high concentrations of suspended solids in base-flow samples at the most downstream station were caused by the discharges from the water-treatment plant. Concentrations of dissolved oxygen measured at the station in the middle of the combined-sewer overflows were less than the Indiana minimum ambient water-quality standard of 4.0 milligrams per liter during all storms. Concentrations of ammonia, oxygen demand, copper, lead, zinc, and fecal coliform bacteria at the stations downstream from the combined-sewer overflows were much higher in storm runoff than in base flow. Increased concentrations of oxygen demand in runoff probably were caused by combined-sewer overflows, urban runoff, and the resuspension of organic material deposited on the streambed. Some of the increased concentrations of lead, zinc, and probably copper can be attributed to the discharge and resuspension of filter backwash

Indiana↗
USGS1993

Policy and procedures for the management and archival storage of data collected for hydrologic investigations, U.S. Geological Survey, Indiana District

This report describes the policy and procedures used by the Indiana District of the U.S. Geological Survey, Water Resources Division, to manage and store data collected during hydrologic investigations. It is the policy of the Indiana District that data collected to meet the objectives of projects for hydrologic investigations be documented, organized, and archieved in a manner that (1) facilitates retrieval, evaluation, and use by other District personnel, and (2) enables verifi- cation of data contained in all reports and computer data bases.

Indiana↗
USGS1990

Effects of surface coal mining and reclamation on the geohydrology of six small watersheds in West-Central Indiana

Six small watersheds in west-central Indiana were selected for study of the hydrologic effects of surface coal mining and reclamation. The watersheds include mined and reclaimed, mined and unreclaimed, and unmined agricultural land uses and are each less than 3 square miles in area. Surface-water, ground-water, and meteorologic data for the 1981 and 1982 water years were used to describe and compare hydrologic systems of the six watersheds and to identify hydrologic effects of mining and reclamation. Discharge at the unreclaimed watersheds was continuous during the study period, whereas discharge at the other watersheds was intermittent and more variable. Peak discharges were greater at the agricultural watersheds than at the unreclaimed watersheds, primarily because of large final-cut lakes in the unreclaimed watersheds. Annual runoff was greatest at the unreclaimed watersheds, intermediate at the agricultural watersheds, and least at the reclaimed watersheds. Hydrologic effects of mining were identified by comparing the hydrologic systems at mined and unreclaimed watersheds with those at unmined agricultural watersheds. These comparisons indicate that surface coal mining without reclamation can increase base flow, annual runoff, and ground-water recharge to the bedrock; reduce peak flow rates and variation in flow; lower the water table in upland areas; change the relation between surface- and ground-water divides; and create numerous, local flow systems in the shallow ground water. Hydrologic effects of reclamation were identified by comparing the hydrologic systems at mined and reclaimed watersheds with those at mined and unreclaimed watersheds. Reclamation can decrease base flow, annual runoff, and recharge to the bedrock; increase peak flow rates, variation in flow, and the response to thunderstorms; reestablish the premining relation between surface- and ground-water divides; and create fewer local flow systems in the shallow ground water. Hydrologic effects of mining and reclamation were identified by comparing the hydrologic systems at mined and reclaimed watersheds with those at unmined agricultural watersheds. The presence or absence of a large final-cut lake in the reclaimed watershed greatly influences the hydrologic systems and the effects of mining and reclamation. Surface coal mining and reclamation can decrease base flow, annual runoff, and peak flow rates; increase the variability of flow and recharge to the bedrock; reestablish the premining relation between surface- and ground-water divides; and lower the water table in upland areas.

Indiana↗
USGS1987

Effects of surface coal mining and reclamation on the geohydrology of six small watersheds in west-central Indiana

The watersheds studied include mined and reclaimed; mined and unreclaimed; and unmined, agricultural land uses, and are each < 3 sq mi in area. Surface water, groundwater, and meteorologic data for the 1981 and 1982 water years were used to describe and compare hydrologic systems of the six watersheds and to identify hydrologic effects of mining and reclamation. Peak discharges were greater at the agricultural watersheds than at the unreclaimed watersheds, primarily because of large final-cut lakes in the unreclaimed watersheds. Annual runoff was greatest at the unreclaimed watersheds, intermediate at the agricultural watersheds, and least at the reclaimed watersheds. Hydrologic effects of mining were identified by comparing the hydrologic systems at mined and unreclaimed watersheds with those at unmined, agricultural watersheds. Comparisons of the hydrologic systems of these watersheds indicate that surface coal mining without reclamation has the potential to increase annual runoff, base flow, and groundwater recharge to the bedrock; reduce peak flow rates and variation in flow; lower the water table in upland areas; change the relation between surface water and groundwater divides; and create numerous, local flow systems in the shallow groundwater. Hydrologic effects of reclamation were identified by comparing the hydrologic systems at mined and reclaimed watersheds with those at mined and unreclaimed watersheds. Reclamation has the potential to decrease annual runoff, base flow, and recharge to the bedrock; increase peak flow rates, variation in flow, and response to thunderstorms; reestablish the premining relation between surface and groundwater divides; and create fewer local flow systems in the shallow groundwater.

Indiana↗
USGS1987

Description of the physical environment and coal-mining history of west-central Indiana, with emphasis on six small watersheds

Information on the geology, geomorphology, soils, climate, hydrology, water use, land use, population, and coal mining history of Clay, Owen, Sullivan, and Vigo Counties in Indiana is summarized. Site-specific information is given on the morphology , geology, soils, land use, coal mining history, and hydrologic instrumentation of the six watersheds which are each less than 3 sq mi in area. The Wabash, White, and Eel Rivers are the major drainages in west-central Indiana. Average annual precipitation is about 39.5 in/yr and average annual runoff is about 13 in/yr. The most productive aquifers are confined or unconfined outwash aquifers located along the major rivers. Bedrock aquifers are regionally insignificant but are the sole source of groundwater for areas that lack outwash, alluvium, or sand and gravel lenses in till. Indiana has more than 17 billion short tons of recoverable coal reserves; about 11% can be mined by surface methods. Almost half of Indiana's surface reserves are in Clay, Owen, Sullivan, and Vigo Counties. More than 50,000 acres in west-central Indiana have been disturbed by surface coal mining from 1941 through 1980. Big Slough and Hooker Creek are streams that drain unmined, agricultural watersheds. Row-crop corn and soybeans are the principal crops. Soils are moderately well drained silt loams, and the watersheds well developed dendritic drainage systems. Unnamed tributaries drain mined and reclaimed watersheds. Ridges of mine spoil have been graded to a gently rolling topography. Soils are well drained and consist of 6 to 12 inches of silt-loam topsoil that was stockpiled and then replaced over shale and sandstone fragments of the graded mine spoil. Grasses and legumes form the vegetative cover in each watershed. Pond Creek and an unnamed tributary to Big Branch are streams that drain mined and unreclaimed watersheds. Soils are very well drained shaly silty loams that have formed on steeply sloping banks. Both watersheds contain numerous impoundments of water and have enclosed areas that do not contribute surface runoff to streamflow. The ridges of mine spoil are covered with pine trees, but much of the soil surface is devoid of vegetation. (Lantz-PTT)

Indiana↗
USGS1987

Statistical analysis of surface-water-quality data in and near the coal-mining region of southwestern Indiana, 1957-80

The Surface Mining Control and Reclamation Act of 1977 requires that applications for coal-mining permits contain information about the water quality of streams at and near a proposed mine. To meet this need for information, streamflow, specific conductance, pH, and concentrations of total alkalinity, sulfate, dissolved solids, suspended solids, total iron, and total manganese at 37 stations were analyzed to determine the spatial and seasonal variations in water quality and to develop equations for predicting water quality. The season of lowest median streamflow was related to the size of the drainage area. Median streamflow was least during fall at 15 of 16 stations having drainage areas greater than 1,000 square miles but was least during summer at 17 of 21 stations having drainage areas less than 1,000 square miles. In general, the season of lowest median specific conductance occurred during the season of highest streamflow except at stations on the Wabash River. Median specific conductance was least during summer at 9 of 9 stations on the Wabash River, but was least during winter or spring (the seasons of highest streamflow) at 27 of the remaining 28 stations. Linear, inverse, semilog, log-log, and hyperbolic regression models were used to investigate the functional relations between water-quality characteristics and streamflow. Of 186 relations investigated, 143 were statistically significant. Specific conductance and concentrations of total alkalinity and sulfate were negatively related to streamflow at all stations except for a positive relation between total alkalinity concentration and streamflow at Patoka River near Princeton. Concentrations of total alkalinity and sulfate were positively related to specific conductance at all stations except for a negative relation at Patoka River near Princeton and for a positive and negative relation at Patoka River at Jasper. Most of these relations are good, have small confidence intervals, and will give reliable predictions of the water-quality variables listed above. The poorest relations are typically at stations in the Patoka River watershed. Suspended-solids concentration was positively related to streamflow at all but two stations on the Patoka River. These relations are poor, have large confidence intervals, and will give less reliable predictions of suspended-solids concentration. Predictive equations for the regional relations between dissolved-solids concentration and specific conductance and between sulfate concentration and specific conductance, and the seasonal patterns of water quality, are probably valid for the coal-mining regions of Illinois and western Kentucky.

Indiana↗
USGS1980

Hydrologic evaluation of a hypothetical coal-mining site near Chrisney, Spencer County, Indiana

Protecting the water resources of the Nation is a major emphasis of the Surface Mining Control and Reclamation Act, PL 95-87. Permanent regulations established for this Act by the Office of Surface Mining (OSM) require the issuance of a permit before mining begins. An application for a mining permit must include an assessment of the hydrologic characteristics of the mining site and adjacent area, and a projection of the potential impacts of mining activities on surface water and ground water. OSM's permanent regulations and guidelines provide little insight on the "how to" aspect of making the required hydrologic assessment. This investigation was completed to improve the understanding of the kinds of information needed to make such assessments by: (a) reviewing the regulations to determine what hydrologic information is required; (b) preparing an example hydrologic assessment based on the regulations; and (c) using the experience gained in (a) and (b) to identify areas lacking or needing additional data to make the required assessment. Hydrologic data for the study area were obtained from published and unpublished reports, maps, aerial photographs, personal interviews with residents in the area of the hypothetical mine site, and discussions with experts in the field. Where data were unavailable, "synthetic" data were generated by extrapolation from proximate or similar watersheds and (or) by assumptions based on experience or theory. Some field data were collected to corroborate and augment information originating from all these sources.

Indiana↗