Geology topics

Charles G. Crawford

Publications and source records attributed to Charles G. Crawford.

At least 19 recordsLinked to original sources

USGS2023

Comparison of surrogate models to estimate pesticide concentrations at six U.S. Geological Survey National Water Quality Network sites during water years 2013–18

During water years 2013–18, the U.S. Geological Survey National Water-Quality Assessment Project sampled the National Water Quality Network for Rivers and Streams year-round and reported on 221 pesticides at 72 sites across the United States. Pesticides are difficult to measure, their concentrations often represent discrete snapshots in time, and capturing peak concentrations is expensive. Three types of regression models were developed to estimate concentrations for two selected pesticides at each of six National Water Quality Network for Rivers and Streams sites. The regression models used continuously measured streamflow and water-quality properties (differing combinations of pH, specific conductance, turbidity, and water temperature); discrete water-quality samples analyzed for atrazine, azoxystrobin, bentazon, bromacil, imidacloprid, simazine, and triclopyr; and time as an additional explanatory variable for seasonality. The modeling approaches included (1) a standard regression that included surrogates (differing combinations of pH, specific conductance, turbidity, and water temperature) and periodic functions (sine-cosine) of pesticide application use as predictor variables; (2) the seasonal wave with flow adjustment model that included a seasonal component and flow anomalies but excluded surrogates; and (3) the seasonal wave with flow adjustment model that included a seasonal component, flow anomalies, and surrogates. Models were evaluated using three measures of model performance: generalized coefficient of determination (generalized R 2 ), Akaike’s Information Criteria, and scale (the estimated standard deviation of the tobit regression error term). Because of low observation numbers, results from this study can be considered a pilot effort with the possibility that some models are overfit. In all cases, estimated pesticide concentrations modeled with base SEAWAVE-Q were better than the standard surrogate regression models; all 39 generalized R 2 values increased by 3–56 percent (median of 25 percent) when compared to the standard surrogate regression models, and all Akaike’s Information Criteria and scale values decreased. The addition of surrogate variables such as pH, specific conductance, turbidity, and water temperature to the base SEAWAVE-Q model to improve estimates of pesticide concentrations resulted in only modest improvements; generalized R 2 values increased by only 0–10 percent (median of 3 percent). In some instances, combinations of the surrogates produced more appreciative improvements in model results, but in those instances, we hypothesize that the surrogates correlated with some unknown measure that directly relates to pesticide transport.

Indiana, Kansas, Missouri, North Dakota, Oregon↗

USGS2019

An evaluation of methods for computing annual water-quality loads

The U.S. Geological Survey publishes information on the mass, or load, of water-quality constituents transported through rivers and streams sampled as part of the operation of the National Water Quality Network (NWQN). This study evaluates methods for computing annual water-quality loads, specifically with respect to procedures currently (2019) used at sites in the NWQN. Near-daily datasets of chloride, total nitrogen, nitrate plus nitrite, total phosphorus, and suspended sediment were subset to determine the accuracy of various load-estimation methods, including linear interpolation, ratio estimators, and linear and weighted-regression methods. Water-quality loads are computed under different sampling strategies and at multiple sampling sites to provide a more complete evaluation of load-estimation methods. Estimation methods were less accurate when computing loads at annual rather than decadal time steps. Depending on the water-quality constituent, annual loads were within comparable accuracy thresholds 21 to 64 percent of the time relative to decadal loads. The accuracy of annual load estimates varied among water-quality constituents, sampling strategies, sampling sites, and estimation methods. Methods were most accurate when estimating chloride and decreased in accuracy when estimating total nitrogen, nitrate plus nitrite, total phosphorus, and suspended-sediment loads. Estimation methods were most likely to compute accurate annual loads when samples were collected frequently (26 samples per year) and when sampling strategies targeted high-flow conditions. For a given water-quality constituent, estimation accuracy differed substantially among sampling sites; estimates were more likely to be accurate at large rivers with less variability in concentration and (or) discharge conditions and were less likely to be accurate at smaller stream sites with more variable streamflow and (or) water-quality concentrations. The Weighted Regressions on Time, Discharge, and Season method with Kalman filtering (WRTDS_K) generally produced the most accurate annual load estimates among sampling sites and water-quality constituents. Although WRTDS_K was the most accurate generally, every estimation method evaluated had the potential to produce accurate (and inaccurate) load estimates depending on the site, constituent, and water year. Linear interpolation and ratio estimators that used samples exclusively from the year being estimated were among the best performing methods for total nitrogen and nitrate plus nitrite loads but were among the least accurate when estimating annual total phosphorus and suspended-sediment loads. Ratio estimation that considered samples from previous years and stratified based on streamflow conditions produced among the most accurate total phosphorus estimates but was among the least accurate for other constituents. Regression-based methods that assumed linear or quadratic relations among the logarithm of water-quality concentrations and streamflow conditions were among the least accurate methods generally, whereas regression-based methods that considered cubic relations among the logarithm of concentration and streamflow and the Weighted Regressions on Time, Discharge, and Season (WRTDS) method were typically more accurate. Methods that adjusted daily estimates computed from regression or weighted-regression methods based on departures from sampled values, such as WRTDS_K and the composite method, improved estimate accuracy for most sites and constituents, but especially for chloride, total nitrogen, nitrate plus nitrite, and suspended-sediment estimates. Investigation of the underlying causes of estimation method bias indicated that sites and years with more variability in concentration and loading conditions, higher slopes in the relation of the logarithm of concentration and discharge, and sampling plans that underrepresented high-flow conditions generally led to less accurate load estimates. Finally, because all methods indicated the capacity to produce biased load estimates, additional work is needed to identify the capacity of new technologies, such as continuous water-quality sensors, to improve the accuracy of annual or shorter term load estimates. Based on findings in this report, the NWQN will continue to publish water-quality loads using LOADEST-based methods that consider multiple transformations of streamflow, as well as season, time, and variables indicative of historical streamflow conditions to maintain consistent methods for stakeholders. However, the NWQN also plans to begin publishing annual load estimates using the WRTDS_K method in 2020 because this method was determined to be the most accurate for a given site, constituent, and water year.

Scientific Investigations Report↗

USGS2017

Methods for computing water-quality loads at sites in the U.S. Geological Survey National Water Quality Network

The U.S. Geological Survey currently (2020) publishes information on concentrations and loads of water-quality constituents at 110 sites across the United States as part of the U.S. Geological Survey National Water Quality Network (NWQN). This report details historical and updated methods for computing water-quality loads at NWQN sites. The primary updates to historical load estimation methods include (1) an adaptation to methods for computing loads to the Gulf of Mexico; (2) the inclusion of loads and trends computed using the Weighted Regressions on Time, Discharge, and Season (WRTDS) and Weighted Regressions on Time, Discharge, and Season with Kalman filtering (WRTDS–K) methods; and (3) the inclusion of loads computed using continuous water-quality data. Loads computed using WRTDS and WRTDS–K and continuous water-quality data are provided along with those computed using historical methods. Various aspects of method updates are evaluated in this report to help users of water-quality loading data determine which estimation methods best suit their particular application.

Open-File Report↗

USGS2017

A method for addressing differences in concentrations of fipronil and three degradates obtained by two different laboratory methods

In October 2012, the U.S. Geological Survey (USGS) began measuring the concentration of the pesticide fipronil and three of its degradates (desulfinylfipronil, fipronil sulfide, and fipronil sulfone) by a new laboratory method using direct aqueous-injection liquid chromatography tandem mass spectrometry (DAI LC–MS/MS). This method replaced the previous method—in use since 2002—that used gas chromatography/mass spectrometry (GC/MS). The performance of the two methods is not comparable for fipronil and the three degradates. Concentrations of these four chemical compounds determined by the DAI LC–MS/MS method are substantially lower than the GC/MS method. A method was developed to correct for the difference in concentrations obtained by the two laboratory methods based on a methods comparison field study done in 2012. Environmental and field matrix spike samples to be analyzed by both methods from 48 stream sites from across the United States were sampled approximately three times each for this study. These data were used to develop a relation between the two laboratory methods for each compound using regression analysis. The relations were used to calibrate data obtained by the older method to the new method in order to remove any biases attributable to differences in the methods. The coefficients of the equations obtained from the regressions were used to calibrate over 16,600 observations of fipronil, as well as the three degradates determined by the GC/MS method retrieved from the USGS National Water Information System. The calibrated values were then compared to over 7,800 observations of fipronil and to the three degradates determined by the DAI LC–MS/MS method also retrieved from the National Water Information System. The original and calibrated values from the GC/MS method, along with measures of uncertainty in the calibrated values and the original values from the DAI LC–MS/MS method, are provided in an accompanying data release.

Open-File Report↗

USGS2016

An evaluation of methods for estimating decadal stream loads

Effective management of water resources requires accurate information on the mass, or load of water-quality constituents transported from upstream watersheds to downstream receiving waters. Despite this need, no single method has been shown to consistently provide accurate load estimates among different water-quality constituents, sampling sites, and sampling regimes. We evaluate the accuracy of several load estimation methods across a broad range of sampling and environmental conditions. This analysis uses random sub-samples drawn from temporally-dense data sets of total nitrogen, total phosphorus, nitrate, and suspended-sediment concentration, and includes measurements of specific conductance which was used as a surrogate for dissolved solids concentration. Methods considered include linear interpolation and ratio estimators, regression-based methods historically employed by the U.S. Geological Survey, and newer flexible techniques including Weighted Regressions on Time, Season, and Discharge (WRTDS) and a generalized non-linear additive model. No single method is identified to have the greatest accuracy across all constituents, sites, and sampling scenarios. Most methods provide accurate estimates of specific conductance (used as a surrogate for total dissolved solids or specific major ions) and total nitrogen – lower accuracy is observed for the estimation of nitrate, total phosphorus and suspended sediment loads. Methods that allow for flexibility in the relation between concentration and flow conditions, specifically Beale’s ratio estimator and WRTDS, exhibit greater estimation accuracy and lower bias. Evaluation of methods across simulated sampling scenarios indicate that (1) high-flow sampling is necessary to produce accurate load estimates, (2) extrapolation of sample data through time or across more extreme flow conditions reduces load estimate accuracy, and (3) WRTDS and methods that use a Kalman filter or smoothing to correct for departures between individual modeled and observed values benefit most from more frequent water-quality sampling.

Journal of Hydrology↗

USGS2015

Regional and temporal differences in nitrate trends discerned from long-term water quality monitoring data

Riverine nitrate (NO 3 ) is a well-documented driver of eutrophication and hypoxia in coastal areas. The development of the elevated river NO 3 concentration is linked to anthropogenic inputs from municipal, agricultural, and atmospheric sources. The intensity of these sources has varied regionally, through time, and in response to multiple causes such as economic drivers and policy responses. This study uses long-term water quality, land use, and other ancillary data to further describe the evolution of river NO 3 concentrations at 22 monitoring stations in the United States (U.S.). The stations were selected for long-term data availability and to represent a range of climate and land-use conditions. We examined NO 3 at the monitoring stations, using a flow-weighting scheme meant to account for interannual flow variability allowing greater focus on river chemical conditions. River NO 3 concentration increased strongly during 1945-1980 at most of the stations and have remained elevated, but stopped increasing during 1981-2008. NO 3 increased to a greater extent at monitoring stations in the Midwest U.S. and less so at those in the Eastern and Western U.S. We discuss 20th Century agricultural development in the U.S. and demonstrate that regional differences in NO 3 concentration patterns were strongly related to an agricultural index developed using principal components analysis. This unique century-scale dataset adds to our understanding of long-term NO 3 patterns in the U.S.

Journal of the American Water Resources Associatio↗

USGS2015

Long-term changes in nitrate conditions over the 20th century in two Midwestern Corn Belt streams

Long-term changes in nitrate concentration and flux between the middle of the 20th century and the first decade of the 21st century were estimated for the Des Moines River and the Middle Illinois River, two Midwestern Corn Belt streams, using a novel weighted regression approach that is able to detect subtle changes in solute transport behavior over time. The results show that the largest changes in flow-normalized concentration and flux occurred between 1960 and 1980 in both streams, with smaller or negligible changes between 1980 and 2004. Contrasting patterns were observed between (1) nitrate export linked to non-point sources, explicitly runoff of synthetic fertilizer or other surface sources and (2) nitrate export presumably associated with point sources such as urban wastewater or confined livestock feeding facilities, with each of these modes of transport important under different domains of streamflow. Surface runoff was estimated to be consistently most important under high-flow conditions during the spring in both rivers. Nitrate export may also have been considerable in the Des Moines River even under some conditions during the winter when flows are generally lower, suggesting the influence of point sources during this time. Similar results were shown for the Middle Illinois River, which is subject to significant influence of wastewater from the Chicago area, where elevated nitrate concentrations were associated with at the lowest flows during the winter and fall. By modeling concentration directly, this study highlights the complex relationship between concentration and streamflow that has evolved in these two basins over the last 50 years. This approach provides insights about changing conditions that only become observable when stationarity in the relationship between concentration and streamflow is not assumed.

Illinois, Iowa↗

USGS2014

Mississippi River nitrate loads from high frequency sensor measurements and regression-based load estimation

Accurately quantifying nitrate (NO 3 – ) loading from the Mississippi River is important for predicting summer hypoxia in the Gulf of Mexico and targeting nutrient reduction within the basin. Loads have historically been modeled with regression-based techniques, but recent advances with high frequency NO 3 – sensors allowed us to evaluate model performance relative to measured loads in the lower Mississippi River. Patterns in NO 3 – concentrations and loads were observed at daily to annual time steps, with considerable variability in concentration-discharge relationships over the two year study. Differences were particularly accentuated during the 2012 drought and 2013 flood, which resulted in anomalously high NO 3 – concentrations consistent with a large flush of stored NO 3 – from soil. The comparison between measured loads and modeled loads (LOADEST, Composite Method, WRTDS) showed underestimates of only 3.5% across the entire study period, but much larger differences at shorter time steps. Absolute differences in loads were typically greatest in the spring and early summer critical to Gulf hypoxia formation, with the largest differences (underestimates) for all models during the flood period of 2013. In additional to improving the accuracy and precision of monthly loads, high frequency NO 3 – measurements offer additional benefits not available with regression-based or other load estimation techniques.

Louisiana↗

USGS2014

A comparison of selected diversity, similarity, and biotic indices for detecting changes in benthic-invertebrate community structure and stream quality

Implementation of advanced wastewater treatment at the two municipal wastewater-treatment plants for Indianapolis, Indiana, resulted in substantial improvement in the quality of the receiving stream and significant changes in the benthic-invertebrate community. Diversity, similarity, and biotic indices were compared to determine which indices best reflected changes in the composition of the biota in the river. None of the indices perfectly reflected the changes in river quality or community structure. Similarity indices, especially percentage similarity, exhibit the most promise of the three classes of indices. Diversity indices were least useful, wrongly indicating that water quality deteriorated after the upgrade of the wastewater-treatment plants. The most descriptive tool in analyzing the data was the percentage of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa present. Using a mixture of indices and other analytical tools, such as EPT, in the analysis of biological data will ensure the most effective investigations of water quality.

Archives of Environmental Contamination and Toxico↗

USGS2014

Long-term trends in alkalinity in large rivers of the conterminous US in relation to acidification, agriculture, and hydrologic modification

Alkalinity increases in large rivers of the conterminous US are well known, but less is understood about the processes leading to these trends as compared with headwater systems more intensively examined in conjunction with acid deposition studies. Nevertheless, large rivers are important conduits of inorganic carbon and other solutes to coastal areas and may have substantial influence on coastal calcium carbonate saturation dynamics. We examined long-term (mid-20th to early 21st century) trends in alkalinity and other weathering products in 23 rivers of the conterminous US. We used a rigorous flow-weighting technique which allowed greater focus on solute trends occurring independently of changes in flow. Increasing alkalinity concentrations and yield were widespread, occurring at 14 and 13 stations, respectively. Analysis of trends in other weathering products suggested that the causes of alkalinity trends were diverse, but at many stations alkalinity increases coincided with decreasing nitrate + sulfate and decreasing cation:alkalinity ratios, which is consistent with recovery from acidification. A positive correlation between the Sen–Thiel slopes of alkalinity increases and agricultural lime usage indicated that agricultural lime contributed to increasing solute concentration in some areas. However, several stations including the Altamaha, Upper Mississippi, and San Joaquin Rivers exhibited solute trends, such as increasing cation:alkalinity ratios and increasing nitrate + sulfate, more consistent with increasing acidity, emphasizing that multiple processes affect alkalinity trends in large rivers. This study was unique in its examination of alkalinity trends in large rivers covering a wide range of climate and land use types, but more detailed analyses will help to better elucidate temporal changes to river solutes and especially the effects they may have on coastal calcium carbonate saturation state.

Science of the Total Environment↗

USGS2013

Watershed Regressions for Pesticides (WARP) models for predicting stream concentrations of multiple pesticides

Watershed Regressions for Pesticides for multiple pesticides (WARP-MP) are statistical models developed to predict concentration statistics for a wide range of pesticides in unmonitored streams. The WARP-MP models use the national atrazine WARP models in conjunction with an adjustment factor for each additional pesticide. The WARP-MP models perform best for pesticides with application timing and methods similar to those used with atrazine. For other pesticides, WARP-MP models tend to overpredict concentration statistics for the model development sites. For WARP and WARP-MP, the less-than-ideal sampling frequency for the model development sites leads to underestimation of the shorter-duration concentration; hence, the WARP models tend to underpredict 4- and 21-d maximum moving-average concentrations, with median errors ranging from 9 to 38% As a result of this sampling bias, pesticides that performed well with the model development sites are expected to have predictions that are biased low for these shorter-duration concentration statistics. The overprediction by WARP-MP apparent for some of the pesticides is variably offset by underestimation of the model development concentration statistics. Of the 112 pesticides used in the WARP-MP application to stream segments nationwide, 25 were predicted to have concentration statistics with a 50% or greater probability of exceeding one or more aquatic life benchmarks in one or more stream segments. Geographically, many of the modeled streams in the Corn Belt Region were predicted to have one or more pesticides that exceeded an aquatic life benchmark during 2009, indicating the potential vulnerability of streams in this region.

Journal of Environmental Quality↗

USGS2013

Assessing total nitrogen in surface-water samples--precision and bias of analytical and computational methods

The characterization of total-nitrogen (TN) concentrations is an important component of many surface-water-quality programs. However, three widely used methods for the determination of total nitrogen—(1) derived from the alkaline-persulfate digestion of whole-water samples (TN-A); (2) calculated as the sum of total Kjeldahl nitrogen and dissolved nitrate plus nitrite (TN-K); and (3) calculated as the sum of dissolved nitrogen and particulate nitrogen (TN-C)—all include inherent limitations. A digestion process is intended to convert multiple species of nitrogen that are present in the sample into one measureable species, but this process may introduce bias. TN-A results can be negatively biased in the presence of suspended sediment, and TN-K data can be positively biased in the presence of elevated nitrate because some nitrate is reduced to ammonia and is therefore counted twice in the computation of total nitrogen. Furthermore, TN-C may not be subject to bias but is comparatively imprecise. In this study, the effects of suspended-sediment and nitrate concentrations on the performance of these TN methods were assessed using synthetic samples developed in a laboratory as well as a series of stream samples. A 2007 laboratory experiment measured TN-A and TN-K in nutrient-fortified solutions that had been mixed with varying amounts of sediment-reference materials. This experiment identified a connection between suspended sediment and negative bias in TN-A and detected positive bias in TN-K in the presence of elevated nitrate. A 2009–10 synoptic-field study used samples from 77 stream-sampling sites to confirm that these biases were present in the field samples and evaluated the precision and bias of TN methods. The precision of TN-C and TN-K depended on the precision and relative amounts of the TN-component species used in their respective TN computations. Particulate nitrogen had an average variability (as determined by the relative standard deviation) of 13 percent. However, because particulate nitrogen constituted only 14 percent, on average, of TN-C, the precision of the TN-C method approached that of the method for dissolved nitrogen (2.3 percent). On the other hand, total Kjeldahl nitrogen (having a variability of 7.6 percent) constituted an average of 40 percent of TN-K, suggesting that the reduced precision of the Kjeldahl digestion may affect precision of the TN-K estimates. For most samples, the precision of TN computed as TN-C would be better (lower variability) than the precision of TN-K. In general, TN-A precision (having a variability of 2.1 percent) was superior to TN-C and TN-K methods. The laboratory experiment indicated that negative bias in TN-A was present across the entire range of sediment concentration and increased as sediment concentration increased. This suggested that reagent limitation was not the predominant cause of observed bias in TN-A. Furthermore, analyses of particulate nitrogen present in digest residues provided an almost complete accounting for the nitrogen that was underestimated by alkaline-persulfate digestion. This experiment established that, for the reference materials at least, negative bias in TN-A was caused primarily by the sequestration of some particulate nitrogen that was refractory to the digestion process. TN-K biases varied between positive and negative values in the laboratory experiment. Positive bias in TN-K is likely the result of the unintended reduction of a small and variable amount of nitrate to ammonia during the Kjeldahl digestion process. Negative TN-K bias may be the result of the sequestration of a portion of particulate nitrogen during the digestion process. Negative bias in TN-A was present across the entire range of suspended-sediment concentration (1 to 14,700 milligrams per liter [mg/L]) in the synoptic-field study, with relative bias being nearly as great at sediment concentrations below 10 mg/L (median of -3.5 percent) as that observed at sediment concentrations up to 750 mg/L (median of -4.4 percent). This lent support to the laboratory-experiment finding that some particulate nitrogen is sequestered during the digestion process, and demonstrated that negative TN-A bias was present in samples with very low suspended-sediment concentrations. At sediment concentrations above 750 mg/L, the negative TN-A bias became more likely and larger (median of -13.2 percent), suggesting a secondary mechanism of bias, such as reagent limitation. From a geospatial perspective, trends in TN-A bias were not explained by selected basin characteristics. Though variable, TN-K bias generally was positive in the synoptic-field study (median of 3.1 percent), probably as a result of the reduction of nitrate. Three alternative approaches for assessing TN in surface water were evaluated for their impacts on existing and future sampling programs. Replacing TN-A with TN-C would remove the bias from subsequent data, but this approach also would introduce discontinuity in historical records. Replacing TN-K with TN-C would lead to the removal of positive bias in TN-K in the presence of elevated nitrate. However, in addition to the issues that may arise from a discontinuity in the data record, this approach may not be applicable to regulatory programs that require the use of total Kjeldahl nitrogen for stream assessment. By adding TN-C to existing TN-A or TN-K analyses, historical-data continuity would be preserved and the transitional period could be used to minimize the impact of bias on data analyses. This approach, however, imposes the greatest burdens on field operations and in terms of analytical costs. The variation in these impacts on different sampling programs will challenge U.S. Geological Survey scientists attempting to establish uniform standards for TN sample collection and analytical determinations.

Scientific Investigations Report↗

USGS2012

Century-scale perspective on water quality in selected river basins of the conterminous United States

Nutrient pollution in the form of excess nitrogen and phosphorus inputs is a well-known cause of water-quality degradation that has affected water bodies across the Nation throughout the 20th century. The recognition of excess nutrients as pollution developed later than the recognition of other water-quality problems, such as waterborne illness, industrial pollution, and organic wastes. Nevertheless, long-term analysis of nutrient pollution is fundamental to our understanding of the current magnitude of the problem, as well the origins and the effects. This report describes the century-scale changes in water quality across a range streams in order to place current water-quality concerns in historical context and presents this history on a national scale as well as for selected river reaches. The primary focus is on nutrient pollution, but the development and societal responses to other water-quality problems also are considered. Land use and agriculture in the selected river reaches also are analyzed to consider how these factors may relate to nutrient pollution. Finally, the availability of relevant nutrient and inorganic carbon data are presented for the selected river reaches. Sources of these data included Federal agencies, State-level reports, municipal public works facilities, public health surveys, and sanitary surveys. The availability of these data extends back more than a century for most of the selected river reaches and suggests that there is a tremendous opportunity to document the development of nutrient pollution in these river reaches.

Scientific Investigations Report↗

USGS2011

Factors affecting stream nutrient loads: A synthesis of regional SPARROW model results for the continental United States

We compared the results of 12 recently calibrated regional SPARROW (SPAtially Referenced Regressions On Watershed attributes) models covering most of the continental United States to evaluate the consistency and regional differences in factors affecting stream nutrient loads. The models – 6 for total nitrogen and 6 for total phosphorus – all provide similar levels of prediction accuracy, but those for major river basins in the eastern half of the country were somewhat more accurate. The models simulate long‐term mean annual stream nutrient loads as a function of a wide range of known sources and climatic (precipitation, temperature), landscape (e.g., soils, geology), and aquatic factors affecting nutrient fate and transport. The results confirm the dominant effects of urban and agricultural sources on stream nutrient loads nationally and regionally, but reveal considerable spatial variability in the specific types of sources that control water quality. These include regional differences in the relative importance of different types of urban (municipal and industrial point vs. diffuse urban runoff) and agriculture (crop cultivation vs. animal waste) sources, as well as the effects of atmospheric deposition, mining, and background (e.g., soil phosphorus) sources on stream nutrients. Overall, we found that the SPARROW model results provide a consistent set of information for identifying the major sources and environmental factors affecting nutrient fate and transport in United States watersheds at regional and subregional scales.

Journal of the American Water Resources Associatio↗

USGS2011

Estimation of suspended-sediment concentration from total suspended solids and turbidity data for Kentucky, 1978-1995

Suspended sediment is a constituent of water quality that is monitored because of concerns about accelerated erosion, nonpoint contamination of water resources, and degradation of aquatic environments. In order to quantify the relationship among different sediment parameters for Kentucky streams, long‐term records were obtained from the National Water Information System of the U.S. Geological Survey. Suspended‐sediment concentration (SSC), the parameter traditionally measured and reported by the U.S. Geological Survey, was statistically compared to turbidity and total suspended solids (TSS), two parameters that are considered surrogate data. A linear regression of log‐transformed observations was used to estimate SSC from TSS; 72% of TSS observations were less than coincident SSC observations; however, the estimated SSC values were almost as likely to be overestimated as underestimated. The SSC‐turbidity relationship also used log‐transformed observations, but required a nonlinear, breakpoint regression that separated turbidity observations ≤6 nephelometric turbidity units. The slope for these low turbidity values was not significantly different than zero, indicating that low turbidity observations provide no real information about SSC; in the case of the Kentucky sediment record, this accounts for 30% of the turbidity observations.

Kentucky↗

USGS2009

Regression models for explaining and predicting concentrations of organochlorine pesticides in fish from streams in the United States

Empirical regression models were developed for estimating concentrations of dieldrin, total chlordane, and total DDT in whole fish from U.S. streams. Models were based on pesticide concentrations measured in whole fish at 648 stream sites nationwide (1992-2001) as part of the U.S. Geological Survey's National Water Quality Assessment Program. Explanatory variables included fish lipid content, estimates (or surrogates) representing historical agricultural and urban sources, watershed characteristics, and geographic location. Models were developed using Tobit regression methods appropriate for data with censoring. Typically, the models explain approximately 50 to 70% of the variability in pesticide concentrations measured in whole fish. The models were used to predict pesticide concentrations in whole fish for streams nationwide using the U.S. Environmental Protection Agency's River Reach File 1 and to estimate the probability that whole-fish concentrations exceed benchmarks for protection of fish-eating wildlife. Predicted concentrations were highest for dieldrin in the Corn Belt, Texas, and scattered urban areas; for total chlordane in the Corn Belt, Texas, the Southeast, and urbanized Northeast; and for total DDT in the Southeast, Texas, California, and urban areas nationwide. The probability of exceeding wildlife benchmarks for dieldrin and chlordane was predicted to be low for most U.S. streams. The probability of exceeding wildlife benchmarks for total DDT is higher but varies depending on the fish taxon and on the benchmark used. Because the models in the present study are based on fish data collected during the 1990s and organochlorine pesticide residues in the environment continue to decline decades after their uses were discontinued, these models may overestimate present-day pesticide concentrations in fish. ?? 2009 SETAC.

Environmental Toxicology and Chemistry↗

USGS2008

Methods and Sources of Data Used to Develop Selected Water-Quality Indicators for Streams and Ground Water for the 2007 Edition of The State of the Nation's Ecosystems Report with Comparisons to the 2002 Edition

The U.S. Geological Survey (USGS) was one of numerous governmental, private, and academic entities that provided input to the report The State of the Nation?s Ecosystems published periodically by the Heinz Center. This report describes the sources of data and methods used by the USGS to develop selected water?quality indicators for the 2007 edition of the Heinz Center report and documents modifications in the data sources and interpretations between the 2002 and 2007 editions of the Heinz Center report. Stream and ground?water quality data collected nationally as part of the USGS National Water-Quality Assessment Program were used to develop the ecosystem indicators for the Heinz Center report, including Core National indicators for the Movement of Nitrogen and Chemical Contamination and for selected ecosystems classified as Farmlands, Forest, Grasslands and Shrublands, Freshwater, and Urban and Suburban. In addition, the USGS provided water?quality and streamflow data collected as part of the National Stream Water Quality Accounting Network and the Federal?State Cooperative Program. The documentation provided herein serves not only as a reference for current and future editions of The State of the Nation?s Ecosystems but also provides critical information for future assessments of changes in contaminant occurrence in streams and ground water of the United States.

Open-File Report↗

USGS2008

Watershed Regressions for Pesticides (WARP) for Predicting Annual Maximum and Annual Maximum Moving-Average Concentrations of Atrazine in Streams

Regression models were developed for predicting annual maximum and selected annual maximum moving-average concentrations of atrazine in streams using the Watershed Regressions for Pesticides (WARP) methodology developed by the National Water-Quality Assessment Program (NAWQA) of the U.S. Geological Survey (USGS). The current effort builds on the original WARP models, which were based on the annual mean and selected percentiles of the annual frequency distribution of atrazine concentrations. Estimates of annual maximum and annual maximum moving-average concentrations for selected durations are needed to characterize the levels of atrazine and other pesticides for comparison to specific water-quality benchmarks for evaluation of potential concerns regarding human health or aquatic life. Separate regression models were derived for the annual maximum and annual maximum 21-day, 60-day, and 90-day moving-average concentrations. Development of the regression models used the same explanatory variables, transformations, model development data, model validation data, and regression methods as those used in the original development of WARP. The models accounted for 72 to 75 percent of the variability in the concentration statistics among the 112 sampling sites used for model development. Predicted concentration statistics from the four models were within a factor of 10 of the observed concentration statistics for most of the model development and validation sites. Overall, performance of the models for the development and validation sites supports the application of the WARP models for predicting annual maximum and selected annual maximum moving-average atrazine concentration in streams and provides a framework to interpret the predictions in terms of uncertainty. For streams with inadequate direct measurements of atrazine concentrations, the WARP model predictions for the annual maximum and the annual maximum moving-average atrazine concentrations can be used to characterize the probable levels of atrazine for comparison to specific water-quality benchmarks. Sites with a high probability of exceeding a benchmark for human health or aquatic life can be prioritized for monitoring.

Open-File Report↗