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Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin

As part of the 2014 Chesapeake Bay Watershed Agreement, all Bay States and the District of Columbia have committed to improving the condition of the Bay, which includes a goal to achieve sustainable fisheries. One outcome under that broad goal is improved effectiveness of fish habitat conservation and preservation efforts. In support of that outcome, the U.S. Geological Survey Eastern Ecological Science Center (USGS-EESC) and the National Oceanic and Atmospheric Association’s National Centers for Coastal Ocean Science (NOAA-NCCOS) are actively developing datasets, methods, and analyses to conduct fish habitat assessments in the Chesapeake Bay watershed, guided by recommendations from a regional stakeholder workshop held by the Chesapeake Bay Program’s (CBP) Fish Habitat Action Team (FHAT) in 2018. The joint USGS and NOAA team has been collaborating on methods for conducting inland and estuarine assessments and exploring whether a seamless headwater to estuary assessment could be developed. The goals of this assessment are to benefit both State and Federal fisheries managers, help advance fisheries science, and provide beneficial information for the public. While past national and regional assessments (e.g. the National Fish Habitat Partnership National Assessment) treated inland and estuarine fish habitat conditions separately due to differences in environments, GIS data representation, and data availability, a seamless habitat assessment could be of value for a broad range of stakeholders as many fish species, several of which are invasive or under federal jurisdiction, use habitats across both inland and estuarine waters. This project developed a pilot framework, explored and tested methods necessary for a finer scale, seamless assessment across both inland and estuarine waters, and demonstrated its use. Although there was interest by the CBP FHAT for the generation of a Baywide fish habitat assessment that spanned tidal salt, tidal fresh, warm non-tidal and cold non-tidal waters, there are a myriad of implementation details and considerations around conducting a Baywide assessment across all four of these general habitat areas. Therefore, the practical need to conduct a tributary-specific pilot assessment arose. At the beginning of this pilot process, members of the FHAT were presented with a decision matrix to choose a study basin using factors such as data availability and tributary size. FHAT members chose the Patuxent River basin, which has been relatively well sampled and studied. Several spatial frameworks were considered before selection of an inclusive gridded framework for summary and analysis that represented inland drainage networks and landscape influences as well as estuarine bathymetry. A suite of landscape and in-water stressor variables were summarized into the framework and were largely generalized over time. In order to assess the viability of the framework, we chose to use species distribution modeling for each of the species to test the framework’s ability to predict habitat use of non-tidal resident, estuarine resident, and migratory species. Tessellated darter (Etheostoma olmstedi), American eel (Anguilla rostrata), and white perch (Morone americana) were chosen as illustrative fish species based on data availability, and differences in life history and habitat use. A nested modeling approach, which involved successive model runs at multiple scales (1000m, 100m, and 10m raster grids) was developed to examine differences in variable importance at different spatial scales and to enhance modeling efficiency. For white perch, a complementary modeling analysis was performed for variables available only in estuarine waters. For all testing, an ensemble modeling approach was conducted, using a suite of potential statistical techniques driven by model strength and variable predictive power. The statistical testing that we conducted was intended only to test the framework and modeling approach, and not to definitively predict all habitats where specific fish species might be present. The modeling we conducted to test the framework did have some limitations. For example, the spatial distribution of favorable habitat areas for white perch was likely influenced by the predominance of fish survey locations near the center channel of the river and the use of generalized in-water conditions. For all species, the use of juvenile and adult fish survey data limits the estimation of habitat use to those life stages. Despite such limitations of the data inputs and modeling approach, we found the framework could seamlessly predict fish habitat distribution across freshwater and tidal environments and integrate the influence of landscape stressors with local in-water factors. The developed framework presented to the Sustainable Fisheries Goal Implementation Team (GIT) and FHAT is informative and could potentially be used for other modeling applications in the Chesapeake Bay watershed and elsewhere. In particular the framework and modeling approach lend themselves to evaluating living resource distributions and underlying habitat conditions in shallow tidal waters and beyond, as recommended by the recent Comprehensive Evaluation of System Response (CESR) report from the Chesapeake Bay Program.

Maryland↗

National water summary 1990-91: Hydrologic events and stream water quality

National Water Summary 1990-91 Hydrologic Events and Stream Water Quality was planned to complement existing Federal-State water-quality reporting to the U.S. Congress that is required by the Clean Water Act of 1972. This act, formally known as the Federal Water Pollution Control Act Amendments of 1972 (Public Law 92-500), and its amendments in 1977,1979,1980,1981,1983, and 1987, is the principal basis for Federal-State cooperation on maintaining and reporting on water quality in the United States. Under section 305(b) of the Clean Water Act, the States must designate uses for waterbodies, biennially assess whether the waterbodies meet designated uses, and report to the U.S. Environmental Protection Agency (EPA), which in turn summarizes the findings of the State assessments in a biennial National Water Quality Inventory report to the Congress. This volume of the National Water Summary uses a nationally consistent data base and methods of statistical analysis to document stream water quality in the United States, Puerto Rico, and the Western Pacific Islands. As a basis for preparation of this report, the U.S. Geological Survey (uses) created a data base of waterquality data from about 2,900 stream water-quality monitoring stations in the United States (Lanfear, 1993). These data, which were extracted primarily from the uses National Water Information System (NWIS) and supplemented with data from the EPA national data base known as STORET, consisted mostly of water-chemistry and physical-sediment data. About 1,400 of these stations met a criterion, used in this volume for trends analysis of data, of having a period of record that began before water year 1980. (A water year is the period from October 1 through September 30 and is identified by the calendar year at the end of the period.) Inasmuch as the determination of trends requires about 10 years of data, few biological data were suitable for trends analysis because biological indicators of water quality were not being collected extensively by water year 1980. Also, because of the lack of long-term data, an assessment of the water quality of lakes and reservoirs is not included in this National Water Summary . Constituent and waterproperty data were selected from this data base and analyzed with statistical techniques that have been used extensively by the uses to summarize conditions and trends in water quality. (Throughout the remainder of this article, the term "constituent" is used also to represent water properties, such as pH and alkalinity.) Selected results from this analysis are presented graphically, both by State and nationally, in this volume. The data base created for preparation of this volume and the resulting nationally consistent statistical analysis of these data provide a unique opportunity to compare and contrast stream water-quality conditions and trends nationally and State by State. Because of the complexity of water quality and the natural seasonal and diurnal variations in the concentrations of waterquality constituents, the determination of trends in water quality requires a relatively long period of record. Trends in constituent concentrations in the State summaries in this volume were calculated for one or more of four periods water years 1970-89,1975-89,1980- 89, and 1982-89. Unfortunately, few water-quality monitoring stations have periods of record of more than 10 or 15 years, and therefore most analyses in the State summaries were done for 8- or 10-year periods. Although interest in water quality has continued to heighten since passage of the Clean Water Act, funds for support of data-collection programs are limited and data records of many of the constituents now of current concern, especially organic constituents, do not exist or are less than 10-years long; consequently only a modest number of organic constituents are presented in this volume. Discussion of conditions and trends in water quality in this National Water Summary also draw upon information about land use, population, water use, application of agricultural chemicals in rural areas, the siting of industrial facilities, and other ancillary information. Because the results of analysis of a restricted number of constituents are presented in this volume, State 305(b) reports also are cited in the discussion of water quality in each State. The following discussion is an overview of the three parts of this 1990-91 National Water Summary - "Hydrologic Conditions and Water-Related Events, Water Years 1990-91," "Hydrologic Perspectives on Water Issues," and "State Summaries of Stream Water Quality."

Water Supply Paper↗

Status and understanding of groundwater quality in the Cascade Range and Modoc Plateau study unit, 2010: California GAMA Priority Basin Project

Groundwater quality in the Cascade Range and Modoc Plateau study unit was investigated as part of the California State Water Resources Control Board’s Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project. The study was designed to provide a statistically unbiased assessment of untreated groundwater quality in the primary aquifer system. The depth of the primary aquifer system for the Cascade Range and Modoc Plateau study unit was delineated by the depths of the screened or open intervals of wells in the State of California’s database of public-supply wells. Two types of assessments were made: a status assessment that described the current quality of the groundwater resource, and an understanding assessment that made evaluations of relations between groundwater quality and potential explanatory factors representing characteristics of the primary aquifer system. The assessments characterize the quality of untreated groundwater, not the quality of treated drinking water delivered to consumers by water distributors. The status assessment was based on water-quality data collected in 2010 by the U.S. Geological Survey from 90 wells and springs (USGS-grid wells) and on water-quality data compiled from the State of California’s regulatory compliance database for samples collected from 240 public-supply wells between September 2007 and September 2010. To provide context, the water-quality data discussed in this report were compared to California and Federal drinking-water regulatory and non-regulatory benchmarks for treated drinking water. Groundwater quality is defined in terms of relative concentrations (RCs), which are calculated by dividing the concentration of a constituent in groundwater by the concentration of the benchmark for that constituent. The RCs for inorganic constituents (major ions, trace elements, nutrients, and radioactive constituents) were classified as “high” (the RC is greater than 1.0, indicating that the concentration is above the benchmark), “moderate” (the RC is from 1.0 to greater than 0.5), or “low” (the RC is less than or equal to 0.5). For organic constituents (volatile organic compounds and pesticides) and special-interest constituents (perchlorate), the boundary between moderate and low RCs was set at 0.1. All benchmarks used for organic constituents were health-based. For inorganic constituents, health-based and aesthetic-based benchmarks were used. Constituents without benchmarks were not considered in the status assessment . The primary metric used for quantifying regional-scale groundwater quality was the aquifer-scale proportion—the areal percentages of the primary aquifer system with high, moderate, and low RCs for a given constituent or class of constituents. The study unit was divided into six study areas on the basis of geologic differences (Eastside Sacramento Valley, Honey Lake Valley groundwater basin, Cascade Range and Modoc Plateau Low Use Basins, Quaternary Volcanic Areas, Shasta Valley and Mount Shasta Volcanic Area, and Tertiary Volcanic Areas), and each study area was divided into equal-area grid cells. Aquifer-scale proportions were calculated for individual constituents and constituent classes for each of the six study areas and for the study unit as a whole by using grid-based (one well per cell) and spatially weighted (many wells per cell) statistical methods. The status assessment showed that inorganic constituents were present at high and moderate RCs in greater proportions of the Cascade Range and Modoc Plateau study unit than were organic constituents. One or more inorganic constituents with health-based benchmarks were present at high RCs in 9.4 percent, and at moderate RCs in 14.7 percent of the primary aquifer system. Arsenic was present at high RCs in approximately 3 percent of the primary aquifer system; boron, molybdenum, uranium, and vanadium each were present at high RCs in approximately 2 percent of the primary aquifer system. One or more inorganic constituents with aesthetic-based benchmarks were present at high RCs in 15.1 percent of the primary aquifer system and at moderate RCs in 4.9 percent. Manganese, iron, and total dissolved solids were present at high RCs in approximately 12 percent, 5 percent, and 2 percent, respectively, of the primary aquifer system. Organic constituents were not detected at high or moderate RCs in the primary aquifer system, and one or more organic constituents were detected at low RCs in approximately 40 percent of the primary aquifer system. Two classes of organic constituents were detected in more than 10 percent of the primary aquifer system: trihalomethanes (chloroform only) and herbicides. The special interest constituent perchlorate was not detected at high RCs, but was detected at moderate RCs in approximately 2 percent of the primary aquifer system. The understanding assessment relied on statistical tests to evaluate relations between concentrations of constituents and values of potential explanatory factors representing geology, land use, well construction, hydrologic conditions, groundwater age, and geochemical conditions. The majority of the high and moderate RCs of arsenic, boron, molybdenum, uranium, and total dissolved solids were in samples from the Honey Lake Valley groundwater basin study area. Groundwater mixing with hydrothermal fluids present in the study area, evaporative concentration of groundwater in the Honey Lake playa, presence of uranium-bearing sediment derived from the adjacent Sierra Nevada, and release of arsenic and other trace elements from sediments under high pH and low dissolved oxygen conditions all appeared to contribute to these elevated concentrations. Thermal springs are in many parts of the Cascade Range and Modoc Plateau study unit and could account for locally elevated concentrations of arsenic, boron, molybdenum, and total dissolved solids in samples from the other study areas. Vanadium concentrations were greater in oxic samples than in anoxic samples, but were not correlated with pH, contrary to expectations from previous studies. Organic constituents were not detected at high or moderate RCs, and the occurrence of low organic constituents at low RCs ranged from 27 percent to 73 percent of the primary aquifers system in the six study areas. The Shasta Valley and Mount Shasta Volcanic study area had significantly greater occurrence of low RCs of herbicides compared to all of the other study areas, which could reflect the greater prevalence of modern groundwater in the Shasta Valley and Mount Shasta Volcanic study area and the presence of potential sources of herbicides, including applications to timberlands and roadside rights-of-way. The Eastside Sacramento Valley study area had the greatest occurrence of low concentrations of chloroform, and chloroform occurrence was most strongly associated with the combination of septic-tank density greater than two tanks per square kilometer and urban land use greater than 10 percent within a radius of 500 meters of the well. These conditions were most prevalent in the Eastside Sacramento Valley study area. The detection frequency of low concentrations of perchlorate was consistent with the probability of occurrence expected under natural conditions, except in the Eastside Sacramento Valley study area, where detection frequencies were much higher than expected and could not be explained by known anthropogenic sources of perchlorate.

California↗

Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014

A statewide study was led to develop regression equations for estimating three selected spring and three selected fall low-flow frequency statistics for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include spring (April through June) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and fall (October through December) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years. Estimates of the three selected spring statistics are provided for 241 U.S. Geological Survey continuous-record streamgages, and estimates of the three selected fall statistics are provided for 238 of these streamgages, using data through June 2014. Because only 9 years of fall streamflow record were available, three streamgages included in the development of the spring regression equations were not included in the development of the fall regression equations. Because of regulation, diversion, or urbanization, 30 of the 241 streamgages were not included in the development of the regression equations. The study area includes Iowa and adjacent areas within 50 miles of the Iowa border. Because trend analyses indicated statistically significant positive trends when considering the period of record for most of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. Geographic information system software was used to measure 63 selected basin characteristics for each of the 211streamgages used to develop the regional regression equations. The study area was divided into three low-flow regions that were defined in a previous study for the development of regional regression equations. Because several streamgages included in the development of regional regression equations have estimates of zero flow calculated from observed streamflow for selected spring and fall low-flow frequency statistics, the final equations for the three low-flow regions were developed using two types of regression analyses—left-censored and generalized-least-squares regression analyses. A total of 211 streamgages were included in the development of nine spring regression equations—three equations for each of the three low-flow regions. A total of 208 streamgages were included in the development of nine fall regression equations—three equations for each of the three low-flow regions. A censoring threshold was used to develop 15 left-censored regression equations to estimate the three fall low-flow frequency statistics for each of the three low-flow regions and to estimate the three spring low-flow frequency statistics for the southern and northwest regions. For the northeast region, generalized-least-squares regression was used to develop three equations to estimate the three spring low-flow frequency statistics. For the northeast region, average standard errors of prediction range from 32.4 to 48.4 percent for the spring equations and average standard errors of estimate range from 56.4 to 73.8 percent for the fall equations. For the northwest region, average standard errors of estimate range from 58.9 to 62.1 percent for the spring equations and from 83.2 to 109.4 percent for the fall equations. For the southern region, average standard errors of estimate range from 43.2 to 64.0 percent for the spring equations and from 78.1 to 78.7 percent for the fall equations. The regression equations are applicable only to stream sites in Iowa with low flows not substantially affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. The regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system application. StreamStats allows users to click on any ungaged stream site and compute estimates of the six selected spring and fall low-flow statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged site are provided. StreamStats also allows users to click on any Iowa streamgage to obtain computed estimates for the six selected spring and fall low-flow statistics.

Iowa↗

Concentration, flux, and the analysis of trends of total and dissolved phosphorus, total nitrogen, and chloride in 18 tributaries to Lake Champlain, Vermont and New York, 1990–2011

Annual concentration, flux, and yield for total phosphorus, dissolved phosphorus, total nitrogen, and chloride for 18 tributaries to Lake Champlain were estimated for 1990 through 2011 using a weighted regression method based on time, tributary streamflows (discharges), and seasonal factors. The weighted regression method generated two series of daily estimates of flux and concentration during the period of record: one based on observed discharges and a second based on a flow-normalization procedure that removes random variation due to year-to-year climate-driven effects. The flownormalized estimate for a given date is similar to an average estimate of concentration or flux that would be made if all of the observed discharges for that date were equally likely to have occurred. The flux bias statistic showed that 68 of the 72 flux regression models were minimally biased. Temporal trends in the concentrations and fluxes were determined by calculating percent changes in flow-normalized annual fluxes for the full period of analysis (1990 through 2010) and for the decades 1990–2000 and 2000–2010. Basinwide, flow-normalized total phosphorus flux decreased by 42 metric tons per year (t/yr) between 1990 and 2010. This net result reflects a basinwide decrease in flux of 21 metric tons (t) between 1990 and 2000, followed by a decrease of 20 t between 2000 and 2010; both results were largely influenced by flux patterns in the large tributaries on the eastern side of the basin. A comparison of results for total phosphorus for the two separate decades of analysis found that more tributaries had decreasing concentrations and flux rates in the second decade than the first. An overall reduction in dissolved phosphorus flux of 0.7 t/yr was seen in the Lake Champlain Basin during the full period of analysis. That very small net change in flux reflects substantial reductions between 1990 and 2000 from eastern tributaries, especially in Otter Creek and the LaPlatte and Winooski Rivers that largely were offset by increases in the Missisquoi and Saranac Rivers in the second decade (between 2000 and 2010). The number of tributaries that had increases in dissolved phosphorus concentrations stayed constant at 13 or 14 during the period of analysis. Total nitrogen concentration and flux for most of the monitored tributaries in the Lake Champlain Basin have decreased since 1990. Between 1990 and 2010, flow-normalized total nitrogen flux decreased by 386 t/yr, which reflects an increase of 440 t/yr between 1990 and 2000 and a decrease of 826 t/yr between 2000 and 2010. All individual tributaries except the Winooski River had decreases in total nitrogen concentration and flux between 2000 and 2010. The decrease in total nitrogen flux over the period of record could be related to the decrease in nitrogen from atmospheric deposition observed in Vermont or to concurrent benefits realized from the implementation of agricultural best-management practices in the Lake Champlain Basin that were designed primarily to reduce phosphorus runoff. For chloride, large increases in flow-normalized concentrations and flux between 1990 and 2000 for 17 of the 18 tributaries diminished to small increases or decreases between 2000 and 2010. Between 1990 and 2010, flow-normalized flux increased by 32,225 t/yr, 78 percent of which (25,163 t) was realized during the first decade, from 1990 through 2000. The five tributaries that had decreasing concentration and flux of chloride between 2000 and 2010 were all on the eastern side of Lake Champlain, possibly related to reductions since 1999 in winter road salt application in Vermont. Positive correlations of phosphorus flux and changes in phosphorus concentration and flux in tributaries with phosphorus inputs to basins from point sources, suggest that point sources have an effect on stream phosphorus chemistry. Several measures of changes in agricultural statistics, such as agricultural land use, acres of land in farms, acres of cropland, and acres of corn for grain or seed, are positively correlated with changes in phosphorus concentration or flux in the tributaries. Negative correlations of the amount of money spent on agricultural best-management practices with changes in phosphorus concentration or flux in the tributaries, suggest that best-management practices may be an effective tool, along with point-source reductions, in making progress towards management goals for phosphorus reductions in Lake Champlain.

New York;Vermont↗

Low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020

A study was conducted by the U.S. Geological Survey, in cooperation with the Ohio Water Development Authority and the Ohio Environmental Protection Agency, to compute low-flow frequency, flow-duration, and harmonic mean flow statistics for long-term streamflow gages and to develop regression equations to estimate those statistics at unregulated, ungaged stream locations in Ohio. The flow statistics were computed with data collected after the 1974 water year because upward trends and statistically significant step changes (occurring after the late 1960s but before 1975) in annual flow statistics were detected at many candidate gages in Ohio. A total of 180 continuous-record gages in Ohio and bordering States were identified as having at least 10 years of daily flow records during the analytical period (water years 1975–2020). Also identified were five low-flow partial-record gages in Ohio that had instantaneous low flows that correlated strongly with daily streamflows at one of the continuous-record gages (also referred to as index gages). For continuous-record gages, the following flow statistics were computed: annual and seasonal minimum 1-, 7-, 30-, and 90-day flows with 2-, 5-, 10-, 20-, and 50-year recurrence intervals; annual and seasonal 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, 50-, 40-, 30-, 20-, and 10-percent duration flows; and the harmonic mean flow. For partial-record gages, estimates were made for annual and seasonal minimum 1-, 7-, 30-, and 90-day low flows with 2-, 10-, and 20-year recurrence intervals and annual and seasonal 98-, 95-, 90-, 85-, and 80-percent duration flows. The drainage basin of each gage was inspected for anthropogenic or karst features that could appreciably affect or regulate low flows. That inspection resulted in data from 53 of the 180 continuous-record gages and the 5 low-flow partial-record gages being categorized as “unregulated” and subsequently used in regression analyses to develop equations for estimating low-flow statistics. Two hundred and sixty potential explanatory variables were tested for this study. In most cases, a streamflow-variability index (SVI) was chosen as the sole explanatory variable for the regression analyses to predict the harmonic mean and annual and seasonal low-flow yields. The exceptions were for one of the September–November low-flow yield statistics and all the December–February yield statistics. Drainage area, decimal longitude, and usually SVI were chosen as the explanatory variables for those exceptions and to predict the 80-percent duration flows. The SVI values used in the model were estimated from a geospatial grid of SVI values developed for this study by using an empirical Bayesian kriging regression prediction. Observations for continuous-record gages used in the regression analyses were weighted as a function of their record length. Weights for partial-record gages were estimated based on the weights determined for their index gages. Equations for low-flow yields were developed by using censored regressions with a censoring level of 0.00001 cubic foot per second per square mile. Numerical constraints were placed on the yield equations if they could compute yields less than the yield censoring level or if the yields did not monotonically decrease with increasing SVI. Logistic-regression equations were developed, with SVI and drainage area as explanatory variables, to estimate the probability that the low-flow statistics were greater than the flow censoring level (0.01 cubic foot per second). The regression equations presented in this report were developed for implementation in the Ohio StreamStats application. The equations are applicable to unregulated streams in Ohio and are not applicable to streams with karst drainage features, diversions, regulation, or other anthropogenic activities that can appreciably affect low flow. The equations were developed by using observations with a range of SVI values from 0.41 to 1.23 log10 cubic foot per second and a range of drainage areas from 0.21 to 540 square miles. The applicability of the equations outside these ranges is not known.

Ohio↗

Effect of land-applied biosolids on surface-water nutrient yields and groundwater quality in Orange County, North Carolina

Land application of municipal wastewater biosolids is the most common method of biosolids management used in North Carolina and the United States. Biosolids have characteristics that may be beneficial to soil and plants. Land application can take advantage of these beneficial qualities, whereas disposal in landfills or incineration poses no beneficial use of the waste. Some independent studies and laboratory analysis, however, have shown that land-applied biosolids can pose a threat to human health and surface-water and groundwater quality. The effect of municipal biosolids applied to agriculture fields is largely unknown in relation to the delivery of nutrients, bacteria, metals, and contaminants of emerging concern to surface-water and groundwater resources. Therefore, the North Carolina Department of Environment and Natural Resources (NCDENR) collaborated with the U.S. Geological Survey (USGS) through the 319 Nonpoint Source Program to better understand the transport of nutrients and bacteria from biosolids application fields to groundwater and surface water and to provide a scientific basis for evaluating the effectiveness of the current regulations. The USGS conducted a paired agricultural watershed study in the Collins Creek and Cane Creek Reservoir watersheds in Orange County, North Carolina. Field activities were conducted from March 2011 through May 2013 at two field study sites, including biosolids field application sites owned by Orange County Water and Sewer Authority (OWASA) in the Collins Creek watershed and a background study site in the Cane Creek watershed that has no fields receiving biosolids applications. Samples of biosolids source material and soil were collected from the land-application fields for laboratory analyses. Soil samples were also collected from a background agricultural field in the Cane Creek watershed that has never received land-applied municipal biosolids. Shallow groundwater samples were collected quarterly from new monitoring wells installed by NCDENR along the edge of the biosolids land-application fields and a background agricultural field for laboratory analyses. Two surface-water monitoring sites were established on Collins Creek to compute continuous streamflow and collect discrete baseflow and stormwater runoff water-quality data upstream and downstream from the biosolids land-application fields. Surface water-quality samples were also collected for baseflow and stormwater runoff conditions at an existing USGS streamgage on Cane Creek to monitor water-quality conditions in the background study watershed. The study primarily focused on nutrients and bacteria; however, data for field properties and water-quality constituents, including metals, major ions, and contaminants of emerging concern (household-, industrial-, and agricultural-use compounds, pharmaceutical compounds, hormones, and antibiotics) also were collected and used in the analyses. There were no exceedances of the 10 elements with designated U.S. Environmental Protection Agency (EPA) ceiling concentrations for land-applied biosolids in any of the biosolids samples. Treatment processes and storage techniques used by OWASA are effective in eliminating Escherichia coli and fecal coliform bacteria from biosolids. Copper, molybdenum, total Kjeldahl nitrogen, and total phosphorus were elevated in the soil from biosolids land-application fields relative to the background field. The relative richness of these constituents in the biosolids land-application fields is consistent with biosolids being the source of the elevated concentrations given the relatively high concentrations of these constituents in the biosolids samples that were collected. Shallow groundwater in the transitional zone wells, which were located adjacent to and topographically downgradient from all the biosolids land-application fields, were found to be statistically different and had higher nitrate concentrations (medians greater than 12 milligrams per liter) than all the other wells sampled as part of the study. Surface-water nutrient concentrations and yields, primarily nitrate, were higher at the monitoring site on Collins Creek downstream from the biosolids land-application fields than the other study sites that drained watersheds without biosolids land application. The largest differences in concentrations between sites were measured at baseflow conditions, which indicate that the main cause of these differences, particularly between Cane Creek and the Collins Creek site downstream from the OWASA application fields, is related to nitrate contribution from the shallow groundwater. Contaminants of emerging concern were detected in approximately 40 percent of the laboratory analyses of the biosolids samples and more frequently in soil samples from the biosolids land-application fields (approximately 40 percent of laboratory analyses) relative to the soil samples from the background field (approximately 12 percent of laboratory analyses). However, contaminants of emerging concern detected in the laboratory analysis for this study do not appear to be good indicators of human-waste contaminants derived from land-applied biosolids in groundwater or surface-water because the number of detections and concentrations at the background wells and surface-water monitoring sites are similar to or higher than those at wells and monitoring sites adjacent to or downstream from the biosolids land-application fields. The data, analysis, and conclusions associated with this study can be used by regulatory agencies, resource managers, and wastewater-treatment operators to (1) better understand the quantity and characteristics of nutrients, bacteria, metals, and contaminants of emerging concern that are transported away from biosolids land-application fields to surface water and groundwater under current regulations for the purposes of establishing effective total maximum daily loads (TMDLs) and restoring impaired water resources, (2) assess how well existing regulations protect waters of the State and potentially recommend effective changes to regulations or land-application procedures, and (3) establish a framework for developing guidance on effective techniques for monitoring and regulatory enforcement of permitted biosolids land-application fields.

North Carolina↗

Methods for estimating magnitude and frequency of floods in Arizona, developed with unregulated and rural peak-flow data through water year 2010

Flooding is among the worst natural disasters responsible for loss of life and property in Arizona, underscoring the importance of accurate estimation of flood magnitude for proper structural design and floodplain mapping. Twenty-four years of additional peak-flow data have been recorded since the last comprehensive regional flood frequency analysis conducted in Arizona. Periodically, flood frequency estimates and regional regression equations must be revised to maintain the accurate estimation of flood frequency and magnitude. Annual peak-flow data collected through water year 2010 were compiled from 448 unregulated streamflow-gaging stations, hereafter referred to as streamgages, in Arizona having a minimum of 10 years of record. Flood frequency estimates were first computed with station (or at-site) skew using the Expected Moments Algorithm with a multiple Grubbs-Beck test to identify multiple potentially influential low flows to fit a Pearson Type III distribution. Next, a multiple step Bayesian least-squares-regression approach was used to determine a new statewide regional skew of −0.09. No basin characteristics analyzed were statistically significant in explaining the variation in skew and as a result, the constant model was chosen as the best regional skew model for the Arizona study area. The mean square error used in Bulletin 17B (B17B) of the Interagency Advisory Committee on Water Data is used to describe the precision of the regional skew. The constant model had a mean square error equal to 0.08, which corresponds to an effective record length of 85 years. This is a marked improvement over a previous Arizona regional skew analysis, with a reported mean square error of 0.31, for a corresponding effective record length of around 17 years. Thus the new regional model had almost five times the information content (as measured by effective record length) of that calculated in USGS Water Supply Paper 2433, published in 1997, or the value of 0.302 reported in the B17B generalized skew map. The flood frequency estimates were recalculated using a weighted skew of the station and regional skew. Station flood frequency estimates for each streamgage are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. Geographical information systems were used to compute basin characteristic information for each streamgage for the purpose of developing regional equations to estimate flood statistics at ungaged basins. Five hydrologic flood regions in Arizona were defined in a multivariate regionalization process based on mean basin elevation, mean annual precipitation, and soil permeability. A regional generalized least-squares-regression analysis was used to develop five sets of equations from 344 nonredundant streamgages, corresponding to five regions, for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities at ungaged basins in Arizona. The regression equations developed for these five regions were based on one or more of the statistically significant explanatory variables: drainage area, mean basin elevation, and mean annual precipitation. Average standard errors of prediction for the regression regions for the five regions ranged from 27 to 122 percent and the pseudo-coefficients of determination (pseudo-R 2 ), a measure of the proportion of peak-flow variation that is explained by the basin characteristics, ranged from 68 to 98 percent. Regression equations for Central Highlands (region 4) had the lowest model error and the greatest pseudo-R 2 metrics. The equations for Colorado Plateau (region 2) regression equations generally had greater model error and lower pseudo-R 2 metrics. The improvement of regional regression equation model error and pseudo-R 2 metrics was related to higher numbers of streamgages, longer period of record, and even spatial coverage within a region. The regional regression equations were integrated into the U.S. Geological Survey’s StreamStats program. The StreamStats program is a national map-based web application that allows the public to easily access published flood frequency and basin characteristic statistics. The interactive web application allows a user to select a point within a watershed (gaged or ungaged) and retrieve flood-frequency estimates derived from the current regional regression equations and geographic information system data within the selected basin. StreamStats provides users with an efficient and accurate means for retrieving the most up to date flood frequency and basin characteristic data. StreamStats is intended to provide consistent statistics, minimize user error, and reduce the need for large datasets and costly geographic information system software.

Arizona↗

Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012

Estimates of the magnitude and frequency of floods are needed across Alaska for engineering design of transportation and water-conveyance structures, flood-insurance studies, flood-plain management, and other water-resource purposes. This report updates methods for estimating flood magnitude and frequency in Alaska and conterminous basins in Canada. Annual peak-flow data through water year 2012 were compiled from 387 streamgages on unregulated streams with at least 10 years of record. Flood-frequency estimates were computed for each streamgage using the Expected Moments Algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows. A multiple Grubbs-Beck test was used to identify potentially influential low floods in the time series of peak flows for censoring in the flood frequency analysis. For two new regional skew areas, flood-frequency estimates using station skew were computed for stations with at least 25 years of record for use in a Bayesian least-squares regression analysis to determine a regional skew value. The consideration of basin characteristics as explanatory variables for regional skew resulted in improvements in precision too small to warrant the additional model complexity, and a constant model was adopted. Regional Skew Area 1 in eastern-central Alaska had a regional skew of 0.54 and an average variance of prediction of 0.45, corresponding to an effective record length of 22 years. Regional Skew Area 2, encompassing coastal areas bordering the Gulf of Alaska, had a regional skew of 0.18 and an average variance of prediction of 0.12, corresponding to an effective record length of 59 years. Station flood-frequency estimates for study sites in regional skew areas were then recomputed using a weighted skew incorporating the station skew and regional skew. In a new regional skew exclusion area outside the regional skew areas, the density of long-record streamgages was too sparse for regional analysis and station skew was used for all estimates. Final station flood frequency estimates for all study streamgages are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. Regional multiple-regression analysis was used to produce equations for estimating flood frequency statistics from explanatory basin characteristics. Basin characteristics, including physical and climatic variables, were updated for all study streamgages using a geographical information system and geospatial source data. Screening for similar-sized nested basins eliminated hydrologically redundant sites, and screening for eligibility for analysis of explanatory variables eliminated regulated peaks, outburst peaks, and sites with indeterminate basin characteristics. An ordinary least‑squares regression used flood-frequency statistics and basin characteristics for 341 streamgages (284 in Alaska and 57 in Canada) to determine the most suitable combination of basin characteristics for a flood-frequency regression model and to explore regional grouping of streamgages for explaining variability in flood-frequency statistics across the study area. The most suitable model for explaining flood frequency used drainage area and mean annual precipitation as explanatory variables for the entire study area as a region. Final regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability discharge in Alaska and conterminous basins in Canada were developed using a generalized least-squares regression. The average standard error of prediction for the regression equations for the various annual exceedance probabilities ranged from 69 to 82 percent, and the pseudo-coefficient of determination (pseudo-R 2 ) ranged from 85 to 91 percent. The regional regression equations from this study were incorporated into the U.S. Geological Survey StreamStats program for a limited area of the State—the Cook Inlet Basin. StreamStats is a national web-based geographic information system application that facilitates retrieval of streamflow statistics and associated information. StreamStats retrieves published data for gaged sites and, for user-selected ungaged sites, delineates drainage areas from topographic and hydrographic data, computes basin characteristics, and computes flood frequency estimates using the regional regression equations.

Alaska↗

Preliminary assessment of a water-quality monitoring program for total maximum daily loads in Johnson County, Kansas, January 2015 through June 2016

Municipalities in Johnson County in northeastern Kansas are required to implement stormwater management programs to reduce pollutant discharges, protect water quality, and comply with applicable water-quality regulations in accordance with National Pollutant Discharge Elimination System permits for stormwater discharge. To this end, municipalities collect grab samples at streams entering and leaving their jurisdiction to determine levels of excessive nutrients, sediment, and fecal bacteria to characterize pollutants and understand the factors affecting them. In 2014, the U.S. Geological Survey and the Johnson County Stormwater Management Program, with input from the Kansas Department of Health and Environment, initiated a 5-year monitoring program to satisfy minimum sampling requirements for each municipality as described by new stormwater permits issued to Johnson County municipalities. The purpose of this report is to provide a preliminary assessment of the monitoring program. The monitoring program is described, a preliminary assessment of the monitoring program design is provided using water-quality data collected during the first 2 years of the program, and the ability of the current monitoring network and sampling plan to provide data sufficient to quantify improvements in water quality resulting from implemented and planned best management practices is evaluated. The information in this initial report may be used to evaluate changes in data collection methods while data collection is still ongoing that may lead to improved data utility. Discrete water-quality samples were collected at 27 sites and analyzed for nutrients, Escherichia coli ( E. coli ) bacteria, total suspended solids, and suspended-sediment concentration. In addition, continuous water-quality data (water temperature, pH, dissolved oxygen, specific conductance, turbidity, and nitrate plus nitrite) were collected at one site to characterize variability and provide a basis for comparison to discrete data. Base flow samples indicated that point sources are likely affecting nutrient concentrations and E. coli bacteria densities at several sites. Concentrations of all analytes in storm runoff samples were characterized by substantial variability among sites and samples. About one-half of the sites, representing different watersheds, had storm runoff samples with nitrogen concentrations greater than 10 milligrams per liter. About one-third of the sites, representing different watersheds, had storm runoff samples with total phosphorus concentrations greater than 3 milligrams per liter. Six sites had samples with E. coli densities greater than 100,000 colonies per 100 milliliters of water. Total suspended solids concentrations of about 12,000 milligrams per liter or greater occurred in samples from three sites. Data collected for this monitoring program may be useful for some general assessment purposes but may also be limited in potential to fully inform stormwater management activities. Valuable attributes of the monitoring program design included incorporating many sites across the county for comparisons among watersheds and municipalities, using fixed-stage samplers to collect multiple samples during single events, collection of base flow samples in addition to storm samples to isolate possible point sources from stormwater sources, and use of continuous monitors to characterize variability. Limiting attributes of the monitoring program design included location of monitoring sites along municipal boundaries to satisfy permit requirements rather than using watershed-based criteria such as locations of tributaries, potential pollutant sources, and implemented management practices. Additional limiting attributes include having a large number of widespread sampling locations, which presented logistical challenges for predicting localized rainfall and collecting and analyzing samples during short timeframes associated with storms, and collecting storm samples at fixed-stage elevations only during the rising limb of storms, which does not characterize conditions over the storm hydrograph. The small number of samples collected per site resulted in a sample size too small to be representative of site conditions, including seasonal and hydrologic variability, and insufficient for meaningful statistical analysis or site-specific modeling. Several measures could be taken to improve data utility and include redesigning the monitoring network according to watershed characteristics, incorporating a nested design in which data are collected at different scales (watershed, subwatershed, and best management practices), increasing sampling frequency, and combining different methods to allow for flexibility to focus on areas and conditions of particular interest. A monitoring design that would facilitate most of these improvements would be to focus efforts on a limited number of watersheds for several years, then cycle to the next set of watersheds for several years, eventually returning to previously monitored watersheds to document changes. Redesign of the water-quality monitoring program requires considerable effort and commitment from municipalities of Johnson County. However, the long-term benefit likely is a monitoring program that results in improved stream conditions and more effective management practices and efficient expenditure of resources.

Kansas↗

Determination of steroid hormones and related compounds in filtered and unfiltered water by solid-phase extraction, derivatization, and gas chromatography with tandem mass spectrometry

A new analytical method has been developed and implemented at the U.S. Geological Survey National Water Quality Laboratory that determines a suite of 20 steroid hormones and related compounds in filtered water (using laboratory schedule 2434) and in unfiltered water (using laboratory schedule 4434). This report documents the procedures and initial performance data for the method and provides guidance on application of the method and considerations of data quality in relation to data interpretation. The analytical method determines 6 natural and 3 synthetic estrogen compounds, 6 natural androgens, 1 natural and 1 synthetic progestin compound, and 2 sterols: cholesterol and 3--coprostanol. These two sterols have limited biological activity but typically are abundant in wastewater effluents and serve as useful tracers. Bisphenol A, an industrial chemical used primarily to produce polycarbonate plastic and epoxy resins and that has been shown to have estrogenic activity, also is determined by the method. A technique referred to as isotope-dilution quantification is used to improve quantitative accuracy by accounting for sample-specific procedural losses in the determined analyte concentration. Briefly, deuterium- or carbon-13-labeled isotope-dilution standards (IDSs), all of which are direct or chemically similar isotopic analogs of the method analytes, are added to all environmental and quality-control and quality-assurance samples before extraction. Method analytes and IDS compounds are isolated from filtered or unfiltered water by solid-phase extraction onto an octadecylsilyl disk, overlain with a graded glass-fiber filter to facilitate extraction of unfiltered sample matrices. The disks are eluted with methanol, and the extract is evaporated to dryness, reconstituted in solvent, passed through a Florisil solid-phase extraction column to remove polar organic interferences, and again evaporated to dryness in a reaction vial. The method compounds are reacted with activated -methyl--trimethylsilyl trifluoroacetamide at 65 degrees Celsius for 1 hour to form trimethylsilyl or trimethylsilyl-enol ether derivatives that are more amenable to gas chromatographic separation than the underivatized compounds. Analysis is carried out by gas chromatography with tandem mass spectrometry using calibration standards that are derivatized concurrently with the sample extracts. Analyte concentrations are quantified relative to specific IDS compounds in the sample, which directly compensate for procedural losses (incomplete recovery) in the determined and reported analyte concentrations. Thus, reported analyte concentrations (or analyte recoveries for spiked samples) are corrected based on recovery of the corresponding IDS compound during the quantification process. Recovery for each IDS compound is reported for each sample and represents an absolute recovery in a manner comparable to surrogate recoveries for other organic methods used by the National Water Quality Laboratory. Thus, IDS recoveries provide a useful tool for evaluating sample-specific analytical performance from an absolute mass recovery standpoint. IDS absolute recovery will differ and typically be lower than the corresponding analyte’s method recovery in spiked samples. However, additional correction of reported analyte concentrations is unnecessary and inappropriate because the analyte concentration (or recovery) already is compensated for by the isotope-dilution quantification procedure. Method analytes were spiked at 10 and 100 nanograms per liter (ng/L) for most analytes (10 times greater spike levels were used for bisphenol A and 100 times greater spike levels were used for 3--coprostanol and cholesterol) into the following validation-sample matrices: reagent water, wastewater-affected surface water, a secondary-treated wastewater effluent, and a primary (no biological treatment) wastewater effluent. Overall method recovery for all analytes in these matrices averaged 100 percent, with overall relative standard deviation of 28 percent. Mean recoveries of the 20 individual analytes for spiked reagent-water samples prepared along with field samples and analyzed in 2009–2010 ranged from 84–104 percent, with relative standard deviations of 6–36 percent. Concentrations for two analytes, equilin and progesterone, are reported as estimated because these analytes had excessive bias or variability, or both. Additional database coding is applied to other reported analyte data as needed, based on sample-specific IDS recovery performance. Detection levels were derived statistically by fortifying reagent water at six different levels (0.1 to 4 ng/L) and range from about 0.4 to 4 ng/L for 16 analytes. Interim reporting levels applied to analytes in this report range from 0.8 to 8 ng/L. Bisphenol A and the sterols (cholesterol and 3-beta-coprostanol) were consistently detected in laboratory and field blanks. The minimum reporting levels were set at 100 ng/L for bisphenol A and at 200 ng/L for the two sterols to prevent any bias associated with the presence of these compounds in the blanks. A minimum reporting level of 2 ng/L was set for 11-ketotestosterone to minimize false positive risk from an interfering siloxane compound emanating as chromatographic-column bleed, from vial septum material, or from other sources at no more than 1 ng/L.

Techniques and Methods↗

Handbook of acute toxicity of chemicals to fish and aquatic invertebrates : summaries of toxicity tests conducted at Columbia National Fisheries Research Laboratory, 1965-78

Acute toxicity is a major subject of research at Columbia National Fisheries Research Laboratory for evaluating the impact of toxic chemicals on fishery resources. The Laboratory has played a leading role in developing research technology for toxicity testing and data interpretation. In 1965-78, more than 400 chemicals were tested against a variety of invertebrates and fish species representative of both cold- and warm-water climates. The use of acute toxicity tests for assessing the potential hazard of chemical contaminants to aquatic organisms is well documented (Boyd 1957; Henderson et al. 1960; Sanders and Cope 1966; Macek and McAllister 1970). Static acute toxicity tests provide rapid and (within limits) reproducible concentration-response curves for estimating toxic effects of chemicals on aquatic organisms. These tests provide a database for determining relative toxicity of a large number of chemicals to a variety of species and for estimating acute effects of chemical spills on natural aquatic systems; they also assist in determining priority and design of additional toxicity studies. Acute toxicity tests usually provide estimates of the exposure concentration causing 50% mortality (LC50) to test organisms during a specified period of time. For certain invertebrates, the effective concentration is based on immobilization, or some other identifiable endpoint, rather than on lethality. The application of the LC50 has gained acceptance among toxicologists and is generally the most highly rated test for assessing potential adverse effects of chemical contaminants to aquatic life (Brungs and Mount 1978; American Institute for Biological Sciences 1978 a ). The literature contains numerous papers dealing with the acute toxicity of chemicals to freshwater organisms. However, there is a tremendous need for a concise compendium of toxicity data covering a large variety of chemicals and test species. This Handbook is a compilation of a large volume of acute toxicity data from the Columbia Laboratory and its field laboratories. It presents definitive acute toxicity data on 271 chemicals tested against a variety of freshwater invertebrates and fishes. The chemicals represent all major groups of pesticides, as well as numerous industrial chemicals. This compilation should serve as a useful database for the many agencies and organizations dealing with research and management programs concerned with the impact of chemicals on aquatic resources. The Columbia Laboratory has played a major role in developing currently used standard methodology for static acute toxicity testing. The use of standardized methodology greatly reduces variation in results. The data presented here have been carefully scrutinized to eliminate tests that failed to follow acceptable procedures. Handling of test organisms and procedures for static toxicity tests followed those described by Lennon and Walker (1964) and Macek and McAllister (1970), and conform well with those recommended by Brauhn and Schoettger (1975) and the Committee on Methods for Toxicity Tests with Aquatic Organisms (1975). The species of fish and invertebrates that were tested are listed in phylogenetic order in Tables 1 and 2. Fish were obtained from Federal and State hatcheries as either eggs or fry. Original stocks of invertebrates were collected and cultured from wild populations with no known source of contamination; these populations were replenished regularly. The invertebrates were cultured in the Laboratory by methods similar to those described by Sanders and Cope (1966). Test chemicals usually consisted of technical or analytical grade samples of known purity. Formulations of the chemicals were also tested when available. When purity of test chemicals was known, all calculated concentrations were based on percent active ingredients. Stock solutions were prepared immediately before each test, with commercial grade acetone as the carrier solvent. Occasionally, ethanol or dimethyl-formamide was substituted. Solvent concentrations did not exceed 0.5 mL/L in final dilution water. Test water (dilution water) was reconstituted from deionized water of at least 10 6 ohms resistivity by the addition of appropriate reagent grade chemicals (Marking 1969). Water was buffered to maintain a pH of 7.2 to 7.5, an alkalinity of 30 to 35 mg/L, and a hardness of 40 to 50 mg/L as CaCO 3 . Test water was mixed thoroughly and aerated before transfer into test chambers. Fish were acclimated to dilution water by gradually changing the water in acclimated tanks from 100% well water to 100% reconstituted water over a 1- to 3-day period at the desired testing temperature. Invertebrates were acclimated from well water to dilution water over a 4- to 6-h period. Toxicity tests were conducted under static conditions without aeration, and the organisms were not fed during acclimation or testing. Temperature of test solutions was maintained within ± 1°C of that required for a given test. Toxicity tests with fish were conducted in 18.9-liter (5-gal) wide-mouthed jars containing 15 liters of test solution. Fingerling fish weighing 0.2 to 1.5 g were tested at each concentration. Caution was taken not to exceed 0.8 g of test organisms per liter of solution. Duplicate test chambers were used to accommodate larger fish. Test chambers varied in size for invertebrates, depending on the species used; volume of test solution ranged from 0.25 to 4 liters. At least 10 organisms were exposed to each concentration for all definitive tests. At least six concentrations were used per toxicity test. The tests began upon initial exposure to the toxicant and continued for 96 h. Immobilization tests with invertebrates were conducted for only 48 h. The number of dead or affected organisms in each test chamber were recorded and the dead organisms were removed every 24 h; general observations on the condition of test organisms were also recorded at these times. Toxicity data were analyzed by a statistical method described by Litchfield and Wilcoxon (1949) to determine LC50 (theoretical estimate of the concentration lethal to 50% of the test animals) and 95% confidence intervals. This method is recommended by the American Public Health Association (1971) and by Sprague (1969) for determining median lethal concentrations. The procedure is easily modified for computing a single LC50 when replicate tests are performed.

Resource Publication↗

Toward improved decision-support tools for Delta Smelt management actions

The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level. Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions. Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance. Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement. • DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions. • DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth. • DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt. There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address. For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models. Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time. Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires: - Engagement of a committed group of technical experts with appropriate expertise. - The development of a coordinated workplan including appropriate project management and tracking. - Dialog between potential users (i.e., managers and policy makers) and technical experts. - Resources to pursue DST development including personnel and computational resources. This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.

White Paper↗

Analysis and review of fishery-dependent data for Hawaiian nearshore noncommercial fisheries

Noncommercial, shore-based fisheries provide economic, social, and cultural services to communities throughout the Hawaiian Islands. The State of Hawai‘i Department of Land and Natural Resources (DLNR), Division of Aquatic Resources (DAR) routinely conducts surveys to monitor noncommercial fisheries such that estimates of fishing effort and catch by gear type can be generated and used to implement more sustainable management practices. DAR executes both the Hawai‘i Marine Recreational Fishery Survey (HMRFS), a nationally standardized survey that focuses on intercepting fishers at access points (i.e., boat ramps) across the main Hawaiian Islands, and a set of roving creel surveys on O‘ahu, Maui Nui, and Kaua‘i that observe and intercept fishers at locations along the shoreline outside of those targeted by HMRFS. The latter set of creel surveys were designed to complement HMRFS by expanding its geographic coverage and thus providing a more representative picture of noncommercial fishing in Hawai‘i. Sustainable management priorities set by DAR rely on the availability of statewide, fishery- dependent data. Thus, we collate information from island-based roving creel surveys into a cohesive Statewide Creel Survey Database. Further, we provide preliminary analyses and describe ways that surveys could be streamlined to improve future data collection, analysis, and utility. In so doing, we synthesize the most detailed information to-date about noncommercial shore-based fisheries of Hawai‘i. The unprecedented spatial and temporal coverage of DAR’s dataset reveals the value of their survey efforts over the last decade to address fishery management needs. Our primary objectives, results, and conclusions are summarized below: 1) Integrate DAR roving creel survey data from different islands into a single Statewide Creel Survey Dataset (Chapter II). We describe the collation of creel survey data from O‘ahu, Maui Nui, and Kaua‘i into a statewide dataset. We also offer ways in which these surveys could be streamlined to meet the needs of managers and decision makers. Briefly, these are to create a statewide strategic plan, standardize the execution of standard operating procedures, centralize the creel survey database and associated metadata, and consider using technology that improves the data pipeline, including transitioning from paper-based to electronic systems for data entry and processing. 2) Assess whether the new Statewide Creel Survey Dataset can provide inputs for length-based stock assessments (Chapter III). Only on Maui were interviews conducted with associated catch data. There was reasonably high taxonomic coverage (42 species from 186 interviews with 310 fishers), but low sample sizes for nearly all species precluded the development of length-based stock assessments. We provide summary statistics from the existing data and briefly discuss how technologies could be used to automate analysis of images of noncommercial catch. 3) Analyze the Statewide Creel Survey Dataset for spatial and temporal patterns in fishing effort (Chapter IV): a. Visualizing noncommercial fishing pressure . We found that fishing effort (mean number of fishers observed per survey event at a site) on O'ahu was over three times greater than that recorded during similar surveys conducted on Maui or Kaua'i. We create maps that display the distribution of angling and spearfishing effort around each of the three islands. b. Factors that predict fishing “hotspots” around Maui . Fishing effort on Maui was associated with areas with more wave power and less parking availability. There were half as many fishers in areas with parking lots than in areas with parking on the road shoulder only. c. Changes in fishing effort during the COVID-19 pandemic . There was no change in fishing effort on O‘ahu during the first year of the pandemic, but there was a 20% decline in year 2 and a 33% decline in year 3, both in comparison to pre-pandemic levels. Pre-pandemic creel survey data were unavailable for Maui and Kaua‘i, but fishing effort on these islands also declined as the pandemic progressed at similar or greater rates than those observed on O‘ahu. 4) Quantify potential bias in survey methods by experimentally deriving fisher detection probabilities of shore-based and drone-based surveys (Chapter V): a. Shore-based surveys . We conducted roving creel surveys for four months at three locations around Hilo Bay, designed to emulate and estimate the efficacy of DAR standard operating procedures. There was high agreement between paired observers in counting fishers, leading to near-perfect detection probabilities of both anglers (94%) and spearfishers (97%), but relatively low agreement and detection probabilities of other fishers (throw net, ‘opihi picking, etc.) (52%). b. Drone-based surveys . We used an unmanned aerial vehicle (UAV; operated by DAR staff) to collect imagery of fishers along the Hilo Bay shoreline. We used still images and video clips (with known fishing activity) to build an online survey that was distributed to DAR and HCFRU personnel, asking them to count and categorize resource users as a snorkeler, spearfisher, angler, or other fisher. Only 40.0% of the responses correctly counted and categorized resource users in the image. Anglers were correctly identified and enumerated in 90.0% of the responses, but the correct response rates of the other three user categories ranged from 67.8% – 79.4%. Snorkelers and anglers tended to be undercounted while spearfishers and other fishers were overcounted. 5) Review the potential for incorporating emerging technologies that will improve, augment, and evolve creel survey data collection, especially for spearfishing (Chapter VI). Within the context of monitoring shore-based noncommercial fishing, we review the use of electronic data entry/processing systems with geospatial and image capabilities, field cameras, drones, smart buoys, citizen science apps, data mining social media, artificial intelligence and machine learning. We highlight several of the challenges and considerations when implementing these technologies into creel surveys and provide a synthesis of options that could be used to better estimate spearfishing. The general conclusion of this assessment is that the DAR roving creel survey program is collecting valuable data that supplement the existing HMRFS efforts. However, there are a number of areas that could be improved to make these efforts a more effective tool for decision-making processes in resource management and conservation: 1) Establishment of clear statewide and island objectives for the Statewide Creel Survey Dataset. Currently, data collection efforts are focused towards addressing a very broad purpose – supplementing the HMRFS data collection efforts. However, the results of the preliminary analyses conducted as part of this project suggest that the data could be used to address other areas of need if these objectives were clearly defined. Further, the design of the creel survey would benefit from greater standardization of survey protocols between islands and an effort to define a) the acceptable margins of error associated with the estimates generated by these data and b) the minimum level of change that the surveys would need to detect to be useful to managers. 2) Centralization of data entry, data quality assessment, and data accessibility. Currently, each DAR office manages data entry, checks the data for errors, and is responsible for managing and storing the data. Instituting a centralized data entry system, particularly an online database that can receive survey data from tablets or smartphones running a standardized data collection application would improve efficiency, reduce data entry errors, and accelerate the availability of data to managers. A substantial amount of time and effort from the project described in this report was devoted to checking the dataset for errors. The development and application of data quality assurance protocols would ensure that the data are reliable and available in a timely fashion to support management decisions. 3) Address lingering questions regarding the efficacy of current survey protocols to capture and characterize the spearfishing component of the noncommercial fishery. The results presented in the report suggest that the current creel survey protocols do a good job detecting spearfishers when present but are not capturing sufficient data about their catch or total effort. There are also questions remaining as to whether the survey times and sites are sufficiently capturing the behavior of spearfishers in Hawai‘i. A more thorough assessment – whether through additional research, alteration of survey design, or review of data by representatives of the spearfishing community – would provide insight on how to use the Statewide Creel Survey Database to inform management of spearfishing. 4) Investigate the integration of technological advancements into the creel survey methods. As priorities and needs are developed and formalized, it would be valuable to consider how various technological advancements might enhance and streamline data collection or open new avenues of inquiry.

Hawaii↗

Application of a Weighted Regression Model for Reporting Nutrient and Sediment Concentrations, Fluxes, and Trends in Concentration and Flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, Results Through Water Year 2012

In the Chesapeake Bay watershed, estimated fluxes of nutrients and sediment from the bay’s nontidal tributaries into the estuary are the foundation of decision making to meet reductions prescribed by the Chesapeake Bay Total Maximum Daily Load (TMDL) and are often the basis for refining scientific understanding of the watershed-scale processes that influence the delivery of these constituents to the bay. Two regression-based flux and trend estimation models, ESTIMATOR and Weighted Regressions on Time, Discharge, and Season (WRTDS), were compared using data from 80 watersheds in the Chesapeake Bay Nontidal Water-Quality Monitoring Network (CBNTN). The watersheds range in size from 62 to 70,189 square kilometers and record lengths range from 6 to 28 years. ESTIMATOR is a constant-parameter model that estimates trends only in concentration; WRTDS uses variable parameters estimated with weighted regression, and estimates trends in both concentration and flux. WRTDS had greater explanatory power than ESTIMATOR, with the greatest degree of improvement evident for records longer than 25 years (30 stations; improvement in median model R 2 = 0.06 for total nitrogen, 0.08 for total phosphorus, and 0.05 for sediment) and the least degree of improvement for records of less than 10 years, for which the two models performed nearly equally. Flux bias statistics were comparable or lower (more favorable) for WRTDS for any record length; for 30 stations with records longer than 25 years, the greatest degree of improvement was evident for sediment (decrease of 0.17 in median statistic) and total phosphorus (decrease of 0.05). The overall between-station pattern in concentration trend direction and magnitude for all constituents was roughly similar for both models. A detailed case study revealed that trends in concentration estimated by WRTDS can operationally be viewed as a less-constrained equivalent to trends in concentration estimated by ESTIMATOR. Estimates of annual mean flow-adjusted (ESTIMATOR) and flow-normalized (WRTDS) concentration for years initially constituting the end of a water-quality record showed a similar degree of variability as data for additional years were incrementally added and the initial estimates “aged.” On the basis of the results of this broad comparison of the two models, the U.S. Geological Survey is adopting WRTDS as the primary model for estimating constituent fluxes and trends throughout the CBNTN. Nutrient and sediment flux and trend estimates, based on WRTDS, are summarized narratively and tabulated in appendixes for all stations for which fluxes or trends were reported through water year 2012. WRTDS also was used to explore the sensitivity of flux and trend estimates to three data-quality issues common in many large-scale monitoring networks and evident in some of the CBNTN records. The potential effects of inconsistency in annual sampling effort and inconsistency in storm sampling effort were explored by way of a subsampling experiment using eight of the most densely sampled long-term (1985–2012) stations in the CBNTN as baseline datasets. From each dataset, a set of 10 “design guideline” subsamples was selected, consisting of 12 monthly samples and 8 targeted storm samples per year. The selection was conducted in a manner that preserved the overall intensity of storm sampling in the baseline data. These 10 subsamples were further manipulated to create “heterogeneous” subsamples by removing storm samples prior to 2003. The maximum relative difference between flow-normalized flux estimated in a single year from any of the 10 design guideline subsamples and values estimated in the corresponding year from baseline data was smallest for dissolved inorganic nitrogen (median of 8 stations = 6 percent of baseline estimate), but more appreciable for total phosphorus and sediment (medians of 22 and 32 percent, respectively). The maximum relative difference between flow-normalized flux estimated from from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data was more pronounced, with medians for 8 stations of 15, 30, and 53 percent of the corresponding baseline estimates for dissolved inorganic nitrogen, total phosphorus, and sediment, respectively. The worst-case maximum relative differences between flow-normalize flux estimated in a single year from the 10 heterogeneous subsamples and values estimated in the corresponding year from baseline data were 25 percent for dissolved inorganic nitrogen, 37 percent for total phosphorus, and 250 percent for sediment. The results for the heterogeneous subsamples indicate that changes in storm sampling frequency can result in appreciable distortion of estimated trends in flow-normalized flux, especially for total phosphorus and sediment. Trend lines estimated from heterogeneous subsamples tended to converge with the trend lines estimated from baseline data after 2003. In contrast, 2003–12 trends based on subsamples truncated by discarding all data prior to the induced heterogeneity in 2003 showed appreciable biases and differences in slope, relative to the corresponding 2003–12 segment of the trend computed from the design guideline subsamples. Overall, the results indicate that for particulate constituents, load and trend estimates computed using long-term records recently converted to CBNTN design guideline sampling protocols will be most reliable if the trend is computed using the entire record, but reported only for the period that design guideline sampling protocols were followed. Inconsistencies related to changing laboratory methods were also examined via two manipulative experiments. In the first experiment, increasing and decreasing “stair-step” patterns of changes in censoring level, overall representing a factor-of-five change in the laboratory reporting limit, were artificially imposed on a 27-year record with no censoring and a period-of-record concentration trend of –68.4 percent. Trends estimated on the basis of the manipulated records were broadly similar to the original trend (–63.6 percent for decreasing censoring levels and –70.3 percent for increasing censoring levels), lending a degree of confidence that the survival regression routines upon which WRTDS is based are generally robust to data censoring. The second experiment considered an abrupt disappearance of low-concentration observations of total phosphorus, associated with a laboratory method change and not reflected through censoring, near the middle of a 28-year record. By process of elimination, an upward shift in the estimated flow-normalize concentration trend line around the same time was identified as a likely artifact resulting from the laboratory method change, although a contemporaneous change in watershed processes cannot be ruled out. Decisions as to how to treat records with potential sampling protocol or laboratory methods-related artifacts should be made on a case-by-case basis, and trend results should be appropriately qualified.

Maryland, New York, Pennsylvania, Virginia↗

Application of dimensionless sediment rating curves to predict suspended-sediment concentrations, bedload, and annual sediment loads for rivers in Minnesota

Consistent and reliable sediment data are needed by Federal, State, and local government agencies responsible for monitoring water quality, planning river restoration, quantifying sediment budgets, and evaluating the effectiveness of sediment reduction strategies. Heightened concerns about excessive sediment in rivers and the challenge to reduce costs and eliminate data gaps has guided Federal and State interests in pursuing alternative methods for measuring suspended and bedload sediment. Simple and dependable data collection and estimation techniques are needed to generate hydraulic and water-quality information for areas where data are unavailable or difficult to collect. The U.S. Geological Survey, in cooperation with the Minnesota Pollution Control Agency and the Minnesota Department of Natural Resources, completed a study to evaluate the use of dimensionless sediment rating curves (DSRCs) to accurately predict suspended-sediment concentrations (SSCs), bedload, and annual sediment loads for selected rivers and streams in Minnesota based on data collected during 2007 through 2013. This study included the application of DSRC models developed for a small group of streams located in the San Juan River Basin near Pagosa Springs in southwestern Colorado to rivers in Minnesota. Regionally based DSRC models for Minnesota also were developed and compared to DSRC models from Pagosa Springs, Colorado, to evaluate which model provided more accurate predictions of SSCs and bedload in Minnesota. Multiple measures of goodness-of-fit were developed to assess the effectiveness of DSRC models in predicting SSC and bedload for rivers in Minnesota. More than 600 dimensionless ratio values of SSC, bedload, and streamflow were evaluated and delineated according to Pfankuch stream stability categories of “good/fair” and “poor” to develop four Minnesota-based DSRC models. The basis for Pagosa Springs and Minnesota DSRC model effectiveness was founded on measures of goodness-of-fit that included proximity of the model(s) fitted line to the 95-percent confidence intervals of the site-specific model, Nash-Sutcliffe Efficiency values, model biases, and deviation of annual sediment loads from each model to the annual sediment loads calculated from measured data. Composite plots comparing Pagosa Springs DSRCs, Minnesota DSRCs, site-specific regression models, and measured data indicated that regionally developed DSRCs (Minnesota DSRC models) more closely approximated measured data for nearly every site. Pagosa Springs DSRC models had markedly larger exponents (slopes) when compared to the Minnesota DSRC models and the site-specific regression models, and over-represent SSC and bedload at streamflows exceeding bankfull. The Nash-Sutcliffe Efficiency values for the Minnesota DSRC model for suspended-sediment concentrations closely matched Nash-Sutcliffe Efficiency values of the site-specific regression models for 12 out of 16 sites. Nash-Sutcliffe Efficiency values associated with Minnesota DSRCs were greater than those associated with Pagosa Springs DSRCs for every site except the Whitewater River near Beaver, Minnesota site. Pagosa Springs DSRC models were less accurate than the mean of the measured data at predicting SSC values for one-half of the sites for good/fair stability sites and one-half of the sites for poor stability sites. Relative model biases were calculated and determined to be substantial (greater than 5 percent) for Pagosa Springs and Minnesota models, with Minnesota models having a lower mean model bias. For predicted annual suspended-sediment loads (SSL), the Minnesota DSRC models for good/fair and poor stream stability sites more closely approximated the annual SSLs calculated from the measured data as compared to the Pagosa Springs DSRC model. Results of data analyses indicate that DSRC models developed using data collected in Minnesota were more effective at compensating for differences in individual stream characteristics across a variety of basin sizes and flow regimes than DSRC models developed using data collected for Pagosa Springs, Colorado. Minnesota DSRC models retained a substantial portion of the unique sediment signatures for most rivers, although deviations were observed for streams with limited sediment supply and for rivers in southeastern Minnesota, which had markedly larger regression exponents. Compared to Pagosa Springs DSRC models, Minnesota DSRC models had regression slopes that more closely matched the slopes of site-specific regression models, had greater Nash-Sutcliffe Efficiency values, had lower model biases, and approximated measured annual sediment loads more closely. The results presented in this report indicate that regionally based DSRCs can be used to estimate reasonably accurate values of SSC and bedload. Practitioners are cautioned that DSRC reliability is dependent on representative measures of bankfull streamflow, SSC, and bedload. It is, therefore, important that samples of SSC and bedload, which will be used for estimating SSC and bedload at the bankfull streamflow, are collected over a range of conditions that includes the ascending and descending limbs of the event hydrograph. The use of DSRC models may have substantial limitations for certain conditions. For example, DSRC models should not be used to predict SSC and sediment loads for extreme streamflows, such as those that exceed twice the bankfull streamflow value because this constitutes conditions beyond the realm of current (2016) empirical modeling capability. Also, if relations between SSC and streamflow and between bedload and streamflow are not statistically significant, DSRC models should not be used to predict SSC or bedload, as this could result in large errors. For streams that do not violate these conditions, DSRC estimates of SSC and bedload can be used for stream restoration planning and design, and for estimating annual sediment loads for streams where little or no sediment data are available.

Minnesota↗

Occurrence of fecal-indicator bacteria and protocols for identification of fecal-contamination sources in selected reaches of the West Branch Brandywine Creek, Chester County, Pennsylvania

The presence of fecal-indicator bacteria indicates the potential presence of pathogens originating from the fecal matter of warm-blooded animals. These pathogens are responsible for numerous human diseases ranging from common diarrhea to meningitis and polio. The detection of fecal-indicator bacteria and interpretation of the resultant data are, therefore, of great importance to water-resource managers. Current (2005) techniques used to assess fecal contamination within the fluvial environment primarily assess samples collected from the water column, either as grab samples or as depth- and (or) width-integrated samples. However, current research indicates approximately 99 percent of all bacteria within nature exist as attached, or sessile, bacteria. Because of this condition, most current techniques for the detection of fecal contamination, which utilize bacteria, assess only about 1 percent of the total bacteria within the fluvial system and are, therefore, problematic. Evaluation of the environmental factors affecting the occurrence and distribution of bacteria within the fluvial system, as well as the evaluation and modification of alternative approaches that effectively quantify the larger population of sessile bacteria within fluvial sediments, will present water-resource managers with more effective tools to assess, prevent, and (or) eliminate sources of fecal contamination within pristine and impaired watersheds. Two stream reaches on the West Branch Brandywine Creek in the Coatesville, Pa., region were studied between September 2002 and August 2003. The effects of sediment particle size, climatic conditions, aquatic growth, environmental chemistry, impervious surfaces, sediment and soil filtration, and dams on observed bacteria concentrations were evaluated. Alternative approaches were assessed to better detect geographic sources of fecal contamination including the use of turbidity as a surrogate for bacteria, the modification and implementation of sandbag bacteria samplers, and the use of optical brighteners. For the purposes of this report, sources of bacteria were defined as geographic locations where elevated concentrations of bacteria are observed within, or expected to enter, the main branch of the West Branch Brandywine Creek. Biologic sources (for example, waterfowl) were noted where applicable; however, no specific study of biologic sources (such as bacterial source tracking) was conducted. Data indicated that specific bacterial populations within fluvial sediments could be related to specific particle-size ranges. This relation is likely the result of the reduced porosity and permeability associated with finer sediments and the ability of specific bacteria to tolerate particular environments. Escherichia coli (E. coli) showed a higher median concentration (2,160 colonies per gram of saturated sediment) in the 0.125 to 0.5-millimeter size range of natural sediments than in other ranges, and enterococcus bacteria showed a higher median concentration (61,830 colonies per gram of saturated sediment) in the 0.062 to 0.25-millimeter size range of natural sediments than in other ranges. There were insufficient data to assess the particle-size relation to fecal coliform bacteria and (or) fecal streptococcus bacteria. Climatic conditions were shown to affect bacteria concentrations in both the water column and fluvial sediments. Drought conditions in 2002 resulted in lower overall bacteria concentrations than the more typically wet year of 2003. E. coli concentrations in fluvial sediment along the Coatesville study reach in 2002 had a median concentration of 92 colonies per gram of saturated sediment; in 2003, the median concentration had risen to 4,752 colonies per gram of saturated sediment. Symbiotic relations between bacteria and aquatic growth were likely responsible for increased bacteria concentrations observed within an impoundment area on the Coatesville study reach. This reach showed evidence of elevated aquatic growth and sharp increases in E. coli concentrations from upstream to downstream through the impoundment area in both 2002 and 2003. In 2003, E. coli concentrations within the waters column increased from 940 colonies per 100 milliliters upstream to 6,000 colonies per 100 milliliters at the dam crest. Given that these bacteria likely resulted from natural bacterial regrowth, the use of E. coli as an indicator of fecal contamination was severely impaired. Variable environmental conditions along the West Branch Brandywine Creek made the common field-chemical parameters of specific conductance, temperature, pH, and dissolved oxygen ineffective and (or) impossible to use for the determination of inputs of fecal contamination. Extreme variations in chemical gradients commonly were related to the urban/industrial signature of the watershed. For example, during base-flow sampling in 2002, specific-conductance values exceeding 1,000 microsiemens per centimeter observed in effluent from a local steel mill. This effluent raised the specific conductance within the West Branch Brandywine from just above 200 microsiemens per centimeter upstream from the outfall to just below 500 microsiemens per centimeter downstream from the outfall. These chemical gradients also, likely, had an effect on the initial colonization of bacteria, the formation of biofilms, and the persistence of certain types of bacteria along the study reach. Data collected in 2003 indicated that nutrients increased during both base-flow and stormflow conditions along the Coatesville study reach. For example, during base-flow sampling in 2003, 20 pounds of phosphorus was shown to enter the West Branch Brandywine Creek along the Coatesville study reach. The largest contributors to this base-flow nutrient load were likely two wastewater-treatment facilities adjacent to the study reach. During stormflow sampling in 2003, 480 pounds of phosphorus was shown to enter the West Branch Brandywine Creek along the Coatesville study reach. Data, along with other research, indicated the largest contributor to this stormflow nutrient load was likely remobilized sediment originating from a large dam impoundment. These elevated nutrient concentrations were considered sufficient to promote accelerated aquatic growth along the reach. Data collected in 2003 showed that wastewater constituents entered the West Branch Brandywine Creek largely from urban storm-sewer systems. Samples from the primary storm sewer for the city of Coatesville had detections for 20 of 69 wastewater constituents. These constituents included both strong and weak indicators of fecal contamination and generally indicated the storm-sewer system along the Coatesville study reach was a likely source of fecal-indicator bacteria and fecal contamination under base-flow conditions. By comparison, 5 constituents were detected in samples from the upstream end of the reach, and 10 constituents were detected in samples from the downstream end of the reach. During stormflow, numbers of detections were similar along the entire length of the study reach-five in samples from the upstream end, eight in samples from the center of the reach, and seven in samples from the downstream end of the reach. These data indicate that point sources (such as culverts and pipes, septic systems, and wastewater-treatment facilities) are not likely the origin of bacteria contamination during stormflow. The bacteria concentrations observed during stormflow events probably result from remobilized sessile bacteria stored within fluvial sediments. In this case, these bacteria should not be considered indicators of current fecal contamination. Impervious surfaces were found to increase bacteria concentrations along the West Branch Brandywine Creek because contaminated runoff from impervious areas generally flows into, and is concentrated within, the confines of the local storm-sewer system. During 2002, storm-sewer outfalls draining impervious areas were associated with all major locations of elevated bacterial concentrations (greater than 1,200 colonies per gram of saturated sediment) in fluvial sediments. During 2003, wetter conditions and overall bacteria concentrations higher than in 2002 resulted in point sources of bacterial contamination becoming less pronounced; however, the storm-sewer system, draining adjacent impervious areas, was still observed to be the primary source of bacteria along the reach. Where stormwater and (or) other runoff from these areas was allowed to infiltrate and (or) flow through wetland and riparian buffers, bacteria concentrations were not observed to be elevated above background levels commonly observed throughout similar areas of the same reach. Two run-of-the-river dams along the Coatesville study reach were evaluated for their effects on observed bacterial concentrations. These dams were shown to have greater or lesser effects on bacterial concentrations depending on the size of the structure and the capacity of the structure to impede flows. The smaller upstream dam had an approximate height of 3 feet and showed little observed effect on measured turbidity values; these data indicated that the dam did not effectively impede the flow of water or sediment within the West Branch Brandywine Creek. Consequently, this small dam did not show any observed effect on bacterial concentrations either upstream or downstream of the structure. The larger dam, near the middle of the reach, had an approximate height of 20 feet and showed greater effects on both turbidity and bacteria concentrations. The capacity of the larger dam to impede flows, combined with nutrients entering the reach, resulted in increased biologic activity throughout the impoundment area. Within this larger impoundment, enterococcus bacteria populations were observed to decrease sharply and E. coli bacteria populations were observed to increase sharply as flow approached the dam crest. All bacteria levels were then observed to drop to background levels, in both the water column and fluvial sediment, immediately downstream from the dam crest. Additional study is required to determine the cause for this rapid die off. Turbidity was assessed as a potential surrogate for E. coli bacteria. Regression analysis indicated higher turbidity levels usually can indicate higher concentrations of bacteria (R2 = 0.67), but the relation was too sporadic on the West Branch Brandywine Creek to use turbidity as a surrogate for estimated bacteria concentrations. Evaluation of data from individual base-flow and stormflow events resulted in variable and generally poor statistical relations between E. coli bacteria and turbidity (R2 values ranged from 0.02 to 0.94). Sandbag samplers were used in 2003 to determine their suitability for the assessment of fecal contamination. Sandbag samplers rely on the ability of bacteria to attach to surfaces and use the larger sessile bacteria populations instead of the more commonly used planktonic bacteria populations. E. coli bacteria concentrations observed in the sandbag samplers, after 1 week in place, were similar to those found within natural sediments collected concurrently. Enterococcus bacteria concentrations within the same sandbag samplers were not similar, and were generally lower, than those observed within the natural sediments. This discrepancy was likely because sand within the samplers was sieved to a size that was likely too coarse for enterococcus bacteria to persist. Optical-brightener samplers were installed along with each sandbag sampler. Optical brighteners are additives used in common household detergents; therefore, detection of optical brighteners, along with elevated fecal-indicator bacteria concentrations, strongly indicates a link to humans. Positive results for optical brighteners were detected only at the outfalls of two sewage-treatment facilities; because of treatment of the effluent from these facilities, these samples did not have elevated bacteria concentrations. The lack of additional positive results was largely because this method is not sensitive to low concentrations of optical brighteners.

Pennsylvania↗