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USGS2004

Estimates of median flows for streams on the 1999 Kansas Surface Water Register

The Kansas State Legislature, by enacting Kansas Statute KSA 82a?2001 et. seq., mandated the criteria for determining which Kansas stream segments would be subject to classification by the State. One criterion for the selection as a classified stream segment is based on the statistic of median flow being equal to or greater than 1 cubic foot per second. As specified by KSA 82a?2001 et. seq., median flows were determined from U.S. Geological Survey streamflow-gaging-station data by using the most-recent 10 years of gaged data (KSA) for each streamflow-gaging station. Median flows also were determined by using gaged data from the entire period of record (all-available hydrology, AAH). Least-squares multiple regression techniques were used, along with Tobit analyses, to develop equations for estimating median flows for uncontrolled stream segments. The drainage area of the gaging stations on uncontrolled stream segments used in the regression analyses ranged from 2.06 to 12,004 square miles. A logarithmic transformation of the data was needed to develop the best linear relation for computing median flows. In the regression analyses, the significant climatic and basin characteristics, in order of importance, were drainage area, mean annual precipitation, mean basin permeability, and mean basin slope. Tobit analyses of KSA data yielded a model standard error of prediction of 0.285 logarithmic units, and the best equations using Tobit analyses of AAH data had a model standard error of prediction of 0.250 logarithmic units. These regression equations and an interpolation procedure were used to compute median flows for the uncontrolled stream segments on the 1999 Kansas Surface Water Register. Measured median flows from gaging stations were incorporated into the regression-estimated median flows along the stream segments where available. The segments that were uncontrolled were interpolated using gaged data weighted according to the drainage area and the bias between the regression-estimated and gaged flow information. On controlled segments of Kansas streams, the median flow information was interpolated between gaging stations using only gaged data weighted by drainage area. Of the 2,232 total stream segments on the Kansas Surface Water Register, 34.5 percent of the segments had an estimated median streamflow of less than 1 cubic foot per second when the KSA analysis was used. When the AAH analysis was used, 36.2 percent of the segments had an estimated median streamflow of less than 1 cubic foot per second. This report supercedes U.S. Geological Survey Water-Resources Investigations Report 02?4292.

Scientific Investigations Report↗

USGS2006

Statewide analysis of the drainage-area ratio method for 34 streamflow percentile ranges in Texas

The drainage-area ratio method commonly is used to estimate streamflow for sites where no streamflow data are available using data from one or more nearby streamflow-gaging stations. The method is intuitive and straightforward to implement and is in widespread use by analysts and managers of surface-water resources. The method equates the ratio of streamflow at two stream locations to the ratio of the respective drainage areas. In practice, unity often is assumed as the exponent on the drainage-area ratio, and unity also is assumed as a multiplicative bias correction. These two assumptions are evaluated in this investigation through statewide analysis of daily mean streamflow in Texas. The investigation was made by the U.S. Geological Survey in cooperation with the Texas Commission on Environmental Quality. More than 7.8 million values of daily mean streamflow for 712 U.S. Geological Survey streamflow-gaging stations in Texas were analyzed. To account for the influence of streamflow probability on the drainage-area ratio method, 34 percentile ranges were considered. The 34 ranges are the 4 quartiles (0-25, 25-50, 50-75, and 75-100 percent), the 5 intervals of the lower tail of the streamflow distribution (0-1, 1-2, 2-3, 3-4, and 4-5 percent), the 20 quintiles of the 4 quartiles (0-5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45-50, 50-55, 55-60, 60-65, 65-70, 70-75, 75-80, 80-85, 85-90, 90-95, and 95-100 percent), and the 5 intervals of the upper tail of the streamflow distribution (95-96, 96-97, 97-98, 98-99 and 99-100 percent). For each of the 253,116 (712X711/2) unique pairings of stations and for each of the 34 percentile ranges, the concurrent daily mean streamflow values available for the two stations provided for station-pair application of the drainage-area ratio method. For each station pair, specific statistical summarization (median, mean, and standard deviation) of both the exponent and bias-correction components of the drainage-area ratio method were computed. Statewide statistics (median, mean, and standard deviation) of the station-pair specific statistics subsequently were computed and are tabulated herein. A separate analysis considered conditioning station pairs to those stations within 100 miles of each other and with the absolute value of the logarithm (base-10) of the ratio of the drainage areas greater than or equal to 0.25. Statewide statistics of the conditional station-pair specific statistics were computed and are tabulated. The conditional analysis is preferable because of the anticipation that small separation distances reflect similar hydrologic conditions and the observation of large variation in exponent estimates for similar-sized drainage areas. The conditional analysis determined that the exponent is about 0.89 for streamflow percentiles from 0 to about 50 percent, is about 0.92 for percentiles from about 50 to about 65 percent, and is about 0.93 for percentiles from about 65 to about 85 percent. The exponent decreases rapidly to about 0.70 for percentiles nearing 100 percent. The computation of the bias-correction factor is sensitive to the range analysis interval (range of streamflow percentile); however, evidence suggests that in practice the drainage-area method can be considered unbiased. Finally, for general application, suggested values of the exponent are tabulated for 54 percentiles of daily mean streamflow in Texas; when these values are used, the bias correction is unity.

Scientific Investigations Report↗

USGS2014

Methods for estimating annual exceedance-probability discharges and largest recorded floods for unregulated streams in rural Missouri

Regression analysis techniques were used to develop a set of equations for rural ungaged stream sites for estimating discharges with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. Basin and climatic characteristics were computed using geographic information software and digital geospatial data. A total of 35 characteristics were computed for use in preliminary statewide and regional regression analyses. Annual exceedance-probability discharge estimates were computed for 278 streamgages by using the expected moments algorithm to fit a log-Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data from water year 1844 to 2012. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized multiple Grubbs-Beck test was used to detect potentially influential low floods. Annual peak flows less than a minimum recordable discharge at a streamgage were incorporated into the at-site station analyses. An updated regional skew coefficient was determined for the State of Missouri using Bayesian weighted least-squares/generalized least squares regression analyses. At-site skew estimates for 108 long-term streamgages with 30 or more years of record and the 35 basin characteristics defined for this study were used to estimate the regional variability in skew. However, a constant generalized-skew value of -0.30 and a mean square error of 0.14 were determined in this study. Previous flood studies indicated that the distinct physical features of the three physiographic provinces have a pronounced effect on the magnitude of flood peaks. Trends in the magnitudes of the residuals from preliminary statewide regression analyses from previous studies confirmed that regional analyses in this study were similar and related to three primary physiographic provinces. The final regional regression analyses resulted in three sets of equations. For Regions 1 and 2, the basin characteristics of drainage area and basin shape factor were statistically significant. For Region 3, because of the small amount of data from streamgages, only drainage area was statistically significant. Average standard errors of prediction ranged from 28.7 to 38.4 percent for flood region 1, 24.1 to 43.5 percent for flood region 2, and 25.8 to 30.5 percent for region 3. The regional regression equations are only applicable to stream sites in Missouri with flows not significantly affected by regulation, channelization, backwater, diversion, or urbanization. Basins with about 5 percent or less impervious area were considered to be rural. Applicability of the equations are limited to the basin characteristic values that range from 0.11 to 8,212.38 square miles (mi 2 ) and basin shape from 2.25 to 26.59 for Region 1, 0.17 to 4,008.92 mi 2 and basin shape 2.04 to 26.89 for Region 2, and 2.12 to 2,177.58 mi 2 for Region 3. Annual peak data from streamgages were used to qualitatively assess the largest floods recorded at streamgages in Missouri since the 1915 water year. Based on existing streamgage data, the 1983 flood event was the largest flood event on record since 1915. The next five largest flood events, in descending order, took place in 1993, 1973, 2008, 1994 and 1915. Since 1915, five of six of the largest floods on record occurred from 1973 to 2012.

Missouri↗

USGS1996

Lake-level frequency analysis for Devils Lake, North Dakota

Two approaches were used to estimate future lake-level probabilities for Devils Lake. The first approach is based on an annual lake-volume model, and the second approach is based on a statistical water mass-balance model that generates seasonal lake volumes on the basis of seasonal precipitation, evaporation, and inflow. Autoregressive moving average models were used to model the annual mean lake volume and the difference between the annual maximum lake volume and the annual mean lake volume. Residuals from both models were determined to be uncorrelated with zero mean and constant variance. However, a nonlinear relation between the residuals of the two models was included in the final annual lakevolume model. Because of high autocorrelation in the annual lake levels of Devils Lake, the annual lake-volume model was verified using annual lake-level changes. The annual lake-volume model closely reproduced the statistics of the recorded lake-level changes for 1901-93 except for the skewness coefficient. However, the model output is less skewed than the data indicate because of some unrealistically large lake-level declines. The statistical water mass-balance model requires as inputs seasonal precipitation, evaporation, and inflow data for Devils Lake. Analysis of annual precipitation, evaporation, and inflow data for 1950-93 revealed no significant trends or long-range dependence so the input time series were assumed to be stationary and short-range dependent. Normality transformations were used to approximately maintain the marginal probability distributions; and a multivariate, periodic autoregressive model was used to reproduce the correlation structure. Each of the coefficients in the model is significantly different from zero at the 5-percent significance level. Coefficients relating spring inflow from one year to spring and fall inflows from the previous year had the largest effect on the lake-level frequency analysis. Inclusion of parameter uncertainty in the model for generating precipitation, evaporation, and inflow indicates that the upper lake-level exceedance levels from the water mass-balance model are particularly sensitive to parameter uncertainty. The sensitivity in the upper exceedance levels was caused almost entirely by uncertainty in the fitted probability distributions of the quarterly inflows. A method was developed for using long-term streamflow data for the Red River of the North at Grand Forks to reduce the variance in the estimated mean. Comparison of the annual lake-volume model and the water mass-balance model indicates the upper exceedance levels of the water mass-balance model increase much more rapidly than those of the annual lake-volume model. As an example, for simulation year 5, the 99-percent exceedance for the lake level is 1,417.6 feet above sea level for the annual lake-volume model and 1,423.2 feet above sea level for the water mass-balance model. The rapid increase is caused largely by the record precipitation and inflow in the summer and fall of 1993. Because the water mass-balance model produces lake-level traces that closely match the hydrology of Devils Lake, the water mass-balance model is superior to the annual lake-volume model for computing exceedance levels for the 50-year planning horizon.

Water Supply Paper↗

USGS2007

Mercury in precipitation in Indiana, January 2001–December 2003

Mercury in precipitation was monitored during 2001 through 2003 at four locations in Indiana as part of the National Atmospheric Deposition Program-Mercury Deposition Network (NADP-MDN). Monitoring stations were operated at Roush Lake near Huntington, Clifty Falls State Park near Madison, Monroe County Regional Airport near Bloomington, and Indiana Dunes National Lakeshore near Porter. At these monitoring stations, precipitation amounts were measured and weekly samples were collected for analysis of total mercury and methylmercury by low-level methods. Wet deposition was computed with the total mercury and methylmercury concentrations and the precipitation amounts. In 3 years of weekly samples collected at the four monitoring stations, the volume-weighted total mercury concentration was 11.5 ng/L (nanograms per liter). As a reference for comparison, the total mercury concentration in 47 percent of the samples analyzed was greater than the Indiana water-quality standard for mercury (12 ng/L, protecting aquatic life) and nearly all of the concentrations exceeded the Indiana water-quality standards for mercury in the Great Lakes system (1.8 ng/L, protecting human health, and 1.3 ng/L, protecting wild mammals and birds). The precipitation-weighted concentrations at three of the monitoring stations in Indiana in 2003 were in the top 40 percent of all monitoring stations in the NADP-MDN and the concentration at Indiana Dunes was the eighth highest in the NADP-MDN for 2003. At the four monitoring stations during the study period, the mean weekly total mercury deposition was 243 ng/m 2 (nanograms per square meter) and mean annual total mercury deposition was 12,623 ng/m 2 . The annual mercury deposition at the four monitoring stations in Indiana in 2003 was in the top 40 percent of all monitoring stations in the NADP-MDN and the annual mercury deposition at the Clifty Falls station was the tenth highest in the NADP-MDN for 2003. For the 3-year period, the median methylmercury concentration in weekly samples was 0.058 ng/L with a maximum of 5.77 ng/L. Normalized methylmercury deposition was 2.09 ng/m 2 per inch of precipitation and methylmercury deposition was 0.7 percent of the total mercury deposition. The annual and mean weekly methylmercury deposition was highest at the Roush Lake station. Among the monitoring stations in the NADP-MDN with methylmercury data, methylmercury deposition at the monitoring stations in Indiana appeared to be higher than at eight stations in Wisconsin and Minnesota for that same time period, although methylmercury concentrations in Indiana were similar to or lower than those in Wisconsin and Minnesota. Geographically, the weekly total mercury concentrations at Indiana Dunes and Clifty Falls were statistically higher than concentrations at Bloomington, although a statistical difference in weekly total mercury deposition was not found among the four monitoring stations. Annual mercury emissions from sources in the vicinity of Indiana Dunes and Clifty Falls in 2001 were more than 10 times those at Bloomington, although other factors may help explain the differences in total mercury concentrations, such as the types of mercury emissions, mercury transport from sources outside Indiana, and meteorological conditions. Mercury concentrations and deposition varied at the four monitoring stations during the 3-year period. Total mercury concentrations in weekly samples ranged from 1.54 to 77 ng/L and weekly mercury deposition ranged from 0.8 to 2,456 ng/m 2 . Data from weekly samples exhibited seasonal patterns. Total mercury concentrations and deposition were highest in spring and summer and lowest in winter. Methylmercury concentrations were highest in winter and methylmercury deposition was highest in spring. Annual precipitation at the four monitoring stations was highest in 2003, exceeding the precipitation normals in spring and summer 2003. Annual mercury deposition in 2003 at the Roush Lake, Clifty Falls, and Indiana Dunes was as much as 41 to 67 percent higher in 2003 than in 2001 or in 2002 at those stations. Total mercury deposition that was more than 10 percent of the mean annual deposition (1,262 ng/m 2 ) was recorded in 11 of 551 weekly samples from the study period. These samples contained approximately 3 inches or more of rain and most were collected in spring and summer 2003. The highest deposition (2,456 ng/m 2 in a sample from Roush Lake) was 15.7 percent of the annual deposition at that station and approximately 10 times the mean weekly deposition for Indiana. High deposition recorded in three weekly samples at Clifty Falls contributed 31 percent of the annual deposition at that station in 2003. Weekly samples with high mercury deposition may help to explain the differences in annual mercury deposition among the four monitoring stations in Indiana.

Indiana↗

USGS2007

Development of relations of stream stage to channel geometry and discharge for stream segments simulated with Hydrologic Simulation Program-Fortran (HSPF), Chesapeake Bay Watershed and adjacent parts of Virginia, Maryland, and Delaware

The U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (USEPA), Chesapeake Bay Program (CBP), Interstate Commission for the Potomac River Basin (ICPRB), Maryland Department of the Environment (MDE), Virginia Department of Conservation and Recreation (VADCR), and University of Maryland (UMD) are collaborating to improve the resolution of the Chesapeake Bay Regional Watershed Model (CBRWM). This watershed model uses the Hydrologic Simulation Program-Fortran (HSPF) to simulate the fate and transport of nutrients and sediment throughout the Chesapeake Bay watershed and extended areas of Virginia, Maryland, and Delaware. Information from the CBRWM is used by the CBP and other watershed managers to assess the effectiveness of water-quality improvement efforts as well as guide future management activities. A critical step in the improvement of the CBRWM framework was the development of an HSPF function table (FTABLE) for each represented stream channel. The FTABLE is used to relate stage (water depth) in a particular stream channel to associated channel surface area, channel volume, and discharge (streamflow). The primary tool used to generate an FTABLE for each stream channel is the XSECT program, a computer program that requires nine input variables used to represent channel morphology. These input variables are reach length, upstream and downstream elevation, channel bottom width, channel bankfull width, channel bankfull stage, slope of the floodplain, and Manning's roughness coefficient for the channel and floodplain. For the purpose of this study, the nine input variables were grouped into three categories: channel geometry, Manning's roughness coefficient, and channel and floodplain slope. Values of channel geometry for every stream segment represented in CBRWM were obtained by first developing regional regression models that relate basin drainage area to observed values of bankfull width, bankfull depth, and bottom width at each of the 290 USGS streamflow-gaging stations included in the areal extent of the model. These regression models were developed on the basis of data from stations in four physiographic provinces (Appalachian Plateaus, Valley and Ridge, Piedmont, and Coastal Plain) and were used to predict channel geometry for all 738 stream segments in the modeled area from associated basin drainage area. Manning's roughness coefficient for the channel and floodplain was represented in the XSECT program in two forms. First, all available field-estimated values of roughness were compiled for gaging stations in each physiographic province. The median of field-estimated values of channel and floodplain roughness for each physiographic province was applied to all respective stream segments. The second representation of Manning's roughness coefficient was to allow roughness to vary with channel depth. Roughness was estimated at each gaging station for each 1-foot depth interval. Median values of roughness were calculated for each 1-foot depth interval for all stations in each physiographic province. Channel and floodplain slope were determined for every stream segment in CBRWM using the USGS National Elevation Dataset. Function tables were generated by the XSECT program using values of channel geometry, channel and floodplain roughness, and channel and floodplain slope. The FTABLEs for each of the 290 USGS streamflow-gaging stations were evaluated by comparing observed discharge to the XSECT-derived discharge. Function table stream discharge derived using depth-varying roughness was found to be more representative of and statistically indistinguishable from values of observed stream discharge. Additionally, results of regression analysis showed that XSECT-derived discharge accounted for approximately 90 percent of the variability associated with observed discharge in each of the four physiographic provinces. The results of this study indicate that the methodology developed to generate FTABLEs for every s

Chesapeake Bay Watershed↗

USGS2017

Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in North Georgia

The U.S. Geological Survey, in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division, developed regional regression equations for estimating selected low-flow frequency and mean annual flow statistics for ungaged streams in north Georgia that are not substantially affected by regulation, diversions, or urbanization. Selected low-flow frequency statistics and basin characteristics for 56 streamgage locations within north Georgia and 75 miles beyond the State’s borders in Alabama, Tennessee, North Carolina, and South Carolina were combined to form the final dataset used in the regional regression analysis. Because some of the streamgages in the study recorded zero flow, the final regression equations were developed using weighted left-censored regression analysis to analyze the flow data in an unbiased manner, with weights based on the number of years of record. The set of equations includes the annual minimum 1- and 7-day average streamflow with the 10-year recurrence interval (referred to as 1Q10 and 7Q10), monthly 7Q10, and mean annual flow. The final regional regression equations are functions of drainage area, mean annual precipitation, and relief ratio for the selected low-flow frequency statistics and drainage area and mean annual precipitation for mean annual flow. The average standard error of estimate was 13.7 percent for the mean annual flow regression equation and ranged from 26.1 to 91.6 percent for the selected low-flow frequency equations. The equations, which are based on data from streams with little to no flow alterations, can be used to provide estimates of the natural flows for selected ungaged stream locations in the area of Georgia north of the Fall Line. The regression equations are not to be used to estimate flows for streams that have been altered by the effects of major dams, surface-water withdrawals, groundwater withdrawals (pumping wells), diversions, or wastewater discharges. The regression equations should be used only for ungaged sites with drainage areas between 1.67 and 576 square miles, mean annual precipitation between 47.6 and 81.6 inches, and relief ratios between 0.146 and 0.607; these are the ranges of the explanatory variables used to develop the equations. An attempt was made to develop regional regression equations for the area of Georgia south of the Fall Line by using the same approach used during this study for north Georgia; however, the equations resulted with high average standard errors of estimates and poorly predicted flows below 0.5 cubic foot per second, which may be attributed to the karst topography common in that area. The final regression equations developed from this study are planned to be incorporated into the U.S. Geological Survey StreamStats program. StreamStats is a Web-based geographic information system that provides users with access to an assortment of analytical tools useful for water-resources planning and management, and for engineering design applications, such as the design of bridges. The StreamStats program provides streamflow statistics and basin characteristics for U.S. Geological Survey streamgage locations and ungaged sites of interest. StreamStats also can compute basin characteristics and provide estimates of streamflow statistics for ungaged sites when users select the location of a site along any stream in Georgia.

Georgia↗

USGS2021

Characterization of factors affecting groundwater levels in and near the former Lake Traverse Indian Reservation, South Dakota, water years 1956–2017

The U.S. Geological Survey (USGS), in cooperation with the Sisseton Wahpeton Oyate, completed a study to characterize water-level fluctuations in observation wells relative to driving factors that affect water levels in and near the historical 1867 boundary of the Lake Traverse Indian Reservation. The study investigated concerns regarding potential effects of groundwater withdrawals and climate conditions on groundwater levels within an area that includes the historical boundary of the reservation and a surrounding area that extends 10 miles in all directions within South Dakota. Characterization of water-level fluctuations in observation wells and relative driving factors was accomplished by statistical trend analysis. Monthly data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) were aggregated to obtain annual and seasonal datasets for total precipitation, minimum air temperature ( T min ), and maximum air temperature ( T max ) for the study area and a surrounding buffer area. Trend tests for gridded data for total precipitation, T min , and T max were completed for annual and seasonal time series for water years 1956–2017, which is about 2 years before the earliest available water-level measurements. A 2-year offset was arbitrarily selected because scrutiny of water-level and precipitation data indicated that responses of groundwater levels for many of the observation wells lagged major changes in precipitation patterns by about 2 years. Statistically significant upward trends were detected for annual precipitation and annual T min for most of the study area and the surrounding buffer area. Statistically significant downward trends in T max were detected for only a few 2.5 arc-minute grid cells; however, the sparsity of the spatial coverage reduces confidence that these are true trends, in contrast to the near completeness of the spatial coverage in upward trends for T min . Spatial distributions of statistically significant trends in seasonal climate data were generally similar to the annual trends, but with substantial differences in the spatial density of the trends. Potential interactions among water levels in observation wells and streamflow were examined through correlation analyses of the annual median water level for each of 76 observation wells versus the annual mean streamflow for each of four area streamgages. Potential interactions among water levels in observation wells and lake levels were examined through correlation analyses involving 25 area lakes. Resulting correlation coefficients were used as part of an approach for selecting a lake to be plotted in conjunction with water-level and precipitation data for each observation well. Groundwater trends for 76 observation wells were analyzed for three separate water-level parameters (minimum, median, and maximum) because wells are measured sporadically, and data are biased towards more frequent measurements during periods of heaviest irrigation demand. Trends in the time series of annual precipitation (from PRISM) starting 2 years earlier than the associated water-level trend also were analyzed for the location of each individual observation well. Sen’s slope and Mann-Kendall p -values were computed for the three water-level parameters and for the annual precipitation time series. Graphs showing results of trend analyses for each observation well also showed changes with time in the sum of licensed groundwater withdrawals within six specified radii (0.5, 1.0, 2.0, 3.0, 4.0, and 5.0 miles) of each well as a qualitative indicator of proximal groundwater demand. Trends in groundwater levels in observation wells in the study area are predominantly upward, with 43 of 76 wells having significant upward trends for at least one of the three water-level parameters and only 8 wells having significant downward trends for at least one water-level parameter. The upward groundwater trends are driven by predominantly upward precipitation trends, with 43 wells (not all the same wells) also having significant upward trends and no wells having significant downward trends. Significant upward precipitation trends were detected for only two of the eight wells with significant downward groundwater trends. Groundwater levels in some observation wells likely are also substantially affected by interactions with surface water, especially with lakes. Water levels in many area lakes increased in response to wet conditions of the early 1990s and have maintained high water levels ever since. It is recognized that in many cases lakes that were selected for plotting with groundwater hydrographs likely are not hydraulically connected with a groundwater system or aquifer associated with an individual well; however, interactions also are plausible for numerous other lakes for which water-level records are not available.

South Dakota↗

USGS1985

Interspecific competition among Hawaiian forest birds

The object of this study was to determine whether interspecific competition modified local geographic distribution, after taking into account the effect of habitat structure. The tendencies for 14 passerine birds to have positive or negative associations were examined, using 7861 sample points in seven native forests on the islands of Hawaii, Maui, and Kauai. All birds were at least partly insectivorous and were fairly common in forested areas, although some fed chiefly on nectar or fruit. Species—pairs were classified as primary or secondary potential competitors based on general dietary similarity. To evaluate the association between species and to account for the effect of individual species habitat preferences, partial correlations were computed for each species—pair in a study area from the simple correlations between the species and 26 habitat variables plus two quadratic terms to represent nonlinearity. The partial correlations represented a short—term ("instantaneous") assessment of the strength of competitive interactions, and did not reflect the accumulation of competitive displacement through time. Of 170 partial correlations in the analysis, only 10 indicated significant negative association. The general pattern was of positive association (76 significantly positive partials), which probably resulted from flocking and from attraction of birds to areas of resource superabundance. Two species showed consistent patterns of negative partial correlations over several adjacent study areas, the Japanese White—eye/Iiwi in montane Hawaii, and the Japanese White—eye/Elepaio in windward Hawaii; both patterns could be reasonably attributed to direct competition. Species—pairs were grouped by the native or exotic status of the component species. Native/exotic pairs had a significantly greater proportion of negative partial correlations (37%) than either native/native pairs (8%) or exotic/exotic pairs (0%). This pattern was consistent across the seven study areas and appeared to reflect the occurrence of interspecific competition along a broad and diffuse ecological "front" between a co—evolved native avifauna and recently introduced exotic species. The role of competition in the pattern was corroborated by the significantly higher proportion of negative partial correlations among species—pairs of primary potential competitors than among those of secondary potential competitors. Our results suggested that °47% of the primary potential competitors among native/exotic species—pairs may experience at least small depressions in local population density due to competition. Although the negative correlations were for the most part small (average negative r = 0.06), one species could eventually replace another as spatial displacement accumulated through time. The Japanese White—eye appeared to have a principal role in native/exotic interactions, with 62% of the partial correlations between it and native primary potential competitor species being negative. Noteworthy implications were that (1) it was important to account for the habitat responses of individual species when studying the role of interspecific competition in modifying small—scale geographic distribution; (2) competition was frequently sporadic in its geographic occurrence and in the species affected, thus supporting Wiens' (1977) theory of competition; and (3) as a consequence, the role of interspecific competition in modifying distribution may be difficult to detect statistically with small data sets.

Hawaii↗

USGS2007

MMA, A Computer Code for Multi-Model Analysis

This report documents the Multi-Model Analysis (MMA) computer code. MMA can be used to evaluate results from alternative models of a single system using the same set of observations for all models. As long as the observations, the observation weighting, and system being represented are the same, the models can differ in nearly any way imaginable. For example, they may include different processes, different simulation software, different temporal definitions (for example, steady-state and transient models could be considered), and so on. The multiple models need to be calibrated by nonlinear regression. Calibration of the individual models needs to be completed before application of MMA. MMA can be used to rank models and calculate posterior model probabilities. These can be used to (1) determine the relative importance of the characteristics embodied in the alternative models, (2) calculate model-averaged parameter estimates and predictions, and (3) quantify the uncertainty of parameter estimates and predictions in a way that integrates the variations represented by the alternative models. There is a lack of consensus on what model analysis methods are best, so MMA provides four default methods. Two are based on Kullback-Leibler information, and use the AIC (Akaike Information Criterion) or AICc (second-order-bias-corrected AIC) model discrimination criteria. The other two default methods are the BIC (Bayesian Information Criterion) and the KIC (Kashyap Information Criterion) model discrimination criteria. Use of the KIC criterion is equivalent to using the maximum-likelihood Bayesian model averaging (MLBMA) method. AIC, AICc, and BIC can be derived from Frequentist or Bayesian arguments. The default methods based on Kullback-Leibler information have a number of theoretical advantages, including that they tend to favor more complicated models as more data become available than do the other methods, which makes sense in many situations. Many applications of MMA will be well served by the default methods provided. To use the default methods, the only required input for MMA is a list of directories where the files for the alternate models are located. Evaluation and development of model-analysis methods are active areas of research. To facilitate exploration and innovation, MMA allows the user broad discretion to define alternatives to the default procedures. For example, MMA allows the user to (a) rank models based on model criteria defined using a wide range of provided and user-defined statistics in addition to the default AIC, AICc, BIC, and KIC criteria, (b) create their own criteria using model measures available from the code, and (c) define how each model criterion is used to calculate related posterior model probabilities. The default model criteria rate models are based on model fit to observations, the number of observations and estimated parameters, and, for KIC, the Fisher information matrix. In addition, MMA allows the analysis to include an evaluation of estimated parameter values. This is accomplished by allowing the user to define unreasonable estimated parameter values or relative estimated parameter values. An example of the latter is that it may be expected that one parameter value will be less than another, as might be the case if two parameters represented the hydraulic conductivity of distinct materials such as fine and coarse sand. Models with parameter values that violate the user-defined conditions are excluded from further consideration by MMA. Ground-water models are used as examples in this report, but MMA can be used to evaluate any set of models for which the required files have been produced. MMA needs to read files from a separate directory for each alternative model considered. The needed files are produced when using the Sensitivity-Analysis or Parameter-Estimation mode of UCODE_2005, or, possibly, the equivalent capability of another program. MMA is constructed using

Techniques and Methods↗

USGS2009

Hydrodynamic simulations of physical aquatic habitat availability for Pallid Sturgeon in the Lower Missouri River, at Yankton, South Dakota, Kenslers Bend, Nebraska, Little Sioux, Iowa, and Miami, Missouri, 2006-07

The objective of this study was to assess the sensitivity of habitat availability in the Lower Missouri River to discharge variation, with emphasis on habitats that might support spawning of the endangered pallid sturgeon. We constructed computational hydrodynamic models for four reaches that were selected because of evidence that sturgeon have spawned in them. The reaches are located at Miami, Missouri (river mile 259.6–263.5), Little Sioux, Iowa (river mile 669.6–673.5), Kenslers Bend, Nebraska (river mile 743.9–748.1), and Yankton, South Dakota reach (river mile 804.8–808.4). The models were calibrated for a range of measured flow conditions, and run for a range of discharges that might be affected by flow modifications from Gavins Point Dam. Model performance was assessed by comparing modeled and measured water velocities. A selection of derived habitat units was assessed for sensitivity to hydraulic input parameters (drag coefficient and lateral eddy viscosity). Overall, model results were minimally sensitive to varying eddy viscosity; varying lateral eddy viscosity by 20 percent resulted in maximum change in habitat units of 5.4 percent. Shallow-water habitat units were most sensitive to variation in drag coefficient with 42 percent change in unit area resulting from 20 percent change in the parameter value; however, no habitat unit value changed more than 10 percent for a 10 percent variation in drag coefficient. Sensitivity analysis provides guidance for selecting habitat metrics that maximize information content while minimizing model uncertainties. To assess model sensitivities arising from topographic variation from sediment transport on an annual time scale, we constructed separate models from two complete independent surveys in 2006 and 2007. The net topographic change was minimal at each site; the ratio of net topographic change to water volume in the reaches at 95 percent exceedance flow was less than 5 percent, indicating that on a reach-average basis, annual topographic change contributed little to habitat area variation. Net erosion occurred at Yankton (the upstream reach) and because erosion was distributed uniformly, there was little affect on many habitat metrics. Topographic change was spatially nonuniform at Little Sioux and Kenslers Bend reaches. Shallow water habitat units and some reach-scale patch statistics (edge density, patch density, and Simpson’s Diversity Index) were affected by these changes. Erosion dominated at the downstream reach but habitat metrics did not vary substantially from 2006 to 2007. Among habitat metrics that were explored, zones of convergent flow were identified as areas that most closely correspond to spawning habitats of other sturgeon species, as identified in the scientific literature, and that are consistent with sparse data on pallid sturgeon spawning locations in the Lower Missouri River. Areas of convergent zone habitat varied little with discharges that would be associated with spring pulsed flows, and relations with discharge changed negligibly between 2006 and 2007. Other habitat measures show how physical habitat varies with discharge and among the four reaches. Wake habitats defined by velocity gradients seem to correspond with migration pathways of adult pallid sturgeon. Habitats with low Froude-number correspond to low energy areas that may accumulate passively transporting particles, organic matter, and larval fish. Among the modeled reaches, Yankton had substantially longer water residence time for equivalent flow exceedances than the other three modeled reaches. Longer residence times result from greater flow resistance in the relatively wide, shallow channel and may be associated with longer residence times of passively transported particulate materials.

Scientific Investigations Report↗

USGS2014

Validation of the SCEC broadband platform V14.3 simulation methods using pseudo spectral acceleration data

This paper summarizes the evaluation of ground motion simulation methods implemented on the SCEC Broadband Platform (BBP), version 14.3 (as of March 2014). A seven-member panel, the authorship of this article, was formed to evaluate those methods for the prediction of pseudo-‐spectral accelerations (PSAs) of ground motion. The panel’s mandate was to evaluate the methods using tools developed through the validation exercise (Goulet et al. ,2014), and to define validation metrics for the assessment of the methods’ performance. This paper summarizes the evaluation process and conclusions from the panel. The five broadband, finite-source simulation methods on the BBP include two deterministic approaches herein referred to as CSM (Anderson, 2014) and UCSB (Crempien and Archuleta, 2014); a band-‐limited stochastic white noise method called EXSIM (Atkinson and Assatourians, 2014); and two hybrid approaches, referred to as G&P (Graves and Pitarka, 2014) and SDSU (Olsen and Takedatsu, 2014), which utilize a deterministic Green’s function approach for periods longer than 1 second and stochastic methods for periods shorter than 1 second. Two acceptance tests were defined to validate the broadband finite‐source ground methods (Goulet et al., 2014). Part A compared observed and simulated PSAs for periods from 0.01 to 10 seconds for 12 moderate to large earthquakes located in California, Japan, and the eastern US. Part B compared the median simulated PSAs to published NGA-‐West1 (Abrahamson and Silva, 2008; Boore and Atkinson, 2008; Campbell and Bozorgnia, 2008; and Chiou and Youngs, 2008) ground motion prediction equations (GMPEs) for specific magnitude and distance cases using a pass-‐fail criteria based on a defined acceptable range around the spectral shape of the GMPEs. For the initial Part A and Part B validation exercises during the summer of 2013, the software for the five methods was locked in at version 13.6 (see Maechling et al., 2014). In the spring of 2014, additional moderate events were considered for the Part A validation, and additional magnitude and distance cases were considered for the Part B validation, for the software locked in at version 14.3. Several of the simulation procedures, specifically UCSB and SDSU, changed significantly between versions 13.6 and 14.3. The CSM code was not submitted in time for the v14.3 evaluation and its detailed performance is not addressed in this paper. As described in Goulet et al. (2014) and Maechling et al. (2014), the BBP generates a variety of products, including three-‐component acceleration time series. A series of post-‐processing codes were developed to provide individual component PSAs and average median horizontal-‐component PSA (referred to as RotD50; Boore, 2010) for oscillator periods ranging from 0.01 to 10 seconds, as well as median PSA values computed using the NGA-‐West 1 GMPEs. The BBP was also configured to provide statistical analysis of simulation results relative to recordings (Part A) and GMPEs (Part B) as described further in sections below. As part of our evaluation, we reviewed documentation provided by each of the developers, which included the technical basis behind the methods and the developer’s self-‐assessments regarding the extrapolation capabilities (in terms of magnitude and distance ranges) of their methods. Two workshops were held in which methods and results were presented, and the panel was given the opportunity to question the developers and to have detailed technical discussions. A SCEC report (Dreger et al., 2013) describes the results of this review for BBP version 13.6. This paper summarizes that work and presents results for the more recent BBP 14.3 validation.

Seismological Research Letters↗

USGS2011

Predicting lake trophic state by relating Secchi-disk transparency measurements to Landsat-satellite imagery for Michigan inland lakes, 2003-05 and 2007-08

Inland lakes are an important economic and environmental resource for Michigan. The U.S. Geological Survey and the Michigan Department of Natural Resources and Environment have been cooperatively monitoring the quality of selected lakes in Michigan through the Lake Water Quality Assessment program. Sampling for this program began in 2001; by 2010, 730 of Michigan’s 11,000 inland lakes are expected to have been sampled once. Volunteers coordinated by the Michigan Department of Natural Resources and Environment began sampling lakes in 1974 and continue to sample (in 2010) approximately 250 inland lakes each year through the Michigan Cooperative Lakes Monitoring Program. Despite these sampling efforts, it still is impossible to physically collect measurements for all Michigan inland lakes; however, Landsat-satellite imagery has been used successfully in Minnesota, Wisconsin, Michigan, and elsewhere to predict the trophic state of unsampled inland lakes greater than 20 acres by producing regression equations relating in-place Secchi-disk measurements to Landsat bands. This study tested three alternatives to methods previously used in Michigan to improve results for predicted statewide Trophic State Index (TSI) computed from Secchi-disk transparency (TSI (SDT)). The alternative methods were used on 14 Landsat-satellite scenes with statewide TSI (SDT) for two time periods (2003– 05 and 2007–08). Specifically, the methods were (1) satellitedata processing techniques to remove areas affected by clouds, cloud shadows, haze, shoreline, and dense vegetation for inland lakes greater than 20 acres in Michigan; (2) comparison of the previous method for producing a single open-water predicted TSI (SDT) value (which was based on an area of interest (AOI) and lake-average approach) to an alternative Gethist method for identifying open-water areas in inland lakes (which follows the initial satellite-data processing and targets the darkest pixels, representing the deepest water, before regression equations are created); and (3) checking to see whether the predicted TSI (SDT) values compared well between two regression equations, one previously used in Michigan and an alternative equation from the hydrologic literature. The combination of improved satellite-data processing techniques and the Gethist method to identify open-water areas in inland lakes during 2003–05 and 2007–08 provided a stronger relation and statistical significance between predicted TSI (SDT) and measured TSI than did the AOI lake-average method; differences in results for the two methods were significant at the 99-percent confidence level. With regard to the comparison of the regression equations, there were no statistically significant differences at the 95-percent confidence level between results from the two equations. The previously used equation, in combination with the Gethist method, yielded coefficient of determination (R 2 ) values of 0.71 and 0.77 for the periods 2003–05 and 2007–08, respectively. The alternative equation, in combination with the Gethist method, yielded R 2 values of 0.74 and 0.75 for 2003–05 and 2007–08, respectively. Predicted TSI (SDT) and measured TSI (SDT) values for lakes used in the regression equations compared well, with R 2 values of 0.95 and 0.96 for predicted TSI (SDT) for 2003–05 and 2007–08, respectively. The R 2 values for statewide predicted TSI (SDT) for all inland lakes with available open-water areas for 2003–05 and 2007–08 were 0.91 and 0.93, respectively. Although the two equations predicted similar trophic-state classes, the alternative equation is planned to be used for future prediction of TSI (SDT) values for Michigan inland lakes, to promote consistency in comparing predicted values between States and for potential use in trend analysis.

Michigan↗

USGS2014

Evaluation of the expected moments algorithm and a multiple low-outlier test for flood frequency analysis at streamgaging stations in Arizona

Flooding is among the costliest natural disasters in terms of loss of life and property in Arizona, which is why the accurate estimation of flood frequency and magnitude is crucial for proper structural design and accurate floodplain mapping. Current guidelines for flood frequency analysis in the United States are described in Bulletin 17B (B17B), yet since B17B’s publication in 1982 (Interagency Advisory Committee on Water Data, 1982), several improvements have been proposed as updates for future guidelines. Two proposed updates are the Expected Moments Algorithm (EMA) to accommodate historical and censored data, and a generalized multiple Grubbs-Beck (MGB) low-outlier test. The current guidelines use a standard Grubbs-Beck (GB) method to identify low outliers, changing the determination of the moment estimators because B17B uses a conditional probability adjustment to handle low outliers while EMA censors the low outliers. B17B and EMA estimates are identical if no historical information or censored or low outliers are present in the peak-flow data. EMA with MGB (EMA-MGB) test was compared to the standard B17B (B17B-GB) method for flood frequency analysis at 328 streamgaging stations in Arizona. The methods were compared using the relative percent difference (RPD) between annual exceedance probabilities (AEPs), goodness-of-fit assessments, random resampling procedures, and Monte Carlo simulations. The AEPs were calculated and compared using both station skew and weighted skew. Streamgaging stations were classified by U.S. Geological Survey (USGS) National Water Information System (NWIS) qualification codes, used to denote historical and censored peak-flow data, to better understand the effect that nonstandard flood information has on the flood frequency analysis for each method. Streamgaging stations were also grouped according to geographic flood regions and analyzed separately to better understand regional differences caused by physiography and climate. The B17B-GB and EMA-MGB RPD-boxplot results showed that the median RPDs across all streamgaging stations for the 10-, 1-, and 0.2-percent AEPs, computed using station skew, were approximately zero. As the AEP flow estimates decreased (that is, from 10 to 0.2 percent AEP) the variability in the RPDs increased, indicating that the AEP flow estimate was greater for EMA-MGB when compared to B17B-GB. There was only one RPD greater than 100 percent for the 10- and 1-percent AEP estimates, whereas 19 RPDs exceeded 100 percent for the 0.2-percent AEP. At streamgaging stations with low-outlier data, historical peak-flow data, or both, RPDs ranged from −84 to 262 percent for the 0.2-percent AEP flow estimate. When streamgaging stations were separated by the presence of historical peak-flow data (that is, no low outliers or censored peaks) or by low outlier peak-flow data (no historical data), the results showed that RPD variability was greatest for the 0.2-AEP flow estimates, indicating that the treatment of historical and (or) low-outlier data was different between methods and that method differences were most influential when estimating the less probable AEP flows (1, 0.5, and 0.2 percent). When regional skew information was weighted with the station skew, B17B-GB estimates were generally higher than the EMA-MGB estimates for any given AEP. This was related to the different regional skews and mean square error used in the weighting procedure for each flood frequency analysis. The B17B-GB weighted skew analysis used a more positive regional skew determined in USGS Water Supply Paper 2433 (Thomas and others, 1997), while the EMA-MGB analysis used a more negative regional skew with a lower mean square error determined from a Bayesian generalized least squares analysis. Regional groupings of streamgaging stations reflected differences in physiographic and climatic characteristics. Potentially influential low flows (PILFs) were more prevalent in arid regions of the State, and generally AEP flows were larger with EMA-MGB than with B17B-GB for gaging stations with PILFs. In most cases EMA-MGB curves would fit the largest floods more accurately than B17B-GB. In areas of the State with more baseflow, such as along the Mogollon Rim and the White Mountains, streamgaging stations generally had fewer PILFs and more positive skews, causing estimated AEP flows to be larger with B17B-GB than with EMA-MGB. The effect of including regional skew was similar for all regions, and the observed pattern was increasingly greater B17B-GB flows (more negative RPDs) with each decreasing AEP quantile. A variation on a goodness-of-fit test statistic was used to describe each method’s ability to fit the largest floods. The mean absolute percent difference between the measured peak flows and the log-Pearson Type 3 (LP3)-estimated flows, for each method, was averaged over the 90th, 75th, and 50th percentiles of peak-flow data at each site. In most percentile subsets, EMA-MGB on average had smaller differences (1 to 3 percent) between the observed and fitted value, suggesting that the EMA-MGB-LP3 distribution is fitting the observed peak-flow data more precisely than B17B-GB. The smallest EMA-MGB percent differences occurred for the greatest 10 percent (90th percentile) of the peak-flow data. When stations were analyzed by USGS NWIS peak flow qualification code groups, the stations with historical peak flows and no low outliers had average percent differences as high as 11 percent greater for B17B-GB, indicating that EMA-MGB utilized the historical information to fit the largest observed floods more accurately. A resampling procedure was used in which 1,000 random subsamples were drawn, each comprising one-half of the observed data. An LP3 distribution was fit to each subsample using B17B-GB and EMA-MGB methods, and the predicted 1-percent AEP flows were compared to those generated from distributions fit to the entire dataset. With station skew, the two methods were similar in the median percent difference, but with weighted skew EMA-MGB estimates were generally better. At two gages where B17B-GB appeared to perform better, a large number of peak flows were deemed to be PILFs by the MGB test, although they did not appear to depart significantly from the trend of the data (step or dogleg appearance). At two gages where EMA-MGB performed better, the MGB identified several PILFs that were affecting the fitted distribution of the B17B-GB method. Monte Carlo simulations were run for the LP3 distribution using different skews and with different assumptions about the expected number of historical peaks. The primary benefit of running Monte Carlo simulations is that the underlying distribution statistics are known, meaning that the true 1-percent AEP is known. The results showed that EMA-MGB performed as well or better in situations where the LP3 distribution had a zero or positive skew and historical information. When the skew for the LP3 distribution was negative, EMA-MGB performed significantly better than B17B-GB and EMA-MGB estimates were less biased by more closely estimating the true 1-percent AEP for 1, 2, and 10 historical flood scenarios.

Arizona↗

USGS1951

Status of the lake trout fishery in Lake Superior

The production of lake trout in the United States waters of Lake Superior was low (only 1,465,000 pounds) in 1879, the first year for which there is a record. Expansion of the fishery must have started soon thereafter, for the take was 3,488,000 pounds in 1885, the next year for which we have statistics, and averaged 3,416,000 pounds in 1885–1892. The years after 1892 can be divided readily into three general periods with average yields as follows: 1893–1907–4,599,000 pounds; 1908–1925–2,168,000 pounds; 1926–1949–3,049,000 pounds. A take of 3 million pounds can be held as “normal” in the modern fishery. During the three periods just listed the percentage contributions of the individual states to the United States total (as computed from the averages for individual periods) ranged from 65.1 to 71.5 for Michigan, 17.5 to 25.6 for Wisconsin, and from 9.3 to 11.0 for Minnesota. In the Canadian (Province of Ontario) waters of Lake Superior the average annual output of lake trout rose from 309,000 pounds in 1871–1882 to 900,000 pounds in 1883–1893, 1,567,000 pounds in 1894–1903, and 2,189,000 pounds in 1904–1918. This last period of relatively high yield was followed by two intervals of successively lower average catches–1,691,000 pounds in 1919–1929 and 1,395,000 pounds in 1930–1949. For the combined United States and Canadian waters of Lake Superior the general trends in the production of lake trout can be described by the following averages: 1879–1,653,000 pounds: 1885–1892–4,325,000 pounds; 1893–1907–6,236,000 pounds; 1908–1949–4,403,000 pounds. The short‐term fluctuations of production during the more recent years give evidence of periodicity in the output of lake trout in Michigan, Ontario, and in the entire lake. Furthermore, these periodic fluctuations tended to be similar in Michigan and Ontario waters. The coefficient of correlation (r) between production in Michigan and Ontario in 1920–1949 (after elimination of trend in the statistics for both areas) had the significant value of 0.456. This correlation suggests that Michigan and Ontario fishermen exploit a common stock or stocks subject to similar fluctuations. Statistics on the production of lake trout in 5 of the 6 statistical districts of the State of Michigan waters of Lake Superior (see Fig. 2 for boundaries of the districts) in 1885 suggest that in most areas the fishery was then in the process of development. Even in 1891–1908 when the general level of production was high, there is evidence that during certain periods the catch in some areas was influenced strongly by factors (such as accessibility to market) other than the natural productivity of the waters. Comparisons of the average annual output of lake trout in the individual districts in 1891–1908 and 1929–1943 (the base period for our modern statistical analysis) reveal an enormous decrease in the Whitefish Bay region (S‐6) from 916,000 pounds in 1891–1908 to only 177,000 pounds in 1929–1943, a substantial drop (from 655,000 pounds to 385,000 pounds) in the Marquette‐Munising area (S‐4), and a small decrease (from 141,000 pounds to 138,000 pounds) in the Black River‐Ontonagon district (S‐2). Among the remaining districts the average yearly take increased from 322,000 pounds in 1891–1908 to 354,000 pounds in 1929–1943 at Isle Royal (S‐1), from 428,000 pounds to 501,000 pounds in the Grand Marais district (S‐5), and from 422,000 pounds to 506,000 pounds in the Keweenaw area (S‐3). These changes in the catch resulted in a westward shifting of production centers. Districts S‐1, S‐2, and S‐3 which together contributed only 30.7 percent of the 1891–1908 catch accounted for 48.4 percent of the take in 1929–1943. The 1929–1949 production in all districts exhibited periodic fluctuations that were characterized by peaks in the middle 1930ˈs and middle 1940ˈs. The indices of abundance or availability as computed from records of the catch of lake trout per unit of fishing effort showed periodic fluctuations in all districts of the State of Michigan waters similar to those of production (the peaks and the intervening minima fell a little earlier in the curves of abundance than in the production curves). For the combined districts the abundance of lake trout, expressed as a percentage of the 1929–1943 mean, stood at 108 in 1929, dropped to 100 in 1931, rose to a 21‐year high of 137 in 1934, decreased to 80 in 1940, increased again to 107 in 1944 and then fell to the 21‐year low of 65 in 1949 (this last decline was interrupted by a small increase in 1947). The 1929–1949 fluctuations in abundance were similar in the 5 mainland districts (S‐2 through S‐6). The coefficients of correlation (r) were significant for all 10 pairings and those for the 4 easterly districts (S‐3 through S‐6) were extremely high (p < 0.001 for all 6 values). Thus we have evidence that the fishermen along the mainland exploit common stocks or stocks in which the factors controlling availability are the same or subject to closely similar fluctuations. The fluctuations in abundance in S‐1 were correlated significantly (p < 0.05) with those in S‐2 but otherwise appeared to be independent of conditions along the mainland. The distinctly cyclic fluctuations that characterized the statistics on production and abundance were much less apparent in the 1929–1949 data on fishing intensity in the State of Michigan districts. In districts S‐3, S‐4, and S‐5 a possible tendency toward a cyclic fluctuation seems to have been obscured by a long‐term upward trend that was becoming stronger toward the end of the 21‐year period. For the combined districts the level of fishing pressure was consistently high after 1943. Over the 6‐year period, 1944–1949, fishing intensity expressed as a percentage of the 1929–1943 mean averaged 142; for the most recent 4 years, 1946–1949 the average intensity index was 151. On the whole, the 1929–1949 production of lake trout in the State of Michigan waters of Lake Superior was unreliable as an indicator of changes in abundance. The coefficient of correlation between the fluctuations of catch and abundance was, to be sure, positive and significant in S‐1 (p < 0.05) and S‐6 (p < 0.01), but in the remaining districts and for the combined districts the values of the coefficient were far below the level of significance. The failure of production to serve better as an indicator of changes in abundance can be attributed to the negative correlation that existed between abundance and fishing intensity (values of r highly significant in every district but S‐6 and for the combined districts). The relationship suggests that fishermen have increased their fishing pressure in order to maintain their production during the recent years of declining abundance. The condition of the lake trout fishery of the State of Michigan waters of Lake Superior must be termed unhealthy and the outlook for the future is not good. Production in 1949 stood at 106 percent of the 1929–1943 mean, but this level of yield was made possible only by fishing intensity that was 162 percent of average; the abundance index in 1949 was only 65. Certainly the stocks of lake trout are in a poor state to withstand the threatened inroads of sea lampreys which have been taken from all parts of Lake Superior and are known to have established spawning runs at least as far west as the Keweenaw Peninsula.

Michigan, Minnesota, Wisconsin↗

USGS2008

Mercury in precipitation in Indiana, January 2004–December 2005

Mercury in precipitation was monitored during 2004–2005 at five locations in Indiana as part of the National Atmospheric Deposition Program–Mercury Deposition Network (NADP–MDN). Monitoring stations were operated at Roush Lake near Huntington, Clifty Falls State Park near Madison, Fort Harrison State Park near Indianapolis, Monroe County Regional Airport near Bloomington, and Indiana Dunes National Lakeshore near Porter. At these monitoring stations, precipitation amounts were measured continuously and weekly samples were collected for analysis of mercury by methods achieving detection limits as low as 0.05 ng/L (nanograms per liter). Wet deposition was computed as the product of mercury concentration and precipitation. The data were analyzed for seasonal patterns, temporal trends, and geographic differences. In the 2 years, 520 weekly samples were collected at the 5 monitoring stations and 448 of these samples had sufficient precipitation to compute mercury wet deposition. The 2-year mean mercury concentration at the five monitoring stations (normalized to the sample volume) was 10.6 ng/L. As a reference for comparison, the total mercury concentration in 41 percent of the samples analyzed was greater than the statewide Indiana water-quality standard for mercury (12 ng/L, protecting aquatic life) and 99 percent of the concentrations exceeded the most conservative Indiana water-quality criterion (1.3 ng/L, protecting wild mammals and birds). The normalized annual mercury concentration at Clifty Falls in 2004 was the fourth highest in the NADP–MDN in eastern North America that year. In 2005, the mercury concentrations at Clifty Falls and Indiana Dunes were the ninth highest in the NADP–MDN in eastern North America. At the five monitoring stations during the study period, the mean weekly total mercury deposition was 0.208 µg/m 2 (micrograms per square meter) and mean annual total mercury deposition was 10.8 µg/m 2 . The annual mercury deposition at Clifty Falls in 2004 and 2005 was in the top 25 percent of the NADP–MDN stations in eastern North America. Mercury concentrations and deposition varied at the five monitoring stations during 2004–2005. Mercury concentrations in wet-deposition samples ranged from 1.2 to 116.6 ng/L and weekly mercury deposition ranged from 0.002 to 1.74 µg/m 2 . Data from weekly samples exhibited seasonal patterns. During April through September, total mercury concentrations and deposition were higher than the median for all samples. Annual precipitation at four of the five monitoring stations was within 10 percent of normal both years, with the exception of Indiana Dunes, where precipitation was 23 percent below normal in 2005. Episodes of high mercury deposition, which were the top 10 percent of weekly mercury deposition at the five monitoring stations, contributed 39 percent of all mercury deposition during 2004–2005. Mercury deposition more than 1.04 µg/m 2 (5 times the mean weekly deposition) was recorded for 12 samples. These episodes of highest mercury deposition were recorded at all five monitoring stations, but the most (7 of 12) were at Clifty Falls and contributed 34.4 percent of the total deposition at that station during 2004–2005. Weekly samples with high mercury deposition may help to explain the differences in annual mercury deposition among the five monitoring stations in Indiana. A statistical evaluation of the monitoring data for 2001–2005 indicated several statistically significant temporal trends. A statewide (5-station) decrease (p = 0.007) in mercury deposition and a statewide decrease (p = 0.059) in mercury concentration were shown. Decreases in mercury deposition (p = 0.061 and p = 0.083) were observed at Roush Lake and Bloomington. A statistically significant trend was not observed for precipitation at the five monitoring stations during this 5-year period. A potential explanation for part of the statewide decrease in mercury concentration and mercury deposition was a 28 percent decrease in the total estimated annual mercury emissions in Indiana between 2002 and 2005. Mercury deposition statistically was correlated most closely to precipitation in the 448 samples, 2004–2005, and this relation was demonstrated by statewide maps of annual precipitation and annual mercury deposition based on precipitation data from 127 National Weather Service Cooperative Observer Program stations. However, one area in southeastern Indiana in the vicinity of Clifty Falls exhibited high mercury deposition that might be related more to mercury concentration than to precipitation. This is because areas with the same range of precipitation as southeastern Indiana were mapped with less mercury deposition. Other data demonstrate a geographic difference for mercury in precipitation in the vicinity of the Clifty Falls monitoring station. The weekly mercury concentrations at Clifty Falls were statistically higher than concentrations at Roush Lake, Fort Harrison, and Bloomington. Clifty Falls data ranked highest among the five monitoring stations for mercury concentration and mercury deposition, 2004–2005, and in the previous 3 years. Episodes of high mercury deposition were recorded most often at Clifty Falls in 2004–2005 and in the previous 3 years. Statistical trends in mercury concentration or mercury deposition were not observed for the Clifty Falls data. A potential explanation for this geographic difference is that annual mercury emissions from sources in the vicinity of Clifty Falls were higher than those at the other stations. Other factors may help explain the differences in total mercury concentrations, such as the types of mercury emissions, mercury transport from stationary sources outside Indiana, and meteorological conditions. Additional data are needed to assign a localized or regional boundary to the area affected by high deposition of mercury near Clifty Falls.

Indiana↗

USGS2009

Pesticides in ground water in selected agricultural land-use areas and hydrogeologic settings in Pennsylvania, 2003-07

This report was prepared by the U.S. Geological Survey (USGS) in cooperation with the Pennsylvania Department of Agriculture (PDA) as part of the Pennsylvania Pesticides and Ground Water Strategy (PPGWS). Monitoring data and extensive quality-assurance data on the occurrence of pesticides in ground water during 2003–07 are presented and evaluated; decreases in the land area used for agriculture and corresponding changes in the use of pesticides also are documented. In the Pennsylvania ground waters assessed since 2003, concentrations of pesticides did not exceed any maximum contaminant or health advisory levels established by the U.S. Environmental Protection Agency; PPGWS actions are invoked by the PDA at fractions of these levels and were needed only in areas designated by the PDA for special ground-water protection. Previous investigations through 1998 of pesticides in Pennsylvania ground water identified land use, as a surrogate for pesticide use, and rock type of the aquifer combined with physiography as key hydrogeologic setting variables for understanding aquifer vulnerability to contamination and the common occurrence of atrazine and metolachlor in ground water. Of 20 major hydrogeologic settings in a framework established in 1999 for pesticide monitoring in Pennsylvania, 9 were identified as priorities for data collection in order to change the monitoring status from "inadequate" to "adequate" for the PPGWS. Agricultural and forested land-use areas are decreasing because of urban and suburban growth. In the nine hydrogeologic settings evaluated using 1992 and 2001 data, decreases of up to 12 percent for agricultural land and 10 percent for forested land corresponded to increases of up to 11 percent for urban land. Changes in agricultural pesticide use were computed from crop data. For example, from 1996 to 2004–05, atrazine use declined by about 15 percent to 1,314,000 lb/yr (pounds per year) and metolachlor use increased by about 20 percent to 895,000 lb/yr; these compounds are the two most-used agricultural pesticides statewide. In 2003–07, a baseline assessment of pesticides was conducted in five of nine hydrogeologic settings with inadequate monitoring data—the Blue Ridge crystalline and Triassic Lowland siliciclastic, Eastern Lake surficial, Devonian-Silurian carbonate, Great Valley siliciclastic, and Northeastern Glaciated surficial settings. Between 20 and 30 wells in each setting were monitored. Of the 126 wells sampled, 96 well-water samples were analyzed for at least 52 pesticide compounds at the USGS National Water Quality Laboratory (NWQL) using a method with a minimum reporting level (MRL) at or above 0.002 µg/L (micrograms per liter). Of the 96 well waters analyzed by NWQL, 43 had measureable concentrations of one or more pesticides. Atrazine and (or) deethylatrazine (CIAT), a degradation product of atrazine, were reported at or above the MRL in 39 of the 43 well waters. Neither atrazine nor CIAT were reported at concentrations exceeding 0.10 µg/L; all measured concentrations in these five settings were below PPGWS action levels. Metolachlor was present in 7 of the 43 well waters with measureable concentrations of 1 or more pesticides; however, concentrations were below the MRL. The other 30 samples (10 of 20 wells in the Blue Ridge crystalline and Triassic Lowland siliciclastic setting and all 20 wells in the Eastern Lake surficial setting) were analyzed for at least 19 pesticide compounds at the Pennsylvania Department of Environmental Protection Laboratory (PADEPL); the PADEPL reported no concentrations of pesticides at or above an MRL of 0.10 µg/L. Statistical tests using the NWQL analytical results showed correlations between pesticide occurrence and two indicators of water-quality degradation—the occurrence of total coliform bacteria and nitrate concentration. A 2 × 2 contingency-table test indicated a relation between presence or absence of atrazine or metolachlor and presence or absence of bacteria only for the 10 wells representing the Blue Ridge crystalline and Triassic Lowland siliciclastic setting. Results of Spearman’s rank test showed strong positive correlations in the Devonian-Silurian carbonate setting between 1) the number of pesticides above the MRLs and nitrate concentration, and 2) concentrations of atrazine and nitrate. Atrazine concentration and nitrate concentration also showed a statistically significant positive correlation in the Great Valley siliciclastic setting. An additional component of baseline monitoring was to evaluate changes in pesticide concentration in water from wells representing hydrogeologic settings most vulnerable to contamination from pesticides. In 2003, 16 wells originally sampled in the 1990s were resampled—4 each in the Appalachian Mountain carbonate, Triassic Lowland siliciclastic, Great Valley carbonate, and Piedmont carbonate settings. Nine of these wells, where pesticide concentrations from 1993 and 2003 were analyzed at the NWQL, were chosen for a paired-sample analysis using concentrations of atrazine and metolachlor. A statistically significant decrease in atrazine concentration was identified using the Wilcoxon signed-rank test (p = 0.004); significant temporal changes in metolachlor concentrations were not observed (p = 0.625). Monitoring in three areas of special ground-water protection, where selected pesticide concentrations in well water were at or above the PPGWS action levels, was done at wells BE 1370 (Berks County, Oley Township), BA 437 (Blair County, North Woodbury Township), and LN 1842 (Lancaster County, Earl Township). Co-occurrence of pesticide-degradation products with parent compounds was documented for the first time in ground-water samples collected from these three wells. Degradation products of atrazine, cyanazine, acetochlor, alachlor, and metolachlor were commonly at larger concentrations than the parent compound in the same water sample. Pesticide occurrence in water from wells neighboring the hot-spot wells was highly variable; however, the same sets of pesticide compounds that were present in wells BA 437, BE 1370, and LN 1842 were present to some degree in water from neighboring wells. To evaluate temporal changes in concentration, nonparametric statistical tests were used to determine overall and seasonal monotonic trends. Concentrations of alachlor, atrazine, metolachlor, and nitrate were examined using the 5-year (2003–07) and the long-term data from wells BA 437 and LN 1842 (1996–2007 and 1995–2007, respectively), and the long-term data for well BE 1370 (1998–2007); results showed either downward trends or no trends. Trends in acetochlor concentrations were tested only at well LN 1842 using the 5-year data; no trends were observed. Homogeneity of trend tests indicated statistically significant downward concentration trends in the long-term data were due to seasonal trends as follows: BA 437—alachlor and atrazine (summer); BE 1370—atrazine and metolachlor (winter) and alachlor (winter and spring); LN 1842—alachlor (summer and fall) and atrazine (spring and fall).

Pennsylvania↗

USGS1998

Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory-Determination of 86 volatile organic compounds in water by gas chromatography/mass spectrometry, including detections less than reporting limits

This report presents precision and accuracy data for volatile organic compounds (VOCs) in the nanogram-per-liter range, including aromatic hydrocarbons, reformulated fuel components, and halogenated hydrocarbons using purge and trap capillary-column gas chromatography/mass spectrometry. One-hundred-four VOCs were initially tested. Of these, 86 are suitable for determination by this method. Selected data are provided for the 18 VOCs that were not included. This method also allows for the reporting of semiquantitative results for tentatively identified VOCs not included in the list of method compounds. Method detection limits, method performance data, preservation study results, and blank results are presented. The authors describe a procedure for reporting low-concentration detections at less than the reporting limit. The nondetection value (NDV) is introduced as a statistically defined reporting limit designed to limit false positives and false negatives to less than 1 percent. Nondetections of method compounds are reported as ?less than NDV.? Positive detections measured at less than NDV are reported as estimated concentrations to alert the data user to decreased confidence in accurate quantitation. Instructions are provided for analysts to report data at less than the reporting limits. This method can support the use of either method reporting limits that censor detections at lower concentrations or the use of NDVs as reporting limits. The data-reporting strategy for providing analytical results at less than the reporting limit is a result of the increased need to identify the presence or absence of environmental contaminants in water samples at increasingly lower concentrations. Long-term method detection limits (LTMDLs) for 86 selected compounds range from 0.013 to 2.452 micrograms per liter (?g/L) and differ from standard method detection limits (MDLs) in that the LTMDLs include the long-term variance of multiple instruments, multiple operators, and multiple calibrations over a longer time. For these reasons, LTMDLs are expected to be slightly higher than standard MDLs. Recoveries for all of the VOCs tested ranged from 36 (tert-butyl formate) to 155 percent (pentachlorobenzene). The majority of the compounds ranged from 85 to 115 percent recovery and had less than 5 percent relative standard deviation for concentrations spiked between 1 to 500 ?g/L in volatile blank-, surface-, and ground-water samples. Recoveries of 60 set spikes at low concentrations ranged from 70 to 114 percent (1,2,3- trimethylbenzene and acetone). Recovery data were collected over 6 months with multiple instruments, operators, and calibrations. In this method, volatile organic compounds are extracted from a water sample by actively purging with helium. The VOCs are collected onto a sorbent trap, thermally desorbed, separated by a Megabore gas chromatographic capillary column, and finally determined by a full-scan quadrupole mass spectrometer. Compound identification is confirmed by the gas chromatographic retention time and by the resultant mass spectrum, typically identified by three unique ions. An unknown compound detected in a sample can be tentatively identified by comparing the unknown mass spectrum to reference spectra in the mass-spectra computer-data system library compiled by the National Institute of Standards and Technology.

Open-File Report↗