The U.S. Geological Survey (USGS), in cooperation with The Nature Conservancy, completed a study to estimate and assess trends in streamflow and annual mean concentrations and flux of nutrients (nitrate plus nitrite, total Kjeldahl nitrogen, and total phosphorus) and total suspended solids at three USGS streamgages (hereafter referred to as “study gages”) on the Upper White River at Muncie (USGS station 03347000), near Nora (USGS station 03351000), and near Centerton (USGS station 03354000), Indiana. Water-quality data used in the analyses were collected by several agencies between calendar years 1991 and 2017, and streamflow (discharge) data were collected by the USGS. For most of the water-quality constituents, there were suitable data to facilitate an analysis of the 26-year period extending from calendar years 1991 to 2017 (water years 1992 to 2017); however, shorter analytical periods were necessary for total Kjeldahl nitrogen for the study gages at Muncie and near Centerton and for total suspended solids for the study gage near Centerton.
Temporal trends in streamflows at the study gages for the period extending from water years 1978 to 2017 were assessed using Exploration and Graphics for RivEr Trends (EGRET) and Mann-Kendall and Pettitt tests. With just one exception, the annual maximum and mean daily streamflows and the annual minimum 7-day mean streamflows at the study gages demonstrated upward trends (increasing streamflows) in the EGRET analyses. The exception was the annual 7-day minimum streamflow at the study gage near Nora, which indicated no trend. Mann-Kendall tests also indicated that the average trend for the annual maximum daily, annual mean daily, and annual 7-day minimum streamflow statistics between water years 1978 and 2017 was upward at each of the study gages; however, only the trends in the annual mean daily streamflows at the study gage at Muncie and the annual maximum daily streamflows at the study gages near Nora and near Centerton were statistically significant at a 0.05 probability level. The Pettitt tests indicated that a statistically significant step trend (abrupt change) in annual mean daily streamflows occurred at each of the study gages around water year 2001.
The seasonal distributions of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen concentrations at the study gages were evaluated to identify patterns and other distinguishing characteristics by examining boxplots of concentrations as a function of month of the year. Seasonal distributions of nitrate plus nitrite concentrations and total suspended solids concentrations differed from each other but were generally similar among the three study gages for a given constituent. Median concentrations of nitrate plus nitrite were highest during the January–June months, whereas median concentrations of total suspended solids were highest during June and July. Seasonal distributions of total phosphorus concentrations were similar at the study gages near Nora and near Centerton, but the seasonal distribution was noticeably different at the study gage at Muncie, which had monthly median concentrations that were substantially lower than at the two downstream study gages (near Nora and near Centerton). The seasonal distribution of total Kjeldahl nitrogen concentrations differed in pattern among the three study gages; however, in general, some of the higher monthly median total Kjeldahl nitrogen concentrations at each study gage were associated with the late spring and summer periods.
The Weighted Regressions on Time, Discharge, and Season (WRTDS) method implemented in EGRET was used to estimate water-year annual mean daily concentrations and flux of nutrients and total suspended solids, as well as estimates of concentrations and flux that were “normalized” to remove the effect of year-to-year variation in streamflow. The approximate coefficients of determination for the WRTDS regression models ranged from a high of 0.82 for total phosphorus for the study gage near Centerton to a low of 0.19 for nitrate plus nitrite for the study gage near Nora.
Loads and yields of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen were estimated for analytical periods consisting of the longest periods of concurrent record at the three study gages. Loads of each of the constituents increased sequentially from the most upstream study gage to the most downstream study gage; however, the same was not true for yields. The highest yields of total suspended solids, total phosphorus, and total Kjeldahl nitrogen occurred at the most upstream study gage (at Muncie); however, the highest yield of nitrate plus nitrite occurred at the most downstream study gage (near Centerton).
WRTDS bootstrap tests were used to assess the magnitude, direction, and likelihood of changes in annual flow-normalized mean daily concentrations and flux of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen at the study gages between water years 1997 and 2017. Changes in flow-normalized concentrations and flux of the constituents between water years 1997 and 2017 were mostly downward (decreasing). The exceptions were likely to highly likely upward (increasing) changes in (1) flow-normalized annual mean daily concentration and annual flux for total suspended solids and total phosphorus at the study gage at Muncie, (2) flow-normalized annual mean daily total phosphorus concentration at the study gage near Centerton, (3) flow-normalized annual flux of total phosphorus at the study gage near Centerton, and (4) flow-normalized annual mean daily nitrate plus nitrite concentration at the study gage near Centerton. Although an upward change in flow-normalized nitrate plus nitrite concentrations was likely at the study gage near Centerton, flow-normalized annual flux of nitrate plus nitrite at that study gage was determined to have a highly likely downward change.
EGRET and Exploration and Graphics for RivEr Trends Confidence Intervals (EGRETci) analyses can be used to improve our understanding of how concentrations and flux change as functions of time and streamflow, as well as provide information on how the relations between streamflow and constituent concentrations have changed within the calendar year between any 2 years included in the analyses. Examples of those uses, illustrating changes between calendar years 1992 and 2017, were given for total suspended solids concentrations at the study gage near Nora and for nitrate plus nitrite concentrations at the study gage near Centerton.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195119","collaboration":"Prepared in cooperation with The Nature Conservancy","usgsCitation":"Koltun, G.F., 2019, Trends in streamflow and concentrations and flux of nutrients and total suspended solids in the Upper White River at Muncie, near Nora, and near Centerton, Indiana: U.S. Geological Survey Scientific Investigations Report 2019–5119, 34 p., https://doi.org/10.3133/sir20195119.","productDescription":"Report: viii, 34 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-109722","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":399602,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109513.htm"},{"id":370134,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VN5RKV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen concentration data for the White River at Muncie, near Nora, and near Centerton, Indiana, 1991–2017"},{"id":370133,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5119/sir20195119.pdf","text":"Report","size":"3.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5119"},{"id":370132,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5119/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Morgan County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.8311,\n 39.2633\n ],\n [\n -84.9667,\n 39.2633\n ],\n [\n -84.9667,\n 40.3608\n ],\n [\n -86.8311,\n 40.3608\n ],\n [\n -86.8311,\n 39.2633\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229–1737
The U.S. Geological Survey (USGS) is developing a multi-resource analysis (MRA) line of products to inform land-use decision makers. Specifically, MRA products will integrate scientific information, include considerations for natural resource interrelations, and quantify the effects of resource management decisions in biophysical, economic, and societal terms. As part of the establishment of the MRA, the USGS, in collaboration with the University of New Mexico, has developed the Net Resources Assessment (NetRA) decision support tool. As a proof of concept analysis, the NetRA was applied to the Piceance basin in Colorado in a hypothetical example to illustrate how resource managers could use the NetRA to consider tradeoffs of natural resources among alternative development plans and land cover patterns within a geographic region.
The NetRA is a policy-relevant approach to assess the availability of multiple natural resources. It is an analytical toolset that may be used to examine the spatiotemporal relations between development of energy and mineral resources and delivery of biological natural resources. The NetRA operates at multiple map scales and contains a set of integrated, compatible submodels with specific data requirements for natural resource stocks, engineering economics, biophysical, and ecological data for ecosystem services stocks, market prices, regulations, and nonmarket values.
The NetRA includes an explicit process to consider the interdependence between development and conservation, which is a crucial consideration in land-management and land-use decisions. The NetRA is used to estimate an expected net resource value (NRV). The NRV is the expected, present value, economic benefit from the extraction of a resource (for example, natural gas) minus the total cost of production, which is the aggregation of the development, production, and social costs. Social costs include private costs plus any external costs. There can be external social benefits associated with natural gas production, such as increased demand for locally produced goods and increased employment in the local area through backward and forward linkages of natural gas production. The NRV is used to compare development outcomes (scenarios) from a range of exploration and development plans for cumulative energy production.
The Piceance basin application of the NetRA uses the NRV to assess the tradeoff between continuous natural gas extraction and the effects to the local populations of Odocoileus hemionus (mule deer) and aquatic species and to consumptive water uses for an area the size and resolution of a USGS energy resource assessment unit. In the proof of concept simulation, the 2.9-square-mile-area of USGS oil and gas assessment unit 50200263 (Piceance basin continuous gas unit of the Mesaverde Total Petroleum System) was gridded into 588 cells. From this area, seven clusters with potential for development and three that cannot be developed were identified; the three clusters that cannot be developed were identified as wilderness study areas, areas of critical environmental concern, and national forests. On the basis of these criteria, there are 118 cells unsuitable for development in the oil and gas assessment unit: 84 are in national forests, 23 are areas of critical environmental concern, and 11 are wilderness study areas. The remaining cells in the oil and gas assessment unit can be developed on both private and public lands.
Two scenarios were considered that are distinguished as plan 1 and plan 2. Plan 1 keeps the amount of land disturbance unchanged and limits the number of development locations to 140 grid cells for the production period, which constrains the amount of the energy resources available for development; the plan requires the usage of the Bureau of Land Management (BLM) unsuitability criteria. Plan 2 also limits the number of development locations to 140 grid cells for the production period but provides a constant volume of energy production by increasing the density of well pads within the cells. The effects of plan 2 to the NRV when there are five wells per pad and five pads per square mile happen mostly in the first 5 years of development, even though the effects on the population of mule deer continue in later years. This outcome is the result of the upfront development and investment costs and the initial effect to the ecosystem services.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195086","collaboration":"Prepared in cooperation with the University of New Mexico","usgsCitation":"Bernknopf, R., Broadbent, C., Adhikari, D., Mamun, S., Tidwell, V., Babis, C., and Pindilli, E., 2019, Multi-resource analysis—A proof of concept study of natural resource tradeoffs in the Piceance Basin, Colorado, using the net resources assessment (NetRA) decision support tool: U.S. Geological Survey Scientific Investigations Report 2019–5086, 40 p., https://doi.org/10.3133/sir20195086.","productDescription":"viii, 40 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088615","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":370122,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/cir1442","text":"Circular 1442","linkHelpText":"- Multi-Resource Analysis—Methodology and synthesis"},{"id":399539,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109512.htm"},{"id":370092,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5086/coverthb.jpg"},{"id":370099,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5086/sir20195086.pdf","text":"Report","size":"8.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5086"}],"country":"United States","state":"Colorado","otherGeospatial":"Piceance Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.22607421875,\n 40.463666324587685\n ],\n [\n -109.09423828125,\n 38.03078569382294\n ],\n [\n -108.12744140625,\n 37.43997405227057\n ],\n [\n -107.07275390625,\n 36.94989178681327\n ],\n [\n -106.2158203125,\n 36.84446074079564\n ],\n [\n -105.8203125,\n 37.142803443716836\n ],\n [\n -105.6884765625,\n 37.49229399862877\n ],\n [\n -105.0732421875,\n 37.125286284966805\n ],\n [\n -104.4580078125,\n 37.666429212090605\n ],\n [\n -104.4140625,\n 37.90953361677018\n ],\n [\n -104.8974609375,\n 38.77121637244273\n ],\n [\n -105.75439453125,\n 39.740986355883564\n ],\n [\n -105.18310546875,\n 40.17887331434696\n ],\n [\n -109.22607421875,\n 40.463666324587685\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Science and Decisions Center
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The New York-Pennsylvania area has a long history of hydrocarbon extraction, and the addition of shale gas extraction methods contributes to landscape disturbance borne by previously developed oil and non-shale gas resources. The main unconventional extraction method used to extract shale gas from the Marcellus Shale located in New York and Pennsylvania is hydraulic fracturing, or “fracking,” although other conventional methods are used extensively. All forms of hydrocarbon extraction disturb the surrounding landscape to some extent, primarily in the form of land clearance and degradation, road construction, and pipeline development, although the effects of these disturbances are not fully understood.
In this study, landscape-change metrics and indicators are used to analyze change in a 10-county region along the New York-Pennsylvania border—the New York counties of Allegany, Steuben, Chemung, Tioga, and Broome, and the Pennsylvania counties of McKean, Potter, Tioga, Bradford, and Susquehanna. This 10-county region was selected due to the differences in policies between the States of New York and Pennsylvania. While fracking occurred extensively in Pennsylvania over the past 10 years or more, the State of New York issued a temporary moratorium against hydraulic fracturing in 2010—citing repercussions that might affect air quality, water quality, and public health—and officially banned hydraulic fracturing in June 2015.
The quantification of landscape disturbance due to hydrocarbon extraction activities is presented in this report as land-use and land-cover (LULC) change between 2004 and 2013 and defined using specific disturbance categories (including well sites, roads, and pipelines) to compare the disturbances and changes, by county, on both sides of the New York-Pennsylvania border. The quantification was accomplished by gathering the signatures of disturbance from high-resolution aerial images, comparing the derived totals of disturbance, and then computing landscape metrics in a geographic information system (GIS) environment.
The collected data represent a summation of landscape disturbance from oil and gas development, as some of the data represented were established decades earlier. The Analytical Tools Interface for Landscape Assessments (ATtILA) software was used to calculate land-cover area and landscape metrics for each shale gas, non-shale gas, oil, and other infrastructure types associated with hydrocarbons across each county and both five-county regions in the study area. The three primary metrics used to describe changes in forest structure were (1)forest area, (2) interior forest area, and (3) forest edge area. The changes in metrics were subsequently evaluated using the Pearson correlation coefficient.
Overall, the disturbed-area footprint in the Pennsylvania region is considerably larger than the disturbed-area footprint in the New York region (13,687.9 hectares [ha] in Pennsylvania; 3,840.5 ha in New York). Disturbance per site is similar, with 1.2 disturbed ha per site in New York and 1.6 disturbed ha per site in Pennsylvania.
In the New York-Pennsylvania 10-county region, hydrocarbon-development and extraction disturbance strongly correlate with a reduction in the percentage of forest for the entire region. This observation also appears to be true in the New York five-county region for forest area. This form of disturbance in the New York five-county region shows significantly correlated changes in forest metrics (–0.4 percent total forest area), particularly in the percentage of interior forest (–1.2 percent total area) and forest edge (+0.7 percent total area). On the other hand, gas and hydrocarbon-development and extraction disturbance (1.0 percent total area) in the Pennsylvania five-county region strongly correlates with a total decline in forest area and agricultural land area (–0.8 percent combined total area) but not with either land-cover class separately.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195096","usgsCitation":"Roig-Silva, C.M., Milheim, L.E., Slonecker, E.T., Kalaly, S., and Chestnut, J., 2019, A comparison of hydrocarbon-related landscape disturbance patterns along the New York-Pennsylvania border, 2004–2013: U.S. Geological Survey Scientific Investigations Report 2019–5096, 23 p., https://doi.org/10.3133/sir20195096.","productDescription":"Report: v, 23 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091180","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":399543,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109511.htm"},{"id":380624,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2019/5096/images/"},{"id":374941,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2019/5096/sir20195096.XML"},{"id":370125,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5096/sir20195096.pdf","text":"Report","size":"3.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5096"},{"id":370027,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5096/coverthb.jpg"},{"id":370021,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TT4Q67","text":"USGS data release","linkHelpText":"Natural gas and oil drilling disturbance in the Marcellus Shale region of the New York-Pennsylvania border"}],"country":"United States","state":"New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.9531,\n 41.4758\n ],\n [\n -75.6325,\n 41.4758\n ],\n [\n -75.6325,\n 42.5783\n ],\n [\n -78.9531,\n 42.5783\n ],\n [\n -78.9531,\n 41.4758\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
Nashville, TN Office
U.S. Geological Survey
640 Grassmere Park Drive
Nashville, TN 37211
In 2005, the U.S. Geological Survey began a cooperative study with the New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment of the Hillview Reservoir in southern Westchester County, New York. The earthen embankment comprises low-permeability glacial clays that were excavated from the site and rest on a veneer of low-permeability glacial deposits that overlie crystalline bedrock. At least two groundwater-flow zones—one shallow and the other deep—overlie the bedrock at the reservoir. As part of the study, slug-test data from 38 screened wells were analyzed to determine the hydraulic conductivity of the sediments in the groundwater-flow zones. Slug-test data were collected from 12 wells at the Hillview Reservoir during August 2007 and from 25 wells at the reservoir and 1 monitoring well south of the reservoir in northern Bronx County in June 2012.
Hydraulic conductivity values at the reservoir ranged from 0.0012 to 2 feet per day. On the southern embankment, hydraulic conductivity ranged from 0.0026 to 1 foot per day for wells screened in the shallow saturated zone; 0.0012 to 2 feet per day for wells screened in the deep saturated zone; and 0.021 to 0.27 foot per day for wells screened in the toe of the southern embankment, where the deep and shallow saturated zones coalesce. A hydraulic conductivity of 0.016 foot per day was determined for a well partially screened in the crystalline-bedrock aquifer, which potentially indicates an interconnection of transmissive fractures near the bedrock surface. The results of four slug-out tests are also included in this report to quality assure the hydraulic conductivity estimates from the slug-in test analysis. The results of the four slug-out tests were within 8 percent of slug-in test results, with an average of less than 2 percent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191102","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Noll, M.L., Chu, A., and Capurso, W.D., 2019, Slug-test analysis of selected wells at an earthen dam site in southern Westchester County, New York: U.S. Geological Survey Open-File Report 2019–1102, 14 p., https://doi.org/10.3133/ofr20191102.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095039","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":399415,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109510.htm"},{"id":369616,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1102/coverthb.jpg"},{"id":369866,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1102/ofr20191102.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1102"},{"id":369619,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J404KW","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Data and analytical type-curve match for selected hydraulic tests in New York State"}],"country":"United States","state":"New York","county":"Westchester County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.87674808502196,\n 40.90551783054535\n ],\n [\n -73.86099815368652,\n 40.90551783054535\n ],\n [\n -73.86099815368652,\n 40.91821491609591\n ],\n [\n -73.87674808502196,\n 40.91821491609591\n ],\n [\n -73.87674808502196,\n 40.90551783054535\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
2045 Route 112, Building 4
Coram, NY 11727
Five integrator sites on streams draining mixed land-use watersheds were sampled monthly from July 2016 to June 2017. Two sites were sampled in the San Joaquin River watershed and one site was sampled in each of the Mokelumne River, Sacramento River, and Ulatis Creek watersheds.
A total of 53 out of 154 pesticides (18 herbicides, 14 insecticides, 13 fungicides, 7 breakdown products, and 1 synergist) were detected in surface-water samples and 95 percent of samples contained mixtures of 2 or more pesticides. The most frequently detected pesticides were the herbicides hexazinone, metolachlor, and diuron (present in 83 percent, 72 percent, and 67 percent of water samples, respectively), the insecticide methoxyfenozide (present in 83 percent of samples), and the fungicides boscalid and azoxystrobin (present in 67 percent and 58 percent of samples, respectively). Pesticide concentrations detected in water samples ranged from below method detection limits to 1,300 nanograms per liter (ng/L) for the insecticide chlorantraniliprole. A total of 4 pesticides (2 herbicides and 2 insecticides) were detected in suspended-sediment samples and 13 percent of suspended-sediment samples contained at least 1 pesticide. Pesticide concentrations detected in suspended-sediment samples ranged from 4.1 to 750 ng/L, both for the herbicide pendimethalin.
Six samples contained the insecticide imidacloprid at concentrations above the U.S. Environmental Protection Agency (EPA) Aquatic Life Benchmark (10 ng/L) for chronic toxicity to aquatic invertebrates. Three samples contained bifenthrin at concentrations above the EPA Aquatic Life Benchmark (1.3 ng/L) for chronic toxicity to invertebrates. One sample contained cyhalothrin at a concentration above the U.S. Aquatic Life Benchmark (3.5 ng/L) for acute toxicity to invertebrates.
Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
In 2016, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers and the Federal Emergency Management Agency, began a study in Arkansas to investigate possible increasing trends in annual peak streamflow data and the possible resulting increase in the annual exceedance probability flood (AEPF) predictions. Temporal trends of peak streamflow were investigated at 15 selected streamgages on unregulated streams in Arkansas having 30 or more years of peak streamflow data through the 2016 water year. For the period of record at each streamgage, the Mann-Kendall trend test indicated that 14 of the 15 streamgages had no statistically significant peak streamflow trends and 1 streamgage had a statistically significant decreasing peak streamflow trend. Visual examination of the locally estimated scatterplot smoothing technique trend lines of the peak streamflow data indicated a possible increasing peak streamflow trend at 8 of the 15 streamgages since the 1990s.
A sequential series analysis of the 1-percent AEPF at each of the 15 selected streamgages was completed by selecting an initial subset of the oldest peak streamflow data from each site to estimate the initial 1-percent AEPF. This initial peak streamflow data subset was subsequently appended with 10-year increments of additional peak streamflow data until the full period of peak streamflow data was analyzed. The maximum increase in the 1-percent AEPF was 113 percent, and the maximum decrease was 31.9 percent.
Percentage differences between the AEPFs derived from regional regression equations presented in the 1995 and 2016 Arkansas flood-frequency reports were compared. The average percentage differences for the 74 selected locations indicate that the 4-, 2-, 1-, and 0.2-percent AEPFs computed using the 2016 regional regression equations were higher by 3.52, 5.10, 8.59, and 13.31 percent, respectively (25-, 50-, 100-, and 500-year recurrence interval floods), than the same percentage AEPFs computed using the 1995 regional regression equations. The average percentage differences between the 1995 and 2016 AEPFs for the 10-percent AEPF (10-year recurrence interval flood) resulted in 2016 AEPF predictions being 0.41 percent higher. For the 50- and 20-percent AEPFs (2- and 5-year recurrence interval floods), the 2016 AEPFs were less than the 1995 AEPFs by 2.53 and 0.31 percent, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195131","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and Federal Emergency Management Agency","usgsCitation":"Ensminger, P.A., and Breaker, B.K., 2019, Flood-frequency comparison from 1995 to 2016 and trends in peak streamflow in Arkansas, water years 1930–2016: U.S. Geological Survey Scientific Investigations Report 2019–5131, 20 p., https://doi.org/10.3133/sir20195131.","productDescription":"vi, 20 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-087618","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":399610,"rank":3,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
640 Grassmere Park, Ste 100
Nashville, TN 37211
The Yorktown-Eastover aquifer system of the Virginia Eastern Shore consists of upper, middle, and lower confined aquifers overlain by correspondingly named confining units and underlain by the Saint Marys confining unit. Miocene- to Pliocene-age marine-shelf sediments observed in 205 boreholes include medium- to coarse-grained sand and shells that compose the aquifers and fine-grained sand, silt, and clay that compose the confining units. The upper confining unit also includes fine-grained and organic-rich back-barrier and estuarine sediments of Pleistocene age. An overlying surficial aquifer is composed mostly of Pleistocene-age nearshore sand and gravel with smaller amounts of cobbles and boulders.
In addition, Pleistocene-age sediments that fill three buried paleochannels are for the first time explicitly delineated here as distinct hydrogeologic units. Two aquifers are composed of medium- to coarse-grained fluvial sand and gravel, and an intervening confining unit is composed of fine-grained estuarine sand, silt, clay, and organic material. Aquifer and confining-unit sediments are also mixed with reworked marine-shelf sediments eroded from the sides of the paleochannels.
Hydrogeologic units of the Yorktown-Eastover aquifer system generally dip eastward, are as much as several tens of feet thick, and have an undulating configuration possibly resulting from the underlying Chesapeake Bay impact crater. Aquifers and confining units are incised by the three paleochannels along an upward-widening and eastward-lengthening series of structural “windows.” Hydrogeologic units within mainstems and branching tributaries of the paleochannels dip southeastward parallel to slopes of the paleochannels, are as much as several tens of feet thick, and laterally abut the Yorktown-Eastover aquifer system along paleochannel sidewalls. The Yorktown-Eastover aquifer system is thereby hydraulically breached by the paleochannels to alternately create barriers to or conduits for groundwater flow.
Results of previously documented aquifer tests at 58 wells indicate that transmissivity is generally greatest in young, shallow, and coarse-grained nearshore and fluvial sediments of the surficial aquifer and paleochannels. Transmissivity progressively decreases with depth in older, deeper, and finer grained marine-shelf sediments of the Yorktown-Eastover aquifer system, probably because they have undergone compaction as a result of greater overburden pressure over longer periods of time.
Compiled chloride concentrations in samples from 330 wells generally increase downward, with most of the samples collected at altitudes above −300 feet and with most concentrations less than 250 milligrams per liter. The saltwater-transition zone has a broad trough-like shape aligned with the peninsula, being relatively shallow along the coastline and deeper along the central “spine.” Because movement of the saltwater is slow, the configuration largely reflects groundwater flow prior to widespread groundwater withdrawals. Fresh groundwater has leaked downward along deep parts of the saltwater-transition zone and leaked upward along shallower parts to discharge at the coast.
The saltwater-transition zone also exhibits an anomalous ridge across the center of the peninsula. Groundwater levels indicate that the saltwater ridge formed primarily by the Exmore paleochannel acting as a large lateral collector drain. Groundwater levels were lowered, and the position of saltwater-transition zone was elevated, by a flow conduit that intercepted groundwater that otherwise would have flowed toward and discharged along the coastline.
Nearly all freshwater on the Virginia Eastern Shore is supplied by groundwater withdrawals, which have lowered water levels, altered hydraulic gradients, and created a concern for saltwater intrusion. Previous characterizations of groundwater conditions that are relied on to manage groundwater development have been limited by a lack of hydrogeologic information, particularly data on buried paleochannels that are critical to safeguarding the groundwater supply. Using recently available expanded information, the U.S. Geological Survey undertook a study in cooperation with the Virginia Department of Environmental Quality during 2016–19 to develop an improved description of the groundwater system called a “hydrogeologic framework.”
The hydrogeologic framework can aid water-supply planning and development by providing information on broad trends in aquifer configurations, hydraulic properties, and proximity to saltwater to avoid chloride contamination. Digital models to evaluate effects of groundwater withdrawals can also be improved with expanded data and capabilities to evaluate paleochannel hydraulic connections and the potential for saltwater movement.
The hydrogeologic framework is limited by the nonuniform distribution of boreholes and the subjective delineation of aquifers and confining units, including those within paleochannels that are regarded as preliminary. The configuration of the saltwater-transition zone is also regarded as preliminary because of the nonuniform distribution of groundwater samples. Low well-sampling frequency precludes characterizing movement of the saltwater-transition zone. A monitoring strategy of sampling and possibly electromagnetic-induction well logging could be used to detect saltwater movement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195093","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"McFarland, E.R., and Beach, T.A., 2019, Hydrogeologic framework of the Virginia Eastern Shore: U.S. Geological Survey Scientific Investigations Report 2019–5093, 26 p., 13 pl., https://doi.org/10.3133/sir20195093.","productDescription":"Report: viii, 26 p.; 13 Plates: 11.00 x 17.00 inches or smaller; Data Release","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-108409","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":369803,"rank":16,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093.pdf","text":"Report","size":"3.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5093"},{"id":369731,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate13.pdf","text":"Plate 13","size":"352 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations and Numbers of Sampled Wells and Altitude of the 250-Milligram-Per-Liter Chloride-Concentration Surface on the Virginia Eastern Shore"},{"id":369730,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate12.pdf","text":"Plate 12","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Confining Unit on the Virginia Eastern Shore"},{"id":369725,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate07.pdf","text":"Plate 7","size":"346 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Middle Confining Unit on the Virginia Eastern 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Shore"},{"id":369728,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate10.pdf","text":"Plate 10","size":"319 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Confining Unit on the Virginia Eastern Shore"},{"id":369727,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate09.pdf","text":"Plate 9","size":"316 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Lower Aquifer on the Virginia Eastern Shore"},{"id":369726,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate08.pdf","text":"Plate 8","size":"350 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Aquifer on the Virginia Eastern 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U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Faced with limitations in data availability, funding, and time constraints, ecologists are often tasked with making predictions beyond the range of their data. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We propose using the trace or determinant of the predictive variance matrix to obtain a scalar value measure that, when paired with a selected cutoff value, allows for delineation between prediction and extrapolation. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we outline novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. The use of our Multivariate Predictive Variance (MVPV) measures and multiple cutoff values when exploring the validity of predictions made from multivariate statistical models can help guide ecological inferences.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0225715","usgsCitation":"Bartley, M., Hanks, E.M., Schliep, E.M., Soranno, P.A., and Wagner, T., 2019, Identifying and characterizing extrapolation in multivariate response data: PLoS ONE, v. 14, no. 12, e0225715, 20 p., https://doi.org/10.1371/journal.pone.0225715.","productDescription":"e0225715, 20 p.","ipdsId":"IP-107783","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":459016,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0225715","text":"Publisher Index Page"},{"id":393911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Bartley, Meridith L.","contributorId":270913,"corporation":false,"usgs":false,"family":"Bartley","given":"Meridith L.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":830122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, Ephraim M.","contributorId":178093,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":830123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schliep, Erin M.","contributorId":171525,"corporation":false,"usgs":false,"family":"Schliep","given":"Erin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":830124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":830125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":830121,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217534,"text":"70217534 - 2019 - BbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey","interactions":[],"lastModifiedDate":"2021-01-21T21:21:24.573822","indexId":"70217534","displayToPublicDate":"2019-12-04T15:18:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5929,"text":"Journal of Open Source Software","active":true,"publicationSubtype":{"id":10}},"title":"BbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey","docAbstract":"This R package contains functions for downloading and munging data from the U.S. Geological Surveys North American Breeding Bird Survey (BBS) via file transfer protocol (FTP)\n(Pardieck, Ziolkowski Jr, Lutmerding, & Hudson, 2018; J. R. Sauer et al., 2017). This package was created to allow the user to bulk-download the BBS point count and related (e.g.,\nroute-level conditions) via FTP, and to quickly subset the data by taxonomic classifications\nand/or geographical locations. This package also maintains data containing the trend and\nannual indices from the most recent (1996-2017) hierarchical population analyses (J. Sauer\net al., 2017).","language":"English","publisher":"JOSS","doi":"10.21105/joss.01768","usgsCitation":"Burnett, J.L., Wszola, L.S., and Palomo-Munoz, G., 2019, BbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey: Journal of Open Source Software, v. 4, no. 44, https://doi.org/10.21105/joss.01768.","productDescription":"1768, 2 p.","startPage":"1768","ipdsId":"IP-111972","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":459020,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21105/joss.01768","text":"Publisher Index Page"},{"id":437264,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93W0EAW","text":"USGS data release","linkHelpText":"bbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey."},{"id":382443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"44","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burnett, Jessica Leigh 0000-0002-0896-5099","orcid":"https://orcid.org/0000-0002-0896-5099","contributorId":248195,"corporation":false,"usgs":true,"family":"Burnett","given":"Jessica","email":"","middleInitial":"Leigh","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":808599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wszola, Lyndsie S.","contributorId":205135,"corporation":false,"usgs":false,"family":"Wszola","given":"Lyndsie","email":"","middleInitial":"S.","affiliations":[{"id":37031,"text":"Nebraska Cooperative Fish & Wildlife Research Unit, University of Nebraska-Lincoln, Lincoln, Nebraska","active":true,"usgs":false}],"preferred":false,"id":808600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palomo-Munoz, Gabriela","contributorId":248196,"corporation":false,"usgs":false,"family":"Palomo-Munoz","given":"Gabriela","email":"","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":808601,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215094,"text":"70215094 - 2019 - Improving predictions of fine particle immobilization in streams","interactions":[],"lastModifiedDate":"2020-10-07T20:12:43.002013","indexId":"70215094","displayToPublicDate":"2019-12-04T15:06:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Improving predictions of fine particle immobilization in streams","docAbstract":"Fine particles are critical to stream ecosystem functioning, influencing in-stream processes from pathogen transmission to carbon cycling, all of which depend on particle immobilization. However, our ability to predict particle immobilization is limited by: (1) availability of combined solute and particle tracer data and (2) identifying parameters that appropriately represent fine particle immobilization, due to the myriad of objective functions and model formulations. We found that improved predictions of the full distribution of possible fine particle residence times requires using an objective function that assesses both the peak and tailing together with solute tracers to constrain in-stream transport processes. The representation of immobilization processes was significantly improved when solute tracer data were combined with a particle model, starkly contrasting the common assumption that fine particles transport as washload. We develop a clear strategy for improving fine particle transport predictions, reshaping the potential role of fine particles in water quality management.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL085849","usgsCitation":"Drummond, J.D., Schmadel, N., Kelleher, C., Packman, A.I., and Ward, A.S., 2019, Improving predictions of fine particle immobilization in streams: Geophysical Research Letters, v. 46, no. 23, p. 13,853-13,861, https://doi.org/10.1029/2019GL085849.","productDescription":"9 p.","startPage":"13,853","endPage":"13,861","ipdsId":"IP-114105","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":459021,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl085849","text":"Publisher Index Page"},{"id":379197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"23","noUsgsAuthors":false,"publicationDate":"2019-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Drummond, Jennifer D.","contributorId":191390,"corporation":false,"usgs":false,"family":"Drummond","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":800820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmadel, Noah M. 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":800821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelleher, Christa","contributorId":242798,"corporation":false,"usgs":false,"family":"Kelleher","given":"Christa","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":800822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Packman, Aaron I.","contributorId":124517,"corporation":false,"usgs":false,"family":"Packman","given":"Aaron","email":"","middleInitial":"I.","affiliations":[{"id":5041,"text":"Department of Civil and Environmental Engineering, Northwestern University, Evanston, Illinois, USA","active":true,"usgs":false}],"preferred":false,"id":800823,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ward, Adam S","contributorId":191363,"corporation":false,"usgs":false,"family":"Ward","given":"Adam","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":800824,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208455,"text":"70208455 - 2019 - Measurement of cyanobacteria bloom magnitude using satellite remote sensing","interactions":[],"lastModifiedDate":"2020-02-11T07:47:17","indexId":"70208455","displayToPublicDate":"2019-12-04T07:43:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Measurement of cyanobacteria bloom magnitude using satellite remote sensing","docAbstract":"Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatio-temporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Ocean Land Color Imager (OLCI) on Sentinel-3 data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-54453-y","usgsCitation":"Mishra, S., Stumpf, R.P., Schaeffer, B., Werdell, P.J., Loftin, K., and Meredith, A., 2019, Measurement of cyanobacteria bloom magnitude using satellite remote sensing: Scientific Reports, no. 1, 18310, 17 p., https://doi.org/10.1038/s41598-019-54453-y.","productDescription":"18310, 17 p.","ipdsId":"IP-111006","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":459026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-54453-y","text":"Publisher Index Page"},{"id":372207,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, 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Understanding what environmental and biological factors influence Sea Lamprey capture in tributaries is crucial to developing efficient trapping methods and reliable abundance estimates. We analyzed data from trapping sites located on eight Great Lakes tributaries using Cormack-Jolly-Seber models and examined how water temperature, discharge, sex, and length influenced Sea Lamprey apparent survival and capture probability. Sea Lamprey apparent survival was negatively associated with water temperature in all tributaries. Additionally, the odds of small Sea Lamprey (≤45 cm) remaining available to capture were 39% less (95% CI: 63% decrease – 1% increase) than large (>45 cm) lamprey odds. These observed relationships were used to investigate if bias in abundance estimates using the pooled-Petersen estimator and Jolly-Seber models was expected to be similar across trapping locations or influenced by variable environmental conditions and biological traits. Pooled-Petersen abundance estimates had a positive bias when datasets were generated from simulated populations with empirical relationships between environmental characteristics and catchability. The degree of bias depended upon changes in stream warming patterns and was not consistent among trapping locations. Jolly-Seber models using data from either weekly-batch-marked or uniquely-marked individuals generated abundance estimate with low bias when data quality was high, but performed poorly in scenarios with few recaptured Sea Lamprey. 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The timing of the designated survey windows was designed to count resident woodcock while minimizing counting of migrating woodcock. Since the implementation of the SGS in 1968, concerns over survey protocols that may bias data have been raised and investigated; however, the extent of survey coverage and the timing of the survey window zones have not been critically investigated. We used 3 years of data collected from male and female woodcock marked with satellite tags to assess the extent of survey coverage and the timing of the SGS survey windows relative to presence of woodcock. SGS coverage encompassed the majority of woodcock breeding-period sites (locations where marked woodcock returned to in spring) within the U.S. (n = 17, 92%) and approximately half of the breeding-period sites in Canada (n = 6, 43%). Thirteen of the 37 monitored woodcock with known breeding-period site arrival dates (35%) were migrating through a survey zone during an active survey window, all in the northernmost 4 of 5 SGS zones. Thirteen woodcock arrived at breeding-period sites after the start of surveys, and all but one of these was located in the northernmost 2 zones. The combination of migration through a SGS zone during the survey window and arrival at breeding-period sites after the beginning of the survey window in northern zones may result in the SGS weighing too heavily the contribution of routes in the southern portion of the primary breeding range, while weighing too lightly the routes in the northern portion of the primary breeding range. We suggest that additional information is necessary to evaluate whether current survey windows are sufficient, or whether they need to be changed.
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The effective area surveyed (EAS) at a listening point is not known, and may vary as a function of land-cover type or other factors. To define the relationship describing distance between vocalizing woodcock and an observer and how cover types influence that relationship, we broadcast a recording of woodcock vocalizations in 2 land-cover types (forest and field) at varying distance. We evaluated the proportion of call broadcasts detected as a function of distance and fit regression curves to detection data to estimate a distance (r*) where the area above the curve at distances <r* was equal to the area under the curve at distances >r*, which allowed determination of the radius of an area where detection probability was effectively 1.0. This EAS had a radius (r*) of 198 m for forest, 384 m for field, and 309 m for both of these land-cover types combined, and an estimated size of 12.3 ha for forest, 46.3 ha for field, and 30.0 ha for both land-cover types combined. We used this information to estimate density of displaying male woodcock based on counts from the SGS in east-central Minnesota that incorporated variation in EAS, probability of detection, survey date, and survey route. Our density estimates (5.0 birds/100 ha in 2009 and 7.1 birds/100 ha in 2010) represent the highest density of singing male American woodcock yet reported, and indicated a substantive increase in density between years.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the eleventh American woodcock symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eleventh American Woodcock Symposium","conferenceDate":"Oct 24-27, 2017","conferenceLocation":"Roscommon, MI","language":"English","publisher":"University of Minnesota Libraries Publishing","doi":"10.24926/AWS.0125","usgsCitation":"Bergh, S.M., and Andersen, D.E., 2019, Estimating density and effective area surveyed for American woodcock, in Proceedings of the eleventh American woodcock symposium, v. 11, Roscommon, MI, Oct 24-27, 2017, p. 193-199, https://doi.org/10.24926/AWS.0125.","productDescription":"7 p.","startPage":"193","endPage":"199","ipdsId":"IP-057491","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":459030,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Stefanie M.","contributorId":272056,"corporation":false,"usgs":false,"family":"Bergh","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":831784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831678,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227628,"text":"70227628 - 2019 - Detection probability and occupancy of American woodcock during Singing-ground surveys","interactions":[],"lastModifiedDate":"2022-01-21T15:17:00.679985","indexId":"70227628","displayToPublicDate":"2019-12-03T09:08:52","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Detection probability and occupancy of American woodcock during Singing-ground surveys","docAbstract":"The Singing-ground Survey (SGS) was designed to exploit the conspicuous breeding-season display of male American woodcock (Scolopax minor; hereafter, woodcock) to monitor these otherwise inconspicuous birds. The SGS was standardized in 1968 and has since been conducted annually to derive an index of abundance and population trend. Counts of singing male woodcock on the SGS have generally declined through time, but without knowledge of the relationship among counts, woodcock abundance, and the factors affecting detection, considerable uncertainty remains in interpretation of SGS data. Using modified SGS protocols, we surveyed SGS routes in Pine County, Minnesota, in 2009 and 2010 and developed models to assess factors associated with detection probability and estimated occupancy. The intercept-only model (i.e., constant detection and occupancy probabilities across sites and no covariates) included overall detection probability of 0.59 (SE = 0.018) in 2009 and 0.66 (SE = 0.017) in 2010 with an occupancy estimate of 0.74 (SE = 0.049) in 2009 and 0.81 (SE = 0.044) in 2010. The best-supported model of detection probability for both years combined included detection as a function of woodcock abundance, observer, date, disturbance level (i.e., ambient noise that interfered with detecting woodcock), and wind speed. High wind speeds were negatively related to detection, different observers had different detection probabilities, date was quadratically related to detection (indicating a mid-period peak in detection), and high woodcock abundance and low disturbance levels were positively related to detection. We provide suggestions for incorporating these resulting into SGS protocol and analyses.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the eleventh American woodcock symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eleventh American Woodcock Symposium","conferenceDate":"Oct 24-27, 2017","conferenceLocation":"Roscommon, MI","language":"English","publisher":"University of Minnesota Libraries Publishing","doi":"10.24926/AWS.0126","usgsCitation":"Bergh, S.M., and Andersen, D.E., 2019, Detection probability and occupancy of American woodcock during Singing-ground surveys, in Proceedings of the eleventh American woodcock symposium, Roscommon, MI, Oct 24-27, 2017, p. 200-208, https://doi.org/10.24926/AWS.0126.","productDescription":"9 p.","startPage":"200","endPage":"208","ipdsId":"IP-043992","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":459032,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Stefanie M.","contributorId":272056,"corporation":false,"usgs":false,"family":"Bergh","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":831415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831416,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206996,"text":"fs20193073 - 2019 - Reach-scale monitoring and modeling of rivers--Expanding hydraulic data collection beyond the cross section","interactions":[],"lastModifiedDate":"2019-12-10T09:20:15","indexId":"fs20193073","displayToPublicDate":"2019-12-02T14:19:38","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3073","displayTitle":"Reach-Scale Monitoring and Modeling of Rivers—Expanding Hydraulic Data Collection Beyond the Cross Section","title":"Reach-scale monitoring and modeling of rivers--Expanding hydraulic data collection beyond the cross section","docAbstract":"For over 125 years, the U.S. Geological Survey streamgage network has provided important\nhydrologic information about rivers and streams throughout the Nation. Traditional streamgage\nmethods provide reliable stage and streamflow data but typically only monitor stage at a single location in a river and require frequent calibration streamflow measurements. Direct measurements are not always feasible, therefore improved sensors and methods\nare being deployed at gages to better document streamflow conditions between measurements. The technology and techniques of reach-scale monitoring allow the U.S. Geological Survey to collect more data across the full range of streamflow without requiring that a hydrographer be present. The U.S. Geological Survey Arizona Water Science Center’s reach-scale monitoring program will enhance the Arizona streamgage network with more accurate streamflow measurements and provide more extensive streamflow records and geomorphological\ndatasets for our agency partners and the public. Reach-scale monitoring installations and techniques are applicable to streams of the western United States and likely throughout the Nation.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193073","collaboration":"Prepared in cooperation with Arizona Department of Transportation","usgsCitation":"Forbes, B.T., Bunch, C.E., DeBenedetto, G., Shaw, C.J., and Gungle, B., 2019, Reach-scale monitoring and modeling of rivers—Expanding hydraulic data collection beyond the cross section: U.S. Geological Survey Fact Sheet 2019–3073, 6p., https://doi.org/10.3133/fs20193073.","productDescription":"6 p.","ipdsId":"IP-075529","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":369839,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3073/fs20193073.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 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\"}}]}","contact":"Director, Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719