Streamflow, nutrient, and sediment concentration data needed to estimate long-term mean daily streamflow and annual constituent loads were compiled from Federal, State, Tribal, and regional agencies, universities, and nongovernmental organizations. The streamflow and loads are used to develop Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models help describe the distribution, sources, and transport of streamflow, nutrients, and sediment in streams throughout five regions of the conterminous United States. After the data were screened, approximately 5,200 streamflow, 3,000 sediment, and 3,300 nutrient sites, sampled by 137 agencies and organizations were identified as having suitable data for calculating the long-term mean daily streamflow and annual nutrient and sediment loads required for SPARROW model estimation. These sites are representative of a wide range in terms of watershed size, contaminant source types, and land-use and other important watershed characteristics. The methods used to estimate long-term mean annual loads include the Beale ratio estimator and Fluxmaster regression method with Kalman smoothing.
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This fact sheet describes water quality and geochemistry of the Little Arkansas River and Equus Beds aquifer during 2001 through 2016 as part of the City of Wichita’s Equus Beds aquifer storage and recovery project in south-central Kansas. The Equus Beds aquifer storage and recovery project was developed to help meet future water demand by pumping water out of the Little Arkansas River (during above-base-flow conditions), treating it using National Primary Drinking Water Regulations as a guideline, and injecting it into the aquifer for later use. Water-quality data were collected and analyzed by the U.S. Geological Survey from 2 Little Arkansas River surface-water sites and 63 Equus Beds groundwater sites, including 38 areal assessment index wells, each of which has a shallow well and a deep well. About 4,700 surface and groundwater samples were collected and analyzed for more than 300 water-quality constituents. About 1,300 groundwater chemistry samples were geochemically modeled. Constituents of concern in the Equus Beds aquifer exceeded their respective Federal criteria throughout the study period and included chloride, sulfate, nitrate plus nitrite, Escherichia coli (E. coli), total coliforms, and dissolved iron and arsenic species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193017","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., Klager, B.J., and Ziegler, A.C., 2019, Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16: U.S. Geological Survey Fact Sheet 2019–3017, 6 p., https://doi.org/10.3133/fs20193017.","productDescription":"Report: 6 p.; Companion Files","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-097042","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":364768,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3017/coverthb.jpg"},{"id":364769,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3017/fs20193017.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3017"},{"id":364770,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026.pdf","text":"SIR 2019–5026","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5026","linkHelpText":" – Water-Quality and Geochemical Variability in the Little Arkansas River and Equus Beds Aquifer, South-Central Kansas, 2001–16"},{"id":364797,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026_appendix01.xlsx","text":"SIR 2019–5026 Appendix Tables","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5026 Appendix Tables","linkHelpText":"– Table 1.1 through Table 1.14"}],"country":"United States","state":"Kansas","otherGeospatial":"Equus Beds Aquifer, Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.83462524414062,\n 37.884608857503785\n ],\n [\n -97.82844543457031,\n 37.85859141570558\n ],\n [\n -97.76664733886719,\n 37.79296501804014\n ],\n [\n -97.57919311523438,\n 37.66805980224121\n ],\n [\n -97.33749389648438,\n 37.684907136008846\n ],\n [\n -97.33062744140625,\n 37.74248523826606\n ],\n [\n -97.35397338867188,\n 37.859675659210005\n ],\n [\n -97.34230041503906,\n 38.03619406237626\n ],\n [\n -97.3443603515625,\n 38.17829073458205\n ],\n [\n -97.40684509277344,\n 38.17613163876633\n ],\n [\n -97.8826904296875,\n 38.171273439283084\n ],\n [\n -97.89985656738281,\n 38.149137543764894\n ],\n [\n -97.83462524414062,\n 37.884608857503785\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
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The city of Wichita’s water supply currently (2019) comes from two primary sources: Cheney Reservoir and the Equus Beds aquifer. The Equus Beds aquifer storage and recovery project was developed to help the city of Wichita meet increasing future water demands. Source water for artificial recharge comes from the Little Arkansas River during above-base-flow conditions, is treated using National Primary Drinking Water Regulations as a guideline, and is injected into the Equus Beds aquifer through recharge wells or surface spreading basins for later use. The Equus Beds aquifer storage and recovery project currently (2019) consists of two coexisting phases. Phase I began in 2007 and captures Little Arkansas River water and indirect streambank diversion well water for aquifer recharge using 4 wells and 2 recharge basins. Phase II began in 2013 and currently (2019) includes a surface-water treatment facility, a river intake facility, eight recharge injection wells, and a third recharge basin. The U.S. Geological Survey, in cooperation with the City of Wichita, completed this study to summarize water-quality and geochemical variability of the Equus Beds aquifer. Data in this report can be used to establish baseline conditions before implementing artificial aquifer recharge further, document groundwater quality, evaluate changing conditions, identify environmental factors affecting groundwater, provide science-based information for decision making, and help meet regulatory monitoring requirements.
Physicochemical properties were measured and water-quality data were collected from 2 Little Arkansas River surface-water sites and 63 Equus Beds aquifer groundwater sites, including 38 areal assessment index wells (IWs) during 2001 through 2016. Data collection included discrete samples and additional continuous measurements at selected sites. Discretely collected samples were analyzed for physicochemical properties, dissolved solids, primary ions, nutrients (nitrogen and phosphorus species), organic carbon, indicator bacteria, trace elements, arsenic species, organic compounds, and radioactivity. This report focuses discussion on aquifer water quality. Federal drinking-water criteria were used to evaluate aquifer water quality. Primary drinking-water criteria are those that are enforceable for public drinking water. Secondary criteria are those that can cause aesthetics or tastes that are unpleasant.
Continuously collected data at a subset of sites included streamflow, groundwater levels, water temperature, specific conductance, pH, oxidation-reduction potential (ORP), dissolved oxygen, turbidity, nitrate plus nitrite, and fluorescent dissolved organic matter. Continuous measurement of physicochemical properties in near-real time allowed characterization of Little Arkansas River surface water and Equus Beds aquifer groundwater during conditions and time scales that would not have been possible otherwise and served as a complement to discrete water-quality sampling. During 2001 through 2016, less than 1 percent of chloride and nitrate plus nitrite, 7 percent of dissolved iron, 48 percent of dissolved manganese, 12 percent of dissolved arsenic, and 39 percent of atrazine detections in surface-water samples exceeded their respective Federal primary or secondary drinking-water criteria. None of the surface-water samples collected exceeded the Federal sulfate criterion, and every sample had detections of total coliform bacteria during the study.
Constituents of concern in the Equus Beds aquifer exceeded their respective Federal criteria throughout the study period and included chloride, sulfate, nitrate plus nitrite, Escherichia coli (E. coli), total coliforms, and dissolved iron and arsenic species. About 5 percent of shallow (less than 80 feet) and 7 percent of deep (greater than 80 feet) IW chloride sample concentrations exceeded the secondary Federal criterion of 250 milligrams per liter (mg/L). Chloride tended to exceed its criterion in shallow and deep wells along the Arkansas River and near Burrton, Kansas, an area with past oil and gas activities. Chloride concentrations near Burrton were larger in the deep parts of the aquifer. About 18 percent of shallow and 13 percent of deep IW sulfate sample concentrations exceeded the secondary Federal criterion of 250 mg/L. Mean sulfate concentrations tended to exceed the criterion in the central part of the study area. Shallow IW mean nitrate plus nitrite (hereafter referred to as “nitrate”) was substantially larger than mean deep IW nitrate. Geochemical conditions in the deeper aquifer reduced forms of nitrogen to species such as ammonia. About 15 percent of shallow and less than 1 percent of deep IW nitrate sample concentrations exceeded the Federal criterion of 10 mg/L. Mean shallow IW nitrate concentrations exceeded the criterion in the northeastern and southeastern parts of the study area; on average, deep IW nitrate concentrations did not exceed the criterion. E. coli and fecal coliform bacteria detections were usually at or near the detection limit. E. coli was detected in 3 percent of shallow and deep IWs, and fecal coliform bacteria were detected in 8 percent of shallow and 6 percent of deep IWs. Total coliforms were detected in 24 percent of shallow and 12 percent of deep IWs. E. coli coliphage was detected in two shallow IW samples (1 percent of samples) at the detection limit and was not detected in deep IW samples.
Dissolved iron was detected in 51 percent of shallow and 62 percent of deep IW samples. Dissolved iron concentrations exceeded the secondary Federal criterion of 0.3 mg/L in 38 percent of shallow and 46 percent of deep IW samples. Mean dissolved iron concentrations were largest mostly in the central and northwest part of the study area corresponding to an area of the aquifer where aquifer material is more clay-rich. The distribution of large dissolved iron concentrations was similar to that of large sulfate concentrations. About 55 percent of shallow and 92 percent of deep IW dissolved manganese samples exceeded the secondary Federal criterion of 0.05 mg/L. Almost all samples from the central and northern parts of the study area had mean dissolved manganese concentrations that exceeded the Federal criterion in the shallow part of the aquifer. Mean dissolved manganese concentrations in the shallow part of the aquifer were substantially large (greater than 1,000 micrograms per liter [μg/L]) in wells near the Little Arkansas River and in the central part of the study area because of chemically reducing conditions in the aquifer that likely related to larger percentages of clay in the aquifer material.
Concentrations of dissolved arsenic species generally were larger in the deep parts of the aquifer. Arsenite was the dominant form of arsenic on average in shallow (52 percent) and deep (55 percent) IWs. About 12 percent of shallow and 34 percent of deep IW dissolved arsenic sample concentrations exceeded the Federal primary drinking criterion of 10 μg/L. Shallow IW dissolved arsenic concentrations were larger near the Little Arkansas River and the center of the study area; large shallow IW dissolved arsenic concentrations (10–50 μg/L) in the center of the study area correspond to areas that have had the most water-level recovery since the historical low in 1993. Mean ORP in shallow IWs generally decreased with increasing water-level depths and were inversely related to mean dissolved arsenic concentrations because of more reducing conditions (smaller ORP) at larger depths below the land surface. Larger dissolved arsenic concentrations in the shallow parts of the aquifer were associated with decreases in water levels and a subsequent decrease in ORP and thus more reducing conditions.
Atrazine was detected in about 58 percent of shallow and 28 percent of deep IWs and did not exceed the primary Federal criterion of 3 μg/L in any groundwater samples. Atrazine concentrations in shallow IWs generally were largest in the northwest part of the study area near the North Branch Kisiwa Creek, and atrazine concentrations in deep IWs generally were largest most often in the southern part of the study area. Gross α radioactivity concentrations exceeded the primary Federal criterion of 15 picocuries per liter in 4 percent of shallow IW samples. Gross α and gross β radioactivity concentrations generally were larger in the southern third of the aquifer.
Most groundwater-sample-simulated minerals saturation indices (SIs) were consistently negative (undersaturated). Minerals that had SI values that were consistently or typically positive (oversaturated) included iron oxide, hydroxide, and quartz-group minerals. Several SI values for arsenic- and manganese-bearing minerals were consistently negative. Some manganese-bearing mineral SI values ranged from undersaturated to oversaturated in shallow and deep IWs during the study. Several carbonate minerals in shallow and deep IWs varied across their equilibrium state. Calcite SI values were larger more often in the deep parts of the aquifer and did not show a clear distributional pattern. Mean and median calcite SI values for shallow and deep IWs were negative (undersaturated) indicating the potential for calcite dissolution if calcite is present for a substantial part of the study period. However, some individual calcite SI values in this study indicated saturation and subsequent calcite precipitation may occur in the study area, potentially resulting in formation of calcite mineral deposits that may reduce efficiency of injection wells. SI values with respect to iron hydroxide varied across their equilibrium states. Mean and median SI values with respect to iron hydroxide were undersaturated in shallow and deep IWs; however, some samples had positive SI values indicating there is potential for iron hydroxide precipitation, possibly caused by leaching and oxidation of iron-containing minerals, like pyrite, in the aquifer material.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195026","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., Klager, B.J., and Ziegler, A.C., 2019, Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16: U.S. Geological Survey Scientific Investigations Report 2019–5026, 79 p., https://doi.org/10.3133/sir20195026.","productDescription":"Report: viii, 79 p.; Appendix Tables: Table 1.1 to Table 1.14; Companion Files","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097040","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":364760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5026/coverthb.jpg"},{"id":364761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5026"},{"id":364771,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026_appendix01.xlsx","text":"Appendix Tables","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5026 Appendix Tables","linkHelpText":" – Table 1.1 through Table 1.14"},{"id":364762,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/fs/2019/3017/fs20193017.pdf","text":"Fact Sheet 2019–3017","size":"4.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3017","linkHelpText":" – Water-Quality and Geochemical Variability in the Little Arkansas River and Equus Beds Aquifer, South-Central Kansas, 2001–16"}],"country":"United States","state":"Kansas","otherGeospatial":"Equus Beds Aquifer, Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.83462524414062,\n 37.884608857503785\n ],\n [\n -97.82844543457031,\n 37.85859141570558\n ],\n [\n -97.76664733886719,\n 37.79296501804014\n ],\n [\n -97.57919311523438,\n 37.66805980224121\n ],\n [\n -97.33749389648438,\n 37.684907136008846\n ],\n [\n -97.33062744140625,\n 37.74248523826606\n ],\n [\n -97.35397338867188,\n 37.859675659210005\n ],\n [\n -97.34230041503906,\n 38.03619406237626\n ],\n [\n -97.3443603515625,\n 38.17829073458205\n ],\n [\n -97.40684509277344,\n 38.17613163876633\n ],\n [\n -97.8826904296875,\n 38.171273439283084\n ],\n [\n -97.89985656738281,\n 38.149137543764894\n ],\n [\n -97.83462524414062,\n 37.884608857503785\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
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Groundwater resources in the Spanish Valley watershed in southern Utah were quantified for the first time since the early 1970s. The primary objectives of this study were (1) to better understand sources of recharge to, groundwater flow directions within, and discharge points for both the valley-fill and Glen Canyon Group aquifers (VFA and GCGA), and (2) to quantify groundwater budget components of the combined VFA and GCGA, including both recharge and discharge. Based on both groundwater chemistry (stable isotopes, major ions, and noble gases) and environmental tracers in vadose-zone pore water of the Navajo Sandstone outcrop along Sand Flats Road, most recharge to the GCGA occurs high in the La Sal Mountains, and not on the sandstone outcrop area. The same groundwater chemistry and environmental tracer evidence from the saturated zone indicates that Pack Creek, rather than GCGA groundwater, is the primary source of recharge to the VFA. Groundwater recharge in the study area occurs mostly from infiltration of precipitation (in the form of snowmelt) at high altitudes. Additional recharge occurs from the infiltration of runoff along losing reaches of stream channels, or as unconsumed surface-water and groundwater irrigation. Average annual recharge to the Moab-Spanish Valley watershed part of the Spanish Valley study area was estimated to be between 9,550 and 30,000 acre-feet. Based on water-levels collected in the current study, groundwater in both the GCGA and the VFA generally moves downgradient parallel to the topographic slope of the watershed towards the Colorado River. Groundwater discharge measurements, and hydraulic-flux estimates at the lower end of Spanish Valley, provide a more robust estimate of the groundwater budget than evaluating recharge. The primary base-flow discharge components in the study area include groundwater discharge to gaining reaches of streams, groundwater discharge to springs, and well withdrawals. Based on 3 years of measurements (2014–16) and hydraulic-flux calculations at the lower end of Spanish Valley, total groundwater discharge was estimated to be 14,000 to 16,000 acre-feet per year (acre-ft/yr) for the entire watershed, or 13,000 to 15,000 acre-ft/yr, excluding the watershed areas of Grandstaff (formerly Negro Bill) and Ice Box Canyons (compared to the 1971 Sumsion estimate of 22,000 acre-ft/yr). The primary difference is this study’s estimate of subsurface outflow to the Colorado River of only 300 to 1,000 acre-ft/yr, compared to 11,000 acre-ft/yr estimated by Sumsion. Because the study period (2014–16) experienced above average precipitation for 2 of the 3 years, the discharge estimates may be slightly higher than long-term average annual discharge from the groundwater system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195062","collaboration":"Prepared in cooperation with the Utah Division of Water Rights, City of Moab, Grand and San Juan Counties, Grand Water and Sewer Service Agency, Utah School and Institutional Trust Lands Administration, The Nature Conservancy, Utah Division of Wildlife Resources, Living Rivers, San Juan Spanish Valley Special Service District, U.S. Bureau of Land Management, and U.S. Forest Service","usgsCitation":"Masbruch, M.D., Gardner, P.M., Nelson, N.C., Heilweil, V.M., Solder, J.E., Hess, M.D., McKinney, T.S., Briggs, M.A., and Solomon, D.K., 2019, Evaluation of groundwater resources in the Spanish Valley Watershed, Grand and San Juan Counties, Utah: U.S. Geological Survey Scientific Investigations Report 2019–5062, 86 p., https://doi.org/10.3133/sir20195062.","productDescription":"Report: x, 86 p.; 3 Plates: 24.00 x 38.00 inches or smaller","numberOfPages":"86","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080057","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":437367,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z39TII","text":"USGS data release","linkHelpText":"Lumped parameter models of groundwater age, Spanish Valley Watershed, Grand and San Juan Counties, Utah"},{"id":366484,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5062/coverthb.jpg"},{"id":366487,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062_plate2.pdf","text":"Plate 2","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062 Plate 2","linkHelpText":"- Stiff Diagrams Showing Major-Ion Composition from Hydrogeologic Units in the Spanish Valley Study Area, Utah"},{"id":366485,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062.pdf","text":"Report","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062"},{"id":366488,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062_plate3.pdf","text":"Plate 3","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062 Plate 3","linkHelpText":"- Water-Level Surface Map and General Direction of Groundwater Movement in the Spanish Valley Study Area, Utah"},{"id":366486,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062_plate1.pdf","text":"Plate 1","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062 Plate 1","linkHelpText":"- Surficial Extent and Cross Sections of Hydrogeologic Units for Selected Locations in the Spanish Valley Study Area, Utah"}],"country":"United States","state":"Utah","county":"Grand County, San Juan County","otherGeospatial":"Spanish Valley Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.74037170410156,\n 38.357810999675664\n ],\n [\n -109.31259155273438,\n 38.357810999675664\n ],\n [\n -109.31259155273438,\n 38.61043215866372\n ],\n [\n -109.74037170410156,\n 38.61043215866372\n ],\n [\n -109.74037170410156,\n 38.357810999675664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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Hennig Brandt's discovery of phosphorus (P) occurred during the early European colonization of the Chesapeake Bay region. Today, P, an essential nutrient on land and water alike, is one of the principal threats to the health of the bay. Despite widespread implementation of best management practices across the Chesapeake Bay watershed following the implementation in 2010 of a total maximum daily load (TMDL) to improve the health of the bay, P load reductions across the bay's 166,000‐km2 watershed have been uneven, and dissolved P loads have increased in a number of the bay's tributaries. As the midpoint of the 15‐yr TMDL process has now passed, some of the more stubborn sources of P must now be tackled. For nonpoint agricultural sources, strategies that not only address particulate P but also mitigate dissolved P losses are essential. Lingering concerns include legacy P stored in soils and reservoir sediments, mitigation of P in artificial drainage and stormwater from hotspots and converted farmland, manure management and animal heavy use areas, and critical source areas of P in agricultural landscapes. While opportunities exist to curtail transport of all forms of P, greater attention is required toward adapting P management to new hydrologic regimes and transport pathways imposed by climate change.
","language":"English","publisher":"Wiley","doi":"10.2134/jeq2019.03.0112","usgsCitation":"Kleinman, P., Fanelli, R., Hirsch, R.M., Buda, A.R., Easton, Z.M., Wainger, L.A., Brosch, C., Lowenfish, M., Collick, A.S., Shirmohammadi, A., Boomer, K., Hubbart, J.A., Bryant, R.B., and Shenk, G., 2019, Phosphorus and the Chesapeake Bay: Lingering issues and emerging concerns for agriculture: Journal of Environmental Quality, v. 48, no. 5, p. 1191-1203, https://doi.org/10.2134/jeq2019.03.0112.","productDescription":"13 p.","startPage":"1191","endPage":"1203","ipdsId":"IP-106511","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":467365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2019.03.0112","text":"Publisher Index Page"},{"id":379941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125,\n 36.92793899776678\n ],\n [\n -75.948486328125,\n 37.23470197166817\n ],\n [\n -75.673828125,\n 37.896530447543\n ],\n [\n -75.816650390625,\n 38.28993659801203\n ],\n [\n -75.8221435546875,\n 38.436379603\n ],\n [\n -76.0858154296875,\n 38.44498466889473\n ],\n [\n -76.0308837890625,\n 38.71980474264237\n ],\n [\n -75.7781982421875,\n 39.614152077002664\n ],\n [\n -76.1956787109375,\n 39.592990390285024\n ],\n [\n -76.7230224609375,\n 39.21948715423953\n ],\n [\n -76.629638671875,\n 38.565347844885466\n ],\n [\n -76.629638671875,\n 38.40194908237822\n ],\n [\n -77.0635986328125,\n 38.487994609214795\n ],\n [\n -77.05810546875,\n 38.21660403859855\n ],\n [\n -76.4373779296875,\n 37.92686760148135\n ],\n [\n -77.04711914062499,\n 38.190704293996504\n ],\n [\n -77.156982421875,\n 38.043765107439675\n ],\n [\n -76.497802734375,\n 37.501010429493284\n ],\n [\n -76.4813232421875,\n 37.322120359451766\n ],\n [\n -76.4813232421875,\n 37.14718209972376\n ],\n [\n -76.234130859375,\n 36.85764758564407\n ],\n [\n -76.11328125,\n 36.92793899776678\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kleinman, Peter","contributorId":244141,"corporation":false,"usgs":false,"family":"Kleinman","given":"Peter","email":"","affiliations":[{"id":48855,"text":"USDA-ARS, Pasture Syst. and Watershed Mgmt. 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The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&gage=desm7, which also forecasts flood hydrographs at this site (site DESM7).
Flood profiles were computed for the stream reach using a one-dimensional model for simulation of water-surface profiles with steady-state (gradually varied) or unsteady-state flow computation options. The model was calibrated by using the theoretical stage-discharge relation at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500), and documented high-water marks from the flood of April 18, 2013.
The hydraulic model was then used to compute 10 water surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum. The profiles ranged from 8.0 ft, or near bankfull, to 17.0 ft, which exceeds the stage that corresponds to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and 1.97-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures and for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195068","collaboration":"Prepared in cooperation with the city of De Soto, Missouri, and Jefferson County, Missouri","usgsCitation":"Heimann, D.C., Voss, J.D., and Rydlund, P.H., Jr., 2019, Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5068, 10 p., https://doi.org/10.3133/sir20195068.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-105218","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":366556,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5068/sir20195068.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Demand for freshwater on the Island of Maui is expected to increase by 45 percent between 2015 and 2035. Groundwater availability on Maui is affected by changes in climate and agricultural irrigation. To evaluate the availability of fresh groundwater under projected future climate conditions and changing agricultural irrigation practices, estimates of groundwater recharge are needed. A water-budget model with a daily computation interval was used to estimate the spatial distribution of recharge on Maui for one present-day and two future-climate scenarios. All three scenarios used 2017 land cover. The two future-climate scenarios, including one wetter than the present-day scenario and one drier than the present-day scenario, were developed using available high-resolution downscaled climate projections. The drier future scenario was developed using projections for a Representative Concentration Pathway warming scenario during 2071–99 with total radiative forcing of 8.5 Watts per square meter by the year 2100 (RCP8.5 2071–99 scenario), whereas the wetter future scenario was developed using projections for a “Special Report on Emissions Scenarios” A1B emission scenario during 2080–99 (A1B 2080–99 scenario). For the RCP8.5 2071–99 scenario, projected mean annual recharge decrease for Maui is about 172 million gallons per day, or about 14 percent less than present-day recharge, which is estimated to be 1,232 million gallons per day. Recharge for the RCP8.5 2071–99 scenario is projected to decrease in 22 of Maui’s 25 aquifer systems, which are defined by the Hawaiʻi Commission on Water Resource Management. For the A1B 2080–99 future scenario, projected mean annual recharge increase for Maui is about 144 million gallons per day, or about 12 percent more than present-day recharge. Recharge for the A1B 2080–99 scenario is projected to increase in 17 of Maui’s 25 aquifer systems. Between the two future scenarios, a total of 11 aquifer systems show similar direction in drying (Kahului, Kama‘ole, Lualaʻilua, Makawao, Olowalu, Pāʻia, Ukumehame, Waikapū) or wetting (Honopou, Kawaipapa, and Waikamoi) changes for recharge. Selectively modifying the climate inputs for the A1B 2080–99 scenario indicates that the projected changes in rainfall account for most of the projected changes in recharge for Maui’s 25 aquifer systems. However, projected changes in reference evapotranspiration and forest-canopy evaporation also can account for a substantial part of the projected changes in recharge where changes in reference evapotranspiration are relatively large and where changes in forest-canopy evaporation extend across large forested areas. Projected changes in daily rainfall frequency have a relatively small but non-negligible impact on recharge estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195064","collaboration":"Prepared in cooperation with the County of Maui Department of Water Supply and the Pacific Regional Integrated Sciences and Assessments Program","usgsCitation":"Mair, A., Johnson A.G., Rotzoll, Kolja, and Oki, D.S., 2019, Estimated groundwater recharge from a water-budget model incorporating selected climate projections, Island of Maui, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2019–5064, 46 p., https://doi.org/10.3133/sir20195064.","productDescription":"Report: vi, 46 p., 3 data releases","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-100732","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":366544,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98W9ABX","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP5 RCP8.5 2071-99 scenario rainfall and 2017 land cover"},{"id":366541,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5064/sir20195064.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5064"},{"id":366542,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91WSOFO","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for average climate conditions, 1978-2007 rainfall and 2017 land cover"},{"id":366540,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5064/coverthb.jpg"},{"id":366543,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9437T2F","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP3 A1B 2080-99 scenario climate and 2017 land cover"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.7474365234375,\n 20.52478875041428\n ],\n [\n -155.8685302734375,\n 20.52478875041428\n ],\n [\n -155.8685302734375,\n 21.099875492701216\n ],\n [\n -156.7474365234375,\n 21.099875492701216\n ],\n [\n -156.7474365234375,\n 20.52478875041428\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
ubsurface coal environments, where biogenic coal-to-methane conversion occurs, are difficult to access, resulting in inherent challenges and expenses for in situexperiments. Previous batch reactor studies provided insights into specific processes, pathways, kinetics, and engineering strategies, but field-relevance is restricted due to limited substrate availability or byproduct accumulation that may influence reactions or metabolisms. In this study, continuous-flow column reactors were used to overcome some batch limitations, improve the understanding of in situconditions, and increase field-relevance for subsurface engineering technology development. The bench-scale reactor system was constructed to investigate the addition of algal amendment for enhancing microbial coal-to-methane conversion previously developed in batch systems. Four reactor columns were packed with coal and inoculated with a microbial consortium from the same Flowers-Goodale coal bed. Two reactors were amended with 13C-labeled algal amendment on day 0, and two were unamended. On day 61, one previously amended and one previously unamended reactor were re-amended. Produced gases were captured in a gas trap, and CH4 and CO2 were quantified. The reactor amended twice produced 1712.6 µmol CH4 (4.6% as 13CH4). The reactor amended only on day 0 produced 1485.5 µmol CH4 (2.6% as 13CH4). The reactor amended only on day 61 produced 278.9 µmol CH4 (3.9% as 13CH4). The reactor with no amendment produced no measurable gases for the duration of the 172-day experiment. Amendment increased the rate of coal-to-methane conversion and total gas production; most of the produced gases were due to coal conversion with only small contributions (<7%) from amendment conversion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fuel.2019.115905","usgsCitation":"Davis, K.J., Platt, G.A., Barnhart, E.P., Hiebart, R., Hyatt, R., Fields, M.W., and Gerlach, R., 2019, Biogenic coal-to-methane conversion can be enhanced with small additions of algal amendment in field-relevant upflow column reactors: Fuel, v. 256, 115905, 8 p., https://doi.org/10.1016/j.fuel.2019.115905.","productDescription":"115905, 8 p.","ipdsId":"IP-106712","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":467367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1557363","text":"Publisher Index Page"},{"id":366902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"256","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Katherine J.","contributorId":203246,"corporation":false,"usgs":false,"family":"Davis","given":"Katherine","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":769184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Platt, George A.","contributorId":218404,"corporation":false,"usgs":false,"family":"Platt","given":"George","email":"","middleInitial":"A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":769185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":769183,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hiebart, Randy","contributorId":218422,"corporation":false,"usgs":false,"family":"Hiebart","given":"Randy","email":"","affiliations":[],"preferred":false,"id":769186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hyatt, Robert","contributorId":218406,"corporation":false,"usgs":false,"family":"Hyatt","given":"Robert","email":"","affiliations":[{"id":39839,"text":"Montana Emergent Technologies","active":true,"usgs":false}],"preferred":false,"id":769187,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":769188,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gerlach, Robin","contributorId":203247,"corporation":false,"usgs":false,"family":"Gerlach","given":"Robin","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":769189,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70207595,"text":"70207595 - 2019 - Predicting surf zone injuries along the Delaware coast using a Bayesian network","interactions":[],"lastModifiedDate":"2019-12-30T16:30:44","indexId":"70207595","displayToPublicDate":"2019-08-14T16:28:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Predicting surf zone injuries along the Delaware coast using a Bayesian network","docAbstract":"Personnel at Beebe Healthcare in Lewes, Delaware, collected surf zone injury (SZI) data for eight summer seasons from 2010 through 2017. Data include, but are not limited to, time of injury, gender, age, and activity. More than 2000 SZI events, including 196 spinal injuries and 6 fatalities, occurred at the five most populated beaches along the 25 miles of Atlantic-fronting coast. SZI are predominantly wave related incidents associated with wading (50.1%), body surfing (18.4%), and body boarding (13.3%). The episodic nature of SZI indicate the importance of linking the environmental conditions and human behavior in the surf zone to predict days with high injury rates. Higher order statistics are necessary to effectively consider all associated factors related to SZI. Two Bayesian networks (BN) were constructed to model SZI and predict changes in injury rate (proportion of injuries to bathers) and injury likelihood (probability of at least one injury occurrence) on an hourly basis. The models incorporate environmental data collected by weather stations, wave gauges, and researcher personnel on the beach. The models include prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential parameters related to injury rates were significant wave height, foreshore slope, and water temperature. Exposure parameters (e.g., air temperature) influenced the number of people in the water, resulting in strong correlation between injury likelihood and the related meteorological conditions (variance reduction > 0.4%). Log likelihood ratio (LLR) scores indicate the network predicts SZI likelihood during any specified hour with more skill than prior predictions with the best performing model improving prediction 69.1% of the time (LLR = 69.1%). An alternative BN predicting injury rate performed worse with the prior probability model out predicting the injury rate network (positive LLR = 36.7%). Issues persist with predicting SZI that have an LLR ≪ -1 (< 5% of 2017 injuries) and occur in conditions different than when most other SZI occur. Better understanding of SZI will improve awareness techniques to both educate beachgoers and assist beach patrol decision making during high risk conditions.","language":"English","publisher":"Springer","doi":"10.1007/s11069-019-03697-y","usgsCitation":"Doelp, M., Puleo, J., and Plant, N.G., 2019, Predicting surf zone injuries along the Delaware coast using a Bayesian network: Natural Hazards, v. 98, no. 2, p. 379-401, https://doi.org/10.1007/s11069-019-03697-y.","productDescription":"22 p.","startPage":"379","endPage":"401","ipdsId":"IP-100096","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":370880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":778661,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204738,"text":"sir20195082 - 2019 - Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotelluric surveys","interactions":[],"lastModifiedDate":"2019-10-07T16:51:39","indexId":"sir20195082","displayToPublicDate":"2019-08-14T09:51:12","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5082","displayTitle":"Characterization of Big Chino Subbasin Hydrogeology near Paulden, Arizona, Using Controlled Source Audio-Frequency Magnetotelluric Surveys","title":"Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotelluric surveys","docAbstract":"The Big Chino subbasin is located in central-northwest Arizona in the transition zone between the Colorado Plateau and the Basin and Range Province. The controlled source audio-frequency magnetotelluric (CSAMT) geophysical method, a low-impact, non-intrusive, electrical resistance sounding technique, was used to evaluate the subsurface hydrogeology of the southern third of the Big Chino subbasin. The Big Chino subbasin is a northwest-trending, late Tertiary graben bordered by the Big Chino Fault along its northeast flank where there is as much as 1,100 meters of displacement. The main water-bearing stratigraphic unit of the basin is Tertiary alluvial-fill sediment. The Devonian Martin Formation provides water to wells near Drake and the Mississippian Redwall Limestone provides water to wells east of the basin and in the Paulden area.
The purpose of the CSAMT surveys was to improve the conceptual model of the aquifer by constraining the basin geometry and identifying stratigraphic units and their subsurface extents. CSAMT methods were used to map the subsurface along 100 kilometers (62 miles) of survey lines across the southern third of the subbasin. Of 21 survey lines, 14 were west of the town of Paulden and another 7 were east of Paulden. Data were cleaned and prepared for entry into Zonge SCS2D software and then inverted to provide a two-dimensional resistivity profile for each survey line. Final inversion models representing the best fit to measured data were compared to driller’s logs or borehole data where present.
Data from the CSAMT lines west and north of Paulden are consistent with thicker alluvial basin deposits that range from 100 meters thick to a few hundred meters thick. Data from the CSAMT lines east of Paulden are consistent with thinner alluvial and basalt deposits overlying Paleozoic Martin Formation and Redwall Limestone, Tapeats Sandstone, and Precambrian granite and schist.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195082","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and Salt River Project","usgsCitation":"Macy, J.P., Gungle, B., and Mason, J.P., 2019, Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotellursurveys: U.S. Geological Survey Scientific Investigations Report 2019–5082, 39 p., https://doi.org/10.3133/sir20195082.\nic ","productDescription":"vii, 39 p.","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-098264","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":366547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5082/sir20195082.pdf","text":"Report","size":"50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The costs of invasive species in the United States alone are estimated to exceed US$100 billion per year so a critical tactic in minimizing the costs of invasive species is the development of effective, early-detection systems. To this end, we evaluated the efficacy of adding environmental (e)DNA surveillance to the U.S. Geological Survey (USGS) streamgage network, which consists of > 8,200 streamgages nationwide systemically visited by USGS hydrologic technicians. Incorporating strategic eDNA sample collection during routine streamgage visits could provide early detection surveillance of aquatic invasive species with minimal additional cost. For this evaluation, USGS hydrologic technicians collected monthly eDNA water samples, May – September 2018, from streamgages downstream of reservoirs in the Columbia River Basin thought to be vulnerable to invasive dreissenid mussel (Dreissenidae spp.) establishment. We tested water samples for dreissenid mussel DNA and also for kokanee (Oncorhynchus nerka) and yellow perch (Perca flavescens) DNA; the two fishes were used to assess if streamgages are adequately located to provide early-detection eDNA surveillance of taxa known to be present in upstream reservoirs. No Columbia River Basin streamgage samples met our criteria for being scored as positive for dreissenid DNA. We did detect kokanee and yellow perch DNA at all streamgages downstream of reservoirs where these species are known to occur. Field collection, laboratory analyses, and personnel time required for collection of four eDNA samples at a streamgage site cost US$500 -US$600 (net). Given these results, incorporating eDNA biosurveillance into routine streamgage visits might decrease costs associated with an invasion since early detection maximizes the potential for eradication, containment, and mitigation.
","language":"English","publisher":"ESA","doi":"10.1002/ecs2.2843","usgsCitation":"Sepulveda, A.J., Schmidt, C., Amberg, J., Hutchins, P.R., Stratton, C., Mebane, C.A., Laramie, M., and Pilliod, D.S., 2019, Adding invasive species bio-surveillance to the U.S. Geological Survey streamgage network: Ecosphere, v. 10, no. 8, e02843, 17 p., https://doi.org/10.1002/ecs2.2843.","productDescription":"e02843, 17 p.","ipdsId":"IP-106819","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":460309,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Center","active":true,"usgs":true}],"preferred":true,"id":768962,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":216342,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":768963,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204877,"text":"70204877 - 2019 - A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge","interactions":[],"lastModifiedDate":"2019-08-21T10:31:44","indexId":"70204877","displayToPublicDate":"2019-08-13T10:22:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge","docAbstract":"Worldwide, eutrophication is threatening lake ecosystems. To support lake management numerous eutrophication models have been developed. Diverse research questions in a wide range of lake ecosystems are addressed by these models. The established models are based on three key approaches: the empirical approach that employs field surveys, the theoretical approach in which models based on first principles are tested against lab experiments, and the process-based approach that uses parameters and functions representing detailed biogeochemical processes. These approaches have led to an accumulation of field-, lab- and model-based knowledge, respectively. Linking these sources of knowledge would benefit lake management by exploiting complementary information; however, the development of a simple tool that links these approaches was hampered by their large differences in scale and complexity. Here we propose a Generically Parameterized Lake eutrophication model (GPLake) that links field-, lab- and model-based knowledge and can be used to make a first diagnosis of lake water quality. We derived GPLake from consumer-resource theory by the principle that lacustrine phytoplankton is typically limited by two resources: nutrients and light. These limitations are captured in two generic parameters that shape the nutrient to chlorophyll-a relations. Next, we parameterized GPLake, using knowledge from empirical, theoretical, and process-based approaches. GPLake generic parameters were found to scale in a comparable manner across data sources. Finally, we show that GPLake can be applied as a simple tool that provides lake managers with a first diagnosis of the limiting factor and lake water quality, using only the parameters for lake depth, residence time and current nutrient loading. With this first-order assessment, lake managers can easily assess measures such as reducing nutrient load, decreasing residence time or changing depth before spending money on field-, lab- or model- experiments to support lake management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.133887","usgsCitation":"Chang, M., Teurlincx, S., DeAngelis, D.L., Janse, J.H., Troost, T.A., van Wijk, D., Mooij, W.M., and Janssen, A., 2019, A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge: Science of the Total Environment, v. 695, 133887, 11 p., https://doi.org/10.1016/j.scitotenv.2019.133887.","productDescription":"133887, 11 p.","ipdsId":"IP-104765","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.133887","text":"Publisher Index Page"},{"id":366781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"695","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chang, Manqi","contributorId":218274,"corporation":false,"usgs":false,"family":"Chang","given":"Manqi","email":"","affiliations":[],"preferred":false,"id":768853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teurlincx, Sven","contributorId":218275,"corporation":false,"usgs":false,"family":"Teurlincx","given":"Sven","email":"","affiliations":[],"preferred":false,"id":768854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":768855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Janse, Jan H.","contributorId":215555,"corporation":false,"usgs":false,"family":"Janse","given":"Jan","email":"","middleInitial":"H.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Troost, Tineke A.","contributorId":218276,"corporation":false,"usgs":false,"family":"Troost","given":"Tineke","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":768857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Wijk, Dianneke","contributorId":215557,"corporation":false,"usgs":false,"family":"van Wijk","given":"Dianneke","email":"","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mooij, Wolf M.","contributorId":215556,"corporation":false,"usgs":false,"family":"Mooij","given":"Wolf","email":"","middleInitial":"M.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Janssen, Annette B. G.","contributorId":200076,"corporation":false,"usgs":false,"family":"Janssen","given":"Annette B. G.","affiliations":[],"preferred":false,"id":768860,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204724,"text":"70204724 - 2019 - Tracking phosphorus and sediment sources and transport from fields and channels in Great Lakes Restoration Initiative priority watersheds","interactions":[],"lastModifiedDate":"2019-08-13T08:21:20","indexId":"70204724","displayToPublicDate":"2019-08-13T08:19:00","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Tracking phosphorus and sediment sources and transport from fields and channels in Great Lakes Restoration Initiative priority watersheds","docAbstract":"A multi-agency, integrated series of studies were initiated in 2017 under the Great Lakes Restoration Initiative (GLRI) by the U.S. Geological Survey, U.S. Forest Service, and the University of Minnesota to quantify the source, downstream travel time, and storage of particulate-bound phosphorus and sediment in agricultural tributaries to the Great Lakes. Of particular interest are contributions at the edge of field, channels, and riparian corridors. Results will be used to help identify upland and stream conservation practices that may reduce phosphorus and sediment inputs to the Great Lakes. The two study watersheds are the 50 km2 Black Creek in the Maumee River basin (Lake Erie) and the 90 km2 Plum Creek in the Lower Fox River basin (Lake Michigan). As part of other GLRI work, Black Creek and Plum Creek have existing, nested, edge-of-field studies in addition to phosphorus and sediment monitoring stations along their mainstems.\n\nSediment-source tracking provides a direct method to quantify suspended sediment, and consequently phosphorus, sources by identifying a minimal set of properties (or fingerprint) that uniquely defines each source of sediment in the basin. This fingerprint can then be used to apportion sources of sediment from agricultural fields as well as other uplands including developed areas, forests, and pastures. These methods can also help distinguish sediment from ditches, ravines and eroding slopes, and streambanks. Multiple tracking methods are being used and adapted for best results in these watersheds, including a suite of trace elements for overall source apportionment in addition to short-term fallout radionuclides beryllium-7 (7Be) and lead-210 (210Pb) for high-flow event-based transport on fields and in stream channels. Tile drain connectivity to the surface is also of interest, especially in the Black Creek watershed.\n\nPreliminary results from the overall source apportionment from analyses of streambed sediment and monthly suspended sediment in Plum Creek indicate that the proportion attributed to different land cover varied by season and events. Further data analyses are being conducted for examining event-based pathways on individual fields, while most basin-wide sampling was monthly. Results from both watersheds will help describe the variations in transport of particulate-bound phosphorus across both steep and gentle landscapes representative of the Great Lakes basin. (this is from IP-101450)","language":"English","publisher":"SEDHYD","collaboration":"US Forest Service, University of Minnesota, EPA","usgsCitation":"Williamson, T.N., Fitzpatrick, F.A., Karwan, D.L., Kolka, R.K., Dobrowolski, E.G., Blount, J.D., and Pawlowski, E.D., 2019, Tracking phosphorus and sediment sources and transport from fields and channels in Great Lakes Restoration Initiative priority watersheds, 13 p.","productDescription":"13 p.","ipdsId":"IP-104838","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":366492,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":366475,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2019/openconf/modules/request.php?module=oc_program&action=view.php&id=79&file=1/79.pdf"}],"country":"United States, Canada","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.46093749999999,\n 41.11246878918088\n ],\n [\n -75.5859375,\n 41.11246878918088\n ],\n [\n -75.5859375,\n 48.86471476180277\n ],\n [\n -92.46093749999999,\n 48.86471476180277\n ],\n [\n -92.46093749999999,\n 41.11246878918088\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":196543,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","email":"fafitzpa@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":768190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karwan, Diana L.","contributorId":207315,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":768191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolka, Randall K.","contributorId":16150,"corporation":false,"usgs":false,"family":"Kolka","given":"Randall","email":"","middleInitial":"K.","affiliations":[{"id":13259,"text":"USDA Forest Service Northern Research Station","active":true,"usgs":false}],"preferred":false,"id":768192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dobrowolski, Edward G. 0000-0001-9840-4609 edobrowo@usgs.gov","orcid":"https://orcid.org/0000-0001-9840-4609","contributorId":5555,"corporation":false,"usgs":true,"family":"Dobrowolski","given":"Edward","email":"edobrowo@usgs.gov","middleInitial":"G.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blount, James D. 0000-0002-0006-3947 jblount@usgs.gov","orcid":"https://orcid.org/0000-0002-0006-3947","contributorId":200231,"corporation":false,"usgs":true,"family":"Blount","given":"James","email":"jblount@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pawlowski, Ethan D.","contributorId":218062,"corporation":false,"usgs":false,"family":"Pawlowski","given":"Ethan","email":"","middleInitial":"D.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":768195,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70207556,"text":"70207556 - 2019 - Geophysical mapping of plume discharge to surface water at a crude oil spill site: Inversion versus machine learning","interactions":[],"lastModifiedDate":"2019-12-24T12:27:15","indexId":"70207556","displayToPublicDate":"2019-08-12T12:15:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1808,"text":"Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical mapping of plume discharge to surface water at a crude oil spill site: Inversion versus machine learning","docAbstract":"The interpretation of geophysical survey results to answer hydrologic, engineering, and geologic questions is critical to diverse problems for management of water, energy, and mineral resources. Although geophysical images provide valuable qualitative insight into subsurface architecture and conditions, translating geophysical images into quantitative information (e.g., saturation, concentration, and hydraulic properties) often involves substantial nonuniqueness and uncertainty owing to the limited resolution of geophysical imaging and uncertainty in petrophysical relations. We have developed a machine-learning approach to address these challenges in the context of a field-based investigation to map zones where a hydrocarbon plume was discharging to surface water at the National Crude Oil Spill Fate and Natural Attenuation Research Site in Bemidji, Minnesota, USA. The two-step approach combines multiple types of geophysical and direct information and effectively bypasses inversion and its associated assumptions. Integrating multifrequency electromagnetic induction, ground-penetrating radar, and fluid-sampling data, we first identify discharge zones and second estimate specific conductance versus depth. Compared with conventional inversion results, the machine-learning results (1) directly address the study objectives (delineating the discharge zones); (2) better extract depth-dependent information from the data, for which sensitivity diminishes rapidly with depth; and (3) quantify the uncertainty of the predictions (i.e., discharge versus nondischarge zones), rather than the uncertainty of the geophysical estimates (i.e., the standard error of estimation for the logarithm of electrical conductivity).
","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/geo2018-0690.1","usgsCitation":"Terry, N., Day-Lewis, F.D., Lane, J., Trost, J.J., and Bekins, B.A., 2019, Geophysical mapping of plume discharge to surface water at a crude oil spill site: Inversion versus machine learning: Geophysics, v. 84, no. 5, p. EN67-EN80, https://doi.org/10.1190/geo2018-0690.1.","productDescription":"14 p.","startPage":"EN67","endPage":"EN80","ipdsId":"IP-105187","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":370676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","city":"Bemidji","otherGeospatial":"National Crude Oil Spill Fate and Natural Attenuation Research Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.0820,\n 47.5775\n ],\n [\n -95.0920,\n 47.5775\n ],\n [\n -95.0920,\n 47.5715\n ],\n [\n -95.0820,\n 47.5715\n ],\n [\n -95.0820,\n 47.5775\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Terry, Neil C. 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","middleInitial":"C.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":778454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":778455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":778456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trost, Jared J. 0000-0003-0431-2151 jtrost@usgs.gov","orcid":"https://orcid.org/0000-0003-0431-2151","contributorId":3749,"corporation":false,"usgs":true,"family":"Trost","given":"Jared","email":"jtrost@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bekins, Barbara A. 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":1348,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":778458,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208502,"text":"70208502 - 2019 - Return of a giant: Coordinated conservation leads to the first wild reproduction of Lahontan Cutthroat Trout in the Truckee River in nearly a century","interactions":[],"lastModifiedDate":"2020-03-11T15:44:57","indexId":"70208502","displayToPublicDate":"2019-08-12T08:35:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1657,"text":"Fisheries","onlineIssn":"1548-8446","printIssn":"0363-2415","active":true,"publicationSubtype":{"id":10}},"title":"Return of a giant: Coordinated conservation leads to the first wild reproduction of Lahontan Cutthroat Trout in the Truckee River in nearly a century","docAbstract":"Many freshwater fish populations have been greatly reduced, with particular loss of migratory fishes. Recovering depleted populations is challenging as threats are often plentiful and complex, especially in arid environments where demands for water resources are high. Here, we describe how a collaborative, multifaceted approach has spurred natural reproduction—a major step towards Lahontan Cutthroat Trout Oncorhynchus clarkii henshawi (LCT) recovery in Pyramid Lake and the Truckee River, Nevada, once home to one of largest freshwater salmonids in North America. The factors limiting LCT were immense, including habitat fragmentation, degradation, and non‐native species attributes common in the declines of native salmonids. Yet for the first time in over 80 years and each year since 2014, adfluvial LCT have spawned in the lower Truckee River, resulting in the production of tens of thousands of young‐of‐year. The progress and positive trajectory towards recovery were driven by a holistic view of the Truckee River watershed beginning in the early 1990's that envisioned bringing numerous conservation building blocks together to expedite the conservation and recovery for the listed fishes of Pyramid Lake. Although additional challenges remain, the LCT recovery program in the Truckee River basin provides a template for the conservation of imperiled fishes.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10350","usgsCitation":"Al-Chokhachy, R., Heki, L., Loux, T., and Peka, R., 2019, Return of a giant: Coordinated conservation leads to the first wild reproduction of Lahontan Cutthroat Trout in the Truckee River in nearly a century: Fisheries, v. 45, no. 2, p. 63-73, https://doi.org/10.1002/fsh.10350.","productDescription":"11 p.","startPage":"63","endPage":"73","ipdsId":"IP-097760","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":372309,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada ","otherGeospatial":"Truckee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.53674316406249,\n 39.71986348549764\n ],\n [\n -119.24011230468749,\n 39.71986348549764\n ],\n [\n -119.24011230468749,\n 39.8928799002948\n ],\n [\n -119.53674316406249,\n 39.8928799002948\n ],\n [\n -119.53674316406249,\n 39.71986348549764\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Al-Chokhachy, Robert 0000-0002-2136-5098","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":222450,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":782178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heki, Lisa","contributorId":222451,"corporation":false,"usgs":false,"family":"Heki","given":"Lisa","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":782179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loux, Tim","contributorId":222452,"corporation":false,"usgs":false,"family":"Loux","given":"Tim","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":782180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peka, Roger","contributorId":222453,"corporation":false,"usgs":false,"family":"Peka","given":"Roger","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":782181,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223737,"text":"70223737 - 2019 - Distribution and movement of steelhead and anglers in the Clearwater River, Idaho","interactions":[],"lastModifiedDate":"2021-09-07T13:24:39.154511","indexId":"70223737","displayToPublicDate":"2019-08-12T07:14:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Distribution and movement of steelhead and anglers in the Clearwater River, Idaho","docAbstract":"Steelhead Oncorhynchus mykiss is a species that is of high economic value that supports popular sport fisheries across the Pacific Northwest. The Clearwater River in Idaho provides a trophy steelhead fishery, and it is home to both wild- and hatchery-origin steelhead. To manage the fishery effectively, information is needed about the spatial and temporal overlap of wild and hatchery steelhead in the Clearwater River, as well as the activity of anglers. We conducted a radiotelemetry study to describe the distribution of steelhead and their final fate in the Clearwater River, and creel surveys were used to describe the distribution of anglers. In total, 289 wild (Potlatch River and Lochsa River) and hatchery (from Dworshak National Fish Hatchery and South Fork Clearwater River) steelhead were radio-tagged at Lower Granite Dam, 51 river kilometers (rkm) downstream from the mouth of the Clearwater River. Fish were monitored upon their entry into the Clearwater River by using mobile tracking surveys (boat and vehicle) and stationary antennas. The majority of wild and hatchery steelhead arrived in the Clearwater River in the fall with the exception of those from the Lochsa River, which arrived in the fall and following spring. Average daily movement of the fish was minimal (range = 0.3–4.7 km/d) and dependent on water temperature and flow. The fates of wild and hatchery steelhead varied. Fish returned to spawning grounds, were harvested by anglers (hatchery fish only), or had unknown fates. Both wild and hatchery steelhead returned at high rates to their natal tributaries and release locations. No straying was observed in either group; however, occasions when steelhead have overshot their natal tributaries and release locations were documented. Spatial and temporal overlap of the distributions of wild and hatchery steelhead was minimal. The distribution of anglers overlapped with that of hatchery steelhead in the fall, winter, and spring. The distributional overlap of anglers and wild steelhead was minimal and largely occurred in September in the lower Clearwater River. This suggests that the Clearwater River has a highly compartmentalized fishery and that current fishing regulations in the Clearwater River are providing for a diversity of angling opportunities while conserving wild steelhead and offering harvest of hatchery fish. The results from this study have important implications for the conservation and management of wild and hatchery steelhead.
In this study potential wetland extents were estimated for 12 river reaches covering about 750 river miles in Indiana and parts of Illinois and Ohio. The study was completed by the U.S. Geological Survey in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service. This study follows and adds to the work completed in a pilot study and determines that potential wetland extents can be estimated using streamflow statistics, streamgage data, and flood-inundation mapping techniques.
The study was designed to assist in the Agricultural Conservation Easement Program. The Agricultural Conservation Easement Program is a voluntary program administered by the Natural Resources Conservation Service that provides technical and financial assistance to private landowners and Tribes to restore, protect, and enhance wetlands in exchange for retiring eligible land from agriculture. For a site to be eligible for wetland restoration, it should be in a zone with sustained or frequent flooding. This study calculated the flows that lasted for a period of 7 consecutive days on average at least once every 2 years (a value termed the “7MQ2”) for all the U.S. Geological Survey streamgages within the selected river reaches. These 7MQ2 flows were related to the stage-discharge tables for each streamgage, and a corresponding water-surface elevation was determined. Maps of estimated wetland extent were prepared using the 7MQ2 inundation elevation data in conjunction with bare-earth land-surface elevation data made publicly available through the online geospatial data clearinghouses of Indiana, Illinois, and Ohio. Flood-inundation mapping techniques were applied with the aid of geographic information system software to generate water-surface planes that represent inundation elevations associated with the 7MQ2 streamflow. Land-surface elevation data from high-resolution digital elevation models were subtracted from the water-surface planes to produce maps of wetland extent. The 12 map products, including datasets and geoprocessing tools, produced from this study will aid the National Resources Conservation Service and its partners with the onsite inundation-zone verification in agricultural land for potential restoration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195063","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service","usgsCitation":"Fowler, K.K., Sperl, B.J., and Kim, M.H., 2019, Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques: U.S. Geological Survey Scientific Investigations Report 2019–5063, 12 p., https://doi.org/10.3133/sir20195063.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097069","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":366436,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGXDJ8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets related to wetland extent maps for 12 stream reaches covering approximately 750 river miles in Indiana"},{"id":424708,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108893.htm","linkFileType":{"id":5,"text":"html"},"description":"108893"},{"id":424707,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108892.htm","linkFileType":{"id":5,"text":"html"},"description":"108892"},{"id":366472,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://wim.usgs.gov/geonarrative/indianawetlands/","text":"USGS story map","linkHelpText":"– Geo-narrative"},{"id":366438,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5063/coverthb2.jpg"},{"id":366435,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5063/sir20195063.pdf","text":"Report","size":"2.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5063"}],"country":"United States","state":"Illinois, Indiana, Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.582763671875,\n 37.21283151445594\n ],\n [\n -83.924560546875,\n 37.21283151445594\n ],\n [\n -83.924560546875,\n 41.934976500546604\n ],\n [\n -88.582763671875,\n 41.934976500546604\n ],\n [\n -88.582763671875,\n 37.21283151445594\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Pterois volitans (lionfish) is a midsize predatory fish commonly found in waters of the western Pacific and Indian Ocean. The species was first documented in Dania Beach, Florida (northwestern Caribbean) in 1985. Since that time the species has expanded its range rapidly to the Northwestern Atlantic Ocean, Gulf of Mexico, and Caribbean Sea. Since its introduction P. volitans has changed community structure and biodiversity of Caribbean reef communities and other coastal tropical ecosystems. Continuous introductions (accidental or intentional), limited natural predators, naïve-range prey behavior, high predation rates on competitors, continuous reproduction, and an extended period of larval dispersal have been the keys for successful invasion and rapid range extension of P. volitans. This invasion has become so severe that it has been recognized as one of the world’s top conservation issues. Here, we review the life history, behavior, and historical and contemporary genetic patterns that facilitate expansion and the colonization process. A greater understanding of lionfish biology, ecology, and the changes related to its present condition as a super-invader could improve current and future management strategies and new detection and response methodologies. We also examine new invasion frontiers that this species has the potential to colonize such as the eastern Pacific. This information will provide managers, the scientific community, and the civil society better tools for eradication, control and management of future invasions of this and other invasive species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsma.2019.100793","usgsCitation":"Diaz-Ferguson, E., and Hunter, M., 2019, Life history, genetics, range expansion and new frontiers of the lionfish (Pterois volitans, Perciformes: Pteroidae) in Latin America: Regional Studies in Marine Science, v. 31, 100793, 8 p., https://doi.org/10.1016/j.rsma.2019.100793.","productDescription":"100793, 8 p.","ipdsId":"IP-107162","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":366779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bahamas, Belize, Bermuda, Cayman Islands, Colombia, Costa Rica, Cuba, Dominian Republic, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, St. Croix, Turks and Caicos, Venezuela, ","otherGeospatial":"Caribbean Sea, Latin America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.3671875,\n 41.77131167976407\n ],\n [\n -74.091796875,\n 40.91351257612758\n ],\n [\n -76.201171875,\n 37.579412513438385\n ],\n [\n -76.552734375,\n 35.24561909420681\n ],\n [\n -78.22265625,\n 34.379712580462204\n ],\n [\n -81.474609375,\n 31.952162238024975\n ],\n [\n -81.9140625,\n 30.221101852485987\n ],\n [\n -81.03515625,\n 28.459033019728043\n ],\n [\n -80.15625,\n 25.958044673317843\n ],\n [\n -80.5078125,\n 25.3241665257384\n ],\n [\n -81.82617187499999,\n 26.352497858154024\n ],\n [\n -82.529296875,\n 27.916766641249065\n ],\n [\n -82.44140625,\n 28.844673680771795\n ],\n [\n -83.75976562499999,\n 30.06909396443887\n ],\n [\n -84.90234375,\n 29.916852233070173\n ],\n [\n -86.5283203125,\n 30.524413269923986\n ],\n [\n -87.62695312499999,\n 30.524413269923986\n ],\n [\n -88.59374999999999,\n 30.524413269923986\n ],\n [\n -89.7802734375,\n 30.221101852485987\n ],\n [\n -89.912109375,\n 29.53522956294847\n ],\n [\n -91.0986328125,\n 29.611670115197377\n ],\n [\n -91.62597656249999,\n 29.916852233070173\n ],\n [\n -92.8564453125,\n 29.878755346037977\n ],\n [\n -96.064453125,\n 29.075375179558346\n ],\n [\n -97.3388671875,\n 28.033197847676377\n ],\n [\n -97.7783203125,\n 26.115985925333536\n ],\n [\n -98.1298828125,\n 22.998851594142913\n ],\n [\n -97.3388671875,\n 19.932041306115536\n ],\n [\n -95.712890625,\n 18.104087015773956\n ],\n [\n -93.0322265625,\n 18.06231230454674\n ],\n [\n -90.966796875,\n 18.312810846425442\n ],\n [\n -89.8681640625,\n 20.715015145512087\n ],\n [\n -87.099609375,\n 21.04349121680354\n ],\n [\n -87.978515625,\n 19.103648251663646\n ],\n [\n -88.8134765625,\n 17.895114303749143\n ],\n [\n -89.20898437499999,\n 15.876809064146757\n ],\n [\n -84.7705078125,\n 15.241789855961722\n ],\n [\n -83.8037109375,\n 14.944784875088372\n ],\n [\n -83.75976562499999,\n 12.340001834116316\n ],\n [\n -83.9794921875,\n 10.833305983642491\n ],\n [\n -83.14453125,\n 9.579084335882534\n ],\n [\n -81.03515625,\n 8.667918002363121\n ],\n [\n -79.0576171875,\n 9.318990192397905\n ],\n [\n -77.82714843749999,\n 8.928487062665504\n ],\n [\n -77.16796875,\n 7.928674801364048\n ],\n [\n -76.728515625,\n 7.667441482726068\n ],\n [\n -76.2451171875,\n 8.407168163601076\n ],\n [\n -74.92675781249999,\n 10.574222078332806\n ],\n [\n -74.0478515625,\n 10.876464994816295\n ],\n [\n -72.24609375,\n 11.523087506868514\n ],\n [\n -71.455078125,\n 12.12526421833159\n ],\n [\n -72.02636718749999,\n 11.092165893502\n ],\n [\n -69.4775390625,\n 10.833305983642491\n ],\n [\n -65.9619140625,\n 10.14193168613103\n ],\n [\n -62.70996093749999,\n 10.18518740926906\n ],\n [\n -61.12792968750001,\n 9.275622176792112\n ],\n [\n -59.72167968749999,\n 14.987239525774244\n ],\n [\n -60.55664062499999,\n 18.396230138028827\n ],\n [\n -66.09375,\n 19.642587534013032\n ],\n [\n -74.53125,\n 26.07652055985697\n ],\n [\n -62.7978515625,\n 32.54681317351514\n ],\n [\n -71.3671875,\n 41.77131167976407\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diaz-Ferguson, Edgardo","contributorId":139668,"corporation":false,"usgs":false,"family":"Diaz-Ferguson","given":"Edgardo","email":"","affiliations":[{"id":12873,"text":"U.S. Fish and Wildlife Service, Conservation Genetics Laboratory, Warm Springs, Georgia","active":true,"usgs":false}],"preferred":false,"id":768848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":207584,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":768849,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206742,"text":"70206742 - 2019 - Examining the extraction efficiency of petroleum-derived dissolved organic matter in contaminated groundwater plumes","interactions":[],"lastModifiedDate":"2019-11-19T19:09:33","indexId":"70206742","displayToPublicDate":"2019-08-09T19:08:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1864,"text":"Ground Water Monitoring and Remediation","active":true,"publicationSubtype":{"id":10}},"title":"Examining the extraction efficiency of petroleum-derived dissolved organic matter in contaminated groundwater plumes","docAbstract":"The extraction efficiency of petroleum-derived dissolved organic matter (DOM) was examined for groundwater samples from an aquifer contaminated with crude oil. Four different types of extraction were used to determine which method is best suited for the analysis of potentially toxic petroleum-derived DOM. The four types were a liquid-liquid extraction (LLE) with dichloromethane (EPA method 3510C), and three solid-phase extraction (SPE) stationary phases that are routinely used for extraction of polar analytes from water. For the LLE, that is selective for non-polar compounds, the extraction efficiency of petroleum-derived DOM decreased downgradient as the petroleum-derived DOM becomes increasingly polar due to biodegradation. In contrast, the average extraction efficiency by the SPE methods was greater than 65 % across the gradient. The results showed that SPE is more efficient for extracting petroleum-derived DOM at hydrocarbon-contaminated sites. The use of a method with greater extraction efficiency for partially-oxidized hydrocarbons may prove useful in determining relationships between their composition and structure and potential for risks to human health or the environment.","language":"English","publisher":"Wiley","doi":"10.1111/gwmr.12349","usgsCitation":"Zito, P., Ghannam, R., Bekins, B.A., and Podgorski, D.C., 2019, Examining the extraction efficiency of petroleum-derived dissolved organic matter in contaminated groundwater plumes: Ground Water Monitoring and Remediation, v. 39, no. 4, p. 25-31, https://doi.org/10.1111/gwmr.12349.","productDescription":"7 p.","startPage":"25","endPage":"31","ipdsId":"IP-107618","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488826,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwmr.12349","text":"Publisher Index Page"},{"id":369351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Zito, Phoebe","contributorId":206101,"corporation":false,"usgs":false,"family":"Zito","given":"Phoebe","email":"","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":775615,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ghannam, Rana","contributorId":220750,"corporation":false,"usgs":false,"family":"Ghannam","given":"Rana","email":"","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":775616,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bekins, Barbara A. 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":1348,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":775614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Podgorski, David C.","contributorId":178153,"corporation":false,"usgs":false,"family":"Podgorski","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":775617,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205112,"text":"70205112 - 2019 - The effects of restored hydrologic connectivity on floodplain trapping vs. release of phosphorus, nitrogen, and sediment along the Pocomoke River, Maryland USA","interactions":[],"lastModifiedDate":"2019-09-03T17:34:57","indexId":"70205112","displayToPublicDate":"2019-08-09T17:24:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"The effects of restored hydrologic connectivity on floodplain trapping vs. release of phosphorus, nitrogen, and sediment along the Pocomoke River, Maryland USA","docAbstract":"River channelization and artificial levees have decreased the hydrologic connectivity of river-floodplain systems around the world. In response, restoration through enhancing connectivity has been advocated to improve the functions of floodplains, but uncertain benefits and the possibility of phosphate release from re-flooded soils has limited implementation. In this study, we measured change in floodplain P, N, and sediment mass balances after restoration along channelized reaches in the lowland Pocomoke River, Maryland USA. Two floodplains (one headwater, one mainstem) restored through partial levee breaches were compared to two additional mainstem floodplains (one natural unchannelized, one unrestored channelized). Potential soluble reactive P (SRP) release from soil cores during experimental laboratory floods; soil P, Fe, and Al fractionation; and deposition and P and N content of sediment were measured before and after the restoration period, as well as in situ inputs and release of SRP and dissolved inorganic N from soils after restorations. Potential SRP release, during both the before and after restoration period, was greatest at the channelized mainstem and restored mainstem sites, lower at the restored headwater site, and small at the natural mainstem site. Both restored sites had smaller potential SRP release after restoration compared to before restoration. In situ SRP release slightly exceeded inputs to soils at connected sites during the post-restoration period, with less net release at the restored sites compared to the natural mainstem site. The magnitude of gross and net SRP release from soils in the field was smaller than, and uncorrelated with, potential SRP release estimated from laboratory experimental floods. Gross soil SRP release rates in the field were predictable using the ratio of soil oxalate-extractable P/Al. Sedimentation inputs of P and N increased at all sites during the post-restoration period, with rates at restored sites intermediate compared to the much higher rates at the natural mainstem site and somewhat lower rates at the channelized mainstem site. These sediment inputs of nutrients were much larger than rates of inorganic P and N release from soils, indicating net trapping of P and N after restoration. Restoring floodplain hydrologic connectivity showed moderate success at increasing the trapping of P, N, and sediment, with relatively little phosphate release, and therefore improving water quality.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2019.08.002","usgsCitation":"Noe, G.E., Boomer, K., Gillespie, J., Hupp, C.R., Martin-Alciati, M., Floro, K., Schenk, E.R., Jacobs, A.K., and Strano, S., 2019, The effects of restored hydrologic connectivity on floodplain trapping vs. release of phosphorus, nitrogen, and sediment along the Pocomoke River, Maryland USA: Ecological Engineering, v. 138, p. 334-352, https://doi.org/10.1016/j.ecoleng.2019.08.002.","productDescription":"19 p.","startPage":"334","endPage":"352","ipdsId":"IP-106687","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467378,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoleng.2019.08.002","text":"Publisher Index Page"},{"id":367160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Pocomoke River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.5859375,\n 38.0091482264894\n ],\n [\n -75.3717041015625,\n 38.08268954483802\n ],\n [\n -75.29891967773438,\n 38.13887716726548\n ],\n [\n -75.19454956054688,\n 38.28885871419223\n ],\n [\n -75.2838134765625,\n 38.43960662292255\n ],\n [\n -75.35110473632812,\n 38.4514377951069\n ],\n [\n -75.4046630859375,\n 38.4514377951069\n ],\n [\n -75.43899536132812,\n 38.429925130409366\n ],\n [\n -75.53237915039062,\n 38.24680876017446\n ],\n [\n -75.61203002929688,\n 38.212288054388175\n ],\n [\n -75.68206787109375,\n 38.04052046968823\n ],\n [\n -75.66696166992186,\n 37.96152331396614\n ],\n [\n -75.5859375,\n 38.0091482264894\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"138","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - 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2019 - Mapping crop residue by combining Landsat and WorldView-3 satellite imagery","interactions":[],"lastModifiedDate":"2019-08-09T12:33:40","indexId":"70204702","displayToPublicDate":"2019-08-09T12:27:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping crop residue by combining Landsat and WorldView-3 satellite imagery","docAbstract":"A unique, multi-tiered approach was applied to map crop-residue cover on the Eastern Shore of the Chesapeake Bay, USA. Field measurements of crop-residue cover were used to calibrate residue mapping using shortwave infrared (SWIR) indices derived from WorldView-3 imagery for an 8-km x 8-km footprint. The resulting map was then used to calibrate and subsequently classify residue mapping of Landsat imagery at a larger spatial resolution and extent. This manuscript describes how the method was applied and presents results in the form of crop-residue cover maps, validation statistics, and quantification of conservation tillage implementation in the agricultural landscape. Overall accuracy for maps derived from Landsat 7 (ETM+) and Landsat 8 (OLI) were comparable at roughly 92% (+/- 10%). Tillage class specific accuracy was also strong and ranged from 75% to 99%. The approach, which employed a 12-band image stack of six tillage spectral indices and six individual Landsat bands, was shown to be adaptable to variable soil-moisture conditions: under dry conditions (Landsat 7, May 14, 2015) the majority of predictive power was attributed to SWIR indices, and under wet conditions (Landsat 8, May 22, 2015) single band reflectance values were more effective at explaining variability in residue cover. Summary statistics of resulting tillage class occurrence matched closely with conservation tillage implementation totals reported by Maryland and Delaware to the Chesapeake Bay Program. This hybrid method combining WorldView-3 and Landsat imagery sources shows promise for monitoring progress in the adoption of conservation tillage practices and for describing crop-residue outcomes associated with a variety of agricultural management practices.","language":"English","publisher":"MDPI","doi":"10.3390/rs11161857","usgsCitation":"Hively, W.D., Shermeyer, J., Lamb, B.T., Daughtry, C.S., Quemada, M., and Keppler, J., 2019, Mapping crop residue by combining Landsat and WorldView-3 satellite imagery: Remote Sensing, v. 11, no. 16, 1857, 21 p., https://doi.org/10.3390/rs11161857.","productDescription":"1857, 21 p.","ipdsId":"IP-090242","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":467379,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11161857","text":"Publisher Index Page"},{"id":366446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Talbot County","otherGeospatial":"Choptank River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.19842529296875,\n 38.565347844885466\n ],\n [\n -75.728759765625,\n 38.565347844885466\n ],\n [\n -75.728759765625,\n 39.02345139405935\n ],\n [\n -76.19842529296875,\n 39.02345139405935\n ],\n [\n -76.19842529296875,\n 38.565347844885466\n ]\n ]\n ]\n }\n }\n ]\n}\n","volume":"11","issue":"16","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":768123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shermeyer, Jacob 0000-0002-8143-2790","orcid":"https://orcid.org/0000-0002-8143-2790","contributorId":218038,"corporation":false,"usgs":true,"family":"Shermeyer","given":"Jacob","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":768124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamb, Brian T.","contributorId":211092,"corporation":false,"usgs":false,"family":"Lamb","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":768125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daughtry, Craig S.T.","contributorId":214079,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S.T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":768126,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quemada, Miguel","contributorId":211094,"corporation":false,"usgs":false,"family":"Quemada","given":"Miguel","email":"","affiliations":[{"id":38180,"text":"School of Agricultural Engineering and CEIGRAM, Technical University of Madrid","active":true,"usgs":false}],"preferred":false,"id":768127,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keppler, Jason","contributorId":218039,"corporation":false,"usgs":false,"family":"Keppler","given":"Jason","email":"","affiliations":[{"id":39731,"text":"Maryland Department of Agriculture, Office of Resource Conservation","active":true,"usgs":false}],"preferred":false,"id":768128,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203807,"text":"sir20195055 - 2019 - Use of set blanks in reporting pesticide results at the U.S. Geological Survey National Water Quality Laboratory, 2001-15","interactions":[],"lastModifiedDate":"2021-05-27T13:27:52.302572","indexId":"sir20195055","displayToPublicDate":"2019-08-09T09:50:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5055","displayTitle":"Use of Set Blanks in Reporting Pesticide Results at the U.S. Geological Survey National Water Quality Laboratory, 2001–15","title":"Use of set blanks in reporting pesticide results at the U.S. Geological Survey National Water Quality Laboratory, 2001-15","docAbstract":"Background.—Pesticide results from the U.S. Geological Survey (USGS) National Water Quality Laboratory (NWQL) are used for water-quality assessments by many agencies and organizations. The USGS is committed to providing data of the highest possible quality to the consumers of its data. A cooperator’s inquiries about specific pesticide detections in water revealed potential laboratory contamination issues for some results. Consequently, the USGS conducted an extensive evaluation of potential low-level contamination related to processing or analysis of water-quality samples at NWQL for 21 pesticide compounds of interest to the cooperator. This is the most comprehensive study of NWQL pesticide quality-control (QC) results to date.
Purpose and scope.—The purpose of this study was to document protocols used by the NWQL to censor pesticide results and to determine the effects of laboratory contamination—as determined from detections in laboratory set blanks—on pesticide detections in groundwater and surface-water samples. More than 30,000 pesticide results from 113 selected batches of samples (2 percent or less of total batches) analyzed by the NWQL during the 15 years from 2001 to 2015 were reviewed. All laboratory results from the selected batches, including results from environmental (surface water and groundwater) and QC (set-blank, blind-blank, and blind-spike) samples, were evaluated. The study includes results for 21 pesticide compounds analyzed in groundwater and surface-water samples collected across the United States. Eleven pesticide compounds were analyzed by a gas chromatography/mass spectrometry method and 10 compounds by a liquid chromatography/mass spectrometry method.
Objectives and methods.—The objectives of this study were to (1) determine the characteristics of laboratory contamination over time, (2) compare distributions of pesticide results in set blanks with distributions in environmental samples, (3) evaluate the potential for false-positive and false-negative reporting of results, and (4) evaluate the effects of reevaluating historical pesticide results using 2017 compound identification protocols on detections of pesticides in groundwater and surface-water samples. The 113 instrument batches selected for this study contained detections of one or more of the 21 pesticide compounds in set blanks or were among those batches with the highest pesticide detection frequencies in set blanks. As a result, the dataset for this study was targeted toward pesticides and batches with laboratory contamination. The objectives were addressed by statistically comparing environmental and set-blank results; computing moving averages of set-blank detection frequencies to identify periods of episodic contamination; and using summary statistics, tabular summaries, and graphical approaches, such as time-series plots and cumulative distribution functions.
Results.—Objective 1: Laboratory contamination, as determined by pesticide detections in set blanks, was found in 13 percent of set-blank results from the 113 targeted batches included in this study (as compared to 6 percent of set-blank results from all 7,620 batches analyzed during the study period). It is estimated that 92 percent of the laboratory contamination during the study period was episodic, meaning that it occurred during discrete periods of time. All 21 of the targeted pesticide compounds had periods of episodic contamination, with most episodes ranging in duration from about 1 to 8 months. The remaining 8 percent of laboratory contamination was random or from a known source (deterministic).
Objective 2: For some compounds, graphs of cumulative distribution functions of the entire distributions of set-blank and environmental samples overlap, suggesting that there is no difference in the distributions of the two types of samples. However, time-series graphs show that detections in set blanks often occur at different times (sometimes separated by years) than detections in environmental samples, indicating clear differences in those distributions, and indicating the importance of evaluating the timing of detections in all sample types.
For most compounds detected in set-blank and environmental samples, detection frequencies were significantly greater in set blanks than in groundwater or surface-water samples (p<0.05). There are several explanations for this finding, including that the 113 batches of samples chosen for this study targeted batches with detections in set blanks or that detections in set-blank samples were historically determined with less stringent identification criteria than for environmental samples (groundwater and surface-water samples).
Objective 3: The false-positive and false-negative rates from blind samples submitted during the study period by the USGS Quality Systems Branch generally were less than 1 and 5 percent, respectively, for the 21 pesticides. The only compound with a false-positive rate greater than 1 percent was flumetsulam (2.6 percent), indicating that there is a higher likelihood of flumetsulam being reported as a detection when it is not present in an environmental sample compared with the reporting of other compounds.
Objective 4: Altogether, for data in targeted batches, NWQL would have reported 0.1 percent of results from groundwater samples and 1.4 percent of results from surface-water samples differently if 2017 identification protocols were applied to historical pesticide results. In most of these cases, detections observed in historical results would change to nondetections. The small percentages of changes that would occur if historical data were reevaluated indicate that historical protocols used by the NWQL to identify detections in environmental samples were robust and produced results that are predominantly consistent with current [2017] practices.
Conclusions.—The NWQL produces high-quality pesticide results at environmentally relevant concentrations. NWQL identification protocols and censoring practices are largely effective at minimizing the reporting of false-positive and false-negative results. Laboratory contamination, when it occurred, tended to occur in episodes; thus, evaluating the timing and magnitude of detections in set blanks relative to detections in environmental samples was determined to be an important consideration for analysis of environmental results. Because NWQL censoring practices do not address all types and occurrences of laboratory contamination, options for additional censoring practices are provided for data users with more specific or stringent data-quality objectives. The methods used to analyze the 21 compounds for this report can similarly be applied to all 173 pesticide compounds that were analyzed by the NWQL during the same time period. This study also has helped to identify potential improvements in reporting USGS data, such as conducting more frequent review of set-blank datasets.
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U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192