Jewel Cave National Monument in the Black Hills of southwestern South Dakota has more than 200 miles of mapped cave passages and several subterranean lakes that have been discovered since 2015. Jewel Cave is one of the world’s longest known caves and its natural beauty and unique natural cave features led U.S. President Theodore Roosevelt to designate the cave as a national monument in 1908. Jewel Cave was naturally formed in the regionally extensive Madison Limestone, which is characterized as a carbonate karst environment (containing caves and sinkholes) with extensive subterranean cave networks and losing streams at the land surface. Preserving and protecting the cave is an important element of the National Park Service mission, and understanding the hydrogeologic connection between the surface and the subsurface is essential for ensuring the preservation and protection of the cave for future generations. A component in preserving and protecting the park includes the improved understanding of groundwater flow and vulnerability of the subsurface, which allows scientists, park managers, the visiting public, and the surrounding communities to better manage, protect, and preserve the site and its unique natural features.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193072","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Valder, J.F., Carter, J.M., Wiles, M.E., and Heimel, S.M., 2019, Groundwater characterization of the Madison aquifer near Jewel Cave National Monument, South Dakota: U.S. Geological Survey Fact Sheet 2019–3072, 6 p., https://doi.org/10.3133/fs20193072.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-112578","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":369683,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20195098","text":"SIR 2019–5098","size":"5.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5098","linkHelpText":"– Generalized Potentiometric-Surface Map and Groundwater Flow Directions in the Madison Aquifer Near Jewel Cave National Monument, South Dakota"},{"id":369681,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3072/coverthb.jpg"},{"id":369682,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3072/fs20193072.pdf","text":"Report","size":"3.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3072"}],"country":"United States","state":"South Dakota","otherGeospatial":"Jewel Cave National Monument, Madison Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.01443481445311,\n 43.51917817047661\n ],\n [\n -103.55781555175781,\n 43.51917817047661\n ],\n [\n -103.55781555175781,\n 43.9058083561574\n ],\n [\n -104.01443481445311,\n 43.9058083561574\n ],\n [\n -104.01443481445311,\n 43.51917817047661\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Here, we present the results of a taxonomic survey of the nematodes parasitizing fishes from the lagoon flats of Palmyra Atoll, Eastern Indo-Pacific. We performed quantitative parasitological surveys of 653 individual fish from each of the 44 species using the intertidal sand flats that border the atoll’s lagoon. We provide morphological descriptions, prevalence, and mean intensities of the recovered seven species of adult nematode (Pulchrascaris chiloscyllii, Capillariidae gen. sp., Cucullanus bourdini, Cucullanus oceaniensis, Pseudascarophis sp., Spinitectus (Paraspinitectus) palmyraensis sp. nov., Philometra pellucida) and three larval stages (Pulchrascaris sp., Hysterothylacium sp., Cucullanus sp.). We recorded: Pulchrascaris chiloscyllii from Carcharhinus melanopterus; Capillariidae gen. sp. from Chaetodon lunula, Lutjanus fulvus, and Ellochelon vaigiensis; Cucullanus bourdini from Arothron hispidus; Cucullanus oceaniensis from Abudefduf sordidus; Pseudascarophis sp. from Chaetodon auriga, Chaetodon lunula, and Mulloidichthys flavolineatus; Spinitectus (Paraspinitectus) palmyraensis sp. nov. from Albula glossodonta; Philometra pellucida from Arothron hispidus; and three larval forms, Pulchrascaris sp. from Acanthurus triostegus, Acanthurus xanthopterus, Rhinecanthus aculeatus, Platybelone argalus, Carangoides ferdau, Carangoides orthogrammus, Caranx ignobilis, Caranx melampygus, Caranx papuensis, Chaetodon auriga, Chanos chanos, Amblygobius phalaena, Asterropteryx semipunctata, Valencienea sexguttata, Kyphosus cinerascens, Lutjanus fulvus, Lutjanus monostigma, Ellochelon vaigiensis, Mulloidichthys flavolineatus, Upeneus taeniopterus, Gymnothorax pictus, Abudefduf septemfasciatus, Abudefduf sordidus, and Stegastes nigricans; Hysterothylacium sp. type MD from Acanthurus triostegus, Carangoides ferdau, Chaetodon lunula, Chanos chanos, Kyphosus cinerascens, Abudefduf sordidus, and Arothron hispidus; and Cucullanus sp. from Caranx ignobilis. Spinitectus (Paraspinitectus) palmyraensis sp. nov. (Cystidicolidae) is described from the intestine of roundjaw bonefish Albula glossodonta. All the nematode species reported in this study represent new geographical records. We discuss how our survey findings compare to other areas of the Indo-Pacific, and the way the relatively numerical dominance of trophically transmitted larval stages likely reflect the intact food web of Palmyra Atoll, which includes a large biomass of large-bodied top predator sharks and ray-finned fishes.
","language":"English","publisher":"Pensoft","doi":"10.3897/zookeys.892.38447","collaboration":"","usgsCitation":"Gonzalez-Solis, D., Soler-Jimenez, L.C., Aguirre-Macedo, M., McLaughlin, J.P., Shaw, J.C., James, A.K., Hechinger, R.F., Kuris, A.M., Lafferty, K.D., and Vidal-Martinez, V.M., 2019, Parasitic nematodes of marine fishes from Palmyra Atoll, East Indo-Pacific, including a new species of Spinitectus (Nematoda, Cystidicolidae): ZooKeys, v. 892, p. 1-26, https://doi.org/10.3897/zookeys.892.38447.","productDescription":"26 p.","startPage":"1","endPage":"26","ipdsId":"IP-110806","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":459100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/zookeys.892.38447","text":"Publisher Index Page"},{"id":374395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Palmyra Atoll","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.1249008178711,\n 5.856133034374881\n ],\n [\n -162.01950073242188,\n 5.856133034374881\n ],\n [\n -162.01950073242188,\n 5.901213306620142\n ],\n [\n -162.1249008178711,\n 5.901213306620142\n ],\n [\n -162.1249008178711,\n 5.856133034374881\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"892","noUsgsAuthors":false,"publicationDate":"2019-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez-Solis, David","contributorId":224406,"corporation":false,"usgs":false,"family":"Gonzalez-Solis","given":"David","email":"","affiliations":[{"id":40876,"text":"El Colegio de la Frontera Sur, unidad Chetumal, Chetumal, Mexico","active":true,"usgs":false}],"preferred":false,"id":788196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soler-Jimenez, Lilia Catherinne","contributorId":200199,"corporation":false,"usgs":false,"family":"Soler-Jimenez","given":"Lilia","email":"","middleInitial":"Catherinne","affiliations":[],"preferred":false,"id":788197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aguirre-Macedo, M Leopoldina","contributorId":224407,"corporation":false,"usgs":false,"family":"Aguirre-Macedo","given":"M Leopoldina","affiliations":[{"id":40877,"text":"Laboratorio de Patología Acuática, Centro de Investigación y de Estudios Avanzados, Mérida","active":true,"usgs":false}],"preferred":false,"id":788198,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McLaughlin, John P. 0000-0002-8756-2123","orcid":"https://orcid.org/0000-0002-8756-2123","contributorId":203516,"corporation":false,"usgs":false,"family":"McLaughlin","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":788199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shaw, Jenny C.","contributorId":189858,"corporation":false,"usgs":false,"family":"Shaw","given":"Jenny","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":788200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"James, Anna K","contributorId":216520,"corporation":false,"usgs":false,"family":"James","given":"Anna","email":"","middleInitial":"K","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":788201,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hechinger, Ryan F.","contributorId":177653,"corporation":false,"usgs":false,"family":"Hechinger","given":"Ryan","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":788202,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kuris, Armand M.","contributorId":189859,"corporation":false,"usgs":false,"family":"Kuris","given":"Armand","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":788203,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":788204,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vidal-Martinez, Victor Manuel","contributorId":200198,"corporation":false,"usgs":false,"family":"Vidal-Martinez","given":"Victor","email":"","middleInitial":"Manuel","affiliations":[],"preferred":false,"id":788205,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70206037,"text":"sir20195117 - 2019 - Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota","interactions":[],"lastModifiedDate":"2019-11-27T09:54:48","indexId":"sir20195117","displayToPublicDate":"2019-11-27T06:42:07","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":"2019-5117","displayTitle":"Groundwater-Flow Model and Analysis of Groundwater and Surface-Water Interactions for the Big Sioux Aquifer, Sioux Falls, South Dakota","title":"Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota","docAbstract":"The city of Sioux Falls, in southeastern South Dakota, is the largest city in South Dakota. The U.S. Geological Survey (USGS), in cooperation with the city of Sioux Falls, completed a groundwater-flow model to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer in the model area.
The model area includes the Big Sioux aquifer and the underlying hydrogeologic units from Dell Rapids, South Dakota, to the confluence of the Big Sioux River and the outlet of the Sioux Falls Diversion Channel in eastern Sioux Falls, S. Dak. The Big Sioux aquifer is the primary aquifer in the model area and the focus of the groundwater-flow model. The Big Sioux River is the largest stream in the model area and is in hydraulic connection with the Big Sioux aquifer.
A conceptual model for the area was constructed and includes a characterization of the hydrogeologic framework, analysis and construction of potentiometric surfaces, and summary of estimated water budget components in the model area. The primary hydrogeologic units in the model area consist of (1) the Big Sioux aquifer, (2) a glacial till confining unit, and (3) bedrock aquifers (Split Rock Creek and Sioux Quartzite aquifers). Sources of groundwater recharge included infiltration of precipitation, stream seepage, and groundwater exchanges among the hydraulically connected Big Sioux aquifer, glacial till confining unit, and bedrock aquifers. Groundwater losses included evapotranspiration, groundwater discharge to streams, and groundwater withdrawal to supply water-use needs.
A numerical groundwater-flow model (numerical model) was constructed and was used to simulate all aspects of the conceptual model for predevelopment (steady-state) and time-varying (transient) monthly conditions for 1950–2017. The numerical model was constructed using the USGS modular hydrologic simulation program, MODFLOW–6, and was calibrated using the Parameter ESTimation software, PEST++.
The transient numerical model was calibrated for steady-state and transient monthly conditions for 1950–2017. Calibration targets were observations of hydraulic head, changes in hydraulic head, monthly mean streamflow (as a rate), and cumulative monthly stream discharge (as a volume). Parameters adjusted during model calibration were horizontal and vertical hydraulic conductivity, specific storage, specific yield, recharge and evapotranspiration multipliers, and streambed hydraulic conductivity. Horizontal and vertical hydraulic conductivity were estimated at pilot points distributed within the model area; specific storage and specific yield were assigned to uniform values in each layer in the model area; recharge and evapotranspiration multipliers were assigned uniformly for every stress period in the numerical model; and streambed hydraulic conductivity values were assigned uniformly between stream confluences.
The final calibrated parameter values of horizontal and vertical hydraulic conductivity, specific yield, specific storage, streambed hydraulic conductivity, recharge, and evapotranspiration were considered reasonable for the hydrogeologic materials and conditions in the model area for 1950–2017.
Overall, simulated hydraulic head altitudes had a linear regression coefficient of determination (R2) of 0.48. Hydraulic head altitude residuals for the glacial till confining unit and bedrock aquifers were typically greater in magnitude when compared to residuals in the Big Sioux aquifer, but simulated hydraulic head altitudes in the Big Sioux aquifer compared favorably with mean observed hydraulic head altitudes and had a linear regression R2 of 0.93.
Simulated streamflow hydrographs matched the general trends of observed increases and decreases in streamflow for USGS streamgages 06482000 (Big Sioux River at Sioux Falls, S. Dak.) and 06482020 (Big Sioux River at North Cliff Avenue at Sioux Falls, S. Dak.), but larger streamflows were overestimated at the first streamgage and underestimated at the second streamgage. The numerical model reasonably estimated cumulative monthly stream discharge for the first 10–15 years of available streamflow records at both USGS streamgages. After the first 10–15 years of available streamflow record, cumulative monthly stream discharge was closely estimated for USGS streamgage 06482000 and underestimated at USGS streamgage 06482020.
Composite sensitivities without regularization were calculated by PEST++ for the calibrated numerical model parameters and were averaged by parameter group. The parameter group with the highest mean composite sensitivity was the recharge multiplier parameter group.
Model simplifications, assumptions, and limitations were necessary for construction of the conceptual and numerical models and for calibration efficiency. Spatial simplification of hydraulic properties could cause the numerical model to misrepresent reactions to changes in localized stresses, such as additional demands for groundwater withdrawal. The numerical model was temporally discretized into monthly periods and required scaling daily rates into representative monthly rates for model input and calibration targets. Based on the comparison between the observed and simulated groundwater levels, monthly mean streamflow and cumulative monthly stream discharge, and general groundwater distribution and flow, the numerical model favorably simulated the flow in the Big Sioux aquifer.
Eventual capture was calculated in the model area using a steady-state numerical groundwater-flow model. The eventual capture map shows areas of higher streamflow capture adjacent to the Big Sioux River north of the city of Sioux Falls and along the lower part of the Sioux Falls Diversion Channel, and areas of lower streamflow capture along aquifer boundaries and near the southern Sioux Quartzite barrier.
The timing of capture was determined using a transient numerical groundwater-flow model to determine the likely captured water sources for 30 years of groundwater withdrawal at three hypothetical wells using three continuous withdrawal rates (112.5, 450.0, and 900.0 gallons per minute). Supply for all three hypothetical wells became capture-dominated after only a short period of continuous withdrawal. Capture stabilized after about 10–15 years for well A, and after 20–25 years for well B, and after about 10–15 years for well C.
The groundwater-flow model is a suitable tool to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer near Sioux Falls, S. Dak. The numerical model can be used to simulate hydrologic scenarios, advance understanding of groundwater budgets, compute system response to stress, and determine likely sources of water supplied to wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195117","collaboration":"Prepared in cooperation with the city of Sioux Falls","usgsCitation":"Davis, K.W., Eldridge, W.G., Valder, J.F., and Valseth, K.J., 2019, Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5117, 86 p., https://doi.org/10.3133/sir20195117.","productDescription":"Report: xi, 86 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-105956","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":369602,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20195013","text":"SIR 2019–5013","linkHelpText":"– Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota"},{"id":369600,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3393","text":"SIM 3393","linkHelpText":"– Delineation of the hydrogeologic framework of the Big Sioux aquifer near Sioux Falls, South Dakota, using airborne electromagnetic data"},{"id":369601,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F79885XC","text":"USGS data release for SIM 3393","linkHelpText":"– Airborne electromagnetic and magnetic survey data, Big Sioux aquifer, October 2015, Sioux Falls, South Dakota"},{"id":369603,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9LUB44J","text":"USGS data release for SIR 2019–5013","linkHelpText":"– Water-level data and AQTESOLV Pro analysis results for slug tests in the Big Sioux Aquifer, Sioux Falls, South Dakota, 2017"},{"id":369535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5117/coverthb.jpg"},{"id":369536,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5117/sir20195117.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5117"},{"id":369537,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O59RO0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-6 model of the Big Sioux aquifer, Sioux Falls, South Dakota"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.06146240234375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.29919735147067\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 57th Annual Report of the River Master of the Delaware River. It covers the 2010 River Master report year, the period from December 1, 2009, to November 30, 2010.
During the report year, precipitation in the upper Delaware River Basin was 49.38 inches or 112 percent of the long-term average. Combined storage in Pepacton, Cannonsville, and Neversink Reservoirs remained high much of the year and did not decline below 80 percent of combined capacity until September 2010. A lower basin drought warning was issued by the Delaware River Basin Commission on September 24, 2010. It automatically ended on October 31, 2010, when the reservoir contents rose above drought levels, due in large part to heavy rainfall during the last week of September. River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 81 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were made during the report year.
The quality of water in the Delaware Estuary between Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at various locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191093","usgsCitation":"Russell, K.L., Ockerman, D., Krejmas, B.E., Paulachok, G.N., and Mason, R.R., Jr., 2019, Report of the River Master of the Delaware River for the period December 1, 2009–November 30, 2010: U.S. Geological Survey Open-File Report 2019–1093, 128 p., https://doi.org/10.3133/ofr20191093.","productDescription":"x, 128 p.","numberOfPages":"142","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-099205","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":369615,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1093/ofr20191093.pdf","text":"Report","size":"3.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1093"},{"id":368735,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1093/coverthb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.278076171875,\n 39.32579941789298\n ],\n [\n -74.608154296875,\n 39.32579941789298\n ],\n [\n -74.608154296875,\n 42.68243539838623\n ],\n [\n -76.278076171875,\n 42.68243539838623\n ],\n [\n -76.278076171875,\n 39.32579941789298\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Deputy Delaware River Master
Office of the Delaware River Master
U.S. Geological Survey
120 Route 209 South
Milford, PA 18337
The main purpose of this study was to demonstrate the utility of the sediment fingerprinting approach to apportion surface-derived sediment, and then age date that portion using short-lived fallout radionuclides. In systems where a large mass of mobile sediment is in channel storage, age dating provides an understanding of the transfer of sediment through the watershed and the time scales over which management actions to reduce sediment loadings may be effective.
In the agricultural Walnut Creek watershed, Iowa, the sediment-fingerprinting approach with elemental analysis was used to apportion the sources of fine-grained sediment (croplands, prairie, unpaved roads, and channel banks). Fallout radionuclides (7Be, 210Pbex) were used to age the portion of suspended sediment that was derived from agricultural topsoil. Age dating was performed at two different scales: 210Pbex which can date sediment to ~ 85 years and 7Be to ~ 1 year.
Sediment fingerprinting results indicated that the majority of suspended sediment is derived from cropland (62%) with streambanks contributing 36%, and prairie, pasture, and unpaved roads each contributing ≤ 1%. The topsoil–derived portion of sediment (primarily agriculture) dated using 210Pbex has ages ranging from 1 to 58 years, and using 7Be, a component of much younger sediment that yields ages ranging from 44 to 205 days. The occurrence of 7Be indicates that some portion of the sediment is young, on the order of months, whereas the dating based on 210Pbex indicates that some of the surface-derived sediment has been in channel storage for decades. Published studies in Walnut Creek indicate that a large component of sediment is stored in the channel bed.
We conclude that the 210Pbex-based ages are a reasonable estimate for the mean age of the surface-derived fraction and that 7Be activities are evidence that there is a smaller fraction of very young sediment in the stream. We propose a geomorphic model where agricultural soil is delivered to the channel and conveyed to the watershed outlet at three time scales: a geologic-millennial time scale, decades, and a young time scale (< 1 year).
","language":"English","publisher":"Springer","doi":"10.1007/s11368-018-2168-z","usgsCitation":"Gellis, A.C., Fuller, C.C., Van Metre, P.C., Filstrup, C.T., Cole, K., and Sabitov, T., 2019, Combining sediment fingerprinting with age-dating sediment using fallout radionuclides for an agricultural stream, Walnut Creek, Iowa, USA: Journal of Soils and Sediments, v. 19, p. 3374-3396, https://doi.org/10.1007/s11368-018-2168-z.","productDescription":"23 p.","startPage":"3374","endPage":"3396","ipdsId":"IP-090014","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":377887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","county":"Jasper County","otherGeospatial":"Walnut Creek","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-93.234,41.8622],[-93.1187,41.8624],[-93.0035,41.8624],[-92.8845,41.8619],[-92.7674,41.8618],[-92.7683,41.776],[-92.768,41.6879],[-92.7683,41.6007],[-92.7567,41.6011],[-92.7564,41.509],[-92.8729,41.5082],[-92.9894,41.5083],[-93.1047,41.5078],[-93.2181,41.5076],[-93.3304,41.5074],[-93.3314,41.6004],[-93.3504,41.6004],[-93.3496,41.688],[-93.3494,41.7757],[-93.3492,41.8624],[-93.234,41.8622]]]},\"properties\":{\"name\":\"Jasper\",\"state\":\"IA\"}}]}","volume":"19","noUsgsAuthors":false,"publicationDate":"2018-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":797341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Metre, Peter C. 0000-0001-7564-9814","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":211144,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":797342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Filstrup, Christopher T.","contributorId":169032,"corporation":false,"usgs":false,"family":"Filstrup","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":797343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cole, Kevin","contributorId":208183,"corporation":false,"usgs":false,"family":"Cole","given":"Kevin","email":"","affiliations":[{"id":37761,"text":"USDA-ARS, National Laboratory for Agriculture and the Environment, 1015 N. University Blvd, Ames. IA 50011","active":true,"usgs":false}],"preferred":false,"id":797344,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sabitov, Timur","contributorId":236885,"corporation":false,"usgs":false,"family":"Sabitov","given":"Timur","email":"","affiliations":[{"id":47559,"text":"Geology and Geophysics, Academy of Science of Uzbekistan","active":true,"usgs":false}],"preferred":false,"id":797345,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208804,"text":"70208804 - 2019 - Marine fog inputs appear to increase methylmercury bioaccumulation in a coastal terrestrial food web","interactions":[],"lastModifiedDate":"2020-03-02T10:01:56","indexId":"70208804","displayToPublicDate":"2019-11-26T09:54:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Marine fog inputs appear to increase methylmercury bioaccumulation in a coastal terrestrial food web","docAbstract":"Coastal marine atmospheric fog has recently been implicated as a potential source of ocean-derived monomethylmercury (MMHg) to coastal terrestrial ecosystems through the process of sea-to-land advection of foggy air masses followed by wet deposition. This study examined whether pumas (Puma concolor) in coastal central California, USA, and their associated food web, have elevated concentrations of MMHg, which could be indicative of their habitat being in a region that is regularly inundated with marine fog. We found that adult puma fur and fur-normalized whiskers in our marine fog-influenced study region had a mean (±SE) total Hg (THg) (a convenient surrogate for MMHg) concentration of 1544 ± 151 ng g−1 (N = 94), which was three times higher (P < 0.01) than mean THg in comparable samples from inland areas of California (492 ± 119 ng g−1, N = 18). Pumas in California eat primarily black-tailed and/or mule deer (Odocoileus hemionus), and THg in deer fur from the two regions was also significantly different (coastal 28.1 ± 2.9, N = 55, vs. inland 15.5 ± 1.5 ng g−1, N = 40). We suggest that atmospheric deposition of MMHg through fog may be contributing to this pattern, as we also observed significantly higher MMHg concentrations in lace lichen (Ramalina menziesii), a deer food and a bioindicator of atmospheric deposition, at sites with the highest fog frequencies. At these ocean-facing sites, deer samples had significantly higher THg concentrations compared to those from more inland bay-facing sites. Our results suggest that fog-borne MMHg, while likely a small fraction of Hg in all atmospheric deposition, may contribute, disproportionately, to the bioaccumulation of Hg to levels that approach toxicological thresholds in at least one apex predator. As global mercury levels increase, coastal food webs may be at risk to the toxicological effects of increased methylmercury burdens.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-019-54056-7","usgsCitation":"Weiss-Penzias, P.S., Bank, M.S., Clifford, D.L., Torregrosa, A.A., Zheng, B., Lin, W., and Wilmers, C.C., 2019, Marine fog inputs appear to increase methylmercury bioaccumulation in a coastal terrestrial food web: Scientific Reports, v. 9, 17611, 11 p., https://doi.org/10.1038/s41598-019-54056-7.","productDescription":"17611, 11 p.","ipdsId":"IP-107344","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":459108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-54056-7","text":"Publisher Index Page"},{"id":372762,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Cruz Mountain coastal region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.7777099609375,\n 36.619936625629215\n ],\n [\n -121.47033691406249,\n 36.619936625629215\n ],\n [\n -121.47033691406249,\n 37.80978395301097\n ],\n [\n -122.7777099609375,\n 37.80978395301097\n ],\n [\n -122.7777099609375,\n 36.619936625629215\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2019-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiss-Penzias, Peter S.","contributorId":222895,"corporation":false,"usgs":false,"family":"Weiss-Penzias","given":"Peter","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":783458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bank, Michael S.","contributorId":10684,"corporation":false,"usgs":true,"family":"Bank","given":"Michael","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":783459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clifford, Deana L.","contributorId":13556,"corporation":false,"usgs":true,"family":"Clifford","given":"Deana","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":783460,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":783461,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zheng, Belle","contributorId":222905,"corporation":false,"usgs":false,"family":"Zheng","given":"Belle","email":"","affiliations":[],"preferred":false,"id":783462,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lin, Wendy","contributorId":222906,"corporation":false,"usgs":false,"family":"Lin","given":"Wendy","email":"","affiliations":[],"preferred":false,"id":783463,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilmers, Christopher C.","contributorId":150642,"corporation":false,"usgs":false,"family":"Wilmers","given":"Christopher","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":783464,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70206894,"text":"70206894 - 2019 - Phenology patterns indicate recovery trajectories of ponderosa pine forests after high-severity fires","interactions":[],"lastModifiedDate":"2019-12-09T15:04:14","indexId":"70206894","displayToPublicDate":"2019-11-26T08:49:29","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":"Phenology patterns indicate recovery trajectories of ponderosa pine forests after high-severity fires","docAbstract":"Post-fire recovery trajectories in ponderosa pine (Pinus ponderosa Laws.) forests of the US Southwest are increasingly shifting away from pre-burn vegetation communities. This study investigated whether phenological metrics derived from a multi-decade remotely sensed imagery time-series could differentiate among grass, evergreen shrub, deciduous, or conifer-dominated replacement pathways. We focused on 10 fires that burned ponderosa pine forests in Arizona and New Mexico, USA before the year 2000. A total of 29 sites with discernable post-fire recovery signals were selected within high-severity burn areas. At each site, we used Google Earth Engine to derive time-series of normalized difference vegetation index (NDVI) signals from Landsat Thematic Mapper, Enhanced Thematic Mapper+, and Operational Line Imager data from 1984 to 2017. We aggregated values to 8- and 16-day intervals, fit Savitzy-Golay filters to each sequence, and extracted annual phenology metrics of amplitude, base value, peak value, and timing of peak value in the Timesat analysis package. Results show that relative to post-fire conditions, pre-burn ponderosa pine forests exhibit significantly lower mean NDVI amplitude (0.14 vs. 0.21), higher mean base NDVI (0.47 vs. 0.22), higher mean peak NDVI (0.60 vs. 0.43), and later mean peak NDVI (day of year 277 vs. 237). Vegetation succession exhibits distinct phenometric characteristics as early as year five (amplitude) and as late as year 20 (timing of peak NDVI). This study confirms the feasibility of leveraging phenology metrics derived from long-term imagery time series to identify and monitor ecological outcomes. This information may be of benefit to land resource managers who seek indicators of future landscape composition to inform management strategies.","language":"English","publisher":"MDPI","doi":"10.3390/rs11232782","usgsCitation":"Walker, J.J., and Soulard, C.E., 2019, Phenology patterns indicate recovery trajectories of ponderosa pine forests after high-severity fires: Remote Sensing, v. 11, no. 23, 2782, https://doi.org/10.3390/rs11232782.","productDescription":"2782","ipdsId":"IP-112858","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":459110,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11232782","text":"Publisher Index Page"},{"id":437276,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y1Z03F","text":"USGS data release","linkHelpText":"Phenology pattern data indicating recovery trajectories of ponderosa pine forests after high-severity fires"},{"id":369699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New Mexico","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-112.538593,37.000674],[-111.278286,37.000465],[-110.50069,37.00426],[-110.490908,37.003566],[-110.47019,36.997997],[-106.877292,37.000139],[-106.869796,36.992426],[-105.447255,36.996017],[-105.1208,36.995428],[-105.029228,36.992729],[-104.338833,36.993535],[-103.734364,36.998041],[-103.002199,37.000104],[-103.002434,36.500397],[-103.041924,36.500439],[-103.040824,36.055231],[-103.043531,34.018014],[-103.064625,32.999899],[-103.064423,32.000518],[-105.428582,32.0006],[-106.125534,32.002533],[-106.618486,32.000495],[-106.619448,31.994733],[-106.623568,31.990999],[-106.631182,31.989809],[-106.636492,31.985719],[-106.639529,31.980348],[-106.638186,31.97682],[-106.630114,31.971258],[-106.626466,31.97069],[-106.623216,31.97291],[-106.619569,31.971578],[-106.619371,31.964777],[-106.624299,31.961054],[-106.625123,31.954531],[-106.622819,31.952891],[-106.614702,31.956],[-106.616136,31.948439],[-106.623659,31.94551],[-106.622529,31.934863],[-106.629747,31.92657],[-106.628663,31.923614],[-106.623933,31.925335],[-106.611846,31.920003],[-106.633668,31.90979],[-106.645479,31.89867],[-106.645646,31.895649],[-106.6429,31.892933],[-106.633927,31.889184],[-106.629197,31.883717],[-106.634873,31.874478],[-106.635926,31.866235],[-106.627808,31.860593],[-106.625763,31.856276],[-106.614637,31.84649],[-106.605845,31.846305],[-106.602045,31.844405],[-106.605267,31.827912],[-106.602727,31.825024],[-106.593826,31.824901],[-106.589045,31.822706],[-106.577244,31.810406],[-106.570944,31.810206],[-106.566844,31.813306],[-106.547144,31.807305],[-106.535843,31.798607],[-106.533043,31.791907],[-106.527943,31.790507],[-106.528543,31.784407],[-108.208394,31.783599],[-108.208573,31.333395],[-108.851105,31.332301],[-110.140512,31.333965],[-111.074825,31.332239],[-111.560194,31.488138],[-112.246102,31.704195],[-112.867074,31.895488],[-113.125961,31.97278],[-113.78168,32.179034],[-114.250775,32.32391],[-114.813613,32.494277],[-114.812635,32.506918],[-114.807726,32.508726],[-114.804694,32.512476],[-114.804958,32.517506],[-114.809723,32.520153],[-114.811576,32.523594],[-114.810563,32.527666],[-114.802181,32.536414],[-114.802018,32.53946],[-114.805966,32.545346],[-114.803883,32.548002],[-114.793769,32.552329],[-114.791551,32.557023],[-114.791988,32.560652],[-114.795959,32.564093],[-114.804429,32.561976],[-114.808929,32.561976],[-114.810517,32.563828],[-114.808929,32.569652],[-114.801877,32.57601],[-114.803987,32.582652],[-114.800441,32.58808],[-114.799683,32.593621],[-114.801548,32.598591],[-114.807906,32.602783],[-114.809393,32.617119],[-114.80739,32.621332],[-114.799302,32.625115],[-114.791179,32.621833],[-114.781872,32.62505],[-114.782235,32.630215],[-114.779215,32.633579],[-114.764382,32.642666],[-114.76495,32.649391],[-114.748,32.664184],[-114.744491,32.678671],[-114.730453,32.698843],[-114.730086,32.704298],[-114.722746,32.713071],[-114.714522,32.73039],[-114.701918,32.745548],[-114.688779,32.737675],[-114.618373,32.728245],[-114.615585,32.728446],[-114.614772,32.734089],[-114.612697,32.734516],[-114.581784,32.734946],[-114.581736,32.742321],[-114.564508,32.742298],[-114.564447,32.749554],[-114.539224,32.749812],[-114.539093,32.756949],[-114.526856,32.757094],[-114.532432,32.776923],[-114.531669,32.791185],[-114.528849,32.796307],[-114.522031,32.801675],[-114.510217,32.816417],[-114.494116,32.823288],[-114.468971,32.845155],[-114.462929,32.907944],[-114.464448,32.913129],[-114.47664,32.923628],[-114.48092,32.935252],[-114.469113,32.952673],[-114.467664,32.966861],[-114.469039,32.972295],[-114.476156,32.975168],[-114.488625,32.969946],[-114.492938,32.971781],[-114.499797,33.003905],[-114.511343,33.023455],[-114.523578,33.030961],[-114.571653,33.036624],[-114.578287,33.035375],[-114.584765,33.028231],[-114.589778,33.026228],[-114.618788,33.027202],[-114.628293,33.031052],[-114.639553,33.045291],[-114.64598,33.048903],[-114.657827,33.033825],[-114.662317,33.032671],[-114.673659,33.041897],[-114.674296,33.057171],[-114.686991,33.070969],[-114.68912,33.076122],[-114.688597,33.082869],[-114.692548,33.085786],[-114.706488,33.08816],[-114.707896,33.097432],[-114.703682,33.113769],[-114.696829,33.131209],[-114.687074,33.142196],[-114.679359,33.159519],[-114.680248,33.169717],[-114.67536,33.185489],[-114.678749,33.203448],[-114.673626,33.223121],[-114.689421,33.24525],[-114.688205,33.247966],[-114.672088,33.258499],[-114.677032,33.27017],[-114.680507,33.273577],[-114.694449,33.279786],[-114.72167,33.286982],[-114.731223,33.302434],[-114.723623,33.31211],[-114.707962,33.323421],[-114.700938,33.337014],[-114.698035,33.352442],[-114.699053,33.361148],[-114.707348,33.376628],[-114.70731,33.382542],[-114.722872,33.398779],[-114.725535,33.404056],[-114.723829,33.406531],[-114.701732,33.408388],[-114.697707,33.410942],[-114.695655,33.415127],[-114.687953,33.417944],[-114.673901,33.418299],[-114.658382,33.413036],[-114.64954,33.413633],[-114.643302,33.416745],[-114.62964,33.428138],[-114.622283,33.447558],[-114.622918,33.456561],[-114.612472,33.470768],[-114.601696,33.481394],[-114.591554,33.499443],[-114.580468,33.506465],[-114.569533,33.509219],[-114.560963,33.516739],[-114.558898,33.531819],[-114.524599,33.552231],[-114.535664,33.568788],[-114.5403,33.580615],[-114.540617,33.591412],[-114.524813,33.611351],[-114.524619,33.61426],[-114.529662,33.622794],[-114.526947,33.637534],[-114.530244,33.65014],[-114.525783,33.657122],[-114.525201,33.661583],[-114.529706,33.668031],[-114.531523,33.675108],[-114.530348,33.679245],[-114.523959,33.685879],[-114.496489,33.696901],[-114.494197,33.707922],[-114.496565,33.719155],[-114.512348,33.734214],[-114.504483,33.750998],[-114.504863,33.760465],[-114.520094,33.799473],[-114.52805,33.814963],[-114.522714,33.818979],[-114.51997,33.825381],[-114.529597,33.848063],[-114.528451,33.854929],[-114.52453,33.858477],[-114.514673,33.858638],[-114.505638,33.864276],[-114.503017,33.867998],[-114.50434,33.876882],[-114.516501,33.885926],[-114.518928,33.891714],[-114.516314,33.896196],[-114.508708,33.90064],[-114.50792,33.903807],[-114.511511,33.911092],[-114.518434,33.917518],[-114.533679,33.926072],[-114.535478,33.934651],[-114.52868,33.947817],[-114.522002,33.955623],[-114.51586,33.958106],[-114.509568,33.957264],[-114.499883,33.961789],[-114.467932,33.992877],[-114.46117,33.994687],[-114.460415,33.999215],[-114.463132,34.00661],[-114.46117,34.010081],[-114.450206,34.012574],[-114.443821,34.016176],[-114.438266,34.022609],[-114.434949,34.037784],[-114.43934,34.057893],[-114.434181,34.087379],[-114.415908,34.107636],[-114.405941,34.11154],[-114.390565,34.110084],[-114.379234,34.115988],[-114.366521,34.118575],[-114.353031,34.133121],[-114.336112,34.134035],[-114.320777,34.138635],[-114.312206,34.144776],[-114.292806,34.166725],[-114.287294,34.170529],[-114.275267,34.17215],[-114.268267,34.17021],[-114.254141,34.173831],[-114.240712,34.183232],[-114.229715,34.186928],[-114.224941,34.193896],[-114.225194,34.203642],[-114.211761,34.211539],[-114.190876,34.230858],[-114.17805,34.239969],[-114.173119,34.247226],[-114.166536,34.249647],[-114.161826,34.257038],[-114.139055,34.259538],[-114.134612,34.263518],[-114.134427,34.266387],[-114.139534,34.295844],[-114.138282,34.30323],[-114.157206,34.317862],[-114.168807,34.339513],[-114.176909,34.349306],[-114.199482,34.361373],[-114.213774,34.36246],[-114.226107,34.365916],[-114.234275,34.376662],[-114.252739,34.3901],[-114.264317,34.401329],[-114.280108,34.403147],[-114.288663,34.406623],[-114.294836,34.421389],[-114.312251,34.432726],[-114.32613,34.437251],[-114.335372,34.450038],[-114.342615,34.451442],[-114.373719,34.446938],[-114.386699,34.457911],[-114.387187,34.462021],[-114.381701,34.47604],[-114.382358,34.495757],[-114.378124,34.507288],[-114.380838,34.529724],[-114.405228,34.569637],[-114.422382,34.580711],[-114.429747,34.591734],[-114.429747,34.595846],[-114.424326,34.602338],[-114.424202,34.610453],[-114.438739,34.621455],[-114.441398,34.630171],[-114.441465,34.64253],[-114.451753,34.654321],[-114.451971,34.666795],[-114.456567,34.677956],[-114.465246,34.691202],[-114.470477,34.711368],[-114.486768,34.7191],[-114.495858,34.727956],[-114.516619,34.736745],[-114.529615,34.750822],[-114.552682,34.766871],[-114.57101,34.794294],[-114.576452,34.8153],[-114.586842,34.835672],[-114.600653,34.847361],[-114.623939,34.859738],[-114.630682,34.866352],[-114.635176,34.875003],[-114.636725,34.889107],[-114.630877,34.907263],[-114.633253,34.924608],[-114.629753,34.938684],[-114.635237,34.965149],[-114.629015,34.986148],[-114.629928,34.99474],[-114.636674,35.008807],[-114.636893,35.028367],[-114.627124,35.044721],[-114.606694,35.058941],[-114.602908,35.068588],[-114.604736,35.07483],[-114.613132,35.083097],[-114.642831,35.096503],[-114.646759,35.101872],[-114.629934,35.118272],[-114.619905,35.121632],[-114.59912,35.12105],[-114.58774,35.123729],[-114.578524,35.12875],[-114.572747,35.138725],[-114.569569,35.163053],[-114.569238,35.18348],[-114.572119,35.200591],[-114.574835,35.205898],[-114.579963,35.20964],[-114.587129,35.262376],[-114.597503,35.296954],[-114.595931,35.325234],[-114.604314,35.353584],[-114.627137,35.409504],[-114.652005,35.429165],[-114.662125,35.444241],[-114.6645,35.449497],[-114.666377,35.466856],[-114.677643,35.489742],[-114.679205,35.499992],[-114.677205,35.513491],[-114.673805,35.517891],[-114.663105,35.524491],[-114.656905,35.534391],[-114.662005,35.545491],[-114.663005,35.56369],[-114.666184,35.577576],[-114.654306,35.59759],[-114.653406,35.610789],[-114.658206,35.619089],[-114.677107,35.641489],[-114.689407,35.651412],[-114.690008,35.664688],[-114.682207,35.678188],[-114.680607,35.685488],[-114.683208,35.689387],[-114.701208,35.701187],[-114.705409,35.708287],[-114.697309,35.733686],[-114.695709,35.755986],[-114.701409,35.769086],[-114.69891,35.790185],[-114.71211,35.806185],[-114.70991,35.810185],[-114.70371,35.814585],[-114.69571,35.830601],[-114.699848,35.843283],[-114.697767,35.854844],[-114.68201,35.863284],[-114.67742,35.874728],[-114.68112,35.885364],[-114.700271,35.901772],[-114.708516,35.912313],[-114.707526,35.92806],[-114.731159,35.943916],[-114.728318,35.95629],[-114.729941,35.962183],[-114.740595,35.975656],[-114.743756,35.985095],[-114.742779,36.009963],[-114.731162,36.021862],[-114.729707,36.028166],[-114.730435,36.031317],[-114.739405,36.037863],[-114.740617,36.041015],[-114.740375,36.049258],[-114.736253,36.05847],[-114.743342,36.070535],[-114.754099,36.07944],[-114.755618,36.087166],[-114.747079,36.097005],[-114.736165,36.104367],[-114.709771,36.107742],[-114.666538,36.117343],[-114.65995,36.124145],[-114.631716,36.142306],[-114.627855,36.141012],[-114.621883,36.13213],[-114.616694,36.130101],[-114.597212,36.142103],[-114.572031,36.15161],[-114.511721,36.150956],[-114.504631,36.145629],[-114.505766,36.131444],[-114.502172,36.128796],[-114.487034,36.129396],[-114.470152,36.138801],[-114.463637,36.139695],[-114.458369,36.138586],[-114.453325,36.130726],[-114.446605,36.12597],[-114.427169,36.136305],[-114.41695,36.145761],[-114.405475,36.147371],[-114.372106,36.143114],[-114.363109,36.130246],[-114.337273,36.10802],[-114.328777,36.105501],[-114.30843,36.082443],[-114.305738,36.074882],[-114.314206,36.066619],[-114.315557,36.059494],[-114.280202,36.046362],[-114.266721,36.029238],[-114.252651,36.020193],[-114.238799,36.014561],[-114.21369,36.015613],[-114.19238,36.020993],[-114.176824,36.027651],[-114.166465,36.027738],[-114.15413,36.023862],[-114.148191,36.028013],[-114.138202,36.041284],[-114.136896,36.059467],[-114.114531,36.095217],[-114.117459,36.100893],[-114.123221,36.104746],[-114.123144,36.111576],[-114.111011,36.119875],[-114.09987,36.121654],[-114.068027,36.180663],[-114.060302,36.189363],[-114.046838,36.194069],[-114.048515,36.289598],[-114.045829,36.442973],[-114.048476,36.49998],[-114.050562,36.656259],[-114.0506,37.000396],[-112.538593,37.000674]]]},\"properties\":{\"name\":\"Arizona\",\"nation\":\"USA \"}}]}","volume":"11","issue":"23","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Jessica J. 0000-0002-3225-0317 jjwalker@usgs.gov","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":169458,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica","email":"jjwalker@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":776179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":776180,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70220223,"text":"70220223 - 2019 - Investigating the accuracy of one‐dimensional volcanic plume models using laboratory experiments and field data","interactions":[],"lastModifiedDate":"2021-04-28T13:13:55.608928","indexId":"70220223","displayToPublicDate":"2019-11-26T08:11:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Investigating the accuracy of one‐dimensional volcanic plume models using laboratory experiments and field data","docAbstract":"During volcanic eruptions, model predictions of plume height are limited by the accuracy of entrainment coefficients used in many plume models. Typically, two parameters are used, α and β, which relate the entrained air speed to the jet speed in the axial and cross‐flow directions, respectively. To improve estimates of these parameters, wind tunnel experiments have been conducted for a range of cross‐wind velocities and turbulence conditions. Measurements are compared directly to computations from the 1‐D plume model, Plumeria, in the near‐field, bending region of the jet. Entrainment coefficients are determined through regression analysis, demonstrating optimal combinations of effective α and β values. For turbulent conditions, all wind speeds overlapped at a single combination, α = 0.06 and β=0.46, each of which are slightly reduced from standard values. Refined coefficients were used to model plume heights for 20 historical eruptions. Model accuracy improves modestly in most cases, agreeing to within 3 km with observed plume heights. For weak eruptions, uncertainty in field measurements can outweigh the effects of these refinements, illustrating the challenge of applying plume models in practice.
The elevated salinity of the Pecos River throughout much of its length is of paramount concern to water users and water managers. Dissolved-solids concentrations in the Pecos River exceed 3,000 milligrams per liter in many of its reaches in the study area, from Santa Rosa Lake, New Mexico, to the confluence of the Pecos River with the Rio Grande, Texas. The salinity of the Pecos River increases downstream and affects the availability of useable water in the Pecos River Basin. In this report, “salinity” and “dissolved-solids concentration” are considered synonymous; both terms are used to refer to the total ionic concentration of dissolved minerals in water. The sources of salinity in the Pecos River Basin are natural (geologic) and anthropogenic, including but not limited to groundwater discharge, springs, and irrigation return flows. Previous studies in the Pecos River Basin were project specific and designed to address salinity issues in specific parts of the basin; therefore, in 2015, the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers, New Mexico Interstate Stream Commission, Texas Commission on Environmental Quality, and Texas Water Development Board assessed the major sources of salinity throughout the extent of the basin where elevated salinity in the Pecos River is well documented (that is, in the drainage area of the Pecos River from Santa Rosa Lake to the confluence of the Pecos River and the Rio Grande). The goal was to gain a better understanding of how specific areas might be contributing to the elevated salinity in the Pecos River and how salinity of the Pecos River has changed over time. This assessment includes a literature review and compilation of previously published salinity-related data, which guided the collection of additional water-quality samples and streamflow gain-loss measurements. Differences in water quality of surface-water and groundwater samples, streamflow measurements, and geophysical data were assessed to gain new insights regarding sources of salinity in the Pecos River Basin and a more detailed assessment of potential areas of elevated salinity in the basin. The datasets compiled for this assessment are available in a companion data release.
The literature review identified several potential sources of salinity inputs to the Pecos River in New Mexico and Texas. In New Mexico, sources of salinity inputs included sinkhole springs discharging into El Rito Creek, the Bitter Lake National Wildlife Refuge inflow to the Pecos River, inflow from the Rio Hondo, including the main channel and a restored channel at the Bitter Lake National Wildlife Refuge referred to as the “Rio Hondo spring channel,” the outflow from Lea Lake at Bottomless Lakes State Park, and the Malaga Bend region of the Pecos River. In Texas, sources of salinity inputs included Salt Creek downstream from Red Bluff Reservoir and the area near the Horsehead Crossing ford on the Pecos River.
The compilation of historical water-quality data revealed a lack of consistent sampling of the same constituents at the same sites along the main stem of the Pecos River, which results in data gaps that hinder the ability to effectively analyze long-term changes in water quality that may help with the understanding of how salinity in the Pecos River has changed over time and identifying the sources of salinity in the Pecos River Basin. To help fill these data gaps, water-quality and streamflow data were collected in the study area in February 2015 by the U.S. Geological Survey. Historical water-quality data and newly collected data from February 2015 were evaluated for selected major-ion concentrations, dissolved-solids concentrations, and deuterium, oxygen, and strontium isotopes. Analysis of the data indicated several areas of increasing salinity in the Pecos River. Most notable increases were in two subreaches of the river, between Acme, N. Mex., and Artesia, N. Mex., and between Orla, Tex., and Grandfalls, Tex. Increasing sodium and chloride concentrations from Acme to Artesia coincided with changes in isotopic ratios within the Pecos River Basin. Changes in isotopic ratios in this reach indicate a likely inflow from an isotopically different source of water compared to the water in the main stem of the Pecos River, such as groundwater inflow, inflow from surface-water features distinct from the main stem of the Pecos River, or both. In the subreach between Orla and Grandfalls, an increase in dissolved-solids concentrations was observed along with a shift in isotope values, indicating that neither evaporative processes in Red Bluff Reservoir nor inflow from Salt Creek likely solely influences the salinity of the Pecos River in this subreach. The highest dissolved-solids concentrations in the Pecos River Basin were measured downstream from Grandfalls, where dissolved-solids concentrations are greater than 16,000 milligrams per liter near Iraan, Tex. Changes in isotopic values (deuterium, oxygen, and strontium) indicate mixing of different waters at several areas along the main stem of the Pecos River. The spatial distribution of the areas of interest from the literature review and the water-quality data are available in the companion data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195071","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, New Mexico Interstate Stream Commission, Texas Commission on Environmental Quality, and Texas Water Development Board","usgsCitation":"Houston, N.A., Thomas, J.V., Ging, P.B., Teeple, A.P., Pedraza, D.E., and Wallace, D.S., 2019, Pecos River Basin salinity assessment, Santa Rosa Lake, New Mexico, to the confluence of the Pecos River and the Rio Grande, Texas, 2015: U.S. Geological Survey Scientific Investigations Report 2019–5071, 75 p., https://doi.org/10.3133/sir20195071.","productDescription":"Report: xi, 75 p.; Data Release","numberOfPages":"91","onlineOnly":"Y","ipdsId":"IP-083306","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":369530,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DB800T","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water Quality, Streamflow Gain Loss, Geologic, and Geospatial Data Used in the Pecos River Basin Salinity Assessment from Santa Rosa Lake, New Mexico to the Confluence of the Pecos River and the Rio Grande, Texas, 1900–2015"},{"id":369528,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5071/coverthb.jpg"},{"id":369529,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5071/sir20195071.pdf","text":"Report","size":"9.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5071"}],"country":"United States","state":"Texas, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.39257812499999,\n 29.916852233070173\n ],\n [\n -101.162109375,\n 29.305561325527698\n ],\n [\n -100.1513671875,\n 30.826780904779774\n ],\n [\n -99.97558593749999,\n 32.32427558887655\n ],\n [\n -101.9970703125,\n 33.797408767572485\n ],\n [\n -103.5791015625,\n 34.74161249883172\n ],\n [\n -105.0732421875,\n 35.567980458012094\n ],\n [\n -106.787109375,\n 36.38591277287651\n ],\n [\n -107.22656249999999,\n 35.53222622770337\n ],\n [\n -107.6220703125,\n 34.34343606848294\n ],\n [\n -105.908203125,\n 32.62087018318113\n ],\n [\n -102.39257812499999,\n 29.916852233070173\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
From 2015 through 2017, the U.S. Geological Survey in cooperation with the New Hampshire Department of Health and Human Services and the New Hampshire Department of Environmental Services studied occurrences of high levels of Escherichia coli (E. coli) bacteria at the Pawtuckaway State Park Beach in Nottingham, New Hampshire. Historic data collected by the New Hampshire Department of Environmental Services indicated that E. coli concentrations in the water typically increased through the beach season to levels considered potentially harmful to beachgoers. During the three beach seasons that were studied, E. coli samples were collected three to four times per week, and water-quality and meteorological data were collected continuously. The Virtual Beach software was used to generate a predictive model for each year of the study (2015–2017), and the model for each of these years was tested with data from the other two. Additionally, data from all study years were combined to generate a comprehensive model to help identify independent variables that might characterize environmental conditions relative to E. coli levels during multiple seasons. The accuracy of the models in predicting the occurrence of high E. coli levels was marginal, but the models did provide insights into the likely mechanisms for increased E. coli levels during the seasons. Variables most important in explaining high E. coli levels were the presence of geese at the beach, the progression of the season, the number of visitors at the beach, and wind vectors relative to beach orientation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195111","collaboration":"Prepared in cooperation with the New Hampshire Department of Health and Human Services and the New Hampshire Department of Environmental Services","usgsCitation":"Coles, J.F., and Bush, K.F., 2019, Evaluating associations between environmental variables and Escherichia coli levels for predictive modeling at Pawtuckaway Beach in Nottingham, New Hampshire, from 2015 to 2017: U.S. Geological Survey Scientific Investigations Report 2019–5111, 28 p., https://doi.org/10.3133/sir20195111.","productDescription":"Report: vii, 28 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101776","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":369290,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5111/coverthb.jpg"},{"id":369288,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5cc70bf4e4b09b8c0b77e5b7","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Data collected at Pawtuckaway Beach in Nottingham, New Hampshire, 2015–2017"},{"id":369409,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5111/sir20195111.pdf","text":"Report","size":"4.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5111"}],"country":"United States","state":"New Hampshire","city":"Nottingham","otherGeospatial":"Pawtuckaway Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.1595630645752,\n 43.08080002811761\n ],\n [\n -71.14797592163086,\n 43.08080002811761\n ],\n [\n -71.14797592163086,\n 43.08650455068649\n ],\n [\n -71.1595630645752,\n 43.08650455068649\n ],\n [\n -71.1595630645752,\n 43.08080002811761\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Pacific salmon acquire most of their biomass in the ocean before returning to spawn and die in coastal streams and lakes, thus providing subsidies of marine‐derived nitrogen (MDN) to freshwater and terrestrial ecosystems. Recent declines in salmon abundance have raised questions of whether managers should mitigate for losses of salmon MDN subsidies. To test the long‐term importance of salmon subsidies to riparian ecosystems, we measured soil nitrogen cycling in response to a 20‐yr manipulation where salmon carcasses were systematically removed from one bank and deposited on the opposite bank along a 2‐km stream in southwestern Alaska. Soil samples were taken at different distances from the stream bank along nine paired transects and measured for organic and inorganic nitrogen concentrations, and nitrogen transformation rates. Marine‐derived nitrogen was measured using 15N/14N for bulk soils, and