The Keanakāko‘i Tephra was deposited from 1500 to ca. 1820 CE, when Kīlauea’s magmatic output was ~2% of the average output during historical times (post–1823 CE). The tephra consists of deposits from numerous phreatomagmatic and phreatic eruptions, three episodes of high lava fountains, and one lava. Fresh glass is available from most tephra units. Major elements and trace elements were determined for glass from 49 tephra units and three pretephra lavas. Olivine crystals from 11 high-MgO tephra glasses were also analyzed. These results were compared to compositions from Kīlauea’s historical period to evaluate ~500 yr of Kīlauea geochemical evolution. Keanakāko‘i Tephra glass composition ranged widely (e.g., 3.4–11.2 wt% MgO). The observed large variations in FeO, CaO, TiO2, and K2O at a given MgO indicate numerous compositionally distinct parental magmas, with the two early nineteenth-century pumice eruptions showing the most diverse compositions. These two magmas were erupted on opposite sides of the caldera and probably tapped different magma bodies. The common occurrence of high-MgO olivine compositions (forsterite [Fo] 88%–89%) in MgO-rich tephra glasses indicates that primitive magma (Mg# 73–74) was routinely supplied to Kīlauea’s summit. Wide ranges and reverse zoning in olivine core compositions from some units show that magma mixing occurred before some eruptions. Modeling of compositional variations within Keanakāko‘i Tephra units using alphaMELTS showed that the most consistent trends for crystal fractionation involved shallow magma (1–2 km), with low water content (0.2 wt% in parental magma) and oxygen fugacity just below the quartz-fayalite-magnetite (QFM) buffer (–0.5 log units). Keanakāko‘i Tephra glasses have lower La/Yb and Nb/Y ratios than historical Kīlauea lavas. Low ratios have been observed during periods of high magma output for historical lava, which is inconsistent with the low magma output at Kīlauea’s summit during 1500–1820 CE. The most likely explanation for this inconsistency is endogenous growth within Kīlauea during this period, following formation of the modern summit caldera. No correlation was found between glass chemistry and eruption style for Keanakāko‘i Tephra deposits. Glass samples from many explosive units have lower Nb/Y and La/Yb ratios compared to glass from high lava-fountain units and historical effusive eruptions. The explosive character of Keanakāko‘i Tephra eruptions was probably caused by interaction of magma with shallow or surface water.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Field volcanology: A tribute to the distinguished career of Don Swanson","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2018.2538(09)","usgsCitation":"Garcia, M., Mucek, A.E., Lynn, K., Swanson, D., and Norman, M.D., 2019, Geochemical evolution of Keanakāko‘i Tephra, Kīlauea Volcano, Hawai‘i, chap. of Field volcanology: A tribute to the distinguished career of Don Swanson, v. 538, p. 203-225, https://doi.org/10.1130/2018.2538(09).","productDescription":"24 p.","startPage":"203","endPage":"225","ipdsId":"IP-088795","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":464628,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.31531936949776,\n 19.45294073344536\n ],\n [\n -155.31531936949776,\n 19.351253042452157\n ],\n [\n -155.18215845732087,\n 19.351253042452157\n ],\n [\n -155.18215845732087,\n 19.45294073344536\n ],\n [\n -155.31531936949776,\n 19.45294073344536\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"538","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":919988,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Garcia, Michael O","contributorId":215129,"corporation":false,"usgs":false,"family":"Garcia","given":"Michael","email":"","middleInitial":"O","affiliations":[],"preferred":false,"id":919989,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Camp, Victor E.","contributorId":236848,"corporation":false,"usgs":false,"family":"Camp","given":"Victor","email":"","middleInitial":"E.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":919990,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Grunder, Anita L.","contributorId":194549,"corporation":false,"usgs":false,"family":"Grunder","given":"Anita","middleInitial":"L.","affiliations":[],"preferred":false,"id":919991,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Garcia, M.O.","contributorId":346802,"corporation":false,"usgs":false,"family":"Garcia","given":"M.O.","affiliations":[{"id":48709,"text":"University of Hawai`i","active":true,"usgs":false}],"preferred":false,"id":919924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mucek, Adonara E.","contributorId":346803,"corporation":false,"usgs":false,"family":"Mucek","given":"Adonara","email":"","middleInitial":"E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":919925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lynn, Kendra J.","contributorId":346804,"corporation":false,"usgs":false,"family":"Lynn","given":"Kendra J.","affiliations":[{"id":82969,"text":"iversity of Delaware","active":true,"usgs":false}],"preferred":false,"id":919926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swanson, Donald A. 0000-0002-1680-3591","orcid":"https://orcid.org/0000-0002-1680-3591","contributorId":229682,"corporation":false,"usgs":true,"family":"Swanson","given":"Donald A.","affiliations":[],"preferred":true,"id":919927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Norman, Marc D.","contributorId":344700,"corporation":false,"usgs":false,"family":"Norman","given":"Marc","email":"","middleInitial":"D.","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":919928,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70200872,"text":"sir20185151 - 2019 - Hydrogeology of Lower Amargosa Valley and groundwater discharge to the Amargosa Wild and Scenic River, Inyo and San Bernardino Counties, California, and adjacent areas in Nye and Clark Counties, Nevada","interactions":[],"lastModifiedDate":"2019-02-07T15:16:00","indexId":"sir20185151","displayToPublicDate":"2019-02-07T08:40:55","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":"2018-5151","title":"Hydrogeology of Lower Amargosa Valley and groundwater discharge to the Amargosa Wild and Scenic River, Inyo and San Bernardino Counties, California, and adjacent areas in Nye and Clark Counties, Nevada","docAbstract":"In 2009, Congress designated certain reaches of the Amargosa River in Inyo County, California between the town of Shoshone and Dumont Dunes as a Wild and Scenic River. As part of the management of the Amargosa Wild and Scenic River, the Bureau of Land Management cooperated with the U.S. Geological Survey to assess the surface and groundwater resources of the Tecopa basin. Groundwater is the primary source of water to the perennial reaches of the Amargosa River. The U.S. Geological Survey studied the surface and groundwater systems in the basin, and assessed the sources and volume of groundwater discharging into the perennial reaches of the Amargosa Wild and Scenic River.
The springs within the Tecopa basin (and the greater Lower Amargosa Valley Hydrographic Area) can be generally grouped by spring type and geographic location. There are four types of groundwater discharge points in the Tecopa basin—regional carbonate-rock springs and seeps, Tecopa Hills springs and seeps, thermal springs and seeps, and Amargosa Canyon hillslope springs and seeps. Results of chemical analysis indicate that water from all of these springs in the Lower Amargosa Valley and particularly in the Tecopa basin, is sourced in the carbonate-rock aquifer, with a local component of recharge. Groundwater is recharged in the Spring Mountains and moves through and around the Nopah and Resting Spring Ranges and into the Tecopa basin. A small (less than 1 cubic foot per second [ft3/s] or 500 acre‑feet per year) component of flow from the Amargosa Desert moves through the river channel alluvium and basin fill from the north.
The location and type of spring appear to be controlled by the geology and geologic structure of the Lower Amargosa Valley. The regional springs (such as Shoshone and Borax Springs) and associated seeps tend to occur along the west side of the basin whereas other carbonate-rock aquifer springs discharge from adjacent mountain ranges, such as the Resting Spring Range, and as a result of low-permeability barriers, such as the Tecopa Hills. The thermal springs and seeps discharge from an area near the town of Tecopa, California. The Amargosa Canyon hillslope springs and seeps discharge directly into the river. Salt-crusted soils adjacent to the river indicate additional areas of diffuse discharge where groundwater is being evaporated.
Perennial flow in the main channel of the Amargosa River appears to originate in an area of thermal springs near Tecopa. Persistent groundwater-fed pools begin to appear along the river channel just to the south of the Tecopa Hills. Flow between these pools is evident, but difficult to measure. During the synoptic seepage measurement survey in February 2014, flow in the Amargosa River at the Tecopa streamgage (U.S. Geological Survey site 10251300, Amargosa River at Tecopa, California) was approximately 1 ft3/s. Just to the south of the Tecopa streamgage, a line of cooler water springs (the Amargosa Canyon hillslope springs) emerges east of the river channel and continues for approximately 1 mile along the Amargosa Canyon wall. At the end of the spring reach, the flow in the river increased to just over 4 ft3/s. Flow then decreased to approximately 3 ft3/s at the confluence of Willow Creek, approximately 3.5 miles downstream. Downstream from the confluence of Willow Creek, the river consistently loses water and was dry just north of Dumont Dunes during the February 2014 synoptic seepage measurement survey.
Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a >50% probability of obtaining weekly clear observations.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11030328","usgsCitation":"Zhou, Q., Rover, J., Brown, J.F., Worstell, B.B., Howard, D., Wu, Z., Gallant, A.L., Rundquist, B., and Burke, M., 2019, Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data: Remote Sensing, v. 11, no. 3, 328, 23 p., https://doi.org/10.3390/rs11030328.","productDescription":"328, 23 p.","ipdsId":"IP-104526","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11030328","text":"Publisher Index Page"},{"id":383590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.2509765625,\n 47.90161354142077\n ],\n [\n -96.3720703125,\n 47.90161354142077\n ],\n [\n -96.3720703125,\n 49.009050809382046\n ],\n [\n -97.2509765625,\n 49.009050809382046\n ],\n [\n -97.2509765625,\n 47.90161354142077\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":223103,"corporation":false,"usgs":true,"family":"Zhou","given":"Qiang","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":810803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":810877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":810876,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Worstell, Bruce B. 0000-0001-8927-3336 worstell@usgs.gov","orcid":"https://orcid.org/0000-0001-8927-3336","contributorId":1815,"corporation":false,"usgs":true,"family":"Worstell","given":"Bruce","email":"worstell@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":810804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176973,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":810878,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":810880,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":810881,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rundquist, Bradley 0000-0002-2572-9792","orcid":"https://orcid.org/0000-0002-2572-9792","contributorId":251983,"corporation":false,"usgs":false,"family":"Rundquist","given":"Bradley","email":"","affiliations":[],"preferred":false,"id":810888,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burke, Morgan","contributorId":251990,"corporation":false,"usgs":false,"family":"Burke","given":"Morgan","email":"","affiliations":[],"preferred":false,"id":810889,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70202020,"text":"70202020 - 2019 - Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl","interactions":[],"lastModifiedDate":"2019-03-15T12:36:43","indexId":"70202020","displayToPublicDate":"2019-02-06T16:20:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl","docAbstract":"Using data on waterfowl band recoveries, we identified spatially explicit hotspots of concentrated waterfowl movement to predict occurrence and spatial spread of a novel influenza A virus (clade 2.3.4.4) introduced from Asia by waterfowl from an initial outbreak in North America in November 2014. In response to the outbreak, the hotspots of waterfowl movement were used to help guide sampling for clade 2.3.4.4 viruses in waterfowl as an early warning for the US poultry industry during the outbreak . After surveillance sampling of waterfowl, we tested whether there was greater detection of clade 2.3.4.4 viruses inside hotspots. We found that hotspots defined using kernel density estimates of waterfowl band recoveries worked well in predicting areas with higher prevalence of the viruses in waterfowl. This approach exemplifies the value of ecological knowledge in predicting risk to agricultural security.
","language":"English","publisher":"Wiley","doi":"10.1111/tbed.13070","usgsCitation":"Franklin, A.B., Bevins, S.N., Ellis, J.W., Miller, R.S., Shriner, S.A., Root, J.J., Walsh, D.P., and DeLiberto, T.J., 2019, Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl: Transboundary and Emerging Diseases, v. 66, no. 2, p. 705-714, https://doi.org/10.1111/tbed.13070.","productDescription":"10 p.","startPage":"705","endPage":"714","ipdsId":"IP-090306","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":361065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"66","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Franklin, Alan B.","contributorId":101999,"corporation":false,"usgs":false,"family":"Franklin","given":"Alan","email":"","middleInitial":"B.","affiliations":[{"id":12434,"text":"USDA, Wildlife Services, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":756726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bevins, Sarah N.","contributorId":212845,"corporation":false,"usgs":false,"family":"Bevins","given":"Sarah","email":"","middleInitial":"N.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":756727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellis, Jeremy W.","contributorId":212846,"corporation":false,"usgs":false,"family":"Ellis","given":"Jeremy","email":"","middleInitial":"W.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":756728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Ryan S.","contributorId":49005,"corporation":false,"usgs":false,"family":"Miller","given":"Ryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":756729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shriner, Susan A.","contributorId":168690,"corporation":false,"usgs":false,"family":"Shriner","given":"Susan","email":"","middleInitial":"A.","affiliations":[{"id":13407,"text":"Colorado State Univ.","active":true,"usgs":false}],"preferred":false,"id":756730,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Root, J. Jeffrey","contributorId":212847,"corporation":false,"usgs":false,"family":"Root","given":"J.","email":"","middleInitial":"Jeffrey","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":756731,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Walsh, Daniel P. 0000-0002-7772-2445 dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":756725,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"DeLiberto, Thomas J.","contributorId":145606,"corporation":false,"usgs":false,"family":"DeLiberto","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":16167,"text":"7United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Disease Program, 4101 LaPorte Ave., Fort Collins, CO, United States of America.","active":true,"usgs":false}],"preferred":false,"id":756732,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202024,"text":"70202024 - 2019 - A scale to characterize the strength and impacts of atmospheric rivers","interactions":[],"lastModifiedDate":"2019-02-06T16:08:40","indexId":"70202024","displayToPublicDate":"2019-02-06T16:08:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"A scale to characterize the strength and impacts of atmospheric rivers","docAbstract":"Atmospheric rivers (ARs) play vital roles in the western United States and related regions globally, not only producing heavy precipitation and flooding, but also providing beneficial water supply. This paper introduces a scale for the intensity and impacts of ARs. Its utility may be greatest where ARs are the most impactful storm type and hurricanes, nor’easters, and tornadoes are nearly nonexistent. Two parameters dominate the hydrologic outcomes and impacts of ARs: vertically integrated water vapor transport (IVT) and AR duration [i.e., the duration of at least minimal AR conditions (IVT ≥ 250 kg m–1s–1)]. The scale uses an observed or predicted time series of IVT at a given geographic location and is based on the maximum IVT and AR duration at that point during an AR event. AR categories 1–5 are defined by thresholds for maximum IVT (3-h average) of 250, 500, 750, 1,000, and 1,250 kg m–1 s–1, and by IVT exceeding 250 kg m–1 s–1 continuously for 24–48 h. If the AR event duration is less than 24 h, it is downgraded by one category. If it is longer than 48 h, it is upgraded one category. The scale recognizes that weak ARs are often mostly beneficial because they can enhance water supply and snowpack, while stronger ARs can become mostly hazardous, for example, if they strike an area with antecedent conditions that enhance vulnerability, such as burn scars or wet conditions. Extended durations can enhance impacts. Short durations can mitigate impacts.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-18-0023.1","usgsCitation":"Ralph, F.M., Rutz, J.J., Cordeira, J.M., Dettinger, M.D., Anderson, M., Reynolds, D., Schick, L.J., and Smallcomb, C., 2019, A scale to characterize the strength and impacts of atmospheric rivers: Bulletin of the American Meteorological Society, v. 100, p. 269-289, https://doi.org/10.1175/BAMS-D-18-0023.1.","productDescription":"21 p.","startPage":"269","endPage":"289","ipdsId":"IP-087000","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":460493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/bams-d-18-0023.1","text":"Publisher Index Page"},{"id":361063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"100","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ralph, F. 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We find no difference in the seasonal flux of the two chromotypes. Although flux of G. ruber is consistently lowest in winter, the flux‐weighted signal exported to marine sediments represents mean annual conditions in the surface mixed‐layer. We observe the same morphological diversity among pink specimens of G. ruber as white. Comparison of the oxygen and carbon isotopic composition (δ18O and δ13C) of two morphotypes (sensu stricto and sensu lato) of pink G. ruber reveals the isotopes to be indistinguishable. The test size distribution within the population varies seasonally, with the abundance of large individuals increasing (decreasing) with increasing (decreasing) sea surface temperature (SST). We find no systematic offsets in the Mg/Ca and δ18O of co‐occurring pink and white G. ruber. The sediment trap data set shows that the Mg/Ca‐temperature sensitivity for both chromotypes is much lower than the canonical 9% per °C, which can likely be attributed to the secondary influence of both salinity and pH on foraminiferal Mg/Ca. Using paired Mg/Ca and δ18O we evaluate the performance of a suite of published equations for calculating SST, sea surface salinity (SSS) and isotopic composition of seawater (δ18Osw), including a new salinity‐δ18Oswrelationship for the northern Gulf of Mexico from water column observations.
","language":"English","publisher":"AGU","doi":"10.1029/2018PA003417","usgsCitation":"Richey, J.N., Thirumalai, K., Khider, D., Reynolds, C., Partin, J.W., and Quinn, T.M., 2019, Considerations for Globigerinoides ruber (white and pink) paleoceanography: Comprehensive insights from a long‐running sediment trap: Paleoceanography and Paleoclimatology, v. 34, no. 3, p. 353-373, https://doi.org/10.1029/2018PA003417.","productDescription":"21 p.","startPage":"353","endPage":"373","ipdsId":"IP-098817","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460495,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018pa003417","text":"Publisher Index Page"},{"id":437581,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KK7UD6","text":"USGS data release","linkHelpText":"Globigerinoides ruber Sediment Trap Data in the Gulf of Mexico"},{"id":361245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Richey, Julie N. 0000-0002-2319-7980 jrichey@usgs.gov","orcid":"https://orcid.org/0000-0002-2319-7980","contributorId":174046,"corporation":false,"usgs":true,"family":"Richey","given":"Julie","email":"jrichey@usgs.gov","middleInitial":"N.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thirumalai, Kaustubh","contributorId":127444,"corporation":false,"usgs":false,"family":"Thirumalai","given":"Kaustubh","email":"","affiliations":[{"id":6732,"text":"Geological Sciences, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":757205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Khider, Deborah","contributorId":213111,"corporation":false,"usgs":false,"family":"Khider","given":"Deborah","email":"","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":757206,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reynolds, Caitlin E. 0000-0002-1724-3055","orcid":"https://orcid.org/0000-0002-1724-3055","contributorId":204634,"corporation":false,"usgs":true,"family":"Reynolds","given":"Caitlin E.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Partin, Judson W.","contributorId":203459,"corporation":false,"usgs":false,"family":"Partin","given":"Judson","email":"","middleInitial":"W.","affiliations":[{"id":36624,"text":"Institute for Geophysics, Jackson School of Geosciences, University of Texas at Austin, J. J. Pickle Research Campus, Building 196, 10100 Burnet Road (R2200), Austin, Texas 78758, USA","active":true,"usgs":false}],"preferred":false,"id":757207,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Quinn, Terrence M.","contributorId":82949,"corporation":false,"usgs":false,"family":"Quinn","given":"Terrence","email":"","middleInitial":"M.","affiliations":[{"id":6732,"text":"Geological Sciences, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":757208,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201856,"text":"fs20193003 - 2019 - The Mississippi Alluvial Plain aquifers—An engine for economic activity","interactions":[],"lastModifiedDate":"2019-02-06T10:20:04","indexId":"fs20193003","displayToPublicDate":"2019-02-05T14:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3003","displayTitle":"The Mississippi Alluvial Plain Aquifers: An Engine for Economic Activity","title":"The Mississippi Alluvial Plain aquifers—An engine for economic activity","docAbstract":"U.S. Geological Survey science supports groundwater resource management in the Mississippi Alluvial Plain region. The USGS Science and Decisions Center is working with the Water Availability and Use Science Program to integrate economics into a sophisticated model of groundwater in the region. The model will quantify the status of the groundwater system and help researchers, stakeholders, and decision-makers understand and manage groundwater resources. Including economics in the model will let users consider the influence of groundwater levels on regional economics and the effects of economic factors on the demand for groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193003","usgsCitation":"Alhassan, M., Lawrence, C., Richardson, S., and Pindilli, E., 2019, The Mississippi Alluvial Plain aquifers—An engine for economic activity: U.S. Geological Survey Fact Sheet 2019–3003, 4 p., https://doi.org/10.3133/fs20193003.","productDescription":"4 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101445","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":361020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3003/coverthb.jpg"},{"id":361021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3003/fs20193003.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3003"},{"id":361022,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RW8Y2A","text":"USGS data release","description":"USGS data release","linkHelpText":"The Mississippi Alluvial Plain Aquifers: An Engine for Economic Activity - Data"}],"country":"United States","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93,\n 29\n ],\n [\n -88.5,\n 29\n ],\n [\n -88.5,\n 38\n ],\n [\n -93,\n 38\n ],\n [\n -93,\n 29\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Science and Decisions Center
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Reston, VA 20192
Email: gs_emeh_sdc@usgs.gov
In the late 1800s, John Wesley Powell, second Director of the U.S. Geological Survey (USGS), proposed gaging the flow of rivers and streams in the Western United States to evaluate the potential for irrigation. Around the same time, several cities in the Eastern United States established primitive streamgages to help design water-supply systems. Streamgaging technology has greatly advanced since the 1800s, and USGS hydrographers have made at least one streamflow measurement at more than 37,000 sites throughout the years. Today, the USGS Groundwater and Streamflow Information Program supports the collection and (or) delivery of both streamflow and water-level information for more than 8,500 sites (continuous or partial record) and water-level information alone for more than 1,700 additional sites. The data are served online—most in near realtime—to meet many diverse needs; more than 640 million requests for streamflow information were fulfilled during the 2017 water year (October 1, 2016‒September 30, 2017).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183081","collaboration":" ","usgsCitation":"Eberts, S.M., Woodside, M.D., Landers, M.N., and Wagner, C.R., 2018, Monitoring the pulse of our Nation's rivers and streams—The U.S. Geological Survey streamgaging network: U.S. Geological Survey Fact Sheet 2018–3081, 2 p., https://doi.org/10.3133/fs20183081.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-101883","costCenters":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":360982,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3081/fs20183081.pdf","text":"Report","size":"5.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3081"},{"id":360981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3081/coverthb2.jpg"}],"contact":"Groundwater and Stream Flow Information Program
U.S. Geological Survey Water Mission Area
12201 Sunrise Valley Drive
Reston, VA 20192
In large, alluvial floodplains dominated by agriculture, small streams have the potential to experience nutrient enrichment affecting algal assemblage structure and metabolism. Nutrient enrichment is largely driven by application of nutrients and altered hydrologic regimes. To inform stressor–response-based nutrient reduction goals for agricultural alluvial plain streams, diatom assemblages were sampled from 25 streams located within the Mississippi Alluvial Plain (MAP) with various land management practices and associated P and N inputs. From August through September 2015, epidendric diatom assemblage samples were collected from instream woody debris. Field nutrient gradients were skewed toward higher concentrations, and ranges of previously reported diatom assemblage response thresholds indicative of oligotrophic conditions were not well represented. Ordination analysis identified a gradient in species composition associated with increasing P and decreasing dissolved oxygen. A significant shift in diatom assemblage structure occurred when total P concentrations in the MAP streams exceeded 0.12 mg L−1. Phosphorus-enriched systems were represented by a distinct set of indicator species, lower abundances of ubiquitous species, greater abundances of highly tolerant species, and greater abundances of high-P indicator species. No relationships were observed among diatom assemblage measures or traits with increasing N. Current results do not address potential criteria for identifying high-quality, oligotrophic streams. However, measures of diatom assemblage structure have potential for helping set benchmarks to reduce nutrient impacts and monitor effects of agricultural best management practices on MAP streams.
","language":"English","publisher":"ACSESS","doi":"10.2134/jeq2018.05.0196","usgsCitation":"Hicks, M.B., and Taylor, J.M., 2019, Diatom assemblage changes in agricultural alluvial plain streams and application for nutrient management: Journal of Environmental Quality, v. 48, no. 1, p. 83-92, https://doi.org/10.2134/jeq2018.05.0196.","productDescription":"10 p.","startPage":"83","endPage":"92","ipdsId":"IP-091585","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":467930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2018.05.0196","text":"Publisher Index Page"},{"id":361026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Yazoo River basin","volume":"48","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hicks, Matthew B. 0000-0001-5516-0296 mhicks@usgs.gov","orcid":"https://orcid.org/0000-0001-5516-0296","contributorId":3778,"corporation":false,"usgs":true,"family":"Hicks","given":"Matthew","email":"mhicks@usgs.gov","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Jason M.","contributorId":212809,"corporation":false,"usgs":false,"family":"Taylor","given":"Jason","email":"","middleInitial":"M.","affiliations":[{"id":38685,"text":"USDA, ARS Sedimentation Lab","active":true,"usgs":false}],"preferred":false,"id":756638,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70214105,"text":"70214105 - 2019 - Enhancement of primary production during drought in a temperate watershed is greater in larger rivers than headwater streams","interactions":[],"lastModifiedDate":"2020-09-23T14:50:20.238443","indexId":"70214105","displayToPublicDate":"2019-02-05T09:38:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7120,"text":"Limnology & Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Enhancement of primary production during drought in a temperate watershed is greater in larger rivers than headwater streams","docAbstract":"Drought is common in rivers, yet how this disturbance regulates metabolic activity across network scales is largely unknown. Drought often lowers gross primary production (GPP) and ecosystem respiration (ER) in small headwaters but by contrast can enhance GPP and cause algal blooms in downstream estuaries. We estimated ecosystem metabolism across a nested network of 13 reaches from headwaters to the main stem of the Connecticut River from 2015 through 2017, which encompassed a pronounced drought. During drought, GPP and ER increased, but with greater enhancement in larger rivers. Responses of GPP and ER were partially due to warmer temperatures associated with drought, particularly in the larger rivers where temperatures during summer drought were > 10°C higher than typical summer baseflow. The larger rivers also had low canopy cover, which allowed primary producers to take advantage of lower turbidity and fewer cloudy days during drought. We conclude that GPP is enhanced by higher temperature, lower turbidity, and longer water residence times that are all a function of low discharge, but ecosystem response in temperate watersheds to these drivers depends on light availability regulated by riparian canopy cover. In larger rivers, GPP increased more than ER during drought, even leading to temporary autotrophy, an otherwise rare event in the typically light‐limited heterotrophic Connecticut River main stem. With climate change, rivers and streams may become warmer and drought frequency and severity may increase. Such changes may increase autotrophy in rivers with broad implications for carbon cycling and water quality in aquatic ecosystems.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.11127","usgsCitation":"Hosen, J.D., Aho, K.S., Appling, A.P., Creech, E., Fair, J., Hall, R.O., Kyzivat, E., Lowenthal, R., Matt, S., Morrison, J., Saiers, J.E., Shanley, J.B., Weber, L., Yoon, B., and Raymond, P.A., 2019, Enhancement of primary production during drought in a temperate watershed is greater in larger rivers than headwater streams: Limnology & Oceanography, v. 64, no. 4, p. 1458-1472, https://doi.org/10.1002/lno.11127.","productDescription":"15 p.","startPage":"1458","endPage":"1472","ipdsId":"IP-100512","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"links":[{"id":467931,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.11127","text":"Publisher Index 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E.","contributorId":191842,"corporation":false,"usgs":false,"family":"Saiers","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":799483,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":799484,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Weber, Lisa","contributorId":241081,"corporation":false,"usgs":false,"family":"Weber","given":"Lisa","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":799485,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Yoon, Bryan","contributorId":241082,"corporation":false,"usgs":false,"family":"Yoon","given":"Bryan","email":"","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":799486,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Raymond, Peter A.","contributorId":172876,"corporation":false,"usgs":false,"family":"Raymond","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":17883,"text":"Yale School of Forestry and Environmental Studies, New Haven, CT","active":true,"usgs":false}],"preferred":false,"id":799487,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70201967,"text":"70201967 - 2019 - Mercury isotopes reveal an ontogenetic shift in habitat use by walleye in lower Green Bay of Lake Michigan","interactions":[],"lastModifiedDate":"2019-02-04T12:46:45","indexId":"70201967","displayToPublicDate":"2019-02-04T12:46:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5022,"text":"Environmental Science & Technology Letters","onlineIssn":"2328-8930","active":true,"publicationSubtype":{"id":10}},"title":"Mercury isotopes reveal an ontogenetic shift in habitat use by walleye in lower Green Bay of Lake Michigan","docAbstract":"In general, fish residing in rivers differ from fish residing in lakes in their mercury (Hg) isotope ratios. Specifically, fish residing in lakes typically show enriched values for the isotope ratios of δ202Hg (mass-dependent fractionation of isotope 202Hg) and Δ199Hg (mass-independent fractionation of isotope 199Hg) compared with fish residing in rivers, because photochemical effects acting on Hg isotope ratios are stronger in lakes than in rivers. Whole-fish determinations of Hg isotope ratios in age-0 and adult (ages 4–11) walleye (Sander vitreus) caught in the Fox River, the main tributary to lower Green Bay of Lake Michigan, were dissimilar. Age-0 fish exhibited a river signature for δ202Hg and Δ199Hg, with means equal to 0.00 and 0.26‰, respectively. Significantly elevated levels of δ202Hg and Δ199Hg were observed in adult fish, indicating that adult fish primarily resided in the bay. Our results implied that the Fox River serves as a nursery area for juvenile walleye in the Fox River–lower Green Bay ecosystem. Moreover, corrections for photochemical fractionation of δ202Hg revealed that age-0 and adult walleye shared the same source of Hg in this ecosystem. In addition, Hg isotope ratios did not significantly differ between the sexes of adult walleye, suggesting that these ratios did not fractionate during the Hg elimination process.
","language":"English","publisher":"ACS","doi":"10.1021/acs.estlett.8b00592","usgsCitation":"Madenjian, C.P., Janssen, S., Lepak, R., Ogorek, J.M., Rosera, T., DeWild, J.F., Krabbenhoft, D.P., Cogswell, S.F., and Holey, M.E., 2019, Mercury isotopes reveal an ontogenetic shift in habitat use by walleye in lower Green Bay of Lake Michigan: Environmental Science & Technology Letters, v. 6, no. 1, p. 8-13, https://doi.org/10.1021/acs.estlett.8b00592.","productDescription":"6 p.","startPage":"8","endPage":"13","ipdsId":"IP-101314","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":360977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":756363,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lepak, Ryan F. 0000-0003-2806-1895","orcid":"https://orcid.org/0000-0003-2806-1895","contributorId":210990,"corporation":false,"usgs":false,"family":"Lepak","given":"Ryan F.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":756366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 jmogorek@usgs.gov","orcid":"https://orcid.org/0000-0002-6327-0740","contributorId":4960,"corporation":false,"usgs":true,"family":"Ogorek","given":"Jacob","email":"jmogorek@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":756367,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosera, Tylor J.","contributorId":212697,"corporation":false,"usgs":false,"family":"Rosera","given":"Tylor J.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":756369,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeWild, John F. 0000-0003-4097-2798 jfdewild@usgs.gov","orcid":"https://orcid.org/0000-0003-4097-2798","contributorId":2525,"corporation":false,"usgs":true,"family":"DeWild","given":"John","email":"jfdewild@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756368,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756364,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cogswell, Stewart F.","contributorId":212698,"corporation":false,"usgs":false,"family":"Cogswell","given":"Stewart","email":"","middleInitial":"F.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":756370,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Holey, Mark E.","contributorId":212699,"corporation":false,"usgs":false,"family":"Holey","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":756371,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70201969,"text":"70201969 - 2019 - Effects of flood inundation, invasion by Phalaris arundinacea, and nitrogen enrichment on extracellular enzyme activity in an Upper Mississippi River floodplain forest","interactions":[],"lastModifiedDate":"2019-06-18T10:04:01","indexId":"70201969","displayToPublicDate":"2019-02-04T12:43:31","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Effects of flood inundation, invasion by Phalaris arundinacea, and nitrogen enrichment on extracellular enzyme activity in an Upper Mississippi River floodplain forest","title":"Effects of flood inundation, invasion by Phalaris arundinacea, and nitrogen enrichment on extracellular enzyme activity in an Upper Mississippi River floodplain forest","docAbstract":"The community structures and ecosystem functions of floodplains are primarily driven by variation in flood inundation. However, global changes, such as invasive species and nutrient enrichment, may alter the effects of flooding in these systems. We added nitrogen (N) to correspond with twice the annual atmospheric deposition rate of the south-west Wisconsin, USA region within mature floodplain forest plots and patches of an invasive grass (reed canarygrass, Phalaris arundinacea) along a floodplain elevation gradient in an Upper Mississippi River floodplain forest. We measured soil physicochemical properties and the activity of six extracellular enzymes during 3 months that varied in flooding conditions. Multivariate analyses (distance-based redundancy analysis) revealed that floodplain elevation, month of sampling, and vegetation type were all significant predictors of variation in soil physicochemical properties, while elevation and month were significant predictors of multivariate extracellular enzyme activity (EEA). The best model for predicting EEA consisted of nitrogen availability, soil porosity, and water filled pore space. Although the categorical fertilization and invasion treatments were not significant predictors of EEA, our results suggest that their effects depend on the degree to which they modify N availability and soil moisture. In this system, spatial and temporal patterns in flooding appear to be the main driver of these properties, but N enrichment and invasion may have the potential to further modify them.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-018-09651-2","usgsCitation":"De Jager, N.R., Swanson, W., Hernandez, D.L., Reich, J., Erickson, R.A., and Strauss, E.A., 2019, Effects of flood inundation, invasion by Phalaris arundinacea, and nitrogen enrichment on extracellular enzyme activity in an Upper Mississippi River floodplain forest: Wetlands Ecology and Management, v. 27, no. 2-3, p. 443-454, https://doi.org/10.1007/s11273-018-09651-2.","productDescription":"12 p.","startPage":"443","endPage":"454","ipdsId":"IP-102848","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437583,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K2RQMG","text":"USGS data release","linkHelpText":"Effects of flood inundation, invasion by Phalaris arundinacea, and nitrogen deposition on extracellular enzyme activity in an UMR forest: Data"},{"id":360976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Upper MIssissippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.40556335449219,\n 43.69071491326582\n ],\n [\n -91.11717224121094,\n 43.69071491326582\n ],\n [\n -91.11717224121094,\n 43.90778718292443\n ],\n [\n -91.40556335449219,\n 43.90778718292443\n ],\n [\n -91.40556335449219,\n 43.69071491326582\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"2-3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":756373,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swanson, Whitney","contributorId":194558,"corporation":false,"usgs":false,"family":"Swanson","given":"Whitney","affiliations":[],"preferred":false,"id":756374,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hernandez, Daniel L.","contributorId":205330,"corporation":false,"usgs":false,"family":"Hernandez","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":33615,"text":"Carleton College","active":true,"usgs":false}],"preferred":false,"id":756375,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reich, Julia","contributorId":205331,"corporation":false,"usgs":false,"family":"Reich","given":"Julia","email":"","affiliations":[{"id":33615,"text":"Carleton College","active":true,"usgs":false}],"preferred":false,"id":756376,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":756377,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Strauss, Eric A.","contributorId":190148,"corporation":false,"usgs":false,"family":"Strauss","given":"Eric","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":756378,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204566,"text":"70204566 - 2019 - Understanding conservation decisions of agriculture producers","interactions":[],"lastModifiedDate":"2019-08-05T10:36:37","indexId":"70204566","displayToPublicDate":"2019-02-03T10:33:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Understanding conservation decisions of agriculture producers","docAbstract":"Most land in the United States (US) is privately owned and used for agriculture. To address the effect of agriculture on wildlife, conservation professionals and organizations need to understand the land use decisions made by farmers and ranchers. We developed a tool for categorizing farmers and ranchers by their conservation land use values (LUVs) to understand how those values affect their land use motivations and resultant decisions. We defined land as the whole natural environment including soil, water, plants, fish, and wildlife. We used principal axis factoring and reliability analysis to identify statements representing human-centered values and nature-centered values of farmers and ranchers. We tested the validity of the combined statements with a survey of South Dakota’s private landowners (N = 4,000, [Jan through May 2016]) resulting in the LUV scale. Crossing the average scores on the human-centered and nature-centered statements identified 4 LUV types: humans first (20%), nature first (29%), interconnected (29%), and disconnected (22%). Analysis of variance and chi-square tests showed that, compared to the humans first and disconnected LUV types, the nature first and interconnected LUV types reported significantly greater importance of the following: most categories of types of wildlife in their land use decisions; conservation-related motivations for participating in a United States Farm Bill Conservation Program; conservation-related motivations for land use decisions; and participation in conservation-related behaviors. Conservation professionals and organizations may use the LUV scale to better understand landowners’ land use decisions to evaluate and inform conservation policy, programs, messaging, and improve conservation outcomes.","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.21643","usgsCitation":"Gigliotti, L.M., and Lily A. Sweikert, 2019, Understanding conservation decisions of agriculture producers: Journal of Wildlife Management, v. 83, no. 4, p. 993-1004, https://doi.org/10.1002/jwmg.21643.","productDescription":"12 p.","startPage":"993","endPage":"1004","ipdsId":"IP-085122","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":366201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"83","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliotti, Larry M. 0000-0002-1693-5113 lgigliotti@usgs.gov","orcid":"https://orcid.org/0000-0002-1693-5113","contributorId":3906,"corporation":false,"usgs":true,"family":"Gigliotti","given":"Larry","email":"lgigliotti@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":767596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lily A. Sweikert","contributorId":217826,"corporation":false,"usgs":false,"family":"Lily A. Sweikert","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":767597,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202172,"text":"70202172 - 2019 - Effects of antecedent streamflow and sample timing on trend assessments of fish, invertebrate, and diatom communities","interactions":[],"lastModifiedDate":"2019-02-12T16:40:10","indexId":"70202172","displayToPublicDate":"2019-02-01T16:40:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Effects of antecedent streamflow and sample timing on trend assessments of fish, invertebrate, and diatom communities","docAbstract":"Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biological condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. A combination of linear regression and Kendall‐tau test for trends were used to evaluate covariate influence on trend estimates. Adjusting for covariates changed the magnitude of trend estimates in two‐thirds of cases on average by 21%, most often reducing the estimated magnitude of the trend. Additionally, covariates influenced the interpretation of over one‐third of trend estimates by either strengthening or weakening trends after adjustment. Our findings clearly indicate that antecedent streamflow and sample timing influences trend estimates and subsequent interpretation. Accounting for covariates during trend analysis will enhance stream monitoring programs by providing a better understanding and interpretation of estimated changes in biological endpoints at monitored sites. Failure to account for antecedent streamflow and sample timing may lead to mischaracterization of a trend and/or misunderstanding of potential causes.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12706","usgsCitation":"Zuellig, R.E., and Carlisle, D.M., 2019, Effects of antecedent streamflow and sample timing on trend assessments of fish, invertebrate, and diatom communities: Journal of the American Water Resources Association, v. 55, no. 1, p. 102-115, https://doi.org/10.1111/1752-1688.12706.","productDescription":"14 p.","startPage":"102","endPage":"115","ipdsId":"IP-091154","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467936,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12706","text":"Publisher Index Page"},{"id":437584,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EEFX0B","text":"USGS data release","linkHelpText":"Datasets used to asses the effects of antecedent streamflow and sample timing on trend assessments of fish, invertebrate and diatom communities (2002-12)"},{"id":361210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Zuellig, Robert E. 0000-0002-4784-2905 rzuellig@usgs.gov","orcid":"https://orcid.org/0000-0002-4784-2905","contributorId":1620,"corporation":false,"usgs":true,"family":"Zuellig","given":"Robert","email":"rzuellig@usgs.gov","middleInitial":"E.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":757093,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203646,"text":"70203646 - 2019 - Ecological consequences of anomalies in atmospheric moisture and snowpack","interactions":[],"lastModifiedDate":"2019-05-30T15:40:41","indexId":"70203646","displayToPublicDate":"2019-02-01T15:39:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Ecological consequences of anomalies in atmospheric moisture and snowpack","docAbstract":"Although increased frequency of extreme‐weather events is one of the most secure predictions associated with contemporary climate change, effects of such events on distribution and abundance of climate‐sensitive species remain poorly understood. Montane ecosystems may be especially sensitive to extreme weather because of complex abiotic and biotic interactions that propagate from climate‐driven reductions in snowpack. Snowpack not only protects subnivean biotas from extreme cold, but also influences forage availability through timing of melt‐off and water availability. We related relative abundances of an alpine mammal, the American pika (Ochotona princeps), to measures of weather and snowpack dynamics over an 8‐yr period that included before and after a year of record‐low snowpack in Washington, USA. We sought to (1) quantify any change in pika abundance associated with the snowpack anomaly and (2) identify aspects of weather and snowpack that influenced abundance of pikas. Pikas showed a 1‐yr lag response to the snowpack anomaly and exhibited marked declines in abundance at elevations below 1,400 m simultaneous with increased abundances at higher elevations. Atmospheric moisture, indexed by vapor pressure deficit (VPD), was especially important, evidenced by strong support for the top‐ranked model that included the interaction of VPD with snowpack duration. Notably, our novel application of VPD from gridded climate data for analyses of animal abundances shows strong potential for improving species distribution models because VPD represents an important aspect of weather that influences the physiology and habitat of biota. Pikas were apparently affected by cold stress without snowpack at mid elevations, whereas changes to forage associated with snowpack and VPD were influential at high and low elevations. Our results reveal context dependency in pika responses to weather and illustrate how snow drought can lead to rapid change in the abundance of subnivean animals.
The San Mateo Creek Basin in New Mexico, USA is located within the Grants Mineral Belt-an area with numerous uranium (U) ore deposits, mines, and milling operations. Six monitoring wells set in an alluvial aquifer near the Homestake Mining Co. Superfund site in the lower San Mateo Creek Basin were logged with a suite of borehole geophysical tools including spectral gamma-ray (SGR), vertically profiled with passive samplers for U and selenium (Se) concentrations, and purged sampled for same constituents. The integrated approach allowed for an assessment on the role of heterogeneity (both physical and chemical) in determining U concentrations in groundwater. Uranium, as measured with SGR logging, is ubiquitous in the alluvial aquifer and the underlying Chinle Group. Aqueous U concentrations appear to be inversely related to thorium (Th) concentrations, as measured by the SGR log, indicating the possibility that U is bound in or adsorbed to clays in the aquifer. The stratigraphy of the alluvium likely plays a role in elevated concentrations of aqueous U. Interbedded clay and sand layers allow for the mobilization of U in oxic sandy layers from U adsorbed in sediments in reduced clay layers. The stratigraphy also plays a role in the degree of mixing of groundwater in the formation and well. Mixing can obscure the ability to identify U sources. Mixing is exacerbated by the relatively long screens (> 20 ft long or > 6.1 m) of the monitoring wells.
","language":"English","publisher":"Springer","doi":"10.1007/s12665-019-8049-y","usgsCitation":"Harte, P.T., Blake, J.M., Thomas, J.V., and Becher, K., 2019, Identifying natural and anthropogenic variability of uranium at the well scale, Homestake Superfund site, near Milan, New Mexico, USA: Environmental Earth Sciences, v. 78, p. 1-19, https://doi.org/10.1007/s12665-019-8049-y.","productDescription":"Article 95; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-080068","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":360932,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.8917,\n 35.2083\n ],\n [\n -107.8417,\n 35.2083\n ],\n [\n -107.8417,\n 35.275\n ],\n [\n -107.8917,\n 35.275\n ],\n [\n -107.8917,\n 35.2083\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Harte, Philip T. 0000-0002-7718-1204 ptharte@usgs.gov","orcid":"https://orcid.org/0000-0002-7718-1204","contributorId":1008,"corporation":false,"usgs":true,"family":"Harte","given":"Philip","email":"ptharte@usgs.gov","middleInitial":"T.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blake, Johanna M. 0000-0003-4667-0096 jmtblake@usgs.gov","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":169698,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna","email":"jmtblake@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755864,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas, Jonathan V. 0000-0003-0903-9713 jvthomas@usgs.gov","orcid":"https://orcid.org/0000-0003-0903-9713","contributorId":2194,"corporation":false,"usgs":true,"family":"Thomas","given":"Jonathan","email":"jvthomas@usgs.gov","middleInitial":"V.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755865,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Becher, Kent 0000-0002-3947-0793 kdbecher@usgs.gov","orcid":"https://orcid.org/0000-0002-3947-0793","contributorId":3863,"corporation":false,"usgs":true,"family":"Becher","given":"Kent","email":"kdbecher@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755866,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200543,"text":"fs20183074 - 2019 - Assessment of undiscovered oil and gas resources in the South Florida basin, 2016","interactions":[],"lastModifiedDate":"2019-02-01T15:50:12","indexId":"fs20183074","displayToPublicDate":"2019-02-01T15:15:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-3074","title":"Assessment of undiscovered oil and gas resources in the South Florida basin, 2016","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated mean undiscovered, technically recoverable resources of 49 million barrels of oil and 18 billion cubic feet of gas in the onshore and State waters part of the South Florida basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183074","usgsCitation":"Roberts-Ashby, T.L., Hackley, P.C., Lohr, C.D., Schenk, C.J., Mercier, T.J., Whidden, K.J., Le, P.A., Tennyson, M.E., Gaswirth, S.B., Woodall, C.A., Brownfield, M.E., Leathers-Miller, H.M., Marra, K.R., and Finn, T.M., 2019, Assessment of undiscovered oil and gas resources in the South Florida basin, 2016: U.S. Geological Survey Fact Sheet 2018–3074, 4 p., https://doi.org/10.3133/fs20183074.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-092946","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":360692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3074/fs20183074.pdf","text":" Report","size":"3.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3074"},{"id":360691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3074/coverthb.jpg"},{"id":360693,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M4VGL9","text":"USGS Data Release","linkHelpText":"USGS Gulf Coast Petroleum Systems, and National and Global Oil and Gas Assessment Projects - USGS Province 50 Assessment Unit Boundaries and Assessment Input Forms"}],"country":"United States","otherGeospatial":"South Florida Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.12,\n 24.38\n ],\n [\n -79.95,\n 24.38\n ],\n [\n -79.95,\n 28.2\n ],\n [\n -83.12,\n 28.2\n ],\n [\n -83.12,\n 24.38\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The Ellenburger–San Saba aquifer discharges spring flows into the overlying Hamilton Creek bed in Burnet County, central Texas. The aquifer is susceptible to contamination from surface‐water reservoirs because of the presence of dissolution cavities that are hydraulically connected to the reservoirs in some locations. There is concern that preferential groundwater seepage from reservoirs into the aquifer in these locations might ultimately degrade the quality of the springwater that enters Hamilton Creek. To investigate preferential groundwater seepage patterns and hydraulic connectivity between surface‐water reservoirs and the Ellenburger–San Saba aquifer, geophysical reconnaissance surveys were completed between July 2017 and January 2018 to map dissolution cavities and locate preferential groundwater seepage within a specific region of the aquifer. Two‐dimensional electric resistivity tomography and self‐potential profiling were utilized, and a simplified, three‐dimensional finite‐element model of the field site was constructed to provide an interpretive aid. The self‐potential data indicated the occurrence of preferential groundwater seepage through a porous seepage conduit that was imaged by the electric resistivity tomography data but did not indicate the occurrence of groundwater seepage through two fluid‐filled dissolution cavities that were imaged by electric resistivity tomography data. Collectively, the surveying and modelling results demonstrate the efficacy of geoelectric methods for mapping the locations of dissolution cavities and preferential groundwater seepage in the electrically resistive karst terrane of the Ellenburger–San Saba aquifer.
","language":"English","publisher":"Wiley","doi":"10.1002/nsg.12023","usgsCitation":"Ikard, S., and Pease, E., 2019, Preferential groundwater seepage in karst terrane inferred from geoelectric measurements: Near Surface Geophysics, v. 17, no. 1, p. 43-53, https://doi.org/10.1002/nsg.12023.","productDescription":"11 p.","startPage":"43","endPage":"53","ipdsId":"IP-091309","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":467939,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nsg.12023","text":"Publisher Index Page"},{"id":437585,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VX0FSC","text":"USGS data release","linkHelpText":"650-m Profiles of Self-Potential, Contact Resistance, and Electric Resistance Tomography Measurements Adjacent to Hamilton Creek, Burnet County, Texas, July 2017 - January 2018"},{"id":360931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Ikard, Scott 0000-0002-8304-4935","orcid":"https://orcid.org/0000-0002-8304-4935","contributorId":212256,"corporation":false,"usgs":true,"family":"Ikard","given":"Scott","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pease, Emily 0000-0001-8295-1632","orcid":"https://orcid.org/0000-0001-8295-1632","contributorId":210588,"corporation":false,"usgs":true,"family":"Pease","given":"Emily","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755917,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202016,"text":"70202016 - 2019 - Valuation of the flood attenuation ecosystem service in Difficult Run, VA, USA","interactions":[],"lastModifiedDate":"2019-02-05T15:03:04","indexId":"70202016","displayToPublicDate":"2019-02-01T15:02:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Valuation of the flood attenuation ecosystem service in Difficult Run, VA, USA","docAbstract":"Floodplains and riparian wetlands provide several ecosystem services that directly benefit people. We present a methodology for valuing the flood attenuation ecosystem service in Difficult Run, a suburban watershed with extensive natural floodplains in northern Virginia. High-resolution lidar-derived data were combined with GIS modeling techniques to produce estimates of flood inundation. We combined the modeled estimates with parcel-level property and primary economic data using a baseline and a counterfactual scenario to estimate the magnitude of flood attenuation and the associated value of the ecosystem service. Our framework brings new models and data to look at floodplains and an alternative land surface scenario in a way that has not previously been done. Annualized avoided property losses totaled $42,184 in the baseline scenario and $115,596 in the counterfactual scenario for the combined 200-, 100-, 50-, 20-, 10-, and 5-year flood events. We estimate the total annualized value of the flood attenuation ecosystem service in Difficult Run is $73,412, which is $77 per hectare of floodplain area and is consistent with similar valuation studies of floodplains. The framework presented here is not specific to the study area and could be deployed at larger spatial areas in other locations. Our methods may better inform land use decision making on the impacts of development in and surrounding floodplain areas.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2018.10.023","usgsCitation":"Lawrence, C.B., Pindilli, E., and Hogan, D.M., 2019, Valuation of the flood attenuation ecosystem service in Difficult Run, VA, USA: Journal of Environmental Management, v. 231, p. 1056-1064, https://doi.org/10.1016/j.jenvman.2018.10.023.","productDescription":"9 p.","startPage":"1056","endPage":"1064","ipdsId":"IP-093393","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":467940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2018.10.023","text":"Publisher Index Page"},{"id":361037,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Difficult Run watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.35,\n 38.85\n ],\n [\n -77.1833,\n 38.85\n ],\n [\n -77.1833,\n 39.0167\n ],\n [\n -77.35,\n 39.0167\n ],\n [\n -77.35,\n 38.85\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"231","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lawrence, Collin B. 0000-0001-9224-5774","orcid":"https://orcid.org/0000-0001-9224-5774","contributorId":212089,"corporation":false,"usgs":true,"family":"Lawrence","given":"Collin","email":"","middleInitial":"B.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":756699,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pindilli, Emily 0000-0002-5101-1266 epindilli@usgs.gov","orcid":"https://orcid.org/0000-0002-5101-1266","contributorId":140262,"corporation":false,"usgs":true,"family":"Pindilli","given":"Emily","email":"epindilli@usgs.gov","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":756700,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hogan, Dianna M. 0000-0003-1492-4514 dhogan@usgs.gov","orcid":"https://orcid.org/0000-0003-1492-4514","contributorId":131137,"corporation":false,"usgs":true,"family":"Hogan","given":"Dianna","email":"dhogan@usgs.gov","middleInitial":"M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":756701,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203462,"text":"70203462 - 2019 - Phenology and species diversity in a Lake Huron ichthyoplankton community: Ecological implications of invasive species dominance","interactions":[],"lastModifiedDate":"2019-05-16T08:04:23","indexId":"70203462","displayToPublicDate":"2019-02-01T14:33:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Phenology and species diversity in a Lake Huron ichthyoplankton community: Ecological implications of invasive species dominance","docAbstract":"Ichthyoplankton communities are dynamic and vary spatiotemporally based on factors such as wind, water currents, and phenology. Nonetheless, ichthyoplankton are an indicator of spawning success in fish populations and examining their community diversity and composition can serve to provide information on ecosystem integrity. Although some ichthyoplankton species may be transient, understanding their distribution in space and time provides information on species composition, abundance, and habitat use during critical early life stages. We sampled the spring-summer ichthyoplankton community during 2008 and 2009 in northern Lake Huron to determine species succession, abundance, and species diversity along physical and environmental gradients. Seasonal succession of species was similar during both years, indicating well-defined patterns in spawning by local populations. Invasive alewife, rainbow smelt, and round goby were the dominant species during both years, with native stickleback species also abundant. Shannon Entropy (H’) increased with increasing water temperature until late summer when H’ declined. H’ decreased with increasing bottom depth and distance to tributary mouth indicating the important ecological role of these habitat features during early life stages. Although ichthyoplankton diversity was comparable to or higher than that reported for other areas of the Great Lakes, the prominence of invasive species in our study is reflective of the degraded state of the Lake Huron fish community, despite large reductions in invasive planktivorous fish since 2004. Continued monitoring of ichthyoplankton communities will be important for measuring the impacts of species invasions or other ecosystem stressors on fish community structure in the Great Lakes.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2018.11.002","usgsCitation":"O’Brien, T.P., Ireland, S., Roseman, E.F., Briggs, A.S., and Taylor, W.W., 2019, Phenology and species diversity in a Lake Huron ichthyoplankton community: Ecological implications of invasive species dominance: Journal of Great Lakes Research, v. 45, no. 1, p. 176-186, https://doi.org/10.1016/j.jglr.2018.11.002.","productDescription":"11 p.","startPage":"176","endPage":"186","ipdsId":"IP-097949","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":460505,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2018.11.002","text":"Publisher Index Page"},{"id":363913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.803466796875,\n 42.94033923363181\n ],\n [\n -79.639892578125,\n 42.94033923363181\n ],\n [\n -79.639892578125,\n 46.49839225859763\n ],\n [\n -84.803466796875,\n 46.49839225859763\n ],\n [\n -84.803466796875,\n 42.94033923363181\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":762772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ireland, Stacey 0000-0001-8568-8980 sireland@usgs.gov","orcid":"https://orcid.org/0000-0001-8568-8980","contributorId":215595,"corporation":false,"usgs":true,"family":"Ireland","given":"Stacey","email":"sireland@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":762773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roseman, Edward F. 0000-0002-5315-9838 eroseman@usgs.gov","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":168428,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward","email":"eroseman@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":762774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Andrew S 0000-0002-0268-9310","orcid":"https://orcid.org/0000-0002-0268-9310","contributorId":215596,"corporation":false,"usgs":false,"family":"Briggs","given":"Andrew","email":"","middleInitial":"S","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":762775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, William W.","contributorId":166927,"corporation":false,"usgs":false,"family":"Taylor","given":"William","email":"","middleInitial":"W.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":762776,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70207449,"text":"70207449 - 2019 - Probabilistic relationships between acid-base chemistry and fish assemblages in streams of the western Adirondack Mountains, New York, USA","interactions":[],"lastModifiedDate":"2019-12-19T13:05:48","indexId":"70207449","displayToPublicDate":"2019-02-01T13:01:43","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic relationships between acid-base chemistry and fish assemblages in streams of the western Adirondack Mountains, New York, USA","docAbstract":"Surface waters across much of the Adirondacks of New York were acidified in the late 20th century but began to recover after the 1990 amendments to the Clean Air Act. Little data, however, were available to characterize biological impacts and predict recovery of fish assemblages in regional streams. Quantitative fish and chemistry surveys were completed in 47 headwater streams during summer 2014-16 to develop logistic (probabilistic) models that characterize the status of contemporary fish assemblages and predict how different N and S deposition loads may affect future fish assemblages. Models for Ali and richness ≥1 species, ANC and total density >400 fish/0.1 ha, ANC and total biomass >1500 g/0.1, presence of Brook Trout, trout density >200 fish/0.1 ha, and trout biomass >1000 g/0.1 ha were suitable for evaluating community and population responses to changes in acid-base chemistry. Anticipated changes in national (US) secondary standards for atmospheric emissions of NOx and SOx to achieve target N and S deposition loads will alter acid-base chemistry and the probabilities for observing various levels of fish metrics in streams across the region and elsewhere.","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2018-0260","usgsCitation":"Baldigo, B., George, S., Sullivan, T.J., Driscoll, C.T., Burns, D., Shoa, S., and Lawrence, G.B., 2019, Probabilistic relationships between acid-base chemistry and fish assemblages in streams of the western Adirondack Mountains, New York, USA: Canadian Journal of Fisheries and Aquatic Sciences, v. 76, no. 11, p. 2013-2026, https://doi.org/10.1139/cjfas-2018-0260.","productDescription":"14 p.","startPage":"2013","endPage":"2026","ipdsId":"IP-098032","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":467943,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2018-0260","text":"Publisher Index Page"},{"id":437586,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4V25","text":"USGS data release","linkHelpText":"Adirondack and Catskill Stream-Fish Survey Dataset (ver. 7.0, December 2023)"},{"id":370496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.311279296875,\n 41.77950486590359\n ],\n [\n -73.9215087890625,\n 41.77950486590359\n ],\n [\n -73.9215087890625,\n 42.36666166373274\n ],\n [\n -75.311279296875,\n 42.36666166373274\n ],\n [\n -75.311279296875,\n 41.77950486590359\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"76","issue":"11","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":221408,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, Scott","contributorId":221409,"corporation":false,"usgs":true,"family":"George","given":"Scott","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Timothy J.","contributorId":196720,"corporation":false,"usgs":false,"family":"Sullivan","given":"Timothy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":778084,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Driscoll, Charles T.","contributorId":167460,"corporation":false,"usgs":false,"family":"Driscoll","given":"Charles","email":"","middleInitial":"T.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":778085,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778086,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shoa, Shuai","contributorId":221410,"corporation":false,"usgs":false,"family":"Shoa","given":"Shuai","email":"","affiliations":[{"id":40368,"text":"Syracuse University, Department of Civil and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":778087,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778088,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202240,"text":"70202240 - 2019 - Tracking changes in nutrient delivery to western Lake Erie: Approaches to compensate for variability and trends in streamflow","interactions":[],"lastModifiedDate":"2019-02-19T11:43:05","indexId":"70202240","displayToPublicDate":"2019-02-01T11:43:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Tracking changes in nutrient delivery to western Lake Erie: Approaches to compensate for variability and trends in streamflow","docAbstract":"Tracking changes in stream nutrient inputs to Lake Erie over multidecadal time scales depends on the use of statistical methods that can remove the influence of year-to-year variability of streamflow but also explicitly consider the influence of long-term trends in streamflow. The methods introduced in this paper include an extended version of Weighted Regressions on Time, Discharge, and Season (WRTDS) modeling that explicitly considers nonstationary streamflow by incorporating information on changes in the frequency distribution of daily measured streamflow (discharge) over time. Soluble reactive phosphorus (SRP) trends in annual flow-normalized fluxes (loads) at five long-term monitoring sites in the western Lake Erie drainage basin show increases of 109 to 322% over the period 1995 to 2015. About one-third of the increase appears attributable to increasing discharge trends, while the remaining two-thirds appears to be driven by changes in concentration versus discharge relationships reflecting higher concentrations for any given discharge during recent years. Trends in total phosphorus and three nitrogen parameters (total nitrogen, nitrate-nitrite, and total Kjeldahl nitrogen) at the 10 sites analyzed were much less pronounced, and commonly show decreases in concentration-discharge relationships accompanied by increases in discharge, resulting in little net change in total flux. Trends in monthly SRP fluxes and discharge, dissolved versus particulate fractions of nutrients, and N:P flux ratios were also evaluated. The methods described here provide tools to more clearly discern the effectiveness of nutrient-control strategies and can serve as ongoing measures of progress, or lack of progress, towards nutrient-reduction goals.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2018.11.012","usgsCitation":"Choquette, A.F., Hirsch, R.M., Murphy, J.C., Johnson, L., and Confesor, R., 2019, Tracking changes in nutrient delivery to western Lake Erie: Approaches to compensate for variability and trends in streamflow: Journal of Great Lakes Research, v. 45, no. 1, p. 21-39, https://doi.org/10.1016/j.jglr.2018.11.012.","productDescription":"19 p.","startPage":"21","endPage":"39","ipdsId":"IP-098855","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":460508,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2018.11.012","text":"Publisher Index Page"},{"id":361336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.27587890625,\n 40.65980593837852\n ],\n [\n -80.4144287109375,\n 40.65980593837852\n ],\n [\n -80.4144287109375,\n 43.113014204188914\n ],\n [\n -85.27587890625,\n 43.113014204188914\n ],\n [\n -85.27587890625,\n 40.65980593837852\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Choquette, Anne F. 0000-0002-7858-1728 achoq@usgs.gov","orcid":"https://orcid.org/0000-0002-7858-1728","contributorId":210699,"corporation":false,"usgs":true,"family":"Choquette","given":"Anne","email":"achoq@usgs.gov","middleInitial":"F.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":757447,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":167405,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":757448,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, L.T.","contributorId":213319,"corporation":false,"usgs":false,"family":"Johnson","given":"L.T.","email":"","affiliations":[{"id":38736,"text":"National Center for Water Quality Research, Heidelberg Univeristy, Tiffin, Ohio","active":true,"usgs":false}],"preferred":false,"id":757449,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Confesor, R. B.","contributorId":213320,"corporation":false,"usgs":false,"family":"Confesor","given":"R. B.","affiliations":[{"id":38737,"text":"National Center for Water Quality Research, Heidelberg University, Tiffin, Ohio","active":true,"usgs":false}],"preferred":false,"id":757450,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237799,"text":"70237799 - 2019 - Aquifer depletion and potential impacts on long-term irrigated agricultural productivity","interactions":[],"lastModifiedDate":"2022-10-24T16:38:20.304723","indexId":"70237799","displayToPublicDate":"2019-02-01T11:27:06","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":12794,"text":"Issue Paper","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"63","title":"Aquifer depletion and potential impacts on long-term irrigated agricultural productivity","docAbstract":"Groundwater is the Earth’s most extracted raw material, with almost 1,000 cubic kilometers per year (800 million acre-feet per year) of groundwater pumped from aquifers around the world. Approximately 70% of groundwater withdrawals worldwide are used to support agricultural production systems, and within the United States, about 71% of groundwater withdrawals are used for irrigating croplands. This percentage of groundwater used to support agriculture is even higher in arid and semi-arid areas, where the only consistent source of irrigation water is groundwater. In these regions, however, the use of groundwater typically far exceeds the rate at which it is naturally replenished, indicating that these critical groundwater resources are being slowly depleted. Within the United States, groundwater depletion has occurred in many important agricultural production regions, including the Great Plains Region (Nebraska, Colorado, Oklahoma, New Mexico, and northern Texas), the Central Valley of California, the Mississippi Embayment Aquifer (Mississippi River lowlands bordering Arkansas and Mississippi), aquifers in southern Arizona, and smaller aquifers in many western states.
The groundwater resource with the greatest long-term depletion is the High Plains (Ogallala) aquifer in the Great Plains Region of the United States, where groundwater levels have declined by more than 50 meters (150 feet) in some areas. The Central Valley of California, however, is experiencing the highest groundwater depletion intensity because of increased use over the last several decades. The most obvious consequences of depleting groundwater resources are the loss of a long-term water supply and the increased costs of pumping groundwater as the water table declines further below the ground surface. There are many other consequences associated with groundwater depletion, however, including the loss of the productivity of groundwater production wells (possibly requiring the construction of new wells); the depletion of the flow of water in rivers, creeks, and lakes when they are hydrologically connected to underlying aquifers; the shifting and subsidence of land surfaces that can occur when groundwater is extracted from aquifers; and the intrusion of high saline, or poor quality, water from other subsurface formations.
The most effective approaches for addressing groundwater depletion focus on reducing or eliminating the imbalance between the inflow and outflow of water to an aquifer. Methods that focus on increasing the inflow to groundwater resources include the development of managed aquifer recharge systems and altering land-use practices to increase the infiltration of water below the land surface. Methods that focus on decreasing groundwater use include the implementation of more efficient irrigation systems, the development of agricultural crops that require less water, and the creation of economic incentives to encourage water conservation. All of these methods should be considered when developing plans to address the long-term consequences of groundwater depletion. In addition, when developing policies that regulate groundwater systems that are being depleted, the potential consequences of groundwater depletion need to be fully assessed to determine the trade-offs that exist between the undesired impacts of groundwater depletion and whether these impacts outweigh the benefits associated with groundwater use.
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Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA","docAbstract":"We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).
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