Sediment from urban impervious surfaces has the potential to be an important vector for contaminants, particularly where stormwater culverts and other buried channels draining large impervious areas exit from underground pipes into open channels. To better understand urban sediment sources and their relation to fallout radionuclides, we collected samples of rainfall, urban sediment (pavement sediment, topsoil), streambank sediment, and fluvial sediment (suspended sediment and bed sediment) for 7Be, 210Pbex, and 137Cs analysis. The results indicate that each rainfall event tags pavement sediment with elevated activities of 7Be and 210Pbex such that runoff from impervious surfaces in the buried channel part of the stream network contains the highest activities. Pavement sediment, because it is characteristically a thin veneer, has a small mass to rainwater ratio resulting in a greater tagging of 7Be and 210Pbex activity than does topsoil on a per gram basis. An unmixing model indicated that suspended-sediment samples collected at the culvert outlet from the buried-channel network are from pavement sediment sources (45 ± 25%) with a smaller component of topsoil (22 ± 19%), and a component from streambanks (32 ± 35%) that we infer to be older channel material and subsoil eroded from within the culvert system. Downstream from the culvert, suspended sediment collected from the open-channel parts of the stream had 7Be and 210Pbex activities that were substantially reduced by the contribution of sediment from streambanks (57 ± 15%), with pavement contributions decreasing to 15 (±9%) and topsoil contributing 28 (±7%). The results highlight the utility of 7Be, 210Pbex, and 137Cs as tracers of urban sediment sources, resulting in a unique radionuclide signature for urban watersheds compared to other sediment-source settings.
Swimming exercise and dissolved oxygen (DO) are important parameters to consider when operating intensive salmonid aquaculture facilities. While previous research has focused on each of these two variables in rainbow trout Oncorhynchus mykiss, studies examining both variables in combination, and their potential interaction, are absent from the scientific literature. Both swimming exercise (usually measured in body lengths per second, or BL/s) and DO can be readily controlled in modern aquaculture systems; therefore, we sought to evaluate the effects of these variables, separately and combined, on several outcomes in rainbow trout including growth performance, fin health and survival. Rainbow trout fry (18 g) were stocked into 12 circular 0.5 m3 tanks, provided with either high (1.5–2 BL/s) or low (approximately 0.5 BL/s) swimming exercise and high (100% saturation) or low (70% saturation) DO, and grown to approximately 1 kg. By the conclusion of the study, higher DO was independently associated with significantly (p < .05) increased growth performance. Significant differences were not noted in other outcomes, namely feed conversion, condition factor and mortality, although caudal and right pectoral fin damage was associated with low oxygen and low swimming exercise treatments respectively. Cardiosomatic index was significantly higher among exercised fish. These results suggest that swimming exercise and DO at saturation during the culture of rainbow trout can be beneficial to producers through improved growth performance and cardiac health.
","language":"English","publisher":"Wiley","doi":"10.1111/are.14600","usgsCitation":"Waldrop, T., Summerfelt, S., Mazik, P.M., Kenney, P.B., and Good, C., 2020, The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss: Aquaculture Research, v. 51, no. 6, p. 2582-2589, https://doi.org/10.1111/are.14600.","productDescription":"8 p.","startPage":"2582","endPage":"2589","ipdsId":"IP-113465","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/are.14600","text":"Publisher Index Page"},{"id":397025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Waldrop, Thomas","contributorId":279449,"corporation":false,"usgs":false,"family":"Waldrop","given":"Thomas","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Summerfelt, Steven","contributorId":279450,"corporation":false,"usgs":false,"family":"Summerfelt","given":"Steven","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kenney, P. Brett","contributorId":279452,"corporation":false,"usgs":false,"family":"Kenney","given":"P.","email":"","middleInitial":"Brett","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":834955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Good, Christopher","contributorId":279454,"corporation":false,"usgs":false,"family":"Good","given":"Christopher","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834956,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228573,"text":"70228573 - 2020 - Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","interactions":[],"lastModifiedDate":"2022-02-14T15:33:01.949286","indexId":"70228573","displayToPublicDate":"2020-03-15T09:19:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","title":"Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","docAbstract":"The combined effects of long-term fire suppression, logging, and overgrazing have negatively impacted many southwestern U.S. forests, resulting in decreased habitat quality for wildlife, and more frequent and severe wildfires. In response, land management agencies are implementing large-scale forest restoration treatments, but data on how wildlife respond to restoration treatments and wildfires are often limited. We investigated bed and den site selection of American black bears (Ursus americanus) using GPS location data and a use/available study design to assess the influence of habitat characteristics, including wildfires, prescribed burns, and thinning treatments on bed and den site selection in the Jemez Mountains, New Mexico. The most supported models suggested that black bears were more likely to select bed sites with a combination of low horizontal visibility (β = −0.007, SE = 0.002; P = 0.002) and high stand basal area (β = 0.013, SE = 0.005; P = 0.004). The highest-ranking model for den site selection indicated that black bears were more likely to select den sites with low horizontal visibility (β = −0.0102, SE = 0.004; P = 0.006). Black bears used all disturbed sites to varying degrees (45% of study area), although 48% of bed sites were located in undisturbed habitat (55% of study area) while only 11% and 2% of bed sites were located in thinned and prescribed burn sites, respectively. Thirty-nine percent of bed sites were located in previous wildfire locations; however, 67% of these sites were in areas with low burn severity. Thirty-eight percent of den sites were located in previously disturbed habitat, 8 of these sites were burned by wildfires. In order to develop effective management plans for black bears, it is essential to understand responses to landscape-scale habitat disturbances due to wildfires and restoration activities, all of which are becoming more prevalent and widespread across southwestern forests. Accounting for the timing, size, and proximity of future restoration efforts would aid in mitigating potential short-term negative effects on black bears.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2020.117904","usgsCitation":"Bard, S.M., and Cain, J.W., 2020, Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires: Forest Ecology and Management, v. 460, p. 1-11, https://doi.org/10.1016/j.foreco.2020.117904.","productDescription":"117904, 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-112372","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":457367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1595500","text":"Publisher Index Page"},{"id":395886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Collaborative Forest Landscape Restoration Program area, Jemez Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.67587280273438,\n 35.65004306288284\n ],\n [\n -106.67587280273438,\n 35.622698214535184\n ],\n [\n -106.62506103515625,\n 35.623256366178964\n ],\n [\n -106.42936706542969,\n 35.85455268869835\n ],\n [\n -106.39503479003906,\n 35.85343961959182\n ],\n [\n -106.39022827148438,\n 36.00800626603582\n ],\n [\n -106.62368774414062,\n 36.00911716117325\n ],\n [\n -106.68960571289062,\n 35.884043325566886\n ],\n [\n -106.86882019042969,\n 35.879592612012026\n ],\n [\n -106.86744689941405,\n 35.821153818963175\n ],\n [\n -106.85714721679688,\n 35.8217105820067\n ],\n [\n -106.85302734374999,\n 35.649485098277204\n ],\n [\n -106.67587280273438,\n 35.65004306288284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"460","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bard, Susan M.","contributorId":264967,"corporation":false,"usgs":false,"family":"Bard","given":"Susan","email":"","middleInitial":"M.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":834645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834644,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209101,"text":"70209101 - 2020 - Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale","interactions":[],"lastModifiedDate":"2020-03-16T16:52:49","indexId":"70209101","displayToPublicDate":"2020-03-14T16:47:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2049,"text":"International Journal of Greenhouse Gas Control","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale","title":"Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale","docAbstract":"Residual oil zones (ROZs) are increasingly gaining interest as potential reservoirs for carbon dioxide (CO2) storage. Here, we present a national- and regional-scale methodology for estimating prospective CO2 storage resources in residual oil zones. This methodology uses a volumetric equation that accounts for CO2 storage as a free phase in pore space and as a dissolved phase in oil and does not assume any oil production associated with CO2 storage. Reservoir modeling and the CO2-SCREEN tool are used to demonstrate that CO2 storage in residual oil zones will predominantly take place in the free phase (approximately 92–97%) with some storage as dissolution in oil (approximately 3–8 %). Based on this preliminary demonstration, the CO2 storage efficiency for ROZs using this national- and regional-scale method ranges from 0.61 to 7.1 %. This range indicates ROZs have a similar efficiency potential for storing CO2 as deep saline formations (0.51–5.4 %).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijggc.2020.103006","usgsCitation":"Sanguinito, S., Singh, H., Myshakin, E.M., Goodman, A.L., Dilmore, R.M., Grant, T.C., Morgan, D., Bromhal, G., Warwick, P., Brennan, S.T., Freeman, P., Karacan, C.O., Gorecki, C., Peck, W., Burton-Kelly, M., Dotzenrod, N., Frailey, S., and Pawar, R., 2020, Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale: International Journal of Greenhouse Gas Control, v. 96, 103006, 8 p., https://doi.org/10.1016/j.ijggc.2020.103006.","productDescription":"103006, 8 p.","ipdsId":"IP-108213","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":457370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1780239","text":"Publisher Index 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Laboratory","active":true,"usgs":false}],"preferred":false,"id":784929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Myshakin, Evgeniy M.","contributorId":220813,"corporation":false,"usgs":false,"family":"Myshakin","given":"Evgeniy","email":"","middleInitial":"M.","affiliations":[{"id":40277,"text":"U.S. Department of Energy","active":true,"usgs":false}],"preferred":false,"id":784930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goodman, Angela L.","contributorId":223391,"corporation":false,"usgs":false,"family":"Goodman","given":"Angela","email":"","middleInitial":"L.","affiliations":[{"id":40708,"text":"United States Department of Energy, National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":784931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dilmore, Robert M.","contributorId":223392,"corporation":false,"usgs":false,"family":"Dilmore","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":40708,"text":"United States Department of Energy, National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":784932,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grant, Timothy C.","contributorId":223393,"corporation":false,"usgs":false,"family":"Grant","given":"Timothy","email":"","middleInitial":"C.","affiliations":[{"id":40708,"text":"United States Department of Energy, National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":784933,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morgan, David","contributorId":223394,"corporation":false,"usgs":false,"family":"Morgan","given":"David","affiliations":[{"id":40708,"text":"United States Department of Energy, National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":784934,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bromhal, Grant","contributorId":177265,"corporation":false,"usgs":false,"family":"Bromhal","given":"Grant","email":"","affiliations":[{"id":17887,"text":"National Energy Technology Laboratory, Department of Energy","active":true,"usgs":false}],"preferred":false,"id":784935,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":205928,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":784927,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Brennan, Sean T. 0000-0002-7102-9359 sbrennan@usgs.gov","orcid":"https://orcid.org/0000-0002-7102-9359","contributorId":559,"corporation":false,"usgs":true,"family":"Brennan","given":"Sean","email":"sbrennan@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":784936,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Freeman, Philip A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":193093,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","email":"pfreeman@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":784937,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":208012,"corporation":false,"usgs":false,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[],"preferred":false,"id":784938,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Gorecki, Charles","contributorId":223395,"corporation":false,"usgs":false,"family":"Gorecki","given":"Charles","email":"","affiliations":[{"id":40709,"text":"Energy & Environmental Research Center, University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":784939,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Peck, Wesley","contributorId":223396,"corporation":false,"usgs":false,"family":"Peck","given":"Wesley","email":"","affiliations":[{"id":40709,"text":"Energy & Environmental Research Center, University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":784940,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Burton-Kelly, Matthew","contributorId":223397,"corporation":false,"usgs":false,"family":"Burton-Kelly","given":"Matthew","email":"","affiliations":[{"id":40709,"text":"Energy & Environmental Research Center, University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":784941,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Dotzenrod, Neil","contributorId":223398,"corporation":false,"usgs":false,"family":"Dotzenrod","given":"Neil","email":"","affiliations":[{"id":40709,"text":"Energy & Environmental Research Center, University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":784942,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Frailey, Scott","contributorId":177268,"corporation":false,"usgs":false,"family":"Frailey","given":"Scott","email":"","affiliations":[],"preferred":false,"id":784943,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Pawar, Rajesh 0000-0003-1422-7532","orcid":"https://orcid.org/0000-0003-1422-7532","contributorId":223399,"corporation":false,"usgs":false,"family":"Pawar","given":"Rajesh","email":"","affiliations":[{"id":37625,"text":"Earth and Environmental Sciences Division, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":784944,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70209158,"text":"70209158 - 2020 - A 'weight of evidence' approach to evaluating structural equation models","interactions":[],"lastModifiedDate":"2020-03-19T19:09:47","indexId":"70209158","displayToPublicDate":"2020-03-13T19:08:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5943,"text":"One Ecosystem","active":true,"publicationSubtype":{"id":10}},"title":"A 'weight of evidence' approach to evaluating structural equation models","docAbstract":"It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.","language":"English","publisher":"Pensoft Publisher","doi":"10.3897/oneeco.5.e50452","usgsCitation":"Grace, J., 2020, A 'weight of evidence' approach to evaluating structural equation models: One Ecosystem, v. 5, e50452, https://doi.org/10.3897/oneeco.5.e50452.","productDescription":"e50452","ipdsId":"IP-115758","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":457375,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/oneeco.5.e50452","text":"Publisher Index Page"},{"id":373395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Grace, James 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":219648,"corporation":false,"usgs":true,"family":"Grace","given":"James","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":785160,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70228651,"text":"70228651 - 2020 - Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty","interactions":[],"lastModifiedDate":"2022-02-16T18:02:33.912037","indexId":"70228651","displayToPublicDate":"2020-03-13T11:56:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty","docAbstract":"Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13–20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2126","usgsCitation":"Pepin, K.M., Smyser, T.J., Davis, A., Miller, R., McKee, S., VerCauteren, K.C., Kendall, W.L., and Slootmaker, C., 2020, Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty: Ecological Applications, v. 30, no. 6, e02126, 15 p., https://doi.org/10.1002/eap.2126.","productDescription":"e02126, 15 p.","ipdsId":"IP-113401","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":457377,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/812305","text":"External Repository"},{"id":396025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Pepin, Kim M.","contributorId":279406,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim","email":"","middleInitial":"M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smyser, Timothy J.","contributorId":279407,"corporation":false,"usgs":false,"family":"Smyser","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Amy J.","contributorId":279408,"corporation":false,"usgs":false,"family":"Davis","given":"Amy J.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Ryan S.","contributorId":279409,"corporation":false,"usgs":false,"family":"Miller","given":"Ryan S.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKee, Sophie","contributorId":279410,"corporation":false,"usgs":false,"family":"McKee","given":"Sophie","email":"","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834937,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"VerCauteren, Kurt C.","contributorId":279413,"corporation":false,"usgs":false,"family":"VerCauteren","given":"Kurt","email":"","middleInitial":"C.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834938,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834932,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Slootmaker, Chris","contributorId":279414,"corporation":false,"usgs":false,"family":"Slootmaker","given":"Chris","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834939,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209076,"text":"70209076 - 2020 - Colorado River flow dwindles as warming-driven loss of reflective snow energizes evaporation","interactions":[],"lastModifiedDate":"2020-03-20T11:05:00","indexId":"70209076","displayToPublicDate":"2020-03-13T10:11:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Colorado River flow dwindles as warming-driven loss of reflective snow energizes evaporation","docAbstract":"The sensitivity of river discharge to climate-system warming is highly uncertain, and the processes that govern river discharge are poorly understood, which impedes climate-change adaptation. 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F.","contributorId":300152,"corporation":false,"usgs":false,"family":"Jasinski","given":"M.","email":"","middleInitial":"F.","affiliations":[{"id":40052,"text":"NASA Goddard","active":true,"usgs":false}],"preferred":false,"id":859495,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borak, J. S.","contributorId":300155,"corporation":false,"usgs":false,"family":"Borak","given":"J.","email":"","middleInitial":"S.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":859496,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Slaughter, Stephen L. 0000-0002-4322-3330","orcid":"https://orcid.org/0000-0002-4322-3330","contributorId":224686,"corporation":false,"usgs":true,"family":"Slaughter","given":"Stephen","email":"","middleInitial":"L.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":859497,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219483,"text":"70219483 - 2020 - Small-scale water deficits after wildfires create long-lasting ecological impacts","interactions":[],"lastModifiedDate":"2021-04-12T11:58:12.438476","indexId":"70219483","displayToPublicDate":"2020-03-13T07:01:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Small-scale water deficits after wildfires create long-lasting ecological impacts","docAbstract":"Ecological droughts are deficits in soil–water availability that induce threshold-like ecosystem responses, such as causing altered or degraded plant-community conditions, which can be exceedingly difficult to reverse. However, 'ecological drought' can be difficult to define, let alone to quantify, especially at spatial and temporal scales relevant to land managers. This is despite a growing need to integrate drought-related factors into management decisions as climate changes result in precipitation instability in many semi-arid ecosystems. We asked whether success in restoration seedings of the foundational species big sagebrush (Artemisia tridentata) was related to estimated water deficit, using the SoilWat2 model and data from >600 plots located in previously burned areas in the western United States. Water deficit was characterized by: (1) the standardized precipitation-evapotranspiration index (SPEI), a coarse-scale drought index, and (2) the number of days with wet and warm conditions in the near-surface soil, where seeds and seedlings germinate and emerge (i.e. days with 0–5 cm deep soil water potential >−2.5 MPa and temperature above 0 °C). SPEI, a widely used drought index, was not predictive of whether sagebrush had reestablished. In contrast, wet-warm days elicited a critical drought threshold response, with successfully reestablished sites having experienced seven more wet-warm days than unsuccessful sites during the first March following summer wildfire and restoration. Thus, seemingly small-scale and short-term changes in water availability and temperature can contribute to major ecosystem shifts, as many of these sites remained shrubless two decades later. These findings help clarify the definition of ecological drought for a foundational species and its imperiled semi-arid ecosystem. Drought is well known to affect the occurrence of wildfires, but drought in the year(s) after fire can determine whether fire causes long-lasting, negative impacts on ecosystems.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab79e4","usgsCitation":"O’Connor, R., Germino, M., Barnard, D.M., Andrews, C.M., Bradford, J., Pilliod, D., Arkle, R.S., and Shriver, R.K., 2020, Small-scale water deficits after wildfires create long-lasting ecological impacts: Environmental Research Letters, v. 15, no. 4, 044001, 11 p., https://doi.org/10.1088/1748-9326/ab79e4.","productDescription":"044001, 11 p.","ipdsId":"IP-114720","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457404,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab79e4","text":"Publisher Index Page"},{"id":437058,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LDKQE2","text":"USGS data release","linkHelpText":"Ecological drought for sagebrush seedings in the Great Basin"},{"id":384960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Idaho, Nevada, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.0810546875,\n 40.84706035607122\n ],\n [\n -113.0712890625,\n 40.84706035607122\n ],\n [\n -113.0712890625,\n 43.32517767999296\n ],\n [\n -118.0810546875,\n 43.32517767999296\n ],\n [\n -118.0810546875,\n 40.84706035607122\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Connor, Rory 0000-0002-6473-0032","orcid":"https://orcid.org/0000-0002-6473-0032","contributorId":222832,"corporation":false,"usgs":true,"family":"O’Connor","given":"Rory","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, David M 0000-0003-1877-3151","orcid":"https://orcid.org/0000-0003-1877-3151","contributorId":222833,"corporation":false,"usgs":false,"family":"Barnard","given":"David","email":"","middleInitial":"M","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":813766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813768,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813769,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arkle, Robert S. 0000-0003-3021-1389","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":218006,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813770,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shriver, Robert K 0000-0002-4590-4834","orcid":"https://orcid.org/0000-0002-4590-4834","contributorId":222834,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert","email":"","middleInitial":"K","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":813771,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70208384,"text":"fs20203006 - 2020 - Pooling resources across organizations — Multisource water-quality data for the Delaware River Basin","interactions":[],"lastModifiedDate":"2022-04-20T18:14:13.866211","indexId":"fs20203006","displayToPublicDate":"2020-03-12T16:33:50","publicationYear":"2020","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":"2020-3006","displayTitle":"Pooling Resources Across Organizations — Multisource Water-Quality Data for the Delaware River Basin","title":"Pooling resources across organizations — Multisource water-quality data for the Delaware River Basin","docAbstract":"The U.S. Geological Survey (USGS) recently launched a pilot Integrated Water Availability Assessment (IWAA) in the Delaware River Basin to explore, test, and refine systems and processes for assessing water availability for human and ecological uses based on water monitoring data. Water-quality monitoring provides citizens, managers, and scientists with the information needed to evaluate the health of aquatic ecosystems and the safety and availability of water for drinking, agriculture, recreation, and other uses. Many organizations collect water-quality data at various sites and sampling frequencies to meet their assessment needs. The result is multiple individual datasets suitable for the specific organization’s needs that also hold great potential if pooled into a much larger dataset sourced from multiple organizations (multisource data). A multisource dataset increases the value and power of multiple single datasets and expands the breadth and depth of available water-quality data to ultimately increase the number and types of questions that can be answered. This fact sheet describes the process of “harmonizing” water-quality data from multiple organizations and presents a recently developed dataset for surface-water quality in the Delaware River Basin. This harmonized multisource surface-water-quality dataset will serve as a resource for analysis and modeling of surface-water quality to support IWAA efforts in the basin. Furthermore, this harmonization process can be expanded and applied to other regional IWAA basins or applied nationally.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203006","collaboration":"Integrated Water Availability Assessments Program","usgsCitation":"Murphy, J.C., and Shoda, M.E., 2020, Pooling resources across organizations — Multisource water-quality data for the Delaware River Basin: U.S. Geological Survey Fact Sheet 2020–3006, 2 p., https://doi.org/10.3133/fs20203006.","productDescription":"Report: 2 p.; Data Release","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-113620","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":373170,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PX8LZO","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin"},{"id":373169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3006/fs20203006.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3006"},{"id":373168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3006/coverthb.jpg"},{"id":399198,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109784.htm"}],"country":"United States","state":"Delaware, Maryland, New York, New Jersey, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.5,\n 38.6\n ],\n [\n -74.333,\n 38.6\n ],\n [\n -74.333,\n 42.5\n ],\n [\n -76.5,\n 42.5\n ],\n [\n -76.5,\n 38.6\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Program Coordinator, Water Availability and Use Science Program
U.S. Geological Survey
Water Resources Mission Area
Email: wausp-info@usgs.gov
","tableOfContents":"Coal mining has been the dominant industry and land use in West Virginia’s southern coal fields since the mid-1800s. Mortality rates for a variety of serious chronic conditions, such as diabetes, heart disease, and some forms of cancer in Appalachian coal mining regions, are higher than in areas lacking substantial coal mining activity within the Appalachian Region or elsewhere in the United States. Causes of the increased mortality and morbidity are not clear, but poor diet, high rates of smoking, socioeconomic factors, and the quality of groundwater used by area residents are all possible contributing factors. This study was conducted by the U.S. Geological Survey in cooperation with the West Virginia Department of Health and Human Resources and the West Virginia Department of Environmental Protection, with grant support from the Centers for Disease Control and Prevention (CDC) to assess the quality of groundwater in southern West Virginia. The data from this assessment of groundwater quality may be used by the CDC and other agencies to potentially investigate the role or lack thereof of groundwater quality with respect to mortality and morbidity rates in the region. The study was conducted in a region where a high density of current or past coal mining combined with a lack of advanced sewage treatment could affect concentrations of commonly occurring constituents plus contaminants, including nitrate, trace metals, major ions, indicator bacteria, radon, hydrogen sulfide, and dissolved hydrocarbons.
Because rural residential wells and mine outfalls are considered private sources of water in the region, and are therefore unregulated and unmonitored, water-quality data are sparse. To fill the data gap and assess the groundwater quality in the region, water-quality samples were collected from 60 sites in a 10-county area. The 60 sites sampled included 46 rural residential homeowner wells and 14 mine outfall discharges used for residential supply. For this study, all samples were collected prior to any filtration or other treatments, typically at the pressure tank, and are indicative of total and dissolved constituents in the untreated water.
Generally, data for the 60 sites indicate that most waters sampled do not exceed thresholds for most U.S. Environmental Protection Agency (EPA) drinking-water standards and U.S. Geological Survey (USGS) drinking-water screening criteria. However, there were several notable exceptions. Turbidity exceeded the 5-Nephelometric Turbidity Unit (NTU) EPA treatment technique (TT) drinking-water standard in 14 of 60 (23 percent) sites sampled and exceeded the 1-NTU TT standard in 51 of 60 (85 percent) sites sampled. Turbidity is common in many wells in southern West Virginia and may be attributed to iron oxyhydroxide precipitates, sediment carried into the aquifers from the shallow soil zone due to improperly constructed or cased wells or transported to the aquifer in shallow stress-relief fracture zones or through permeable bedding-plane partings. For the sites sampled, 31 of 60 (52 percent) had pH values at, above, or below the upper and lower range of the EPA Secondary Maximum Contaminant Level (SMCL, 6.5–8.5 standard units). Of those 31 sites, 28 (90 percent) were indicative of acidic corrosive water and 3 (10 percent) were indicative of alkaline water.
The Langelier Saturation Index (LSI), which is a measure of the corrosivity of the water, was computed for all sites sampled for the study. Eighty-two percent of the sites sampled had waters that were classified as corrosive, based on a LSI less than −0.5. Corrosive water has the potential to leach lead, copper, and other metals from lead, copper, galvanized, or lead-tin soldered connections in water lines. The chloride to sulfate mass ratio also was assessed with the alkalinity to indicate the potential to promote galvanic corrosion (PPGC) of water lines and plumbing fixtures. Only one of the sites (1.7 percent) classified as a corrosive water site, had a PPGC considered high; the remaining sites were classified as having either a moderate (53.3 percent) or low (45 percent) PPGC. Therefore, the type of plumbing systems sampled for this study may be affected by corrosive water, but the potential for leaching trace metals and other constituents from residential plumbing systems containing older galvanized pipes or lead-tin soldered copper pipes is moderate to low.
The indicator bacteria total coliform and Escherichia coli (E. coli) also were detected in groundwater samples to varying degrees. Total coliforms, which are a broad class of indicator bacteria, are common in groundwater in southern West Virginia and were detected in 39 of the 60 sites (65 percent) sampled. The presence of total coliform bacteria is a potential indicator of surface contamination, due to improperly constructed or cased wells, or infiltration of soil or other surface contaminants into the aquifer or well bore. E. coli bacteria, however, are much more indicative of fecal contamination of groundwater from either human or animal sources, and 14 of the 60 (23 percent) sites sampled had detections of E. coli. Although only a few strains of E. coli are known pathogens, their presence in groundwater may be an indicator of other related pathogens such as viruses and should be regarded as a serious potential issue. Water treatment such as chlorination, ozonation, or ultraviolet light may be appropriate to kill potential pathogenic bacteria or viruses in the source water.
Manganese and iron were prevalent contaminants in the groundwater samples collected for this study, with 30 of 60 (50 percent) sites analyzed for manganese and 25 of 60 (42 percent) sites analyzed for iron exceeding the proposed 50- and 300-micrograms per liter (µg/L) SMCL drinking-water standards, respectively, for aesthetic criteria such as taste, odor, or staining of plumbing fixtures. Fourteen of the 60 sites sampled (23 percent) had concentrations of manganese that exceeded the 300-µg/L USGS health-based screening level, and 1 site exceeded the 1,600-µg/L EPA drinking-water equivalent level, which is based on a lifetime exposure level. Sodium is another common constituent in groundwater within the study area. Sodium has an EPA health-based value (HBV) of 20 milligrams per liter (mg/L) for individuals who are on a sodium-restricted diet for blood pressure or other health reasons. Sodium concentrations exceeded the 20-mg/L EPA HBV in 27 of 60 (45 percent) samples.
Radon, a naturally occurring carcinogenic radioactive gas known to cause lung cancer, was detected at concentrations at or exceeding the proposed 300-picocuries per liter (pCi/L) EPA Maximum Contaminant Level (MCL) in 12 of the 60 (20 percent) sites sampled. Sites with radon gas concentrations exceeding the 300-pCi/L proposed MCL have the potential for airborne concentrations of radon to exceed the 4-pCi/L indoor air standard. Inhalation of radon can cause lung cancer, and the 4-pCi/L indoor air standard is based on an inhalation standard. Therefore, homeowners whose wells have radon gas concentrations exceeding 300 pCi/L may be advised to have their indoor air tested to determine if indoor air concentrations exceed the 4-pCi/L indoor air standard established by the EPA.
Various factors were analyzed statistically and graphically to determine whether they have an influence on groundwater quality within the study area, including topographic setting, well depth, type of mining (surface or underground), type of site (well or mine outfall), and geologic formation. Only geologic formation and the type of site sampled had strong statistical correlations with one or more of the constituents of concern for this study. The overall chemistry of outfalls (mine outfalls) and wells was significantly different, with a much higher dissolved oxygen content in outfalls than in wells. The dissolved oxygen content is the primary component driving the oxidation and reduction of minerals, and the precipitation of minerals that are saturated or super saturated with respect to various cations and anions. Median dissolved oxygen concentrations for the outfalls sampled was 8.75 mg/L, and only 0.4 mg/L for the wells sampled.
Median concentrations of sulfate and selenium were much higher in waters from the outfalls sampled, with median concentrations of 73.75 mg/L and 2.35 µg/L, respectively, compared to the wells sampled, which had median concentrations of 18.3 mg/L and less than (<) the 0.05-µg/L method detection limit, respectively. The maximum selenium concentration was for a well, with a concentration of 16.6 µg/L. The geochemical processes that control sulfate and selenium concentrations in groundwater are similar and are the result of the oxidation of sulfide minerals such as pyrite and ferroselite. Iron and manganese concentrations were elevated in most of the wells sampled, with median concentrations of 269.5 and 124.5 µg/L, respectively, but were rarely detected in the outfalls sampled, with median concentrations of < 4.0 and < 0.4 µg/L, respectively. The difference in iron and manganese between wells and outfalls is indicative of the role of dissolved oxygen on processes controlling groundwater chemistry in the region.
Three principal geologic formations were assessed for the study, and the overall chemistry for the Pocahontas, New River, and Kanawha Formations varied substantially with respect to several constituents. Concentrations of calcium, magnesium, and total dissolved solids were highest for sites sampled in the Pocahontas Formation, with median concentrations of 41.9, 18.6, and 312 mg/L, respectively. For constituents that are commonly associated with mining activity, the highest concentrations were for sites sampled in the New River Formation, with median concentrations of iron and manganese of 2,450 µg/L and 482 µg/L, respectively, and a median pH of 6.35 standard units. Concentrations of barium also were elevated in samples collected from sites in the New River Formation, with a median barium concentration of 184 µg/L. The source of the barium is not fully known but may be associated with commingling of shallow groundwater with deeper brines or dissolution of the mineral barite. The highest median sulfate concentrations were from sites sampled in the Pocahontas Formation, with a median concentration of 64.0 mg/L. Of the 12 sites at or exceeding the 300-pCi/L proposed drinking-water standard for radon, 8 (67 percent of MCL exceedances) were for sites deriving water from the Kanawha Formation, 3 (25 percent of MCL exceedances) were for sites deriving water from the New River Formation, and only 1 site was for water from the Pocahontas Formation (8 percent of proposed MCL exceedances).
Dissolved hydrocarbons, including methane, ethane, propane, propene, n- and i-butane, 1-butene, n- and i-pentane, pentane, 2- and 3-ethyl pentane, hexane, and benzene were analyzed in samples collected from 59 of the 60 sites to assess the potential occurrence and sources of these trace gases in groundwater within the study area. Results of the analysis indicate that most of the gas is of shallow biogenic origin, possibly associated with coal-bed methane, but a subset of samples has a gas signature and a chloride to bromide ratio indicative of potential mixing with deeper thermogenic gases. Only 2 of the 59 (3.3 percent) sites sampled had concentrations of methane gas, which is a highly combustible and explosive gas, exceeding the 10 milligrams per kilogram level of concern established by the U.S. Office of Surface Mining Reclamation and Enforcement.
Principal components analysis was used to assess the primary geochemical processes occurring in the aquifers sampled. The first principal component had significant positive loadings for bromide, chloride, silica, ammonia, barium, iron, manganese, and arsenic, and significant negative loadings for dissolved oxygen, potassium, nitrate, and uranium, and reflects reduction and oxidation (redox) processes occurring in deeper anoxic groundwater or shallow oxic groundwater. The strong positive loadings for iron, manganese, barium, and arsenic are correlated with reducing conditions often found deeper in the aquifer. More oxic water is correlated with oxidation of nitrogen species to nitrate and environmental mobilization of uranium and sulfate in shallow wells and mine outfalls.
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U.S. Geological Survey
11 Dunbar Street
Charleston, WV 25301
Hurricane Katrina made landfall in New Orleans, Louisiana as a Category 3 storm in August 2005. Storm surges, levee failures, and the low-lying nature of New Orleans led to widespread flooding, damage to over 70% of occupied housing, and evacuation of 80–90% of city residents. Only 57% of the city's black population has returned. Many residents complain of gentrification following rebuilding efforts. Climate gentrification is a recently described phenomenon whereby the effects of climate change, most notably rising sea levels and more frequent flooding and storm surges, alter housing values in a way that leads to gentrification.
To examine the climate gentrification following hurricane Katrina by (1) estimating the associations between flooding severity, ground elevation, and gentrification and (2) whether these relationships are modified by neighborhood level pre- and post-storm sociodemographic factors.
Lidar data collected in 2002 were used to determine elevation. Water gauge height of Lake Ponchartrain was used to estimate flood depth. Using census tracts as a proxy for neighborhoods, demographic, housing, and economic data from the 2000 decennial census and the 2010 and 2015 American Community Survey 5-year estimates US Census records were used to determine census tracts considered eligible for gentrification (median income < 2000 Orleans Parish median income). A gentrification index was created using tract changes in education level, population above the poverty limit, and median household income. Proportional odds ordinal logistic regression was used with product terms to test for effect measure modification by sociodemographic factors.
Census tracts eligible for gentrification in 2000 were 80.2% black. Median census tract flood depth was significantly lower in areas eligible to undergo gentrification (0.70 m vs. 1.03 m). Residents of gentrification-eligible tracts in 2000 were significantly more likely to be black, less educated, lower income, unemployed, and rent their home rather than own. In 2015 in these same eligible tracts, areas that underwent gentrification became significantly whiter, more educated, higher income, less unemployed, and more likely to live in a multi-unit dwelling. Gentrification was inversely associated with flood depth and directly associated with ground elevation in eligible tracts. Marginal effect modification was detected by the effect of pre-storm black race on the relationships of flood depth and elevation with gentrification.
Gentrification was strongly associated with higher ground elevation in New Orleans. These results provide evidence to support the idea of climate gentrification described in other low-elevation major metropolitan areas like Miami, FL. High elevation, low-income, demographically transitional areas in particular – that is areas that more closely resemble high-income area demographics, may be vulnerable to future climate gentrification.
This chapter introduces background and historical information on how structural equation modeling (SEM) came to be developed. Then, the main differences between SEM and earlier multivariate methods are explained. The chapter describes three main applications of SEM: path analysis, factor analysis, and hybrid models. Some computer programs are recommended for these applications. The step-by-step section goes over how to estimate structural models with AMOS and R. The chapter concludes with two example applications of SEM in the planning field.
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We use dynamic rupture modeling to address the questions of why the first earthquake did not propagate through the right‐lateral fault in one larger event, whether stress changes from the M 6.4 were necessary for the M 7.1 to occur, and how the Ridgecrest earthquakes affected the nearby Garlock Fault. We find that dynamic clamping and shear stress reduction confined surface rupture in the M 6.4 to the left‐lateral fault. We also find that stress changes from the M 6.4 were not necessary to allow a M 7.1 on the right‐lateral fault but that they affected the slip and likely accelerated the timing of the M 7.1. Lastly, we find that the Ridgecrest earthquakes may have brought the central Garlock Fault closer to failure.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL086020","usgsCitation":"Lozos, J.C., and Harris, R.A., 2020, Dynamic rupture simulations of the M6.4 and M7.1 July 2019 Ridgecrest, California earthquakes: Geophysical Research Letters, v. 47, no. 7, e2019GL086020, 9 p., https://doi.org/10.1029/2019GL086020.","productDescription":"e2019GL086020, 9 p.","ipdsId":"IP-112946","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":457415,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl086020","text":"Publisher Index Page"},{"id":375246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.95996093749999,\n 34.37064492478658\n ],\n [\n -115.34545898437499,\n 34.37064492478658\n ],\n [\n -115.34545898437499,\n 36.84446074079564\n ],\n [\n -118.95996093749999,\n 36.84446074079564\n ],\n [\n -118.95996093749999,\n 34.37064492478658\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Lozos, Julian C.","contributorId":146525,"corporation":false,"usgs":false,"family":"Lozos","given":"Julian","email":"","middleInitial":"C.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":790058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Ruth A. 0000-0002-9247-0768 harris@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-0768","contributorId":786,"corporation":false,"usgs":true,"family":"Harris","given":"Ruth","email":"harris@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":790059,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228345,"text":"70228345 - 2020 - Ecological prediction at macroscales using big data: Does sampling design matter?","interactions":[],"lastModifiedDate":"2022-02-09T23:31:04.189125","indexId":"70228345","displayToPublicDate":"2020-03-11T17:23:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Ecological prediction at macroscales using big data: Does sampling design matter?","docAbstract":"Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially-extensive dataset to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8 million km2 area. We estimated model predictive performance for different subsets of the dataset to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in datasets from some forms of targeted sampling may limit predictive performance, compiling existing spatially-extensive datasets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2123","usgsCitation":"Patricia A. Soranno, Cheruvelil, K.S., Boyang Liu, Wang, Q., Pang-Ning Tan, Jiayu Zhou, King, K.B., Ian M. McCullough, Joseph Stachelek, Bartley, M., Filstrup, C.T., Hanks, E., Lapierre, J., Lottig, N.R., Schliep, E., Wagner, T., and Webster, K.E., 2020, Ecological prediction at macroscales using big data: Does sampling design matter?: Ecological Applications, v. 30, no. 6, e02123, 13 p., https://doi.org/10.1002/eap.2123.","productDescription":"e02123, 13 p.","ipdsId":"IP-110739","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Patricia A. Soranno","contributorId":275249,"corporation":false,"usgs":false,"family":"Patricia A. 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The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.
Erosion related to glacial activity produces enormous amounts of sediment. However, sediment mobilization in glacial systems is extremely complex. Sediment is derived from headwalls, slopes along the margins of glaciers, and basal erosion; however, the rates and relative contributions of each are unknown. To test and quantify conceptual models for sediment generation and transport in a simple valley glacier system, we collected samples for 10Be analysis from the Kahiltna Glacier, which flows off Denali, the tallest mountain in North America. We collected angular quartz clasts on bedrock ledges from a high mountainside above the equilibrium line altitude (ELA), amalgamated clast samples from medial moraines, and sand samples from the river below the glacier. We also collected sand from nine other rivers along the south flank of the Alaska Range. In the upper catchment of the Kahiltna drainage system, toppling, rockfall, and slab collapse are significant erosional processes. Erosion rates of hundreds of millimeters per thousand years were calculated from 10Be concentrations. The 10Be concentrations in amalgamated samples from medial moraines showed concentrations much lower than those measured from the high mountainside, a result of the incorporation of thick, and effectively unexposed, blocks into the moraine, as well as the incorporation of material from lower-elevation nearby slopes above the moraines. The 10Be sediment samples from downstream of the Kahiltna Glacier terminus showed decreasing concentrations with increasing distance from the moraine, indicating the incorporation of material that was less exposed to cosmic rays, most likely from the glacier base as well as from slopes downstream of the glacier. Taken together, 10Be concentrations in various samples from the Kahiltna drainage system indicated erosion rates of hundreds of millimeters per thousand years, which is typical of tectonically active terrains. We also measured 10Be concentrations from river sediment samples collected from across the south flank of the Alaska Range. Calculation of basinwide weighted erosion rates that incorporated hypsometric curves produced unrealistically high erosion rates, which indicates that the major source of sediment was not exposed to cosmic rays and was primarily derived from the base of glaciers. Moreover, the apparently high erosion rates suggest that parts of each drainage system are not in erosional steady state with respect to cosmogenic isotope accumulation.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02190.1","usgsCitation":"Matmon, A., and Haeussler, P., 2020, Sediment sources and transport by the Kahiltna Glacier and other catchments along the south side of the Alaska Range, Alaska: Geosphere, v. 16, no. 3, p. 787-805, https://doi.org/10.1130/GES02190.1.","productDescription":"19 p.","startPage":"787","endPage":"805","ipdsId":"IP-113858","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":457429,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02190.1","text":"Publisher Index Page"},{"id":382463,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Range, Kahiltna Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154,\n 64\n ],\n [\n -154,\n 61\n ],\n [\n -146,\n 61\n ],\n [\n -146,\n 64\n ],\n [\n -154,\n 64\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-10","publicationStatus":"PW","contributors":{"editors":[{"text":"Team, ASTER","contributorId":248231,"corporation":false,"usgs":false,"family":"Team","given":"ASTER","email":"","affiliations":[{"id":49834,"text":"CNRS-Aix-Marseille University, Aix en Provence, France","active":true,"usgs":false}],"preferred":false,"id":808640,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Matmon, Ari","contributorId":196405,"corporation":false,"usgs":false,"family":"Matmon","given":"Ari","email":"","affiliations":[],"preferred":false,"id":808638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":808639,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208809,"text":"sir20195127 - 2020 - An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset","interactions":[],"lastModifiedDate":"2022-04-25T19:26:27.608939","indexId":"sir20195127","displayToPublicDate":"2020-03-10T10:15:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5127","displayTitle":"An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution Dataset","title":"An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset","docAbstract":"The National Hydrography Dataset Plus, Version 2.1 (NHDPlusV2.1) is an attribute-rich digital stream network for the conterminous United States, serving as a foundational infrastructure for reporting hydrologic information at both regional and national scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW) is a process-based statistical model that relies on a digital hydrologic network like NHDPlusV2.1 to establish spatial relations between quantities of monitored contaminant loads and contaminant sources, accounting for the physical characteristics along flow paths affecting contaminant transport. The U.S. Geological Survey National Water Quality Assessment project adopted and modified the medium-resolution NHDPlusV2.1 network for use as the primary framework supporting SPARROW modeling. This report describes the enhancements made to improve the routing capabilities and the value-added attributes of NHDPlusV2.1 to support modeling and other hydrologic analyses. These enhancements include corrections to inconsistencies in network/routing information, filling in missing attribute values of associated characteristics, accounting of water use affecting flow, new variables useful for interpreting network data, revised flowline attributes such as slope and flow, and incorporation of ancillary spatial data into the network. The resulting dataset containing the enhancements to the network is named E2NHDPlusV2_US. Although the enhancements described in the report were developed for use in SPARROW modeling, the enhancements are expected to be useful for a wide variety of hydrologic studies within the United States.
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National Water Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20191-0002
Ecological communities are controlled by multiple, interacting abiotic and biotic factors that influence the distribution, abundance, and diversity of species. These processes jointly determine resource availability, resource competition, and ultimately species richness. For many terrestrial ecosystems in dryland climates, soil water availability is the most frequent limiting resource for plant species. We used field sampling coupled with process‐based soil water balance modeling to explore the relative importance of multiple macroclimatic, ecohydrological, and biotic variables on plant species and functional type richness at the landscape scale in dryland plant communities.
Dryland plant communities dominated by big sagebrush (Artemisia tridentata ) that span climatic and elevational gradients in Wyoming, USA.
We quantified species richness at 1,000 m2 and used multiple regression to determine whether mean climatic conditions, multiple metrics of soil moisture from a soil water balance model (SOILWAT2), soil physical and chemical properties, and shrub stand structure (biotic) variables were related to species and functional type richness.
Species richness varied between 16 and 54 across sites. We found that species and functional type richness were related to both macroclimate and ecohydrology, but ecohydrology explained slightly more variation than climate. Biotic variables were always secondary to macroclimate and ecohydrology in our models. Variance partitioning revealed that large portions of variability in species (~54%), forb (~47%), and grass (~40%) richness were explained by ecohydrological variables.
Our results highlight the importance of the spatial and temporal distribution of soil water for dryland plant species richness and suggest that documenting the ways in which climate, vegetation, and soil properties interact to determine soil water availability is critical for understanding biodiversity patterns in dryland plant communities. This work has relevance for other mid‐latitude, shrub‐dominated dryland plant communities where soil water availability strongly influences ecosystem structure and function.
","language":"English","publisher":"Wiley","doi":"10.1111/jvs.12874","usgsCitation":"Jordan, S., Palmquist, K.A., Bradford, J., and Lauenroth, W.K., 2020, Soil water availability shapes species richness in mid-latitude shrub steppe plant communities: Journal of Vegetation Science, v. 31, no. 4, p. 646-657, https://doi.org/10.1111/jvs.12874.","productDescription":"12 p.","startPage":"646","endPage":"657","ipdsId":"IP-101810","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":376122,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-05-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Jordan, Samuel E. 0000-0001-6074-3330","orcid":"https://orcid.org/0000-0001-6074-3330","contributorId":228826,"corporation":false,"usgs":false,"family":"Jordan","given":"Samuel E.","affiliations":[],"preferred":false,"id":792148,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmquist, Kyle A.","contributorId":169517,"corporation":false,"usgs":false,"family":"Palmquist","given":"Kyle","email":"","middleInitial":"A.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":792149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":792150,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":792151,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215559,"text":"70215559 - 2020 - Probabilistic categorical groundwater salinity mapping from airborne electromagnetic data adjacent to California’s Lost Hills and Belridge oil fields","interactions":[],"lastModifiedDate":"2020-10-23T14:06:56.145727","indexId":"70215559","displayToPublicDate":"2020-03-10T09:01:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic categorical groundwater salinity mapping from airborne electromagnetic data adjacent to California’s Lost Hills and Belridge oil fields","docAbstract":"Growing water stress has led to emerging interest in protecting fresh and brackish groundwater as a potential supplement to water supplies and raised questions about factors that could affect the future quality of fresh and brackish aquifers. Limited well infrastructure, particularly in regions where elevated salinity has led to limited historical groundwater development, hinders traditional mapping of salinity distributions through groundwater sampling. This paper presents a quantitative salinity mapping approach of the upper 300 m using high‐resolution, regionally comprehensive resistivity models derived from Bayesian inversion of an airborne electromagnetic survey adjacent to the Lost Hills and Belridge oil fields in the southwestern San Joaquin Valley of California. Using local water quality observations as an interpretational foundation, a probabilistic approach yields maps of fresh, saline, and brackish groundwater while quantifying joint uncertainty inherited from the geophysical data and interpretational relations. Saline and fresh regions are mapped with relatively high confidence in many locations, while areas of lower confidence, particularly at depth, can be mapped as their most probable salinity category while reflecting the relative uncertainty in the interpretation. These maps identify a stratified salinity structure, where saline water commonly occurs in the surficial aquifer overlying fresher groundwater in the Tulare aquifer, separated by regional confining clay layers. Downgradient of unlined surface water diversions, recharge of imported surface water results in relatively fresh groundwater throughout the depth of investigation.