In this article, we describe the use of diagnostic timescales as simple tools for illuminating how aquatic ecosystems work, with a focus on coastal systems such as estuaries, lagoons, tidal rivers, reefs, deltas, gulfs, and continental shelves. Intending this as a tutorial as well as a review, we discuss relevant fundamental concepts (e.g., Lagrangian and Eulerian perspectives and methods, parcels, particles, and tracers), and describe many of the most commonly used diagnostic timescales and definitions. Citing field-based, model-based, and simple algebraic methods, we describe how physical timescales (e.g., residence time, flushing time, age, transit time) and biogeochemical timescales (e.g., for growth, decay, uptake, turnover, or consumption) are estimated and implemented (sometimes together) to illuminate coupled physical-biogeochemical systems. Multiple application examples are then provided to demonstrate how timescales have proven useful in simplifying, understanding, and modeling complex coastal aquatic systems. We discuss timescales from the perspective of “holism”, the degree of process richness incorporated into them, and the value of clarity in defining timescales used and in describing how they were estimated. Our objective is to provide context, new applications and methodological ideas and, for those new to timescale methods, a starting place for implementing them in their own work.
","language":"English","publisher":"MDPI","doi":"10.3390/w12102717","usgsCitation":"Lucas, L., and Deleersnijder, E., 2020, Timescale methods for simplifying, understanding and modeling biophysical and water quality processes in coastal aquatic ecosystems: A review: Water, v. 12, no. 10, 2717, 65 p., https://doi.org/10.3390/w12102717.","productDescription":"2717, 65 p.","ipdsId":"IP-119708","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":455205,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12102717","text":"Publisher Index Page"},{"id":386640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":818032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deleersnijder, Eric 0000-0003-0346-9667","orcid":"https://orcid.org/0000-0003-0346-9667","contributorId":260499,"corporation":false,"usgs":false,"family":"Deleersnijder","given":"Eric","email":"","affiliations":[{"id":52602,"text":"Université catholique de Louvain, Institute of Mechanics, Materials and Civil Engineering (IMMC) & Earth and Life Institute (ELI), Louvain-la-Neuve, Belgium","active":true,"usgs":false}],"preferred":false,"id":818033,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70214236,"text":"sim3460 - 2020 - Potentiometric surfaces, 2011–12, and water-level differences between 1995 and 2011–12, in wells of the “200-foot,” “500-foot,” and “700-foot” sands of the Lake Charles area, southwestern Louisiana","interactions":[],"lastModifiedDate":"2020-09-30T12:20:54.19763","indexId":"sim3460","displayToPublicDate":"2020-09-28T10:47:44","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3460","displayTitle":"Potentiometric Surfaces, 2011–12, and Water-Level Differences Between 1995 and 2011–12, in Wells of the “200-Foot,” “500-Foot,” and “700-Foot” Sands of the Lake Charles Area, Southwestern Louisiana","title":"Potentiometric surfaces, 2011–12, and water-level differences between 1995 and 2011–12, in wells of the “200-foot,” “500-foot,” and “700-foot” sands of the Lake Charles area, southwestern Louisiana","docAbstract":"Water levels were determined in 90 wells to prepare 2011–12 potentiometric surfaces focusing primarily on the “200-foot,” 500-foot,” and “700-foot” sands of the Lake Charles area, which are part of the Chicot aquifer system underlying Calcasieu and Cameron Parishes of southwestern Louisiana. These three aquifers provided 34 percent of the total water withdrawn and 93 percent of the groundwater withdrawn in Calcasieu and Cameron Parishes in 2012 (84.5 million gallons per day [Mgal/d]). This work was completed by the U.S. Geological Survey, in cooperation with the Louisiana Department of Transportation and Development, to assist in developing and evaluating groundwater-resource management strategies. The highest water levels determined in wells screened in the “200-foot,” “500-foot,” and “700-foot” sands were about 8 feet (ft) above the National Geodetic Vertical Datum of 1929 (NGVD 29), 2 ft below NGVD 29, and 14 ft below NGVD 29, respectively, and were located in northwestern Calcasieu Parish. The lowest water levels determined in wells screened in the “200-foot,” “500-foot,” and “700-foot” sands were approximately 50, 80, and 70 ft below NGVD 29, respectively, and were located in the southern Lake Charles metropolitan area, to the west of Prien Lake, and between the cities of Lake Charles and Sulphur, respectively. The primary groundwater flow direction in these three aquifers was radially towards pumping centers overlying the water-level lows. Comparisons of water-level differences in 42 wells measured in 1995 and 2011–12 indicated that the maximum increases in water levels for wells screened in the “200-foot,” “500-foot,” and “700-foot” sands were approximately 7, 31, and 19 ft, respectively. Water-level increases coincided with a decline in total groundwater withdrawals during the period (about 25 Mgal/d from 1995 to 2012) from these sands. More specifically, withdrawals from the “500-foot” sand affected water levels in wells screened in the “200-foot” and “700-foot” sands because the three are hydraulically connected and withdrawals from the “500-foot” sand were greater by volume than withdrawals from the “200-foot” and “700-foot” sands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3460","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., and Griffith, J.M., 2020, Potentiometric surfaces, 2011–12, and water-level differences between 1995 and 2011–12, in wells of the “200-foot,” “500-foot,” and “700-foot” sands of the Lake Charles area, southwestern Louisiana: U.S. Geological Survey Scientific Investigations Map 3460, 4 sheets, 11-p. pamphlet, https://dx.doi.org/10.3133/sim3460.","productDescription":"Pamphlet: viii, 11 p.; 4 Sheets: 32.00 x 28.00 inches or smaller","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-055171","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":378720,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3460/sim3460_sheet4.pdf","text":"Sheet 4","size":"932 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3460 Sheet 4"},{"id":378719,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3460/sim3460_sheet3.pdf","text":"Sheet 3","size":"1.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3460 Sheet 3"},{"id":378715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3460/coverthb.jpg"},{"id":378718,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3460/sim3460_sheet2.pdf","text":"Sheet 2","size":"1.70 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3460 Sheet 2"},{"id":378716,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3460/sim3460_pamphlet.pdf","text":"Pamphlet","size":"499 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3460 Pamphlet"},{"id":378717,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3460/sim3460_sheet1.pdf","text":"Sheet 1","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3460 Sheet 1"}],"country":"United States","state":"Louisiana","otherGeospatial":"Lake Charles area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.812255859375,\n 29.6594160549124\n ],\n [\n -92.61474609375,\n 29.6594160549124\n ],\n [\n -92.61474609375,\n 30.524413269923986\n ],\n [\n -93.812255859375,\n 30.524413269923986\n ],\n [\n -93.812255859375,\n 29.6594160549124\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Anthropogenic dewatering of aquatic habitats can cause stranding and mortality of burrowed larval lampreys; however, the effects of dewatering have not been quantified. We assessed: (a) changes in spatial distribution, abundance, and emergence of larvae dewatered at Leaburg Reservoir (OR); (b) emergence and mortality of larvae dewatered in a laboratory; and (c) bias, precision, and interpretation of field results by simulation and modeling of laboratory results. In the field, we examined the distribution, abundance (by N‐mixture model), and density of larvae by electrofishing at randomly selected sites before dewatering and after refill, and assessed the emergence rate by observation and excavation during dewatering. Due to dewatering in the field, about 42% of larvae emerged and spatial distribution changed toward sites dewatered less than 20 hours. Estimated average density decreased from 10.8 larvae/m2 before dewatering to 2.3 larvae/m2 after refilling, suggesting that abundance declined by 79%; simulation suggested this decline ranged 71–84% (interquartile range). In the laboratory, we examined the emergence and mortality rates of larvae dewatered 0–48 hrs. The emergence rate in the laboratory was similar to that in the field. Mortality rate increased with hours dewatered and was higher for emerged than burrowed larvae. Laboratory estimates of mortality rate predicted a 61% decline in abundance if only burrowed larvae survived and a 54% decline if both burrowed and emerged larvae survived. Abundance declines in the field could be from mortality (e.g., desiccation, predation) and relocation to watered habitat. Our results indicate dewatering can substantially affect spatial distribution and abundance of larval lampreys in freshwater ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3730","usgsCitation":"Harris, J.E., Skalicky, J.J., Liedtke, T.L., Weiland, L.K., Clemens, B.J., and Gray, A.E., 2020, Effects of dewatering on behavior, distribution, and abundance of larval lampreys: River Research and Applications, v. 36, no. 10, p. 2001-2012, https://doi.org/10.1002/rra.3730.","productDescription":"12 p.","startPage":"2001","endPage":"2012","ipdsId":"IP-119069","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":382054,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Leaburg Reservoir, McKenzie River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.068603515625,\n 43.75522505306928\n ],\n [\n -121.36596679687499,\n 43.75522505306928\n ],\n [\n -121.36596679687499,\n 45.706179285330855\n ],\n [\n -124.068603515625,\n 45.706179285330855\n ],\n [\n -124.068603515625,\n 43.75522505306928\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Harris, Julianne E. 0000-0003-1343-5911","orcid":"https://orcid.org/0000-0003-1343-5911","contributorId":247527,"corporation":false,"usgs":false,"family":"Harris","given":"Julianne","email":"","middleInitial":"E.","affiliations":[{"id":49569,"text":"U.S. Fish and Wildlife Service, Columbia River Fish and Wildlife Conservation Office, 1211 SE Cardinal Court, Suite 100, Vancouver, Washington 98683","active":true,"usgs":false}],"preferred":false,"id":807857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Skalicky, Joseph J. 0000-0002-6467-5037","orcid":"https://orcid.org/0000-0002-6467-5037","contributorId":247528,"corporation":false,"usgs":false,"family":"Skalicky","given":"Joseph","email":"","middleInitial":"J.","affiliations":[{"id":49569,"text":"U.S. Fish and Wildlife Service, Columbia River Fish and Wildlife Conservation Office, 1211 SE Cardinal Court, Suite 100, Vancouver, Washington 98683","active":true,"usgs":false}],"preferred":false,"id":807858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liedtke, Theresa L. 0000-0001-6063-9867 tliedtke@usgs.gov","orcid":"https://orcid.org/0000-0001-6063-9867","contributorId":2999,"corporation":false,"usgs":true,"family":"Liedtke","given":"Theresa","email":"tliedtke@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":807859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weiland, Lisa K. 0000-0002-9729-4062 lweiland@usgs.gov","orcid":"https://orcid.org/0000-0002-9729-4062","contributorId":3565,"corporation":false,"usgs":true,"family":"Weiland","given":"Lisa","email":"lweiland@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":807860,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clemens, Benjamin J.","contributorId":195098,"corporation":false,"usgs":false,"family":"Clemens","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":807861,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gray, Ann E.","contributorId":195113,"corporation":false,"usgs":false,"family":"Gray","given":"Ann","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":807862,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70214530,"text":"70214530 - 2020 - Modeling soil porewater salinity in mangrove forests (Everglades, Florida, USA) impacted by hydrological restoration and a warming climate","interactions":[],"lastModifiedDate":"2020-09-30T14:56:14.381095","indexId":"70214530","displayToPublicDate":"2020-09-26T09:49:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Modeling soil porewater salinity in mangrove forests (Everglades, Florida, USA) impacted by hydrological restoration and a warming climate","docAbstract":"Hydrology is a critical driver controlling mangrove wetlands structural and functional attributes at different spatial and temporal scales. Yet, human activities have negatively affected hydrology, causing mangrove diebacks and coverage loss worldwide. In fact, the assessment of mangrove water budgets, impacted by natural and human disturbances, is limited due to a lack of long-term data and information that hinders our understanding of how changes in hydroperiod and salinity control mangrove productivity and spatial distribution. In this study, we implemented a mass balance-based hydrological model (RHYMAN) that explicitly considers groundwater discharge in the Shark River estuary (SRE, southwestern Everglades) located in a karstic geomorphic setting and influenced by regional hydrological restoration. We used long-term hydroperiod and porewater salinity (PWS) datasets obtained from 2004 to 2016 for model calibration and validation and to determine spatiotemporal variability in water levels and PWS at three riverine mangrove sites (downstream, SRS-6; midstream, SRS-5; upstream, SRS-4) along SRE. Model results agree with a distinct PWS pattern along the estuarine salinity gradient where the highest PWS occurs at SRS-6 (mean: 25, range: 22–30 ppt), followed by SRS-5 (17, 14–25 ppt) and SRS-4 (5, 3–13 ppt). A commensurate increase in PWS over a thirteen-year period indicates a long-term reduction in freshwater inflow coupled with sea-level rise (SLR). Increasing freshwater scenario simulation results show a significant reduction (17–27%) in PWS along the estuary in contrast with a high SLR scenario when salinity increases up to 1.1 to 2.5 times that of control values. Model results show that freshwater inflow and SLR are key drivers controlling mangrove wetlands PWS in this karstic coastal region. Given its relatively simple structure, this mass balance-based hydrological model could be used in other environmental settings to evaluate potential habitat and regime shifts due to changes in hydrology and PWS under regional hydrological restoration management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2020.109292","usgsCitation":"Zhao, X., Rivera-Monroy, V.H., Wang, H., Xue, Z., Tsai, C., Willson, C.S., Castañeda-Moya, E., and Twilley, R.R., 2020, Modeling soil porewater salinity in mangrove forests (Everglades, Florida, USA) impacted by hydrological restoration and a warming climate: Ecological Modelling, v. 436, 109292, 18 p., https://doi.org/10.1016/j.ecolmodel.2020.109292.","productDescription":"109292, 18 p.","ipdsId":"IP-117526","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455213,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.lsu.edu/civil_engineering_pubs/1184","text":"Publisher Index Page"},{"id":378913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.9085693359375,\n 25.06072125231416\n ],\n [\n -80.3814697265625,\n 25.06072125231416\n ],\n [\n -80.3814697265625,\n 26.48532391504829\n ],\n [\n -81.9085693359375,\n 26.48532391504829\n ],\n [\n -81.9085693359375,\n 25.06072125231416\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"436","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhao, Xiaochen","contributorId":219696,"corporation":false,"usgs":false,"family":"Zhao","given":"Xiaochen","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":799834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivera-Monroy, Victor H. 0000-0003-2804-4139","orcid":"https://orcid.org/0000-0003-2804-4139","contributorId":200322,"corporation":false,"usgs":false,"family":"Rivera-Monroy","given":"Victor","email":"","middleInitial":"H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":799835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Hongqing 0000-0002-2977-7732","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":219641,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":799836,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xue, Zuo 0000-0003-4018-0248","orcid":"https://orcid.org/0000-0003-4018-0248","contributorId":241655,"corporation":false,"usgs":false,"family":"Xue","given":"Zuo","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":799837,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tsai, Cheng-Feng","contributorId":241949,"corporation":false,"usgs":false,"family":"Tsai","given":"Cheng-Feng","email":"","affiliations":[],"preferred":false,"id":799838,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Willson, C. S.","contributorId":90440,"corporation":false,"usgs":false,"family":"Willson","given":"C.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":799839,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Castañeda-Moya, E. 0000-0001-7759-4351","orcid":"https://orcid.org/0000-0001-7759-4351","contributorId":241657,"corporation":false,"usgs":false,"family":"Castañeda-Moya","given":"E.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":799840,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Twilley, Robert R.","contributorId":34585,"corporation":false,"usgs":false,"family":"Twilley","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":799841,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70214645,"text":"70214645 - 2020 - From lava to water: A new era at Kīlauea","interactions":[],"lastModifiedDate":"2020-10-01T17:43:09.036638","indexId":"70214645","displayToPublicDate":"2020-09-25T12:34:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3879,"text":"Eos, Earth and Space Science News","active":true,"publicationSubtype":{"id":10}},"title":"From lava to water: A new era at Kīlauea","docAbstract":"No abstract available.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020EO149557","usgsCitation":"Nadeau, P.A., Diefenbach, A., Hurwitz, S., and Swanson, D., 2020, From lava to water: A new era at Kīlauea: Eos, Earth and Space Science News, 11 p., https://doi.org/10.1029/2020EO149557.","productDescription":"11 p.","ipdsId":"IP-118975","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":455216,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020eo149557","text":"Publisher Index Page"},{"id":378964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.29329299926758,\n 19.39471557731923\n ],\n [\n -155.23681640625,\n 19.39471557731923\n ],\n [\n -155.23681640625,\n 19.4395612768183\n ],\n [\n -155.29329299926758,\n 19.4395612768183\n ],\n [\n -155.29329299926758,\n 19.39471557731923\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nadeau, Patricia A. 0000-0002-6732-3686","orcid":"https://orcid.org/0000-0002-6732-3686","contributorId":215616,"corporation":false,"usgs":true,"family":"Nadeau","given":"Patricia","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":800326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diefenbach, Angela K. 0000-0003-0214-7818","orcid":"https://orcid.org/0000-0003-0214-7818","contributorId":204743,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Angela K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":800327,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":800328,"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":800329,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70214965,"text":"70214965 - 2020 - Integrating physical and economic data into experimental water accounts for the United States: Lessons and opportunities","interactions":[],"lastModifiedDate":"2020-10-03T15:10:16.780202","indexId":"70214965","displayToPublicDate":"2020-09-25T10:06:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1477,"text":"Ecosystem Services","active":true,"publicationSubtype":{"id":10}},"title":"Integrating physical and economic data into experimental water accounts for the United States: Lessons and opportunities","docAbstract":"Water management increasingly involves tradeoffs, making its accounting highly relevant in our interconnected world. Physical and economic data about water in many nations are becoming more widely integrated through application of the System of Environmental-Economic Accounts for Water (SEEA-Water), which enables the tracking of linkages between water and the economy. We present the first national and subnational SEEA-Water accounts for the United States. We compile accounts for water: (1) physical supply and use, (2) productivity, (3) quality, and (4) emissions for roughly the years 2000 to 2015. Total U.S. water use declined by 22% from 2000 to 2015, falling in 44 states though groundwater use increased in 21 states. Water-use reductions, combined with economic growth, led to increases in water productivity for the overall national economy (65%), mining (99%), and agriculture (68%). Surface-water quality trends were most evident at regional levels, and differed by water-quality constituent and region. This work provides (1) a baseline of recent historical water resource trends and their value in the U.S., and (2) a roadmap for the completion of future accounts for water, a critical ecosystem service. Our work also aids in the interpretation of ecosystem accounts in the context of long-term water resources trends.
heir value in the U.S., and (2) a roadmap for the completion of future accounts for water, a critical ecosystem service. Our work also aids in the interpretation of ecosystem accounts in the context of long-term water resources trends.
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L.","contributorId":211867,"corporation":false,"usgs":false,"family":"Hass","given":"Julie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":800452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glynn, Pierre D. 0000-0001-8804-7003 pglynn@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7003","contributorId":2141,"corporation":false,"usgs":true,"family":"Glynn","given":"Pierre","email":"pglynn@usgs.gov","middleInitial":"D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":800453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wentland, Scott","contributorId":211876,"corporation":false,"usgs":false,"family":"Wentland","given":"Scott","affiliations":[{"id":38340,"text":"Bureau of Economic Analysis","active":true,"usgs":false}],"preferred":false,"id":800454,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vardon, Michael","contributorId":211875,"corporation":false,"usgs":false,"family":"Vardon","given":"Michael","email":"","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":800455,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fay, John P.","contributorId":207571,"corporation":false,"usgs":false,"family":"Fay","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":800456,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70214056,"text":"ofr20201096 - 2020 - Field evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor","interactions":[],"lastModifiedDate":"2022-10-25T13:56:58.33759","indexId":"ofr20201096","displayToPublicDate":"2020-09-24T11:47:39","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1096","displayTitle":"Field Evaluation of the Sequoia Scientific LISST-ABS Acoustic Backscatter Sediment Sensor","title":"Field evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor","docAbstract":"Sequoia Scientific’s LISST-ABS is a submersible acoustic instrument that measures the acoustic backscatter sensor (ABS) concentration at a point within a river, stream, or creek. Compared to traditional physical methods for measuring suspended-sediment concentration (SSC), sediment surrogates like the LISST-ABS offer continuous data that can be calibrated with physical SSC samples. Data were collected at 10 U.S. Geological Survey streamflow-gaging stations between January 10, 2016, and February 21, 2018, across the contiguous United States to test the accuracy and effectiveness of using the LISST-ABS as a surrogate for measuring the concentration of suspended sediment in a dynamic fluvial system. Correlation coefficients (Pearson’s r values) relating the ABS concentration and SSC from physical samples ranged from r = 0.718 to r = 0.956 at the 10 stations with the mean percentage of fines (percentage of the sediment less than 62.5 microns in diameter) ranging from 65 to 100 percent (with minimum and maximum values of 18 and 100 percent, respectively). The LISST-ABS instruments used in this field evaluation were factory-calibrated to accurately determine SSC for grains in the diameter range of 75–90 microns. Note that the sensor responds to grains of arbitrary sizes, but the accuracy varies at sizes other than this calibration size. For operational use, regression models could be determined for the ABS concentrations and SSC values or the instrument could be recalibrated to sediments for each fluvial environment. However, such calibrations were beyond the scope of this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201096","collaboration":"Federal Interagency Sedimentation Project and Observing Systems Division","usgsCitation":"Manaster, A.E., Straub, T.D., Wood, M.S., Bell, J.M., Dombroski, D.E., and Curran, C.A., 2020, Field evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor: U.S. Geological Survey Open-File Report 2020–1096, 26 p., https://doi.org/10.3133/ofr20201096.","productDescription":"Report: v, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-116096","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":378643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1096/coverthb.jpg"},{"id":378644,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1096/ofr20201096.pdf","text":"Report","size":"3.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1096"},{"id":378645,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LROJE4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for field evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The karstic Edwards and Trinity aquifers are classified as major sources of water in south-central Texas by the Texas Water Development Board. During 2018–20 the U.S. Geological Survey, in cooperation with the Edwards Aquifer Authority, mapped and described the geologic framework and hydrostratigraphy of the rocks composing the Edwards and Trinity aquifers in northern Medina County from field observations of the surficial expressions of the rocks. The thicknesses of the mapped lithostratigraphic members and hydrostratigraphic units were also estimated from field observations.
The Cretaceous-age rocks (listed in ascending order) in the study area are part of the Trinity Group (lower and upper members of the Glen Rose Limestone), Edwards Group (Kainer Formation [and its stratigraphic equivalent, the Fort Terrett Formation] and Person Formation), Devils River Limestone, Washita Group (Georgetown Formation, Del Rio Clay, and Buda Limestone), Eagle Ford Group, Austin Group, Taylor Group, and Late Cretaceous igneous intrusive rocks. The groups and formations are composed primarily of relatively thick layers of clays, shales, and limestone. The igneous rocks are coarse-grained ultramafic in composition.
The principal structural feature in northern Medina County is the Balcones fault zone, which is the result of late Oligocene and early Miocene extensional faulting and fracturing resulting from the eastern Edwards Plateau uplift. In the Balcones fault zone, most of the faults in the study area are high-angle to vertical, en echelon, normal faults that are predominately downthrown to the southeast.
Hydrostratigraphically, the rocks exposed in the study area (listed in descending order from land surface as they appear in a stratigraphic column) are igneous, the upper confining unit to the Edwards aquifer, the Edwards aquifer, the upper zone of the Trinity aquifer, and the upper part of the middle zone of the Trinity aquifer. The karstic carbonate Edwards and Trinity aquifers developed as a result of their original depositional history, primary and secondary porosity, diagenesis, fracturing, and faulting. These factors have resulted in development of modified porosity, permeability, and transmissivity within and between the aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3461","collaboration":"Prepared in cooperation with the Edwards Aquifer Authority","usgsCitation":"Clark, A.K., Morris, R.E., and Pedraza, D.E., 2020, Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas: U.S. Geological Survey Scientific Investigations Map 3461, 13 p. pamphlet, 1 pl., scale 1:24,000, https://doi.org/10.3133/sim3461.","productDescription":"Report: vi, 13 p.; Sheet: 48 inches x 36 inches; Data Release","numberOfPages":"23","onlineOnly":"N","ipdsId":"IP-112816","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":378661,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HHMBX8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial dataset of the geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas, at 1:24,000 scale"},{"id":378659,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3461/sim3461_pamphlet.pdf","text":"Pamphlet","size":"1.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3461 Pamphlet"},{"id":378658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3461/coverthb1.jpg"},{"id":378660,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3461/sim3461.pdf","text":"Map sheet","size":"30.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3461"}],"country":"United States","state":"Texas","county":"Medina County","otherGeospatial":"Edwards and Trinity Aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.46609497070312,\n 29.31514119318728\n ],\n [\n -98.79867553710936,\n 29.31514119318728\n ],\n [\n -98.79867553710936,\n 29.6510621496229\n ],\n [\n -99.46609497070312,\n 29.6510621496229\n ],\n [\n -99.46609497070312,\n 29.31514119318728\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Aquatic waterfowl, particularly those in the order Anseriformes and Charadriiformes, are the ecological reservoir of avian influenza viruses (AIVs). Dabbling ducks play a recognized role in the maintenance and transmission of AIVs. Furthermore, the pathogenesis of highly pathogenic AIV (HPAIV) in dabbling ducks is well characterized. In contrast, the role of diving ducks in HPAIV maintenance and transmission remains unclear. In this study, the pathogenesis of a North American A/Goose/1/Guangdong/96-lineage clade 2.3.4.4 group A H5N2 HPAIV, A/Northern pintail/Washington/40964/2014, in diving sea ducks (surf scoters, Melanitta perspicillata) was characterized.
Intrachoanal inoculation of surf scoters with A/Northern pintail/Washington/40964/2014 (H5N2) HPAIV induced mild transient clinical disease whilst concomitantly shedding high virus titers for up to 10 days post-inoculation (dpi), particularly from the oropharyngeal route. Virus shedding, albeit at low levels, continued to be detected up to 14 dpi. Two aged ducks that succumbed to HPAIV infection had pathological evidence for co-infection with duck enteritis virus, which was confirmed by molecular approaches. Abundant HPAIV antigen was observed in visceral and central nervous system organs and was associated with histopathological lesions.
Collectively, surf scoters, are susceptible to HPAIV infection and excrete high titers of HPAIV from the respiratory and cloacal tracts whilst being asymptomatic. The susceptibility of diving sea ducks to H5 HPAIV highlights the need for additional research and surveillance to further understand the contribution of diving ducks to HPAIV ecology.
","language":"English","publisher":"Springer","doi":"10.1186/s12917-020-02579-x","usgsCitation":"Luczo, J.M., Prosser, D., Pantin-Jackwood, M.J., Berlin, A., and Spackman, E., 2020, The pathogenesis of a North American H5N2 clade 2.3.4.4 group A highly pathogenic avian influenza virus in surf scoters (Melanitta perspicillata): BMC Veterinary Research, v. 16, 351, 10 p., https://doi.org/10.1186/s12917-020-02579-x.","productDescription":"351, 10 p.","ipdsId":"IP-115428","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455237,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12917-020-02579-x","text":"Publisher Index Page"},{"id":378746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2020-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Luczo, Jasmine M.","contributorId":241114,"corporation":false,"usgs":false,"family":"Luczo","given":"Jasmine","email":"","middleInitial":"M.","affiliations":[{"id":48207,"text":"USDA SEPRL","active":true,"usgs":false}],"preferred":false,"id":799587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":799588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pantin-Jackwood, Mary J.","contributorId":197094,"corporation":false,"usgs":false,"family":"Pantin-Jackwood","given":"Mary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":799589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berlin, Alicia 0000-0002-5275-3077 aberlin@usgs.gov","orcid":"https://orcid.org/0000-0002-5275-3077","contributorId":168416,"corporation":false,"usgs":true,"family":"Berlin","given":"Alicia","email":"aberlin@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":799590,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spackman, Erica","contributorId":53647,"corporation":false,"usgs":false,"family":"Spackman","given":"Erica","email":"","affiliations":[],"preferred":false,"id":799591,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215716,"text":"70215716 - 2020 - Does the Darcy-Buckingham Law apply to flow through unsaturated porous rock?","interactions":[],"lastModifiedDate":"2020-10-28T13:20:09.284709","indexId":"70215716","displayToPublicDate":"2020-09-23T08:15:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Does the Darcy-Buckingham Law apply to flow through unsaturated porous rock?","docAbstract":"Soft sediment deformation structures are common in fine-grained pyroclastic deposits and are often taken, along with other characteristics, to indicate that deposits were emplaced in a wet and cohesive state. At Ubehebe Crater (Death Valley, California, USA), deposits were emplaced by multiple explosions, both directly from pyroclastic surges and by rapid remobilization of fresh, fine-ash-rich deposits off steep slopes as local granular flows. With the exception of the soft sediment deformation structures themselves, there is no evidence of wet deposition. We conclude that deformation was a result of destabilization of fresh, fine-grained deposits with elevated pore-gas pressure and dry cohesive forces. Soft sediment deformation alone is not sufficient to determine whether parent pyroclastic surges contained liquid water and caused wet deposition of strata.
During winter 2017–18 coastal areas of New England were impacted by the January 4, and March 2–4, 2018, nor’easters. The U.S. Geological Survey (USGS), under an interagency agreement with the Federal Emergency Management Agency (FEMA), collected total water level data (the combination of tide, storm surge, wave runup and setup, and freshwater input) using the North American Vertical Datum of 1988 (NAVD 88) from high-water marks and continuous water-level sensors, to better understand the areal extent, timing, and impact of coastal flooding from strong storms.
During the January 4, 2018, nor’easter the National Oceanic and Atmospheric Administration (NOAA) Boston, Massachusetts, tide gage recorded the highest total water level on record of 9.66 ft. During the March 2–4, 2018, nor’easter, the Boston tide gage recorded its third highest total water level on record of 9.16 ft.
After the January and March 2018 nor’easter storms, the USGS deployed field teams that identified and flagged high-water marks along the coastlines of eastern Massachusetts in January and from Portland, Maine, south to the Connecticut-New York State border in March. In preparation for the approach of the March 2018 nor’easter, the USGS deployed 35 temporary water-level sensors along the coastline of New England to collect total water level data during the storm. Total water level data were also collected at 28 tide gages and 14 coastal streamgages (affected tidally or by tidal backwater during coastal storms) in New England during both nor’easters.
Total water level elevations at 71 high-water marks collected after the January 2018 nor’easter in coastal areas of eastern Massachusetts ranged from 5.8 to 15.1 feet (ft), with an average elevation of 9.4 ft and a median elevation of 9.6 ft. Total water level elevations at 10 tide gages and 7 coastal streamgages from Portland to Cape Cod Bay ranged from 4.8 to 11.2 ft, with an average of 9.1 ft and a median of 9.6 ft. Following the March 2018 nor’easter, 111 high-water marks were collected along the New England coastline. Of the 111 high-water marks, 100 were along the eastern coastline of New England from Portland to Cape Cod and had elevations that ranged from 5.3 to 15.1 ft, with an average of 8.9 ft and a median of 8.6 ft. The remaining 11 high-water marks along the southern coastline of New England in Connecticut, Rhode Island, and Massachusetts had elevations that ranged from 3.1 to 7.5 ft, with an average of 4.3 ft and a median of 4.9 ft. Total water level elevations for 19 USGS temporary water-level sensors from Portland to Cape Cod Bay ranged from 6.2 to 10.4 ft, with an average of 8.4 ft and a median of 8.7 ft. Total water level elevations at 10 tide gages and 6 coastal streamgages from Portland to Cape Cod Bay ranged from 7.8 to 10.8 ft, with an average of 9.1 ft and a median of 9.2 ft.
There were 10 tide gages and 5 coastal streamgages with data from both nor’easters from Portland to Cape Cod Bay; for the January nor’easter, the average and median elevations were about 0.3 and 0.5 ft higher, respectively, than for the March nor’easter. At the 52 high-water mark locations with data for both nor’easters in Massachusetts, the average and median elevations were 0.1 and 0.4 ft higher, respectively, for the January nor’easter than for the March nor’easter.
At 10 tide gages along the coastline from Portland to Cape Cod Bay, the observed peak total water level elevations for the January nor’easter ranged from 1.6 to 3.7 ft higher than the concurrent predicted elevations, with an average of 2.8 ft and a median of 3.0 ft higher. For the March nor’easter, the observed peak total water level elevations ranged from 1.8 to 4.0 ft higher than the concurrent predicted elevations, with an average of 2.7 ft and a median of 3.0 ft higher. This is approximately the amount of storm surge that was experienced during the highest tides of the two nor’easters along the coastline from Portland to Cape Cod Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205048","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Bent, G.C., and Taylor, N.J., 2020, Total water level data from the January and March 2018 nor’easters for coastal areas of New England: U.S. Geological Survey Scientific Investigations Report 2020–5048, 47 p., https://doi.org/10.3133/sir20205048.","productDescription":"Report: vii, 47 p.; 2 Data Releases","numberOfPages":"47","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108335","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":378670,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://stn.wim.usgs.gov/FEV/#NoreasterofMarch2018","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- Flood Event Viewer, March 2018 nor’easter"},{"id":378666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5048/coverthb.jpg"},{"id":378669,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://stn.wim.usgs.gov/FEV/#NoreasterJanuary2018","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- Flood Event Viewer, January 2018 nor’easter"},{"id":378667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5048/sir20205048.pdf","text":"Report","size":"4.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5048"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.86279296875,\n 44.80132682904856\n ],\n [\n -67.203369140625,\n 45.213003555993964\n ],\n [\n -67.32421875,\n 45.120052841530544\n ],\n [\n -67.412109375,\n 45.282617057517406\n ],\n [\n -67.423095703125,\n 45.606352077118316\n ],\n [\n -67.752685546875,\n 45.72152152227954\n ],\n [\n -67.796630859375,\n 47.092565552235705\n ],\n [\n -68.31298828125,\n 47.368594345213374\n ],\n [\n -68.92822265625,\n 47.21956811231547\n ],\n [\n -69.027099609375,\n 47.42065432071318\n ],\n [\n -69.2138671875,\n 47.45780853075031\n ],\n [\n -70.048828125,\n 46.70973594407157\n ],\n [\n -70.279541015625,\n 46.17983040759436\n ],\n [\n -70.455322265625,\n 45.71385093029221\n ],\n [\n -70.77392578125,\n 45.4524242413431\n ],\n [\n -71.015625,\n 45.298075138707965\n ],\n [\n -71.290283203125,\n 45.298075138707965\n ],\n [\n -71.510009765625,\n 44.99588261816546\n ],\n [\n -71.619873046875,\n 44.55133484083592\n ],\n [\n -72.015380859375,\n 44.33956524809713\n ],\n [\n -72.45483398437499,\n 43.42100882994726\n ],\n [\n -72.53173828125,\n 42.90011265525328\n ],\n [\n -72.4658203125,\n 42.76314586689492\n ],\n [\n -73.27880859375,\n 42.771211138625894\n ],\n [\n -73.509521484375,\n 42.09822241118974\n ],\n [\n -73.54248046875,\n 41.31082388091818\n ],\n [\n -73.740234375,\n 41.07935114946899\n ],\n [\n -73.641357421875,\n 41.00477542222947\n ],\n [\n -72.861328125,\n 41.253032440653186\n ],\n [\n -72.059326171875,\n 41.20345619205131\n ],\n [\n -70.345458984375,\n 41.30257109430557\n ],\n [\n -69.971923828125,\n 41.178653972331674\n ],\n [\n -69.80712890625,\n 41.236511201246216\n ],\n [\n -69.93896484375,\n 42.04113400940807\n ],\n [\n -70.57617187499999,\n 42.73894375124377\n ],\n [\n -69.98291015625,\n 43.6599240747891\n ],\n [\n -66.97265625,\n 44.645208223744035\n ],\n [\n -66.86279296875,\n 44.80132682904856\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Mercury (Hg) has been a contaminant of concern for several decades in South Florida, particularly in the Florida Everglades. The transport and bioavailability of Hg in aquatic systems is intimately linked to dissolved organic carbon (DOC). In aquatic systems, Hg can be converted to methylmercury (MeHg), which is the form of Hg that bioaccumulates in food webs. The bioaccumulation of MeHg poses significant health risks to wildlife and humans. Fish consumption advisories triggered by elevated Hg levels first appeared in the 1980s in South Florida. Multiple structures regulate freshwater distribution to Everglades National Park, including S-12D. This report summarizes seasonal and annual concentration and load data from late September 2013 to April 2017 for the total of (1) filter-passing total mercury (FTHg), (2) filter-passing methylmercury (FMeHg), (3) particulate total mercury (PTHg), (4) particulate methylmercury (PMeHg) and, (5) DOC discharged through control structure S-12D. The loads of Hg fractions and DOC at control structure S-12D were determined by pairing discharge data with constituent concentrations estimated by empirical models based on surrogate in situ water-quality measurements.
Calculated concentrations of DOC ranged from 12.8 milligrams per liter (mg/L) to 27.9 mg/L with a mean of 18.8 mg/L during the study period. Annual loads of DOC ranged from 3,950 tons in 2015 to 10,900 tons in 2016. DOC loads increased linearly with an increase in flow, and the highest monthly DOC load of 1,630 tons was observed in February 2016.
Calculated concentrations of FTHg ranged from 0.35 to 1.55 nanograms per liter (ng/L) with a mean of 0.85 ng/L during the study period. Calculated concentrations of FMeHg ranged from 0.06 ng/L to 0.24 ng/L with a mean of 0.14 ng/L during the study period. Generally, FTHg and FMeHg concentrations were lower during periods of decreased flow and higher during periods of increased flow. Calculated PTHg concentrations ranged from 0.09 ng/L to 4.19 ng/L with a mean of 0.58 ng/L during the study period. Calculated PMeHg concentrations ranged from below the limit of detection <0.01 ng/L to 0.29 ng/L with a mean of 0.03 ng/L during the study period.
Loads of Hg were often zero or lowest from November to May, owing to the lack of flow or low-flow conditions. FTHg and FMeHg loads increased linearly with an increase in flow and typically were highest from June to October. During periods of increasing flow or following changes in gate operations, PTHg and PMeHg constituted a greater percentage of the total Hg load. Annual loads of total Hg (filter-passing and particulate) ranged from 254 grams in 2015 to 658 grams in 2016. FTHg was the predominant contributor to the total Hg load. Information presented herein provides the first assessment of DOC and Hg loads to Everglades National Park through control structure S-12D using continuous in situ measurements of discharge and constituent surrogates and compares the surrogate model approach to loads calculated from monthly sampling. Analysis of calculated and observed loads demonstrates the significance of flow data on calculating constituent loads.
Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Slightly brackish groundwater contaminated by polycyclic aromatic hydrocarbons (PAHs) at a Superfund site in the Central Valley of California was pumped from 250 feet below land surface to a water storage tank using solar power and then gravity‐fed into 18, 330‐gallon intermediate bulk containers (totes) as follows:
Five totes contained planting medium with three salt‐tolerant hybrid poplar trees per tote (n = 15);
Seven totes contained planting medium with three salt‐tolerant hybrid poplar trees per tote and inoculated with the naturally occurring, PAH‐degrading endophyte Pseudomonas putida PD1 (n = 21);
Three totes contained planting medium only (n = 0);
One tote contained groundwater with three PD1‐inoculated trees (n = 3) and one tote contained groundwater with three regular trees (n = 3); and
One tote contained groundwater only (n = 0).
All trees grew well during the 7‐month growing season in spite of the area's hot, dry air temperature, little precipitation, tote‐influent chloride concentrations of 290 mg/L, and tote‐influent naphthalene concentrations that ranged from 650 to 5100 mg/L. PD1‐inoculated trees initially had 56% larger tree area (tree height × tree width) than regular trees and up to 69% larger tree area by the end of the growing season, indicating some conferred phytoprotection to the PAH contamination. All trees had similar trunk caliper (diameter) and leaf chlorophyll content by the end of the growing season. Total naphthalene removal ranged from 88% to 100% across all totes. The lowest naphthalene removal of 88% was observed in a tote that contained only planting medium and indicates substantial adsorption of naphthalene onto the high organic content of the planting medium. Contaminant removal due to uptake by the hybrid poplar trees was confirmed by the detection of naphthalene in in vivo passive samplers placed in tree trunks. Benzene, toluene, ethylbenzene, total xylenes, 2‐methylnaphthalene, 1,2,4‐trimethylbenzene, and isopropylbenzene were also detected. These results from the pilot‐scale study indicate that a full‐scale application of using salt‐tolerant hybrid poplar trees at this site could effectively decrease naphthalene concentrations in groundwater pumped from the deep aquifer. These initial results provide hope for similar application at other contaminated sites characterized by groundwater at considerable depths, especially at Superfund sites where costly pump‐and‐treat systems have been used long term to treat low levels of groundwater contamination.
","language":"English","publisher":"Wiley","doi":"10.1002/rem.21664","usgsCitation":"Landmeyer, J.E., Rock, S., Freeman, J., Nagle, G., Samolis, M., Levine, H., Cook, A., and O’Neill, H., 2020, Phytoremediation of slightly brackish, polycyclic aromatic hydrocarbon‐contaminated groundwater from 250 ft below land surface: A pilot‐scale study using salt‐tolerant, endophyte‐enhanced hybrid poplar trees at a Superfund site in the Central Valley of California, April‒November 2019: Remediation Journal, v. 31, no. 1, p. 73-89, https://doi.org/10.1002/rem.21664.","productDescription":"17 p.","startPage":"73","endPage":"89","ipdsId":"IP-118071","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":455249,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8686211","text":"External Repository"},{"id":382092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.904296875,\n 40.413496049701955\n ],\n [\n -121.728515625,\n 40.44694705960048\n ],\n [\n -122.16796875,\n 40.74725696280421\n ],\n [\n -122.958984375,\n 41.07935114946899\n ],\n [\n -123.3984375,\n 40.34654412118006\n ],\n [\n -122.9150390625,\n 38.92522904714054\n ],\n [\n -121.55273437499999,\n 37.19533058280065\n ],\n [\n -120.05859375,\n 35.92464453144099\n ],\n [\n -117.8173828125,\n 34.05265942137599\n ],\n [\n -116.3671875,\n 33.394759218577995\n ],\n [\n -117.59765625,\n 35.71083783530009\n ],\n [\n -120.234375,\n 37.50972584293751\n ],\n [\n -121.904296875,\n 40.413496049701955\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Landmeyer, James E. 0000-0002-5640-3816","orcid":"https://orcid.org/0000-0002-5640-3816","contributorId":216137,"corporation":false,"usgs":true,"family":"Landmeyer","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rock, Steven","contributorId":247586,"corporation":false,"usgs":false,"family":"Rock","given":"Steven","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":808014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, John 0000-0003-3403-9360","orcid":"https://orcid.org/0000-0003-3403-9360","contributorId":247587,"corporation":false,"usgs":false,"family":"Freeman","given":"John","email":"","affiliations":[{"id":49585,"text":"Intrinsyx Technologies Corporation","active":true,"usgs":false}],"preferred":false,"id":808015,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagle, Greg","contributorId":247588,"corporation":false,"usgs":false,"family":"Nagle","given":"Greg","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":808016,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Samolis, Mark","contributorId":247589,"corporation":false,"usgs":false,"family":"Samolis","given":"Mark","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":808017,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Levine, Herb","contributorId":218950,"corporation":false,"usgs":false,"family":"Levine","given":"Herb","email":"","affiliations":[{"id":39943,"text":"U.S. EPA, REGION 9","active":true,"usgs":false}],"preferred":false,"id":808018,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cook, Anna-Marie","contributorId":247590,"corporation":false,"usgs":false,"family":"Cook","given":"Anna-Marie","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":808019,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"O’Neill, Harry","contributorId":247591,"corporation":false,"usgs":false,"family":"O’Neill","given":"Harry","email":"","affiliations":[{"id":49586,"text":"Beacon Environmental Services, Inc.","active":true,"usgs":false}],"preferred":false,"id":808020,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70215472,"text":"70215472 - 2020 - Occurrence and spatiotemporal dynamics of pharmaceuticals in a temperate-region wastewater effluent-dominated stream: Variable inputs and differential attenuation yield evolving complex exposure mixtures","interactions":[],"lastModifiedDate":"2020-10-21T12:04:11.156236","indexId":"70215472","displayToPublicDate":"2020-09-22T07:00:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Occurrence and spatiotemporal dynamics of pharmaceuticals in a temperate-region wastewater effluent-dominated stream: Variable inputs and differential attenuation yield evolving complex exposure mixtures","docAbstract":"Effluent-dominated streams are becoming increasingly common in temperate regions and generate complex pharmaceutical mixture exposure conditions that may impact aquatic organisms via drug–drug interactions. Here, we quantified spatiotemporal pharmaceutical exposure concentrations and composition mixture dynamics during baseflow conditions at four sites in a temperate-region effluent-dominated stream (upstream, at, and progressively downstream from effluent discharge). Samples were analyzed monthly for 1 year for 109 pharmaceuticals/degradates using a comprehensive U.S. Geological Survey analytical method and biweekly for 2 years focused on 14 most common pharmaceuticals/degradates. We observed a strong chemical gradient with pharmaceuticals only sporadically detected upstream from the effluent. Seventy-four individual pharmaceuticals/degradates were detected, spanning 5 orders of magnitude from 0.28 to 13 500 ng/L, with 38 compounds detected in >50% of samples. “Biweekly” compounds represented 77 ± 8% of the overall pharmaceutical concentration. The antidiabetic drug metformin consistently had the highest concentration with limited in-stream attenuation. The antihistamine drug fexofenadine inputs were greater during warm- than cool-season conditions but also attenuated faster. Differential attenuation of individual pharmaceuticals (i.e., high = citalopram; low = metformin) contributed to complex mixture evolution along the stream reach. This research demonstrates that variable inputs over multiple years and differential in-stream attenuation of individual compounds generate evolving complex mixture exposure conditions for biota, with implications for interactive effects.
High precision triple oxygen isotope measurements of carbonates can better constrain temperatures and oxygen isotope compositions of seawater through geologic time than 18O/16O measurements alone, but lack of a definitive calibration has hindered progress. In this study, we fluorinated both carbonate and water samples to measure quantitatively the triple oxygen isotope composition of each phase. We compared the oxygen isotope fractionation between carbonate and water for different carbonate materials: calcite synthesized with and without carbonic anhydrase, abiogenic calcite from Devils Hole, and extant biogenic calcite and aragonite of marine origin. We found similar 1000lnα18Occ-wt values for all materials and combined the results with the high temperature experimental data of O'Neil et al. (1969), resulting in the following fractionation equation (T in Kelvins)
The population in the Dallas-Fort Worth metropolitan area in northern Texas is rapidly growing, resulting in a rapid increase in the demand for potable water and an increase in the discharge of wastewater treatment plant effluent. An assessment of compounds of emerging concern (CECs) in samples collected at potable water and wastewater treatment plants in Dallas and downstream from Dallas in the Trinity River was completed by the U.S. Geological Survey in cooperation with the City of Dallas, Dallas Water Utilities. CECs are synthetic or naturally occurring chemicals that are not commonly monitored in the environment but can enter the environment and cause known or suspected adverse ecological or human health effects. CECs can enter the environment through nonpoint sources (for example, runoff) and point sources (for example, concentrated animal feeding operations and treated-effluent discharge from wastewater treatment plants), which can increase concentrations of CECs especially in highly populated areas. CECs include pharmaceuticals (prescription and nonprescription), steroidal hormones, stanols, sterols, detergents and detergent metabolites (hereinafter referred to as “detergents”), personal-use products, pesticides, polycyclic aromatic hydrocarbons (PAHs), flame retardants, plasticizers, and other organic compounds used in everyday domestic, agricultural, and industrial applications. The release of CECs to the environment went largely unrecognized until relatively recently. Increased loading of certain CECs to the environment, combined with advancements in laboratory analysis methods that resulted in appreciably lower detection levels, brought greater attention to the release of CECs. In addition, synthesis of new chemicals or changes in use and disposal of existing chemicals can create new sources of CECs. Some CECs are endocrine disrupting compounds (EDCs), which can elicit adverse effects on development, behavior, and reproduction of wildlife and can cause dysfunction of human and wildlife endocrine (hormone) systems.
Results of studies in the United States and Europe indicate that CECs, their metabolites, and industrial, agricultural, and household wastewater products are present in the aquatic environment, water treatment plants, and septic systems. CECs, especially pharmaceuticals, are of interest because of their persistence, widespread use, and potential to cause adverse effects in humans and nontargeted organisms. There is also concern that some CECs and EDCs resist degradation of water treatment processes at potable water treatment plants (PWTPs) and wastewater treatment plants (WWTPs) and that treated-effluent discharge could contain compounds that negatively affect biota living in receiving waters. Therefore, CECs and EDCs are more likely to be detected in environmental samples collected near areas of high population density where treated effluent from WWTPs can contribute substantially to receiving waters.
The U.S. Geological Survey, in cooperation with the City of Dallas, Dallas Water Utilities, evaluated the occurrence and concentrations of selected CECs in samples collected at PWTPs and WWTPs in Dallas and downstream from the Dallas-Fort Worth metropolitan area in the Trinity River, Texas, from August 2009 to December 2013. Water samples were collected at three PWTP sites, two WWTP sites, and five study sites on the Trinity River; all sites where samples were collected were in or downstream from Dallas. These water samples were analyzed for 120 CECs, including human-health pharmaceuticals (prescription and nonprescription), antibiotics, steroidal hormones, stanols, sterols, detergents, personal-use products (flavors and fragrances), pesticides and repellents, industrial wastewater compounds, disinfection compounds, PAHs, flame retardants, and plasticizers. Additionally, bed-sediment samples were collected at each of the five Trinity River sites. The bed-sediment samples were analyzed for 57 CECs.
In general, the water treatment processes at PWTPs and WWTPs were effective at reducing detections and concentrations of CECs to undetectable levels or transforming the compounds into degradates that were not analyzed. There were 14 and 73 CECs detected in raw water and in untreated-influent water at PWTPs and WWTPs, respectively. Of these, 11 of the 14 CECs detected in raw-water samples and 44 of the 73 CECs detected in untreated-influent samples were not detected in finished water or in treated-effluent water samples, respectively, indicating that these compounds were removed or degraded to compounds that were not analyzed. Some CECs, however, are resistant to degradation and were detected in untreated and treated water at PWTPs and at WWTPs. The three CECs detected at PWTPs in raw-water and finished-water samples were tris(dichloroisopropyl)phosphate, benzophenone, and methyl salicylate. At WWTPs, 29 CECs were detected, including carbamazepine, sulfamethoxazole, 4-androstene-3,17-dione, 3-beta-coprostanol, acetyl-hexamethyl-tetrahydronaphthalene (AHTN), hexahydro-hexamethyl-cyclopenta-benzopyran (HHCB), 1,4-dichlorobenzene, tribromomethane, benzophenone, and tris(dichloroisopropyl)phosphate, in untreated and treated water, indicating that treatment processes likely did not remove or degrade these compounds.
Of the 23 CECs detected in stream-water samples collected at 5 sites on the Trinity River in or near Dallas, 10 CECs (carbamazepine, sulfamethoxazole, caffeine, 3-beta-coprostanol, cholesterol, HHCB, benzophenone, triethyl citrate, tributyl phosphate, and tris(dichloroisopropyl)phosphate) were detected at all 5 sites. The 10 CECs detected in water samples collected at all 5 sites on the Trinity River were also detected in treated-effluent water at WWTPs.
Eleven of the 57 targeted CECs were detected in bed-sediment samples collected at study sites on the Trinity River. Of these 11 CECs, only 2 (beta-sitosterol and cholesterol) were detected in bed-sediment samples at all 5 sites on the Trinity River. Nine of these 11 CECs were not detected in any water-column sample, likely because of the strong hydrophobic characteristics of these compounds.
Results from water treatment plants indicate that the water treatment process is less effective for removing or degrading compounds that are engineered to be resistant to degradation. These results also indicate the presence of CECs and EDCs at locations upstream from PWTPs in Dallas. Results from Trinity River main-stem sites indicate that some compounds are naturally attenuated during transport, but a few are persistent throughout the study reach. Many CECs and EDCs are hydrophobic and were only detected in bed sediment, indicating multiple pathways through which CECs can persist in the environment.
In general, concentrations of CECs in the Dallas-Fort Worth metropolitan area were similar to those found in metropolitan areas nationwide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195019","collaboration":"Prepared in cooperation with the City of Dallas, Dallas Water Utilities","usgsCitation":"Churchill, C.J., Baldys, S., III, Gunn, C.L., Mobley, C.A., and Quigley, D.P., 2020, Compounds of emerging concern detected in water samples from potable water and wastewater treatment plants and detected in water and bed-sediment samples from sites on the Trinity River, Dallas, Texas, 2009–13: U.S. Geological Survey Scientific Investigations Report 2019–5019, 57 p., https://doi.org/10.3133/sir20195019.","productDescription":"Report: vii, 57 p.; Data Release","numberOfPages":"69","onlineOnly":"Y","ipdsId":"IP-063824","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":378598,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QUPBZK","text":"USGS data release","linkHelpText":"Detections and concentrations of compounds of emerging concern at water treatment plants and in the Trinity River in or near Dallas, Texas, 2009–13"},{"id":378597,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5019/sir20195019.pdf","text":"Report","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5019"},{"id":378596,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5019/coverthb.jpg"}],"country":"United States","state":"Texas","city":"Dallas","otherGeospatial":"Trinity River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.0147705078125,\n 32.22674287041067\n ],\n [\n -96.21826171874999,\n 32.22674287041067\n ],\n [\n -96.21826171874999,\n 32.91648534731439\n ],\n [\n -97.0147705078125,\n 32.91648534731439\n ],\n [\n -97.0147705078125,\n 32.22674287041067\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Streamflow data and statistics are vitally important for proper protection and management of the water quality and water quantity of Alabama streams. Such data and statistics are generally available at U.S. Geological Survey streamflow-gaging stations, also referred to as streamgages or stations, but are often needed at ungaged stream locations. To address this need, the U.S. Geological Survey, in cooperation with numerous Alabama State agencies and organizations, developed regional regression equations for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in Alabama that are not substantially affected by tides, regulation, diversions, or other anthropogenic influences. A small percentage of the streamgages included in this study experience zero flows during certain periods; thus, the final low-flow frequency regression equations were developed by using weighted left-censored regression analyses to analyze the flow data in an unbiased manner, with weights based on number of years of record.
The equations developed include the annual minimum 1- and 7-day average streamflows with a 10-year recurrence interval (referred to as the 1Q10 and 7Q10 flows), the annual minimum 7-day average streamflow with a 2-year recurrence interval (referred to as the 7Q2 flow), and the mean annual flow using data from 174 streamgages from Alabama and surrounding States. For the 1Q10, 7Q2, and 7Q10 low-flow frequency statistics, the regional regression equations are functions of drainage area, streamflow-variability index, mean annual precipitation, and percentage of the drainage basin located in the Piedmont and Southeastern Plains ecoregions. The mean annual flow regression equation is a function of drainage area, mean annual precipitation, and percentage of the drainage basin located in the Southeastern Plains ecoregion. For the mean annual flow regression equation, the average standard error of estimate was 11.2 percent. For the selected low-flow frequency equations, the average standard errors of estimate ranged from 18.1 to 38.8 percent.
The regional regression equations developed from this investigation have been incorporated into the U.S. Geological Survey StreamStats application for Alabama. StreamStats (https://streamstats.usgs.gov/ss/) is a web-based geographic information system application that delineates drainage basins at selected stream locations and then generates the needed basin characteristics for available regional regression equations. Along with the low-flow frequency equations developed in this investigation, the StreamStats application also has regional regression equations for estimating flood-frequency statistics at locations on rural and urban streams in Alabama.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205099","collaboration":"Prepared in cooperation with Alabama Power; Alabama Farmers Federation; Alabama Association of Conservation Districts; Alabama Association of Resource Conservation and Development Councils; Alabama Department of Agriculture and Industries; Alabama Department of Conservation and Natural Resources— Wildlife and Freshwater Fisheries Division; Alabama Department of Economic and Community Affairs—Office of Water Resources; Alabama Department of Environmental Management; Alabama Soil and Water Conservation Committee; Choctawhatchee, Pea and Yellow Rivers Watershed Management Authority; Geological Survey of Alabama; and The University of Alabama—Alabama Water Institute","usgsCitation":"Feaster, T.D., Kolb, K.R., Painter, J.A., and Clark, J.M., 2020, Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in Alabama (ver. 1.1, November 20, 2020): U.S. Geological Survey Scientific Investigations Report 2020–5099, 21 p., https://doi.org/10.3133/sir20205099.","productDescription":"Report: vii, 21 p.; Appendixes: 4; Data Release; Version History","numberOfPages":"34","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-114774","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":378594,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P994UFS7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supporting data for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in Alabama"},{"id":380616,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5099/versionHist.txt","text":"Version History","size":"1.47 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5099 Version History"},{"id":378593,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5099/sir20205099_appendix2.xlsx","text":"Appendix 2","size":"49.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5099 Appendix 2","linkHelpText":"— U.S. Geological Survey streamgages and independent and dependent variables used in the low-flow frequency and mean annual flow regression analyses for ungaged locations on streams in Alabama"},{"id":378592,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5099/sir20205099_appendix2.csv","text":"Appendix 2","size":"27.6 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5099 Appendix 2","linkHelpText":"— U.S. Geological Survey streamgages and independent and dependent variables used in the low-flow frequency and mean annual flow regression analyses for ungaged locations on streams in 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\"}}]}","edition":"Version 1.0: September 21, 2020; Version 1.1: September 29, 2020; Version 1.2: November 20, 2020","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, Tennessee 37211
The Clean Water Act (CWA) requires States to identify waters that are impaired for designated uses. These waters are published through a State’s §303(d) list. The CWA also requires that a total maximum daily load (TMDL) be completed for each water body to calculate the maximum amount of contaminants that can be present in that water body and still meet water-quality standards. The Mississippi Department of Environmental Quality (MDEQ) uses a statewide monitoring and assessment strategy to collect benthic macroinvertebrate community data to assess the health of streams and rivers and to identify impaired waters. Waters that are found to be impaired based on the macroinvertebrate community data are listed on the Mississippi §303(d) list, and the cause of impairment is listed as “biological impairment.” Although the CWA requires TMDLs to be developed for applicable contaminants identified in the §303(d) list, TMDLs cannot be computed for stream reaches in Mississippi listed for biological impairment because the actual stressors causing the impairment have not yet been determined. The MDEQ and other water-resource managers in Mississippi require a framework for stressor identification in biologically impaired streams and rivers. This report is organized to (1) provide a general overview of biological impairment and stressor identification in stream ecosystems and (2) provide a detailed framework for stressor identification of Mississippi streams that are biologically impaired. The intent is for the framework to reduce subjectivity, provide consistency, and allow for adaptation as the science evolves. The stressor identification framework for Mississippi involves six key steps:
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
There is an active debate regarding the influence that climate has on the risk of armed conflict, which stems from challenges in assembling unbiased datasets, competing hypotheses on the mechanisms of climate influence, and the difficulty of disentangling direct and indirect climate effects. We use gridded historical non-state conflict records, satellite data, and land surface models in a structural equation modeling approach to uncover the direct and indirect effects of climate on violent conflicts in Africa and the Middle East (ME). We show that climate–conflict linkages in these regions are more complex than previously suggested, with multiple mechanisms at work. Warm temperatures and low rainfall direct effects on conflict risk were stronger than indirect effects through food and water supplies. Warming increases the risk of violence in Africa but unexpectedly decreases this risk in the ME. Furthermore, at the country level, warming decreases the risk of violence in most West African countries. Overall, we find a non-linear response of conflict to warming across countries that depends on the local temperature conditions. We further show that magnitude and sign of the effects largely depend on the scale of analysis and geographical context. These results imply that extreme caution should be exerted when attempting to explain or project local climate–conflict relationships based on a single, generalized theory.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/aba97d","usgsCitation":"Helman, D., Zaitchik, B., and Funk, C., 2020, Climate has contrasting direct and indirect effects on armed conflicts: Environmental Research Letters, v. 15, 104017, 12 p., https://doi.org/10.1088/1748-9326/aba97d.","productDescription":"104017, 12 p.","ipdsId":"IP-118530","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":455254,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aba97d","text":"Publisher Index Page"},{"id":421737,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Africa, Middle East","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 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]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2020-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Helman, David","contributorId":330683,"corporation":false,"usgs":false,"family":"Helman","given":"David","affiliations":[{"id":37540,"text":"John Hopkins University","active":true,"usgs":false}],"preferred":false,"id":885582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zaitchik, Benjamin F.","contributorId":330684,"corporation":false,"usgs":false,"family":"Zaitchik","given":"Benjamin F.","affiliations":[{"id":37540,"text":"John Hopkins University","active":true,"usgs":false}],"preferred":false,"id":885583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","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":885584,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226676,"text":"70226676 - 2020 - Neonicotinoid insecticide concentrations in agricultural wetlands and associations with aquatic invertebrate communities","interactions":[],"lastModifiedDate":"2021-12-03T13:03:38.319596","indexId":"70226676","displayToPublicDate":"2020-09-20T07:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":682,"text":"Agriculture, Ecosystems and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Neonicotinoid insecticide concentrations in agricultural wetlands and associations with aquatic invertebrate communities","docAbstract":"Neonicotinoids are considered a superior insecticide for agricultural pest management, although their impacts on non-target insects is a rising concern. Aside from laboratory and mesocosm studies, limited research has been directed towards the role neonicotinoids may have in structuring aquatic invertebrate communities in field settings. Therefore, we simultaneously collected aquatic invertebrate and surface water samples from 26 wetlands within a highly modified agricultural landscape of Nebraska’s Rainwater Basin during spring 2015. Water samples were tested for six different neonicotinoids, nutrients, and physical properties. Trace levels of clothianidin and imidacloprid were the only neonicotinoids detected, occurring in 85% and 15%, respectively, of wetlands sampled. All measurements for clothianidin and imidacloprid were below chronic toxicity benchmarks set by the United States Environmental Protection Agency. Neonicotinoid concentrations were significantly lower (W26, 0.05 = 42.5) at wetlands with vegetative buffer strips >50 m wide compared to wetlands with vegetative buffers strips <50 m. Although neonicotinoids were below benchmark concentrations proposed by government regulations, a significant negative association between neonicotinoid concentrations and aquatic invertebrate biomass was observed across all wetlands studied (Parameter Estimate = -0.031; SE = 0.014).
Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground‐based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite‐based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high‐resolution imagery from Unmanned Aerial Systems (UAS) to map fine‐scale patterns of biocrusts. We collected sub‐centimeter, true color 3‐band imagery at 10 plots in sagebrush and pinyon‐juniper woodland communities in a semiarid ecosystem in the southwestern US and used object‐based image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon‐juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon‐juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon‐juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon‐juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type.
Hydrological and bioclimatic processes that lead to drought may stress plants and wildlife, restructure plant community type and architecture, increase monotypic stands and bare soils, facilitate the invasion of non‐native plant species and accelerate soil erosion. Our study focuses on the impact of a paucity of Colorado River surface flows from the United States (U.S.) to Mexico. We measured change in riparian plant greenness and water use over the past two decades using remotely sensed measurements of vegetation index (VI), evapotranspiration (ET), and a new annualized Phenology Assessment Metric (PAM) for ET. We measure these long‐term (2000‐2019) metrics and their short‐term (2014‐2019) response to an environmental, pulse flow in 2014, as prescribed under Minute 319 of the 1944 Water Treaty between the two nations. In subsequent years, small directed flows were provided to restoration areas under Minute 323. We use 250 m MODIS and 30 m Landsat imagery to evaluate three vegetation indices (NDVI, EVI, EVI2). We select EVI2 to parameterize an optical‐based ET algorithm and test the relationship between ET from Landsat and MODIS by regression approaches. Our analyses show significant decreases in VIs and ET for both the 20‐year and post‐pulse 5‐year periods. Over the last 20 years, EVI Landsat declined 34% (30% by EVIMODIS) and ETLandsat‐EVI declined 38% (27% by ETMODIS‐EVI), overall ca. 1.61 mmd‐1 or 476 mmyr‐1 drop in ET. Over the 5 years since the 2014 pulse flow, EVI Landsat declined 20% (13% by EVIMODIS) and ETLandsat‐EVI declined 23% (4% by ETMODIS‐EVI) with a 0.77 mmd‐1 or a 209 mmyr‐1 5‐year drop in ET. Data and change maps show the pulse flow contributed enough water to slow the rate of loss, but only for the very short‐term (1‐2 years). These findings are critically important as they suggest further deterioration of biodiversity, wildlife habitat and key ecosystem services due to anthropogenic diversions of water in the U.S. and Mexico and from land clearing, fires, and plant‐related drought which affect hydrological processes.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13911","usgsCitation":"Nagler, P.L., Barreto-Muñoz, A., Chavoshi Borujeni, S., Jarchow, C., Gómez‐Sapiens, M., Nouri, H., Herrmann, S.M., and Didan, K., 2020, Ecohydrological responses to surface flow across borders: Two decades of changes in vegetation greenness and water use in the riparian corridor of the Colorado River Delta: Hydrological Processes, v. 34, no. 25, p. 4851-4883, https://doi.org/10.1002/hyp.13911.","productDescription":"33 p.; Data Release","startPage":"4851","endPage":"4883","ipdsId":"IP-117414","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":378804,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":436784,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98PGDJ1","text":"USGS data release","linkHelpText":"Colorado River Delta Project: A compilation of vegetation indices, phenology assessment metrics, estimates of evapotranspiration and change maps for seven reaches of the delta's 150 km region, for nearly the last two decades"}],"country":"Mexico, United States","otherGeospatial":"Colorado River delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.15869140624999,\n 31.606609719226917\n ],\n [\n -114.521484375,\n 31.606609719226917\n ],\n [\n -114.521484375,\n 32.76880048488168\n ],\n [\n -115.15869140624999,\n 32.76880048488168\n ],\n [\n -115.15869140624999,\n 31.606609719226917\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"25","noUsgsAuthors":false,"publicationDate":"2020-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":799708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":799709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chavoshi Borujeni, Sattar","contributorId":241612,"corporation":false,"usgs":false,"family":"Chavoshi Borujeni","given":"Sattar","email":"","affiliations":[{"id":48363,"text":"Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Centre, AREEO, Isfahan, Iran","active":true,"usgs":false}],"preferred":false,"id":799710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":799711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gómez‐Sapiens, Marth M.","contributorId":241615,"corporation":false,"usgs":false,"family":"Gómez‐Sapiens","given":"Marth M.","affiliations":[],"preferred":false,"id":799732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nouri, Hamideh","contributorId":178847,"corporation":false,"usgs":false,"family":"Nouri","given":"Hamideh","affiliations":[],"preferred":false,"id":799733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Herrmann, Stefanie M. 0000-0002-4069-2019","orcid":"https://orcid.org/0000-0002-4069-2019","contributorId":20234,"corporation":false,"usgs":true,"family":"Herrmann","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":799734,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Didan, Kamel","contributorId":130999,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":799735,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70218272,"text":"70218272 - 2020 - Application of a new species-richness based flow ecology framework for assessing flow reduction effects on aquatic communities","interactions":[],"lastModifiedDate":"2021-02-23T13:33:50.365096","indexId":"70218272","displayToPublicDate":"2020-09-19T07:30:01","publicationYear":"2020","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":"Application of a new species-richness based flow ecology framework for assessing flow reduction effects on aquatic communities","docAbstract":"Water‐resources managers are challenged with maintaining a balance among beneficial uses throughout river networks and need robust means of assessing potential risks to aquatic life resulting from flow alterations. This study generated ecological limit functions from species‐streamflow relations to quantify potential fish richness response to flow alteration and compared results to currently accepted streamflow management guidelines. Modeled responses of absolute richness change were watershed specific and varied among sample sets derived from hydrologic unit classifications of different sizes (large HUC 6 basins to regional scale HUC 8). With a 20% flow reduction, 10% of HUC 8 predicted a richness decrease in one or more taxa. While absolute richness change was consistent across streams within a HUC, percent richness change was stream size dependent. Comparisons with Instream Flow Incremental Methodology habitat models predicted habitat loss greater than percent richness change; however, predictions for habitat and richness decreased similarly as stream size decreased. Watershed‐specific responses from flow reductions could allow water‐resources management decisions to be made locally based on the predicted richness change for certain sized streams. Quantitative results highlight the utility of a richness‐based framework for generating watershed‐specific risk assessments that validate and inform currently employed water‐resources management practices.