Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fate of total nitrogen (TN) in two sub-basins of the Grande River Basin (GRB; 43,000 km2). Land under cultivation for sugar cane, urban land, and point source inputs from wastewater treatment plants was estimated to each contribute approximately 30% of the TN load at the outlet, with pasture land contributing about 10% of the load. Hypothetical assessments of wastewater treatment plant upgrades and the building of new facilities that could treat currently untreated urban runoff suggest that these management actions could potentially reduce loading at the outlet by as much as 20–25%. This study highlights the ability of SPARROW and the RSPARROW mapping tool to assist with the development and evaluation of management actions aimed at reducing nutrient pollution and eutrophication. The freely available RSPARROW modeling tool provides new opportunities to improve understanding of the sources, delivery, and transport of water-quality contaminants in watersheds throughout the world.
","language":"English","publisher":"MDPI","doi":"10.3390/w12102911","usgsCitation":"Miller, M., de Souza, M.L., Alexander, R.B., Gorman Sanisaca, L.E., de Amorim Teixeira, A., and Appling, A.P., 2020, Application of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil: Water, v. 12, no. 10, 2911, 20 p., https://doi.org/10.3390/w12102911.","productDescription":"2911, 20 p.","ipdsId":"IP-122604","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":455023,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12102911","text":"Publisher Index Page"},{"id":436752,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FZV0Z0","text":"USGS data release","linkHelpText":"RSPARROW Model Archive Files for the Grande River Basin TN SPARROW Model"},{"id":379649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Grande River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -50.95458984374999,\n -20.324023603422507\n ],\n [\n -49.32861328125,\n -21.46329344189928\n ],\n [\n -48.284912109375,\n -22.451648819126202\n ],\n [\n -46.73583984375,\n -23.29181053244191\n ],\n [\n -45.37353515625,\n -22.61401087437028\n ],\n [\n -44.05517578124999,\n -21.881889807629257\n ],\n [\n -43.5498046875,\n -21.125497636606266\n ],\n [\n -45.736083984375,\n -20.33432561683554\n ],\n [\n -46.35131835937499,\n -20.478481600090554\n ],\n [\n -46.966552734375,\n -20.014645445341355\n ],\n [\n -47.647705078125,\n -19.797717490704724\n ],\n [\n -48.944091796875,\n -19.9526963975442\n ],\n [\n -49.32861328125,\n -19.652934210612436\n ],\n [\n -50.28442382812499,\n -19.425153718960143\n ],\n [\n -50.86669921875,\n -19.756364230752375\n ],\n [\n -50.95458984374999,\n -20.324023603422507\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Souza, Marcelo L","contributorId":243598,"corporation":false,"usgs":false,"family":"de Souza","given":"Marcelo","email":"","middleInitial":"L","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard B 0000-0001-9166-0626","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":243599,"corporation":false,"usgs":false,"family":"Alexander","given":"Richard","email":"","middleInitial":"B","affiliations":[{"id":38108,"text":"NA","active":true,"usgs":false}],"preferred":false,"id":802667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorman Sanisaca, Lillian E. 0000-0003-1711-3864","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":210381,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de Amorim Teixeira, Alexandre","contributorId":243600,"corporation":false,"usgs":false,"family":"de Amorim Teixeira","given":"Alexandre","email":"","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":802670,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208918,"text":"cir1464 - 2020 - Estimated groundwater withdrawals from principal aquifers in the United States, 2015","interactions":[],"lastModifiedDate":"2020-10-19T11:35:18.292013","indexId":"cir1464","displayToPublicDate":"2020-10-16T15:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1464","displayTitle":"Estimated Groundwater Withdrawals from Principal Aquifers in the United States, 2015","title":"Estimated groundwater withdrawals from principal aquifers in the United States, 2015","docAbstract":"In 2015, about 84,600 million gallons per day (Mgal/d) of groundwater were withdrawn in the United States for various uses including public supply, self-supplied domestic, industrial, mining, thermoelectric power, aquaculture, livestock, and irrigation. Of this total, about 94 percent (79,200 Mgal/d) was withdrawn from principal aquifers, which are defined as regionally extensive aquifers or aquifer systems that have the potential to be used as sources of water of suitable quality and quantity to meet various needs. The remaining 6 percent (5,400 Mgal/d) was withdrawn from other, nonprincipal aquifers in the United States.
Sixty-six principal aquifers belonging to 5 major lithologic groups have been identified and delineated in the United States, including Puerto Rico and the U.S. Virgin Islands. Of the water withdrawn from principal aquifers in 2015, 81 percent (63,900 Mgal/d) was from the unconsolidated and semiconsolidated sand and gravel lithologic group, 7.1 percent (5,630 Mgal/d) was from the igneous and metamorphic-rock lithologic group, 6.8 percent (5,360 Mgal/d) was from the carbonate-rock lithologic group, 3.4 percent (2,680 Mgal/d) was from the sandstone lithologic group, and 2.2 percent (1,710 Mgal/d) was from the sandstone and carbonate-rock lithologic group.
The most heavily pumped of the 24 principal aquifers and aquifer systems within the unconsolidated and semiconsolidated sand and gravel lithologic group were the High Plains aquifer (12,300 Mgal/d), Mississippi River Valley alluvial aquifer (12,100 Mgal/d), Central Valley aquifer system (11,100 Mgal/d), and Basin and Range basin-fill aquifers (7,390 Mgal/d). Withdrawals for irrigation were 48,100 Mgal/d and accounted for 75 percent of the total withdrawals from this lithologic group. Although unconsolidated sand and gravel aquifers are widely distributed and were used as sources of water in all States except Hawaii and the U.S. Virgin Islands, 56 percent of the total withdrawn from unconsolidated and semiconsolidated sand and gravel aquifers was in just four States: California (15,600 Mgal/d), Arkansas (9,560 Mgal/d), Nebraska (5,570 Mgal/d), and Texas (4,830 Mgal/d).
The most heavily pumped of the seven principal aquifers within the igneous and metamorphic-rock lithologic group were the Snake River Plain (2,930 Mgal/d) and Columbia Plateau basaltic-rock aquifers (1,080 Mgal/d), which are located in the northwestern United States and together accounted for 71 percent of the water withdrawn from this lithologic group. Withdrawals for irrigation were 4,190 Mgal/d and accounted for more than 74 percent of the total withdrawals from this lithologic group. Seventy-eight percent of the withdrawals from igneous and metamorphic-rock aquifers were in three States: Idaho (3,230 Mgal/d), Washington (614 Mgal/d), and Oregon (528 Mgal/d).
The most heavily pumped of the 15 principal aquifers and aquifer systems within the carbonate-rock lithologic group were the Floridan aquifer system (3,180 Mgal/d) and the Biscayne aquifer (679 Mgal/d), which are in the southeastern United States and together accounted for almost 72 percent of the withdrawals from this lithologic group. Withdrawals for public supply (2,440 Mgal/d) and irrigation (1,610 Mgal/d) together accounted for almost 76 percent of the total withdrawals from this lithologic group. Although water was withdrawn from carbonate-rock aquifers in 35 States, 71 percent of the total withdrawn was in Florida (3,020 Mgal/d) and Georgia (785 Mgal/d).
The most heavily pumped of the 15 principal aquifers within the sandstone lithologic group was the Cambrian-Ordovician aquifer system (921 Mgal/d), which is in the north-central United States and accounted for 34 percent of the water withdrawn from this lithologic group. Withdrawals for public supply were 1,030 Mgal/d and accounted for 38 percent of the total withdrawals from this lithologic group. Although sandstone aquifers were used as sources of water in 32 States, 45 percent of the total withdrawn from sandstone aquifers was in five States: Minnesota (321 Mgal/d), Wisconsin (319 Mgal/d), Kansas (193 Mgal/d), Illinois (187 Mgal/d), and Pennsylvania (179 Mgal/d).
The most heavily pumped of the five principal aquifers and aquifer systems within the sandstone and carbonate-rock lithologic group were the Edwards-Trinity aquifer system (661 Mgal/d) in the south-central United States and the Valley and Ridge aquifers (551 Mgal/d) of the eastern United States, which together accounted for 71 percent of total withdrawals from this lithologic group. Withdrawals from sandstone and carbonate-rock aquifers for public-supply (713 Mgal/d), irrigation (469 Mgal/d), and self-supplied domestic (253 Mgal/d) uses accounted for about 84 percent of the total withdrawals from this lithologic group. Although water was withdrawn from sandstone and carbonate-rock aquifers in 25 States, 65 percent of the total withdrawn was in Texas (651 Mgal/d), Pennsylvania (238 Mgal/d), and Florida (223 Mgal/d).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1464","usgsCitation":"Lovelace, J.K., Nielsen, M.G., Read, A.L., Murphy, C.J., and Maupin, M.A., 2020, Estimated groundwater withdrawals from principal aquifers in the United States, 2015 (ver. 1.2, October 2020): U.S. Geological Survey Circular 1464, 70 p., https://doi.org/10.3133/cir1464.","productDescription":"Report: vii, 70 p.; Data Release","numberOfPages":"82","onlineOnly":"N","ipdsId":"IP-107784","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":374027,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1464/coverthb3.jpg"},{"id":375193,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1464/versionHist.txt","text":"Version History","description":"CIR 1464 Version 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U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, Tennessee 37211
On October 18, 2015, a large flood caused considerable damage in Grapevine Canyon near Death Valley Scotty Historic District, in Death Valley National Park, California. Significant channel changes had limited the applicability of previously created flood-inundation maps to current conditions. Predicted flood-inundation maps for Scotty’s Castle were updated using one-dimensional hydraulic models. A digital terrain model was created for the study area using a terrestrial laser scanner for use in the hydraulic models. Estimations of the 4, 2, 1, 0.5, and 0.2-percent annual exceedance probability flood streamflows (previously known as the 25, 50, 100, 250, and 500-year floods) were computed from regional flood regression equations. The estimated flood streamflows were used with the hydraulic models to compute water surface elevations that were mapped on the digital terrain model. The results indicate inundation of the visitor center and park offices occurs by the 4-percent annual exceedance probability flood. Bridge and embankment overtopping occurs by the 2-percent annual exceedance probability flood. Sections of Grapevine Canyon Road and the parking lot are inundated by the 4-percent annual exceedance probability flood and above streamflows. None of the computed streamflows reach Scotty’s Castle main building.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205082","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Morris, C.M., Welborn, T.L., and Minear, J.T., 2020, Delineation of flood-inundation areas in Grapevine Canyon near Scotty’s Castle, Death Valley National Park, California: U.S. Geological Survey Scientific Investigations Report 2020–5082, 27 p., https://doi.org/10.3133/sir20205082.","productDescription":"Report: vi, 27 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-091560","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":379474,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IPKW55","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial Data, Tabular Data, and Surface-Water Model Archive for Delineation of Flood-Inundation Areas in Grapevine Canyon Near Scotty's Castle, Death Valley National Park, California"},{"id":379390,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5082/sir20205082.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5082"},{"id":379389,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5082/coverthb2.jpg"}],"country":"United States","state":"California","otherGeospatial":"Death Valley National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.960205078125,\n 36.5670120564234\n ],\n [\n -116.8670654296875,\n 36.5670120564234\n ],\n [\n -116.8670654296875,\n 37.19095471582605\n ],\n [\n -117.960205078125,\n 37.19095471582605\n ],\n [\n -117.960205078125,\n 36.5670120564234\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Stream and river ecosystems provide subsidies of emergent adult aquatic insects and other resources to terrestrial food webs, and this lotic–land subsidy has garnered much attention in recent research. Here, we critically examine a list of biotic and abiotic variables—including productivity, dominant taxa, geomorphology, and weather—that should be important in affecting the nature of these subsidy dynamics between lotic and terrestrial ecosystems, especially the pathway from emergent aquatic insects to terrestrial predators. We also explore how interactions between these variables can lead to otherwise unexpected patterns in the importance of aquatic subsidies to terrestrial food webs. Utilizing a match-mismatch framework developed previously, we identify how these variables and interactions may be affected by a broad suite of stressors in addition to contaminants: climate change, land-use conversion, damming and water abstraction, and species invasions and extinctions. These stressors may all act to modify and potentially exacerbate the effects of contaminants on subsidies. The available literature on many variables is sparse, despite strong theoretical underpinnings supporting their importance for lotic–land subsidies. Notably, these understudied variables include those related to physical geomorphology and the structure of the stream/river and floodplain/riparian zone as well as species-specific interactions between aquatic and terrestrial organisms. We suggest that more explicit characterization of these variables and more research directly linking broad-scale stressors to subsidy resource–consumer interactions can help provide a more mechanistic understanding to lotic–land subsidy dynamics within a changing environment.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and ecological subsidies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_7","usgsCitation":"Muehlbauer, J., Larsen, S., Jonsson, M., and Emilson, E.J., 2020, Variables affecting resource subsidies from streams and rivers to land and their susceptibility to global change stressors, chap. of Contaminants and ecological subsidies, p. 129-155, https://doi.org/10.1007/978-3-030-49480-3_7.","productDescription":"27 p.","startPage":"129","endPage":"155","ipdsId":"IP-090826","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":381024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Muehlbauer, Jeffrey 0000-0003-1808-580X","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":221739,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":806231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, Stefano","contributorId":169188,"corporation":false,"usgs":false,"family":"Larsen","given":"Stefano","email":"","affiliations":[{"id":13099,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":806232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jonsson, Micael","contributorId":245462,"corporation":false,"usgs":false,"family":"Jonsson","given":"Micael","email":"","affiliations":[{"id":49198,"text":"Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden","active":true,"usgs":false}],"preferred":false,"id":806233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Emilson, Erik J.S.","contributorId":245463,"corporation":false,"usgs":false,"family":"Emilson","given":"Erik","email":"","middleInitial":"J.S.","affiliations":[{"id":49199,"text":"Natural Resources Canada, Canadian Forest ServiceGreat Lakes Forestry Centre, Sault Ste. Marie, Canada","active":true,"usgs":false}],"preferred":false,"id":806234,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216147,"text":"70216147 - 2020 - Metamorphosis and the impact of contaminants on ecological subsidies","interactions":[],"lastModifiedDate":"2020-11-06T14:36:25.684398","indexId":"70216147","displayToPublicDate":"2020-10-16T08:30:29","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Metamorphosis and the impact of contaminants on ecological subsidies","docAbstract":"Animals with complex life histories such as aquatic insects and amphibians link freshwater and terrestrial ecosystems when they transition from water to land during development. This transition requires metamorphosis from juvenile to adult life stages. Metamorphosis is a stressful and ecologically sensitive life history event. Exposure to contaminants during juvenile development (before or during metamorphosis) can disrupt the complex process of metamorphosis, thereby altering the flow of organisms from water to land. This chapter reviews how ecological stressors impact the timing and success of metamorphosis. Key ideas include: (1) metamorphosis is a key event in the movement of subsidies from water to land, (2) mortality during metamorphosis is enhanced in the presence of contaminants, and (3) juvenile responses to contaminants may not predict adult responses, due to death during metamorphosis. Metamorphosis is a critical life history stage that should be accounted for in ecotoxicological studies.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and ecological subsidies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_6","usgsCitation":"Wesner, J., Kraus, J.M., Henry, B.L., and Kerby, J., 2020, Metamorphosis and the impact of contaminants on ecological subsidies, chap. of Contaminants and ecological subsidies, p. 111-125, https://doi.org/10.1007/978-3-030-49480-3_6.","productDescription":"15 p.","startPage":"111","endPage":"125","ipdsId":"IP-113160","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":380261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Wesner, Jeff","contributorId":211583,"corporation":false,"usgs":false,"family":"Wesner","given":"Jeff","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":804230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henry, Brianna L.","contributorId":239984,"corporation":false,"usgs":false,"family":"Henry","given":"Brianna","email":"","middleInitial":"L.","affiliations":[{"id":48079,"text":"Natural Resources Conservation Service, Beltsville, MD","active":true,"usgs":false}],"preferred":false,"id":804232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kerby, Jacob","contributorId":244593,"corporation":false,"usgs":false,"family":"Kerby","given":"Jacob","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":804233,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216144,"text":"70216144 - 2020 - Introduction: Ecological subsidies as a framework for understanding contaminant fate, exposure, and effects at the land-water interface","interactions":[],"lastModifiedDate":"2020-11-06T14:28:58.413961","indexId":"70216144","displayToPublicDate":"2020-10-16T08:25:06","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Introduction: Ecological subsidies as a framework for understanding contaminant fate, exposure, and effects at the land-water interface","docAbstract":"Ecologists have long recognized that ecological subsidies (the flow of organic matter, nutrients, and organisms between ecosystems) can strongly affect ecosystem processes and community structure in the recipient ecosystem. Animal movements, organic matter flows, and food web dynamics between linked aquatic and terrestrial systems can also influence contaminant fate, exposure, and effects at the land-water interface. Here and in this book, we develop a broad framework that highlights two important ways that ecological subsidies and contaminants interact. Ecological subsidies from the donor system can drive exposure to recipient systems, and contaminant exposures in the donor system can control subsidies and contaminant fluxes to the recipient systems. In the case of prey movement between ecosystems, subsidies drive exposure when contaminants present in aquatic environments bioaccumulate in the tissues of prey organisms at levels that are relatively non-toxic to the prey themselves. Conversely, exposure in the aquatic system can limit subsidies when pollutants are relatively toxic to prey organisms themselves and the magnitude of the subsidy (i.e., biomass of aquatic insects emerging to the terrestrial environment) is reduced. These effects of contaminants on subsidies are shaped by other global stressors that are ubiquitous in aquatic-riparian ecosystems (e.g., climate and land use change, species extinction and invasion, and eutrophication). As our understanding of these ecological and toxicological processes advances, there are increasing opportunities to make landscape-scale predictions of contaminant and animal fluxes and to integrate this knowledge of aquatic-riparian linkages into managing contaminant risks. Through these efforts to integrate the fields of ecology and ecotoxicology on this subject, we expect to gain greater insight on the ecological effects of contaminants on linked ecosystems as well as the ways in which food web dynamics and ecosystem processes can themselves govern the fate, transport, and exposure to contaminants in the environment.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and Ecological Subsidies: The Land-Water Interface","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_1","usgsCitation":"Walters, D., Kraus, J.M., and Mills, M.A., 2020, Introduction: Ecological subsidies as a framework for understanding contaminant fate, exposure, and effects at the land-water interface, chap. of Contaminants and Ecological Subsidies: The Land-Water Interface, p. 1-14, https://doi.org/10.1007/978-3-030-49480-3_1.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-116208","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":380259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Walters, David 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":147135,"corporation":false,"usgs":true,"family":"Walters","given":"David","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mills, Marc A.","contributorId":141085,"corporation":false,"usgs":false,"family":"Mills","given":"Marc","email":"","middleInitial":"A.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":804229,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216156,"text":"70216156 - 2020 - Synthesis: A framework for predicting the dark side of ecological subsidies","interactions":[],"lastModifiedDate":"2020-11-06T14:20:09.778211","indexId":"70216156","displayToPublicDate":"2020-10-16T08:17:35","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Synthesis: A framework for predicting the dark side of ecological subsidies","docAbstract":"In this chapter, we synthesize the state of the science regarding ecological subsidies and contaminants at the land-water interface and suggest research and management approaches for linked freshwater-terrestrial ecosystems. Specifically, we focus on movements of animals with complex life histories and the detrital inputs associated with animal and plant matter delivered to freshwaters. We present a framework based on the physicochemical parameters of contaminants and how they shape the relationship between contaminant persistence within resource subsidies (“dark side” of subsidies) and movement of resource subsidies (“bright side” of subsidies) across ecosystem boundaries. This relationship between the “dark side” and “bright side” of subsidies defines an important parameter space that allows researchers and practitioners to predict the potential impacts of aquatic contaminants on resource subsidies and their interaction with other stressors on consumers. Ecological factors such as ecosystem productivity, community composition, and consumer prey preference shape the ecotoxicological outcomes of aquatic contamination on subsidies. Landscape factors such as lithology, hydrogeomorphology, hydroperiod, and land use underlie chemical, toxicological, and ecological patterns and provide the context within which effects of contaminants play out. Finally, effects of contaminants combine with effects of other global stressors on timing, quality, and quantity of subsidies that drive responses to contaminants at the land-water interface. Understanding the “dark side” of ecological subsidies requires expertise from multiple disciplines. We attempt to synthesize current knowledge from those disciplines and generate conceptual models that ecologists can use to guide future research in understanding cross-ecosystem subsidies and contaminant fate and effects.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and ecological subsidies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_14","usgsCitation":"Kraus, J.M., Wessner, J., and Walters, D., 2020, Synthesis: A framework for predicting the dark side of ecological subsidies, chap. of Contaminants and ecological subsidies, p. 343-372, https://doi.org/10.1007/978-3-030-49480-3_14.","productDescription":"30 p.","startPage":"343","endPage":"372","ipdsId":"IP-114721","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":380257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wessner, Jeff","contributorId":244602,"corporation":false,"usgs":false,"family":"Wessner","given":"Jeff","email":"","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":804244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":205915,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804245,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216141,"text":"70216141 - 2020 - Cross-ecosystem linkages and trace metals at the land-water interface","interactions":[],"lastModifiedDate":"2020-11-06T14:16:25.956662","indexId":"70216141","displayToPublicDate":"2020-10-16T08:12:20","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Cross-ecosystem linkages and trace metals at the land-water interface","docAbstract":"At low concentrations, trace metals are critical for sustaining life on Earth. However, at high concentrations, they become a global contaminant with particularly strong effects on freshwater communities. These effects can propagate to terrestrial ecosystems in part by altering production and community structure of adult aquatic insect emergence and aquatic insect-mediated metal fluxes to terrestrial insectivores. Here we highlight mechanisms driving effects of trace metals on aquatic organisms in general, aquatic insects specifically, and insectivorous consumers at the land-water interface. Specifically, we focus on how trace metals impact and bioaccumulate in aquatic organisms and communities and how these changes propagate through aquatic food web interactions and insect metamorphosis to alter fluxes of aquatically derived prey and trace metals to terrestrial consumers. Ultimately, trace metals impact food webs at the land-water interface by altering aquatic insect prey composition and availability for aquatic insectivores and by reducing aquatic insect subsidies to terrestrial consumers, and not by increasing exposure to trace metals in prey. Exposure of terrestrial insectivores to trace metals in prey is decoupled from aqueous concentrations due to high rates of metal excretion during insect metamorphosis from aquatic larvae to terrestrial adult. These effects increase reliance of aquatic insectivores on terrestrial insect prey subsidies and/or lead to declines and behavioral changes in terrestrial insectivore populations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and ecological subsidies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_5","usgsCitation":"Kraus, J.M., and Pomeranz, J., 2020, Cross-ecosystem linkages and trace metals at the land-water interface, chap. of Contaminants and ecological subsidies, p. 91-109, https://doi.org/10.1007/978-3-030-49480-3_5.","productDescription":"19 p.","startPage":"91","endPage":"109","ipdsId":"IP-109559","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":380256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":804225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pomeranz, Justin F.","contributorId":149789,"corporation":false,"usgs":false,"family":"Pomeranz","given":"Justin F.","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":804226,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215275,"text":"ofr20201108 - 2020 - Aquifer transmissivity in Nassau, Queens, and Kings Counties, New York, estimated from specific-capacity tests at production wells","interactions":[],"lastModifiedDate":"2020-10-16T12:33:17.218141","indexId":"ofr20201108","displayToPublicDate":"2020-10-15T15:50:00","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":"2020-1108","displayTitle":"Aquifer Transmissivity in Nassau, Queens, and Kings Counties, New York, Estimated From Specific-Capacity Tests at Production Wells","title":"Aquifer transmissivity in Nassau, Queens, and Kings Counties, New York, estimated from specific-capacity tests at production wells","docAbstract":"As part of a cooperative effort between the U.S. Geological Survey and the New York State Department of Environmental Conservation to evaluate the sustainability of Long Island’s sole-source aquifer system, the transmissivities of four aquifers were estimated from specific-capacity tests at 447 production wells in Nassau, Queens, and Kings Counties on Long Island, New York. The specific-capacity test data, which included pumping rate, pumping duration, and drawdown, were obtained from published and unpublished records of driller-reported acceptance tests collected at production wells screened in the upper glacial, Jameco, Magothy, or Lloyd aquifers. Pumping rates from the production wells during the tests generally were greater than 400 gallons per minute and ranged up to 1,800 gallons per minute. Pumping duration generally was 8 hours or more. Transmissivities were estimated from the specific-capacity data by the Cooper-Jacob approximation of the Theis equation. The transmissivity estimates are considered rough approximations because the aquifers do not meet the ideal assumptions of the method, well losses and partial penetration were not accounted for, and aquifer storage coefficients were not known but were only estimated from available data.
The transmissivities estimated from production wells screened in the upper glacial aquifer in the outwash plain south of the moraine generally were greater than those of the aquifer north of the moraine. The transmissivities estimated from the wells screened in the upper glacial aquifer south of the moraine typically ranged (as defined by the 10th and 90th percentiles) from 3,800 to 15,000 feet squared per day (ft2/d), with a median value of 7,300 ft2/d. The transmissivities estimated from the wells screened in the upper glacial aquifer north of the moraine typically ranged from 2,100 to 7,400 ft2/d, with a median value of 4,400 ft2/d. The Jameco aquifer generally had the highest estimated transmissivities of all the aquifers analyzed. The estimated transmissivities for the Jameco aquifer typically ranged from 5,500 to 43,000 ft2/d, with a median value of 16,000 ft2/d. The Magothy and Lloyd aquifers had similar estimated transmissivities. The transmissivities estimated for the Magothy aquifer typically ranged from 2,700 to 13,000 ft2/d, with a median of 7,100 ft2/d. The estimated transmissivities of the Lloyd typically ranged from 3,000 to 14,000 ft2/d, with a median of 7,200 ft2/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201108","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Williams, J.H., Woodley, M., and Finkelstein, J.S., 2020, Aquifer transmissivity in Nassau, Queens, and Kings Counties, New York, estimated from specific-capacity tests at production wells: U.S. Geological Survey Open-File Report 2020–1108, 7 p., https://doi.org/10.3133/ofr20201108.","productDescription":"Report: iv, 7 p.; Dataset; Application Site","numberOfPages":"7","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108170","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":379362,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1108/coverthb.jpg"},{"id":379363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1108/ofr20201108.pdf","text":"Report","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1108"},{"id":379365,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://ny.water.usgs.gov/maps/aq-test/","text":"Aquifer Test Locator","linkFileType":{"id":5,"text":"html"}},{"id":379364,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System database","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","county":"Nassau County, Queens County, Kings County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0972900390625,\n 40.43858586704331\n ],\n [\n -73.388671875,\n 40.43858586704331\n ],\n [\n -73.388671875,\n 41.000629848685385\n ],\n [\n -74.0972900390625,\n 41.000629848685385\n ],\n [\n -74.0972900390625,\n 40.43858586704331\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Despite low species diversity and primary production, trophic structure (e.g., top predator species, predator size) is surprisingly variable among Arctic lakes. We investigated trophic structure in lakes of arctic Alaska containing arctic char Salvelinus alpinus using stomach contents and stable isotope ratios in two geographically-close but hydrologically-distinct lake clusters to investigate how these fish may interact and compete for limited food resources. Aside from different lake connectivity patterns (‘leaky’ versus ‘closed’), differing fish communities (up to five versus only two species) between lake clusters allowed us to test trophic hypotheses including: (1) arctic char are more piscivorous, and thereby grow larger and obtain higher trophic positions, in the presence of other fish species; and, (2) between arctic char size classes, resource polymorphism is more prominent, and thereby trophic niches are narrower and overlap less, in the absence of other predators. Regardless of lake cluster, we observed little direct evidence of arctic char consuming other fishes, but char were larger (mean TL = 468 vs 264 mm) and trophic position was higher (mean TP = 4.0 vs 3.8 for large char) in lakes with other fishes. Further, char demonstrated less intraspecific overlap when other predators were present whereas niche overlap was up to 100% in closed, char only lakes. As hydrologic characteristics (e.g., lake connectivity, water temperatures) will change across the Arctic owing to climate change, our results provide insight regarding potential concomitant changes to fish interactions and increase our understanding of lake trophic structure to guide management and conservation goals.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-020-04776-9","usgsCitation":"Klobucar, S.L., and Budy, P., 2020, Trophic structure of apex fish communities in closed versus leaky lakes of arctic Alaska: Oecologia, v. 194, p. 491-504, https://doi.org/10.1007/s00442-020-04776-9.","productDescription":"14 p.","startPage":"491","endPage":"504","ipdsId":"IP-109849","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Brooks Range, Toolik Field Station","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.69558715820312,\n 68.3996855982224\n ],\n [\n -149.20257568359375,\n 68.3996855982224\n ],\n [\n -149.20257568359375,\n 68.64455609820665\n ],\n [\n -149.69558715820312,\n 68.64455609820665\n ],\n [\n -149.69558715820312,\n 68.3996855982224\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"194","noUsgsAuthors":false,"publicationDate":"2020-10-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Klobucar, Stephen L.","contributorId":274993,"corporation":false,"usgs":false,"family":"Klobucar","given":"Stephen","email":"","middleInitial":"L.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":833550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833549,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216544,"text":"70216544 - 2020 - Climate sensitivity of water use by riparian woodlands at landscape scales","interactions":[],"lastModifiedDate":"2020-12-14T16:56:03.212697","indexId":"70216544","displayToPublicDate":"2020-10-15T10:46:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Climate sensitivity of water use by riparian woodlands at landscape scales","docAbstract":"Semi‐arid riparian woodlands face threats from increasing extractive water demand and climate change in dryland landscapes worldwide. Improved landscape‐scale understanding of riparian woodland water use (evapotranspiration, ET) and its sensitivity to climate variables is needed to strategically manage water resources, as well as to create successful ecosystem conservation and restoration plans for potential climate futures. In this work, we assess the spatial and temporal variability of Cottonwood (Populus fremontii)‐Willow (Salix gooddingii) riparian gallery woodland ET and its relationships to vegetation structure and climate variables for 80 km of the San Pedro River corridor in southeastern Arizona, USA, between 2014 and 2019. We use a novel combination of publicly available remote sensing, climate and hydrological datasets: cloud‐based Landsat thermal remote sensing data products for ET (Google Earth Engine EEFlux), Landsat multispectral imagery and field data‐based calibrations to vegetation structure (leaf‐area index, LAI), and open‐source climate and hydrological data. We show that at landscape scales, daily ET rates (6–10 mm day−1) and growing season ET totals (400–1,400 mm) matched rates of published field data, and modelled reach‐scale average LAI (0.80–1.70) matched lower ranges of published field data. Over 6 years, the spatial variability of total growing season ET (CV = 0.18) exceeded that of temporal variability (CV = 0.10), indicating the importance of reach‐scale vegetation and hydrological conditions for controlling ET dynamics. Responses of ET to climate differed between perennial and intermittent‐flow stream reaches. At perennial‐flow reaches, ET correlated significantly with temperature, whilst at intermittent‐flow sites ET correlated significantly with rainfall and stream discharge. Amongst reaches studied in detail, we found positive but differing logarithmic relationships between LAI and ET. By documenting patterns of high spatial variability of ET at basin scales, these results underscore the importance of accurately accounting for differences in woodland vegetation structure and hydrological conditions for assessing water‐use requirements. Results also suggest that the climate sensitivity of ET may be used as a remote indicator of subsurface water resources relative to vegetation demand, and an indicator for informing conservation management priorities.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13942","usgsCitation":"Mayes, M., Caylor, K.K., Singer, M.B., Stella, J., Roberts, D., and Nagler, P.L., 2020, Climate sensitivity of water use by riparian woodlands at landscape scales: Hydrological Processes, v. 34, no. 25, p. 4884-4903, https://doi.org/10.1002/hyp.13942.","productDescription":"10 p.","startPage":"4884","endPage":"4903","ipdsId":"IP-120214","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455038,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://orca.cardiff.ac.uk/id/eprint/135647/","text":"External Repository"},{"id":380788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"San Pedro River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58563232421875,\n 31.3348710339506\n ],\n [\n -109.79461669921875,\n 31.3348710339506\n ],\n [\n -109.79461669921875,\n 32.15468722002481\n ],\n [\n -110.58563232421875,\n 32.15468722002481\n ],\n [\n -110.58563232421875,\n 31.3348710339506\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"25","noUsgsAuthors":false,"publicationDate":"2020-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Mayes, Marc","contributorId":245241,"corporation":false,"usgs":false,"family":"Mayes","given":"Marc","email":"","affiliations":[],"preferred":false,"id":805665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caylor, Kelly K.","contributorId":245242,"corporation":false,"usgs":false,"family":"Caylor","given":"Kelly","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":805666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singer, Michael B.","contributorId":168369,"corporation":false,"usgs":false,"family":"Singer","given":"Michael","email":"","middleInitial":"B.","affiliations":[{"id":25268,"text":"University of St Andrews, UK","active":true,"usgs":false}],"preferred":false,"id":805667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stella, John C","contributorId":149423,"corporation":false,"usgs":false,"family":"Stella","given":"John C","affiliations":[{"id":17732,"text":"Professor, Dept of Forest & Natural Resources Mgmt, SUNY at ESF","active":true,"usgs":false}],"preferred":false,"id":805668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, Dar","contributorId":13721,"corporation":false,"usgs":true,"family":"Roberts","given":"Dar","affiliations":[],"preferred":false,"id":805669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":805569,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215581,"text":"70215581 - 2020 - Interaction between watershed features and climate forcing affects habitat profitability for juvenile salmon","interactions":[],"lastModifiedDate":"2020-10-23T12:40:54.251341","indexId":"70215581","displayToPublicDate":"2020-10-15T07:37:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Interaction between watershed features and climate forcing affects habitat profitability for juvenile salmon","docAbstract":"Opportunities for growth and survival of aquatic organisms are spatially and temporally variable as habitat conditions across watersheds respond to interacting climatic, geomorphic, and hydrologic conditions. As conservation efforts often focus on identifying and protecting critical habitats, it is important to understand how this spatial and temporal variation in habitat quality affects the production dynamics of populations. Here, we use microchemical records preserved in otoliths to reconstruct juvenile habitat‐use by sockeye salmon that survived to spawn in a single population on the Alaska Peninsula. Successful individuals demonstrated a diverse array of juvenile behavioral strategies both within and among years. Importantly, the dominant juvenile behavioral strategy used by successful individuals changed among years, suggesting shifts in the relative benefits of different rearing habitats. The growth benefits of remaining in a more productive rearing lake were greatest in warm years indicating environmental influence on relative habitat quality. However, we found no strong relationship between the amount of growth accumulated in the productive rearing lake and overall population productivity across years. These results highlight the dynamic nature of habitat conditions and the beneficial effect of maintaining connectivity between diverse habitats for population productivity. When short‐term studies are used to demonstrate the relative values of different habitats to species of conservation concern, there is a distinct risk of under‐valuing habitats that may be critically important under alternative environmental conditions. In particular, land‐use decisions that reduce the range of habitat options available to species may erode a population’s ability to withstand environmental change over the long term.
The Utica and Marcellus Shale Plays in the Appalachian Basin are the fourth and first largest natural gas producing plays in the United States, respectively. Hydrocarbon production generates large volumes of brine (“produced water”) that must be disposed of, treated, or reused. Though Marcellus brines have been studied extensively, there are few studies from the Utica Shale Play. This study presents new brine chemical analyses from 16 Utica Shale Play wells in Ohio and Pennsylvania. Results from Na–Cl–Br systematics and stable and radiogenic isotopes suggest that the Utica Shale Play brines are likely residual pore water concentrated beyond halite saturation during the formation of the Ordovician Beekmantown evaporative sequence. The narrow range of chemistry for the Utica Shale Play produced waters (e.g., total dissolved solids = 214–283 g/L) over both time and space implies a consistent composition for disposal and reuse planning. The amount of salt produced annually from the Utica Shale Play is equivalent to 3.4% of the annual U.S. halite production. Utica Shale Play brines have radium activities 580 times the EPA maximum contaminant level and are supersaturated with respect to barite, indicating the potential for surface and aqueous radium hazards if not properly disposed of.
Soil respiration is a primary component of the terrestrial carbon cycle. However, predicting the response of soil respiration to climate change remains a challenge due to the complex interactions between environmental drivers, especially plant phenology, temperature, and soil moisture. In this study, we use a 1‐D diffusion‐reaction model to calculate depth‐resolved CO2 production rates from soil CO2 concentrations and surface efflux observations in a subalpine meadow in the East River watershed, CO. Modeled rates are compared to in situ soil temperature and moisture conditions and MODIS satellite enhanced vegetation index (EVI) representing plant phenology across three hydrologically distinct growing seasons from 2016–2018. While soil respiration correlated with temperature on diel timescales (p < 0.05), seasonal variability was dominated by soil moisture and plant phenology (p < 0.05). We observed significant respiration increases in response to precipitation events; however, magnitude and duration were significantly higher in 2017 than 2016 despite similar wetting characteristics. Based on MODIS EVI, we suggest that the respiration response to rainfall is controlled by plant phenology, which in turn reflects the capacity of plants to respond to precipitation via increased photosynthesis and autotrophic respiration, behavior that is not captured in typical soil respiration pulse models. Projected changes in montane climate such as earlier snowmelt and prolonged fore‐summer drought may decrease soil respiration fluxes by decreasing the overlap between peak productivity and the summer monsoon. Finally, we observed significant late season CO2 fluxes from the deep subsoil (>165 cm) that support growing evidence for the importance of subsoil processes in driving integrated respiration fluxes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JG005924","usgsCitation":"Winnick, M., Lawrence, C.R., McCormick, M., Druhan, J., and Maher, K., 2020, Soil respiration response to rainfall modulated by plant phenology in a montane meadow, East River, Colorado, USA: Journal of Geophysical Research Biogeosciences, v. 125, no. 10, e2020JG005924, 20 p., https://doi.org/10.1029/2020JG005924.","productDescription":"e2020JG005924, 20 p.","ipdsId":"IP-108485","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":455072,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1664387","text":"External Repository"},{"id":381100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"East River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.061767578125,\n 38.50626606567193\n ],\n [\n -106.82968139648436,\n 38.50626606567193\n ],\n [\n -106.82968139648436,\n 38.922023851268925\n ],\n [\n -107.061767578125,\n 38.922023851268925\n ],\n [\n -107.061767578125,\n 38.50626606567193\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Winnick, Mathew","contributorId":245458,"corporation":false,"usgs":false,"family":"Winnick","given":"Mathew","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":806219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawrence, Corey R. 0000-0001-6143-7781","orcid":"https://orcid.org/0000-0001-6143-7781","contributorId":202390,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","email":"","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":806220,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Maeve","contributorId":245459,"corporation":false,"usgs":false,"family":"McCormick","given":"Maeve","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":806221,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Druhan, Jennifer","contributorId":245460,"corporation":false,"usgs":false,"family":"Druhan","given":"Jennifer","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":806222,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maher, Kate","contributorId":245461,"corporation":false,"usgs":false,"family":"Maher","given":"Kate","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":806223,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215356,"text":"70215356 - 2020 - Determining habitat limitations of Maumee River walleye production to western Lake Erie fish stocks: Documenting a spawning ground barrier","interactions":[],"lastModifiedDate":"2020-11-30T16:39:00.408384","indexId":"70215356","displayToPublicDate":"2020-10-09T08:22:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Determining habitat limitations of Maumee River walleye production to western Lake Erie fish stocks: Documenting a spawning ground barrier","docAbstract":"Tributaries provide spawning habitat for three of four major sub-stocks of Lake Erie walleye (Sander vitreus). Despite anthropogenic degradation and the extirpation of other potamodromous species, the Maumee River, Ohio, USA continues to support one of the largest fish migrations in the Laurentian Great Lakes. To determine if spawning habitat availability and quality could limit production of Maumee River walleye, two habitat suitability models were created for the lower 51 km of the Maumee River and the distribution and numbers of walleye eggs deposited in a 25 km stretch of river were assessed. Walleye eggs were collected using a diaphragm pump at 7 and 10 sites from March/April to May 2014 and 2015. The habitat suitability models showed that <3% of the river yielded ‘good’ walleye spawning habitat and 11–38% yielded ‘moderate’ walleye spawning habitat, depending on the model. However, a large set of rapids at river kilometer 28 and more than five river kilometers of less suitable habitat separated areas of ‘good’ habitat. The rapids may present a migratory barrier for many spawning walleye, as modeled water velocities exceed maximum estimated walleye swim speeds 71–100% of days during pre-spawn migration and spawning during the study period. In both study years, there was a sharp decline in mean egg numbers from spawning sites downstream of the rapids (439.7 eggs/2 min tow ± 990.6 SD) to upstream sites (5.9 eggs/2 min tow ± 19.4 SD). Physical barriers like rapids may reduce spawning habitat connectivity and could limit walleye production in the Maumee River.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.08.022","usgsCitation":"Schmidt, B., Tucker, T., Collier, J., Mayer, C., Roseman, E., Stott, W., and Pritt, J., 2020, Determining habitat limitations of Maumee River walleye production to western Lake Erie fish stocks: Documenting a spawning ground barrier: Journal of Great Lakes Research, v. 46, no. 6, p. 1661-1673, https://doi.org/10.1016/j.jglr.2020.08.022.","productDescription":"13 p.","startPage":"1661","endPage":"1673","ipdsId":"IP-115670","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":436758,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9214IQU","text":"USGS data release","linkHelpText":"Walleye (Sander vitreus) egg deposition and spawning habitat suitability in the Maumee River, OH (2014-2015)"},{"id":379460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.36975097656249,\n 41.68111756290652\n ],\n [\n -83.47412109375,\n 41.75492216766298\n ],\n [\n -84.232177734375,\n 41.47977575214487\n ],\n [\n -84.638671875,\n 41.31082388091818\n ],\n [\n -84.5947265625,\n 41.08763212467916\n ],\n [\n -83.81469726562499,\n 41.33970040774419\n ],\n [\n -83.5015869140625,\n 41.51269075845857\n ],\n [\n -83.36975097656249,\n 41.68111756290652\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Brian 0000-0001-7067-6194","orcid":"https://orcid.org/0000-0001-7067-6194","contributorId":242674,"corporation":false,"usgs":false,"family":"Schmidt","given":"Brian","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":801850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tucker, Taaja 0000-0003-1534-4677","orcid":"https://orcid.org/0000-0003-1534-4677","contributorId":217908,"corporation":false,"usgs":true,"family":"Tucker","given":"Taaja","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":801851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collier, Jessica","contributorId":242677,"corporation":false,"usgs":false,"family":"Collier","given":"Jessica","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":801852,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mayer, Christine","contributorId":237769,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","affiliations":[{"id":47604,"text":"University of Toledo, Lake Erie Center","active":true,"usgs":false}],"preferred":false,"id":801853,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":801854,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stott, Wendylee 0000-0002-5252-4901 wstott@usgs.gov","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":191249,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":801855,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pritt, Jeremy J.","contributorId":138591,"corporation":false,"usgs":false,"family":"Pritt","given":"Jeremy J.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":801856,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215551,"text":"70215551 - 2020 - Getting to the root of restoration: Considering root traits for improved restoration outcomes under drought and competition","interactions":[],"lastModifiedDate":"2020-11-30T16:51:47.983035","indexId":"70215551","displayToPublicDate":"2020-10-08T08:29:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Getting to the root of restoration: Considering root traits for improved restoration outcomes under drought and competition","docAbstract":"A foundational goal of trait‐based ecology, including trait‐based restoration, is to link specific traits to community assembly, biodiversity, and ecosystem function. Despite a growing awareness of the importance of belowground traits for ecological processes, a synthesis of how to root traits can inform restoration of terrestrial plant communities is lacking. We reviewed and summarized existing literature focused on root traits in relation to plant performance measures (i.e. survival, establishment, productivity) in the contexts of drought and competition (including invasion). Root traits related to belowground resource acquisition (e.g. high specific root length, deep roots) are frequently related to drought avoidance (i.e. a plant strategy based on optimizing water uptake to maintain function), whereas studies relating root traits to drought tolerance (i.e. a plant strategy that allows plants to withstand low hydration) remain limited. More studies have linked root traits to plant competitive effects (i.e. the influence of a plant has on neighbors) than to competitive responses (i.e. a plant's ability to resist the effects of neighbors). Because plants with acquisitive traits decrease resources to the detriment of neighbors, root traits associated with rapid resource acquisition (e.g. high specific root length) may be important for understanding competitive effects. Albeit more limited, research suggests root traits associated with resource conservation or stress tolerance (e.g. high root tissue density, high root diameter) may elucidate mechanisms related to competitive responses. Re‐vegetation outcomes may be improved by considering root traits, but only if clear links are made between traits and plant performance in varied contexts.","language":"English","publisher":"Wiley","doi":"10.1111/rec.13291","usgsCitation":"Garbowski, M., Avera, B., Bertram, J.H., Courkamp, J., Gray, J., Hein, K., Lawrence, R., McIntosh, M., McClelland, S., Post, A., Slette, I.J., Winkler, D.E., and Brown, C.S., 2020, Getting to the root of restoration: Considering root traits for improved restoration outcomes under drought and competition: Restoration Ecology, v. 28, no. 6, p. 1384-1395, https://doi.org/10.1111/rec.13291.","productDescription":"12 p.","startPage":"1384","endPage":"1395","ipdsId":"IP-120212","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455097,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13291","text":"Publisher Index Page"},{"id":379646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Garbowski, M.","contributorId":243608,"corporation":false,"usgs":false,"family":"Garbowski","given":"M.","affiliations":[],"preferred":false,"id":802708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avera, B.","contributorId":243609,"corporation":false,"usgs":false,"family":"Avera","given":"B.","email":"","affiliations":[],"preferred":false,"id":802709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bertram, J. H.","contributorId":243610,"corporation":false,"usgs":false,"family":"Bertram","given":"J.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":802710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Courkamp, J.S.","contributorId":243611,"corporation":false,"usgs":false,"family":"Courkamp","given":"J.S.","email":"","affiliations":[],"preferred":false,"id":802711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, J.","contributorId":100683,"corporation":false,"usgs":true,"family":"Gray","given":"J.","affiliations":[],"preferred":false,"id":802712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hein, K.M.","contributorId":243612,"corporation":false,"usgs":false,"family":"Hein","given":"K.M.","email":"","affiliations":[],"preferred":false,"id":802713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lawrence, R.","contributorId":101430,"corporation":false,"usgs":false,"family":"Lawrence","given":"R.","email":"","affiliations":[],"preferred":false,"id":802714,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McIntosh, M.","contributorId":243613,"corporation":false,"usgs":false,"family":"McIntosh","given":"M.","email":"","affiliations":[],"preferred":false,"id":802715,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McClelland, S.","contributorId":95633,"corporation":false,"usgs":false,"family":"McClelland","given":"S.","email":"","affiliations":[],"preferred":false,"id":802716,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Post, A.","contributorId":51033,"corporation":false,"usgs":false,"family":"Post","given":"A.","email":"","affiliations":[],"preferred":false,"id":802717,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Slette, Ingrid J.","contributorId":187583,"corporation":false,"usgs":false,"family":"Slette","given":"Ingrid","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":802718,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":802719,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Brown, C. S.","contributorId":80675,"corporation":false,"usgs":false,"family":"Brown","given":"C.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":802720,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70214611,"text":"sim3463 - 2020 - Bathymetry of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado, 2019","interactions":[],"lastModifiedDate":"2020-10-07T23:40:27.941947","indexId":"sim3463","displayToPublicDate":"2020-10-07T15:35:00","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":"3463","displayTitle":"Bathymetry of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado, 2019","title":"Bathymetry of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado, 2019","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Air Force Academy (USAFA), carried out bathymetric and topographic surveys to characterize the volume of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado. Bathymetric maps of each lake and reservoir are presented with figures of the elevation-volume curves. The bathymetric surveys were carried out from October 15, 2019, to December 12, 2019, using a manually operated, boat-mounted, single-beam echo sounder integrated with a Real-Time Kinematic Global Navigation Satellite Systems receiver. Topographic surveys were carried out during the same time period using Real-Time Kinematic Global Navigation Satellite System to collect elevation data at and above the water surface and up to the elevation of the dam or spillway at the time of the surveys. The topographic and bathymetric datasets were imported into Esri ArcMap 10.7.1. The combined survey points were then interpolated into digital elevation models, which were used to determine lake or reservoir volumes that correspond to water-surface elevations between the lakebed and the approximate top of the dam or spillway.
This report provides an updated characterization of storage capacity and improved understanding of present (2019) water capacity in the lakes and reservoirs at the USAFA. In addition, these surveys serve as a baseline that could be compared with future surveys of the lakes and reservoirs. The differences in these and future surveys could then be used to determine sedimentation infill rates and provide estimates of the lifespan of the lakes and reservoirs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3463","collaboration":"Prepared in cooperation with the U.S. Air Force Academy","usgsCitation":"Kohn, M.S., and Hempel, L.A., 2020, Bathymetry of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado, 2019: U.S. Geological Survey Scientific Investigations Map 3463, pamphlet 12 p., https://doi.org/10.3133/sim3463.","productDescription":"Pamphlet: vi, 12 p.; 1 Sheet: 36.00 x 32.00 inches; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114390","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":378914,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3463/coverthb.jpg"},{"id":378915,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3463/sim3463_map.pdf","text":"Map","size":"9.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3463"},{"id":378917,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LTH0RO","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Survey and Bathymetric Data of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1-3, and Non-Potable Reservoirs 1-4 at the U.S. Air Force Academy, Colorado, 2019 (ver. 1.1, June 2020)"},{"id":378916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3463/sim3463_pamphlet.pdf","text":"Pamphlet","size":"1.17 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Colorado","city":"Colorado Springs","otherGeospatial":"U.S. Airforce Academy","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.91188049316406,\n 38.91561302513129\n ],\n [\n -104.77867126464842,\n 38.93163900447185\n ],\n [\n -104.8267364501953,\n 39.03731965210478\n ],\n [\n -104.92767333984374,\n 39.03731965210478\n ],\n [\n -104.91188049316406,\n 38.91561302513129\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
Methods were developed for measuring immune function in Micropterus dolomieu (smallmouth bass). The ultimate objective is to monitor and evaluate changes over time in immune status and disease resistance in conjunction with other characteristics of fish health and environmental stressors. To test these methods for utility in ecotoxicological studies, 192 smallmouth bass, age 2 years and older, were collected from three sites within the Susquehanna River Basin and one site in the Ohio River Basin during spring and fall 2016 and 2017. The anterior kidney was aseptically removed and homogenized for leukocyte isolation. Leukocytes were tested for bactericidal activity against two species of bacteria; respiratory burst activity when stimulated with phorbol 12-myristate 13-acetate; and mitogenesis activity when stimulated with concanavalin A, phytohemagglutinin, and lipopolysaccharide. Tissues were preserved for histopathological analyses.
Two of the sites were part of a monitoring program at which surface-water samples were collected monthly (bimonthly in spring) for chemical contaminants. Significant seasonal and (or) site differences in all three immune function tests were observed. Interpretations of seasonal trends in immune function of wild fish or correlations with environmental variables and other factors are difficult to make owing to the complex nature of the immune response and the environment. Differences in immune function could potentially be related to a variety of confounding factors; therefore, additional endpoints and repeated sampling over an extended period are essential to draw conclusions on the immune status of wild fish.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201077","collaboration":"Prepared in cooperation with Pennsylvania Department of Environmental Protection","usgsCitation":"Smith, C.R., Ottinger, C.A., Walsh, H.L., and Blazer, V.S., 2020, Development of a suite of functional immune assays and initial assessment of their utility in wild smallmouth bass health assessments: U.S. Geological Survey Open-File Report 2020–1077, 23 p., https://doi.org/10.3133/ofr20201077.","productDescription":"vii, 23 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118051","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":379041,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1077/ofr20201077.pdf","text":"Report","size":"13.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1077"},{"id":379040,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1077/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Tionesta Lake, Pine Creek, Upper Juniata River, West Branch Mahantango Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.79919433593749,\n 41.30257109430557\n ],\n [\n -77.38220214843749,\n 41.30257109430557\n ],\n [\n -77.38220214843749,\n 42.00032514831621\n ],\n [\n -79.79919433593749,\n 42.00032514831621\n ],\n [\n -79.79919433593749,\n 41.30257109430557\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
To characterize eruption activity of the iconic Old Faithful Geyser in Yellowstone National Park over past centuries, we obtained 41 new radiocarbon dates of mineralized wood preserved in the mound of silica that precipitated from erupted waters. Trees do not grow on active geyser mounds, implying that trees grew on the Old Faithful Geyser mound during a protracted period of eruption quiescence. Rooted stumps and root crowns located on higher parts of the mound are evidence that at the time of tree growth, the geyser mound closely resembled its current appearance. The range of calibrated radiocarbon dates (1233–1362 CE) is coincident with a series of severe multidecadal regional droughts toward the end of the Medieval Climate Anomaly, prior to the onset of the Little Ice Age. Climate models project increasingly severe droughts by mid‐21st century, suggesting that geyser eruptions could become less frequent or completely cease.
The endangered Silver Chub (Macrhybopsis storeriana, Kirtland 1844) is native to North America and primarily riverine, with the only known large‐lake population in Lake Erie. Once a major component of the Lake Erie fish community, it declined and became nearly extirpated in the mid‐1900s. Recent collections in western Lake Erie suggest that Silver Chub may be able to recover, but their habitat and distribution are poorly known. A recent work showed an extensive area of western Lake Erie with the potential to support large numbers of Silver Chub, but was based on a geographically limited dataset. We developed a neural network‐based species distribution model for the Silver Chub in western Lake Erie, improved by new synoptic data and using habitat variables resistant to anthropogenic activities. The Potential model predictions were compared with a model that included anthropogenic‐sensitive variables. The Potential model used 10 habitat variables and performed well, explaining > 99% of data variation and had generally low error rates. Predictions indicated that a large area of the waters approximately 2–9 m deep contained Appropriate habitat and the highest abundances should be supported by habitat in a wide arc through the western end of the basin. The model indicated that Appropriate Silver Chub habitat was associated with relatively deep water, near coastal wetlands, where effective fetch is less than average. Disturbance model predictions were similar, but predicted poorer Silver Chub habitat in more areas than that predicted by the Potential model. Our Potential model reveals Appropriate habitat conditions for Silver Chub and its spatial distribution, indicating that extensive areas of western Lake Erie could support Silver Chub. Comparisons with Disturbance model predictions demonstrate that Potential model predictions may be used in conjunction with analyses of degrading conditions in the system to better conserve and manage for this endangered species.
Evapotranspiration (ET) is needed in a range of applications in hydrology, climatology, ecology, and agriculture. Remote sensing-based estimation is the only viable and economical method for ET estimation over large areas. The current Landsat satellites provide images every 16 days limiting the ability to capture biophysical changes affecting ET. Thus, we explored the potential integration of Landsat 8 and Sentinel-2 data for estimating ET using a surface energy balance model. The results indicate the proposed Landsat-Sentinel data fusion approach substantially reduced relative errors from 48% to 10% on area-wide and from 49% to 17% on pixel-wide compared to linear interpolation between two Landsat images. The proposed approach had a better agreement with expected actual ET maps across high-vegetation conditions than in low-vegetation conditions. The finer temporal resolution and better accuracy of ET maps based on Landsat-Sentinel integration is of great importance in managing limited water resources.
The ability of wetlands to accrete organic matter in response to rising sea level is a key to landscape resilience, especially in light of reduced sediment availability consequent to dam construction and channelization. This study examined the degradation of cattail (Typha spp.) and tule (Schoenoplectus acutus) litters in restored wetlands through the lens of lignin, a major structural biopolymer in vascular plants with degradation characteristics very sensitive to oxic versus anoxic conditions. A series of litterbags were deployed during the first 10 years after flooding of Deep (55 cm) and Shallow (25 cm) restored wetlands. As emergent marsh vegetation spread through the maturing wetlands, anoxic conditions were more prevalent and overall degradation rates of litter in litterbags were lower. In later experiments in the maturing wetlands, lignin was progressively enriched in litter as evidenced by carbon-normalized yields (Λ8) that increased in tule starting materials from 6.3 to 7.1 mg 100 mgOC–1 to as high as 9.9 mg 100 mgOC–1, and in cattail starting materials from 5.9 to 7.0 mg 100 mgOC–1 to as high as 10.9 mg 100 mgOC–1. However, in an experiment initiated soon after the restored wetlands were constructed, Λ8 in tule litter decreased from 6.8 to 3.6 mg 100 mgOC–1, highlighting the prevalence of initial oxic conditions. With the exception of the early oxic conditions for tule, there was an overall trend of decreasing lignin acid-to-aldehyde ratios with litter degradation, which runs counter to most studies in the literature. We hypothesize that this reflects the utilization of more oxygen-rich lignin components as electron acceptors in redox reactions. No consistent differences were observed in degradation patterns between the Shallow and Deep wetlands. There were distinct differences in lignin degradation in cattail (more resistant) versus tule (less resistant), which indicates that although anoxia may be the dominant control on organic matter accretion in wetlands, specific types of vegetation in restored or constructed wetlands affects organic matter preservation, and hence accretion. Thus, selective management of predominant species in wetlands may prove important for the ability of wetlands to maintain emergent vegetation during sea level rise and to preserve the overall stability of wetland soils.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2020.564603","usgsCitation":"Hernes, P.J., Miller, R.L., Dyda, R.Y., and Bergamaschi, B.A., 2020, Vegetation vs. anoxic controls on degradation of plant litter in a restored wetland: Frontiers in Environmental Science, v. 8, 564603, 11 p., https://doi.org/10.3389/fenvs.2020.564603.","productDescription":"564603, 11 p.","ipdsId":"IP-119215","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":502354,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":502494,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2020.564603","text":"Publisher Index Page"}],"country":"United States","state":"California","otherGeospatial":"Twitchell Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.68381283679834,\n 38.12416147877542\n ],\n [\n -121.68755028169036,\n 38.08707073917063\n ],\n [\n -121.67928640312594,\n 38.08780599701319\n ],\n [\n -121.66757273973434,\n 38.09442402508458\n ],\n [\n -121.65265808360306,\n 38.09467806713366\n ],\n [\n -121.63544357092064,\n 38.08904952104504\n ],\n [\n -121.6280522656029,\n 38.09589585522204\n ],\n [\n -121.62668326258334,\n 38.099398949521174\n ],\n [\n -121.61151764873418,\n 38.10516475690591\n ],\n [\n -121.61684341930066,\n 38.11624520495312\n ],\n [\n -121.65443009534332,\n 38.11969426991299\n ],\n [\n -121.66258531326154,\n 38.12254968333488\n ],\n [\n -121.66666292222067,\n 38.12397739004583\n ],\n [\n -121.67074053117977,\n 38.12540509675678\n ],\n [\n -121.68381283679834,\n 38.12416147877542\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Hernes, Peter J. 0000-0001-7908-0936","orcid":"https://orcid.org/0000-0001-7908-0936","contributorId":329589,"corporation":false,"usgs":false,"family":"Hernes","given":"Peter","middleInitial":"J.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":959139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Robin L. 0000-0001-6931-3898","orcid":"https://orcid.org/0000-0001-6931-3898","contributorId":369566,"corporation":false,"usgs":true,"family":"Miller","given":"Robin","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dyda, Rachael Y. 0000-0002-4616-7231","orcid":"https://orcid.org/0000-0002-4616-7231","contributorId":369567,"corporation":false,"usgs":false,"family":"Dyda","given":"Rachael","middleInitial":"Y.","affiliations":[{"id":28024,"text":"UCDavis","active":true,"usgs":false}],"preferred":false,"id":959141,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959142,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70213240,"text":"sir20205054 - 2020 - Estimating flood magnitude and frequency on streams and rivers in Connecticut, based on data through water year 2015","interactions":[],"lastModifiedDate":"2020-10-06T21:49:41.168644","indexId":"sir20205054","displayToPublicDate":"2020-10-06T16:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5054","displayTitle":"Estimating Flood Magnitude and Frequency on Streams and Rivers in Connecticut, Based on Data Through Water Year 2015","title":"Estimating flood magnitude and frequency on streams and rivers in Connecticut, based on data through water year 2015","docAbstract":"The U.S. Geological Survey, in cooperation with the Connecticut Department of Transportation, updated flood-frequency estimates with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively) for 141 streamgages in Connecticut and 11 streamgages in adjacent States using annual peak-flow data through water year 2015. Peak-flow regression equations were derived for estimating flows at ungaged stream sites with annual exceedance probabilities from 50 to 0.2 percent. Methods for estimating prediction intervals for the peak-flow regression equations are presented. The regression equations are applicable for basins in Connecticut with drainage areas ranging from 0.69 to 325 square miles that are not affected by flood-control regulation or flow diversions.
The flood discharges for select annual exceedance probabilities were estimated following new (2018) national guidelines for flood-frequency analyses. New guidelines have improved statistical methods for flood-frequency analysis including (1) the expected moments algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record and (2) the generalized multiple Grubbs-Beck test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Additionally, a new regional skew (0.37) derived for New England was used in the flood-frequency analysis for the streamgages.
Annual peak flows were analyzed for trends for four time periods (30, 50, 70, and 90 years) through 2015. Trend results show some statistical evidence of increasing peak flows in each of the time periods analyzed; however, multidecadal climate cycles may be influencing the number and magnitude of the trends. Historical peak-flow trends in and near Connecticut do not offer clear and convincing evidence for incorporating trends into flood-frequency analyses. For this study, the traditional assumption of stationarity is used with no adjustment for trends.
Generalized least squares regression techniques were used to develop the final set of multivariable regression equations for estimating flood discharges with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. The standard error of prediction for the regional regression equations ranged from 26.3 to 45.0 percent. The standard error of prediction was slightly smaller in the current study compared to the 2004 study, indicating an improvement in the predictive ability of the equations (6 percent smaller at the 50-percent annual exceedance probability to about 1 percent smaller at the 1-percent annual exceedance probability). Generalized least squares regression techniques also were used to develop a one-variable (drainage-area-only) equation. Drainage-area-only equations can be used as an alternative to the multiexplanatory variable statewide regression equations if decreased accuracy is acceptable.
The revised statistical procedures and additional streamgage data applied in the current study result in a more accurate representation of peak-flow conditions in Connecticut than was previously available. The regional regression equations will be integrated in the U.S. Geological Survey StreamStats program, which estimates basin and climatic characteristics and streamflow statistics at user-selected ungaged stream sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205054","collaboration":"Prepared in cooperation with the Connecticut Department of Transportation","usgsCitation":"Ahearn, E.A., and Hodgkins, G.A., 2020, Estimating flood magnitude and frequency on streams and rivers in Connecticut, based on data through water year 2015: U.S. Geological Survey Scientific Investigations Report 2020–5054, 42 p., https://doi.org/10.3133/sir20205054.","productDescription":"Report: v, 42 p.; 2 Tables; 2 Data Releases","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108818","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":378387,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EWHAYW","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Worksheet for computing annual exceedance probability flood discharges and prediction intervals at stream sites in Connecticut"},{"id":378386,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4F751","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Flood frequency and source data used in regional regression analysis of annual peak flows in Connecticut"},{"id":378381,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5054/coverthb.png"},{"id":378382,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5054/sir20205054.pdf","text":"Report","size":"6.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5054"},{"id":378383,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5054/sir20205054_table01.xlsx","text":"Table 1","size":"35.3 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Descriptions of U.S. Geological Survey streamgages in Connecticut and adjacent States used in the flood-frequency analysis and regionalization of peaks flows in Connecticut"},{"id":378384,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5054/sir20205054_table01.csv","text":"Table 1","size":"33.5 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Descriptions of U.S. Geological Survey streamgages in Connecticut and adjacent States used in the flood-frequency analysis and regionalization of peaks flows in Connecticut"}],"country":"United 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Procedures for undertaking and analyzing recovery from aquifer tests of 13 to 132 seconds (described in reports cited herein) were applied to 51 domestic drilled wells that penetrated bedrock outside major valleys in the part of the Appalachian Plateau of New York drained by the Susquehanna River. Transmissivities calculated from these tests ranged over three orders of magnitude in both the Catskill-Cattaraugus lithofacies (shales, mudstones, siltstones, medium to coarse sandstones, pebbly sandstones) and the Chemung-Hamilton lithofacies (shales, mudstones, siltstones, fine to medium sandstones). Median transmissivity values were 0.000425 foot squared per second (36.7 feet squared per day) in the Catskill-Cattaraugus lithofacies and 0.00055 foot squared per second (47.5 feet squared per day) in the Chemung-Hamilton lithofacies. The distributions of transmissivity values within the two lithofacies were likewise similar. The range and median values of transmissivity were also nearly the same on lower and midlevel hillsides and were only slightly greater on a few upper hillsides. Transmissivities estimated from such easily arranged and analyzed tests may be appropriate for estimating groundwater flux under the small gradients that prevail under natural conditions, but not under larger drawdowns and steeper gradients near clusters of domestic wells. Four of the 51 wells tested were also pumped for 10 to 32 minutes; analysis by the Theis recovery method yielded transmissivities consistent with the brief tests for 2 wells, but 7 to 9 times smaller for 2 wells.
Transmissivity values estimated by the PICKINGmodel were not significantly different from values estimated by an automated application of the Picking method (PPC-Recovery) at a probability of 95 percent. Transmissivities calculated by either method from data for time intervals of 120 seconds or less may be of limited practical value because they apply only to a small volume of bedrock close to the pumped well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205087","usgsCitation":"Randall, A.D., and Mills, A.C., 2020, Transmissivity estimated from brief aquifer tests of domestic wells and compared with bedrock lithofacies and position on hillsides in the Appalachian Plateau of New York: U.S. Geological Survey Scientific Investigations Report 2020–5087, 21 p., https://doi.org/10.3133/sir20205087.","productDescription":"Report: iv, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091051","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":378953,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KLZD9M","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Field Data From Brief Aquifer Tests of Domestic Wells Penetrating Bedrock in the Appalachian Plateau of New York and Best Fits to Theoretical Curves of Aquifer Properties"},{"id":378951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5087/coverthb.jpg"},{"id":378952,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5087/sir20205087.pdf","text":"Report","size":"1.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5087"}],"country":"United States","state":"New York","otherGeospatial":"Appalachian Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.2391357421875,\n 42.0125705565935\n ],\n [\n -74.37744140625,\n 42.0125705565935\n ],\n [\n -74.37744140625,\n 43.03677585761058\n ],\n [\n -78.2391357421875,\n 43.03677585761058\n ],\n [\n -78.2391357421875,\n 42.0125705565935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
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