An extensive fauna of at least 77 taxa is reported from the basal Wilson Grove Formation in a small quarry just north of the town of Bloomfield, Sonoma County, California. The fauna represents intertidal to shallow subtidal water depths and water temperatures interpreted from the fauna, consistent with the latitude of the fossil locality (37° north) during the late Miocene. The fauna from Bloomfield Quarry is unusually large and diverse from such a small area. It consists of thousands of specimens of 4 brachiopod, 42 mollusk (28 bivalves and 14 gastropods), 6 arthropod (1 crab, 1 shrimp, and 4 barnacles), and 25 vertebrate (3 sharks, 1 ray, 8 bony fishes, 9 marine mammals, and 4 birds) taxa. Unusual in the fauna is the abundant and diverse brachiopod fauna, the diverse barnacle fauna, which was described previously, and the extensive and diverse vertebrate fauna. Most significant among the vertebrates is the walrus fauna, which is the most diverse assemblage of walrus yet reported worldwide from a single locality.
A single strontium (Sr) isotope age determination of about 8 million years (megaannum, Ma) from a pectinid mollusk is consistent with a new age determination of the overlying, informally named Roblar tuff as described by Sarna-Wojcicki in 1992 (6.203±0.011 Ma) and previously reported age determinations (recalculated here) from basalt (9.27±0.06 Ma) underlying these deposits. The Roblar tuff at Bloomfield Quarry can be correlated with other sites, including the Delgada Fan offshore northern California and the Coalinga anticline in California’s Central Valley. These age determinations conform with the “Jacalitos” California provincial molluscan stage age, the Hemphillian North American Land Mammal age determined from the fossils, and is part of the International Tortonian Stage of the Miocene.
Geology, Minerals, Energy and Geophysics Science Center
U.S. Geological Survey
345 Middlefield Road
Mail Stop 973
Menlo Park, CA 94025
Background: Alternative protein sources to fishmeal in fish feeds are needed.
Objectives: Evaluate rearing performance of adult rainbow trout (Oncorhynchus mykiss) (initial weight 139.0 ±1.5 g, length 232.9 ± 0.8 mm, mean ± SE) fed one of the two isonitrogenous and isocaloric diets (46% protein, 16% lipid) and reared at one of the two levels of exercise (water velocities of either 3.6 cm/s or 33.2 cm/s).
Methods: Protein in the control diet was based on fishmeal. In the experimental diet, bioprocessed soybean meal replaced approximately 60% of the fishmeal. Fish were fed by hand once-per-day to near satiation, and the food was increased daily. The experiment lasted 90-days.
Results: There were no significant differences in gain, percent gain, or specific growth rate between the dietary treatments. However, the amount of food fed and feed conversion ratio was significantly lower in the 60% bioprocessed soybean meal diet. Intestinal morphology, relative fin length, splenosomatic index, hepatosomatic index, and viscerosomatic index were not significantly different in the trout fed either diet. Fish reared at 3.6 cm/s had a significantly lower feed conversion ratio (1.02 ± 0.02) than fish reared at 33.2 cm/s (1.13 ± 0.02). However, there were no significant differences in gain, percent gain, specific growth rate, or percentage mortality in fish reared with or without exercise. No significant interactions were observed between diet and exercise (higher water velocity).
Conclusion:Based on these results, at least 60% of the fishmeal in adult rainbow trout diets can be replaced by bioprocessed soybean meal, even if higher water velocities are used to exercise the fish.
","language":"English","publisher":"Bentham Open","doi":"10.2174/1874196701907010001","usgsCitation":"Jill M. Voorhees, Barnes, M.E., Chipps, S.R., and Brown, M.L., 2019, Effects of exercise and bioprocessed soybean meal diets during rainbow trout rearing: The Open Biology Journal, v. 7, p. 1-13, https://doi.org/10.2174/1874196701907010001.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-097773","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467659,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2174/1874196701907010001","text":"Publisher Index Page"},{"id":395660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jill M. Voorhees","contributorId":275091,"corporation":false,"usgs":false,"family":"Jill M. Voorhees","affiliations":[{"id":37104,"text":"South Dakota Department of Game, Fish and Parks","active":true,"usgs":false}],"preferred":false,"id":833631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, Michael E.","contributorId":275092,"corporation":false,"usgs":false,"family":"Barnes","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":56698,"text":"South Dakota Department of Game, Fish, and Parks","active":true,"usgs":false}],"preferred":false,"id":833632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Michael L.","contributorId":275093,"corporation":false,"usgs":false,"family":"Brown","given":"Michael","email":"","middleInitial":"L.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":833634,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202350,"text":"sir20185105 - 2019 - Hydrologic Influences on Water Levels at Three Oaks Recreation Area, Crystal Lake, Illinois, April 14 through September 27, 2016","interactions":[],"lastModifiedDate":"2019-04-30T12:50:01","indexId":"sir20185105","displayToPublicDate":"2019-04-29T15:30:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5105","displayTitle":"Hydrologic Influences on Water Levels at Three Oaks Recreation Area, Crystal Lake, Illinois, April 14 through September 27, 2016","title":"Hydrologic Influences on Water Levels at Three Oaks Recreation Area, Crystal Lake, Illinois, April 14 through September 27, 2016","docAbstract":"Hydrologic influences on water levels were investigated at Three Oaks Recreation Area (TORA), a former sand-and-gravel quarry converted into recreational lakes in Crystal Lake, Illinois. From 2009 to 2015, average water levels in the lakes declined nearly 4 feet. It was not clear if these declines were related to variations in weather (precipitation or evaporation) or other hydrologic influences such as municipal supply pumping or nearby quarry operations. Data were collected using three approaches to determine the possibility of such hydrologic influences. First, water levels were collected at 15 minute intervals at three wells equipped with pressure transducers from April 14 through September 27, 2016. The continuous data allowed assessment of lake and well water level responses to precipitation, pumping influences, and quarry operations. Second, a single-day synoptic water-level survey was completed to create a water table map to determine groundwater flow directions. Third, single-well aquifer tests (slug tests) were completed on the three data-collection wells to estimate the aquifer’s horizontal hydraulic conductivity. Collectively, these data were used to determine the velocity and volume of water entering and exiting TORA.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20185105","collaboration":"Prepared in cooperation with the City of Crystal Lake, Illinois","usgsCitation":"Gahala, A.M., 2019, Hydrologic influences on water levels at Three Oaks Recreation Area, Crystal Lake, Illinois, April 14 through September 27, 2016: U.S. Geological Survey Scientific Investigations Report 2019–5105, 22 p., https://doi.org/10.3133/sir20185105.","productDescription":"Report: vii, 22 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-082111","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":437479,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SA4LZZ","text":"USGS data release","linkHelpText":"Water level test data for groundwater monitoring wells near Three Oaks Recreational Area, Crystal Lake, Illinois"},{"id":362987,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5b801758e4b05f6e32194c4b","text":"USGS data release","description":"USGS data release","linkHelpText":"Water Level Test Data for Groundwater Monitoring Wells Near Three Oaks Recreational Area, Crystal Lake, Illinois"},{"id":362986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5105/sir20185105.pdf","text":"Report","size":"3.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5105"},{"id":362985,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5105/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Crystal Lake","otherGeospatial":"Three Oaks Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.32527160644531,\n 42.165184775416826\n ],\n [\n -88.23446273803711,\n 42.165184775416826\n ],\n [\n -88.23446273803711,\n 42.226610675467626\n ],\n [\n -88.32527160644531,\n 42.226610675467626\n ],\n [\n -88.32527160644531,\n 42.165184775416826\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, Wisconsin 53562
During the last 50 years, construction of dams in the western United States declined. This is partly because of increasing recognition of diverse and unintended social-ecological consequences of dams. Today, resource managers are recognizing the wide array of tradeoffs and are including a more diverse group of stakeholders in decision making for individual dams. Yet decisions at the regional scale maintain a focus on a limited number of resources and objectives, leading to inefficient and inequitable outcomes. Social-ecological changes compounded by climate change challenge this management paradigm. Increasing water demands for humans and the environment and renewed interest in hydropower present opportunities for operations that include climate change mitigation and adaptation strategies while considering tradeoffs and equitable responses at the regional scale.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cosust.2019.04.002","usgsCitation":"Bair, L.S., Yackulic, C.B., Schmidt, J.C., Perry, D.M., Kirchhoff, C.J., Chief, K., and Colombi, B.J., 2019, Incorporating social-ecological considerations into basin-wide responses to climate change in the Colorado River Basin: Current Opinion in Environmental Sustainability, v. 37, p. 14-19, https://doi.org/10.1016/j.cosust.2019.04.002.","productDescription":"6 p.","startPage":"14","endPage":"19","ipdsId":"IP-106843","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":467663,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cosust.2019.04.002","text":"Publisher Index Page"},{"id":364187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado River Basin","volume":"37","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bair, Lucas S. 0000-0002-9911-3624 lbair@usgs.gov","orcid":"https://orcid.org/0000-0002-9911-3624","contributorId":5270,"corporation":false,"usgs":true,"family":"Bair","given":"Lucas","email":"lbair@usgs.gov","middleInitial":"S.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":763325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":763326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmidt, John C.","contributorId":207751,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37627,"text":"Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":763327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Denielle M.","contributorId":215885,"corporation":false,"usgs":false,"family":"Perry","given":"Denielle","email":"","middleInitial":"M.","affiliations":[{"id":39324,"text":"School of Earth and Sustainability, Northern Arizona University, Flagstaff, Arizona 86011, USA","active":true,"usgs":false}],"preferred":false,"id":763328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kirchhoff, Christine J.","contributorId":215886,"corporation":false,"usgs":false,"family":"Kirchhoff","given":"Christine","email":"","middleInitial":"J.","affiliations":[{"id":39325,"text":"Connecticut Institute for Resilience and Climate Adaptation, Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, USA","active":true,"usgs":false}],"preferred":false,"id":763329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chief, Karletta","contributorId":147055,"corporation":false,"usgs":false,"family":"Chief","given":"Karletta","email":"","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":763330,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Colombi, Benedict J.","contributorId":215887,"corporation":false,"usgs":false,"family":"Colombi","given":"Benedict","email":"","middleInitial":"J.","affiliations":[{"id":39326,"text":"School of Anthropology, University of Arizona, Tucson, Arizona 85721, USA","active":true,"usgs":false}],"preferred":false,"id":763331,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209634,"text":"70209634 - 2019 - Denitrification in the river network of a mixed land use watershed: Unpacking the complexities","interactions":[],"lastModifiedDate":"2020-05-04T17:25:53.684335","indexId":"70209634","displayToPublicDate":"2019-04-27T14:03:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Denitrification in the river network of a mixed land use watershed: Unpacking the complexities","docAbstract":"River networks have the potential to permanently remove nitrogen through denitrification. Few studies have measured denitrification rates within an entire river network or assessed how land use affect rates at larger spatial scales. We sampled 108 sites throughout the network of the Fox River watershed, Wisconsin, to determine if land use influence sediment denitrification rates, and to identify zones of elevated sediment denitrification rates (hot spots) within the river network. Partial least squares regression models identified variables from four levels of organization (river bed sediment, water column, riparian zone, and watershed) that best predicted denitrification rates throughout the river network. Nitrate availability was the most important predictor of denitrification rates, while land cover was not always a good predictor of local-scale nitrate concentrations. Thus, land cover and denitrification rate were not strongly related across the Fox River watershed. A direct relationship between denitrification rate and watershed land cover occurred only in the Wolf River sub-watershed, the least anthropogenically disturbed of the sub-watersheds. Denitrification hot spots were located throughout the river network, regardless of watershed land use, with hot spot location being determined primarily by nitrate availability. In the Fox River watershed, when nitrate was abundant, river bed sediment character influenced denitrification rate, with higher denitrification rates at sites with fine, organic sediments. These findings suggest that denitrification occurring throughout an entire river network, from headwater streams to larger rivers, can help reduce nitrogen loads to downstream water bodies.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-019-00565-6","usgsCitation":"Kreiling, R., Richardson, W.B., Bartsch, L., Thoms, M.C., and Christensen, V.G., 2019, Denitrification in the river network of a mixed land use watershed: Unpacking the complexities: Biogeochemistry, v. 143, p. 327-346, https://doi.org/10.1007/s10533-019-00565-6.","productDescription":"20 p.","startPage":"327","endPage":"346","numberOfPages":"20","ipdsId":"IP-096829","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437480,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93RTNVY","text":"USGS data release","linkHelpText":"Great Lakes Restoration Initiative Project 49 Fox River Basin 2016 and 2017 Data"},{"id":374060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fox River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.8736572265625,\n 43.45690646829029\n ],\n [\n -87.6873779296875,\n 43.45690646829029\n ],\n [\n -87.6873779296875,\n 45.79816953017265\n ],\n [\n -89.8736572265625,\n 45.79816953017265\n ],\n [\n -89.8736572265625,\n 43.45690646829029\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"143","noUsgsAuthors":false,"publicationDate":"2019-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Kreiling, Rebecca 0000-0002-9295-4156 rkreiling@usgs.gov","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":147679,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","email":"rkreiling@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":787291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":787292,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":787293,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":787294,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christensen, Victoria G. 0000-0003-4166-7461 vglenn@usgs.gov","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":2354,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","email":"vglenn@usgs.gov","middleInitial":"G.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":787295,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203217,"text":"70203217 - 2019 - Global virtual water trade and the hydrological cycle: Patterns, drivers, and socio-environmental impacts","interactions":[],"lastModifiedDate":"2019-04-29T13:52:51","indexId":"70203217","displayToPublicDate":"2019-04-26T13:52:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Global virtual water trade and the hydrological cycle: Patterns, drivers, and socio-environmental impacts","docAbstract":"The increasing global demand for farmland products is placing unprecedented pressure on the global agricultural system and its water resources. Many regions of the world, that are affected by a chronic water scarcity relative to their population, strongly depend on the import of agricultural commodities and associated embodied (or virtual) water. The globalization of water through virtual water trade is leading to a displacement of water use and a disconnection between human populations and the water resources they rely on. Despite the recognized importance of these phenomena in reshaping the patterns of water dependence through teleconnections between consumers and producers, their effect on global and regional water resources has just started to be quantified. This review investigates the global spatiotemporal dynamics, drivers, and impacts of virtual water trade through an integrated analysis of surface water, groundwater, and root-zone soil moisture consumption for agricultural production; it evaluates how virtual water flows compare to the major “physical water fluxes” in the Earth System; and provides a new reconceptualization of the hydrologic cycle to account also for the role of water redistribution by the hidden ‘virtual water cycle’.","language":"English","publisher":"IOP Scence","doi":"10.1088/1748-9326/ab05f4","usgsCitation":"D’Odorico, P., Carr, J., Dalin, C., Dell’Angelo, J., Konar, M., Laio, F., Ridolfi, L., Rosa, L., Suweis, S., Tamea, S., and Tuninetti, M., 2019, Global virtual water trade and the hydrological cycle: Patterns, drivers, and socio-environmental impacts: Environmental Research Letters, v. 14, no. 5, 34 p., https://doi.org/10.1088/1748-9326/ab05f4.","productDescription":"34 p.","ipdsId":"IP-101097","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467667,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab05f4","text":"Publisher Index Page"},{"id":363319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Odorico, Paolo","contributorId":209957,"corporation":false,"usgs":false,"family":"D’Odorico","given":"Paolo","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":761706,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":761705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dalin, Carole","contributorId":215134,"corporation":false,"usgs":false,"family":"Dalin","given":"Carole","email":"","affiliations":[{"id":39184,"text":"Institute for Sustainable Resources, University College, London, UK","active":true,"usgs":false}],"preferred":false,"id":761707,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dell’Angelo, Jampel","contributorId":215135,"corporation":false,"usgs":false,"family":"Dell’Angelo","given":"Jampel","email":"","affiliations":[{"id":39185,"text":"Department of Environmental Policy Analysis, Institute for Environmental Studies, Vrije Universiteit Amsterdam, NL","active":true,"usgs":false}],"preferred":false,"id":761708,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Konar, Megan","contributorId":215136,"corporation":false,"usgs":false,"family":"Konar","given":"Megan","email":"","affiliations":[{"id":39186,"text":"Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":761709,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Laio, Francesco","contributorId":215137,"corporation":false,"usgs":false,"family":"Laio","given":"Francesco","email":"","affiliations":[{"id":39187,"text":"Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, IT","active":true,"usgs":false}],"preferred":false,"id":761710,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ridolfi, Luca","contributorId":124519,"corporation":false,"usgs":false,"family":"Ridolfi","given":"Luca","email":"","affiliations":[{"id":5039,"text":"Department of Environment, Land, and Infrastructure Engineering, Politecnico di Torino, Torino, Italy","active":true,"usgs":false}],"preferred":false,"id":761711,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rosa, Lorenzo","contributorId":209959,"corporation":false,"usgs":false,"family":"Rosa","given":"Lorenzo","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":761712,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Suweis, Samir","contributorId":209965,"corporation":false,"usgs":false,"family":"Suweis","given":"Samir","email":"","affiliations":[{"id":38039,"text":"University of Padova","active":true,"usgs":false}],"preferred":false,"id":761713,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tamea, Stefania","contributorId":215138,"corporation":false,"usgs":false,"family":"Tamea","given":"Stefania","email":"","affiliations":[{"id":39187,"text":"Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, IT","active":true,"usgs":false}],"preferred":false,"id":761714,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Tuninetti, Marta","contributorId":215139,"corporation":false,"usgs":false,"family":"Tuninetti","given":"Marta","email":"","affiliations":[{"id":39187,"text":"Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, IT","active":true,"usgs":false}],"preferred":false,"id":761715,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70202851,"text":"sir20195023 - 2019 - Erosion monitoring along selected bank locations of the Coosa River in Alabama using terrestrial light detection and ranging (T–lidar) technology, 2014–17","interactions":[],"lastModifiedDate":"2019-04-26T15:01:44","indexId":"sir20195023","displayToPublicDate":"2019-04-26T12:37:33","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5023","displayTitle":"Erosion Monitoring Along Selected Bank Locations of the Coosa River in Alabama Using Terrestrial Light Detection and Ranging (T–Lidar) Technology, 2014–17","title":"Erosion monitoring along selected bank locations of the Coosa River in Alabama using terrestrial light detection and ranging (T–lidar) technology, 2014–17","docAbstract":"The Alabama Power Company operates a series of dams on the Coosa River in east central Alabama. Seven dams impound the river to form six reservoirs: Weiss Lake, H Neely Henry Lake, Logan Martin Lake, Lay Lake, Lake Mitchell, and Lake Jordan. Streamflow below these reservoirs is primarily controlled by power generation at the dams, and there is ongoing concern about the stability of selected stream banks downstream from the dams. During relicensing in the early 2000s, the Alabama Power Company and stakeholders identified particular areas of concern to monitor and document the extent of erosion. The U.S. Geological Survey, in cooperation with the Alabama Power Company, conducted a 3-year monitoring program, from 2014 to 2017, of the geomorphic conditions of six selected reaches along the Coosa River. The six reaches included two downstream from H Neely Henry Dam near Gadsden, two downstream from Logan Martin Dam near Vincent, and two downstream from Walter Bouldin Dam near Wetumpka, Alabama. The geomorphic monitoring was conducted using boat- and tripod-mounted terrestrial light detection and ranging technology. Site LM–108, an island in the Coosa River downstream from Logan Martin Dam, exhibited the greatest amount of normalized erosion, 2.05 cubic meters per square meter of area, likely because this site experiences head-on flow from the river. Bank retreat at the upstream end of the island (LM–108) was estimated at 2.9 meters for the study period. The remaining five reaches were exposed to shear flow from the river; the greatest amount of normalized erosion, 0.467 cubic meter per square meter of area, was exhibited by site WB–106 on the right bank downstream from Walter Bouldin Dam. Results of the comparisons of terrestrial light detection and ranging scans indicated that intervals between scans that exhibited the greatest amounts of erosion generally corresponded to periods of above-median flow, and that intervals between scans that exhibited the least amounts of erosion, or deposition, generally corresponded to periods of below-median flow. Relatively smaller surface areas could be surveyed at some sites because inundation or dense vegetation obscured parts of the banks, suggesting that, in future investigations, it may be preferable to conduct scans during periods of leaf-off and low flow to avoid bias introduced by parts of the banks of interest being inundated or obscured by vegetation.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195023","collaboration":"Prepared in cooperation with the Alabama Power Company","usgsCitation":"Huizinga, R.J., and Wagner, D.M., 2019, Erosion monitoring along selected bank locations of the Coosa River in Alabama using terrestrial light detection and ranging (T–lidar) technology, 2014–17: U.S. Geological Survey Scientific Investigations Report 2019–5023, 28 p., https://doi.org/10.3133/sir20195023.","productDescription":"Report: vii, 28 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-102291","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":363234,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5023/sir20195023.pdf","text":"Report","size":"7.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5023"},{"id":363233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5023/coverthb.jpg"},{"id":363235,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GF0SS8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Erosion Monitoring along the Coosa River, Alabama, using Terrestrial Light Detection and Ranging (T-LiDAR) Technology, 2014–2017"}],"country":"United States","state":"Alabama","otherGeospatial":"Coosa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.75079345703125,\n 32.45415593941475\n ],\n [\n -85.3857421875,\n 32.45415593941475\n ],\n [\n -85.3857421875,\n 34.309412579370544\n ],\n [\n -86.75079345703125,\n 34.309412579370544\n ],\n [\n -86.75079345703125,\n 32.45415593941475\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
A case study of groundwater contamination is a detailed study of a single site contaminated with a chemical or mixture that is known to be a problem at many sites. The goal of case studies is to provide insights into the physical, chemical, and biological processes controlling migration, natural attenuation, or remediation of common groundwater contaminants. Ideally, processes occurring at a case study site are representative of other sites so that knowledge gained from these intensive studies can be applied at thousands of sites where fewer data are available. Several characteristics of case studies contribute to their value. First, they may have tens to hundreds of monitoring wells, compared to fewer than ten wells at some contaminated sites. Second, some case studies continue for many years or even decades, providing insights into temporal progression of slow processes. Third, analytical methods prohibitively expensive for routine use or under development may be tested at case study sites. Finally, the ongoing characterization typical of case study sites builds a foundation of knowledge that facilitates sophisticated experimental design and testing of new methods. This article is divided into sections based on the contaminant type because the chemical and biological processes required for remediation vary for each contaminant. Most importantly, some contaminants can be biodegraded whereas metals and radionuclides cannot be destroyed but can be immobilized or rendered less toxic. The emphasis is on case studies of natural processes that control the fate and transport of contaminants in groundwater rather than on active remediation methods. The principles learned from these studies may form the basis for design of remedial strategies. The organic contaminants are divided into: petroleum hydrocarbons, fuel oxygenates, coal tar and wastes from manufactured gas plants, and chlorinated solvents. The inorganic contaminants covered are metals and radionuclides, arsenic, and nitrate. Case studies of mixed waste plumes from landfills are also described. Experimental sites where contaminants have been introduced into an aquifer as an emplaced source or a controlled release may not meet the above definition of case studies, but some are included because the overall goal is to impart lessons learned from detailed field studies. It is impossible to cover all case studies in this short format. Conversely, focusing on one or two does not convey the breadth of research results in entire range of case studies. Instead, the strategy is to describe the evolution of knowledge for each contaminant class while providing citations of relevant case studies. Much of the progress in understanding of the fate of contaminants in groundwater is based on laboratory studies; thus whenever possible, papers that included both field and laboratory results have been included among the citations. Two topics of growing importance have not been covered. These are the fate of pharmaceuticals in groundwater and discharge of contaminant plumes to surface water. These topics merit coverage in the future as knowledge grows and case studies increase in number.
During 2015–17, the U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Forest Service (Forest Service), carried out a study to characterize the hydrology and water chemistry in two study areas within the Daniel Boone National Forest. One study area was within the Rock Creek drainage and the other study area included the Wildcat and Addison Branch drainages. Both study areas historically were mined for coal prior to the Surface Mining Control and Reclamation Act of 1977 and contain abandoned coal mine sites that have since been the focus of remediation efforts. Synoptic surveys of streamflow and water-quality properties (water temperature, pH, specific conductance, and dissolved oxygen) of Rock Creek were done during November 2015 and May 2016, and surveys of Wildcat and Addison Branches were done during June 2016 and May 2017. Streamflow measurements were used to quantify contributions from tributaries and to compute streamflow gain and loss in designated reaches. Discrete measurements of water temperature, pH, specific conductance, and dissolved oxygen were used to evaluate conditions during a short timeframe and for comparison between study areas. Study designs for the two study areas differed because there was an operating streamgage on Rock Creek near Yamacraw, Kentucky (station number 03410590) where streamflow and water-quality properties (water temperature, specific conductance, pH, dissolved oxygen, and turbidity) were monitored continuously, while Addison and Wildcat Branches were ungaged. Several hydrograph separation methods were used to estimate base flow and runoff at the Rock Creek gage. These data will be used by the Forest Service to evaluate the current (2018) conditions and plan remediation efforts.
The water quality at Rock Creek was less affected by acid mine drainage (AMD) than the Wildcat or Addison Branches. Appreciable losing reaches, where water flowed underground, were identified in both study areas. All losing reaches coincided with karst topography. Streamflow increased in areas with openings to underground mine tunnels, known as portals.
Six hydrograph separation methods (Base-flow index [BFI; standard and modified], HYSEP [fixed interval, sliding interval, and local minimum], and PART) were applied to daily mean streamflow collected from August 2015 to August 2017 at station number 03410590. The hydrograph separation methods partition total streamflow into base flow and streamflow that originated from surface runoff. Base flow typically reacts slowly to precipitation infiltration and is largely sustained by groundwater discharge. The estimated daily base flow and runoff made with the different separation methods are not highly different. On average, base flow accounted for more total streamflow than surface runoff during the study period, irrespective of method.
Water temperature, pH, dissolved oxygen, specific conductance, and turbidity values were measured from July 2016 through July 2017 with a continuous monitor installed at station number 03410590. Nearly neutral pH values that ranged from 6.8 to 7.9 standard units likely limited metal solubility in the surface water. The continuous specific conductance values ranged between 30 and 259 microsiemens per centimeter at 25 degrees Celsius. The previous remediation efforts are likely continuing to improve the effect of AMD in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195006","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Forest Service ","usgsCitation":"Cherry, M.A., 2019, Streamflow gain and loss, hydrograph separation, and water quality of abandoned mine lands in the Daniel Boone National Forest, eastern Kentucky, 2015–17: U.S. Geological Survey Scientific Investigations Report 2019–5006, 36 p., https://doi.org/10.3133/sir20195006.","productDescription":"Report: viii, 36 p.;Data Release","numberOfPages":"49","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091989","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":363195,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5006/sir20195006.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5006"},{"id":363196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX78D9","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow and water-quality data for selected streams in the Daniel Boone National Forest, eastern Kentucky, 2015–17"},{"id":363194,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5006/coverthb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Daniel Boone National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.67437744140625,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 36.63536611993544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
We assess monthly temperature and precipitation data produced by four statistically based techniques that were used to downscale general circulation models (GCMs) in the Climate Model Intercomparison Program Phase 5 (CMIP5) (Taylor et al., 2012). We drive a simple water-balance model with the downscaled data to demonstrate the effect of the methods on the cold season hydrology of three, snow dominated regions in the western U.S. Independent of substantial variation among the GCM simulations over the regions (maximum range of ~3.5 °C and 50% change in precipitation), the four methods produce disparate high resolution representations of the magnitude and spatial patterns of future temperature and precipitation simulated by the models that range for up to ~3 °C and 30% change in precipitation that propagate into the hydrologic simulations. Temperature-dependent snowfall, accumulation, and melt in the model are sensitive to how atmospheric lapse rates are applied in the gridded observations that are used to remove the bias in raw GCM temperatures. By the end of the century the same downscaling method (Bias Corrected Spatial Disaggregation) yields a loss of cold-season snowpack of 34% over the Greater Yellowstone Area under a constant lapse rate ( 6.5°C km-1), whereas spatially variable lapse rates nearly double the loss to 66%, highlighting the roll of both lapse rates and high elevation stations in the bias correction dataset. The two newest downscaling methods (Multivariate Adaptive Constructed Analogs and Localized Constructed Analogs) preserve the magnitude of change simulated GCMs better than the other methods and the produce comparable hydrologic projections. Because the downscaled data from the methods vary spatially and by GCM, the downscaled data should be evaluated carefully as part of the process of using downscaled climate products to drive hydrological models over the area of interest.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023458","usgsCitation":"Alder, J.R., and Hostetler, S.W., 2019, The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data: Water Resources Research, v. 55, no. 3, p. 2279-2300, https://doi.org/10.1029/2018WR023458.","productDescription":"22 p.","startPage":"2279","endPage":"2300","ipdsId":"IP-097120","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437482,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9EB1C","text":"USGS data release","linkHelpText":"Data Release for The dependence of hydroclimate projections in snow-dominated regions of the western U.S. on the choice of statistically downscaled climate data"},{"id":363240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana,Nevada, New Mexico, Oregon, Washington, Wyoming","otherGeospatial":"Columbia River Basin, Greater Yellowstone Area, Sierra Nevada, Upper Colorado Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.33300781249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 34.66935854524543\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":761576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":761577,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203386,"text":"70203386 - 2019 - Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization","interactions":[],"lastModifiedDate":"2019-06-18T12:02:06","indexId":"70203386","displayToPublicDate":"2019-04-25T09:33:18","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization","docAbstract":"Chronic exposure to geogenic arsenic via drinking water is a worldwide health concern. However, effects of well installation and operation on arsenic concentrations and mobilization are not well understood. This knowledge gap impacts both reliable detection of arsenic in drinking water and effective public health recommendations to reduce exposure to arsenic. This study examines changes in arsenic and redox geochemistry over one year following installation of 254 new domestic water wells in three regions of the north-central USA that commonly have elevated arsenic concentrations. Our regions' geologic settings share some important characteristics with other high-arsenic aquifers: igneous bedrock aquifers; or late Pleistocene-age glacial sand and gravel aquifers interbedded with aquitards. Over the study, arsenic concentrations increased by 16% or more in 25% of wells in glacial aquifer regions, and the redox conditions changed towards more reducing. In wells in the bedrock region, there was no significant change in arsenic concentrations, and redox conditions changed towards more oxidizing. Our findings illustrate the importance of understanding short- to moderate-term impacts of well installation and operation on arsenic and aqueous chemistry, as it relates to human exposure. Our study informs water quality sampling requirements, which currently do not consider the implications sampling timing with respect to well installation. Evaluating arsenic concentrations in samples from new wells in the context of general regional pH and redox conditions can provide information regarding the degree of disequilibrium created by well drilling. Our analysis approach may be transferable and scalable to similar aquifer settings across the globe.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.04.362","usgsCitation":"Erickson, M., Malenda, H.F., Berquist, E.C., and Ayotte, J.D., 2019, Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization: Science of the Total Environment, v. 678, p. 681-691, https://doi.org/10.1016/j.scitotenv.2019.04.362.","productDescription":"11 p.","startPage":"681","endPage":"691","ipdsId":"IP-090484","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467673,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.04.362","text":"Publisher Index Page"},{"id":363660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"678","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":762442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malenda, Helen F. 0000-0003-4143-6460","orcid":"https://orcid.org/0000-0003-4143-6460","contributorId":211885,"corporation":false,"usgs":false,"family":"Malenda","given":"Helen","email":"","middleInitial":"F.","affiliations":[{"id":38341,"text":"Colorodo School of Mines","active":true,"usgs":false}],"preferred":true,"id":762443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berquist, Emily C.","contributorId":202174,"corporation":false,"usgs":false,"family":"Berquist","given":"Emily","email":"","middleInitial":"C.","affiliations":[{"id":36357,"text":"Minnesota Department of Health","active":true,"usgs":false}],"preferred":false,"id":762444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 jayotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1892-2738","contributorId":149619,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph","email":"jayotte@usgs.gov","middleInitial":"D.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":762445,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203185,"text":"70203185 - 2019 - Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”","interactions":[],"lastModifiedDate":"2019-04-25T06:29:42","indexId":"70203185","displayToPublicDate":"2019-04-25T06:23:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”","docAbstract":"Over the last decade, studies at the Shale Hills Critical Zone Observatory (Shale Hills) have greatly expanded knowledge of weathering in previously understudied, shale-mantled terrains, as well as Earth's Critical Zone as a whole. Among the many discoveries made was the importance of redistribution and losses of micron-sized particles during development of shale-derived soils. A geochemical fingerprint of this process for Al and Fe was illustrated quantitatively by Jin et al. (2010). Subsequent papers, too numerous to list in a Comment, built upon this new recognition by evaluating the spatial and temporal aspects element mobilization. Recently, Kim et al. (2018) examined the composition of suspended, generally micron-sized particles in the Shale Hills stream, along with the dissolved load, across seasons and ranges of discharge.
One prominent conclusion from Kim et al. (2018) is that Zr is essentially immobile at Shale Hills. Such a broad conclusion is in direct contradiction with one from Bern and Yesavage (2018) that Zr has been mobilized from soils at Shale Hills, and the losses relative to soil parent material are significant (median 41%). The point is important, because assuming Zr immobility is necessary to index gains and losses of other elements using the open-chemical-system transport function (τ). Both papers draw upon patterns and calculations using elemental concentration data from Shale Hills and attempt to construct conceptual frameworks to explain the results. Here, the argument is made that the understanding of substantial Zr mobility from soils at Shale Hills described by Bern and Yesavage (2018) is more accurate. Additionally, issues with adaptations of the standard τ equations used in Kim et al. (2018) and some previous papers are also addressed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.02.014","usgsCitation":"Bern, C.R., and Yesavage, T., 2019, Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”: Earth and Planetary Science Letters, v. 514, p. 166-168, https://doi.org/10.1016/j.epsl.2019.02.014.","productDescription":"3 p.","startPage":"166","endPage":"168","ipdsId":"IP-102182","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":363221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Shale Hills Critical Zone Observatory ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.17596435546875,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.4323142901375\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"514","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bern, Carleton R. 0000-0002-8980-1781 cbern@usgs.gov","orcid":"https://orcid.org/0000-0002-8980-1781","contributorId":201152,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton","email":"cbern@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yesavage, Tiffany 0000-0001-9433-763X","orcid":"https://orcid.org/0000-0001-9433-763X","contributorId":215057,"corporation":false,"usgs":false,"family":"Yesavage","given":"Tiffany","email":"","affiliations":[{"id":39167,"text":"USGS Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":false}],"preferred":false,"id":761538,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216034,"text":"70216034 - 2019 - Estimation bias in water-quality constituent concentrations and fluxes: A synthesis for Chesapeake Bay rivers and streams","interactions":[],"lastModifiedDate":"2020-11-04T00:26:49.503344","indexId":"70216034","displayToPublicDate":"2019-04-24T18:23:50","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Estimation bias in water-quality constituent concentrations and fluxes: A synthesis for Chesapeake Bay rivers and streams","docAbstract":"Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias. This work was aimed to (1) synthesize and compare WRTDS estimation bias for constituent concentrations and fluxes for rivers and streams in the Chesapeake Bay watershed (including headwater sites) and (2) identify controlling factors from five broad categories (watershed size, sampling practice, concentration and discharge conditions, land use, and geology). Five major constituents were considered, namely, suspended sediment (SS), total phosphorus (TP), total nitrogen (TN), orthophosphate (PO4), and nitrate-plus-nitrite (NOx). For both concentration and flux, estimation bias follows the general order of SS > TP > PO4 > TN ≈ NOx. Median TN and NOx bias statistics were near zero, with an equal distribution of small positive and negative bias. TP, PO4, and SS each showed a median positive bias across sites of <18% for flux and <7% for concentration. Particulate constituents, especially SS, tend to have larger bias at sites with smaller sampling frequencies, shorter sampling record lengths, and smaller watershed sizes. Results of multivariate models showed that both flux and concentration biases are most affected by concentration and discharge variabilities and the length of concentration record. In comparison, flux bias of particulate constituents is more affected by flow variability, whereas flux bias of dissolved constituents is more affected by concentration variability. Moreover, analysis using classification and regression trees provided additional information on how the factors affected flux bias: when all site-constituent combinations are considered, large flux biases are more likely associated with sites that have large concentration and discharge variabilities, small lengths of concentration record, and small sampling frequencies. These results may be useful for identifying sites with large biases, modifying monitoring practice at existing sites to reduce those biases, and choosing new monitoring locations in the Chesapeake watershed and beyond.
The Little Colorado River in Arizona, U.S.A. has undergone substantial geomorphic change since the early 1900s. We analyzed hydrologic and geomorphic data at different spatial and temporal scales to determine the type, magnitude, and rate of geomorphic change that has occurred since the early 20th century. Since the 1920s, there have been 4 alternating periods of high and low total-annual flow. Peak-flow magnitude, however, has progressively declined. In some reaches, the channel has narrowed between 72 and 88% since the 1930s. Increases in sinuosity in wide alluvial valleys have resulted in reductions in channel slope by ~21 to 32%; channel bed aggradation up to 1.4 m has also occurred in some reaches. Newly developed floodplains have been colonized by dense stands of vegetation that appear to have stabilized these surfaces. Large, long duration floods may cause some channel widening, and meander migration, however, these floods are infrequent, and narrowing resumes shortly thereafter. Channel narrowing, increases in sinuosity, decreases in slope, and increases in vegetative roughness appear to have caused biogeomorphic feedbacks, thereby exacerbating sediment deposition, and disrupting flood conveyance. In recent decades, there has been an increase in the travel time of floods up to ~100% compared to floods of the 1940s and 1950s, and this has likely led to increased flood attenuation, contributing to decreases in peak-flow magnitude. The progressive increase in water development in parts of the basin has also likely played some role in the progressive declines in peak flow over the duration of the study.
","language":"English","publisher":"The Geological Society of America","doi":"10.1130/B35047.1","usgsCitation":"Dean, D.J., and Topping, D.J., 2019, Geomorphic change and biogeomorphic feedbacks in a dryland river: The Little Colorado River, Arizona, USA: GSA Bulletin, Repository Item: 2019158; 23 p., https://doi.org/10.1130/B35047.1.","productDescription":"Repository Item: 2019158; 23 p.","ipdsId":"IP-099021","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437486,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XPWIBM","text":"USGS data release","linkHelpText":"Geomorphic Change Data for the Little Colorado River, Arizona, USA"},{"id":363278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Little Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.412353515625,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 35.54116627999815\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":215067,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761569,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761570,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202128,"text":"ofr20191010 - 2019 - Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas","interactions":[],"lastModifiedDate":"2019-04-26T15:38:27","indexId":"ofr20191010","displayToPublicDate":"2019-04-24T14:35:00","publicationYear":"2019","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":"2019-1010","displayTitle":"Geochemistry and Mineralogy of Soils Collected in the Lower Rio Grande Valley, Texas","title":"Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas","docAbstract":"Presented in this report are the chemical and mineralogical results of a soil study conducted in the lower Rio Grande valley, Texas. Samples were collected from soils formed on Holocene alluvial flood-plain and distributary channel deposits of the Rio Grande, flood plain and meander-belt deposits of the Pliocene Goliad Formation, and the Pleistocene Lissie and Beaumont Formations. The lower Rio Grande valley is located on the old distributary delta of the Rio Grande. The watersheds on the U.S. side of the delta no longer drain into the Rio Grande but are part of a complex system of irrigation channels and wastewater drains that flow into the lower Laguna Madre. The results of the study have been used to map concealed geologic units and identify potential mosquito breeding habitat.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191010","collaboration":" ","usgsCitation":"Whitney, H.A., Solano, F., and Hubbard, B.E., 2019, Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas: U.S. Geological Survey Open-File Report 2019–1010, 92 p., https://doi.org/10.3133/ofr20191010.","productDescription":"Report: v, 92 p.; 6 Tables","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062701","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":363123,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1010/coverthb.jpg"},{"id":363124,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1010"},{"id":363125,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table01.xlsx","text":"Table 1","size":"70.9 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Geochemical analyses of soil samples collected in 2003–04, by element and method of analysis, lower Rio Grande valley, Texas\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":363126,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table02.xlsx","text":"Table 2","size":"64.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Geochemical analyses of soil samples collected in 2007, by element and method of analysis, lower Rio Grande valley, Texas"},{"id":363127,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table03.xlsx","text":"Table 3","size":"18.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Univariate statistics and percentiles of analytical results for soil samples collected in 2003 and 2004, lower Rio Grande valley, Texas"},{"id":363128,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table04.xlsx","text":"Table 4","size":"19.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Univariate statistics and percentiles of analytical results for soil samples collected in 2007, lower Rio Grande valley, Texas"},{"id":363129,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table05.xlsx","text":"Table 5","size":"31.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Mineralogy of all soil samples collected in 2003, 2004, and 2007, lower Rio Grande valley, Texas"},{"id":363130,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table06.xlsx","text":"Table 6","size":"16.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Summary statistics of mineral content of soils by geologic formation (Page and others, 2005) as determined by x‐ray diffraction"}],"country":"United States","state":"Texas","otherGeospatial":"Rio Grande Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.1845703125,\n 25.686087780724858\n ],\n [\n -97.1136474609375,\n 25.686087780724858\n ],\n [\n -97.1136474609375,\n 26.76277822801415\n ],\n [\n -99.1845703125,\n 26.76277822801415\n ],\n [\n -99.1845703125,\n 25.686087780724858\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Mineral and Energy Resources Center
U.S. Geological Survey
MS 954 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
The Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic responses and streamflow. The U.S. Geological Survey (USGS), in cooperation with the New Mexico Department of Homeland Security and Emergency Management, developed procedures to apply the PRMS model to simulate the effects of fire on hydrologic responses.
A PRMS model was built of the upper Rio Hondo Basin from the headwaters to approximately 19 miles downstream from the USGS streamgage Rio Hondo above Chavez Canyon near Hondo, New Mexico, by using 24 hydrologic response units (HRUs), or hydrologically similar subareas, from the National Hydrologic Model. A quasi-graphical user interface was created to easily query and analyze published PRMS sensitivity-analysis data. Simulation of mean daily streamflow was most sensitive to parameters related to snowmelt or infiltration throughout the upper Rio Hondo Basin. In the basin’s eastern and northern HRUs, flashiness and timing of streamflow were most sensitive to interflow; in many western-basin HRUs (higher elevations), flashiness of streamflow was most sensitive to soil moisture parameters, and timing of streamflow was most sensitive to infiltration and evapotranspiration parameters.
The PRMS model was calibrated for the fire-affected North Fork Eagle Creek subwatershed by comparing modeled to observed daily streamflow for the nonfrozen (May through October) period for a prefire and postfire time period. The prefire model was calibrated for the period 2007–12 before the 2012 fire, and the postfire model was calibrated for a 2-year (2014–15) period after the fire. Model parameterization combined manual adjustment of 8 parameters on the basis of prior knowledge and automated adjustment of the most sensitive parameters by using the Let Us Calibrate interface. A gridded, daily precipitation dataset that captured the spatial heterogeneity across the study watershed was used as the precipitation input for calibration. Model performance was assessed as satisfactory by using standard statistical measures for prefire and postfire periods.
The calibrated model was run by using data from a single precipitation gage to better represent the effect of localized, extreme storms on postfire hydrologic response. The calibrated models for prefire and postfire conditions simulated streamflows with greater consistency than the uncalibrated model for the corresponding (prefire or postfire) period of hydrographic record. The effect of fire on streamflow was found to be primarily a shift from streamflow dominated by base flow prior to fire to streamflow dominated by surface runoff after fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195022","collaboration":"Prepared in cooperation with the New Mexico Department of Homeland Security and Emergency Management","usgsCitation":"Douglas-Mankin, K.R., and Moeser, C.D., 2019, Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5022, 25 p., https://doi.org/10.3133/sir20195022.","productDescription":"Report: vi, 25 p.; Data Release","numberOfPages":"36","ipdsId":"IP-094970","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":363146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KD1X7Q","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model input and output for prefire and postfire hydrologic simulations in the Upper Rio Hondo Basin, New Mexico using the Precipitation-Runoff Modeling System (PRMS)"},{"id":363157,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5022/coverthb2.jpg"},{"id":363145,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5022/sir20195022.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5022"}],"country":"United States","state":"New Mexico","county":"Lincoln County, Otero County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.83610534667969,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.33741240611175\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113
A binational study was initiated to update statistical equations that are used to estimate the magnitude and frequency of peak flows on streams in Manitoba and Ontario, Canada, and Minnesota that are contained within the binational Lake of the Woods–Rainy River Basin upstream from Kenora, Ontario, Canada. Hydraulic engineers use peak streamflow data to inform designs of bridges, culverts, and dams, and water managers use peak streamflow data to inform regulation and planning activities. However, long-term streamflow measurements are available at few locations along the more than 20,000 miles of stream/ditch networks within the binational Lake of the Woods–Rainy River Basin upstream from Kenora, Ontario, Canada.
Estimates of peak-flow magnitudes for 66.7-, 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities equivalent to annual flood-frequency recurrence intervals of 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals, respectively, are presented for 49 streamgages in Minnesota and adjacent areas in the Province of Ontario, Canada, based on data collected through water year 2013. Peak-flow frequency information was subsequently used in regression analyses to develop equations relating peak flows for selected recurrence intervals to various basin and climatic characteristics.
The study area includes 49 streamgages located in the binational Lake of the Woods–Rainy River Basin upstream from Kenora, Ontario, Canada, and is represented by southern portions of the Canadian Provinces of Manitoba (2 percent) and Ontario (56 percent) and the northern portion of the U.S. State of Minnesota (42 percent). The study area was represented by three regions that were defined in previous studies in the U.S. State of Minnesota and another in the Canadian Province of Ontario. The two Minnesota regions A and B were developed using a multiple regression method and hydrologic landscape units were used to validate regions in Minnesota. The Ontario region A was developed using a multiple regression method and standardized residuals from the 100-year recurrence intervals.
Canadian maximum instantaneous peak-flow data were converted from a calendar year to a water year (October 1 to September 30) and where the annual maximum instantaneous peak-flow value was not available in HYDAT, the Sangal method was applied to known average daily flow values to estimate an annual maximum instantaneous peak-flow value. Geographic information system software was used to calculate eight characteristics investigated as potential explanatory variables in the regression analyses.
The procedure for estimating peak-flow frequency for selected exceedance probabilities for a specific ungaged site depends on whether the site is near a streamgage on the same stream or is on an ungaged stream. For an ungaged site near a streamgage on the same stream, the drainage-area ratio method can be used. For an ungaged site on an ungaged stream, the regional regression equations developed for this study should be used.
All equations presented in this study will be incorporated into StreamStats, a web-based geographic information system tool developed by the U.S. Geological Survey. StreamStats allows users to obtain streamflow statistics, basin characteristics, and other information for user-selected locations on streams through an interactive map.
Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The dynamic, multiscale nature of stream systems makes it challenging to establish basic ecological principles to guide stream fish conservation and management. For example, finer-scale instream habitat is often constrained by coarser-scale characteristics driving observed species distributions. Additionally, instream environmental variability can result in patchy species distributions within general upstream–downstream occurrence patterns (i.e., variation around a common theme). Groundwater contribution, an often-overlooked habitat characteristic in warmwater systems, has numerous influences on the instream environment and can play a role in fish habitat-use patterns and assemblage structure. We identified multiscale instream habitat characteristics associated with the occurrence probability of 20 Ozark Highland stream fishes. Fishes were surveyed using tow-barge electrofishing in 76 channel unit complexes (i.e., riffle-to-riffle habitat sequences) nested in 20 reaches of northwest Oklahoma and southwest Missouri. We used a multiscale, multispecies generalized linear mixed model to identify relationships between fish occurrence and both channel unit complex- and reach-scale variables. Stream fishes were more likely to occur in larger or deeper channel unit complexes. Fish occurrence was also associated with different levels of reach-scale groundwater contribution, bankfull width-to-depth ratio, and percent instream cover. Ten fishes, typically associated with warmer water temperatures, had lower occurrence probabilities in reaches with higher groundwater contribution, whereas Banded Sculpin Cottus carolinae and Creek Chub Semotilus atromaculatus occurrence probabilities were higher. There was no relationship between occurrence probabilities and instream cover for 11 fishes. The occurrence probabilities in relation to varying amounts of instream cover for the other nine stream fishes was dependent on bankfull width-to-depth ratio, where the direction and magnitude of the relationships varied among stream fishes. The variation in occurrence relationships can be attributed to thermal preferences, environmental interactions, and the use of multiple habitat types. Our findings demonstrate the multiscale nature of fish occurrence relationships and how conservation and management may benefit from considering this complexity when developing holistic instream habitat enhancement strategies.
","language":"English","publisher":"American Society of Ichthyologists and Herpetologists","doi":"10.1643/CE-18-099","usgsCitation":"Mollenhauer, R., Zhou, Y., and Brewer, S.K., 2019, Multiscale habitat factors explain variability in stream fish occurrence in the Ozark Highlands ecoregion, USA: Copeia, v. 107, no. 2, p. 219-231, https://doi.org/10.1643/CE-18-099.","productDescription":"13 p.","startPage":"219","endPage":"231","ipdsId":"IP-099822","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Ozark Highlands ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.1910400390625,\n 36.465471886798134\n ],\n [\n -93.9935302734375,\n 36.465471886798134\n ],\n [\n -93.9935302734375,\n 36.99816565700228\n ],\n [\n -95.1910400390625,\n 36.99816565700228\n ],\n [\n -95.1910400390625,\n 36.465471886798134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mollenhauer, Robert","contributorId":274327,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhou, Yan","contributorId":274328,"corporation":false,"usgs":false,"family":"Zhou","given":"Yan","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832909,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}