In the United States, national chemical water quality criteria for the protection of aquatic life assume that aquatic ecosystems have sufficient resiliency to recover from criteria exceedences occurring up to once every 3 years. This resiliency assumption was critically reviewed through two approaches: 1) synthesis of case studies and 2) population modeling. The population modeling examined differences in recovery of species with widely different life histories. One invertebrate (Hyalella azteca) and four fish species were modeled (fathead minnow, brook trout, lake trout, and shortnose sturgeon) with various disturbance magnitudes and intervals. The synthesis of ecosystem case studies showed generally faster recoveries for insect communities rather than fish, and recoveries from pulse (acute) disturbances were often faster than recoveries from press (chronic) disturbances. When the recovery dataset excluded severe disturbances that seemed unrepresentative of common facility discharge upsets that might cause criteria exceedences, the median recovery time was 1 year, 81% of the cases were considered recovered within 3 years, and 95% were considered recovered within 10 years. The modeling projected that short-lived fish species with high recovery times could thrive despite enduring 50% mortality disturbances every other year. However, long-lived fish species had longer recovery times and declined under the 1 disturbance every 3 years scenario. Overall, the analyses did not refute the long-standing judgements that 3 years is generally sufficient for recovery from non-repetitive, moderate intensity disturbances of a magnitude up to 2X the chronic criteria in waters without other pollution sources or stresses. However, these constraints may not always be met and if long-lived fish species are a concern, longer return intervals such as 5 to 10 years could be indicated.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5471","usgsCitation":"Mebane, C.A., 2022, The capacity of freshwater ecosystems to recover from exceedances of aquatic life criteria: Environmental Toxicology and Chemistry, v. 41, no. 12, p. 2887-2910, https://doi.org/10.1002/etc.5471.","productDescription":"24 p.","startPage":"2887","endPage":"2910","ipdsId":"IP-125552","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":446640,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5471","text":"Publisher Index Page"},{"id":406944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852244,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70235866,"text":"fs20223068 - 2022 - Database of biodiversity, habitat, and aquatic-resource quantification tools used in market-based conservation — 2022 update","interactions":[],"lastModifiedDate":"2022-09-27T13:31:01.57704","indexId":"fs20223068","displayToPublicDate":"2022-08-26T06:25:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3068","displayTitle":"Database of Biodiversity, Habitat, and Aquatic-Resource Quantification Tools Used in Market-Based Conservation — 2022 Update","title":"Database of biodiversity, habitat, and aquatic-resource quantification tools used in market-based conservation — 2022 update","docAbstract":"Market-based conservation makes use of economic incentives to promote actions that avoid, minimize, or compensate for detrimental effects on natural resources and the environment. Examples of market-based conservation mechanisms include aquatic-resource (such as, streams, wetlands, and estuaries) compensatory mitigation, conservation banking, habitat exchanges, and payments for ecosystem services. A critical component in the operation of these market-based conservation mechanisms is the methods (hereafter referred to as “quantification tools”) used to assess existing (sometimes referred to as “baseline”) or potential site conditions. Quantification tools are used to assign values to the benefits provided by preservation, restoration, or enhancement actions, as well as the negative effects of human activities (for example, infrastructure development, energy extraction, and anthropogenic disasters).
In 2018, the U.S. Geological Survey (USGS) published a database describing the attributes of 69 quantification tools developed for United States conservation markets. The database focused on tools used for species-based mitigation, payments for ecosystem services, and ecolabel programs (Chiavacci and Pindilli, 2020). Recently, the USGS, in collaboration with the U.S. Environmental Protection Agency, revised the original database by updating the existing tool information, adding newly developed tools, and broadening the scope to include tools developed for compensatory mitigation under the Clean Water Act Section 404 Regulatory Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223068","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Chiavacci, S.J., French, E.D., and Morgan, J.A., 2022, Database of biodiversity, habitat, and aquatic-resource quantification tools used in market-based conservation — 2022 update: U.S. Geological Survey Fact Sheet 2022–3068, 2 p., https://doi.org/10.3133/fs20223068.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-140564","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":405533,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223068/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3068"},{"id":406471,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79G5M3X","text":"USGS data release","linkHelpText":"Database of Biodiversity, Habitat, and Aquatic Resource Quantification Tools Used for Market-based Conservation in the United States (ver. 2.0, June 2022)"},{"id":405535,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3068/fs20223068.XML"},{"id":405534,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3068/images/"},{"id":405509,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3068/fs20223068.pdf","text":"Report","size":"558 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3068"},{"id":405508,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3068/coverthb3.jpg"}],"contact":"Science and Decisions Center
12201 Sunrise Valley Drive
Reston, VA 20192
A radium industry existed between about 1914 and 1920 in Denver, Colorado, with operations located along the South Platte River. Sites associated with that industry were contaminated with radium and uranium processing residues and were incorporated into clean-up efforts as Operating Units (OUs) of the Denver Radium Superfund Site. Concentrations of uranium exceeding the U.S. Environmental Protection Agency maximum contaminant level of 0.03 milligrams per liter for drinking water are present in shallow groundwater at OU8. However, previous studies have shown concentrations of dissolved uranium can be naturally high in shallow groundwater of the South Platte River valley compared to other rivers of the world. This report compares dissolved uranium concentrations measured by the U.S. Geological Survey across the South Platte River valley to data collected at the OU8 of the Denver Radium Superfund Site. The U.S. Geological Survey data represent 5 distinct urban or agricultural geographic areas and included 230 sampling events at 114 wells during 1993 to 2013. The OU8 data represent 13 wells and groundwater discharge locations sampled during the years 2017 and 2018. Dissolved uranium concentrations were statistically significantly greater for both years of the OU8 data compared to three datasets from shallow groundwater beneath urban areas in the Denver metropolitan area. However, compared to OU8, concentrations were significantly greater in shallow groundwater from an agricultural area of the South Platte River valley distant from Denver. Additionally, each of the urban area datasets contained some individual dissolved uranium concentrations greater than the greatest concentrations from the two OU8 datasets. Thus, naturally occurring concentrations of dissolved uranium in shallow groundwater that are greater than those observed at OU8 are not uncommon in the South Platte River valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20225085","collaboration":"Prepared in cooperation the U.S. Environmental Protection Agency","usgsCitation":"Bern, C.R., 2022, Examination of dissolved uranium concentrations in regional shallow groundwater relative to Operable Unit 8 of the Denver Radium Superfund Site: U.S. Geological Survey Scientific Investigations Report 2022–5085, 16 p., https://doi.org/10.3133/sir20225085.","productDescription":"Report: vi, 16 p.; Database","onlineOnly":"Y","ipdsId":"IP-135030","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":405601,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":405600,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://semspub.epa.gov/work/08/100005517.pdf","text":"U.S. Environmental Protection Agency [EPA], 2018b—","linkHelpText":"Fifth five-year review report for Denver radium superfund site, Denver County, Colorado"},{"id":405596,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5085/coverthb.jpg"},{"id":405598,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5085/ofr20225085.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5085"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.07598876953125,\n 39.115144700901475\n ],\n [\n -103.75213623046875,\n 39.115144700901475\n ],\n [\n -103.75213623046875,\n 39.8992015115692\n ],\n [\n -105.07598876953125,\n 39.8992015115692\n ],\n [\n -105.07598876953125,\n 39.115144700901475\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
The Great Lakes Basin covers around 536,393 square kilometers, and the Great Lakes hold more than 5,400 cubic miles of water, accounting for more than 20 percent of the world’s fresh surface water supply. The Great Lakes provide a source of drinking water to tens of millions of people in Canada and the United States and support one of the most diverse ecosystems in the world. Increasing urbanization combined with aging infrastructure and more extreme storm events because of changing weather patterns creates stormwater management challenges for communities across the Great Lakes region. A variety of green infrastructure (GI) practices, designed to decrease runoff and improve water quality, have been implemented throughout the region in response to these challenges; however, implementation often remains limited to local efforts and with little coordination among various levels of government because of, at least in part, a lack of clear standards for stormwater, limited funding, and a general uncertainty in the type and expected performance of these practices. City planners, engineers, and political leaders often see GI investment as riskier than other alternatives despite studies that determined, in most cases, practices can either reduce or not affect costs.
This report summarizes selected published reports and data sources from studies done in Great Lakes states and compares the measured effects of various GI practices and their applicability in different settings around the Great Lakes. By summarizing selected published reports and data sources from studies done in Great Lakes states, this report provides foundational information for U.S. Geological Survey scientists and their local and national partners to assess the ability of GI to reduce stormwater runoff in Great Lakes urban areas. GI includes a variety of stormwater management techniques designed to mimic natural hydrologic processes like infiltration and evapotranspiration, which can decrease the volume of water running into sewers and streams. It can also improve water quality by trapping sediment, nutrients, and other contaminants. A variety of landscape practices can be incorporated into urban areas as GI, but the discussion here is limited to vegetated basins, vegetated channels, permeable pavement, urban tree canopy, and green roofs. Other types of GI, such as downspout disconnection, rainwater harvesting, and wet and dry detention basins were not included because hydrologic function and associated components are not widely monitored or evaluated in literature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1496","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Baker, N.T., Sullivan, D.J., Selbig, W.R., Haefner, R.J., Lampe, D.C., Bayless, R., and McHale, M.R., 2022, Green infrastructure in the Great Lakes—Assessment of performance, barriers, and unintended consequences: U.S. Geological Survey Circular 1496, 70 p., https://doi.org/10.3133/cir1496.","productDescription":"Report: ix, 70 p.; 1 Table","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-128488","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Flowing lava and water have dramatically different physical properties but can form similar hydraulic structures, including undular hydraulic jumps, or standing wave trains. In water flows, undular hydraulic jumps are evidence of critical flow (Froude number ∼1) and open-channel hydraulic theory provides a powerful tool for estimating flow depth and velocity. Monitoring these parameters in an active lava channel is inherently challenging, but essential for calculating lava discharge (effusion rate), a primary control on the rate of flow front advance and ultimate flow runout distance. We analyze undular hydraulic jumps in both water and lava flows to assess the conditions under which they form and, by extension, the potential use of critical flow theory to estimate, in real time, lava flow velocity, depth, and discharge. Experimental data for water flows show that these structures mark the transition from supercritical to subcritical flow. Undular hydraulic jumps in the near-vent lava channel of the 2018 lower East Rift Zone eruption of Kīlauea, Hawaiʻi also reflect critical flow conditions; their wavelengths scale with flow depth and velocity, consistent with hydraulic theory. Calculated lava effusion rates are similar to estimates made using more traditional approaches (Jeffreys', 1925, https://doi.org/10.1080/14786442508634662, equation based on lava viscosity, density, and channel slope) and with lava volumes derived from topographic-change mapping. From this we conclude that critical flow phenomena show great potential to track flow dynamics and inform hazard assessment for a wide range of geophysical fluids.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006666","usgsCitation":"Dietterich, H., Grant, G., Fasth, B., Major, J., and Cashman, K., 2022, Can lava flow like water? Assessing applications of critical flow theory to channelized basaltic lava flows: Journal of Geophysical Research - Earth Surface, v. 127, no. 9, e2022JF006666, 26 p., https://doi.org/10.1029/2022JF006666.","productDescription":"e2022JF006666, 26 p.","ipdsId":"IP-138706","costCenters":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":406841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Dietterich, Hannah R. 0000-0001-7898-4343","orcid":"https://orcid.org/0000-0001-7898-4343","contributorId":212771,"corporation":false,"usgs":true,"family":"Dietterich","given":"Hannah R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":852036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Gordon E.","contributorId":30881,"corporation":false,"usgs":false,"family":"Grant","given":"Gordon E.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":852037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fasth, Becky","contributorId":296636,"corporation":false,"usgs":false,"family":"Fasth","given":"Becky","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":852038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Major, J. J. 0000-0003-2449-4466","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":29461,"corporation":false,"usgs":true,"family":"Major","given":"J. J.","affiliations":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":852039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cashman, Katharine V.","contributorId":40097,"corporation":false,"usgs":false,"family":"Cashman","given":"Katharine V.","affiliations":[],"preferred":false,"id":852040,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236759,"text":"70236759 - 2022 - The influence of submerged coastal structures on nearshore flows and wave runup","interactions":[],"lastModifiedDate":"2022-09-19T12:09:25.337399","indexId":"70236759","displayToPublicDate":"2022-08-25T07:07:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"The influence of submerged coastal structures on nearshore flows and wave runup","docAbstract":"Engineered and natural submerged coastal structures (e.g., submerged breakwaters and reefs) modify incident wave fields and thus can alter hydrodynamic processes adjacent to coastlines. Although submerged structures are generally assumed to promote beach protection by dissipating waves offshore and creating sheltered conditions in their lee, their interaction with waves can result in mean wave-driven circulation patterns that may either promote shoreline accretion or erosion. Here, we analyse the mean flow patterns and shoreline water levels (wave runup) in the lee of idealised impermeable submerged structures with a phase-resolved nonhydrostatic numerical model. Waves propagating over submerged structures can drive either a 2-cell mean (wave-averaged) circulation, which is characterised by diverging flows behind the structure and at the shoreline, or 4-cell circulation, with converging flows at the shoreline and diverging flows in the immediate lee of the structure. The numerical results show that the mode of circulation can be predicted with a set of relationships depending on the incoming wave heights, the structure crest level, and distance to the shoreline (or structure depth). Qualitative agreement between the mean flow and proxies for the sediment transport using an energetics approach suggest that the mean flow can be a robust proxy for inferring sediment transport patterns. For the cases considered, the submerged structures had a minimal influence on shoreline wave setup and wave runup despite the wave energy dissipation by the structures due to alongshore wave energy fluxes in the lee. Consequently, these results suggest that the coastal protection provided by the range of impermeable submerged structures we modelled is primarily due to their capacity to promote beach accretion.
Stream synoptic sampling studies that include flow estimates derived from the stream tracer dilution method are now commonly performed to identify sources and processes controlling solute transport to streams. However, a limitation of this mass-loading approach is its inability to identify the side of the stream on which a source is located in the common case where loading is largely from groundwater discharge. Such bank-specific loading information can be particularly valuable at mining-affected sites where both mining-related and natural background metal sources are often closely intermingled. Here we combine the stream mass-loading approach with data from pairs of shallow hand-installed streambank wells located on opposite sides of a gaining metal-impacted headwater stream to estimate bank-specific metal loading rates. Study results successfully identify a right-bank zone in the upper half of the primary study reach as the dominant source of loading, where groundwater discharge is elevated in metals due to natural weathering of sulfide-rich bedrock. A left-bank zone in the lower half of the primary study reach was also identified as a secondary, yet still substantial, loading source, where groundwater is likely impacted by portal discharge and/or waste piles associated with an adjacent abandoned mine. Determining the dominance of the left-bank mining-related source compared to other potential right-bank natural sulfide weathering sources would not have been possible without the streambank wells. Streambank wells also enabled collection of dissolved gas samples for groundwater dating. Computed piston-flow 3H/3He groundwater ages show little to no correlation with either metal concentrations or loading rates, suggesting that groundwater residence time and flow path variations exert little influence on groundwater discharge chemistry compared to variations in bedrock and soil composition. This study thus provides proof of concept that the proposed method of combining streambank well data with stream tracer injection and synoptic sampling can provide useful information on bank-specific mass loading rates and sources of contaminants affecting stream water quality.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2022.105425","usgsCitation":"Manning, A.H., Runkel, R.L., Morrison, J.M., Wanty, R., and Walton-Day, K., 2022, Incorporating streambank wells in stream mass loading studies to more effectively identify sources of solutes in stream water: Applied Geochemistry, v. 145, 105425, 14 p., https://doi.org/10.1016/j.apgeochem.2022.105425.","productDescription":"105425, 14 p.","ipdsId":"IP-141156","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},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":446668,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2022.105425","text":"Publisher Index Page"},{"id":435717,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V0J8FS","text":"USGS data release","linkHelpText":"Geochemistry and Environmental Tracer Data for Groundwater, Stream Water, and Soil and Sediment from North Quartz Creek, Colorado"},{"id":408264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"North Quartz Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.52,\n 38.60\n ],\n [\n -106.44344329833984,\n 38.60\n ],\n [\n -106.44344329833984,\n 38.65870536210694\n ],\n [\n -106.52,\n 38.65870536210694\n ],\n [\n -106.52,\n 38.60\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":854531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morrison, Jean M. 0000-0002-6614-8783 jmorrison@usgs.gov","orcid":"https://orcid.org/0000-0002-6614-8783","contributorId":994,"corporation":false,"usgs":true,"family":"Morrison","given":"Jean","email":"jmorrison@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":854533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wanty, Richard 0000-0002-2063-6423","orcid":"https://orcid.org/0000-0002-2063-6423","contributorId":209899,"corporation":false,"usgs":true,"family":"Wanty","given":"Richard","affiliations":[],"preferred":true,"id":854534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854535,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70245487,"text":"70245487 - 2022 - Stakeholder engagement to guide decision-relevant water data delivery","interactions":[],"lastModifiedDate":"2023-06-23T16:12:23.251924","indexId":"70245487","displayToPublicDate":"2022-08-24T11:01:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Stakeholder engagement to guide decision-relevant water data delivery","docAbstract":"Water resources management and policy making require access to reliable scientific data. However, water managers may need to overcome various obstacles to accessing data. For example, insufficient technological infrastructures, low data literacy, and data format complexities often inhibit data user access. Thus, it is imperative to include stakeholders in the design of data delivery systems. The United States Geological Survey's Water Resources Mission Area is currently developing Integrated Water Availability Assessments (IWAAs) — multi-extent, stakeholder driven, near real-time water availability census and prediction for human and ecological uses. To provide appropriate user accessibility to data delivery systems developed for IWAAs, a user-centered design process including stakeholder focus groups was used to determine potential water data user needs and preferences. Focus groups identified five types of potential users: Public sector water resources managers, Public sector water resources manager data analysts, Industry and private companies, Tribal Nations, and Nonprofit organizations. Different water data user types depended on diverse spatial and temporal scale data. Public sector water resources managers benefitted most from data synthesized into user-friendly platforms and Public sector water resources data analysts preferred easy access to raw data. These findings can support the development of a water data delivery platform that meets a variety of user needs.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13055","usgsCitation":"Restrepo-Osorio, D., Stoltz, A.D., and Herman-Mercer, N.M., 2022, Stakeholder engagement to guide decision-relevant water data delivery: Journal of the American Water Resources Association (JAWRA), v. 58, no. 6, p. 1531-1546, https://doi.org/10.1111/1752-1688.13055.","productDescription":"14 p.","startPage":"1531","endPage":"1546","ipdsId":"IP-130669","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":435718,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YI55UG","text":"USGS data release","linkHelpText":"Datasets from the focus group series of stakeholder engagement efforts to inform integrated water availability assessment data delivery"},{"id":418404,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Restrepo-Osorio, Diana 0000-0003-4230-0055 drestrepo-osorio@usgs.gov","orcid":"https://orcid.org/0000-0003-4230-0055","contributorId":189352,"corporation":false,"usgs":true,"family":"Restrepo-Osorio","given":"Diana","email":"drestrepo-osorio@usgs.gov","affiliations":[],"preferred":true,"id":876138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoltz, Amanda D. 0000-0003-4656-6125","orcid":"https://orcid.org/0000-0003-4656-6125","contributorId":311692,"corporation":false,"usgs":true,"family":"Stoltz","given":"Amanda","email":"","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":876139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herman-Mercer, Nicole M. 0000-0001-5933-4978 nhmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-5933-4978","contributorId":3927,"corporation":false,"usgs":true,"family":"Herman-Mercer","given":"Nicole","email":"nhmercer@usgs.gov","middleInitial":"M.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":876141,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237000,"text":"70237000 - 2022 - Tracing the sources and depositional history of mercury to coastal northeastern U.S. lakes","interactions":[],"lastModifiedDate":"2022-10-31T14:49:02.430423","indexId":"70237000","displayToPublicDate":"2022-08-24T10:41:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1566,"text":"Environmental Science: Processes and Impacts","active":true,"publicationSubtype":{"id":10}},"title":"Tracing the sources and depositional history of mercury to coastal northeastern U.S. lakes","docAbstract":"Mercury (Hg) deposition was reconstructed in sediment cores from lakes in two coastal U.S. National Parks: Acadia National Park (ANP) and Cape Cod National Seashore (CCNS), to fill an important spatial gap in Hg deposition records and to explore changing sources of Hg and processes affecting Hg accumulation in these coastal sites. Recent Hg deposition chronology was assessed using (1) a newly developed lead-210 (210Pb) based sediment age model which employs 7Be to constrain deposition and sediment mixing of 210Pb-excess, (2) coinciding Pb flux and isotope ratios (206Pb/207Pb), and (3) Hg isotope ratios and their response to changes in Hg flux. At both sites, Hg flux increased substantially from pre-1850 levels, with accumulation in ANP peaking in the 1970s, whereas in CCNS, Hg levels were highest in recent sediments. Negative values of δ202Hg and Δ199Hg indicated terrestrially-derived Hg was a major constituent of Hg flux to Sargent Mountain Pond, ANP, although recent decreases in Hg flux were in agreement with precipitation Hg records, indicating a rapid watershed response. By contrast, δ202Hg and Δ199Hg profiles in Long Pond, CNNS reflect direct Hg deposition, but disturbances in the sedimentary record were indicated by bomb fallout radionuclide inventories and by peaks in both Pb and Hg isotope depth profiles. These cores provided poor reconstructions of atmospheric deposition and reveal responses that are decoupled from emissions reduction due to complex post-depositional redistribution of atmospheric metals including Hg. The application of multiple tracers of Hg deposition provide insight into the sources and pathways governing Hg accumulation in these lakes.
","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D2EM00214K","usgsCitation":"Taylor, V., Landis, J.D., and Janssen, S., 2022, Tracing the sources and depositional history of mercury to coastal northeastern U.S. lakes: Environmental Science: Processes and Impacts, v. 24, p. 1805-1820, https://doi.org/10.1039/D2EM00214K.","productDescription":"16 p.","startPage":"1805","endPage":"1820","ipdsId":"IP-142940","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, Massachusetts","otherGeospatial":"Acadia National Park, Cape Cod National Seashore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.40568542480469,\n 44.21863119293724\n ],\n [\n -68.17,\n 44.21863119293724\n ],\n [\n -68.17,\n 44.44995770844175\n ],\n [\n -68.40568542480469,\n 44.44995770844175\n ],\n [\n -68.40568542480469,\n 44.21863119293724\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.01380920410156,\n 41.74467659677642\n ],\n [\n -69.88128662109375,\n 41.74467659677642\n ],\n [\n -69.88128662109375,\n 41.949787926673416\n ],\n [\n -70.01380920410156,\n 41.949787926673416\n ],\n [\n -70.01380920410156,\n 41.74467659677642\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Vivien F.","contributorId":296971,"corporation":false,"usgs":false,"family":"Taylor","given":"Vivien F.","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":853015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landis, Joshua D.","contributorId":211459,"corporation":false,"usgs":false,"family":"Landis","given":"Joshua","email":"","middleInitial":"D.","affiliations":[{"id":38249,"text":"Department of Earth Sciences, Dartmouth College, Hanover, NH","active":true,"usgs":false}],"preferred":false,"id":853016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853017,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266889,"text":"70266889 - 2022 - Sedimentary geochemistry of deepwater slope deposits in southern Lake Tanganyika (East Africa): Effects of upwelling and minor lake level oscillations","interactions":[],"lastModifiedDate":"2025-05-14T14:38:39.481043","indexId":"70266889","displayToPublicDate":"2022-08-23T09:33:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2451,"text":"Journal of Sedimentary Research","onlineIssn":"1938-3681","printIssn":"1527-1404","active":true,"publicationSubtype":{"id":10}},"title":"Sedimentary geochemistry of deepwater slope deposits in southern Lake Tanganyika (East Africa): Effects of upwelling and minor lake level oscillations","docAbstract":"Lake Tanganyika ranks among the most valuable modern analogs for understanding depositional processes of carbonaceous sediments in ancient tropical rifts. Prior research on Lake Tanganyika has emphasized the importance of bottom-water anoxia, depositional processes (hemipelagic settling versus gravity flows), and large-scale (100s of meters) lake level change on the quality of sedimentary organic matter content. Here, facies analysis and numerous organic geochemical tools (elemental, carbon isotope, and programmed pyrolysis) were applied to a radiocarbon-dated core from southern Lake Tanganyika to investigate the accumulation of carbonaceous sediments in a deepwater slope environment influenced by high-frequency climatic fluctuations accompanied by only minor (10s of meters) lake level changes. Considerable variability in lithofacies and geochemistry characterizes the ∼ 1030-year-long core record, chiefly driven by climate-mediated changes to the lake's upwelling system. Laminated diatom oozes and sapropels with mean total organic carbon (TOC) concentrations and hydrogen indices of 6.9 wt.% and 385 mg hydrocarbon/g TOC, respectively, characterize sediments deposited during periods of strong upwelling and variable water levels. Silty sediments deposited via gravity-flow processes were likewise rich in organic matter, likely due to preservation-enhancing bottom-water anoxia. Dilution by reworked tephra was the chief constraint on organic enrichment at the study site. Data from this study reveal that oscillations in atmospheric and limnological processes in the absence of major shoreline movements can result in geochemically diverse deepwater slope sediments, which have implications for improving depositional models of petroliferous continental rift basins.
","language":"English","publisher":"Society for Sedimentary Geology","doi":"10.2110/jsr.2021.104","usgsCitation":"McGlue, M., Ellis, G.S., Brannon, M., Latimer, J., Stone, J., Ivory, S., Mganza, N., Soreghan, M.J., and Scholz, C., 2022, Sedimentary geochemistry of deepwater slope deposits in southern Lake Tanganyika (East Africa): Effects of upwelling and minor lake level oscillations: Journal of Sedimentary Research, v. 92, no. 8, p. 721-738, https://doi.org/10.2110/jsr.2021.104.","productDescription":"18 p.","startPage":"721","endPage":"738","ipdsId":"IP-128441","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":485933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Tanganyika","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 27.668481366027635,\n -3.1820760248469355\n ],\n [\n 27.668481366027635,\n -9.112434947743992\n ],\n [\n 31.779025954731935,\n -9.112434947743992\n ],\n [\n 31.779025954731935,\n -3.1820760248469355\n ],\n [\n 27.668481366027635,\n -3.1820760248469355\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"92","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"McGlue, Michael M.","contributorId":225229,"corporation":false,"usgs":false,"family":"McGlue","given":"Michael M.","affiliations":[{"id":41081,"text":"Department of Geosciences, The University of Arizona, Tucson AZ","active":true,"usgs":false}],"preferred":false,"id":937045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellis, Geoffrey S. 0000-0003-4519-3320 gsellis@usgs.gov","orcid":"https://orcid.org/0000-0003-4519-3320","contributorId":1058,"corporation":false,"usgs":true,"family":"Ellis","given":"Geoffrey","email":"gsellis@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":937046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brannon, McKenzie A","contributorId":355181,"corporation":false,"usgs":false,"family":"Brannon","given":"McKenzie A","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":937047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Latimer, Jennifer C","contributorId":355182,"corporation":false,"usgs":false,"family":"Latimer","given":"Jennifer C","affiliations":[{"id":17777,"text":"Indiana State University","active":true,"usgs":false}],"preferred":false,"id":937048,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stone, Jeffery S","contributorId":355183,"corporation":false,"usgs":false,"family":"Stone","given":"Jeffery S","affiliations":[{"id":17777,"text":"Indiana State University","active":true,"usgs":false}],"preferred":false,"id":937049,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ivory, Sarah J.","contributorId":138493,"corporation":false,"usgs":false,"family":"Ivory","given":"Sarah J.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":937050,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mganza, Neema E","contributorId":355184,"corporation":false,"usgs":false,"family":"Mganza","given":"Neema E","affiliations":[{"id":84721,"text":"Tanzania Petroleum Development Corporation","active":true,"usgs":false}],"preferred":false,"id":937051,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Soreghan, Michael J.","contributorId":347062,"corporation":false,"usgs":false,"family":"Soreghan","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":83052,"text":"School of Geosciences, University of Oklahoma, Norman, OK, 73019, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":937052,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scholz, Christopher A.","contributorId":149267,"corporation":false,"usgs":false,"family":"Scholz","given":"Christopher A.","affiliations":[{"id":17692,"text":"Syracuse University, Syracuse NY","active":true,"usgs":false}],"preferred":false,"id":937053,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70235790,"text":"sir20225086 - 2022 - Water-quality data and trends in the Rapid Creek Basin, South Dakota, 1970–2020","interactions":[],"lastModifiedDate":"2022-09-27T12:25:42.596256","indexId":"sir20225086","displayToPublicDate":"2022-08-22T11:27:22","publicationYear":"2022","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":"2022-5086","displayTitle":"Water-Quality Data and Trends in the Rapid Creek Basin, South Dakota, 1970–2020","title":"Water-quality data and trends in the Rapid Creek Basin, South Dakota, 1970–2020","docAbstract":"Surface-water-quality data in the Rapid Creek Basin in South Dakota were compiled to assess basic trends in the water quality of Rapid Creek. Spatial and temporal patterns in water quality were described for major ions, sediment, total suspended solids, nutrients, field measurements, bacteria, and select metals for the period of 1970–2020, and a water-quality trend analysis was completed for sites with enough data for selected constituents.
Major ions and total suspended solids had higher median concentrations in the lower basin (downstream from the city of Rapid City) relative to the upper and middle basins. Nutrient concentrations were generally low, and increased concentrations were only detected at the sites downstream from the City of Rapid City Water Reclamation Facility. Fecal indicator bacteria (Escherichia coli and fecal coliform) concentrations were highest downstream from the main urbanized area of Rapid City.
Water-quality trends were analyzed for total dissolved solids, specific conductance, calcium, magnesium, total suspended solids, total phosphorus, dissolved phosphorus, and total Kjeldahl nitrogen for the period of 1979–2019. Concentrations for major ions and total dissolved solids typically changed by less than 15 percent. Total dissolved solids concentrations upstream from Rapid City were generally decreasing, whereas concentrations downstream were generally increasing. The flow-averaged geometric mean concentration of total dissolved solids at three sites upstream from Rapid City decreased overall by 3–5 percent, and concentrations at two sites downstream from Rapid City increased by at least 7 percent between 1979 and 2019. Trends in specific conductance in the Rapid Creek Basin were mixed with alternating increasing and decreasing trends at many of the sites between 1979 and 2014. Total suspended solids concentrations were observed to be decreasing at two sites analyzed for trends. Concentrations in total phosphorus were observed to be decreasing at every site analyzed for trends between 1989 and 2014. Significant downward trends in total Kjeldahl nitrogen were observed at two sites in the lower Rapid Creek Basin for the trend period of 1999–2019. The decreases in total suspended solids and nutrient concentrations in the Rapid Creek Basin could be related to several processes such as the implementation of a stormwater management plan in Rapid City, improvements to the water reclamation facility downstream from Rapid City, and residual climatic effects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225086","collaboration":"Prepared in cooperation with the City of Rapid City","usgsCitation":"Tatge, W.S., Hoogestraat, G.K., and Nustad, R.A., 2022, Water-quality data and trends in the Rapid Creek Basin, South Dakota, 1970–2020: U.S. Geological Survey Scientific Investigations Report 2022–5086, 67 p., https://doi.org/10.3133/sir20225086.","productDescription":"Report: viii, 67 p.; Data Release; Dataset","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-133856","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":405392,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225086/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405347,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5086/sir20225086.XML"},{"id":405350,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":405346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5086/sir20225086.pdf","text":"Report","size":"21.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5086"},{"id":405345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5086/coverthb.jpg"},{"id":405349,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D8BSXR","text":"USGS data release","linkHelpText":"Model scripts and water-quality data for trends in the Rapid Creek Basin, South Dakota, 1970–2020"},{"id":405348,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5086/images"}],"country":"United States","state":"South Dakota","otherGeospatial":"Rapid Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.0185546875,\n 43.84443209873525\n ],\n [\n -102.64251708984374,\n 43.84443209873525\n ],\n [\n -102.64251708984374,\n 44.213709909702054\n ],\n [\n -104.0185546875,\n 44.213709909702054\n ],\n [\n -104.0185546875,\n 43.84443209873525\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Decades of research on the effects of urbanization on stream ecology have shown that urban stream problems are inherently wicked. These problems are wicked in the sense that they are difficult to solve because information is incomplete, changing, or conflicting and because finding potential solutions often requires input from stakeholders who can have conflicting and competing values. The 5th Symposium on Urbanization and Stream Ecology (SUSE5) in February 2020 brought together diverse perspectives from scientists, managers, practitioners, and local communities. Participants at SUSE5 discussed the state of the science in urban stream ecology and worked through in-depth case studies in teams to tackle complex real-world problems in urban stream management. The papers in this special series on urbanization and stream ecology include empirical research studies and synthesis papers sparked by discussions at SUSE5 and advance multidisciplinary solutions to wicked urban stream problems.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/721470","usgsCitation":"Fork, M.L., Hopkins, K.G., Chappell, J., Hawley, R.J., Kaushal, S., Murphy, B.M., Rios-Touma, B., and Roy, A.H., 2022, Urbanization and stream ecology: Moving the bar on multidisciplinary solutions to wicked urban stream problems: Freshwater Science, v. 41, no. 3, 6 p., https://doi.org/10.1086/721470.","productDescription":"6 p.","ipdsId":"IP-142003","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":405387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fork, Megan L.","contributorId":139659,"corporation":false,"usgs":false,"family":"Fork","given":"Megan","email":"","middleInitial":"L.","affiliations":[{"id":12868,"text":"Nicholas School of the Environment, Duke University, Durham, NC, USA","active":true,"usgs":false}],"preferred":false,"id":849355,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":849356,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chappell, Jessica","contributorId":219589,"corporation":false,"usgs":false,"family":"Chappell","given":"Jessica","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":849357,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawley, Robert J.","contributorId":167574,"corporation":false,"usgs":false,"family":"Hawley","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":24758,"text":"Sustainable Streams, LLC, Louisville, KY","active":true,"usgs":false}],"preferred":false,"id":849358,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kaushal, Sujay S.","contributorId":210125,"corporation":false,"usgs":false,"family":"Kaushal","given":"Sujay S.","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":849359,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Brian M. 0000-0002-7670-2469","orcid":"https://orcid.org/0000-0002-7670-2469","contributorId":292734,"corporation":false,"usgs":false,"family":"Murphy","given":"Brian","email":"","middleInitial":"M.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":849360,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rios-Touma, Blanca","contributorId":295395,"corporation":false,"usgs":false,"family":"Rios-Touma","given":"Blanca","email":"","affiliations":[{"id":63858,"text":"Universidad de Las Américas","active":true,"usgs":false}],"preferred":false,"id":849361,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":849362,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237919,"text":"70237919 - 2022 - Whole-ecosystem experiment illustrates short timescale hydrodynamic, light, and nutrient control of primary production in a terminal slough","interactions":[],"lastModifiedDate":"2022-11-01T13:50:50.001339","indexId":"70237919","displayToPublicDate":"2022-08-21T08:28:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Whole-ecosystem experiment illustrates short timescale hydrodynamic, light, and nutrient control of primary production in a terminal slough","docAbstract":"Estuaries are among the most productive of aquatic ecosystems. Yet the collective understanding of patterns and drivers of primary production in estuaries is incomplete, in part due to complex hydrodynamics and multiple controlling factors that vary at a range of temporal and spatial scales. A whole-ecosystem experiment was conducted in a deep, pelagically dominated terminal channel of the Sacramento-San Joaquin Delta (California, USA) that seasonally appears to become nitrogen limited, to test whether adding calcium nitrate would stimulate primary productivity or increase phytoplankton density. Production did not respond consistently to fertilization, in part because nitrate and phytoplankton were dispersed away from the manipulated area within 1–3 days. Temporal and spatial patterns of gross primary production were more strongly related to stratification and light availability (i.e., turbidity) than nitrogen, highlighting the role of hydrodynamics in regulating system production. Similarly, chlorophyll was positively related not only to stratification but also to nitrogen—with a positive interaction—suggesting stratification may trigger nutrient limitation. The average rate of primary production (4.3 g O2 m−2 d−1), metabolic N demand (0.023 mg N L−1 d−1), and ambient dissolved inorganic nitrogen concentration (0.03 mg N L−1) indicate that nitrogen can become limiting in time and space, especially during episodic stratification events when phytoplankton are isolated within the photic zone, or farther upstream where water clarity increases, dispersive flux decreases, and stratification is stronger and more frequent. The role of hydrodynamics in organizing habitat connectivity and regulating physical and chemical processes at multiple temporal and spatial scales is critical for determining resource availability and evaluating biogeochemical processes in estuaries.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-022-01111-8","usgsCitation":"Loken, L.C., Sadro, S., Lenoch, L., Stumpner, P., Dahlgren, R.A., Burau, J.R., and Van Nieuwenhuyse, E.E., 2022, Whole-ecosystem experiment illustrates short timescale hydrodynamic, light, and nutrient control of primary production in a terminal slough: Estuaries and Coasts, v. 45, p. 2428-2449, https://doi.org/10.1007/s12237-022-01111-8.","productDescription":"22 p.","startPage":"2428","endPage":"2449","ipdsId":"IP-136358","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":446716,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-022-01111-8","text":"Publisher Index Page"},{"id":408986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River Deep Water Ship Channel","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.5493802954393,\n 38.55374191573719\n ],\n [\n -121.70672314658754,\n 38.55374191573719\n ],\n [\n -121.70672314658754,\n 38.26810056353631\n ],\n [\n -121.5493802954393,\n 38.26810056353631\n ],\n [\n -121.5493802954393,\n 38.55374191573719\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2022-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Loken, Luke C. 0000-0003-3194-1498 lloken@usgs.gov","orcid":"https://orcid.org/0000-0003-3194-1498","contributorId":195600,"corporation":false,"usgs":true,"family":"Loken","given":"Luke","email":"lloken@usgs.gov","middleInitial":"C.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":856203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sadro, Steven 0000-0002-6416-3840","orcid":"https://orcid.org/0000-0002-6416-3840","contributorId":139662,"corporation":false,"usgs":false,"family":"Sadro","given":"Steven","email":"","affiliations":[{"id":12871,"text":"Marine Science Institute, University of California, Santa Barbara, CA, USA","active":true,"usgs":false}],"preferred":false,"id":856204,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lenoch, Leah 0000-0003-4613-0858","orcid":"https://orcid.org/0000-0003-4613-0858","contributorId":270181,"corporation":false,"usgs":true,"family":"Lenoch","given":"Leah","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":856205,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stumpner, Paul 0000-0002-0933-7895 pstump@usgs.gov","orcid":"https://orcid.org/0000-0002-0933-7895","contributorId":5667,"corporation":false,"usgs":true,"family":"Stumpner","given":"Paul","email":"pstump@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":856206,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dahlgren, Randy A 0000-0002-8961-875X","orcid":"https://orcid.org/0000-0002-8961-875X","contributorId":269424,"corporation":false,"usgs":false,"family":"Dahlgren","given":"Randy","email":"","middleInitial":"A","affiliations":[{"id":7082,"text":"University of California - 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In Chesapeake Bay, a well-studied and intensively monitored estuary in North America, the challenge of synthesizing data on water quality and land use as factors related to a key habitat, submerged aquatic vegetation, was tackled by a team of scientists and resource managers operating at multiple levels of governance (state, federal). The synthesis effort took place over a two-year period (2016–2018), and the results were communicated widely to a) scientists via peer review publications and conference presentations; b) resource managers via web materials and workshop presentations; and c) the public through newspaper articles, radio interviews, and podcasts. The synthesis effort was initiated by resource managers at the United States Environmental Protection Agencys’ Chesapeake Bay Program and 16 scientist participants were recruited from a diversity of organizations. Multiple short, immersive workshops were conducted regularly to conceptualize the problem, followed by data analysis and interpretation that supported the preparation of the synthetic products that were communicated widely. Reflections on the process indicate that there are a variety of structural and functional requirements, as well as enabling conditions, that need to be considered to achieve successful outcomes from synthesis efforts.
The U.S. Geological Survey maintains a network of hydrologic monitoring stations across Kansas in cooperation with Federal, State, Tribal, and local agencies. During water year 2021, this network included 230 real-time surface water data collection sites, referred to as “streamgages.” A water year is the 12-month period from October 1 through September 30 and is designated by the calendar year in which it ends. These real-time data are routinely collected and calibrated by U.S. Geological Survey personnel via regular measurements of streamflow and water levels. Analyses of these data provide an understanding of hydrologic conditions in the State critical to the management of water resources, industrial and agricultural uses, protection of life and property during flooding, operation of reservoirs, recreation, and other activities.
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U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The maps and graphs in this summary describe national streamflow conditions for water year 2021 (a water year is the period from October 1 to September 30 and is designated by the year in which it ends; for example, water year 2021 was from October 1, 2020, to September 30, 2021) in the context of streamflow ranks relative to the 92-year period of water years 1930–2021. Annual runoff in the Nation’s rivers and streams during water year 2021 (9.43 inches) was higher than the long-term (1930–2021) mean annual runoff of 9.42 inches for the contiguous United States. Nationwide, the 2021 streamflow ranked the 46th highest out of the 92 years.
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415 National Center
Reston, Virginia 20192
The glacial geology and hydrogeology of valley-fill aquifers and their surrounding uplands are described within a 112-square-mile area in southern Otsego and northwestern Delaware Counties, New York, centered around the City of Oneonta. The major valleys include those of the Susquehanna River, Otego Creek, Charlotte Creek, and Schenevus Creek. A variety of data were analyzed to provide a broad picture of the glacial deposits, hydrogeologic framework, aquifer occurrence, and water-resource potential in the area. Both valley-fill and bedrock aquifers are used for water supply within the study area. The valley-fill aquifers consist of coarse-grained stratified drift, are mostly limited to the larger valleys, and have well yields that typically are much greater than those obtained from the bedrock aquifers. The bedrock aquifers generally have lower well yields, are the sole source of groundwater in upland areas, and are tapped in valley areas where sediments are very silty or are absent.
Through and non-through valleys and their orientations relative to ice flow have resulted in a variety of deglacial environments and deposits, some of which depart from glacial stratigraphy typically observed elsewhere in central New York. In comparison to through valleys with low in-valley divides, the regional thinning of ice over the high bedrock divides of the non-through valleys resulted in the earlier and more widespread stagnation of glacial ice, development of dead-ice sinks, and earlier diversion of meltwater from ice north of the divides. As the main through valley in the study area, the Susquehanna River valley is characterized by multiple inferred ice-margin positions with associated outwash deposition or ice-contact deposits. Throughout the study area, valleys orientated parallel or subparallel to the ice flow facilitated the development of long ice tongues; valleys oriented perpendicular to the ice flow led to little ice-tongue development, but they did facilitate the deposition of the extensive kame moraines that now occupy several-mile-long valley reaches. Lacustrine sediments were deposited in proglacial lakes. These sediments underlie most valleys that were oriented parallel and subparallel to ice flow, but they are largely absent in the Charlotte Creek valley, which was oriented perpendicular to the ice flow and now contains an extensive kame moraine. Beneath these lacustrine deposits, sand and gravel were deposited as subaqueous fans, eskers, and the distal parts of delta (kame) terraces, each with variable silt content.
The presence of coarse-grained stratified deposits, their saturated thicknesses, and their recharge potential are the primary controls on aquifer locations in the study area. The most widespread aquifers in the study area consist of sand and gravel and are confined mostly beneath lacustrine deposits. Confined aquifer yields are enhanced by hydraulic connections with unconfined ice-contact deposits along the valley walls, especially where tributary streams cross these deposits and provide additional recharge through streambed infiltration. The Susquehanna River and other large valley creeks provide a potentially large source of recharge to aquifers where groundwater withdrawals from nearby production wells induce infiltration of river water into aquifers. Unconfined aquifers are present where ice-contact deposits extend below the valley floor and are sufficiently saturated. Most surficial outwash deposits in the study area are thinly saturated; thus their water-resource potential is likely to be limited.
The upland areas contain very little stratified drift; therefore, characterization was limited to delineating areas of thick till and thin, or absent, till. Recharge of bedrock aquifers is greatest in areas overlain by thin till or where bedrock is exposed at land surface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225069","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Heisig, P.M., and Fleisher, P.J., 2022, Glacial geology and hydrogeology of valley-fill aquifers in the Oneonta area, Otsego and Delaware Counties, New York: U.S. Geological Survey Scientific Investigations Report 2022–5069, 35 p., 1 pl., https://doi.org/10.3133/sir20225069.","productDescription":"Report: vii, 35 p.; 1 Plate: 36.00 × 40.00 inches; 1 Figure: 25.00 × 17.00 inches ; Data Releases","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118408","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":405214,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCQS14","text":"USGS data release","linkHelpText":"Geospatial datasets of the glacial geology and hydrogeology of valley-fill aquifers in the Oneonta area, Otsego and Delaware Counties, New York"},{"id":405211,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225069/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5069"},{"id":405199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5069/coverthb.jpg"},{"id":405219,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2022/5069/sir20225069_plate01.pdf","text":"Plate 1","size":"177 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of glacial geology and hydrogeology of valley-fill aquifers in the Oneonta area, Otsego and Delaware Counties, New York [layered pdf; to toggle layers, download the file (right-click and select \"Save link as...\") and open it with Adobe Acrobat Reader]"},{"id":405210,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5069/sir20225069.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5069"},{"id":405212,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5069/sir20225069.XML"},{"id":405213,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5069/images/"},{"id":405218,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2022/5069/sir20225069_fig04a.pdf","text":"Figure 4A","size":"423 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Primary longitudinal hydrogeologic section A.1–A.1′ and secondary longitudinal hydrogeologic section A.2–A.2′ along the Susquehanna River valley, Otsego County, New York"},{"id":405216,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGQUJL","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio (HVSR) soundings in Broome, Chenango, Franklin, Orange, Rensselaer, and Saratoga Counties, New York, and Susquehanna County, Pennsylvania 2010–2019"},{"id":405215,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92NSO7T","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio soundings and depth-to-bedrock data for valley-fill aquifers in the Oneonta area, Otsego and Delaware Counties, New York, 2016–2018"}],"country":"United States","state":"New York","county":"Delaware County, Otsego County","otherGeospatial":"Oneonta area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.1667,\n 42.4167\n ],\n [\n -74.9167,\n 42.4167\n ],\n [\n -74.9167,\n 42.5833\n ],\n [\n -75.1667,\n 42.5833\n ],\n [\n -75.1667,\n 42.4167\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
Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the “true\" value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2022.118968","usgsCitation":"Mutzner, L., Furrer, V., Castebrunet, H., Dittmer, U., Fuchs, S., Gernjak, W., Gromaire, M., Matzinger, A., Mikkelsen, P.S., Selbig, W.R., and Vezzaro, L., 2022, A decade of monitoring micropollutants in urban wet-weather flows: What did we learn?: Water Research, v. 223, 118968, 14 p., https://doi.org/10.1016/j.watres.2022.118968.","productDescription":"118968, 14 p.","ipdsId":"IP-140246","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":446726,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2022.118968","text":"Publisher Index Page"},{"id":407377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"223","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mutzner, Lena","contributorId":296932,"corporation":false,"usgs":false,"family":"Mutzner","given":"Lena","email":"","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":852914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Furrer, Viviane","contributorId":296933,"corporation":false,"usgs":false,"family":"Furrer","given":"Viviane","email":"","affiliations":[{"id":64243,"text":"Swiss Federal Institute of Aquatic Science and Technology","active":true,"usgs":false}],"preferred":false,"id":852915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castebrunet, Helene","contributorId":296934,"corporation":false,"usgs":false,"family":"Castebrunet","given":"Helene","email":"","affiliations":[{"id":13426,"text":"University of Lyon","active":true,"usgs":false}],"preferred":false,"id":852916,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dittmer, Ulrich","contributorId":296935,"corporation":false,"usgs":false,"family":"Dittmer","given":"Ulrich","email":"","affiliations":[{"id":64244,"text":"Technical University Kaiserslautern","active":true,"usgs":false}],"preferred":false,"id":852917,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuchs, Stephan","contributorId":296936,"corporation":false,"usgs":false,"family":"Fuchs","given":"Stephan","email":"","affiliations":[{"id":64245,"text":"Karlsruhe Institute of Technology (KIT)","active":true,"usgs":false}],"preferred":false,"id":852918,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gernjak, Wolfgang","contributorId":296937,"corporation":false,"usgs":false,"family":"Gernjak","given":"Wolfgang","email":"","affiliations":[{"id":64246,"text":"ICRA, Catalan Institute for Water Research","active":true,"usgs":false}],"preferred":false,"id":852919,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gromaire, Marie-Christine","contributorId":296938,"corporation":false,"usgs":false,"family":"Gromaire","given":"Marie-Christine","email":"","affiliations":[{"id":64247,"text":"Leesu, École des Ponts ParisTech","active":true,"usgs":false}],"preferred":false,"id":852920,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Matzinger, Andreas","contributorId":296939,"corporation":false,"usgs":false,"family":"Matzinger","given":"Andreas","email":"","affiliations":[{"id":64248,"text":"Kompetenzzentrum Wasser Berlin (KWB)","active":true,"usgs":false}],"preferred":false,"id":852921,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mikkelsen, Peter Steen","contributorId":296940,"corporation":false,"usgs":false,"family":"Mikkelsen","given":"Peter","email":"","middleInitial":"Steen","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":852922,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852923,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Vezzaro, Luca","contributorId":296941,"corporation":false,"usgs":false,"family":"Vezzaro","given":"Luca","email":"","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":852924,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70235777,"text":"70235777 - 2022 - How does precipitation variability control bedload response across a mountainous channel network in a maritime climate?","interactions":[],"lastModifiedDate":"2022-08-18T15:04:08.1626","indexId":"70235777","displayToPublicDate":"2022-08-18T09:51:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"How does precipitation variability control bedload response across a mountainous channel network in a maritime climate?","docAbstract":"Modeled stream discharge is often used to drive sediment transport models across channel networks. Because sediment transport varies non-linearly with flow rates, discharge modeled from daily total precipitation distributed evenly over 24-hrs may significantly underestimate actual bedload transport capacity. In this study, we assume bedload transport capacity determined from a hydrograph resulting from the use of hourly (1-h) precipitation is a close approximation of actual transport capacity and quantify the error introduced into a network-scale bedload transport model driven by daily precipitation at channel network locations varying from lowland pool-riffle channels to upland colluvial channels in a watershed where snow accumulation and melt can affect runoff processes. Transport capacity is determined using effective stresses and the Wilcock and Crowe (2003) equations and expressed in terms of transport capacity normalized by the bankfull value. We find that, depending on channel network location, cumulative error can range from 10 - 20% to more than two orders of magnitude. Surprisingly, variation in flow rates due to differences in hillslope and channel runoff do not seem to dictate the network locations where the largest errors in predicted bedload transport capacity occur. Rather, spatial variability of the magnitude of the effective-bankfull-excess shear stress and changes in runoff due to snow accumulation and melt exert the greatest influence. These findings have implications for flood-hazard and aquatic habitat models that rely on modeled sediment transport driven by coarse-temporal-resolution climate data.","language":"English","publisher":"Wiley","doi":"10.1029/2021WR030358","usgsCitation":"Keck, J., Istanbulluoglu, E., Lundquist, J., Bandaragoda, C., Jaeger, K.L., Mauger, G.S., and Horner-Devine, A., 2022, How does precipitation variability control bedload response across a mountainous channel network in a maritime climate?: Water Resources Research, v. 58, no. 8, e2021WR030358, 28 p., https://doi.org/10.1029/2021WR030358.","productDescription":"e2021WR030358, 28 p.","ipdsId":"IP-142534","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":405308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Sauk River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.66671752929688,\n 47.89148526708789\n ],\n [\n -120.69030761718749,\n 47.89148526708789\n ],\n [\n -120.69030761718749,\n 48.47565256743914\n ],\n [\n -121.66671752929688,\n 48.47565256743914\n ],\n [\n -121.66671752929688,\n 47.89148526708789\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Keck, Jeffrey 0000-0002-0646-8574","orcid":"https://orcid.org/0000-0002-0646-8574","contributorId":295347,"corporation":false,"usgs":false,"family":"Keck","given":"Jeffrey","email":"","affiliations":[{"id":63850,"text":"University of Washington; Washington State Dept of Natrual Resources","active":true,"usgs":false}],"preferred":false,"id":849240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Istanbulluoglu, Erkan 0000-0001-9453-4676","orcid":"https://orcid.org/0000-0001-9453-4676","contributorId":295348,"corporation":false,"usgs":false,"family":"Istanbulluoglu","given":"Erkan","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":849241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lundquist, Jessica 0000-0003-2193-5633","orcid":"https://orcid.org/0000-0003-2193-5633","contributorId":295349,"corporation":false,"usgs":false,"family":"Lundquist","given":"Jessica","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":849242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bandaragoda, Christina 0000-0003-1617-1288","orcid":"https://orcid.org/0000-0003-1617-1288","contributorId":295350,"corporation":false,"usgs":false,"family":"Bandaragoda","given":"Christina","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":849243,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849244,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mauger, Guillaume S.","contributorId":138608,"corporation":false,"usgs":false,"family":"Mauger","given":"Guillaume","email":"","middleInitial":"S.","affiliations":[{"id":12463,"text":"Climate Impacts Group, College of the Environment, University of Washington","active":true,"usgs":false}],"preferred":false,"id":849245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Horner-Devine, Alex 0000-0003-2323-7150","orcid":"https://orcid.org/0000-0003-2323-7150","contributorId":295351,"corporation":false,"usgs":false,"family":"Horner-Devine","given":"Alex","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":849246,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70235762,"text":"70235762 - 2022 - Natural infrastructure in dryland streams (NIDS) can establish regenerative wetland sinks that reverse desertification and strengthen climate resilience","interactions":[],"lastModifiedDate":"2022-08-18T14:51:15.245065","indexId":"70235762","displayToPublicDate":"2022-08-18T09:43:51","publicationYear":"2022","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":"Natural infrastructure in dryland streams (NIDS) can establish regenerative wetland sinks that reverse desertification and strengthen climate resilience","docAbstract":"In this article we describe the natural hydrogeomorphological and biogeochemical cycles of dryland fluvial ecosystems that make them unique, yet vulnerable to land use activities and climate change. We introduce Natural Infrastructure in Dryland Streams (NIDS), which are structures naturally or anthropogenically created from earth, wood, debris, or rock that can restore implicit function of these systems. This manuscript further discusses the capability of and functional similarities between beaver dams and anthropogenic NIDS, documented by decades of scientific study. In addition, we present the novel, evidence-based finding that NIDS can create wetlands in water-scarce riparian zones, with soil organic carbon stock as much as 200 to 1400 Mg C/ha in the top meter of soil. We identify the key restorative action of NIDS, which is to slow the drainage of water from the landscape such that more of it can infiltrate and be used to facilitate natural physical, chemical, and biological processes in fluvial environments. Specifically, we assert that the rapid drainage of water from such environments can be reversed through the restoration of natural infrastructure that once existed. We then explore how NIDS can be used to restore the natural biogeochemical feedback loops in these systems. We provide examples of how NIDS have been used to restore such feedback loops, the lessons learned from installation of NIDS in the dryland streams of the southwestern United States, how such efforts might be scaled up, and what the implications are for mitigating climate change effects. Our synthesis portrays how restoration using NIDS can support adaptation to and protection from climate-related disturbances and stressors such as drought, water shortages, flooding, heatwaves, dust storms, wildfire, biodiversity losses, and food insecurity.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.157738","usgsCitation":"Norman, L., Lal, R., Wohl, E., Fairfax, E., Gellis, A.C., and Pollock, M.M., 2022, Natural infrastructure in dryland streams (NIDS) can establish regenerative wetland sinks that reverse desertification and strengthen climate resilience: Science of the Total Environment, v. 849, 157738, 20 p., https://doi.org/10.1016/j.scitotenv.2022.157738.","productDescription":"157738, 20 p.","ipdsId":"IP-137646","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science 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0000-0001-7435-5013","orcid":"https://orcid.org/0000-0001-7435-5013","contributorId":194945,"corporation":false,"usgs":false,"family":"Wohl","given":"Ellen","affiliations":[],"preferred":false,"id":849204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fairfax, Emily","contributorId":295332,"corporation":false,"usgs":false,"family":"Fairfax","given":"Emily","email":"","affiliations":[{"id":63844,"text":"California State University Channel Islands, Department of Environmental Science and Research Management, Camarillo, CA 93012","active":true,"usgs":false}],"preferred":false,"id":849205,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849206,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pollock, Michael M.","contributorId":295333,"corporation":false,"usgs":false,"family":"Pollock","given":"Michael","email":"","middleInitial":"M.","affiliations":[{"id":63846,"text":"NOAA Fisheries-Northwest Fisheries Science Center, Watershed Program, Bainbridge Island, WA 98110, USA","active":true,"usgs":false}],"preferred":false,"id":849207,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70235758,"text":"70235758 - 2022 - Using machine learning to improve predictions and provide insight into fluvial sediment transport","interactions":[],"lastModifiedDate":"2022-08-18T14:43:16.365462","indexId":"70235758","displayToPublicDate":"2022-08-18T09:36:27","publicationYear":"2022","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":"Using machine learning to improve predictions and provide insight into fluvial sediment transport","docAbstract":"A thorough understanding of fluvial sediment transport is critical to addressing many environmental concerns such as exacerbated flooding, degradation of aquatic habitat, excess nutrients, and the economic challenges of restoring aquatic systems. Fluvial sediment samples are integral for addressing these environmental concerns but cannot be collected at every river and time of interest. Therefore, to gain a better understanding for rivers where direct measurements have not been made, extreme gradient boosting machine learning (ML) models were developed and trained to predict suspended sediment and bedload from sampling data collected in Minnesota, United States (U.S.), by the U.S. Geological Survey. Approximately 400 watershed (full upstream area), catchment (nearby landscape), near-channel, channel, and streamflow features were retrieved or developed from multiple sources, reduced to approximately 30 uncorrelated features, and used in the final ML models. The results indicate suspended sediment and bedload ML models explain approximately 70% of the variance in the datasets. Important features used in the models were interpreted with Shapley additive explanation (SHAP) plots, which provided insight into sediment transport processes. The most important features in the models were developed to normalize streamflow by the 2-year recurrence interval and quantify the rate of change in streamflow (slope), which helped account for sediment hysteresis. Generally, this study also showed a combination of mostly watershed and catchment geospatial features were important in ML models that predict sediment transport from physical samples. This study is a promising step forward in making fluvial sediment transport predictions using machine learning models trained by physically collected samples. The approach developed here can be used wherever similar datasets exists and will be useful for landscape and water management.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14648","usgsCitation":"Lund, J.W., Groten, J.T., Karwan, D.L., and Babcock, C., 2022, Using machine learning to improve predictions and provide insight into fluvial sediment transport: Hydrological Processes, v. 36, no. 8, e14648, 21 p., https://doi.org/10.1002/hyp.14648.","productDescription":"e14648, 21 p.","ipdsId":"IP-133936","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":446739,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14648","text":"Publisher Index Page"},{"id":435725,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VOPSEJ","text":"USGS data release","linkHelpText":"Extreme gradient boosting machine learning models, suspended sediment, 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William 0000-0002-8830-4468","orcid":"https://orcid.org/0000-0002-8830-4468","contributorId":211157,"corporation":false,"usgs":true,"family":"Lund","given":"J.","email":"","middleInitial":"William","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Groten, Joel T. 0000-0002-0441-8442 jgroten@usgs.gov","orcid":"https://orcid.org/0000-0002-0441-8442","contributorId":173464,"corporation":false,"usgs":true,"family":"Groten","given":"Joel","email":"jgroten@usgs.gov","middleInitial":"T.","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":849195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karwan, Diana L.","contributorId":207315,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":849196,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Babcock, Chad","contributorId":150502,"corporation":false,"usgs":false,"family":"Babcock","given":"Chad","email":"","affiliations":[{"id":18039,"text":"Department of Geography, Michigan State University, East Lansing, Michigan USA","active":true,"usgs":false}],"preferred":false,"id":849197,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70235773,"text":"70235773 - 2022 - Metabolic costs associated with seawater acclimation in a euryhaline teleost, the fourspine stickleback (Apeltes quadracus)","interactions":[],"lastModifiedDate":"2022-08-18T14:35:38.312092","indexId":"70235773","displayToPublicDate":"2022-08-18T09:28:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12550,"text":"Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Metabolic costs associated with seawater acclimation in a euryhaline teleost, the fourspine stickleback (Apeltes quadracus)","title":"Metabolic costs associated with seawater acclimation in a euryhaline teleost, the fourspine stickleback (Apeltes quadracus)","docAbstract":"The cost of osmoregulation in teleosts has been debated for decades, with estimates ranging from one to 30 % of routine metabolic rate. The variation in the energy budget appears to be greater for euryhaline fish due to their ability to withstand dynamic salinity levels. In this study, a time course of metabolic and physiological responses of the euryhaline fourspine stickleback (Apeltes quadracus) acclimated to freshwater (FW) and then exposed to seawater (SW) was examined. There was 18% mortality in the first 3 days following exposure to SW, with no mortalities in the FW control group. Gill Na+/K+-ATPase (NKA) activity, an index of osmoregulatory capacity, increased 2.6-fold in SW fish peaking on days 7 and 14. Gill citrate synthase activity, an index of aerobic capacity, was 50–62% greater in SW than FW fish and peaked on day 7. Tissue water content was significantly lower in the SW fish on day 1 only, returning to FW levels by day 3. Routine metabolic rate was decreased within 24 h of SW exposure and was maintained slightly (8–22%) but significantly lower in SW compared to FW water controls throughout the 2-week experiment. These results indicate that elevated salinity resulted in increased SW osmoregulatory and aerobic capacity in the gill, but with a reduced whole animal metabolic rate to this euryhaline species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cbpb.2022.110780","usgsCitation":"Norstog, J.L., McCormick, S.D., and Kelly, J.T., 2022, Metabolic costs associated with seawater acclimation in a euryhaline teleost, the fourspine stickleback (Apeltes quadracus): Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, v. 262, 110780, https://doi.org/10.1016/j.cbpb.2022.110780.","productDescription":"110780","ipdsId":"IP-136345","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":405304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"262","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norstog, Jessica L. 0000-0002-5495-5131","orcid":"https://orcid.org/0000-0002-5495-5131","contributorId":295345,"corporation":false,"usgs":false,"family":"Norstog","given":"Jessica","email":"","middleInitial":"L.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":849236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":849237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelly, John T.","contributorId":150626,"corporation":false,"usgs":false,"family":"Kelly","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":18054,"text":"University of New Haven","active":true,"usgs":false}],"preferred":false,"id":849238,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235724,"text":"sir20225043 - 2022 - Water-quality conditions and constituent loads, water years 2013–19, and water-quality trends, water years 1983–2019, in the Scituate Reservoir drainage area, Rhode Island","interactions":[],"lastModifiedDate":"2026-04-09T17:35:58.64895","indexId":"sir20225043","displayToPublicDate":"2022-08-17T19:45:00","publicationYear":"2022","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":"2022-5043","displayTitle":"Water-Quality Conditions and Constituent Loads, Water Years 2013–19, and Water-Quality Trends, Water Years 1983–2019, in the Scituate Reservoir Drainage Area, Rhode Island","title":"Water-quality conditions and constituent loads, water years 2013–19, and water-quality trends, water years 1983–2019, in the Scituate Reservoir drainage area, Rhode Island","docAbstract":"The Scituate Reservoir is the primary source of drinking water for more than 60 percent of the population of Rhode Island. From October 1, 1982, to September 30, 2019, water years (WYs) 1983–2019 (a water year is the period between October 1 and September 30 and is designated by the year in which it ends), the Providence Water Supply Board maintained a fixed-frequency sampling program at 37 stations to monitor water quality in tributaries to the Scituate Reservoir. The U.S. Geological Survey (USGS), in cooperation with the Providence Water Supply Board, has measured streamflow at selected streamgages in the Scituate Reservoir drainage area since WY 1994, monitored water quality at selected stations since WY 2009, and conducted targeted base-flow and stormflow sampling at five stations in WYs 2016–19. Daily loads and yields of constituents (chloride, nitrite, nitrate, total coliform bacteria, Escherichia coli, and orthophosphate) were determined for sampled days during WYs 2013–19, and trends were examined for the entire period of record, predominantly WYs 1983–2019. USGS water-quality data were used to determine annual loads and yields of chloride and sodium for WYs 2013–19 at 14 stations, and nutrients and suspended sediment for WYs 2016–19 at 5 stations.
Tributaries in the Scituate Reservoir drainage area for WYs 2013–19 were slightly acidic (pH values less than 7.0 standard units) and often below the recommended pH range of 6.5 to 8.5 standard units, as described by the U.S. Environmental Protection Agency (EPA) in the secondary drinking-water regulations. Most measurements of water color in the tributaries were greater than the EPA secondary drinking-water regulation of 15 platinum-cobalt units. Chloride concentrations in Providence Water Supply Board samples rarely exceeded the EPA secondary drinking-water regulation for chloride (250 milligrams per liter); however, chloride concentrations estimated from continuous measurements of specific conductance exceeded the EPA criterion continuous concentration recommended for freshwater (230 milligrams per liter) for short periods ranging from 10 minutes to 26 hours at two streamgages.
Positive trends in pH, color, alkalinity, and chloride at more than half of the monitoring stations were identified for WYs 1983–2019. Fewer than half of the stations had significant trends in turbidity values, and significant trends varied in direction (positive or negative trends). Trend tests were not performed on total coliform bacteria, Escherichia coli, and nitrate concentrations because of analytical method changes that coincide with abrupt shifts in the magnitude and distribution of concentration data.
The median of daily loads and yields of chloride, nitrite, nitrate, orthophosphate, and bacteria determined for each Providence Water Supply Board sample in WYs 2013–19 varied across the 37 monitoring stations, but yields were generally greater at stations in the Moswansicut and Regulating Reservoir subbasins. Average daily yields of chloride and sodium estimated from continuous records of specific-conductance and streamflow data at 14 stations ranged from 42 to 310 kilograms per square mile per day and 28 to 180 kilograms per square mile per day, respectively. The mean annual yields of total phosphorus, total nitrogen, and suspended sediment determined for five stations ranged from 16 to 78 kilograms per square mile, from 370 to 2,100 kilograms per square mile, and from 5,000 to 13,000 kilograms per square mile, respectively. More than half of the nutrient and suspended sediment loads occurred during stormflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225043","collaboration":"Prepared in cooperation with the Providence Water Supply Board","usgsCitation":"Spaetzel, A.B., and Smith, K.P., 2022, Water-quality conditions and constituent loads, water years 2013–19, and water-quality trends, water years 1983–2019, in the Scituate Reservoir drainage area, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2022–5043, 102 p., https://doi.org/10.3133/sir20225043.","productDescription":"Report: xiv, 102 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-128796","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":405192,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5043/images/"},{"id":405190,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98XCK0R","text":"USGS data release","linkHelpText":"Water-quality, streamflow, and quality-control data supporting estimation of nutrient and sediment loads in the Scituate Reservoir drainage area, Rhode Island, water years 2016–19"},{"id":502398,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113397.htm","linkFileType":{"id":5,"text":"html"}},{"id":405191,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5043/sir20225043.XML"},{"id":405188,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5043/sir20225043.pdf","text":"Report","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5043"},{"id":405187,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5043/coverthb.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir drainage area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.79840087890625,\n 41.724180549563606\n ],\n [\n -71.52786254882812,\n 41.724180549563606\n ],\n [\n -71.52786254882812,\n 41.96459591213679\n ],\n [\n -71.79840087890625,\n 41.96459591213679\n ],\n [\n -71.79840087890625,\n 41.724180549563606\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532