Sediment from urban impervious surfaces has the potential to be an important vector for contaminants, particularly where stormwater culverts and other buried channels draining large impervious areas exit from underground pipes into open channels. To better understand urban sediment sources and their relation to fallout radionuclides, we collected samples of rainfall, urban sediment (pavement sediment, topsoil), streambank sediment, and fluvial sediment (suspended sediment and bed sediment) for 7Be, 210Pbex, and 137Cs analysis. The results indicate that each rainfall event tags pavement sediment with elevated activities of 7Be and 210Pbex such that runoff from impervious surfaces in the buried channel part of the stream network contains the highest activities. Pavement sediment, because it is characteristically a thin veneer, has a small mass to rainwater ratio resulting in a greater tagging of 7Be and 210Pbex activity than does topsoil on a per gram basis. An unmixing model indicated that suspended-sediment samples collected at the culvert outlet from the buried-channel network are from pavement sediment sources (45 ± 25%) with a smaller component of topsoil (22 ± 19%), and a component from streambanks (32 ± 35%) that we infer to be older channel material and subsoil eroded from within the culvert system. Downstream from the culvert, suspended sediment collected from the open-channel parts of the stream had 7Be and 210Pbex activities that were substantially reduced by the contribution of sediment from streambanks (57 ± 15%), with pavement contributions decreasing to 15 (±9%) and topsoil contributing 28 (±7%). The results highlight the utility of 7Be, 210Pbex, and 137Cs as tracers of urban sediment sources, resulting in a unique radionuclide signature for urban watersheds compared to other sediment-source settings.
Streamflow generation in mountain watersheds is strongly influenced by snow accumulation and melt, and multiple studies have found that snow loss leads to earlier snowmelt timing and declines in annual streamflow. However, hydrologic responses to snow loss are heterogeneous, and not all areas experience streamflow declines. This research examines whether streamflow generation is different for rainfall versus snowmelt inputs. We compiled a sample of 57 small U.S. Geological Survey watersheds in the western United States containing a Natural Resource Conservation Service Snow Telemetry site and having ratios of mean annual peak snow water equivalent to precipitation ratios >0.25. Daily streamflow was separated into quickflow and baseflow using a digital filter, and quickflow was then divided into quickflow response intervals using thresholds in quickflow slope. Each quickflow response interval was categorized by its fraction of input from snowmelt. Most sites exhibited two streamflow generation peaks each year, with one peak in the winter when runoff efficiency is greatest, and the second in the spring during peak snowmelt input. On average, study watersheds were dominated by snowmelt inputs (70%), and snowmelt and mixed inputs usually generated greater streamflow than rainfall because of higher inputs and longer durations. However, rainfall produced high streamflow generation in winter, when watersheds have their highest runoff efficiency (81%) across all input types. We demonstrate that while snowmelt is important for streamflow generation due to high input over long periods, increases in rain and mixed input during wet winter periods can countervail tendencies for reduced streamflow with declining snowpacks.
The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104692","usgsCitation":"Stanley, T.A., Kirschbaum, D.B., Sobieszczyk, S., Jasinski, M.F., Borak, J.S., and Slaughter, S.L., 2020, Building a landslide hazard indicator with machine learning and land surface models: Environmental Modelling & Software, v. 129, 104692, 15 p., https://doi.org/10.1016/j.envsoft.2020.104692.","productDescription":"104692, 15 p.","ipdsId":"IP-114297","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":457392,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2020.104692","text":"Publisher Index Page"},{"id":410787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, 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The restoration of protection provided by forests from post-fire floods and debris flows depends on the recovery of infiltration and attendant reduction of infiltration-excess surface runoff generation. This work examines seven years of post-fire infiltration measurements and temporal relations fit to soil-hydraulic properties from the Colorado Front Range, USA to assess infiltration recovery with increasing time since fire. Point-scale Green-Ampt simulations of infiltration across a full spectrum of rainfall events are used to evaluate infiltration changes and shifts in surface runoff generation thresholds with post-fire temporal recovery. Measured and simulated infiltration generally recovered monotonically with increasing time since fire. This indicates a reduced vulnerability to infiltration-excess runoff generation as time elapses, with the greatest risk in the first two years after the fire. The threshold for infiltration-excess runoff advances with increasing time to rainfall events with higher intensity and greater return intervals; by the third year after wildfire only extreme events (30-100 year recurrence) generate surface runoff and by the fifth and seventh year even extreme rainfall events typically fail to generate surface runoff. Remotely-sensed vegetation indices suggest linked, or at least contemporaneous, recovery of understory vegetation and field-saturated hydraulic conductivity at this field site, suggesting coincident recovery of multiple hillslope properties impacting surface runoff generation. This work indicates that the closing of the window of disturbance after wildfire, relative to infiltration-excess runoff generation and corresponding flash flood and debris flow hazards, relies on coupled assessments of hillslope property recovery and stochasticity of high-intensity rainfall. Post-fire hazard assessments using static hillslope properties could fail to predict flash floods and debris flows associated with infrequent extreme rainfall events that strike during the post-fire recovery period when hillslope properties are partially recovered.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.124765","collaboration":"","usgsCitation":"Ebel, B., 2020, Temporal evolution of measured and simulated infiltration following wildfire in the Colorado Front Range, USA: Shifting thresholds of runoff generation and hydrologic hazards: Journal of Hydrology, v. 585, https://doi.org/10.1016/j.jhydrol.2020.124765.","productDescription":"124765, 16 p.","startPage":"124765","ipdsId":"IP-111116","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457401,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2020.124765","text":"Publisher Index Page"},{"id":437057,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCNFD9","text":"USGS data release","linkHelpText":"Green-Ampt infiltration modeling following wildfire in the Colorado Front Range, USA"},{"id":374881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Colorado Front Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.270751953125,\n 39.52522954427751\n ],\n [\n -104.52392578125,\n 39.52522954427751\n ],\n [\n -104.52392578125,\n 40.901057866884024\n ],\n [\n -106.270751953125,\n 40.901057866884024\n ],\n [\n -106.270751953125,\n 39.52522954427751\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"585","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":789317,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70208809,"text":"sir20195127 - 2020 - An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset","interactions":[],"lastModifiedDate":"2022-04-25T19:26:27.608939","indexId":"sir20195127","displayToPublicDate":"2020-03-10T10:15:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5127","displayTitle":"An Enhanced Hydrologic Stream Network Based on the NHDPlus Medium Resolution Dataset","title":"An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset","docAbstract":"The National Hydrography Dataset Plus, Version 2.1 (NHDPlusV2.1) is an attribute-rich digital stream network for the conterminous United States, serving as a foundational infrastructure for reporting hydrologic information at both regional and national scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW) is a process-based statistical model that relies on a digital hydrologic network like NHDPlusV2.1 to establish spatial relations between quantities of monitored contaminant loads and contaminant sources, accounting for the physical characteristics along flow paths affecting contaminant transport. The U.S. Geological Survey National Water Quality Assessment project adopted and modified the medium-resolution NHDPlusV2.1 network for use as the primary framework supporting SPARROW modeling. This report describes the enhancements made to improve the routing capabilities and the value-added attributes of NHDPlusV2.1 to support modeling and other hydrologic analyses. These enhancements include corrections to inconsistencies in network/routing information, filling in missing attribute values of associated characteristics, accounting of water use affecting flow, new variables useful for interpreting network data, revised flowline attributes such as slope and flow, and incorporation of ancillary spatial data into the network. The resulting dataset containing the enhancements to the network is named E2NHDPlusV2_US. Although the enhancements described in the report were developed for use in SPARROW modeling, the enhancements are expected to be useful for a wide variety of hydrologic studies within the United States.
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National Water Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20191-0002
The principle sources of trace elements entering upper Tenmile Creek, Montana, during September 2011, four trace metals and the metalloid arsenic, were identified and quantified by combining and analyzing streamflow data determined from tracer injection with trace-element concentrations and related water-quality data determined from synoptic sampling. The study reach was along upper Tenmile Creek, beginning downstream from the city of Helena’s diversion and extending 5,020 feet downstream. Results from the 2011 study, completed by the U.S. Geological Survey in cooperation with the Montana Department of Environmental Quality, were compared to results from a similar study conducted in 1998 to assess the effectiveness of mine reclamation and remediation work to reduce trace-element loading to upper Tenmile Creek, which has been ongoing throughout the drainage basin.
Main-stem concentrations of most trace elements analyzed were generally greater in 1998 than in 2011. However, the State of Montana human-health criteria for total-recoverable cadmium and arsenic were exceeded in parts of upper Tenmile Creek, and concentrations of cadmium and zinc exceeded the acute aquatic-life criteria at all main-stem sites during both studies. Total-recoverable copper concentrations observed in 2011 exceeded the chronic aquatic-life criterion upstream from the Lee Mountain adit, whereas, in 1998, all sites exceeded the acute aquatic-life criteria.
Direct comparison of loads from the 1998 and 2011 tracer studies were complicated by the differences in hydrologic conditions. Streamflow in 1998 was about 10 percent of the 2011 streamflow. The Lee Mountain Mine and Susie Lode adit were identified as major contributors of trace elements to upper Tenmile Creek in both studies. However, trace-element loading from the Lee Mountain Mine area was substantially reduced between 1998 and 2011. Total-recoverable loads of all trace elements showed substantial loss in 1998 but increased in 2011 downstream from the Susie Lode adit to the end of the study reach. This reach was one of the primary sources of trace-element loading to upper Tenmile Creek in 2011. This difference indicated that the streambed may act as a sink or a source for trace elements, depending on hydrologic conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195126","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Cleasby, T., and Eldridge, S.L.C., 2020, Quantification of trace element loading in the upper Tenmile Creek drainage basin near Rimini, Montana, September 2011: U.S. Geological Survey Scientific Investigations Report 2019–5126, 40 p., https://doi.org/10.3133/sir20195126.","productDescription":"Report: vii, 40 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-043897","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":399607,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109755.htm"},{"id":372808,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System database","linkHelpText":"– USGS water data for the Nation"},{"id":372807,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5126/sir20195126.pdf","text":"Report","size":"6.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5126"},{"id":372806,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5126/coverthb.jpg"}],"country":"United States","state":"Montana","county":"Lewis and Clark County","city":"Rimini","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.2533,\n 46.4808\n ],\n [\n -112.2444,\n 46.4808\n ],\n [\n -112.2444,\n 46.5008\n ],\n [\n -112.2533,\n 46.5008\n ],\n [\n -112.2533,\n 46.4808\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Management of water resources in the transboundary Rio Grande/Río Bravo Basin (the Basin) presents challenges for state and Federal entities in the United States and Mexico making management decisions on shared water resources. Damming, channelization, water availability, and allocation are governed by water rights and water-sharing agreements. Data and information sharing are important aspects of transboundary cooperation, but differences in format, content, spatial and temporal resolution, and language hinder collaboration. In addition, data on the kinds and geographic distribution of water governance and management institutions across the Basin have not been consistently documented. Existing data disparities parallel the hydrological and social fragmentation of the Basin.
Seeking to underscore the interdependence between social and environmental processes in the Basin, anthropologists and modelers collaborated to develop a socio-environmental geodatabase. This geodatabase is a first step in modeling the social components of decision making and their connectivity to environmental processes across the Basin. The geodatabase is available in an open-access domain and contains geospatial data related to water and land governance, hydrology, water use and hydraulic infrastructure, socioeconomics, and the biophysical environment necessary to advance the understanding of basin dynamics. Having these data documented and compiled in a central location serves as a resource to help decision makers better understand upstream and downstream social-environmental characteristics. This knowledge is useful for developing sustainable water management policies in a region where water resources are increasingly under pressure from climatic, environmental, and human-related changes.
","language":"English","publisher":"Collaborative Conservation and Adaptation Strategy Toolbox (CCAST)","usgsCitation":"Villa, J., 2020, A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin: CCAST Case Study on Actionable Science, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-123961","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":398832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395033,"type":{"id":15,"text":"Index Page"},"url":"https://arcg.is/0bava9"}],"country":"Mexico, United States","state":"Chihuahua, New Mexico, Texas","otherGeospatial":"Rio Grande/Río Bravo basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.12493896484374,\n 31.421631960419596\n ],\n [\n -105.90545654296875,\n 31.421631960419596\n ],\n [\n -105.90545654296875,\n 32.58384932565662\n ],\n [\n -107.12493896484374,\n 32.58384932565662\n ],\n [\n -107.12493896484374,\n 31.421631960419596\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Villa, Jennifer 0000-0002-4774-7166","orcid":"https://orcid.org/0000-0002-4774-7166","contributorId":245824,"corporation":false,"usgs":true,"family":"Villa","given":"Jennifer","email":"","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832043,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70217011,"text":"70217011 - 2020 - Causal effect of impervious cover on annual flood magnitude for the United States","interactions":[],"lastModifiedDate":"2020-12-28T12:49:18.302259","indexId":"70217011","displayToPublicDate":"2020-03-05T06:30:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Causal effect of impervious cover on annual flood magnitude for the United States","docAbstract":"Despite consensus that impervious surfaces increase flooding, the magnitude of the increase remains uncertain. This uncertainty largely stems from the challenge of isolating the effect of changes in impervious cover separate from other factors that also affect flooding. To control for these factors, prior study designs rely on either temporal or spatial variation in impervious cover. We leverage both temporal and spatial variation in a panel data regression design to isolate the effect of impervious cover on floods. With 39 years of data from 280 U.S. streamgages, we estimate that a one percentage point increase in impervious basin cover causes a 3.3% increase in annual flood magnitude (95%CI: 1.9%, 4.7%) on average. Using 2,109 streamgages, some of which have upstream regulation and/or overlapping basins, we estimate a larger effect: 4.6% (CI: 3.5%, 5.6%). The approach introduced here can be extended to estimate the causal effects of other drivers of hydrologic change.
Sediment is one of the leading pollutants in rivers and streams across the United States (US) and the world. Between 1992 and 2012, concentrations of annual mean suspended sediment decreased at over half of the 137 stream sites assessed across the contiguous US. Increases occurred at less than 25 % of the sites, and the direction of change was uncertain at the remaining 25 %. Sediment trends were characterized using the Weighted Regressions on Time, Discharge, and Season (WRTDS) model, and decreases in sediment ranged from −95 % to −8.5 % of the 1992 concentration. To explore potential drivers of these changes, the sediment trends were (1) parsed into two broad contributors of change, changes in land management versus changes in the streamflow regime, and (2) grouped by land use of the watershed and correlated to concurrent changes in land use or land cover (land use/cover), hydrology and climate variables and static/long-term watershed characteristics. At 83 % of the sites, changes in land management (captured by changes in the concentration–streamflow relationship over time; C–Q relationship) contributed more to the change in the sediment trend than changes in the streamflow regime alone (i.e., any systematic change in the magnitude, frequency or timing of flows). However, at >50 % of the sites, changes in the streamflow regime contributed at least a 5 % change in sediment, and at 11 sites changes in the streamflow regime contributed over half the change in sediment, indicating that at many sites changes in streamflow were not the main driver of changes in sediment but were often an important supporting factor. Correlations between sediment trends and concurrent changes in land use/cover, hydrology and climate were often stronger at sites draining watersheds with more homogenous, human-related land uses (i.e., agricultural and urban lands) compared to mixed-use or undeveloped lands. At many sites, decreases in sediment occurred despite small-to-moderate increases in the amount of urban or agricultural land in the watershed, suggesting conservation efforts and best-management practices (BMPs) used to reduce sediment runoff to streams may be successful, up to a point, as lands are converted to urban and agricultural uses.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-24-991-2020","usgsCitation":"Murphy, J.C., 2020, Changing suspended sediment in United States rivers and streams: Linking sediment trends to changes in land use/cover, hydrology and climate: Hydrology and Earth System Sciences, v. 24, p. 991-1010, https://doi.org/10.5194/hess-24-991-2020.","productDescription":"20 p.","startPage":"991","endPage":"1010","ipdsId":"IP-105905","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":457510,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-24-991-2020","text":"Publisher Index Page"},{"id":372922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n 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Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":783837,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211932,"text":"70211932 - 2020 - Mercury export from Arctic great rivers","interactions":[],"lastModifiedDate":"2020-08-11T21:05:02.516262","indexId":"70211932","displayToPublicDate":"2020-03-02T16:04:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Mercury export from Arctic great rivers","docAbstract":"Land–ocean linkages are strong across the circumpolar north, where the Arctic Ocean accounts for 1% of the global ocean volume and receives more than 10% of the global river discharge. Yet estimates of Arctic riverine mercury (Hg) export constrained from direct Hg measurements remain sparse. Here, we report results from a coordinated, year-round sampling program that focused on the six major Arctic rivers to establish a contemporary (2012–2017) benchmark of riverine Hg export. We determine that the six major Arctic rivers exported an average of 20 000 kg y–1 of total Hg (THg, all forms of Hg). Upscaled to the pan-Arctic, we estimate THg flux of 37 000 kg y–1. More than 90% of THg flux occurred during peak river discharge in spring and summer. Normalizing fluxes to watershed area (yield) reveals higher THg yields in regions where greater denudation likely enhances Hg mobilization. River discharge, suspended sediment, and dissolved organic carbon predicted THg concentration with moderate fidelity, while suspended sediment and water yields predicted THg yield with high fidelity. These findings establish a benchmark in the face of rapid Arctic warming and an intensifying hydrologic cycle, which will likely accelerate Hg cycling in tandem with changing inputs from thawing permafrost and industrial activity.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.9b07145","usgsCitation":"Zolkos, S., Krabbenhoft, D.P., Suslova, A., Tank, S.E., McClelland, J.W., Spencer, R.G., Shiklomanov, A., Zhulidov, A.V., Gurtovaya, T., Zimov, N., Zimov, S., Mutter, E., Kutny, L., Amos, E., and Holmes, R.M., 2020, Mercury export from Arctic great rivers: Environmental Science & Technology, v. 54, no. 7, p. 4140-4148, https://doi.org/10.1021/acs.est.9b07145.","productDescription":"9 p.","startPage":"4140","endPage":"4148","ipdsId":"IP-115773","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Russia, United States","volume":"54","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Zolkos, Scott 0000-0001-9945-6945","orcid":"https://orcid.org/0000-0001-9945-6945","contributorId":238024,"corporation":false,"usgs":false,"family":"Zolkos","given":"Scott","email":"","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":795852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"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":795853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suslova, Anya","contributorId":238025,"corporation":false,"usgs":false,"family":"Suslova","given":"Anya","email":"","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":795854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tank, Suzanne E. 0000-0002-5371-6577","orcid":"https://orcid.org/0000-0002-5371-6577","contributorId":238026,"corporation":false,"usgs":false,"family":"Tank","given":"Suzanne","email":"","middleInitial":"E.","affiliations":[{"id":47684,"text":"Department of Biological Sciences, University of Alberta","active":true,"usgs":false}],"preferred":false,"id":795855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McClelland, James W. 0000-0001-9619-8194","orcid":"https://orcid.org/0000-0001-9619-8194","contributorId":238027,"corporation":false,"usgs":false,"family":"McClelland","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":47685,"text":"Marine Science Institute, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":795856,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spencer, Robert G. M. 0000-0003-0777-0748","orcid":"https://orcid.org/0000-0003-0777-0748","contributorId":238028,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert","email":"","middleInitial":"G. M.","affiliations":[{"id":47686,"text":"Department of Earth, Ocean and Atmospheric Science, Florida State University","active":true,"usgs":false}],"preferred":false,"id":795857,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shiklomanov, Alexander","contributorId":238029,"corporation":false,"usgs":false,"family":"Shiklomanov","given":"Alexander","affiliations":[{"id":47687,"text":"Institute for the Study of Earth, Oceans, and Space, University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":795858,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhulidov, Alexander V.","contributorId":238030,"corporation":false,"usgs":false,"family":"Zhulidov","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":47688,"text":"South Russia Centre for Preparation and Implementation of International Projects, Rostov-on-Don, Russia","active":true,"usgs":false}],"preferred":false,"id":795859,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gurtovaya, Tatiana","contributorId":238031,"corporation":false,"usgs":false,"family":"Gurtovaya","given":"Tatiana","email":"","affiliations":[{"id":47688,"text":"South Russia Centre for Preparation and Implementation of International Projects, Rostov-on-Don, Russia","active":true,"usgs":false}],"preferred":false,"id":795860,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zimov, Nikita","contributorId":238032,"corporation":false,"usgs":false,"family":"Zimov","given":"Nikita","email":"","affiliations":[{"id":47689,"text":"Northeast Science Station, Far Eastern Branch of Russian Academy of Science, Chersky, Russia","active":true,"usgs":false}],"preferred":false,"id":795861,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zimov, Sergey","contributorId":238033,"corporation":false,"usgs":false,"family":"Zimov","given":"Sergey","email":"","affiliations":[{"id":47689,"text":"Northeast Science Station, Far Eastern Branch of Russian Academy of Science, Chersky, Russia","active":true,"usgs":false}],"preferred":false,"id":795862,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mutter, Edda A.","contributorId":238034,"corporation":false,"usgs":false,"family":"Mutter","given":"Edda A.","affiliations":[{"id":47690,"text":"˚Yukon River Inter-Tribal Watershed Council, Anchorage, Alaska","active":true,"usgs":false}],"preferred":false,"id":795863,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kutny, Les","contributorId":238035,"corporation":false,"usgs":false,"family":"Kutny","given":"Les","email":"","affiliations":[{"id":47691,"text":"Western Arctic Research Centre, Inuvik, Northwest Territories, Canada","active":true,"usgs":false}],"preferred":false,"id":795864,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Amos, Edwin","contributorId":238036,"corporation":false,"usgs":false,"family":"Amos","given":"Edwin","email":"","affiliations":[{"id":47691,"text":"Western Arctic Research Centre, Inuvik, Northwest Territories, Canada","active":true,"usgs":false}],"preferred":false,"id":795865,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Holmes, Robert M.","contributorId":178901,"corporation":false,"usgs":false,"family":"Holmes","given":"Robert","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":795866,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70209070,"text":"70209070 - 2020 - Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018","interactions":[],"lastModifiedDate":"2020-03-16T17:06:02","indexId":"70209070","displayToPublicDate":"2020-03-02T14:40:52","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018","docAbstract":"We sampled juvenile wild Steelhead Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize populations and investigate life-history metrics, particularly migratory patterns. We used Passive Integrated Transponder (PIT)-tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead have been planted in the Wind River subbasin since 1997, and hatchery adults are estimated to be less than one percent of spawners in most years (pers comm. Thomas Buehrens, Washington Department of Fish and Wildlife). Our repeated headwater sampling of consistent sites in the Wind River subbasin has also allowed us to track relative abundance of Brook Trout, a non-native species to the Wind River. Our work is contributing to understanding of Steelhead population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, where our PTISs are helping to quantify adult response.
Data from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administration’s (BPA) Research Monitoring and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (www.cbfish.org/ProgramStrategy.mvc/ViewProgramStrategySummary/1). Specifically this work addresses the sub-strategies of: 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and to Uncertainties Research regarding differing life histories of a wild Steelhead population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives. Our headwaters parr PIT tagging, WDFW parr, smolt, and adult tagging and our instream PTISs are providing data on movements and life histories of parr, smolt, and adult Steelhead.
During summer 2018, we PIT-tagged 1,592 age-0 and age-1 Steelhead parr in headwater areas of the Wind River subbasin to characterize population traits and investigate life-history diversity, including growth and pre-smolt downstream movement. Repeat headwater sampling and smolt trap operations provide opportunities for recapture, and instream PTISs and Columbia River infrastructure provide opportunity for detection of PIT-tagged fish. Throughout the year, we maintained a series of six instream PTISs to monitor movement of tagged Steelhead parr, smolts, and adults.
Detections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. Substantial numbers of parr make downstream movements as age-1 fish. We have estimated that from 15 to 33 percent of parr tagged as age-0 fish make downstream migrations at age-1 for additional rearing. We have estimated that from 1 to 27 percent of parr tagged as age-1 fish make downstream migrations during fall. These findings raise many questions about parr rearing strategies, habitat use, and success of these migrants and suggest a need for broader monitoring of juvenile Steelhead in some river systems to fully document juvenile production. Long-term monitoring of PIT-tagged fish is providing information on contribution of various life-history strategies to smolt production and adult returns.
Movements of PIT-tagged adult Steelhead were recorded at instream PTISs. These data have allowed assessment of adult returns to tributary watersheds within the Wind River subbasin. Detection efficiency of adult PIT-tagged Steelhead at our primary adult-monitoring PTIS in Trout Creek has been greater than 92 percent during 6 of the past 7 years. This is providing excellent data to estimate adult returns to this watershed. Determination of adult use of tributary watersheds is providing data to help evaluate the efficacy of the removal of Hemlock Dam on Trout Creek. Hemlock Dam, located at rkm 2.0 of Trout Creek, was removed in summer 2009. The dam contributed to hydrologic impairment of Trout Creek and had potential negative effects on Steelhead. The improvements made to the upper Wind River PTIS (site code WRU at rkm 28.3; better site characteristics and grid power) during 2016 and 2017, and a planned new site in the Mine Reach of the upper Wind River, will allow estimates of subbasin adult escapement like those in Trout Creek.
During 2018, we also completed planning and permitting with U.S. Forest Service for a new PTIS site at rkm 36 of the Wind River (the Mine Reach, mentioned above). This site will replace two sites (one in Paradise Creek and one at rkm 41 of the Wind River), which had operational challenges due to lack of adequate solar power and winter difficulties. The new Mine Reach PTIS site at rkm 36, will have better solar exposure, fewer winter operations difficulties, and provide opportunity to detect fish from juvenile sampling sites that were downstream of the previous two PTISs. The more consistent operation of the new Mine Reach PTIS site will increase our ability to estimate migrant abundance as all the juveniles tagged upstream of it will be subject to the same potential detection history, instead of three different potential detection histories as before. Additionally, with the new Mine Reach PTIS site lower in the watershed, it will subject more PIT-tagged adult Steelhead to detection and provide ability to generate a nonbiased adult-detection efficiency estimate for the WRU PTIS at rkm 28.3 of the Wind River. This will provide the opportunity to estimate yearly adult Steelhead abundance to the upper Wind watershed area. Permitting is complete and some supplies have been purchased to build and install this new site in 2019.
Repeat sampling at consistent locations in the subbasin has allowed investigation into juvenile Steelhead growth patterns. Growth rates (relative change in weight) of age-0 PIT-tagged parr during summer are similar across the subbasin, but lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly growth for parr tagged at age-0 is similar across the subbasin. Yearly growth for parr tagged at age-1 is lowest in Martha Creek, but similar elsewhere.
Non-native Brook Trout are present in portions of the subbasin, chiefly the Trout Creek watershed, and repeat sampling has allowed us to index their prevalence. Percentage of catch that is Brook Trout at each of four sample sites in Trout Creek have declined from the period 1998 – 2003 to the period 2011 – 2018. There was a pattern of decline in percent of catch and number of Brook Trout at the Trout Creek sites from 2011 through 2016, though a slight upward trend during 2017 and 2018 has been evident.
Evaluating and planning restoration efforts are of interest to many managers and agencies to ensure efficient use of resources. The evaluation of various life-histories of Steelhead within the Wind River subbasin will provide information to better track populations, and to direct habitat restoration and water allocation planning. Movement of Steelhead parr raises many questions regarding estimating juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT tagging of age-0 and age-1 Steelhead parr are increasingly allowing us to investigate such questions. Increasingly detailed Viable Salmonid Population information, such as that provided by PIT-tagging and instream PTISs networks like those in the Wind River subbasin, provide data to inform policy and management, as life-history strategies and production bottlenecks are identified and understood.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Jezorek, I.G., 2020, Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018, 74 p.","productDescription":"74 p.","ipdsId":"IP-115314","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373226,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P170098"}],"country":"United States","state":"Washington","otherGeospatial":"Wind River subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.34374999999999,\n 45.69083283645816\n ],\n [\n -120.62988281249999,\n 45.69083283645816\n ],\n [\n -120.62988281249999,\n 46.649436163350245\n ],\n [\n -122.34374999999999,\n 46.649436163350245\n ],\n [\n -122.34374999999999,\n 45.69083283645816\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian G. 0000-0002-3842-3485 ijezorek@usgs.gov","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":3572,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","email":"ijezorek@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":784716,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70208889,"text":"70208889 - 2020 - Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses","interactions":[],"lastModifiedDate":"2020-03-04T15:12:43","indexId":"70208889","displayToPublicDate":"2020-03-01T15:07:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses","docAbstract":"Hydrology and water quality (suspended sediment, total nitrogen, ammonia, total Kjeldahl nitrogen, nitrate plus nitrite, and total phosphorus (TP)) were monitored in two small agricultural drainages in northwestern Mississippi to document changes in water quality that coincided with the implementation of BMPs in upstream drainages. Using an event-based dataset and bootstrapping techniques, we tested for difference and equivalence in median event concentration and differences in concentration-discharge (C-Q) relationships between an early and late period at each site, where most of the major BMP implementation occurred during the early period. Results for one site were inconclusive. None of the constituents had statistically different or equivalent event concentrations between the periods, indicating a lack of evidence to tell whether water quality had changed or stayed the same, and only TP had a significantly higher C-Q slope during the late period. At the other site, more than half the constituents had a significantly different median, slope, or intercept between periods, indicating a 35% or more decrease in event concentration following a period of intense BMP implementation. These mixed results could be due to variety of differences between the sites including BMP implementation, production practices, and crops. We also used the monitoring data to generate synthetic data and perform a simulation-based power analysis to explore the ability to detect change under 25 scenarios of sampled event counts and hypothetical percent changes. The simulation-based power analysis indicated that high natural variability in event concentration and flow hindered our ability to detect change. Based on our monitoring, data analysis, and power analysis, we provide recommendations for future monitoring.","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.75.2.218","usgsCitation":"Murphy, J.C., Hicks, M.B., and Stocks, S.J., 2020, Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses: Journal of Soil and Water Conservation, v. 75, no. 2, p. 218-230, https://doi.org/10.2489/jswc.75.2.218.","productDescription":"13 p.","startPage":"218","endPage":"230","ipdsId":"IP-091590","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":457542,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.75.2.218","text":"Publisher Index Page"},{"id":437076,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75H7FJJ","text":"USGS data release","linkHelpText":"Hydrologic event-based water-quality and streamflow data for three oxbow tributaries in northwestern Mississippi, 2007-2016"},{"id":372917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Bee Lake, Lake Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.889892578125,\n 32.35212281198644\n ],\n [\n -90.186767578125,\n 33.15594830078649\n ],\n [\n -90.098876953125,\n 33.93424531117312\n ],\n [\n -90.208740234375,\n 34.96699890670367\n ],\n [\n -90.54931640625,\n 34.67839374011646\n ],\n [\n -90.802001953125,\n 34.27083595165\n ],\n [\n -91.0546875,\n 33.925129700072\n ],\n [\n -91.1865234375,\n 33.63291573870479\n ],\n [\n -91.153564453125,\n 33.27543541298162\n ],\n [\n -91.131591796875,\n 32.80574473290688\n ],\n [\n -91.043701171875,\n 32.44488496716713\n ],\n [\n -90.889892578125,\n 32.35212281198644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":167405,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":783845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hicks, Matthew B. 0000-0001-5516-0296 mhicks@usgs.gov","orcid":"https://orcid.org/0000-0001-5516-0296","contributorId":3778,"corporation":false,"usgs":true,"family":"Hicks","given":"Matthew","email":"mhicks@usgs.gov","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783846,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stocks, Shane J. 0000-0003-1711-3071 sjstocks@usgs.gov","orcid":"https://orcid.org/0000-0003-1711-3071","contributorId":3811,"corporation":false,"usgs":true,"family":"Stocks","given":"Shane","email":"sjstocks@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783898,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208171,"text":"70208171 - 2020 - Preserving meander bend geometry through scale","interactions":[],"lastModifiedDate":"2020-08-28T12:39:57.506553","indexId":"70208171","displayToPublicDate":"2020-03-01T13:01:09","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preserving meander bend geometry through scale","docAbstract":"Stream meander geometry is a function of hydrologic, geologic, and anthropogenic forces. Meander morphometrics are used in geomorphic classification, ecological characterization, and tectonic and hydrologic change detection. Thus, detailed measurement and classification of meander geometry is imperative to multiscale representation of hydrographic features, which raises important questions. What meander geometries are important to preserve in multi-scale databases? How are geometries measured? How are they preserved? Is the choice between preservation of geometry or use of classification attributes? Questions related to multiscale measurement and representation of hydrographic features continue to emerge with increased spatial and temporal data collection.
A key metric for understanding meander bend geometry is sinuosity. The most common measure of sinuosity is the length of a feature divided by the distance between stream head and mouth. The measure relays deviation from a straight line but nothing about meander wavelength. There is not a clear consensus on methods for measuring meander geometry, much less efficiently, at scales made viable with increased data resolution. Here we propose a method for automated characterization of meander wavelength or bend radius. The method, termed Scale-Specific Sinuosity (S3), is a derivation from the Richardson plot. The Richardson (1961) plot is a classic means of calculating fractal dimension of natural line features and describes feature length (ℓ) given increasing vertex spacing, or step size (S), plotted on a log-log plot. The S3 metric is defined as negative one times the slope of a Richardson plot for a given stride length. This paper demonstrates utility of S3 for estimating changes in sinuosity with scale change.
","conferenceTitle":"Second Annual SPARC Workshop, Scale and Spatial Analytics","conferenceDate":"February 10-11, 2020","conferenceLocation":"Tempe, AZ","language":"English","publisher":"Arizona State University","usgsCitation":"Shavers, E.J., Stanislawski, L., Buttenfield, B.P., and Kronenfeld, B.J., 2020, Preserving meander bend geometry through scale, Second Annual SPARC Workshop, Scale and Spatial Analytics, Tempe, AZ, February 10-11, 2020, 3 p.","productDescription":"3 p.","ipdsId":"IP-113570","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":377952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377950,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://sgsup.asu.edu/sparc/ScaleWorkshop"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":780795,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":780796,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":780797,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kronenfeld, Barry J. 0000-0002-9518-2462","orcid":"https://orcid.org/0000-0002-9518-2462","contributorId":207104,"corporation":false,"usgs":false,"family":"Kronenfeld","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":780798,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211876,"text":"70211876 - 2020 - Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","interactions":[],"lastModifiedDate":"2020-08-12T15:04:28.21349","indexId":"70211876","displayToPublicDate":"2020-02-29T10:39:46","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","docAbstract":"The Snake River volcanic province (SRP) has long been considered a target for geothermal development. It overlies a thermal anomaly that extends deep into the mantle and represents one of the highest heat flow provinces in North America, but systematic exploration been hindered by lack of a conceptual model. Play Fairway Analysis (PFA) is a methodology adapted from the petroleum industry that integrates data at the regional or basin scale to define favorable plays for exploration in a systematic fashion. The success of play fairway analysis in geothermal exploration depends critically on defining a systematic methodology that is grounded in theory and adapted to the geologic and hydrologic framework of real geothermal systems. \nThis study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the Snake River Plain: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for Heat were heat flow, the distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Permeability was derived from stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetic and gravity data. Data for Seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment (CRS) maps for Heat, Permeability and Seal, which were combined to create a Composite Common Risk Segment (CCRS) map for all of southern Idaho that reflects the risk associated with geothermal resource exploration and helps to identify favorable resource tracks. \nOur data suggests that important undiscovered geothermal resources may be located in several areas of the SRP, including the western SRP (associated with buried lineaments capped by lacustrine sediment), at lineament intersections in the central SRP, and along the margins of the eastern SRP. These blind resources are associated with temperatures sufficient to support electricity production, and may be exploitable with existing deep drilling technology. We are testing our methodology by drilling a geothermal test well in Camas Prairie, ID, confirm our predictions of permeability and reservoir temperature.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: 45th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"45th Workshop on Geothermal Reservoir Engineering 2020","conferenceDate":"February 10-12, 2020","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford Geothermal Program","usgsCitation":"Shervais, J., Glen, J.M., Siler, D.L., Liberty, L., Nielson, D., Garg, S., Dobson, P., Gasperikova, E., Sonnenthal, E., Newell, D., Evans, J.E., DeAngelo, J., Peacock, J., Earney, T.E., Schermerhorn, W.D., and Neupane, G., 2020, Play fairway analysis in geothermal exploration: The Snake River plain volcanic province, in Proceedings: 45th workshop on geothermal reservoir engineering, Stanford, CA, February 10-12, 2020, p. 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negatively affect protected area landscapes and resources and soil erosion is frequently cited as the most significant long-term impact to recreational trails. Comprehensive modeling of soil loss on trails can identify influential factors that managers can manipulate to design and manage more sustainable trails. Field measurements assessed soil loss as the mean vertical depth along 135 trail transects across the Appalachian Trail sampled along three 5km trail segments in the White Mountains National Forest of New Hampshire. Using LiDAR data to accurately measure terrain characteristics that influence trail erosion can improve predictive models of trail system soil loss. Borrowing from geomorphic and agricultural soil erosion models, this study evaluated a variety of terrain and hydrology characteristics to model trail soil loss at three spatial scales: transect, trail corridor, and watershed. The model for each spatial scale and a combined model are presented. The adjusted R2 explaining variation in soil loss is 0.57 using variables from all spatial scales, a substantial improvement on previous trail erosion models. Environmental and trail design factors such as slope and watershed flow length were found to be significantly correlated to soil loss and have implications for sustainable trail design and management.","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2020.103765","usgsCitation":"Eagleston, H., and Marion, J.L., 2020, Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA: Landscape and Urban Planning, v. 198, 103765, 9 p., https://doi.org/10.1016/j.landurbplan.2020.103765.","productDescription":"103765, 9 p.","ipdsId":"IP-088107","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457565,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/98678","text":"External 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Hampshire\",\"nation\":\"USA \"}}]}","volume":"198","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eagleston, Holly","contributorId":173611,"corporation":false,"usgs":false,"family":"Eagleston","given":"Holly","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":783511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marion, Jeffrey L. 0000-0003-2226-689X jeff_marion@usgs.gov","orcid":"https://orcid.org/0000-0003-2226-689X","contributorId":3614,"corporation":false,"usgs":true,"family":"Marion","given":"Jeffrey","email":"jeff_marion@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":783510,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221103,"text":"70221103 - 2020 - Towards reproducible environmental modeling for decision support: A worked example","interactions":[],"lastModifiedDate":"2021-06-03T12:05:09.55066","indexId":"70221103","displayToPublicDate":"2020-02-28T07:20:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8916,"text":"Frontiers in Earth Science, Hydrosphere","active":true,"publicationSubtype":{"id":10}},"title":"Towards reproducible environmental modeling for decision support: A worked example","docAbstract":"A fully worked example of decision-support-scale uncertainty quantification (UQ) and parameter estimation (PE) is presented. The analyses are implemented for an existing groundwater flow model of the Edwards aquifer, Texas, USA, and are completed in a script-based workflow that strives to be transparent and reproducible. High-dimensional PE is used to history-match simulated outputs to corresponding state observations of spring flow and groundwater level. Then a hindcast of a historical drought is made. Using available state observations recorded during drought conditions, the combined UQ and PE analyses are shown to yield an ensemble of model results that bracket the observed hydrologic responses. All files and scripts used for the analyses are placed in the public domain to serve as a template for other practitioners who are interested in undertaking these types of analyses.
","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.00050","usgsCitation":"White, J.T., Foster, L.K., Fienen, M., Knowling, M.J., Hemmings, B., and Winterle, J.R., 2020, Towards reproducible environmental modeling for decision support: A worked example: Frontiers in Earth Science, Hydrosphere, v. 28, 50, 11 p., https://doi.org/10.3389/feart.2020.00050.","productDescription":"50, 11 p.","ipdsId":"IP-115342","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":457567,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00050","text":"Publisher Index Page"},{"id":437084,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AUZMI7","text":"USGS data release","linkHelpText":"Towards reproducible environmental modeling for decision support: a worked example"},{"id":386114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"southern-central Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.96435546875,\n 28.76765910569123\n ],\n [\n -96.83349609375,\n 28.76765910569123\n ],\n [\n -96.83349609375,\n 30.14512718337613\n ],\n [\n -100.96435546875,\n 30.14512718337613\n ],\n [\n -100.96435546875,\n 28.76765910569123\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469 jwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":167708,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jwhite@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Linzy K. 0000-0002-7373-7017","orcid":"https://orcid.org/0000-0002-7373-7017","contributorId":259186,"corporation":false,"usgs":true,"family":"Foster","given":"Linzy","email":"","middleInitial":"K.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knowling, Matthew J.","contributorId":251909,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hemmings, Brioch","contributorId":259187,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Winterle, James R.","contributorId":259189,"corporation":false,"usgs":false,"family":"Winterle","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":52328,"text":"Edwards Aquifer Authority","active":true,"usgs":false}],"preferred":false,"id":816777,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208792,"text":"70208792 - 2020 - Causal factors for pesticide trends in streams of the United States: Atrazine and deethylatrazine","interactions":[],"lastModifiedDate":"2020-03-02T06:42:23","indexId":"70208792","displayToPublicDate":"2020-02-28T06:41:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Causal factors for pesticide trends in streams of the United States: Atrazine and deethylatrazine","docAbstract":"Pesticides are important for agriculture in the United States, and atrazine is one of the most widely used and widely detected pesticides in surface water. A better understanding of the mechanisms by which atrazine and its degradation product, deethylatrazine, increase and decrease in surface waters can help inform future decisions for water-quality improvement. This study considers causal factors for trends in pesticide concentration in streams in the United States and models the causal factors, other than use, in structural equation models. The structural equation models use a concomitant trend in corn and a latent variable model indicating moisture supply and management. The moisture supply and management latent variable incorporates long-term moisture conditions in the individual watersheds by using the Palmer Hydrologic Drought Index; human influence on the hydrologic cycle through the percent of the watershed drained by tile drains in 2012; and the base-flow contribution to streamflow, using the base-flow index. The structural equation models explain 77% and 38% of the variability in atrazine and deethylatrazine trends, respectively, across the conterminous United States. 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We describe a collaborative effort to develop and apply the Integrated Ecosystem Model (IEM) for Alaska and northwestern Canada to explore how climate change influences interactions among disturbance regimes, permafrost integrity, hydrology, and vegetation, and how these dynamics in turn influence resource management decisions. This process emphasizes co-production of knowledge among decision makers, scientists, major funders, partners, and stakeholders. We highlight research findings based on IEM applications in Arctic Alaska, as well as successes and challenges of the co-production process. The overall framework and lessons from our work with the IEM are relevant to other collaborative efforts outside the Arctic that aim to develop a decision-support tool or an undertaking of equivalent scope.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2176","usgsCitation":"Euskirchen, E.S., Timm, K., Breen, A.L., Gray, S., Rupp, T., Martin, P., Reynolds, J.H., Sesser, A., Murphy, K., Littell, J., Bennett, A., Bolton, W.R., Carman, T., Genet, H., Griffith, B., Kurkowski, T., Lara, M.J., Marchenko, S., Nicolsky, D., Santosh, P., Romanovsky, V., Rutter, R., Tucker, C., and McGuire, A.D., 2020, Co-producing knowledge: The Integrated Ecosystem Model for resource management in Arctic Alaska: Frontiers in Ecology and the Environment, v. 18, no. 8, p. 447-455, https://doi.org/10.1002/fee.2176.","productDescription":"9 p.","startPage":"447","endPage":"455","ipdsId":"IP-094448","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":485836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -133.03771862405338,\n 54.449282262495586\n ],\n [\n -124.23849104872949,\n 54.963179322366955\n ],\n [\n -127.37957259839732,\n 62.539236061020546\n ],\n [\n -134.5159081711619,\n 69.49951413324942\n ],\n [\n -160.0873123544818,\n 71.48715481528723\n ],\n [\n -168.2315176008282,\n 68.43184600082128\n ],\n [\n -167.50613138738822,\n 64.4563020433356\n ],\n [\n -167.8878826181007,\n 59.901416725449195\n ],\n [\n -158.89957316923457,\n 57.913366649388536\n ],\n [\n -169.2280660138636,\n 53.60184015623466\n ],\n [\n -170.94410510519103,\n 53.21196387103788\n ],\n [\n -169.6494550660574,\n 52.08508422023331\n ],\n [\n -152.2209018203102,\n 56.88215554508227\n ],\n [\n -146.25556902863565,\n 59.80908327331997\n ],\n [\n -141.68648886181515,\n 59.433840122176434\n ],\n [\n -133.03771862405338,\n 54.449282262495586\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Euskirchen, Eugenie S.","contributorId":207139,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugenie","email":"","middleInitial":"S.","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":936972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Timm, Kristin","contributorId":139461,"corporation":false,"usgs":false,"family":"Timm","given":"Kristin","email":"","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":936973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breen, Amy L.","contributorId":81396,"corporation":false,"usgs":true,"family":"Breen","given":"Amy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":936974,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gray, Stephen T. 0000-0002-0959-3418 sgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-3418","contributorId":209851,"corporation":false,"usgs":true,"family":"Gray","given":"Stephen","email":"sgray@usgs.gov","middleInitial":"T.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":936776,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rupp, T. 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David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":936775,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70210990,"text":"70210990 - 2020 - Evaluation of soil zone processes and a novel radiocarbon correction approach for groundwater with mixed sources","interactions":[],"lastModifiedDate":"2020-07-10T13:54:41.871444","indexId":"70210990","displayToPublicDate":"2020-02-27T08:48:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of soil zone processes and a novel radiocarbon correction approach for groundwater with mixed sources","docAbstract":"Estimates of groundwater age based on 14C is often limited by the uncertainty in geochemical processes that alter the 14C concentration measured in water and the composition (δ13C and 14C) of carbon sources needed to appropriately parametrize 14C adjustment models. Estimated ages for samples that contain a mixture of young and old groundwater will be particularly sensitive to model parametrization as relatively small additions of modern 14C from recent recharge can mask the presence and amount of old groundwater. A novel multi-model approach based on inverse geochemical modeling and lumped parameter modeling of age tracers (3H, 3Hetrit, and SF6) was used to better constrain 14C dilution caused by dissolution of carbonates in the unsaturated zone or shallow parts of the Glacial aquifer, which extends over 2000 miles across the northern contiguous United States. Calibration of 14C inverse geochemical models to LPM computed 14C concentrations in modern water indicated that 14C of soil zone and shallow aquifer carbonates were not 14C-dead (0 pmC), as is typically assumed for 14C correction models. 14C of such carbonates was on average about 53 pmC (ranged 0-110 pmC, n = 72). This information was used to correct 14C concentrations for water recharged entirely before 1950 and water that is a mixture of pre- and post-1950 water. The multi-model approach developed here was compared to an analytical 14C-adjustment model (Revised Fontes and Garnier) that assumed solid carbonates were 14C-dead. 14C corrections using the analytical adjustment model tended to over-correct final 14C concentrations by 21 pmC and underestimates mean ages by 40% for groundwater mixtures. In fact, 14C corrections based on analytical model yielded negative ages (14C > 120 pmC) in nearly 36% of mixed samples. This work presents a new approach to constraining 14C corrections and age estimates of mixtures of young and old groundwater. 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2020 - Water withdrawals, uses, and trends in Florida, 2015","interactions":[],"lastModifiedDate":"2022-04-25T20:27:23.096539","indexId":"sir20195147","displayToPublicDate":"2020-02-26T07:43:08","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5147","displayTitle":"Water Withdrawals, Uses, and Trends in Florida, 2015","title":"Water withdrawals, uses, and trends in Florida, 2015","docAbstract":"In 2015, the total amount of water withdrawn in Florida was estimated to be 15,319 million gallons per day (Mgal/d). Saline water accounted for 9,598 Mgal/d (63 percent) and freshwater accounted for 5,721 Mgal/d (37 percent) of the total. Groundwater accounted for 3,604 Mgal/d (63 percent) of freshwater withdrawals and surface water accounted for the remaining 2,117 Mgal/d (37 percent). Surface-water sources accounted for 9,401 Mgal/d (98 percent) of the saline-water withdrawals, and groundwater sources accounted for the remaining 198 Mgal/d (2 percent). The majority of groundwater withdrawals (almost 62 percent) in 2015 were from the Floridan aquifer system, which is used throughout most of the State while the majority of fresh surface-water withdrawals (52 percent) occurred in the Southern Florida Subregion, a hydrologic unit that includes Lake Okeechobee and canals in the Everglades Agricultural Area. Groundwater provided drinking water (public supplied and self-supplied) for 18.324 million people (92 percent of Florida’s population), and fresh surface water provided drinking water for 1.491 million people (8 percent).
Overall, public supply accounted for 39 percent of the total freshwater withdrawals (ground and surface) and 53 percent of groundwater withdrawals, followed by agricultural self-supplied uses, which accounted for 37 percent of the total freshwater withdrawals and 28 percent of groundwater withdrawals. Other self-supplied groundwater withdrawals include commercial-industrial-mining self-supplied (8 percent), recreational-landscape irrigation and domestic self-supplied (5 percent each), and power generation (less than 1 percent). Agricultural self-supplied withdrawals accounted for 51 percent of fresh surface-water withdrawals, followed by power generation (19 percent), public supply (15 percent), recreational-landscape irrigation (10 percent), and commercial-industrial-mining self-supplied (5 percent).
In 1975, agricultural water withdrawals accounted for 43 percent of the total freshwater withdrawals, followed by power generation (24 percent) and public supply (17 percent). By 2000, agricultural withdrawals increased to 48 percent of the total freshwater withdrawals, followed by public supply (30 percent). For 2015, agricultural self-supplied decreased to 37 percent of total freshwater withdrawals, and was surpassed by public supply at 39 percent. Over the 40-year period between 1975 and 2015, increases in freshwater withdrawals caused by large gains in population and the expansion of irrigated acreage were offset by decreases in water used for power generation and commercial-industrial-mining withdrawals. Since 2000, however, irrigated acreage has decreased statewide because of crop disease, storm damage, and urbanization. This decline, coupled with large gains in water conservation measures in the farming industry, has led to agricultural withdrawals in Florida being less than public-supply withdrawals for the first time since water-use data were first reported in 1965.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195147","collaboration":"Prepared in cooperation with the Florida Department of Agricultural and Consumer Services","usgsCitation":"Marella, R.L., 2020, Water withdrawals, uses, and trends in Florida, 2015: U.S. Geological Survey Scientific Investigations Report 2019–5147, 52 p., https://doi.org/10.3133/sir20195147.","productDescription":"Report: vii, 52 p.; Data Release","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-093230","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":372545,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5147/sir20195147.pdf","text":"Report","size":"2.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
To update and expand a part of the Federal Emergency Management Agency Flood Insurance Study, the U.S. Geological Survey, the Muskingum Watershed Conservancy District, and the Stark County Commissioners began a cooperative study. The study consisted of hydrologic and hydraulic analyses for selected reaches of 14 streams in Stark County, Ohio: Broad-Monter Creek, Chatham Ditch, East Branch Nimishillen Creek, Fairhope Ditch, Firestone Ditch, Hayden Ditch, Middle Branch Nimishillen Creek, Middle Branch Nimishillen Creek Tributary Number 1, Nimishillen Creek, Reemsnyder Ditch, Sherrick Run, unnamed stream, West Branch Nimishillen Creek, and Zimber Ditch. The study totaled nearly 50 miles of stream reaches.
Instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities were estimated using historical streamflow data from the streamgages Nimishillen Creek at North Industry, Ohio (U.S. Geological Survey station number 03118500), and Middle Branch Nimishillen Creek at Canton, Ohio (U.S. Geological Survey station number 03118000), regional flood regression equations, and streamflow urbanization techniques.
The annual exceedance probability streamflows were then used in a Hydrologic Engineering Center-River Analysis System step-backwater model to determine water-surface profiles, flood-inundation boundaries for the 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probability floods, and a regulatory floodway along a selected reach of each stream. Model input included DEM-derived cross sections supplemented with field surveys of open channel cross sections and hydraulic structures, field estimates of roughness values, and annual exceedance probability flood estimates from regional regression equations and historical streamflow data. Flood-inundation boundaries were mapped for the 1- and 0.2-percent annual exceedance probability floods and a regulatory floodway for each stream reach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205011","collaboration":"Prepared in cooperation with Stark County and the Muskingum Watershed Conservancy District","usgsCitation":"Ostheimer, C.J. and Whitehead, M.T, 2020, Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio: U.S. Geological Survey Scientific Investigations Report 2020–5011, 15 p., https://doi.org/10.3133/sir20205011.","productDescription":"Report: iv, 15 p.; 4 Appendixes; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106471","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":399632,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109726.htm"},{"id":372523,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5011/coverthb.jpg"},{"id":372525,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix1.pdf","text":"Appendix 1","size":"3.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 1","linkHelpText":"– Technical Support Data Notebook"},{"id":372526,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix2.pdf","text":"Appendix 2","size":"780 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 2","linkHelpText":"– Floodway data tables"},{"id":372524,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011.pdf","text":"Report","size":"1.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011"},{"id":372527,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix3.pdf","text":"Appendix 3","size":"1.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 3","linkHelpText":"– Water-surface profiles"},{"id":372528,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix4.pdf","text":"Appendix 4","size":"8.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 4","linkHelpText":"– Flood-inundation maps"},{"id":372529,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YQJ8B7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic models for selected streams in Stark County, Ohio"}],"country":"United States","state":"Ohio","county":"Stark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.0864,40.9879],[-81.0865,40.9839],[-81.0866,40.978],[-81.0869,40.9013],[-81.0873,40.728],[-81.0922,40.7285],[-81.1001,40.7281],[-81.1989,40.7292],[-81.1991,40.7224],[-81.2373,40.7237],[-81.241,40.6507],[-81.2755,40.651],[-81.2791,40.6511],[-81.304,40.6518],[-81.3173,40.6519],[-81.4372,40.6529],[-81.4365,40.6584],[-81.4395,40.6625],[-81.4467,40.6657],[-81.4589,40.6654],[-81.4675,40.6555],[-81.6489,40.6346],[-81.6491,40.6681],[-81.6483,40.7371],[-81.648,40.9145],[-81.4201,40.9064],[-81.4164,40.9889],[-81.3932,40.9887],[-81.1059,40.9882],[-81.0925,40.988],[-81.0864,40.9879]]]},\"properties\":{\"name\":\"Stark\",\"state\":\"OH\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Suite 100
Columbus, OH 43229–1737