Pathogenic fungi are increasingly associated with epidemics in wildlife populations. Snake fungal disease (SFD, also referred to as Ophidiomycosis) is an emerging threat to snakes, taxa that are elusive and difficult to sample. Thus, assessments of the effects of SFD on populations have rarely occurred. We used a field technique to enhance detection, Passive Integrated Transponder (PIT) telemetry, and a multi‐state capture–mark–recapture model to assess SFD effects on short‐term (within‐season) survival, movement, and surface activity of two wild snake species, Regina septemvittata (Queensnake) and Nerodia sipedon (Common Watersnake). We were unable to detect an effect of disease state on short‐term survival for either species. However, we estimated Bayesian posterior probabilities of >0.99 that R. septemvittata with SFD spent more time surface‐active and were less likely to permanently emigrate from the study area. We also estimated probabilities of 0.98 and 0.87 that temporary immigration and temporary emigration rates, respectively, were lower in diseased R. septemvittata. We found evidence of elevated surface activity and lower temporary immigration rates in diseased N. sipedon, with estimated probabilities of 0.89, and found considerably less support for differences in permanent or temporary emigration rates. This study is the first to yield estimates for key demographic and behavioral parameters (survival, emigration, surface activity) of snakes in wild populations afflicted with SFD. Given the increase in surface activity of diseased snakes, future surveys of snake populations could benefit from exploring longer‐term demographic consequences of SFD and recognize that disease prevalence in surface‐active animals may exceed that of the population as a whole.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2251","usgsCitation":"McKenzie, J.M., Price, S.J., Connette, G.M., Bonner, S.J., and Lorch, J.M., 2020, Effects of snake fungal disease on short‐term survival, behavior, and movement in free‐ranging snakes: Ecological Applications, v. 31, no. 2, e02251, https://doi.org/10.1002/eap.2251.","productDescription":"e02251","ipdsId":"IP-123269","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":383707,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-15","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenzie, Jennifer M.","contributorId":212841,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":811193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Price, Steven J. 0000-0002-2388-0579","orcid":"https://orcid.org/0000-0002-2388-0579","contributorId":57738,"corporation":false,"usgs":false,"family":"Price","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":811194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connette, Grant M.","contributorId":212844,"corporation":false,"usgs":false,"family":"Connette","given":"Grant","email":"","middleInitial":"M.","affiliations":[{"id":37784,"text":"Smithsonian Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":811195,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonner, Simon J","contributorId":252946,"corporation":false,"usgs":false,"family":"Bonner","given":"Simon","email":"","middleInitial":"J","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":811196,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":811197,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216106,"text":"70216106 - 2020 - Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST v3.4) model","interactions":[],"lastModifiedDate":"2020-11-05T14:23:09.49639","indexId":"70216106","displayToPublicDate":"2020-11-02T08:20:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST v3.4) model","docAbstract":"The coupled biophysical interactions between submerged aquatic vegetation (SAV), hydrodynamics (currents and waves), sediment dynamics, and nutrient cycling have long been of interest in estuarine environments. Recent observational studies have addressed feedbacks between SAV meadows and their role in modifying current velocity, sedimentation, and nutrient cycling. To represent these dynamic processes in a numerical model, the presence of SAV and its effect on hydrodynamics (currents and waves) and sediment dynamics was incorporated into the open-source Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. In this study, we extend the COAWST modeling framework to account for dynamic changes of SAV and associated epiphyte biomass. Modeled SAV biomass is represented as a function of temperature, light, and nutrient availability. The modeled SAV community exchanges nutrients, detritus, dissolved inorganic carbon, and dissolved oxygen with the water-column biogeochemistry model. The dynamic simulation of SAV biomass allows the plants to both respond to and cause changes in the water column and sediment bed properties, hydrodynamics, and sediment transport (i.e., a two-way feedback). We demonstrate the behavior of these modeled processes through application to an idealized domain and then apply the model to a eutrophic harbor where SAV dieback is a result of anthropogenic nitrate loading and eutrophication. These cases demonstrate an advance in the deterministic modeling of coupled biophysical processes and will further our understanding of future ecosystem change.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/gmd-13-5211-2020","usgsCitation":"Kalra, T., Ganju, N., and Testa, J.M., 2020, Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST v3.4) model: Geoscientific Model Development, v. 13, no. 11, p. 5211-5228, https://doi.org/10.5194/gmd-13-5211-2020.","productDescription":"18 p.","startPage":"5211","endPage":"5228","ipdsId":"IP-102944","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-13-5211-2020","text":"Publisher Index Page"},{"id":380185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":804107,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":804108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Testa, Jeremy M.","contributorId":244524,"corporation":false,"usgs":false,"family":"Testa","given":"Jeremy","email":"","middleInitial":"M.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":804109,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237973,"text":"70237973 - 2020 - High-frequency data reveal deicing salts drive elevated specific conductance and chloride along with pervasive and frequent exceedances of the U.S. Environmental Protection Agency aquatic life criteria for chloride in urban streams","interactions":[],"lastModifiedDate":"2022-11-02T11:44:45.440534","indexId":"70237973","displayToPublicDate":"2020-11-02T06:43:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"High-frequency data reveal deicing salts drive elevated specific conductance and chloride along with pervasive and frequent exceedances of the U.S. Environmental Protection Agency aquatic life criteria for chloride in urban streams","docAbstract":"Increasing specific conductance (SC) and chloride concentrations [Cl] negatively affect many stream ecosystems. We characterized spatial variability in SC, [Cl], and exceedances of Environmental Protection Agency [Cl] criteria using nearly 30 million high-frequency observations (2–15 min intervals) for SC and modeled [Cl] from 93 sites across three regions in the eastern United States: Southeast, Mid-Atlantic, and New England. SC and [Cl] increase substantially from south to north and within regions with impervious surface cover (ISC). In the Southeast, [Cl] weakly correlates with ISC, no [Cl] exceedances occur, and [Cl] concentrations are constant with time. In the Mid-Atlantic and New England, [Cl] and [Cl] exceedances strongly correlate with ISC. [Cl] criteria are frequently exceeded at sites with greater than 9–10% ISC and median [Cl] higher than 30–80 mg/L. Tens to hundreds of [Cl] exceedances observed annually at most of these sites help explain previous research where stream ecosystems showed changes at (primarily nonwinter) [Cl] as low as 30–40 mg/L. Mid-Atlantic chronic [Cl] exceedances occur primarily in December–March. In New England, exceedances are common in nonwinter months. [Cl] is increasing at nearly all Mid-Atlantic and New England sites with the largest increases at sites with higher [Cl].
Wildfire is a worldwide phenomenon that is expected to increase in extent and severity in the future, due to fuel accumulations, shifting land management practices, and climate change. It immediately affects the landscape by removing vegetation, depositing ash, influencing water-repellent soil formation, and physically weathering boulders and bedrock. These changes typically lead to increased erosion through sheetwash, rilling, dry ravel, and increased mass movement in the form of floods, debris flow, rockfall, and landslides. These process changes bring about landform changes as hillslopes are lowered and stream channels aggrade or incise at increased rates. Furthermore, development of alluvial fans, debris fans, and talus cones are enhanced. The window of disturbance to the landscape caused by wildfire is typically on the order of 3–4 years, with some effects persisting up to 30 years.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference module in earth systems and environmental sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","doi":"10.1016/B978-0-12-818234-5.00017-1","usgsCitation":"Santi, P., and Rengers, F.K., 2020, Wildfire and landscape change, chap. of Reference module in earth systems and environmental sciences, HTML Document, https://doi.org/10.1016/B978-0-12-818234-5.00017-1.","productDescription":"HTML Document","ipdsId":"IP-119751","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":382559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Santi, Paul M.","contributorId":247562,"corporation":false,"usgs":false,"family":"Santi","given":"Paul M.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":807909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807910,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222481,"text":"70222481 - 2020 - Wildfire and Earth surface processes","interactions":[],"lastModifiedDate":"2021-08-02T15:49:34.707051","indexId":"70222481","displayToPublicDate":"2020-11-01T08:31:55","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Wildfire and Earth surface processes","docAbstract":"Wildfire is a worldwide phenomenon that is expected to increase in extent and severity in the future, due to fuel accumulations, shifting land management practices, and climate change. It immediately affects the landscape by removing vegetation, depositing ash, influencing water-repellent soil formation, and physically weathering boulders and bedrock. These changes typically lead to increased erosion through sheetwash, rilling, dry ravel, and increased mass movement in the form of floods, debris flow, rockfall, and landslides. These process changes bring about landform changes as hillslopes are lowered and stream channels aggrade or incise at increased rates. Furthermore, development of alluvial fans, debris fans, and talus cones are enhanced. The window of disturbance to the landscape caused by wildfire is typically on the order of 3–4 years, with some effects persisting up to 30 years.
The Wetlands Component of the USDA's Conservation Effects Assessment Project (CEAP-Wetlands) is a multi-agency effort advancing science related to quantifying and interpreting effects and effectiveness of conservation practices and programs on ecosystem services provided by wetlands in agricultural landscapes. This special section originated from a symposium held at the 73rd Soil and Water Conservation Society's International Annual Conference in Albuquerque New Mexico, July 29 to August 1, 2018. The symposium was jointly organized by the USDA Natural Resources Conservation Service and the US Geological Survey. To facilitate CEAP-Wetlands efforts, several regional assessments were conducted across the United States. These regional assessments were designed to address science gaps hindering wetland conservation and to develop tools facilitating conservation assessments. Conservation decisions affect not just agricultural wetlands, but also the services that these complex ecosystems provide to society. Papers in this special section of the Journal of Soil and Water Conservation present key findings and recent advances from several CEAP-Wetlands regional assessments and discuss the significant contributions of each assessment to an ever-increasing understanding of wetland ecosystems and their provisioning of ecosystem services. Modeling efforts using the Agricultural Policy and Environmental eXtender (APEX) and other process-based models are an integral component of CEAP-Wetlands. Results of these modeling efforts are also presented, and conservation implications are discussed.
Wind fetch is defined as the unobstructed distance that wind can travel over water in a constant direction. Fetches are limited by landforms surrounding the body of water. Fetch is an important characteristic of open water because longer fetches can result in larger wind-generated waves. The larger waves, in turn, can increase shoreline erosion and sediment resuspension (Rohweder and others 2012). Increases in sediment resuspension lead to increases in water turbidity, which in turn decreases light penetration and, therefore, create conditions less conducive to aquatic plant growth (Giblin and others 2010).
A wind fetch model was developed by David Finlayson, U. S. Geological Survey, Pacific Science Center, while he was a Ph.D. student at the University of Washington (Finlayson 2005). This method calculates effective fetch using the recommended procedure of the Shore Protection Manual (USACE 1984). Scientists at the United States Geological Survey, Upper Midwest Environmental Sciences Center (UMESC) and the United States Army Corps of Engineers (USACE) further refined this model (Rohweder and others 2012) and structured it to operate using the most recent version of the ArcMap Geographic Information System platform (Esri, 2019). At the time the analysis was performed, the version of ArcMap used was 10.7.1. The model refined in 2012 was used for the analyses described in this report.
Using this model, UMESC performed an analysis to model weighted wind fetch for the Upper Mississippi River System (UMRS) corresponding to three separate time periods of land cover spatial data acquisition (1989, 2000, and 2010/2011). The purpose of the analysis was to examine how fetch varies over time and space within the UMRS for potential management applications. For more detailed information on the wind fetch model, examine the USGS Open-File Report by Rohweder and others (2012).
","language":"English","publisher":"U.S. Army Corps of Engineers, Mississippi River Restoration Program","usgsCitation":"Rohweder, J.J., and Rogala, J.T., 2020, Upper Mississippi River system weighted wind fetch analysis (1989, 2000, 2010/2011): Contract Report, ii, 26 p.","productDescription":"ii, 26 p.","ipdsId":"IP-119011","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":382758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":382757,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://umesc.usgs.gov/documents/reports/2020/umrr_ltrm_weighted_wind_fetch_101620.pdf"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.3955078125,\n 39.8928799002948\n ],\n [\n -88.9453125,\n 40.64730356252251\n ],\n [\n -87.64892578125,\n 41.44272637767212\n ],\n [\n -87.71484375,\n 41.918628865183045\n ],\n [\n -88.0224609375,\n 42.27730877423709\n ],\n [\n -88.83544921874999,\n 41.83682786072714\n ],\n [\n -89.45068359374999,\n 41.393294288784865\n ],\n [\n -90.50537109375,\n 40.39676430557203\n ],\n [\n -90.3955078125,\n 39.8928799002948\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.5166015625,\n 37.77071473849609\n ],\n [\n -90,\n 38.53097889440024\n ],\n [\n -90.15380859375,\n 39.2832938689385\n ],\n [\n -90.68115234375,\n 40.53050177574321\n ],\n [\n -89.8681640625,\n 41.96765920367816\n ],\n [\n -89.89013671875,\n 42.47209690919285\n ],\n [\n -91.07666015625,\n 44.11914151643737\n ],\n [\n -94.2626953125,\n 45.98169518512228\n ],\n [\n -94.85595703125,\n 46.10370875598026\n ],\n [\n -95.16357421875,\n 45.5679096098613\n ],\n [\n -92.92236328125,\n 44.29240108529005\n ],\n [\n -91.73583984374999,\n 43.068887774169625\n ],\n [\n -90.98876953125,\n 41.85319643776675\n ],\n [\n -91.91162109375,\n 40.56389453066509\n ],\n [\n -91.73583984374999,\n 39.45316112807394\n ],\n [\n -90.24169921875,\n 38.048091067457236\n ],\n [\n -90,\n 37.405073750176925\n ],\n [\n -89.5166015625,\n 37.77071473849609\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":802002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogala, James T. 0000-0002-1954-4097 jrogala@usgs.gov","orcid":"https://orcid.org/0000-0002-1954-4097","contributorId":2651,"corporation":false,"usgs":true,"family":"Rogala","given":"James","email":"jrogala@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":802003,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215785,"text":"sir20205094 - 2020 - Geochemical assessment of groundwater in the Big Chino subbasin, Arizona, 2011–18","interactions":[],"lastModifiedDate":"2020-10-30T15:26:07.378654","indexId":"sir20205094","displayToPublicDate":"2020-10-29T20:57:35","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5094","displayTitle":"Geochemical Assessment of Groundwater in the Big Chino Subbasin, Arizona, 2011–18","title":"Geochemical assessment of groundwater in the Big Chino subbasin, Arizona, 2011–18","docAbstract":"A geochemical characterization of groundwater in the Big Chino subbasin of Arizona was conducted by the U.S. Geological Survey, in cooperation with the City of Prescott, the Town of Prescott Valley, and the Salt River Project, to understand groundwater evolution through the study area and the source of water to springs along the gaining reach of the Verde River just downstream from its confluence with Granite Creek. Samples were collected between 2011 and 2018 in groundwater wells completed in basin-fill and carbonate aquifers and at selected springs, including two discrete springs discharging along the aforementioned stretch of the Verde River. Five newly installed monitoring wells completed in the carbonate aquifer were sampled in 2018. Water-quality results obtained from these samples include the first known geochemical data for carbonate groundwater beneath the basin-fill in the Big Chino subbasin downgradient from Walnut Creek near Paulden, Arizona, as well as other parts of the study area without previous data. Groundwater samples were collected and analyzed for major ions, arsenic, nutrients, stable isotopes of oxygen and hydrogen (δ18O and δ2H), strontium isotopes (87Sr/86Sr), carbon-14, isotopes of carbon (δ13C), and noble gases.
Significant differences in groundwater geochemistry between the basin-fill and carbonate aquifers were driven primarily by higher pH, tritium, and δ18O and δ2H in the basin-fill aquifer samples and higher specific conductance and higher concentrations of calcium, sodium, bicarbonate, fluoride, and arsenic in the carbonate aquifer samples. All but one sample from the carbonate aquifer and two samples from the basin-fill aquifer exceeded the U.S. Environmental Protection Agency (EPA) drinking water standard for arsenic of 10 micrograms per liter. One basin-fill aquifer sample exceeded the EPA drinking water standard for fluoride of 4 milligrams per liter, and one carbonate aquifer sample exceeded the EPA secondary drinking water standard for fluoride of 2 milligrams per liter. A component of modern groundwater recharged following aboveground nuclear testing beginning in the mid-1950s is present in some basin-fill and spring groundwater from this study. Groundwater that can be dated using radiocarbon decay is also present in the study area, with four groundwater samples indicating possible recharge during the Pleistocene with groundwater ages ranging from approximately 34,600 to 13,300 years before present. Other groundwater sampled during this study that can dated using radiocarbon decay ranged in age from about 7,500 to 1,100 years before present, indicating possible recharge during the Holocene.
The gaining reach of the Verde River downstream from the confluence with Granite Creek shows areal changes in temperature, pH, and specific conductance, indicating multiple zones of groundwater input. Surface-water samples for analyses of δ18O and δ2H have been collected at the Verde River near Paulden, Ariz. streamgage (09503700) during discharge measurements since 2009, and a trend analysis of the δ18O and δ2H data indicated no significant trend exists for the 10-year period of record. Additional groundwater samples from the carbonate aquifer beneath the basin-fill upgradient and downgradient from Walnut Creek would provide valuable information to understand groundwater evolution along the Big Chino subbasin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205094","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and the Salt River Project","usgsCitation":"Beisner, K.R., and Jones, C.J.R., 2020, Geochemical assessment of groundwater in the Big Chino subbasin, Arizona, 2011–18: U.S. Geological Survey Scientific Investigations Report 2020–5094, 49 p., https://doi.org/10.3133/sir20205094.","productDescription":"Report: viii, 49 p.; 2 Appendixes; 2 Data Releases","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113409","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":379927,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HMZNIK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Carbon and strontium isotopic data for rock, soil, and soil gas from the Big Chino Sub-Basin, Arizona, 2017 and 2018"},{"id":379924,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094_appendix_1.csv","text":"Appendix 1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5094 Appendix 1","linkHelpText":"— Groundwater Geochemistry Data for Samples Collected by the U.S. Geological Survey from the Big Chino Subbasin Between 2011 and 2018"},{"id":379923,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094.pdf","text":"Report","size":"37.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5094"},{"id":379926,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P909LD47","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water quality parameters in the Verde River below Granite Creek, Arizona, June 2018"},{"id":379922,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5094/coverthb.jpg"},{"id":379925,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094_appendix_1.xlsx","text":"Appendix 1","size":"33.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5094 Appendix 1","linkHelpText":"— Groundwater Geochemistry Data for Samples Collected by the U.S. Geological Survey from the Big Chino Subbasin Between 2011 and 2018"}],"country":"United States","state":"Arizona","otherGeospatial":"Big Chino subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.18389892578125,\n 34.3366324743773\n ],\n [\n -111.8463134765625,\n 34.3366324743773\n ],\n [\n -111.8463134765625,\n 35.1154153142536\n ],\n [\n -113.18389892578125,\n 35.1154153142536\n ],\n [\n -113.18389892578125,\n 34.3366324743773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, NM 87111
We present measurements of the density, hydraulic conductivity, and specific discharge of a widespread firn aquifer in Antarctica, within the Wilkins Ice Shelf. At the field site, the aquifer is 16.2 m thick, starting at 13.4 m from the snow surface and transitioning from water‐saturated firn to ice at 29.6 m. Hydraulic conductivity derived from slug tests show a geometric mean value of 1.4 ± 1.2 × 10−4 m s−1, equivalent to permeability of 2.6 ± 2.2 × 10−11 m2. A borehole dilution test indicates an average specific discharge value of 1.9 ± 2.8 × 10−6 m s−1. Ground‐penetrating radar profiles and a groundwater flow model show the aquifer is draining laterally into a large nearby rift. Our findings indicate that the firn aquifer in the vicinity of the field site is likely not in a steady state and its presence likely contributed to past ice shelf instability.
","language":"English","publisher":"Wiley","doi":"10.1029/2020GL089552","usgsCitation":"Montgomery, L., Miege, C., MIller, J., Wallin, B., Miller, O.L., Scambos, T.A., Solomon, D., Forster, R., and Koenig, L., 2020, Hydrologic properties of a highly permeable firn aquifer in the Wilkins Ice Shelf, Antarctica: Geophysical Research Letters, v. 47, e2020GL089552, 10 p., https://doi.org/10.1029/2020GL089552.","productDescription":"e2020GL089552, 10 p.","ipdsId":"IP-119455","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":454923,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020gl089552","text":"External Repository"},{"id":382525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Wilkins Ice Sheet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.54022216796875,\n -71.79883675782347\n ],\n [\n -70.400390625,\n -71.79883675782347\n ],\n [\n -70.400390625,\n -71.54143894204527\n ],\n [\n -71.54022216796875,\n -71.54143894204527\n ],\n [\n -71.54022216796875,\n -71.79883675782347\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2020-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Montgomery, Lynn","contributorId":244036,"corporation":false,"usgs":false,"family":"Montgomery","given":"Lynn","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":803105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miege, C.","contributorId":248303,"corporation":false,"usgs":false,"family":"Miege","given":"C.","email":"","affiliations":[],"preferred":false,"id":808855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MIller, Julie","contributorId":248311,"corporation":false,"usgs":false,"family":"MIller","given":"Julie","email":"","affiliations":[],"preferred":false,"id":808856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wallin, Bruce","contributorId":248312,"corporation":false,"usgs":false,"family":"Wallin","given":"Bruce","email":"","affiliations":[],"preferred":false,"id":808857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scambos, Ted A.","contributorId":57367,"corporation":false,"usgs":true,"family":"Scambos","given":"Ted","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Olivia L. 0000-0002-8846-7048","orcid":"https://orcid.org/0000-0002-8846-7048","contributorId":216556,"corporation":false,"usgs":true,"family":"Miller","given":"Olivia","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803106,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Solomon, D Kip","contributorId":146290,"corporation":false,"usgs":false,"family":"Solomon","given":"D Kip","affiliations":[{"id":7215,"text":"University of Utah Dept. of Geography","active":true,"usgs":false}],"preferred":false,"id":808859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Forster, Richard","contributorId":172149,"corporation":false,"usgs":false,"family":"Forster","given":"Richard","affiliations":[{"id":26993,"text":"University of Utah, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":808860,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Koenig, Lora","contributorId":248313,"corporation":false,"usgs":false,"family":"Koenig","given":"Lora","affiliations":[],"preferred":false,"id":808861,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215981,"text":"70215981 - 2020 - Harvester ant seed removal in an invaded sagebrush ecosystem: Implications for restoration","interactions":[],"lastModifiedDate":"2020-12-29T21:46:00.683246","indexId":"70215981","displayToPublicDate":"2020-10-29T08:11:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Harvester ant seed removal in an invaded sagebrush ecosystem: Implications for restoration","docAbstract":"A better understanding of seed movement in plant community dynamics is needed, especially in light of disturbance‐driven changes and investments into restoring degraded plant communities. A primary agent of change within the sagebrush‐steppe is wildfire and invasion by non‐native forbs and grasses, primarily cheatgrass (Bromus tectorum). Our objectives were to quantify seed removal and evaluate ecological factors influencing seed removal within degraded sagebrush‐steppe by granivorous Owyhee harvester ants (Pogonomyrmex salinus Olsen). In 2014, we sampled 76 harvester ant nests across 11 plots spanning a gradient of cheatgrass invasion (40%–91% cover) in southwestern Idaho, United States. We presented seeds from four plant species commonly used in postfire restoration at 1.5 and 3.0 m from each nest to quantify seed removal. We evaluated seed selection for presented species, monthly removal, and whether biotic and abiotic factors (e.g., distance to nearest nest, temperature) influenced seed removal. Our top model indicated seed removal was positively correlated with nest height, an indicator of colony size. Distance to seeds and cheatgrass canopy cover reduced seed removal, likely due to increased search and handling time. Harvester ants were selective, removing Indian ricegrass (Achnatherum hymenoides) more than any other species presented. We suspect this was due to ease of seed handling and low weight variability. Nest density influenced monthly seed removal, as we estimated monthly removal of 1,890 seeds for 0.25 ha plots with 1 nest and 29,850 seeds for plots with 15 nests. Applying monthly seed removal to historical restoration treatments across the western United States showed harvester ants can greatly reduce seed availability at degraded sagebrush sites; for instance, fourwing saltbush (Atriplex canescens) seeds could be removed in <2 months. Collectively, these results shed light on seed removal by harvester ants and emphasize their potential influence on postfire restoration within invaded sagebrush communities.
The retreat of glaciers in response to global warming has the potential to trigger landslides in glaciated regions around the globe. Landslides that enter fjords or lakes can cause tsunamis, which endanger people and infrastructure far from the landslide itself. Here we document the ongoing movement of an unstable slope (total volume of 455 million m3) in Barry Arm, a fjord in Prince William Sound, Alaska. The slope moved rapidly between 2010 and 2017, yielding a horizontal displacement of 120 m, which is highly correlated with the rapid retreat and thinning of Barry Glacier. Should the entire unstable slope collapse at once, preliminary tsunami modeling suggests a maximum runup of 300 m near the landslide, which may have devastating impacts on local communities. Our findings highlight the need for interdisciplinary studies of recently deglaciated fjords to refine our understanding of the impact of climate change on landslides and tsunamis.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL089800","usgsCitation":"Dai, C., Higman, B., Lynett, P.J., Jacquemart, M., Howat, I., Liljedahl, A.K., Dufresne, A., Freymueller, J.T., Geertsema, M., Jones, M.W., and Haeussler, P., 2020, Detection and assessment of a large and potentially tsunamigenic periglacial landslide in Barry Arm, Alaska: Geophysical Research Letters, e2020GL089800, 9 p., https://doi.org/10.1029/2020GL089800.","productDescription":"e2020GL089800, 9 p.","ipdsId":"IP-122213","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":454932,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl089800","text":"Publisher Index Page"},{"id":380255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Barry Arm","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.18436382537658,\n 61.20521067964421\n ],\n [\n -148.18436382537658,\n 61.11202000604544\n ],\n [\n -148.0222489470055,\n 61.11202000604544\n ],\n [\n -148.0222489470055,\n 61.20521067964421\n ],\n [\n -148.18436382537658,\n 61.20521067964421\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Dai, Chunli 0000-0003-1840-8699","orcid":"https://orcid.org/0000-0003-1840-8699","contributorId":244604,"corporation":false,"usgs":false,"family":"Dai","given":"Chunli","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":804250,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Higman, 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Colorado","active":true,"usgs":false}],"preferred":false,"id":804253,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howat, Ian 0000-0002-8072-6260","orcid":"https://orcid.org/0000-0002-8072-6260","contributorId":244607,"corporation":false,"usgs":false,"family":"Howat","given":"Ian","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":804254,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liljedahl, Anna K. 0000-0001-7114-6443","orcid":"https://orcid.org/0000-0001-7114-6443","contributorId":150135,"corporation":false,"usgs":false,"family":"Liljedahl","given":"Anna","email":"","middleInitial":"K.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":804255,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dufresne, Anja 0000-0001-7777-3317","orcid":"https://orcid.org/0000-0001-7777-3317","contributorId":244608,"corporation":false,"usgs":false,"family":"Dufresne","given":"Anja","email":"","affiliations":[{"id":48946,"text":"Aachen University, Germany","active":true,"usgs":false}],"preferred":false,"id":804256,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Freymueller, Jeffery T. 0000-0003-0614-0306","orcid":"https://orcid.org/0000-0003-0614-0306","contributorId":244609,"corporation":false,"usgs":false,"family":"Freymueller","given":"Jeffery","email":"","middleInitial":"T.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":804257,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Geertsema, Marten","contributorId":197464,"corporation":false,"usgs":false,"family":"Geertsema","given":"Marten","email":"","affiliations":[],"preferred":false,"id":804258,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jones, Melissa Ward 0000-0002-3401-2515","orcid":"https://orcid.org/0000-0002-3401-2515","contributorId":244610,"corporation":false,"usgs":false,"family":"Jones","given":"Melissa","email":"","middleInitial":"Ward","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":804259,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":804260,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70215985,"text":"70215985 - 2020 - Nitrate in streams during winter low‐flow conditions as an indicator of legacy nitrate","interactions":[],"lastModifiedDate":"2020-11-30T16:30:57.387972","indexId":"70215985","displayToPublicDate":"2020-10-29T07:48:13","publicationYear":"2020","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":"Nitrate in streams during winter low‐flow conditions as an indicator of legacy nitrate","docAbstract":"Winter low‐flow (LF) conditions in streams provide a potential opportunity to evaluate the importance of legacy nitrate in catchments due to the dominance of slow‐flow transport pathways and lowered biotic activity. In this study, the concentration, flux, and trend of nitrate in streams during winter low‐flow conditions were analyzed at 320 sites in the conterminous United States. LF flow‐normalized nitrate concentrations varied from <0.1 to >20 mg‐N L‐1 and LF conditions contributed between 2% and 98% of the winter nitrate flux. LF nitrate concentrations generally exceeded 2.5 mg‐N L‐1 in the upper Midwest, with smaller regions of high LF nitrate concentrations in eastern Texas and along the northern mid‐Atlantic coast. Groundwater was inferred to be the primary or sole contributor of nitrate to streams during winter LF conditions at 140 of our 320 sites. Among these 140 sites, nitrate from groundwater comprised 45% or more of the winter nitrate flux at a quarter of the sites. Among the same 140 sites, concentrations of nitrate in streams during winter LF conditions generally increased between 2002 and 2012 at sites where 40% or more of the winter flux was from groundwater, suggesting that concentrations of nitrate in the contributing groundwater system were increasing. Using metrics developed herein, we characterize the potential importance of legacy nitrate at sites in this study and discuss methods to characterize sites with fewer samples than required by our models or at sites without continuous stream discharge measurements.
Modeling forest change effects on snow is critical to resource management. However, many models either do not appropriately model canopy structure or cannot represent fine‐scale changes in structure following a disturbance. We applied a 1 m2 resolution energy budget snowpack model at a forested site in New Mexico, USA, affected by a wildfire, using input data from lidar to represent prefire and postfire canopy conditions. Both scenarios were forced with 37 years of equivalent meteorology to simulate the effect of fire‐mediated canopy change on snowpack under varying meteorology. Postfire, the simulated snow distribution was substantially altered, and despite an overall increase in snow, 32% of the field area displayed significant decreases, resulting in higher snowpack variability. The spatial differences in snow were correlated with the change in several direction‐based forest structure metrics (aspect‐based canopy edginess and gap area). Locations with decreases in snow following the fire were on southern aspects that transitioned to south facing canopy edges, canopy gaps that increased in size to the south, or where large trees were removed. Locations with largest increases in snow occurred where all canopy was removed. Changes in canopy density metrics, typically used in snow models to represent the forest, did not fully explain the effects of fire on snow distribution. This explains why many models are not able to represent greater postfire variability in snow distribution and tend to predict only increases in snowpack following a canopy disturbance event despite observational studies showing both increases and decreases.
In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in‐stream water quality and process interactions, soil health and land management, and (peri‐)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027721","usgsCitation":"Fu, B., Horsburgh, J., Jakeman, A.J., Gaultieri, C., Arnold, T.W., Marshall, L.A., Green, T.R., Quinn, N.W., Volk, M., Hunt, R., Vezzaro, L., Croke, B., Jakeman, J., Snow, V.O., and Rashleigh, B., 2020, Modeling water quality in watersheds: From here to the next generation: Water Resources Research, v. 56, no. 11, e2020WR027721, 28 p., https://doi.org/10.1029/2020WR027721.","productDescription":"e2020WR027721, 28 p.","ipdsId":"IP-123332","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":454942,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr027721","text":"Publisher Index Page"},{"id":383140,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Fu, Baihua 0000-0003-2494-0518","orcid":"https://orcid.org/0000-0003-2494-0518","contributorId":174165,"corporation":false,"usgs":false,"family":"Fu","given":"Baihua","email":"","affiliations":[],"preferred":false,"id":810074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horsburgh, J. S. 0000-0002-0768-3196","orcid":"https://orcid.org/0000-0002-0768-3196","contributorId":248851,"corporation":false,"usgs":false,"family":"Horsburgh","given":"J. S.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":810075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jakeman, Anthony J. 0000-0001-5282-2215","orcid":"https://orcid.org/0000-0001-5282-2215","contributorId":173848,"corporation":false,"usgs":false,"family":"Jakeman","given":"Anthony","email":"","middleInitial":"J.","affiliations":[{"id":17939,"text":"The Australian National University","active":true,"usgs":false}],"preferred":false,"id":810076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gaultieri, C 0000-0002-3717-1618","orcid":"https://orcid.org/0000-0002-3717-1618","contributorId":248852,"corporation":false,"usgs":false,"family":"Gaultieri","given":"C","email":"","affiliations":[{"id":50045,"text":"University of Napoli","active":true,"usgs":false}],"preferred":false,"id":810077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arnold, Todd W.","contributorId":36058,"corporation":false,"usgs":false,"family":"Arnold","given":"Todd","email":"","middleInitial":"W.","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":810078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marshall, Lucy A. 0000-0003-0450-4292","orcid":"https://orcid.org/0000-0003-0450-4292","contributorId":198080,"corporation":false,"usgs":false,"family":"Marshall","given":"Lucy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":810079,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Green, Tim R 0000-0002-1441-8008","orcid":"https://orcid.org/0000-0002-1441-8008","contributorId":248853,"corporation":false,"usgs":false,"family":"Green","given":"Tim","email":"","middleInitial":"R","affiliations":[{"id":39550,"text":"U.S. Department of Agriculture, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":810080,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Quinn, Nigel W. T. 0000-0003-3333-4763","orcid":"https://orcid.org/0000-0003-3333-4763","contributorId":248854,"corporation":false,"usgs":false,"family":"Quinn","given":"Nigel","email":"","middleInitial":"W. T.","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":810081,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Volk, Martin 0000-0003-0064-8133","orcid":"https://orcid.org/0000-0003-0064-8133","contributorId":247479,"corporation":false,"usgs":false,"family":"Volk","given":"Martin","email":"","affiliations":[{"id":13477,"text":"Washington Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":810082,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":16118,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810083,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Vezzaro, L. 0000-0001-6344-7131","orcid":"https://orcid.org/0000-0001-6344-7131","contributorId":248855,"corporation":false,"usgs":false,"family":"Vezzaro","given":"L.","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":810084,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Croke, Barry 0000-0001-9216-1554","orcid":"https://orcid.org/0000-0001-9216-1554","contributorId":248856,"corporation":false,"usgs":false,"family":"Croke","given":"Barry","email":"","affiliations":[{"id":27305,"text":"Australia National University","active":true,"usgs":false}],"preferred":false,"id":810085,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Jakeman, John 0000-0002-3517-337X","orcid":"https://orcid.org/0000-0002-3517-337X","contributorId":248857,"corporation":false,"usgs":false,"family":"Jakeman","given":"John","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":810086,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Snow, Valerie O 0000-0002-6911-8184","orcid":"https://orcid.org/0000-0002-6911-8184","contributorId":248846,"corporation":false,"usgs":false,"family":"Snow","given":"Valerie","email":"","middleInitial":"O","affiliations":[{"id":50044,"text":"AgResearch","active":true,"usgs":false}],"preferred":false,"id":810087,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rashleigh, Brenda 0000-0002-0806-686X","orcid":"https://orcid.org/0000-0002-0806-686X","contributorId":242708,"corporation":false,"usgs":false,"family":"Rashleigh","given":"Brenda","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":810088,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70228467,"text":"70228467 - 2020 - Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment","interactions":[],"lastModifiedDate":"2022-02-14T12:04:27.764111","indexId":"70228467","displayToPublicDate":"2020-10-28T11:13:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment","docAbstract":"Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails (Rallus obsoletus yumanensis) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108734","usgsCitation":"Harrity, E.J., Stevens, B., and Conway, C.J., 2020, Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment: Biological Conservation, v. 250, 108734,10 p., https://doi.org/10.1016/j.biocon.2020.108734.","productDescription":"108734,10 p.","ipdsId":"IP-116214","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California, Nevada","otherGeospatial":"Colorado River Basin, Salton Sink","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.840087890625,\n 32.52828936482526\n ],\n [\n -114.796142578125,\n 32.44024912337551\n ],\n [\n -114.08752441406249,\n 32.697177359290635\n ],\n [\n -114.246826171875,\n 33.55055114384406\n ],\n [\n -114.29077148437499,\n 33.8247936182649\n ],\n [\n -113.873291015625,\n 34.31621838080741\n ],\n [\n -114.356689453125,\n 34.88593094075317\n ],\n [\n -114.63134765625001,\n 34.858890491257796\n ],\n [\n -115.916748046875,\n 33.128351191631566\n ],\n [\n -114.840087890625,\n 32.52828936482526\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"250","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harrity, Eamon J.","contributorId":275852,"corporation":false,"usgs":false,"family":"Harrity","given":"Eamon","email":"","middleInitial":"J.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":834366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":834367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834365,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216654,"text":"70216654 - 2020 - Modest residual effects of short-term warming, altered hydration, and biocrust successional state on dryland soil heterotrophic carbon and nitrogen cycling","interactions":[],"lastModifiedDate":"2020-11-27T17:09:15.373856","indexId":"70216654","displayToPublicDate":"2020-10-28T11:04:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7439,"text":"Frontiers in Ecology and Evolution Section Biogeography and Macroecology","active":true,"publicationSubtype":{"id":10}},"title":"Modest residual effects of short-term warming, altered hydration, and biocrust successional state on dryland soil heterotrophic carbon and nitrogen cycling","docAbstract":"Biological soil crusts (biocrusts) on the Colorado Plateau may fuel carbon (C) and nitrogen (N) cycling of soil heterotrophic organisms throughout the region. Late successional moss and lichen biocrusts, in particular, can increase soil C and N availability, but some data suggest these biocrust types will be replaced by early successional cyanobacterial biocrusts as the region undergoes warming and aridification. In this study, we evaluated the short-term interactive effects of biocrust successional state and elevated temperature on soil heterotrophic C and N cycling (specifically, soil respiration, N2O emissions, microbial biomass C and N, and soluble C and N). We collected soils following an 87-day greenhouse mesocosm experiment where the soils had been topped with different biocrust successional states (moss-dominated, cyanobacteria-dominated, or no biocrust) and had experienced different temperatures (ambient and warmed), under an artificial precipitation regime. Following this pre-incubation mesocosm phase, the soils were assessed using a short-term (2-day) laboratory incubation to determine the cumulative effect of the elevated temperature and altered biocrust successional state on the temperature sensitivity of soil heterotrophic C and N cycling. We found that there were interactive effects of biocrust successional state and exposure to warmer temperatures during the mesocosm phase under greenhouse conditions on the rate and temperature sensitivity of soil heterotrophic C and N cycling in laboratory incubations. Soils collected from beneath late successional biocrusts exhibited higher C and N cycling rates than those from beneath early successional crusts, while warming reduced both the magnitude and the temperature sensitivity of C and N cycling. The inhibiting effect of warming, was most evident in soils from beneath late successional biocrusts, which, during the mesocosm phase, also exhibited the greatest reductions in gross primary production and respiration in response to the warming treatment. Taken together, these data suggest that an overall effect of climate warming may be increasing resource limitation of the soil heterotrophic C and N cycles in the region, which may magnify alterations associated with the changes in biocrust community structure documented in previous studies. Overall, results from this study suggest that soil heterotrophic biogeochemical cycling is affected by interactions between temperature and the biocrust community that lives atop the mineral soil, with important implications for C and N cycling into the future.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2020.467157","usgsCitation":"Tucker, C., Ferrenberg, S., and Reed, S., 2020, Modest residual effects of short-term warming, altered hydration, and biocrust successional state on dryland soil heterotrophic carbon and nitrogen cycling: Frontiers in Ecology and Evolution Section Biogeography and Macroecology, v. 8, 467157, 17 p., https://doi.org/10.3389/fevo.2020.467157.","productDescription":"467157, 17 p.","ipdsId":"IP-110869","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2020.467157","text":"Publisher Index Page"},{"id":380845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","city":"Castle Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.66140747070312,\n 38.50519140240356\n ],\n [\n -109.26040649414062,\n 38.50519140240356\n ],\n [\n -109.26040649414062,\n 38.78085193143006\n ],\n [\n -109.66140747070312,\n 38.78085193143006\n ],\n [\n -109.66140747070312,\n 38.50519140240356\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Tucker, Colin 0000-0002-4539-7780 ctucker@usgs.gov","orcid":"https://orcid.org/0000-0002-4539-7780","contributorId":167487,"corporation":false,"usgs":true,"family":"Tucker","given":"Colin","email":"ctucker@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferrenberg, Scott","contributorId":217143,"corporation":false,"usgs":false,"family":"Ferrenberg","given":"Scott","affiliations":[{"id":39569,"text":"Department of Biology, New Mexico State University, Las Cruces, NM 88001, USA","active":true,"usgs":false}],"preferred":false,"id":805740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805741,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216386,"text":"70216386 - 2020 - Mussel community assessment tool for the Upper Mississippi River system","interactions":[],"lastModifiedDate":"2020-11-13T14:53:45.631508","indexId":"70216386","displayToPublicDate":"2020-10-28T08:47:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5254,"text":"Freshwater Mollusk Biology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Mussel community assessment tool for the Upper Mississippi River system","docAbstract":"Upper Mississippi River (UMR) resource managers need a quantitative means of evaluating the health of mussel assemblages to measure effects of management and regulatory actions, assess restoration techniques, and inform regulatory tasks. Our objective was to create a mussel community assessment tool (MCAT), consisting of a suite of metrics and scoring criteria, to consistently compare the relative health of UMR mussel assemblages. We developed an initial MCAT using quantitative data from 25 sites and 10 metrics. Metrics fell in five broad groups: conservation status and environmental sensitivity, taxonomic composition, population processes, abundance, and diversity. Metric scoring categories were based on quartile analysis: 25% scoring as good, 50% scoring as fair, and 25% scoring as poor. Scores were meant to facilitate establishing management priorities and mitigation options for the conservation of mussels. Scoring categories assumed that a healthy mussel assemblage consists of species with a variety of reproductive and life-history strategies, a low percentage of tolerant species, and a high percentage of sensitive species; shows evidence of adequate recruitment, a variety of age classes, and low mortality; and has high abundance, species richness, and species and tribe evenness. Metrics were validated using a modified Delphi technique. MCAT metrics generally reflected the professional opinions of UMR resource managers and provided a consistent evaluation technique with uniform definitions that managers could use to evaluate mussel assemblages. Additional data sets scored a priori by UMR resource managers were used to further validate metrics, resulting in data from 33 sites spanning over 980 km of the UMR. Initial and revised MCAT scores were similar, indicating that data represent the range of mussel assemblages in the UMR. Mussel assemblages could be evaluated using individual metrics or a composite score to suit management purposes. With additional data, metrics could be calibrated on a local scale or applied to other river systems.
","language":"English","publisher":"BioOne","doi":"10.31931/fmbc.v23i2.2020.109-123","usgsCitation":"Dunn, H.L., Zigler, S.J., and Newton, T., 2020, Mussel community assessment tool for the Upper Mississippi River system: Freshwater Mollusk Biology and Conservation, v. 23, no. 2, p. 109-123, https://doi.org/10.31931/fmbc.v23i2.2020.109-123.","productDescription":"15 p.","startPage":"109","endPage":"123","ipdsId":"IP-100031","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":454949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc.v23i2.2020.109-123","text":"Publisher Index Page"},{"id":380503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.9560546875,\n 38.85682013474361\n ],\n [\n -90.3076171875,\n 39.13006024213511\n ],\n [\n -91.043701171875,\n 39.690280594818034\n ],\n [\n -91.307373046875,\n 40.26276066437183\n ],\n [\n -90.791015625,\n 41.1290213474951\n ],\n [\n -90.87890625,\n 41.31907562295139\n ],\n [\n -90.2197265625,\n 41.566141964768384\n ],\n [\n -89.9560546875,\n 42.032974332441405\n ],\n [\n -90.50537109375,\n 42.54498667313236\n ],\n [\n -90.59326171875,\n 42.70665956351041\n ],\n [\n -90.90087890624999,\n 42.867912483915305\n ],\n [\n -90.966796875,\n 43.16512263158296\n ],\n [\n -90.999755859375,\n 43.492782808225\n ],\n [\n -91.1865234375,\n 43.96909818325171\n ],\n [\n -91.790771484375,\n 44.35527821160296\n ],\n [\n -92.1533203125,\n 44.53567453241317\n ],\n [\n -92.779541015625,\n 44.87144275016589\n ],\n [\n -93.087158203125,\n 44.68427737181225\n ],\n [\n -92.2412109375,\n 44.315987905196906\n ],\n [\n -91.834716796875,\n 44.02442151965934\n ],\n [\n -91.5380859375,\n 43.8503744993026\n ],\n [\n -91.241455078125,\n 43.22118973298753\n ],\n [\n -91.25244140624999,\n 43.092960677116295\n ],\n [\n -91.1865234375,\n 42.71473218539458\n ],\n [\n -90.758056640625,\n 42.50450285299051\n ],\n [\n -90.3955078125,\n 42.09007006868398\n ],\n [\n -90.50537109375,\n 41.672911819602085\n ],\n [\n -91.19750976562499,\n 41.50034959128928\n ],\n [\n -91.29638671875,\n 41.19518982948959\n ],\n [\n -91.20849609375,\n 41.03793062246529\n ],\n [\n -91.395263671875,\n 40.73893324113601\n ],\n [\n -91.62597656249999,\n 40.413496049701955\n ],\n [\n -91.746826171875,\n 40.027614437486655\n ],\n [\n -91.483154296875,\n 39.42770738465604\n ],\n [\n -90.966796875,\n 39.172658670429946\n ],\n [\n -90.703125,\n 38.79690830348427\n ],\n [\n -89.9560546875,\n 38.85682013474361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Heidi L.","contributorId":244888,"corporation":false,"usgs":false,"family":"Dunn","given":"Heidi","email":"","middleInitial":"L.","affiliations":[{"id":49009,"text":"EcoAnalysts, Inc.","active":true,"usgs":false}],"preferred":false,"id":804848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zigler, Steven J. 0000-0002-4153-0652 szigler@usgs.gov","orcid":"https://orcid.org/0000-0002-4153-0652","contributorId":2410,"corporation":false,"usgs":true,"family":"Zigler","given":"Steven","email":"szigler@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newton, Teresa 0000-0001-9351-5852 tnewton@usgs.gov","orcid":"https://orcid.org/0000-0001-9351-5852","contributorId":150098,"corporation":false,"usgs":true,"family":"Newton","given":"Teresa","email":"tnewton@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804850,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267767,"text":"70267767 - 2020 - Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the Big Bend region of the Rio Grande","interactions":[],"lastModifiedDate":"2025-05-30T16:03:05.271414","indexId":"70267767","displayToPublicDate":"2020-10-28T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the Big Bend region of the Rio Grande","docAbstract":"Alteration of flow regimes by anthropogenic activities is one of the primary environmental problems in riverine systems. Understanding how hydrologic conditions can affect ontogenetic habitat shifts of imperiled fishes is important in order to develop conservation and management strategies for each life-history stage. We examined relationships between the abundance of young-of-the-year (YOY) Rio Grande Blue Sucker and various abiotic variables in the Trans-Pecos region of the Rio Grande in Texas, USA. We used open N-mixture modeling to better understand the factors affecting ontogenetic habitat shifts of the imperiled aridland river fish. In addition, we examined differences in Rio Grande Blue Sucker total length among three mesohabitat types (pool, riffle, and run). The results of open N-mixture modeling suggested that as pool area increased, the abundance of YOY Rio Grande Blue Sucker increased. Total length of YOY Rio Grande Blue Sucker also significantly differed among the three mesohabitat types. The total lengths of YOY Rio Grande Blue Sucker in pool habitats were lower than in other mesohabitats, suggesting that YOY Rio Grande Blue Sucker undergo ontogenetic habitat shifts into greater current velocity habitats as they grow. The habitat associations we documented support the growing body of research emphasizing the importance of maintaining sufficient and appropriately timed flows to avoid prolonged low flows that limit habitat availability for native fish species during sensitive life stages in the Rio Grande and other aridland rivers.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10641-020-01038-8","usgsCitation":"Miyazono, S., Pease, A., Fritts, S., and Grabowski, T.B., 2020, Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the Big Bend region of the Rio Grande: Environmental Biology of Fishes, v. 103, p. 1471-1480, https://doi.org/10.1007/s10641-020-01038-8.","productDescription":"10 p.","startPage":"1471","endPage":"1480","ipdsId":"IP-118286","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Rio Grande in the Big Bend region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.91886105019951,\n 29.728911907450694\n ],\n [\n -103.91886105019951,\n 28.965625411076672\n ],\n [\n -102.76942901752302,\n 28.965625411076672\n ],\n [\n -102.76942901752302,\n 29.728911907450694\n ],\n [\n -103.91886105019951,\n 29.728911907450694\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Miyazono, Seiji","contributorId":356122,"corporation":false,"usgs":false,"family":"Miyazono","given":"Seiji","affiliations":[{"id":37463,"text":"TTU","active":true,"usgs":false}],"preferred":false,"id":938781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pease, Allison A.","contributorId":356124,"corporation":false,"usgs":false,"family":"Pease","given":"Allison A.","affiliations":[{"id":37463,"text":"TTU","active":true,"usgs":false}],"preferred":false,"id":938782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fritts, Sarah","contributorId":356126,"corporation":false,"usgs":false,"family":"Fritts","given":"Sarah","affiliations":[{"id":84915,"text":"tsu","active":true,"usgs":false}],"preferred":false,"id":938783,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":938780,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","interactions":[{"subject":{"id":47512,"text":"twri04A3 - 2002 - Statistical methods in water resources","indexId":"twri04A3","publicationYear":"2002","noYear":false,"displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources"},"predicate":"SUPERSEDED_BY","object":{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","indexId":"tm4A3","publicationYear":"2020","noYear":false,"title":"Statistical methods in water resources"},"id":1}],"lastModifiedDate":"2024-08-13T14:02:36.434133","indexId":"tm4A3","displayToPublicDate":"2020-10-27T09:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-A3","displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources","docAbstract":"This text began as a collection of class notes for a course on applied statistical methods for hydrologists taught at the U.S. Geological Survey (USGS) National Training Center. Course material was formalized and organized into a textbook, first published in 1992 by Elsevier as part of their Studies in Environmental Science series. In 2002, the work was made available online as a USGS report.
The text has now been updated as a USGS Techniques and Methods Report. It is intended to be a text in applied statistics for hydrology, environmental science, environmental engineering, geology, or biology that addresses distinctive features of environmental data. For example, water resources data tend to have many variables with a lower bound of zero, tend to be more skewed than data from many other disciplines, commonly contain censored data (less than values), and assumptions that the data are normally distributed are not appropriate. Computer-intensive methods (bootstrapping and permutation tests) now improve upon and replace the dependence on t-intervals, t-tests, and analysis of variance. A new chapter on sampling design addresses questions such as “How many observations do I need?” The chapter also presents distribution-free methods to help plan sampling efforts. The trends chapter has been updated to include the WRTDS (Weighted Regressions on Time, Discharge, and Season) method for analysis of water-quality data. This new version contains updated graphics and updated guidance on the use of statistical techniques. The text utilizes R, a programming language and open-source software environment, for all exercises and most graphics, and the R code used to generate figures and examples is provided for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4A3","usgsCitation":"Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p., https://doi.org/10.3133/tm4a3. [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chap. A3, version 1.1.]","productDescription":"Report: xxii, 458 p.; Data Release","numberOfPages":"484","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-089727","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":418371,"rank":5,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/tm/04/a03/Errata_Sheet.pdf","text":"Errata Sheet","size":"136 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Errata Sheet"},{"id":379731,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://store.usgs.gov/product/533012","text":"Print Version Available"},{"id":374999,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JWL6XR","text":"USGS data release","linkHelpText":"Statistical Methods in Water Resources - Supporting Materials"},{"id":375013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/a03/tm4a3.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-A3"},{"id":375000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/a03/coverthb.jpg"}],"publicComments":"Techniques and Methods 4-A3 supersedes Techniques of Water-Resources Investigations, book 4, chapter A3, version 1.1.","contact":"Chief, Analysis and Prediction Branch
Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Dr., Mail Stop 415
Reston, VA 20192
This report provides an overview of existing hydrologic information describing the quality, quantity, and extent of the major surface-water and groundwater resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma. Hydrologic information is provided for five major river systems (Cimarron River, North Canadian River, Canadian River, Washita River, and North Fork Red River), two reservoirs (Foss Reservoir and Canton Lake), and eight aquifers consisting of the alluvial aquifers associated with each of the five major river systems and three major bedrock aquifers (Ogallala aquifer, Elk City aquifer, and Rush Springs aquifer).
Types of information provided about rivers and reservoirs for the Cheyenne and Arapaho Tribal jurisdictional area include diversion sites and amounts of water allocated and diverted for permitted uses in 2015; treated wastewater discharge sites and amounts discharged in 2015; and characteristics describing water-quality field properties, major ions, nutrients, and selected trace elements. Major ions, nutrients, and selected trace elements are compared to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water. Additionally, statistics are provided describing daily, monthly, and annual streamflow characteristics at 12 U.S. Geological Survey streamgages. Streamflow statistics include the magnitudes and frequencies of floods, base-flow characteristics, and long-term streamflow trends.
Types of information provided about the aquifers include amounts of water allocated and pumped for permitted uses in 2015; characteristics of groundwater describing water-quality field properties, major ions, nitrate (measured as nitrogen), and selected trace elements with comparisons to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water; groundwater levels and long-term changes in water levels; and ranges of hydraulic conductivity, aquifer recharge, specific yield, transmissivity, and well yields from reports and groundwater-flow models.
Surface water is used primarily for irrigation and mining and other nonconsumptive uses in the Cheyenne and Arapaho Tribal jurisdictional area, except from the Washita and North Fork Red Rivers, where water is treated for use as a public-water supply. Large concentrations of dissolved solids are the primary limiting factor affecting the use of surface water. Median concentrations of dissolved solids in surface water range from less than 1,000 milligrams per liter (mg/L) in samples from the North Canadian River to greater than 9,000 mg/L in samples from the Cimarron River. Large dissolved solids concentrations are correlated with hard water. Median hardness as calcium carbonate concentrations in surface water ranges from 427 mg/L in samples from Canton Lake to 1,000 mg/L in samples from the Washita River.
In 2015, groundwater was used at more than twice the rate of surface water in the Cheyenne and Arapaho Tribal jurisdictional area. Although the alluvial aquifers are considered reliably good sources of water in the Cheyenne and Arapaho Tribal jurisdictional area, concentrations of nitrate (measured as nitrogen) exceed the maximum contaminant level of 10 mg/L established by the U.S. Environmental Protection Agency for finished drinking water in parts of all of the alluvial aquifers. Water from the three major bedrock aquifers is used for irrigation, mining, public-water supply, and other uses; however, large concentrations of dissolved solids, nitrate (measured as nitrogen), and naturally occurring trace elements such as arsenic and uranium may limit the use of groundwater as a source of public-water supply in some areas. As of 2015, the depletion of groundwater from the major aquifers in west-central Oklahoma is a minor concern to the Oklahoma Water Resources Board. Groundwater levels and other hydrologic information show that recharge rates exceed the rates of water pumped from aquifers, except in areas that may be affected locally by groundwater depletions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205105","collaboration":"Prepared in cooperation with the Cheyenne and Arapaho Tribes of Oklahoma and the Bureau of Indian Affairs","usgsCitation":"Becker, C.J., and Varonka, M.S., 2020, Water resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma, with an analysis of data gaps through 2015 (ver. 1.1, January 2021): U.S. Geological Survey Scientific Investigations Report 2020–5105, 158 p., 1 app., https://doi.org/10.3133/sir20205105..","productDescription":"xi, 158 p.","numberOfPages":"175","onlineOnly":"Y","ipdsId":"IP-109610","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":382059,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5105/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5105 Version History"},{"id":379749,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5105/sir20205105.pdf","text":"Report","size":"36.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5105"},{"id":379748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5105/coverthb2.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Cheyenne and Arapaho Tribal Jurisdictional Area","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.3595,35.1163],[-99.4067,35.1161],[-99.409,35.1148],[-99.4129,35.1134],[-99.4163,35.1134],[-99.4196,35.1143],[-99.4219,35.1138],[-99.4258,35.1129],[-99.4281,35.1111],[-99.4337,35.1097],[-99.4393,35.111],[-99.4432,35.1124],[-99.4449,35.116],[-99.7833,35.1161],[-99.7839,35.1151],[-99.7852,35.028],[-99.8897,35.0277],[-100.0008,35.0295],[-100.0014,35.1818],[-100.0015,35.2034],[-100.0013,35.2689],[-100.0012,35.293],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-100.0015,35.9478],[-100.002,36.0539],[-100.0025,36.1891],[-100.003,36.3134],[-100.0031,36.3348],[-100.0038,36.4998],[-100.0044,36.5849],[-100.0045,36.5917],[-99.6193,36.5916],[-99.605,36.5917],[-99.6043,36.506],[-99.6038,36.3051],[-99.6034,36.2457],[-99.5954,36.2457],[-99.5976,36.1639],[-99.382,36.1645],[-98.9565,36.1587],[-98.7878,36.1613],[-98.7428,36.1625],[-98.7251,36.1634],[-98.6362,36.1636],[-98.634,36.1636],[-98.3177,36.1645],[-98.2122,36.1656],[-98.1057,36.1658],[-97.6759,36.1663],[-97.6765,36.0715],[-97.6763,35.984],[-97.6761,35.8973],[-97.6757,35.7253],[-97.6753,35.5506],[-97.6719,35.5506],[-97.6729,35.4639],[-97.6733,35.3763],[-97.6729,35.335],[-97.6836,35.3351],[-97.6898,35.3338],[-97.6948,35.3339],[-97.7016,35.3353],[-97.7033,35.3353],[-97.7073,35.334],[-97.7118,35.3313],[-97.7181,35.3287],[-97.7231,35.3278],[-97.7288,35.3274],[-97.7367,35.328],[-97.7423,35.3298],[-97.749,35.3326],[-97.7546,35.3354],[-97.7635,35.3414],[-97.7759,35.3434],[-97.7917,35.3408],[-97.829,35.3348],[-97.838,35.3354],[-97.8464,35.3368],[-97.8565,35.341],[-97.8582,35.3428],[-97.8614,35.3551],[-97.8636,35.3588],[-97.8669,35.3615],[-97.8703,35.3629],[-97.8754,35.3625],[-97.8811,35.3608],[-97.8845,35.3572],[-97.8885,35.3545],[-97.8936,35.3513],[-97.9009,35.3514],[-97.9082,35.3528],[-97.9105,35.3533],[-97.915,35.3556],[-97.925,35.3612],[-97.9351,35.3649],[-97.9368,35.3672],[-97.9378,35.3744],[-97.9395,35.3763],[-97.9429,35.3772],[-97.9474,35.3777],[-97.9491,35.3764],[-97.9502,35.3745],[-97.9492,35.3668],[-97.951,35.3596],[-97.9527,35.3555],[-97.955,35.3528],[-97.9562,35.3519],[-97.9663,35.3525],[-97.9697,35.3529],[-97.9736,35.3543],[-97.9843,35.3599],[-97.9994,35.365],[-98.0184,35.3765],[-98.0985,35.3767],[-98.305,35.3744],[-98.3051,35.5437],[-98.3085,35.541],[-98.3119,35.5401],[-98.3142,35.5406],[-98.3193,35.5429],[-98.3193,35.5438],[-98.3187,35.5479],[-98.3181,35.5502],[-98.6199,35.552],[-98.6209,35.4639],[-98.6216,35.2038],[-98.616,35.2038],[-98.6177,35.0994],[-98.621,35.0981],[-98.6255,35.1035],[-98.6294,35.1167],[-98.6355,35.1231],[-98.6406,35.1231],[-98.6429,35.1145],[-98.6451,35.1113],[-98.6496,35.1141],[-98.6513,35.1177],[-98.649,35.1195],[-98.6485,35.1213],[-98.6485,35.1231],[-98.6513,35.125],[-98.6575,35.1236],[-98.6665,35.1209],[-98.6738,35.1187],[-98.6778,35.1082],[-98.6822,35.1101],[-98.6856,35.1078],[-98.6985,35.1115],[-98.7042,35.111],[-98.7109,35.1065],[-98.7132,35.1065],[-98.721,35.1138],[-98.7255,35.1115],[-98.7317,35.1129],[-98.7351,35.1029],[-98.7379,35.102],[-98.7401,35.107],[-98.748,35.1166],[-98.8244,35.1176],[-98.9312,35.1168],[-98.9807,35.1173],[-99.0425,35.1168],[-99.2555,35.1161],[-99.3595,35.1163]]]},\"properties\":{\"name\":\"Beckham\",\"state\":\"OK\"}}]}","edition":"Version 1.0: October 27, 2020; 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U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The city of Summerset is a growing community in west South Dakota. The Sun Valley Estates subdivision in the north part of the city was developed on unconsolidated deposits surrounded by steep terrain. During years with greater than normal precipitation, particularly in 2019, groundwater levels increased in the unconsolidated deposits and caused damage to stormwater systems, sewer infrastructure, and houses with basements. The U.S. Geological Survey, in cooperation with the City of Summerset, completed a study of the hydrogeology and groundwater flow in the alluvial aquifer part of the unconsolidated deposits in north Summerset to understand the groundwater system in the area and to provide hydrogeologic information in support of future development planning.
The study area included most of the Sun Valley Estates subdivision in the north part of the city of Summerset in the east Black Hills of west South Dakota. About 0.7 square mile of water-bearing alluvial deposits is included in the study area. Precipitation in the study area from 2017 to 2019 was compared to the monthly normal values at a nearby climate site. The largest departure from normal was in May 2019 with precipitation exceeding the monthly normal by about 5 inches (in.). All months in 2019, except March, exceeded the monthly normal precipitation. Cumulative departure from normal precipitation in 2019 increased from about 4 in. greater than normal in January to about 18 in. greater than normal in December.
The geologic setting of the study area is characterized by the surrounding Black Hills. Unconsolidated Quaternary-age deposits overlie consolidated to partially consolidated Mesozoic-age and Paleozoic-age shales, sandstones, and limestones. Surficial deposits of alluvium and other unconsolidated deposits are the primary surficial geologic units in the study area and form the components of the alluvium hydrogeologic unit of the study area. Results from previous studies of alluvium along nearby Rapid Creek estimated hydraulic conductivity to range from 89 to 2,292 feet per day (ft/d), transmissivity to range from 1,001 to 32,083 feet squared per day, and storage coefficients to range from 0.0002 to 0.16. Hydraulic conductivity and transmissivity generally decreased downstream along Rapid Creek (west to east). Slug tests were completed August 16, 2019, at two observation wells completed in the alluvial aquifer in the Sun Valley Estates subdivision to determine hydraulic conductivity. Hydraulic conductivity estimated from AQTESOLV curve-fitting analysis using the Bouwer-Rice method for all slug-in and slug-out trials from two observation wells in the study ranged from 0.20 to 0.26 ft/d for well 441318103220001 (SunValley1 well) and from 0.54 to 14 ft/d for well 441319103215701 (SunValley2 well). The mean, median, and standard deviation of all trials at both wells were 4.3 ft/d, 0.8 ft/d, and 5.6 ft/d, respectively.
The extent of the alluvial aquifer was determined by geologic maps and lithologic logs. Alluvial deposits in the study area extend to about 1 mile in the north–south direction and about 1.5 miles in the southeast–northwest direction. The direction of groundwater flow was estimated using water-level records and topographic maps. The resulting potentiometric map indicated that groundwater in the alluvial aquifer under the Sun Valley Estates subdivision originates from higher elevations of the west part of the area of interest and from streams in the southeast part. Recharge and evapotranspiration estimates were results from a Soil-Water Balance model that calculated a matrix of recharge for 2019 with values ranging from 0 to 11.4 in. and an annual mean value of 5.1 in. across the study area. Soil-Water Balance-estimated potential evapotranspiration for 2019 ranged from 28.90 to 28.75 in. and the estimated annual mean was 28.86 in. across the study area. Estimated groundwater budget components for the alluvial aquifer in the area of interest included inflows and outflows. Total estimated groundwater budget components for inflows for 2019 were about 66 percent from recharge, 33 percent from streamflow, and 1 percent from inflow from adjacent aquifers. Total estimated outflows were about 99-percent evapotranspiration and less than 1-percent outflow to adjacent aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205097","issn":"2328-0328","usgsCitation":"Eldridge, W.G., and Anderson, T.M., 2020, Hydrogeology and groundwater flow in alluvial deposits, north Summerset, South Dakota: U.S. Geological Survey Scientific Investigations Report 2020–5097, 31 p., https://doi.org/10.3133/sir20205097.","productDescription":"Report: vii, 31 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-116994","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":379700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5097/coverthb.jpg"},{"id":379703,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","description":"USGS data release","linkHelpText":"USGS Water Data for the Nation"},{"id":379702,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TKVMXU","text":"USGS data release","description":"USGS data release","linkHelpText":"Soil-Water Balance model for alluvial deposits in Summerset, South Dakota"},{"id":379701,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5097/sir20205097.pdf","text":"Report","size":"5.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5097"}],"country":"United States","state":"South Dakota","city":"Sommerset","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.37310791015625,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.15856343854312\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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Excessive nutrient inputs from tributary streams and rivers contribute to harmful algal blooms and coastal ecosystem degradation worldwide. However, the role that small tributaries play in coastal nutrient dynamics remains unknown because most monitoring and regulatory efforts focus only on the largest tributaries. We combined a 6-d sampling effort with discharge modeling to characterize nutrient inputs from nearly all watersheds draining to the world’s fifth largest lake. We found that streams are particularly likely to promote eutrophication in coastal ecosystems because they deliver water with higher concentrations of nutrients that are readily available to algae. Thus, our findings indicate that efforts to control nutrient loading could be enhanced by looking beyond the largest tributaries to include smaller streams.
The Midcontinent Rift System (MRS) is a 1.1 Ga sequence of voluminous basaltic eruptions and multiple intrusions followed by widespread sedimentation that extends across the Midcontinent and northern Great Lakes region of North America. Previous workers have commonly used seismic-reflection data (Great Lakes International Multidisciplinary Program on Crustal Evolution [GLIMPCE] line A) to demonstrate that the northern rift margin in central Lake Superior developed as a normal growth fault that was structurally inverted to a reverse fault during a compressional event after rifting had ended. A prominent, curvilinear aeromagnetic anomaly that extends from Isle Royale, Michigan, to Superior Shoal in central Lake Superior, Ontario (the IR-SS anomaly), is commonly presented as a manifestation of this reverse fault. We have integrated multidisciplinary geophysical analyses (seismic-reflection, seismic-refraction, aeromagnetic, and gravity), physical-property information (density, magnetic susceptibility and remanence, and compressional-wave velocity), and geologic concepts to develop an alternate interpretation of the rift margin along GLIMPCE line A, where it intersects the IR-SS anomaly. Our new model indicates that a normal fault is the dominant structure at the northern rift margin along line A, contrary to the original rift-margin paradigm, which asserts that compressional structures are the dominant features preserved today. Integral to this alternate model is a newly interpreted, prerift sedimentary basin intruded by sills in northern Lake Superior. Our alternate model of the northern rift margin has implications for interpreting the style, scale, and timing of extension, rift-related intrusion, and compression during development of the MRS.
Lava that erupted during the 2014–2015 Holuhraun eruption in Iceland flowed into a proglacial river system, resulting in aqueous cooling of the lava and an ephemeral hydrothermal system. We carried out a monitoring study of this system from 2015 to 2018 to document the cooling of the lava over this time, using thermocouple measurements and data-logging sensors. The heat loss rate from advection through this hydrothermal system in August 2015 was ~5.5 × 108 W; since eruption, aqueous cooling likely accounted for ~1% of the total heat loss from the lava. This estimate excludes steam losses from fumaroles as well as any groundwater that was not released to the surface, and thus is a lower bound. Near the terminus of the flow, advection of heat by flowing water may have locally accounted for tens of percent of the total cooling of that part of the flow. Our data quantify the importance of water cooling for this lava flow and can be compared with models to better understand lava–water interactions more generally. We also provide detailed methods for simple, low-cost monitoring of similar instances in the future.
As climate warming and precipitation increase at high latitudes, permafrost terrains across the circumpolar north are poised for intensified geomorphic activity and sediment mobilization that are expected to persist for millennia. In previously glaciated permafrost terrain, ice-rich deposits are associated with large stores of reactive mineral substrate. Over geological timescales, chemical weathering moderates atmospheric CO2 levels, raising the prospect that mass wasting driven by terrain consolidation following thaw (thermokarst) may enhance weathering of permafrost sediments and thus climate feedbacks. The nature of these feedbacks depends upon the mineral composition of sediments (weathering sources) and the balance between atmospheric exchange of CO2 vs. fluvial export of carbonate alkalinity (Σ[