Exposure to high concentration geogenic arsenic via groundwater is a worldwide health concern. Well installation introduces oxic drilling fluids and hypochlorite (a strong oxidant) for disinfection, thus inducing geochemical disequilibrium. Well installation causes changes in geochemistry lasting 12 + months, as illustrated in a recent study of 250 new domestic wells in Minnesota, north-central United States. One study well had extremely high initial arsenic (1550 µg/L) that substantially decreased after 15 months (5.2 µg/L). The drilling and development of the study well were typical and ordinary; nothing observable indicated the very high initial arsenic concentration. We hypothesized that oxidation of arsenic-containing sulfides (which lowers pH) combined with low pH dissolution of arsenic-bearing Fe (oxyhydr)oxides caused the very high arsenic concentration. Geochemical equilibrium considerations and modeling supported our hypothesis. Groundwater equilibrium redox conditions are poised at the Fe(III)(s)/Fe(II)(aq) stability boundary, indicating arsenic-bearing Fe (oxyhydr)oxide mineral sensitivity to pH and redox changes. Changing groundwater geochemistry can have negative implications for home water treatment (e.g., reduced arsenic removal efficiency, iron fouling), which can lead to ongoing but unrecognized hazard of arsenic exposure from domestic well water. Our results may inform arsenic mobilization processes and geochemical sensitivity in similarly complex aquifers in Southeast Asia and elsewhere.
Lawns as a landcover change substantially alter evapotranspiration, CO2, and energy exchanges and are of rising importance considering their spatial extent. We contrast eddy covariance (EC) flux measurements collected in the Denver, Colorado, USA metropolitan area in 2011 and 2012 over a lawn and a xeric tallgrass prairie. Close linkages between seasonal vegetation development, energy fluxes, and net ecosystem exchange (NEE) of CO2 were found. Irrigation of the lawn modified energy and CO2 fluxes and greatly contributed to differences observed between sites. Due to greater water inputs (precipitation + irrigation) at the lawn in this semi-arid climate, energy partitioning at the lawn was dominated by latent heat (LE) flux. As a result, evapotranspiration (ET) of the lawn was more than double that of tallgrass prairie (2011: 639(±17) mm vs. 302(±9) mm; 2012: 584(±15) mm vs. 265(±7) mm). NEE for the lawn was characterized by a longer growing season, higher daily net uptake of CO2, and growing season NEE that was also more than twice that of the prairie (2011: −173(±23) g C m−2 vs. -81(±10) g C m−2; 2012: −73(±22) g C m−2 vs. -21(±8) g C m−2). During the drought year (2012), temperature and water stress greatly influenced the direction and magnitude of CO2 flux at both sites. The results suggest that lawns in Denver can function as carbon sinks and conditionally contribute to the mitigation of carbon emissions - directly by CO2 uptake and indirectly through effects of evaporative cooling on microclimate and energy use.
The presence of many pathogens varies in a predictable manner with latitude, with infections decreasing from the equator towards the poles. We investigated the geographic trends of pathogens infecting a widely distributed carnivore: the gray wolf (Canis lupus). Specifically, we investigated which variables best explain and predict geographic trends in seroprevalence across North American wolf populations and the implications of the underlying mechanisms. We compiled a large serological dataset of nearly 2000 wolves from 17 study areas, spanning 80° longitude and 50° latitude. Generalized linear mixed models were constructed to predict the probability of seropositivity of four important pathogens: canine adenovirus, herpesvirus, parvovirus, and distemper virus—and two parasites: Neospora caninum and Toxoplasma gondii. Canine adenovirus and herpesvirus were the most widely distributed pathogens, whereas N. caninum was relatively uncommon. Canine parvovirus and distemper had high annual variation, with western populations experiencing more frequent outbreaks than eastern populations. Seroprevalence of all infections increased as wolves aged, and denser wolf populations had a greater risk of exposure. Probability of exposure was positively correlated with human density, suggesting that dogs and synanthropic animals may be important pathogen reservoirs. Pathogen exposure did not appear to follow a latitudinal gradient, with the exception of N. caninum. Instead, clustered study areas were more similar: wolves from the Great Lakes region had lower odds of exposure to the viruses, but higher odds of exposure to N. caninum and T. gondii; the opposite was true for wolves from the central Rocky Mountains. Overall, mechanistic predictors were more informative of seroprevalence trends than latitude and longitude. Individual host characteristics as well as inherent features of ecosystems determined pathogen exposure risk on a large scale. This work emphasizes the importance of biogeographic wildlife surveillance, and we expound upon avenues of future research of cross-species transmission, spillover, and spatial variation in pathogen infection.
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D.","contributorId":251696,"corporation":false,"usgs":false,"family":"Cluff","given":"H.","email":"","middleInitial":"D.","affiliations":[{"id":50376,"text":"Government of the Northwest Territories","active":true,"usgs":false}],"preferred":false,"id":810333,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"MacNulty, Daniel R.","contributorId":179179,"corporation":false,"usgs":false,"family":"MacNulty","given":"Daniel R.","affiliations":[],"preferred":false,"id":810334,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Watts, David L.","contributorId":214781,"corporation":false,"usgs":false,"family":"Watts","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":810335,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Roffler, G.","contributorId":251697,"corporation":false,"usgs":false,"family":"Roffler","given":"G.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":810336,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Schwantje, Helen M.","contributorId":190378,"corporation":false,"usgs":false,"family":"Schwantje","given":"Helen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":810337,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Hebblewhite, Mark","contributorId":190188,"corporation":false,"usgs":false,"family":"Hebblewhite","given":"Mark","email":"","affiliations":[],"preferred":false,"id":810338,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Beckman, K.","contributorId":251698,"corporation":false,"usgs":false,"family":"Beckman","given":"K.","email":"","affiliations":[{"id":50377,"text":"Alaska Dept of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":810339,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Hudson, P. J.","contributorId":236937,"corporation":false,"usgs":false,"family":"Hudson","given":"P.","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":810340,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70218713,"text":"70218713 - 2021 - Indicators of volcanic eruptions revealed by global M4+ earthquakes","interactions":[],"lastModifiedDate":"2021-03-08T16:13:29.870904","indexId":"70218713","displayToPublicDate":"2021-02-12T10:07:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Indicators of volcanic eruptions revealed by global M4+ earthquakes","docAbstract":"Determining whether seismicity near volcanoes is due primarily to tectonic or magmatic processes is a challenging but critical endeavor for volcanic eruption forecasting and detection, especially at poorly monitored volcanoes. Global statistics on the occurrence and timing of earthquakes near volcanoes both within and outside of eruptive periods reveal patterns in eruptive seismicity that may improve our ability to discern magmatically driven seismicity from purely tectonic seismicity. In this paper, we catalog magnitude four and greater (M4+) earthquakes near volcanoes globally and compute statistics on their occurrence with respect to various eruptive and volcanic attributes, evaluating their utility as diagnostic indicators of eruptions. Using a 2‐week time window and a 30 km radius around the volcanoes, we find that 11% of eruptions are preceded by at least one M4+ earthquake, but only 1% of such earthquakes is followed by eruption. However, earthquakes located 5–15 km from the volcano, those with normal faulting mechanisms and/or large nondouble‐couple components, and those occurring as groups are more commonly associated with eruptions, providing significant forecasting utility in some cases. Similarly, certain volcanoes are more likely to exhibit such precursors, such as those with long repose periods. We illustrate the use of these data in eruption forecasting scenarios, including rapid identification of analogous earthquake sequences at other volcanoes. When integrated within the context of multiparametric, multidisciplinary probabilistic assessments of volcanic activity, global earthquake statistics can improve eruption forecasts, and our work provides a model for use on other rapidly expanding global volcanological databases.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB021294","usgsCitation":"Pesicek, J.D., Ogburn, S.E., and Prejean, S., 2021, Indicators of volcanic eruptions revealed by global M4+ earthquakes: Journal of Geophysical Research, v. 126, no. 3, e2020JB021294, 28 p., https://doi.org/10.1029/2020JB021294.","productDescription":"e2020JB021294, 28 p.","ipdsId":"IP-124151","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":453474,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jb021294","text":"Publisher Index Page"},{"id":384229,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pesicek, Jeremy D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":202042,"corporation":false,"usgs":true,"family":"Pesicek","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":811480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ogburn, Sarah E. 0000-0002-4734-2118","orcid":"https://orcid.org/0000-0002-4734-2118","contributorId":204751,"corporation":false,"usgs":true,"family":"Ogburn","given":"Sarah","email":"","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":811481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prejean, Stephanie 0000-0003-0510-1989 sprejean@usgs.gov","orcid":"https://orcid.org/0000-0003-0510-1989","contributorId":172404,"corporation":false,"usgs":true,"family":"Prejean","given":"Stephanie","email":"sprejean@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":811482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218675,"text":"70218675 - 2021 - Airborne geophysical imaging of weak zones on Iliamna Volcano, Alaska: Implications for slope stability","interactions":[],"lastModifiedDate":"2021-03-05T13:52:47.045359","indexId":"70218675","displayToPublicDate":"2021-02-12T07:44:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Airborne geophysical imaging of weak zones on Iliamna Volcano, Alaska: Implications for slope stability","docAbstract":"Water‐saturated, hydrothermally altered rocks reduce the strength of volcanic edifices and increase the potential for sector collapses and far‐traveled mass flows of unconsolidated debris. Iliamna Volcano is an andesitic stratovolcano located on the western side of the Cook Inlet, ∼225 km southwest of Anchorage and is a source of repeated avalanches. The widespread snow and ice cover on Iliamna Volcano make surface alteration difficult to identify. However, intense hydrothermal alteration significantly reduces both the electrical resistivity and magnetization of volcanic rock and can therefore be identified with airborne geophysical measurements. We use airborne electromagnetic and magnetic data to map snow and ice thickness and identify underlying alteration zones at Iliamna Volcano, Alaska. Resistivities were calculated to an average depth of >300 m, and a 3‐D susceptibility model extends from the surface to the base of the volcano, about 3,000 m below the summit. Geophysical models image low resistivity (<30 ohm‐m) and low susceptibilities near the summit of Iliamna and below its older vent complex, with the low susceptibilities indicating alteration up to ∼800 m in thickness. Thin conductors (∼50–100 m thick) on the edifice slopes coincide with recorded locations of repeated debris avalanches over the past ∼60 years and are attributed to saturated zones at high elevation. Three‐dimensional slope stability models based upon the geophysically constrained alteration distribution suggest the edifice of Iliamna is unstable and could lead to collapse scars ∼400 m deep near the current and former vent complexes.
Stratigraphic, lithologic, foraminiferal, and radiocarbon analyses indicate that at least four abrupt mud-over-peat contacts are recorded across three sites (Jacoby Creek, McDaniel Creek, and Mad River Slough) in northern Humboldt Bay, California, USA (∼44.8°N, −124.2°W). The stratigraphy records subsidence during past megathrust earthquakes at the southern Cascadia subduction zone ∼40 km north of the Mendocino Triple Junction. Maximum and minimum radiocarbon ages on plant macrofossils from above and below laterally extensive (>6 km) contacts suggest regional synchroneity of subsidence. The shallowest contact has radiocarbon ages that are consistent with the most recent great earthquake at Cascadia, which occurred at 250 cal yr B.P. (1700 CE). Using Bchron and OxCal software, we model ages for the three older contacts of ca. 875 cal yr B.P., ca. 1120 cal yr B.P., and ca. 1620 cal yr B.P.
For each of the four earthquakes, we analyze foraminifera across representative mud-over-peat contacts selected from McDaniel Creek. Changes in fossil foraminiferal assemblages across all four contacts reveal sudden relative sea-level (RSL) rise (land subsidence) with submergence lasting from decades to centuries. To estimate subsidence during each earthquake, we reconstructed RSL rise across the contacts using the fossil foraminiferal assemblages in a Bayesian transfer function. The coseismic subsidence estimates are 0.85 ± 0.46 m for the 1700 CE earthquake, 0.42 ± 0.37 m for the ca. 875 cal yr B.P. earthquake, 0.79 ± 0.47 m for the ca. 1120 cal yr B.P. earthquake, and ≥0.93 m for the ca. 1620 cal yr B.P. earthquake. The subsidence estimate for the ca. 1620 cal yr B.P. earthquake is a minimum because the pre-subsidence paleoenvironment likely was above the upper limit of foraminiferal habitation. The subsidence estimate for the ca. 875 cal yr B.P. earthquake is less than (<50%) the subsidence estimates for other contacts and suggests that subsidence magnitude varied over the past four earthquake cycles in southern Cascadia.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35701.1","usgsCitation":"Padgett, J., Engelhart, S.E., Kelsey, H., Witter, R., Cahill, N., and Hemphill-Haley, E., 2021, Timing and amount of southern Cascadia earthquake subsidence over the past 1700 years at northern Humboldt Bay, California, USA: GSA Bulletin, v. 133, no. 9-10, p. 2137-2156, https://doi.org/10.1130/B35701.1.","productDescription":"20 p.","startPage":"2137","endPage":"2156","ipdsId":"IP-122222","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":453481,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/b35701.1","text":"External Repository"},{"id":400683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Humboldt Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.57946777343751,\n 40.43440488077008\n ],\n [\n -123.81042480468749,\n 40.43440488077008\n ],\n [\n -123.81042480468749,\n 41.31082388091818\n ],\n [\n -124.57946777343751,\n 41.31082388091818\n ],\n [\n -124.57946777343751,\n 40.43440488077008\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","issue":"9-10","noUsgsAuthors":false,"publicationDate":"2021-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Padgett, Jason S.","contributorId":257829,"corporation":false,"usgs":false,"family":"Padgett","given":"Jason S.","affiliations":[{"id":52130,"text":"Department of Geology, Humboldt State University, Arcata, California 95524, USA; Department of Geography, Durham University, Durham, DH1 3LE, UK","active":true,"usgs":false}],"preferred":false,"id":843182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engelhart, Simon E.","contributorId":60104,"corporation":false,"usgs":false,"family":"Engelhart","given":"Simon","email":"","middleInitial":"E.","affiliations":[{"id":6923,"text":"University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":843183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelsey, Harvey M.","contributorId":206893,"corporation":false,"usgs":false,"family":"Kelsey","given":"Harvey M.","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":843184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":4528,"corporation":false,"usgs":true,"family":"Witter","given":"Robert C.","email":"rwitter@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":843185,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cahill, Niamh","contributorId":150754,"corporation":false,"usgs":false,"family":"Cahill","given":"Niamh","email":"","affiliations":[{"id":18091,"text":"University College Dublin","active":true,"usgs":false},{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":843186,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hemphill-Haley, Eileen","contributorId":194373,"corporation":false,"usgs":false,"family":"Hemphill-Haley","given":"Eileen","affiliations":[{"id":35736,"text":"Hemphill-Haley Consulting, McKinleyville, CA","active":true,"usgs":false}],"preferred":false,"id":843187,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219505,"text":"70219505 - 2021 - Partial migration and spawning movements of humpback chub in the Little Colorado River are better understood using data from autonomous PIT tag antennas","interactions":[],"lastModifiedDate":"2021-08-17T16:01:40.065816","indexId":"70219505","displayToPublicDate":"2021-02-12T06:33:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Partial migration and spawning movements of humpback chub in the Little Colorado River are better understood using data from autonomous PIT tag antennas","docAbstract":"Choosing whether or not to migrate is an important life history decision for many fishes. Here we combine data from physical captures and detections on autonomous passive integrated transponder (PIT) tag antennas to study migration in an endangered fish, the humpback chub (Gila cypha). We develop hidden Markov mark-recapture models with and without antenna detections and find that the model fit without antenna detections misses a large proportion of fish and underestimates migration and survival probabilities. We then assess survival and growth differences associated with life history strategy and migration for different demographic groups (small male, small female, large male, large female). We find large differences in survival according to life history strategy, where residents had much lower over-winter survival than migrants. However, within the migratory life history strategy, survival and growth were similar for active migrants and skipped migrants for all demographic groups. We discuss some common challenges to incorporating detections from autonomous antennas into population models and demonstrate how these data can provide insight about fish movement and life history strategies.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2020-0291","usgsCitation":"Dzul, M.C., Kendall, W.L., Yackulic, C., Winkelman, D.L., Van Haverbeke, D.R., and Yard, M.D., 2021, Partial migration and spawning movements of humpback chub in the Little Colorado River are better understood using data from autonomous PIT tag antennas: Canadian Journal of Fisheries and Aquatic Sciences, v. 78, no. 8, p. 1057-1072, https://doi.org/10.1139/cjfas-2020-0291.","productDescription":"16 p.","startPage":"1057","endPage":"1072","onlineOnly":"N","ipdsId":"IP-121398","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436513,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95KA0XI","text":"USGS data release","linkHelpText":"Humpback chub spring and fall capture histories in the Little Colorado River, 2009-2019"},{"id":384979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Little Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.28851318359375,\n 35.67737855391475\n ],\n [\n -111.29425048828125,\n 35.67737855391475\n ],\n [\n -111.29425048828125,\n 36.43896124085945\n ],\n [\n -112.28851318359375,\n 36.43896124085945\n ],\n [\n -112.28851318359375,\n 35.67737855391475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William Louis 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":257230,"corporation":false,"usgs":false,"family":"Kendall","given":"William","email":"","middleInitial":"Louis","affiliations":[{"id":51981,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, 201 J.V.K. Wagar Building 1484 Campus Delivery, Fort Collins, CO 80523, USA","active":true,"usgs":false}],"preferred":false,"id":813825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":813827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Haverbeke, David Randall","contributorId":257231,"corporation":false,"usgs":false,"family":"Van Haverbeke","given":"David","email":"","middleInitial":"Randall","affiliations":[{"id":51983,"text":"Arizona Fish and Wildlife Conservation Office, U.S. Fish and Wildlife Service, 2500 S Pine Knoll Dr., Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":813828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813829,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248718,"text":"70248718 - 2021 - Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality","interactions":[],"lastModifiedDate":"2023-09-28T13:38:30.568584","indexId":"70248718","displayToPublicDate":"2021-02-11T08:39:19","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":91,"text":"Technical Report","active":true,"publicationSubtype":{"id":1}},"title":"Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality","docAbstract":"This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.
","language":"English","publisher":"Department of Energy","doi":"10.2172/1769795","usgsCitation":"Varadharajan, C., Kumar, V., Willard, J., Zwart, J.A., Sadler, J.M., Weierbach, H., Perciano, T., Mueller, J., Hendrix, V., and Christianson, D., 2021, Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality: Technical Report, 5 p., https://doi.org/10.2172/1769795.","productDescription":"5 p.","ipdsId":"IP-126904","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":453494,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1769795","text":"External Repository"},{"id":421340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Varadharajan, Charuleka","contributorId":242712,"corporation":false,"usgs":false,"family":"Varadharajan","given":"Charuleka","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":883289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":883290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883292,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weierbach, Helen","contributorId":290549,"corporation":false,"usgs":false,"family":"Weierbach","given":"Helen","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883293,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perciano, Talita 0000-0002-2388-1803","orcid":"https://orcid.org/0000-0002-2388-1803","contributorId":290546,"corporation":false,"usgs":false,"family":"Perciano","given":"Talita","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883294,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Juliane 0000-0001-8627-1992","orcid":"https://orcid.org/0000-0001-8627-1992","contributorId":290539,"corporation":false,"usgs":false,"family":"Mueller","given":"Juliane","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883295,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hendrix, Valerie 0000-0001-9061-8952","orcid":"https://orcid.org/0000-0001-9061-8952","contributorId":290533,"corporation":false,"usgs":false,"family":"Hendrix","given":"Valerie","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883296,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Christianson, Danielle","contributorId":265829,"corporation":false,"usgs":false,"family":"Christianson","given":"Danielle","email":"","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":883297,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70225537,"text":"70225537 - 2021 - Linking decomposition rates of soil organic amendments to their chemical composition","interactions":[],"lastModifiedDate":"2021-10-21T12:03:44.991455","indexId":"70225537","displayToPublicDate":"2021-02-11T07:00:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9533,"text":"Soil Research","active":true,"publicationSubtype":{"id":10}},"title":"Linking decomposition rates of soil organic amendments to their chemical composition","docAbstract":"The stock of organic carbon contained within a soil represents the balance between inputs and losses. Inputs are defined by the ability of vegetation to capture and retain carbon dioxide, effects that management practices have on the proportion of captured carbon that is added to soil and the application organic amendments. The proportion of organic amendment carbon retained is defined by its rate of mineralisation. In this study, the rate of carbon mineralisation from 85 different potential soil organic amendments (composts, manures, plant residues and biosolids) was quantified under controlled environmental conditions over a 547 day incubation period. The composition of each organic amendment was quantified using nuclear magnetic resonance and mid- and near-infrared spectroscopies. Cumulative mineralisation of organic carbon from the amendments was fitted to a two-pool exponential model. Multivariate chemometric algorithms were derived to allow the size of the fast and slow cycling pools of carbon to be predicted from the acquired spectroscopic data. However, the fast and slow decomposition rate constants could not be predicted suggesting that prediction of the residence time of organic amendment carbon in soil would likely require additional information related to soil type, environmental conditions, and management practices in use at the site of application.
Machine-learning models developed by the U.S. Geological Survey were used to predict iron concentrations and the probability of dissolved oxygen (DO) concentrations exceeding a threshold of 1 milligram per liter (mg/L) in groundwater in aquifers of the Mississippi embayment physiographic region. DO and iron concentrations are driven by and reflect the oxidation-reduction (redox) conditions in groundwater. Predictions from boosted regression trees, a type of machine-learning model, of iron concentration and DO threshold probability were used to categorize redox zones in the Mississippi River Valley alluvial aquifer (MRVA), middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ). Model predictions indicated that DO concentrations greater than 1 mg/L are uncommon across the MRVA. DO events (where the predicted probability was greater than 0.5) tended to occur on the margins of the MRVA and in upland areas where MCAQ and LCAQ units crop out at the surface or are at shallow depth. Predicted iron concentrations were higher in the MRVA than in the MCAQ and LCAQ. Uncertainty in predicted iron concentrations tended to be high in areas where measured concentrations were also high, resulting in small areas (encompassing less than 1.5 percent of the areal extent of the MRVA) of predicted iron concentrations that exceeded 100,000 micrograms per liter. Despite the large magnitude of overpredicted iron concentrations, the general proportion and spatial distribution of predicted iron concentrations reflected observed concentrations in groundwater wells. Where the probability of exceeding a DO concentration of 1 mg/L was 0.8 or more and the iron concentration was less than 1,000 micrograms per liter, aquifers were categorized as oxic. Oxic conditions were mostly in the uplands where MCAQ and LCAQ units crop out at the margins of the modeled area. The MRVA was mostly anoxic, which was controlled by DO threshold probabilities less than 0.1. The predictions and redox zones support conceptual models of redox conditions in the Mississippi embayment. The MRVA is predominantly anoxic with high iron concentrations. In the Claiborne aquifers (including the MCAQ and LCAQ), groundwater flows along regional flow paths toward the axis of the Mississippi embayment (the approximate location of the Mississippi River), the residence time in the aquifer increases, DO is consumed, and iron concentrations generally increase. Elevated concentrations of trace elements, such as manganese and arsenic, are often associated with reducing conditions in anoxic and mixed anoxic zones, but other factors such as sediment mineralogy affect the occurrence and distribution of these constituents.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3468","collaboration":"National Water Quality Program","usgsCitation":"Knierim, K.J., Kingsbury, J.A., and Haugh, C.J., 2021, Machine-learning predictions of redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers, south-central United States: U.S. Geological Survey Scientific Investigations Map 3468, 16 p., 3 sheets, https://doi.org/10.3133/sim3468.","productDescription":"Pamphlet: v, 16 p.; 3 Sheets: 34.3 inches x 24.7 inches or smaller; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-117970","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":383180,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N108JM","text":"USGS data release","linkHelpText":"Machine-learning model predictions and rasters of dissolved oxygen probability, iron concentration, and redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers"},{"id":383179,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3468/sim3468_sheet03.pdf","text":"Sheet 3","size":"3.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3468 Sheet 3"},{"id":383178,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3468/sim3468_sheet02.pdf","text":"Sheet 2","size":"6.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3468 Sheet 2"},{"id":383177,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3468/sim3468_sheet01.pdf","text":"Sheet 1","size":"7.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3468 Sheet 1"},{"id":383176,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3468/sim3468_pamphlet.pdf","text":"Pamphlet","size":"1.55MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3468 Pamphlet"},{"id":383175,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3468/coverthb2.jpg"}],"country":"United States","state":"Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Claiborne Aquifer, Mississippi Rier Valley Alluvial Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.04296874999999,\n 33.568861182555565\n ],\n [\n -94.04296874999999,\n 32.40779154205701\n ],\n [\n -93.33984375,\n 31.71882222408327\n ],\n [\n -91.95556640625,\n 31.147006308556566\n ],\n [\n -91.351318359375,\n 31.11879439598953\n ],\n [\n -90.274658203125,\n 31.522361470421437\n ],\n [\n -87.978515625,\n 31.5504526754715\n ],\n [\n -87.593994140625,\n 31.728167146023935\n ],\n [\n -87.78076171875,\n 32.45415593941475\n ],\n [\n -89.12109375,\n 32.93492866908233\n ],\n [\n -88.934326171875,\n 35.567980458012094\n ],\n [\n -88.8134765625,\n 37.23032838760387\n ],\n [\n -89.329833984375,\n 37.03763967977139\n ],\n [\n -90.362548828125,\n 36.48314061639213\n ],\n [\n -91.86767578124999,\n 35.25459097465022\n ],\n [\n -94.04296874999999,\n 33.568861182555565\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The Soldier Meadows spring complex provides habitat for the desert dace, an endemic and threatened fish. The spring complex has been altered with the construction of irrigation ditches that remove water from natural stream channels. Irrigation ditches generally provide lower quality habitat for the desert dace. Land and wildlife management agencies are interested in increasing habitat extent and quality by filling in irrigation ditches and restoring streamflow to natural channels. The U.S. Geological Survey measured streamflow, surveyed topography, and combined light detection and ranging data to create a two-dimensional hydraulic model of the study area to understand how restoration would change streamflow extents and hydraulic characteristics. Streamflow measurements indicate that, except for a section of one irrigation ditch at the upstream end of the study area, the total volume of streamflow diverted into the irrigation ditches in the study area was minimal. Hydraulic modeling indicates filling in the irrigation ditch at the upper end of the study area would return streamflow to the natural channel, resulting in an increase in natural channel surface water extent, and a reduction of irrigation ditch surface water flow. The result would be a more heterogenous natural stream channel, ranging from shallow and slow to narrow and fast.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205143","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Morris, C.M., 2021, Evaluation of streamflow extent and hydraulic characteristics of a restored channel at Soldier Meadows, Black Rock Desert–High Rock Canyon Emigrant Trails National Conservation Area, Nevada: U.S. Geological Survey Scientific Investigations Report 2020–5143, 22 p., https://doi.org/10.3133/sir20205143.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-110000","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":383124,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O0GII7","linkHelpText":"Geospatial data and surface-water model archive for evaluation of streamflow extent and hydraulic characteristics of a restored channel at Soldier Meadows, Black Rock Desert–High Rock Canyon Emigrant Trails National Conservation Area, Nevada"},{"id":383123,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5143/images"},{"id":383122,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5143/sir20205143.xml"},{"id":383121,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5143/sir20205143.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383120,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5143/covrthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Black Rock Desert, High Rock Canyon Emigrant Trails National Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.40765380859375,\n 40.734770989672406\n ],\n [\n -118.35845947265625,\n 40.734770989672406\n ],\n [\n -118.35845947265625,\n 41.45919537950706\n ],\n [\n -119.40765380859375,\n 41.45919537950706\n ],\n [\n -119.40765380859375,\n 40.734770989672406\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
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Green Peter and Foster Dams have altered natural seasonal temperature patterns in the South and Middle Santiam Rivers of the Willamette River Basin in northwestern Oregon. Cold-water releases from Green Peter Dam, upstream of Foster Lake, contribute to the cool-water conditions at Foster Dam. In summer, unseasonably cold water typically is discharged from Foster Dam into the Foster Dam fish ladder, which may be one factor contributing to the low numbers of upstream migrating Chinook salmon (Oncorhynchus tshawytscha) that enter the fish ladder. The U.S. Army Corps of Engineers is leading efforts to improve conditions for Chinook salmon upstream and downstream of these dams by considering structural alterations to Foster Dam and by exploring changes to the way the dams are operated.
The U.S. Geological Survey assisted the U.S. Army Corps of Engineers by using previously calibrated numerical models of flow and water quality for Green Peter and Foster Lakes and for the South Santiam River downstream of Foster Dam. These two-dimensional hydrodynamic and water-quality (CE-QUAL-W2) models were used to test scenarios of altered dam operations and alternate water-management strategies. Results of these scenarios provide information and insights into how the mixing and thermal characteristics of the lakes are affected by dam operations, how the mixing and timing of upstream source waters reaching Foster Dam are affected by dam operations, how river and fish-ladder temperature targets might be achieved, and how quickly (or slowly) such changes in the lakes and downstream river reaches occur, relative to typical unmodified operations at Green Peter and Foster Dams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205145","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Sullivan, A.B., and Rounds, S.A., 2021, Modeling water temperature response to dam operations and water management in Green Peter and Foster Lakes and the South Santiam River, Oregon: U.S. Geological Survey Scientific Investigations Report 2020–5145, 27 p., https://doi.org/10.3133/sir20205145.","productDescription":"Report: vi, 27 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-117626","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":383125,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5145/coverthb.jpg"},{"id":383126,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5145/sir20205145.pdf","text":"Report","size":"12.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5145"},{"id":383127,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C1YRV3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"CE–QUAL–W2 water-quality model for Green Peter and Foster Lakes and the South Santiam River, Oregon: 2002-2011"}],"country":"United States","state":"Oregon","otherGeospatial":"Foster Lake, Green Peter Lake, South Santiam River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.08807373046875,\n 44.29436701558004\n ],\n [\n -122.09518432617186,\n 44.29436701558004\n ],\n [\n -122.09518432617186,\n 44.775986224030376\n ],\n [\n -123.08807373046875,\n 44.775986224030376\n ],\n [\n -123.08807373046875,\n 44.29436701558004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Coastal margins are important areas of materials flux that link terrestrial and marine ecosystems. Consequently, climate-mediated changes to coastal terrestrial ecosystems and hydrologic regimes have high potential to influence nearshore ocean chemistry and food web dynamics. Research from tightly coupled, high-flux coastal ecosystems can advance understanding of terrestrial–marine links and climate sensitivities more generally. In the present article, we use the northeast Pacific coastal temperate rainforest as a model system to evaluate such links. We focus on key above- and belowground production and hydrological transport processes that control the land-to-ocean flow of materials and their influence on nearshore marine ecosystems. We evaluate how these connections may be altered by global climate change and we identify knowledge gaps in our understanding of the source, transport, and fate of terrestrial materials along this coastal margin. Finally, we propose five priority research themes in this region that are relevant for understanding coastal ecosystem links more broadly.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biaa171","usgsCitation":"Bidlack, A.L., Bisbing, S., Buma, B., Diefenderfer, H., Fellman, J., Floyd, W., Giesbrecht, I., Lally, A., Lertzman, K., Perakis, S.S., Butman, D., D'Amore, D., Fleming, S.W., Hood, E.W., Hunt, B.K., Kiffney, P., McNicol, G., Menounos, B., and Tank, S.E., 2021, Climate-mediated changes to linked terrestrial and marine ecosystems across the northeast Pacific coastal temperate rainforest margin: BioScience, biaa171, 15 p., https://doi.org/10.1093/biosci/biaa171.","productDescription":"biaa171, 15 p.","ipdsId":"IP-107280","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":453506,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biaa171","text":"Publisher Index 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Ken","contributorId":257053,"corporation":false,"usgs":false,"family":"Lertzman","given":"Ken","email":"","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":813741,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813742,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Butman, David","contributorId":224754,"corporation":false,"usgs":false,"family":"Butman","given":"David","affiliations":[{"id":16962,"text":"U. Washington","active":true,"usgs":false}],"preferred":false,"id":813743,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"D'Amore, David","contributorId":168446,"corporation":false,"usgs":false,"family":"D'Amore","given":"David","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":813744,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Fleming, Sean W.","contributorId":140495,"corporation":false,"usgs":false,"family":"Fleming","given":"Sean","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":813745,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Hood, Eran W.","contributorId":198165,"corporation":false,"usgs":false,"family":"Hood","given":"Eran","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":813746,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hunt, Brianna K.","contributorId":245137,"corporation":false,"usgs":false,"family":"Hunt","given":"Brianna","email":"","middleInitial":"K.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":813747,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kiffney, Peter","contributorId":242881,"corporation":false,"usgs":false,"family":"Kiffney","given":"Peter","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":813748,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"McNicol, Gavin 0000-0002-6655-8045","orcid":"https://orcid.org/0000-0002-6655-8045","contributorId":217391,"corporation":false,"usgs":false,"family":"McNicol","given":"Gavin","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":813749,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Menounos, Brian","contributorId":225514,"corporation":false,"usgs":false,"family":"Menounos","given":"Brian","email":"","affiliations":[{"id":41154,"text":"Geography Program and Natural Resources and Environmental Studies Institute, University of Northern British Columbia","active":true,"usgs":false}],"preferred":false,"id":813750,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Tank, Suzanne E.","contributorId":150795,"corporation":false,"usgs":false,"family":"Tank","given":"Suzanne","email":"","middleInitial":"E.","affiliations":[{"id":18102,"text":"University of Alberta, Edmonton, Canada","active":true,"usgs":false}],"preferred":false,"id":813751,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70218701,"text":"70218701 - 2021 - Computational methodology to analyze the effect of mass transfer rate on attenuation of leaked carbon dioxide in shallow aquifers","interactions":[],"lastModifiedDate":"2021-04-16T13:59:46.362577","indexId":"70218701","displayToPublicDate":"2021-02-10T07:11:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7747,"text":"Acta Polytechnica","active":true,"publicationSubtype":{"id":10}},"title":"Computational methodology to analyze the effect of mass transfer rate on attenuation of leaked carbon dioxide in shallow aquifers","docAbstract":"Exsolution and re-dissolution of CO2 gas within heterogeneous porous media are investigated using experimental data and mathematical modeling. In a set of bench-scale experiments, water saturated with CO2 under a given pressure is injected into a 2-D water-saturated porous media system, causing CO2 gas to exsolve and migrate upwards. A layer of fine sand mimicking a heterogeneity within a shallow aquifer is present in the tank to study accumulation and trapping of exsolved CO2. Then, clean water is injected into the system and the accumulated CO2 dissolves back into the flowing water. Simulated exsolution and dissolution mass transfer processes are studied using both nearequilibrium and kinetic approaches and compared to experimental data under conditions that do and do not include lateral background water flow. The mathematical model is based on the mixed hybrid finite element method that allows for accurate simulation of both advection- and diffusion- dominated processes.
","language":"English","publisher":"Czech Technical University","doi":"10.14311/AP.2021.61.0077","usgsCitation":"Fucik, R., Solovsky, J., Plampin, M.R., Wu, H., Mikyska, J., and Illangasekare, T.H., 2021, Computational methodology to analyze the effect of mass transfer rate on attenuation of leaked carbon dioxide in shallow aquifers: Acta Polytechnica, v. 61, no. SI, 12 p., https://doi.org/10.14311/AP.2021.61.0077.","productDescription":"12 p.","ipdsId":"IP-114423","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":453509,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14311/ap.2021.61.0077","text":"Publisher Index Page"},{"id":384057,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"SI","noUsgsAuthors":false,"publicationDate":"2021-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Fucik, Radek 0000-0001-7040-9184","orcid":"https://orcid.org/0000-0001-7040-9184","contributorId":254378,"corporation":false,"usgs":false,"family":"Fucik","given":"Radek","email":"","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":811427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Solovsky, Jakub","contributorId":254380,"corporation":false,"usgs":false,"family":"Solovsky","given":"Jakub","email":"","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":811428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plampin, Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":811429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Hao","contributorId":254382,"corporation":false,"usgs":false,"family":"Wu","given":"Hao","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":811430,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mikyska, Jiri","contributorId":254383,"corporation":false,"usgs":false,"family":"Mikyska","given":"Jiri","email":"","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":811431,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Illangasekare, Tissa H.","contributorId":194933,"corporation":false,"usgs":false,"family":"Illangasekare","given":"Tissa","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":811432,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218211,"text":"70218211 - 2021 - Environmental and anthropogenic drivers of contaminants in agricultural watersheds with implications for land management","interactions":[],"lastModifiedDate":"2021-02-19T19:40:48.384164","indexId":"70218211","displayToPublicDate":"2021-02-09T13:33:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Environmental and anthropogenic drivers of contaminants in agricultural watersheds with implications for land management","docAbstract":"If not managed properly, modern agricultural practices can alter surface and groundwater quality and drinking water resources resulting in potential negative effects on aquatic and terrestrial ecosystems. Exposure to agriculturally derived contaminant mixtures has the potential to alter habitat quality and negatively affect fish and other aquatic organisms. Implementation of conservation practices focused on improving water quality continues to increase particularly in agricultural landscapes throughout the United States. The goal of this study was to determine the consequences of land management actions on the primary drivers of contaminant mixtures in five agricultural watersheds in the Chesapeake Bay, the largest watershed of the Atlantic Seaboard in North America where fish health issues have been documented for two decades. Surface water was collected and analyzed for 301 organic contaminants to determine the benefits of implemented best management practices (BMPs) designed to reduce nutrients and sediment to streams in also reducing contaminants in surface waters. Of the contaminants measured, herbicides (atrazine, metolachlor), phytoestrogens (formononetin, genistein, equol), cholesterol and total estrogenicity (indicator of estrogenic response) were detected frequently enough to statistically compare to seasonal flow effects, landscape variables and BMP intensity. Contaminant concentrations were often positively correlated with seasonal stream flow, although the magnitude of this effect varied by contaminant across seasons and sites. Land-use and other less utilized landscape variables including biosolids, manure and pesticide application and percent phytoestrogen producing crops were inversely related with site-average contaminant concentrations. Increased BMP intensity was negatively related to contaminant concentrations indicating potential co-benefits of BMPs for contaminant reduction in the studied watersheds. The information gained from this study will help prioritize ecologically relevant contaminant mixtures for monitoring and contributes to understanding the benefits of BMPs on improving surface water quality to better manage living resources in agricultural landscapes inside and outside the Chesapeake Bay watershed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.145687","usgsCitation":"Smalling, K., Devereux, O., Gordon, S.E., Phillips, P.J., Blazer, V., Hladik, M.L., Kolpin, D., Meyer, M., Sperry, A., and Wagner, T., 2021, Environmental and anthropogenic drivers of contaminants in agricultural watersheds with implications for land management: Science of the Total Environment, v. 774, 145687, 14 p., https://doi.org/10.1016/j.scitotenv.2021.145687.","productDescription":"145687, 14 p.","ipdsId":"IP-118914","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science 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Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":810437,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70217852,"text":"ofr20201102 - 2021 - Using high resolution satellite and telemetry data to track flooded habitats, their use by waterfowl, and evaluate effects of drought on waterfowl and shorebird bioenergetics in California","interactions":[],"lastModifiedDate":"2021-02-10T18:00:22.216537","indexId":"ofr20201102","displayToPublicDate":"2021-02-09T10:33:12","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1102","displayTitle":"Using High Resolution Satellite and Telemetry Data to Track Flooded Habitats, Their Use by Waterfowl, and Evaluate Effects of Drought on Waterfowl and Shorebird Bioenergetics in California","title":"Using high resolution satellite and telemetry data to track flooded habitats, their use by waterfowl, and evaluate effects of drought on waterfowl and shorebird bioenergetics in California","docAbstract":"Wetland managers in the Central Valley of California, a dynamic hydrological landscape, require information regarding the amount and location of existing wetland habitat to make decisions on how to best use water resources to support multiple wildlife objectives, particularly during drought. Scientists from the U.S. Geological Survey Western Ecological Research Center (WERC), Point Blue Conservation Science (Point Blue), and the U.S. Fish and Wildlife Service (USFWS) partnered to learn how wetland and flooded agricultural habitats used by waterfowl and shorebirds change during the non-breeding season (July–April) particularly during drought. During extreme drought conditions, the ability to provide sufficient water for wildlife often depends on the timing of water deliveries to managed wetlands and winter-flooded crop fields and decisions on whether to fallow croplands. Waterfowl and shorebirds could be particularly affected by these decisions because they typically rest and feed in flooded habitats. Poor habitat conditions resulting from spatially or temporally suboptimal water deliveries (that is, mismatch between waterfowl habitat needs and timing and location of flooded habitats) could reduce waterfowl hunting opportunities and body condition. Point Blue scientists developed a system for near real-time tracking of habitats used by waterfowl, shorebirds, and some other wetland-dependent “waterbirds” (www.pointblue.org/watertracker) and to assess the impacts of drought on habitat availability and on waterfowl and shorebird bioenergetics. The WERC researchers linked these data with near real-time tracking (telemetry) data of duck locations throughout the Valley. The team used these two datasets to relate duck locations to open-water characteristics and to learn how waterfowl use habitats under spatially and temporally changing conditions during drought and non-drought periods. We found that recent extreme drought (2013–15) significantly changed the timing and magnitude of flooding and consequently reduced the availability of habitats used by waterfowl and shorebirds more than other recent historic droughts 2000–11. Drought reduced irrigations of moist soil seed plants and thus there was lower food energy available for waterfowl. Analyses using bioenergetics models indicated that, overall, extreme drought increased food energy deficits (total number of deficit days) for shorebirds and waterfowl. Our analysis indicated a strong direct relationship between duck locations and classified habitat derived from open-water data during the wintering period (October–March). This result helps confirm the application of dynamic water data to identify flooded areas that provide waterfowl habitat. Presence of open water at a 1-hectare resolution can be used effectively to identify flooded landscape areas available as habitat for ducks. Our discoveries from evaluating use of space by ducks also indicated that nighttime feeding locations of ducks were concentrated nearby primary roosts and that foraging distances could depend on hydrologic dynamics of location (Suisun Marsh versus California excluding Suisun Marsh) and time of season (early, middle, late). Other results indicated that some areas on the California landscape with extremely reliable water supplies could receive consistent use by ducks year after year (in essence, almost drought proof). The Water Tracker is set up to automatically track wetland habitat and food availability each year and is making these data available to water and wetland managers. Results from this research are a significant step toward understanding how waterfowl and shorebird habitats can be optimally managed on the landscape to support desired populations of these migratory birds during extreme drought.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201102","collaboration":"Prepared in cooperation with the Southwest Climate Adaptation Science Center of the U.S. Geological Survey and the Regional Inventory and Monitoring Program of the U.S. Fish and Wildlife Service","usgsCitation":"Matchett, E.L., Reiter, M., Overton, C.T., Jongsomjit, D., and Casazza, M.L., 2021, Using high resolution satellite and telemetry data to track flooded habitats, their use by waterfowl, and evaluate effects of drought on waterfowl and shorebird bioenergetics in California: U.S. Geological Survey Open-File Report 2020–1102, 59 p., https://doi.org/10.3133/ofr20201102.","productDescription":"Report: xi, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","ipdsId":"IP-102884","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":383074,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2020/1102/images"},{"id":383073,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P922KDU6","linkHelpText":"Classification of waterfowl habitat and quantification of interannual space use and movement distance from primary roosts to night feeding locations by waterfowl in California for October–March of 2015 through 2018"},{"id":383071,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1102/ofr20201102.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383070,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1102/covrthb.jpg"},{"id":383072,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2020/1102/ofr20201102.xml"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 36.06686213257888\n ],\n [\n -119.44335937499999,\n 35.137879119634185\n ],\n [\n -118.828125,\n 34.813803317113155\n ],\n [\n -118.30078125,\n 35.137879119634185\n ],\n [\n -118.49853515625,\n 35.71083783530009\n ],\n [\n -119.39941406249999,\n 37.33522435930639\n ],\n [\n -120.47607421874999,\n 38.16911413556086\n ],\n [\n -120.89355468749999,\n 38.58252615935333\n ],\n [\n -121.22314453124999,\n 39.11301365149975\n ],\n [\n -121.640625,\n 39.977120098439634\n ],\n [\n -121.97021484374999,\n 40.74725696280421\n ],\n [\n -122.3876953125,\n 41.0130657870063\n ],\n [\n -122.84912109375,\n 40.613952441166596\n ],\n [\n -122.87109375,\n 40.07807142745009\n ],\n [\n -122.6953125,\n 38.993572058209466\n ],\n [\n -122.08007812499999,\n 37.68382032669382\n ],\n [\n -121.37695312499999,\n 36.96744946416934\n ],\n [\n -120.234375,\n 35.99578538642032\n ],\n [\n -120.234375,\n 36.06686213257888\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Reliable predictions and accompanying uncertainty estimates of coastal evolution on decadal to centennial time scales are increasingly sought. So far, most coastal change projections rely on a single, deterministic realization of the unknown future wave climate, often derived from a global climate model. Yet, deterministic projections do not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, we present an ensemble Kalman filter shoreline change model to predict coastal erosion and uncertainty due to waves at a variety of time scales. We compare shoreline change projections, simulated with and without ensemble wave forcing conditions by applying ensemble wave time series produced by a computationally efficient statistical downscaling method. We demonstrate a sizable (site-dependent) increase in model uncertainty compared with the unrealistic case of model projections based on a single, deterministic realization (e.g., a single time series) of the wave forcing. We support model-derived uncertainty estimates with a novel mathematical analysis of ensembles of idealized process models. Here, the developed ensemble modeling approach is applied to a well-monitored beach in Tairua, New Zealand. However, the model and uncertainty quantification techniques derived here are generally applicable to a variety of coastal settings around the world.
","language":"English","publisher":"Wiley","doi":"10.1029/2019JF005506","usgsCitation":"Vitousek, S., Cagigal, L., Montano, J., Rueda, A., Mendez, F., Coco, G., and Barnard, P.L., 2021, The application of ensemble wave forcing to quantify uncertainty of shoreline change predictions: JGR Earth Surface, v. 126, no. 7, e2019JF005506, 43 p., https://doi.org/10.1029/2019JF005506.","productDescription":"e2019JF005506, 43 p.","ipdsId":"IP-116481","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453518,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019jf005506","text":"External Repository"},{"id":388152,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":821574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagigal, Laura","contributorId":264473,"corporation":false,"usgs":false,"family":"Cagigal","given":"Laura","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":821575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montano, Jennifer","contributorId":264474,"corporation":false,"usgs":false,"family":"Montano","given":"Jennifer","email":"","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":821576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rueda, Ana","contributorId":264475,"corporation":false,"usgs":false,"family":"Rueda","given":"Ana","affiliations":[{"id":41638,"text":"University of Cantabria","active":true,"usgs":false}],"preferred":false,"id":821577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mendez, Fernando","contributorId":264476,"corporation":false,"usgs":false,"family":"Mendez","given":"Fernando","affiliations":[{"id":41638,"text":"University of Cantabria","active":true,"usgs":false}],"preferred":false,"id":821578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coco, Giovanni","contributorId":264477,"corporation":false,"usgs":false,"family":"Coco","given":"Giovanni","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":821579,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":821580,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223896,"text":"70223896 - 2021 - Export of photolabile and photoprimable dissolved organic carbon from the Connecticut River","interactions":[],"lastModifiedDate":"2021-09-14T11:42:29.429084","indexId":"70223896","displayToPublicDate":"2021-02-09T10:02:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Export of photolabile and photoprimable dissolved organic carbon from the Connecticut River","docAbstract":"Dissolved organic carbon (DOC) impacts water quality, the carbon cycle, and the ecology of aquatic systems. Understanding what controls DOC is therefore critical for improving large-scale models and best management practices for aquatic ecosystems. The two main processes of DOC transformation and removal, photochemical and microbial DOC degradation, work in tandem to modify and remineralize DOC within natural waters. Here, we examined both the photo- and microbial remineralization of DOC (photolability and biolability), and the indirect phototransformation of DOC into biolabile DOC (photoprimed biolability) for samples that capture the spatiotemporal and hydrological gradients of the Connecticut River watershed. The majority of DOC exported from this temperate watershed was photolabile and the concentration of photolabile DOC correlated with UV absorbance at 254 nm (r2 = 0.86). Phototransformation of DOC also increased biolability, and the total photolabile DOC (sum of photolabile and photoprimed biolabile DOC) showed a stronger correlation with UV absorbance at 254 nm (r2 = 0.92). We estimate that as much as 49% (SD = 3.3%) and 10% (SD = 1.1%) of annual DOC export from the Connecticut River is directly photolabile and photoprimable, respectively. Thus, 2.82 Gg C year−1 (SD = 0.67 Gg C year−1) or 1.13 Mg C km−2 year−1 (SD = 0.27 km−2 year−1) of total photolabile DOC escapes photochemical degradation within the river network to be exported from the Connecticut River each year.
","language":"English","publisher":"Springer","doi":"10.1007/s00027-021-00778-8","usgsCitation":"Yoon, B., Hosen, J.D., Kyzivat, E., Fair, J., Weber, L.C., Aho, K.S., Lowenthal, R., Matt, S., Sobczak, W.V., Shanley, J.B., Morrison, J., Saiers, J.E., Stubbins, A., and Raymond, P.A., 2021, Export of photolabile and photoprimable dissolved organic carbon from the Connecticut River: Aquatic Sciences, v. 83, 23, 17 p., https://doi.org/10.1007/s00027-021-00778-8.","productDescription":"23, 17 p.","ipdsId":"IP-094783","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":389152,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Connecticut River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.26806640624999,\n 41.36031866306708\n ],\n [\n -72.13623046875,\n 41.95131994679697\n ],\n [\n -72.18017578125,\n 42.293564192170095\n ],\n [\n -72.24609375,\n 42.8115217450979\n ],\n [\n -72.18017578125,\n 43.197167282501276\n ],\n [\n -71.91650390625,\n 43.89789239125797\n ],\n [\n -71.60888671875,\n 44.134913443750726\n ],\n [\n -71.279296875,\n 44.465151013519616\n ],\n [\n -71.21337890625,\n 44.98034238084973\n ],\n [\n -71.08154296875,\n 45.3521452458518\n ],\n [\n -71.4990234375,\n 45.27488643704891\n ],\n [\n -71.9384765625,\n 45.07352060670971\n ],\n [\n -72.333984375,\n 44.08758502824516\n ],\n [\n -72.70751953125,\n 43.32517767999296\n ],\n [\n -72.88330078125,\n 42.84375132629021\n ],\n [\n -72.99316406249999,\n 42.309815415686664\n ],\n [\n -73.037109375,\n 41.49212083968776\n ],\n [\n -72.70751953125,\n 41.21172151054787\n ],\n [\n -72.35595703125,\n 41.19518982948959\n ],\n [\n -72.26806640624999,\n 41.36031866306708\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Yoon, B. 0000-0002-8959-3855","orcid":"https://orcid.org/0000-0002-8959-3855","contributorId":245946,"corporation":false,"usgs":false,"family":"Yoon","given":"B.","email":"","affiliations":[{"id":49377,"text":"Department of Marine and Environmental Sciences, Northeastern University, Boston, MA USA","active":true,"usgs":false}],"preferred":false,"id":823178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hosen, Jacob D.","contributorId":149188,"corporation":false,"usgs":false,"family":"Hosen","given":"Jacob","email":"","middleInitial":"D.","affiliations":[{"id":17663,"text":"Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, Maryland, United States","active":true,"usgs":false}],"preferred":false,"id":823179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kyzivat, Ethan","contributorId":241078,"corporation":false,"usgs":false,"family":"Kyzivat","given":"Ethan","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":823180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fair, Jennifer H","contributorId":241077,"corporation":false,"usgs":false,"family":"Fair","given":"Jennifer H","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":823181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weber, Lisa C.","contributorId":124586,"corporation":false,"usgs":true,"family":"Weber","given":"Lisa","email":"","middleInitial":"C.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":823182,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aho, Kelly S.","contributorId":241075,"corporation":false,"usgs":false,"family":"Aho","given":"Kelly","email":"","middleInitial":"S.","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":823183,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lowenthal, Rachel","contributorId":241079,"corporation":false,"usgs":false,"family":"Lowenthal","given":"Rachel","email":"","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":823184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Matt, Serena","contributorId":194108,"corporation":false,"usgs":false,"family":"Matt","given":"Serena","affiliations":[],"preferred":false,"id":823185,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sobczak, W. V.","contributorId":41983,"corporation":false,"usgs":true,"family":"Sobczak","given":"W.","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":823186,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823187,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Morrison, Jonathan 0000-0002-1756-4609 jmorriso@usgs.gov","orcid":"https://orcid.org/0000-0002-1756-4609","contributorId":2274,"corporation":false,"usgs":true,"family":"Morrison","given":"Jonathan","email":"jmorriso@usgs.gov","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823188,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Saiers, James E.","contributorId":191842,"corporation":false,"usgs":false,"family":"Saiers","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":823189,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Stubbins, Aron","contributorId":80949,"corporation":false,"usgs":true,"family":"Stubbins","given":"Aron","affiliations":[],"preferred":false,"id":823190,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Raymond, Peter A.","contributorId":172876,"corporation":false,"usgs":false,"family":"Raymond","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":17883,"text":"Yale School of Forestry and Environmental Studies, New Haven, CT","active":true,"usgs":false}],"preferred":false,"id":823191,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70254486,"text":"70254486 - 2021 - Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX)","interactions":[],"lastModifiedDate":"2024-05-28T14:47:15.444274","indexId":"70254486","displayToPublicDate":"2021-02-09T09:42:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7749,"text":"Frontiers in Climate","active":true,"publicationSubtype":{"id":10}},"title":"Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX)","docAbstract":"The mitigation of losses due to extreme climate events and long-term climate adaptation requires climate informed decision-making. In the past few decades, several remote sensing and modeled-based Earth observations (EOs) have been developed to provide an unprecedented global overview and routine monitoring of climate and its impacts on vegetation and hydrologic conditions, with the goal of supporting informed decision-making. However, their usage in decision-making is particularly limited in climate-risk vulnerable and in situ data-scarce regions such as sub-Saharan Africa, due to lack of access to EOs. Here, we describe the Early Warning eXplorer (EWX), which was developed to address this crucial limitation and facilitate the application of EOs in decision-making, particularly in the food and water-insecure regions of the world. First, the EWX's core framework, which includes (i) the Viewer, (ii) GeoEngine, and (iii) Support Applications, is described. Then, a comprehensive overview of the Viewer, which is a web-based interface used to access EOs, is provided. This includes a description of (i) the maps and associated features to access gridded EO data and anomalies for different temporal averaging periods, (ii) time series graphs and associated features to access EOs aggregated over polygons such as administrative boundaries, and (iii) commonly used EOs served by the EWX that provide assessments of climate and vegetation conditions. Next, examples are provided to demonstrate how EWX can be used to monitor development, progression, spatial extent, and severity of climate-driven extreme events to support timely decisions related to mitigation of food insecurity and flooding impacts. Finally, the value of a regional implementation of EWX at the Regional Centre for Mapping of Resources for Development (RCMRD) in Nairobi, Kenya, is highlighted. Regional implementation of the EWX facilitates access to regionally focused EOs and their availability at polygon boundaries most relevant to the local decision-makers. Similar instances of EWX implemented in other regions, especially those susceptible to food and water security, will likely further enhance the application of EOs for informed decision-making.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2020.583509","usgsCitation":"Shukla, S., Landsfeld, M., Anthony, M., Budde, M., Husak, G., Rowland, J., and Funk, C., 2021, Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX): Frontiers in Climate, v. 2, 583509, 16 p., https://doi.org/10.3389/fclim.2020.583509.","productDescription":"583509, 16 p.","ipdsId":"IP-120483","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453527,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2020.583509","text":"Publisher Index Page"},{"id":429328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":901558,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landsfeld, Martin","contributorId":192380,"corporation":false,"usgs":false,"family":"Landsfeld","given":"Martin","affiliations":[],"preferred":false,"id":901559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anthony, Michelle 0000-0001-6646-2134","orcid":"https://orcid.org/0000-0001-6646-2134","contributorId":336955,"corporation":false,"usgs":false,"family":"Anthony","given":"Michelle","affiliations":[{"id":80923,"text":"KBR Technical Support Services Contract (TSSC)","active":true,"usgs":false}],"preferred":false,"id":901560,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Budde, Michael 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Husak, Greg 0000-0003-2647-7870","orcid":"https://orcid.org/0000-0003-2647-7870","contributorId":331302,"corporation":false,"usgs":false,"family":"Husak","given":"Greg","email":"","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":901562,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901563,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901564,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219165,"text":"70219165 - 2021 - Seasonal impoundment alters patterns of tidal wetland plant diversity across spatial scales","interactions":[],"lastModifiedDate":"2021-03-29T13:00:49.94313","indexId":"70219165","displayToPublicDate":"2021-02-09T07:56:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal impoundment alters patterns of tidal wetland plant diversity across spatial scales","docAbstract":"Understanding patterns of biodiversity is a key goal of ecology and is especially pressing in the current human‐caused biodiversity crisis. In wetland ecosystems, human impacts are centered around hydrologic manipulation including the common practice of wetland diking and impoundment. Constraining how wetland management influences plant biodiversity patterns across spatial scales will provide information on how best to modify actions to preserve biodiversity and ecosystem function in managed wetlands. Here, we compare patterns of plant diversity and species presence, abundance, and community composition at several spatial scales among tidal wetlands along an estuarine salinity gradient and managed wetlands that were formerly tidal. Managed impounded wetlands had decreased alpha and gamma diversity of rare species, with less than 60% of the species richness found in tidal brackish wetlands at several spatial scales. There was little change in the overall pattern of alpha, beta, and gamma diversity for common species in impounded wetlands; however, dominant tidal brackish species, primarily perennial rhizomatous graminoids, were replaced with management target plants and non‐native annual grasses in impounded wetlands. This species replacement led to over 60% of impounded sites being classified as containing novel plant assemblages. An additional 25% of impounded sites were classified as containing tidal saline plant assemblages, suggesting potential soil salinization. Along the estuarine gradient, patchiness and codominance of common plant species drove high diversity and turnover in tidal brackish wetlands, while it remains unclear whether tidal fresh or brackish wetlands maximize rare plant diversity. With reduced species richness, altered functional dominants, and novel or saline assemblages, impounded brackish wetlands may require careful water management to balance native plant biodiversity, associated ecosystem processes, and waterfowl requirements.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3366","usgsCitation":"Jones, S., Janousek, C.N., Casazza, M.L., Takekawa, J., and Thorne, K., 2021, Seasonal impoundment alters patterns of tidal wetland plant diversity across spatial scales: Ecosphere, v. 12, no. 2, e03366, 19 p., https://doi.org/10.1002/ecs2.3366.","productDescription":"e03366, 19 p.","ipdsId":"IP-121980","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453532,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3366","text":"Publisher Index Page"},{"id":436516,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZG1Y72","text":"USGS data release","linkHelpText":"Impounded and tidal wetland plant diversity and composition across spatial scales, San Francisco Bay-Delta, California, USA (2016-2018)"},{"id":384713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.93701171874999,\n 36.89719446989036\n ],\n [\n -121.57470703125,\n 36.89719446989036\n ],\n [\n -121.57470703125,\n 38.976492485539396\n ],\n [\n -122.93701171874999,\n 38.976492485539396\n ],\n [\n -122.93701171874999,\n 36.89719446989036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Scott 0000-0002-1056-3785","orcid":"https://orcid.org/0000-0002-1056-3785","contributorId":215602,"corporation":false,"usgs":true,"family":"Jones","given":"Scott","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":813087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813088,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takekawa, John Y. 0000-0003-0217-5907","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":203805,"corporation":false,"usgs":false,"family":"Takekawa","given":"John Y.","affiliations":[{"id":36724,"text":"Audubon California, Richardson Bay Audubon Center and Sanctuary, Tiburon, CA","active":true,"usgs":false}],"preferred":false,"id":813089,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813090,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217900,"text":"70217900 - 2021 - Microbial and viral indicators of pathogens and human health risks from recreational exposure to waters impaired by fecal contamination","interactions":[],"lastModifiedDate":"2021-02-10T13:57:39.234893","indexId":"70217900","displayToPublicDate":"2021-02-09T07:55:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5896,"text":"Journal of Sustainable Water in the Built Environment","active":true,"publicationSubtype":{"id":10}},"title":"Microbial and viral indicators of pathogens and human health risks from recreational exposure to waters impaired by fecal contamination","docAbstract":"Fecal indicator bacteria (FIB) (e.g., fecal coliforms, Escherichia coli, and enterococci) have been used for decades to monitor for and protect the public from waterborne pathogens from fecal contamination. However, FIB may not perform well at predicting the presence of waterborne pathogens or human health outcomes from recreational exposure to fecal-contaminated surface waters. Numerous factors can influence the relationship between FIB and pathogens or human health outcomes, including the source(s) of contamination, the type of pathogen(s) present, differences in the survival and behavior of FIB and pathogens in the wastewater conveyance and treatment process, and varying environmental conditions. As a result, different indicators, such as source-specific microbial source tracking (MST) markers and viral fecal indicators, have been used as possible surrogates to better approximate pathogen abundance and human health risks in recreational waters. The performance of these alternative indicators has been mixed, with some promise of viral indicators better approximating viral pathogens than bacterial fecal indicators, and FIB generally more closely associated with bacterial and protozoal pathogen presence than human MST markers. Many of the assays to detect and quantify fecal indicators and pathogens are polymerase chain reaction-based assays, which detect and quantify nucleic acid [deoxyribonucleic acid (DNA) and ribonucleic acid (RNA)] sequences specific to a target of interest. Recent advances in DNA and RNA sequencing technologies may push the field toward metabarcoding approaches, where multiple targets can be detected and quantified simultaneously. Metabarcoding is currently more applicable to bacterial and protozoal assessments than viral assessments based on a lack of universal metabarcoding markers for viruses. Innovative technologies, such as biosensors and nanotechnologies, may provide more sensitive and accurate tools to detect and quantify pathogens. When a specific pathogen is of concern for a recreational water body, a practical approach in estimating the likelihood of human health outcomes is the application of quantitative microbial risk assessments (QMRAs). Quantitative microbial risk assessments can be used to model the likelihood of pathogen-specific human health outcomes from recreational exposure as a function of a surrogate indicator. Inputs for QMRAs include the ratio between the indicator to be monitored and the pathogen of interest, the concentration of the indicator, the amount of water ingested, and the likelihood of the health outcome based on the estimated amount of pathogen consumed. There are numerous unknowns about the behavior and survival of fecal indicators and pathogens in environmental waters. Developing accurate models to predict pathogen concentrations from fecal indicators in recreational waters will require a better understanding of these unknowns. Current methods and technologies for detecting and quantifying fecal indicators and pathogens are limited due to the rare and patchy nature of pathogens. Technological advances may help improve sensitivity for detecting and quantifying pathogens.
Basic data on river bathymetry is critical for numerous applications in river research and management and is increasingly obtained via remote sensing, but the noisy, pixelated appearance of image‐derived depth maps can compromise subsequent analyses. We hypothesized that this noise originates from reflectance from an irregular water surface and introduced a framework for mitigating these effects by Inferring Bathymetry from Averaged River Images (IBARI). This workflow produces time‐averaged images from video frames stabilized to account for platform motion and/or computes a spatial average from an ensemble simulated by randomly shifting images relative to themselves. We used field observations of water depth and helicopter‐based videos from a clear‐flowing river to assess the potential of this approach to improve depth retrieval. Our results indicated that depths inferred from averaged images were more accurate and precise than those inferred from single frames; observed versus predicted regression R2 increased from 0.80 to 0.88. In addition, IBARI significantly enhanced the texture of image‐derived depth maps, leading to smoother, more coherent representations of channel morphology. Depth retrieval improved with image sequence duration, but the number of images was more important than the length of time encompassed; shorter acquisitions at higher frame rates would economize data collection. We also demonstrated the potential to scale up the IBARI workflow by producing a mosaic of bathymetric maps derived from averaged images acquired at several hovering waypoints distributed along a 2.36 km reach. This approach is well‐suited to data collected from helicopters and small unmanned aircraft systems.
In the spring of 2019, ice sheets transported down-stream during a large streamflow rise event in the lower Minnesota River destroyed an index-velocity streamgage at the Minnesota River at Fort Snelling State Park, Minnesota (U.S. Geological Survey station 05330920; hereafter referred to as “Ft. Snelling”). The streamgage previously used an acoustic Doppler velocity meter to provide instantaneous streamflow and suspended-sedimentation concentration (SSC) data in backwater conditions caused by the confluence with the Mississippi River. In response, the U.S. Geological Survey cooperated with the U.S. Army Corps of Engineers and Lower Minnesota River Watershed District to develop linear regression models that estimate SSCs and suspended-sand concentrations (sand) at the destroyed streamgage using streamflow data from an upstream site Minnesota River near Jordan, Minn. (U.S. Geological Survey station 05330000, hereafter referred to as “Jordan”).
Simple linear regression models were developed for selected positions on the streamflow hydrograph to estimate SSC and sand at Ft. Snelling from the streamflow at Jordan. Statistically significant models could not be developed for estimating SSC at low streamflows and sand at high streamflows. Models developed to estimate sand were more uncertain than models used to estimate SSC, and models using streamflow to predict SSC and sand were more uncertain than models using acoustic backscatter to predict SSC. Annual loads of SSC and sand estimated from these models show the dynamic nature of sediment transport and storage in this section of the lower Minnesota River. These models and the associated ancillary data can help with management decisions that are crucial in managing aquatic habitat, supporting power production, and commercial navigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211005","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers and Lower Minnesota River Watershed District","usgsCitation":"Groten, J.T., Hendrickson, J.S., and Loomis, L.R., 2021, Estimation of suspended sediment at a discontinued streamgage on the lower Minnesota River at Fort Snelling State Park, Minnesota: U.S. Geological Survey Open-File Report 2021–1005, 12 p., https://doi.org/10.3133/ofr20211005.","productDescription":"Report: vi, 12 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-121668","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":383100,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1005/ofr20211005.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1005"},{"id":383101,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AIULOQ","text":"USGS data release","linkHelpText":"Suspended-sediment and sand concentrations, streamflow, acoustic data, linear regression models, and loads for the Lower Minnesota River, 2012 -2019"},{"id":383108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1005/coverthb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Fort Snelling State Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.22311401367188,\n 44.819107339295684\n ],\n [\n -93.1801986694336,\n 44.85124448203336\n ],\n [\n -93.16577911376953,\n 44.879471418146686\n ],\n [\n -93.14414978027344,\n 44.89187715629887\n ],\n [\n -93.15067291259766,\n 44.89503897537852\n ],\n [\n -93.17436218261719,\n 44.89601180781499\n ],\n [\n -93.19427490234375,\n 44.89114748105545\n ],\n [\n -93.19599151611328,\n 44.87557887053108\n ],\n [\n -93.21247100830078,\n 44.859275967357476\n ],\n [\n -93.22208404541016,\n 44.85270483540896\n ],\n [\n -93.23856353759766,\n 44.84102097157541\n ],\n [\n -93.23856353759766,\n 44.82763029742812\n ],\n [\n -93.22895050048828,\n 44.81862027505869\n ],\n [\n -93.22311401367188,\n 44.819107339295684\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Southern California has a long history of damaging debris flows after wildfire. Despite recurrent loss, forecasts of the frequency and magnitude of postfire debris flows are not available for the region like they are for earthquakes. Instead, debris flow hazards are typically assessed in a reactive manner after wildfires. Such assessments are crucial for evaluating debris flow risk by postfire emergency response teams; however, time between the fire and first rainstorm is often insufficient to fully develop and implement effective emergency response plans like those in place for earthquakes. Here, we use both historical distributions of fire and precipitation frequency and empirical models of postfire debris flow likelihood and volume to map the expected frequency and magnitude of postfire debris flows across southern California. We find that at least small debris flows can be expected almost every year, while major debris flows capable of damaging 40 or more structures have a recurrence interval between 10 and 13 years, a return interval that is comparable to a magnitude 6.7 earthquake. A sensitivity analysis to possible future changes in current fire and precipitation regimes indicates that debris flow activity in southern California is more sensitive to increases in precipitation intensity than increases in fire frequency and severity. Projected increases in rainfall intensity of 18% result in an overall 110% increase in the probability of major debris flows. Our results, in combination with an assessment of exposure, can be used to prioritize watersheds for further analysis and possible prefire mitigation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020EF001735","usgsCitation":"Kean, J.W., and Staley, D.M., 2021, Forecasting the frequency and magnitude of postfire debris flows across southern California: Earth's Future, v. 9, no. 3, e2020EF001735, 19 p., https://doi.org/10.1029/2020EF001735.","productDescription":"e2020EF001735, 19 p.","ipdsId":"IP-124894","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453549,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020ef001735","text":"Publisher Index Page"},{"id":436518,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91GIT04","text":"USGS data release","linkHelpText":"Gridded estimates of postfire debris flow frequency and magnitude for southern California"},{"id":384885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.76171875,\n 32.565333160841035\n ],\n [\n -114.2578125,\n 32.565333160841035\n ],\n [\n -114.2578125,\n 35.209721645221386\n ],\n [\n -120.76171875,\n 35.209721645221386\n ],\n [\n -120.76171875,\n 32.565333160841035\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":813546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":813547,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}