Introduced species are frequently implicated in declines of native species. In many cases, however, evidence linking introduced species to native declines is weak. Failure to make strong inferences regarding the role of introduced species can hamper attempts to predict population viability and delay effective management responses. For many species, mark–recapture analysis is the more rigorous form of demographic analysis. However, to our knowledge, there are no mark–recapture models that allow for joint modeling of interacting species. Here, we introduce a two‐species mark–recapture population model in which the vital rates (and capture probabilities) of one species are allowed to vary in response to the abundance of the other species. We use a simulation study to explore bias and choose an approach to model selection. We then use the model to investigate species interactions between endangered humpback chub (Gila cypha) and introduced rainbow trout (Oncorhynchus mykiss) in the Colorado River between 2009 and 2016. In particular, we test hypotheses about how two environmental factors (turbidity and temperature), intraspecific density dependence, and rainbow trout abundance are related to survival, growth, and capture of juvenile humpback chub. We also project the long‐term effects of different rainbow trout abundances on adult humpback chub abundances. Our simulation study suggests this approach has minimal bias under potentially challenging circumstances (i.e., low capture probabilities) that characterized our application and that model selection using indicator variables could reliably identify the true generating model even when process error was high. When the model was applied to rainbow trout and humpback chub, we identified negative relationships between rainbow trout abundance and the survival, growth, and capture probability of juvenile humpback chub. Effects on interspecific interactions on survival and capture probability were strongly supported, whereas support for the growth effect was weaker. Environmental factors were also identified to be important and in many cases stronger than interspecific interactions, and there was still substantial unexplained variation in growth and survival rates. The general approach presented here for combining mark–recapture data for two species is applicable in many other systems and could be modified to model abundance of the invader via other modeling approaches.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2166","usgsCitation":"Yackulic, C.B., Korman, J., Yard, M., and Dzul, M.C., 2018, Inferring species interactions through joint mark–recapture analysis: Ecology, v. 99, no. 4, p. 812-821, https://doi.org/10.1002/ecy.2166.","productDescription":"10 p.","startPage":"812","endPage":"821","ipdsId":"IP-086832","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437980,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZC81T9","text":"USGS data release","linkHelpText":"Humpback Chub (Gila cypha) and Rainbow Trout Joint Mark-Recapture Data and Model, Colorado River, Arizona"},{"id":352758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-21","publicationStatus":"PW","scienceBaseUri":"5afee6f7e4b0da30c1bfbfde","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":731652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":2889,"corporation":false,"usgs":true,"family":"Yard","given":"Michael D.","email":"myard@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":731651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":731650,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196198,"text":"70196198 - 2018 - Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands","interactions":[],"lastModifiedDate":"2018-03-26T10:12:45","indexId":"70196198","displayToPublicDate":"2018-03-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands","docAbstract":"Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2017.12.008","usgsCitation":"Poitras, T.B., Villarreal, M.L., Waller, E.K., Nauman, T.W., Miller, M.E., and Duniway, M.C., 2018, Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands: Journal of Arid Environments, v. 153, p. 76-87, https://doi.org/10.1016/j.jaridenv.2017.12.008.","productDescription":"12 p.","startPage":"76","endPage":"87","ipdsId":"IP-084812","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468897,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2017.12.008","text":"Publisher Index Page"},{"id":437977,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SWDWLS","text":"USGS data release","linkHelpText":"Grassland State and Transition Map of Canyonlands National Park Needles District and Indian Creek Grazing Allotment"},{"id":352759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.20797729492188,\n 37.81737834565083\n ],\n [\n -109.58862304687499,\n 37.81737834565083\n ],\n [\n -109.58862304687499,\n 38.494443887725055\n ],\n [\n -110.20797729492188,\n 38.494443887725055\n ],\n [\n -110.20797729492188,\n 37.81737834565083\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"153","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6f7e4b0da30c1bfbfe0","contributors":{"authors":[{"text":"Poitras, Travis B. 0000-0001-8677-1743 tpoitras@usgs.gov","orcid":"https://orcid.org/0000-0001-8677-1743","contributorId":195168,"corporation":false,"usgs":true,"family":"Poitras","given":"Travis","email":"tpoitras@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":731644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":731643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waller, Eric K. 0000-0002-9169-9210","orcid":"https://orcid.org/0000-0002-9169-9210","contributorId":203496,"corporation":false,"usgs":true,"family":"Waller","given":"Eric","email":"","middleInitial":"K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":true,"id":731645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Mark E.","contributorId":91580,"corporation":false,"usgs":false,"family":"Miller","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":6959,"text":"National Park Service Southeast Utah Group","active":true,"usgs":false}],"preferred":false,"id":731648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731647,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196193,"text":"70196193 - 2018 - Nonhydrostatic and surfbeat model predictions of extreme wave run-up in fringing reef environments","interactions":[],"lastModifiedDate":"2018-03-28T10:57:12","indexId":"70196193","displayToPublicDate":"2018-03-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Nonhydrostatic and surfbeat model predictions of extreme wave run-up in fringing reef environments","docAbstract":"The accurate prediction of extreme wave run-up is important for effective coastal engineering design and coastal hazard management. While run-up processes on open sandy coasts have been reasonably well-studied, very few studies have focused on understanding and predicting wave run-up at coral reef-fronted coastlines. This paper applies the short-wave resolving, Nonhydrostatic (XB-NH) and short-wave averaged, Surfbeat (XB-SB) modes of the XBeach numerical model to validate run-up using data from two 1D (alongshore uniform) fringing-reef profiles without roughness elements, with two objectives: i) to provide insight into the physical processes governing run-up in such environments; and ii) to evaluate the performance of both modes in accurately predicting run-up over a wide range of conditions. XBeach was calibrated by optimizing the maximum wave steepness parameter (maxbrsteep) in XB-NH and the dissipation coefficient (alpha) in XB-SB) using the first dataset; and then applied to the second dataset for validation. XB-NH and XB-SB predictions of extreme wave run-up (Rmax and R2%) and its components, infragravity- and sea-swell band swash (SIG and SSS) and shoreline setup (<η>), were compared to observations. XB-NH more accurately simulated wave transformation but under-predicted shoreline setup due to its exclusion of parameterized wave-roller dynamics. XB-SB under-predicted sea-swell band swash but overestimated shoreline setup due to an over-prediction of wave heights on the reef flat. Run-up (swash) spectra were dominated by infragravity motions, allowing the short-wave (but not wave group) averaged model (XB-SB) to perform comparably well to its more complete, short-wave resolving (XB-NH) counterpart. Despite their respective limitations, both modes were able to accurately predict Rmax and R2%.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2018.03.007","usgsCitation":"Lashley, C.H., Roelvink, D., van Dongeren, A.R., Buckley, M.L., and Lowe, R.J., 2018, Nonhydrostatic and surfbeat model predictions of extreme wave run-up in fringing reef environments: Coastal Engineering, v. 137, p. 11-27, https://doi.org/10.1016/j.coastaleng.2018.03.007.","productDescription":"17 p.","startPage":"11","endPage":"27","ipdsId":"IP-092741","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468894,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2018.03.007","text":"Publisher Index Page"},{"id":352760,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"137","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6f8e4b0da30c1bfbfe2","contributors":{"authors":[{"text":"Lashley, Christopher H.","contributorId":203483,"corporation":false,"usgs":false,"family":"Lashley","given":"Christopher","email":"","middleInitial":"H.","affiliations":[{"id":36631,"text":"IHE-Delft Institute for Water Education","active":true,"usgs":false}],"preferred":false,"id":731601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roelvink, Dano","contributorId":139950,"corporation":false,"usgs":false,"family":"Roelvink","given":"Dano","email":"","affiliations":[{"id":13328,"text":"UNESCO-IHE","active":true,"usgs":false}],"preferred":false,"id":731602,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Dongeren, Ap R.","contributorId":203482,"corporation":false,"usgs":false,"family":"van Dongeren","given":"Ap","email":"","middleInitial":"R.","affiliations":[{"id":36257,"text":"Deltares","active":true,"usgs":false}],"preferred":false,"id":731600,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buckley, Mark L. 0000-0002-1909-4831","orcid":"https://orcid.org/0000-0002-1909-4831","contributorId":203481,"corporation":false,"usgs":true,"family":"Buckley","given":"Mark","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":731599,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lowe, Ryan J.","contributorId":152265,"corporation":false,"usgs":false,"family":"Lowe","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":731603,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195781,"text":"sim3389 - 2018 - Geologic map of the Nepenthes Planum Region, Mars","interactions":[],"lastModifiedDate":"2023-03-20T18:10:06.609924","indexId":"sim3389","displayToPublicDate":"2018-03-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3389","title":"Geologic map of the Nepenthes Planum Region, Mars","docAbstract":"This map product contains a map sheet at 1:1,506,000 scale that shows the geology of the Nepenthes Planum region of Mars, which is located between the cratered highlands that dominate the southern hemisphere and the less-cratered sedimentary plains that dominate the northern hemisphere. The map region contains cone- and mound-shaped landforms as well as lobate materials that are morphologically similar to terrestrial igneous or mud vents and flows. This map is part of an informal series of small-scale (large-area) maps aimed at refining current understanding of the geologic units and structures that make up the highland-to-lowland transition zone. The map base consists of a controlled Thermal Emission Imaging System (THEMIS) daytime infrared image mosaic (100 meters per pixel resolution) supplemented by a Mars Orbiter Laser Altimeter (MOLA) digital elevation model (463 meters per pixel resolution). The map includes a Description of Map Units and a Correlation of Map Units that describes and correlates units identified across the entire map region. The geologic map was assembled using ArcGIS software by Environmental Systems Research Institute (http://www.esri.com). The ArcGIS project, geodatabase, base map, and all map components are included online as supplemental data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3389","usgsCitation":"Skinner, J.A., Jr., and Tanaka, K.L., 2018, Geologic map of the Nepenthes Planum Region, Mars: U.S. Geological Survey Scientific Investigations Map 3389, pamphlet 11 p., scale 1:1,506,000, https://doi.org/10.3133/sim3389.","productDescription":"Map: 45.60 x 38.82 inches; Pamphlet: i, 11 p.; Metadata, Spatial Data; Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078987","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":437979,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95837GN","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3389 Geologic Map of the Nepenthes Planum Region, Mars"},{"id":352459,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3389/coverthb.jpg"},{"id":352467,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3389/sim3389_gis.zip","text":"GIS Files","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3389"},{"id":352463,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3389/sim3389_readme.txt","text":"Read Me","size":"4 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3389"},{"id":352462,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3389/sim3389_pamphlet.pdf","text":"Pamphlet","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3389"},{"id":352461,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3389/sim3389_mapsheet.pdf","text":"Map","size":"74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3389"},{"id":352460,"rank":2,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3389/sim3389_geomap_metadata.xml","size":"7 KB","description":"SIM 3389 Metadata"},{"id":400823,"rank":7,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P95837GN","text":"Interactive map","linkHelpText":"- Geologic map of the Nepenthes Planum Region, Mars, 1:1,506,000, Skinner et al. (2018)"}],"contact":"Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Hydrothermal alteration can create low‐permeability zones, potentially resulting in elevated pore‐fluid pressures, within a volcanic edifice. Strength reduction by rock alteration and high pore‐fluid pressures have been suggested as a mechanism for edifice flank instability. Here we combine numerical models of multiphase heat transport and groundwater flow with a slope‐stability code that incorporates three‐dimensional distributions of strength and pore‐water pressure to address the following questions: (1) What permeability distributions and contrasts produce elevated pore‐fluid pressures in a stratovolcano? (2) What are the effects of these elevated pressures on flank stability? (3) Finally, what are the effects of magma intrusion on potential flank failure in an edifice? Simulation results show that under a range of plausible parameters, water tables in a stratovolcano can be elevated or perched. These elevated water tables result in universally lower stability (lower factor of safety) compared with equivalent dry edifices, indicating a higher likelihood of flank collapse. Low‐permeability (<1 × 10−17 m2) layers such as altered pyroclastic deposits or breccias can result in locally saturated regions (perched water) and lower factors of safety near the ground surface but may actually reduce liquid water saturation and pore pressures in the core of the edifice and thus may favor small, shallow collapses over larger, deeper collapses. Magma intrusion into the base of the edifice increases pore‐fluid pressures and decreases the factor of safety. However, the shear strength of edifice rocks also exerts a significant control on stability, so both mechanical properties and pore‐fluid pressures are important for stability assessments.
Climate warming may alter the quantity and timing of groundwater discharge to streams in high alpine watersheds due to changes in the timing of the duration of seasonal freezing in the subsurface and snowmelt recharge. It is imperative to understand the effects of seasonal freezing and recharge on groundwater discharge to streams in warming alpine watersheds as streamflow originating from these watersheds is a critical water resource for downstream users. This study evaluates how climate warming may alter groundwater discharge due to changes in seasonally frozen ground and snowmelt using a 2‐D coupled flow and heat transport model with freeze and thaw capabilities for variably saturated media. The model is applied to a representative snowmelt‐dominated watershed in the Rocky Mountains of central Colorado, USA, with snowmelt time series reconstructed from a 12 year data set of hydrometeorological records and satellite‐derived snow covered area. Model analyses indicate that the duration of seasonal freezing in the subsurface controls groundwater discharge to streams, while snowmelt timing controls groundwater discharge to hillslope faces. Climate warming causes changes to subsurface ice content and duration, rerouting groundwater flow paths but not altering the total magnitude of future groundwater discharge outside of the bounds of hydrologic parameter uncertainties. These findings suggest that frozen soil routines play an important role for predicting the future location of groundwater discharge in watersheds underlain by seasonally frozen ground.
","language":"English","publisher":"AGU","doi":"10.1002/2017WR022098","usgsCitation":"Evans, S.G., Ge, S., Voss, C.I., and Molotch, N.P., 2018, The role of frozen soil in groundwater discharge predictions for warming alpine watersheds: Water Resources Research, v. 54, no. 3, p. 1599-1615, https://doi.org/10.1002/2017WR022098.","productDescription":"17 p.","startPage":"1599","endPage":"1615","ipdsId":"IP-093839","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":468898,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr022098","text":"Publisher Index Page"},{"id":352744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.64702033996582,\n 40.03182061333687\n ],\n [\n -105.57663917541504,\n 40.03182061333687\n ],\n [\n -105.57663917541504,\n 40.05902304741144\n ],\n [\n -105.64702033996582,\n 40.05902304741144\n ],\n [\n -105.64702033996582,\n 40.03182061333687\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-07","publicationStatus":"PW","scienceBaseUri":"5afee6f8e4b0da30c1bfbff2","contributors":{"authors":[{"text":"Evans, Sarah G.","contributorId":203464,"corporation":false,"usgs":false,"family":"Evans","given":"Sarah","email":"","middleInitial":"G.","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":731568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ge, Shemin","contributorId":203465,"corporation":false,"usgs":false,"family":"Ge","given":"Shemin","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":731569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Voss, Clifford I. 0000-0001-5923-2752 cvoss@usgs.gov","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":1559,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford","email":"cvoss@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":731567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Molotch, Noah P. 0000-0003-4733-8060","orcid":"https://orcid.org/0000-0003-4733-8060","contributorId":203466,"corporation":false,"usgs":false,"family":"Molotch","given":"Noah","email":"","middleInitial":"P.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":731570,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196187,"text":"70196187 - 2018 - Functional group, biomass, and climate change effects on ecological drought in semiarid grasslands","interactions":[],"lastModifiedDate":"2020-09-01T14:12:00.758312","indexId":"70196187","displayToPublicDate":"2018-03-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2319,"text":"Journal of Geophysical Research G: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Functional group, biomass, and climate change effects on ecological drought in semiarid grasslands","docAbstract":"Water relations in plant communities are influenced both by contrasting functional groups (grasses, shrubs) and by climate change via complex effects on interception, uptake and transpiration. We modelled the effects of functional group replacement and biomass increase, both of which can be outcomes of invasion and vegetation management, and climate change on ecological drought (soil water potential below which photosynthesis stops) in 340 semiarid grassland sites over 30‐year periods. Relative to control vegetation (climate and site‐determined mixes of functional groups), the frequency and duration of drought were increased by shrubs and decreased by annual grasses. The rankings of shrubs, control vegetation, and annual grasses in terms of drought effects were generally consistent in current and future climates, suggesting that current differences among functional groups on drought effects predict future differences. Climate change accompanied by experimentally‐increased biomass (i.e. the effects of invasions that increase community biomass, or management that increases productivity through fertilization or respite from grazing) increased drought frequency and duration, and advanced drought onset. Our results suggest that the replacement of perennial temperate semiarid grasslands by shrubs, or increased biomass, can increase ecological drought both in current and future climates.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JG004173","usgsCitation":"Wilson, S.D., Schlaepfer, D., Bradford, J.B., Lauenroth, W.K., Duniway, M.C., Hall, S.A., Jamiyansharav, K., Jia, G., Lkhagva, A., Munson, S.M., Pyke, D.A., and Tietjen, B., 2018, Functional group, biomass, and climate change effects on ecological drought in semiarid grasslands: Journal of Geophysical Research G: Biogeosciences, v. 123, no. 3, p. 1072-1085, https://doi.org/10.1002/2017JG004173.","productDescription":"14 p.","startPage":"1072","endPage":"1085","ipdsId":"IP-093452","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":468900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017jg004173","text":"Publisher Index Page"},{"id":352743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-25","publicationStatus":"PW","scienceBaseUri":"5afee6f8e4b0da30c1bfbff0","contributors":{"authors":[{"text":"Wilson, Scott D.","contributorId":181519,"corporation":false,"usgs":false,"family":"Wilson","given":"Scott","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":731572,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schlaepfer, Daniel R.","contributorId":105189,"corporation":false,"usgs":false,"family":"Schlaepfer","given":"Daniel R.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":731573,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731574,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":731575,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731576,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hall, Sonia A.","contributorId":181518,"corporation":false,"usgs":false,"family":"Hall","given":"Sonia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":731577,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jamiyansharav, Khishigbayar","contributorId":181522,"corporation":false,"usgs":false,"family":"Jamiyansharav","given":"Khishigbayar","email":"","affiliations":[],"preferred":false,"id":731578,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jia, Gensuo","contributorId":181520,"corporation":false,"usgs":false,"family":"Jia","given":"Gensuo","email":"","affiliations":[],"preferred":false,"id":731579,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lkhagva, Ariuntsetseg","contributorId":181521,"corporation":false,"usgs":false,"family":"Lkhagva","given":"Ariuntsetseg","email":"","affiliations":[],"preferred":false,"id":731580,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731581,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":731571,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tietjen, Britta","contributorId":181517,"corporation":false,"usgs":false,"family":"Tietjen","given":"Britta","email":"","affiliations":[],"preferred":false,"id":731582,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70194780,"text":"sir20175159 - 2018 - Model methodology for estimating pesticide concentration extremes based on sparse monitoring data","interactions":[],"lastModifiedDate":"2018-03-22T15:35:37","indexId":"sir20175159","displayToPublicDate":"2018-03-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5159","title":"Model methodology for estimating pesticide concentration extremes based on sparse monitoring data","docAbstract":"This report describes a new methodology for using sparse (weekly or less frequent observations) and potentially highly censored pesticide monitoring data to simulate daily pesticide concentrations and associated quantities used for acute and chronic exposure assessments, such as the annual maximum daily concentration. The new methodology is based on a statistical model that expresses log-transformed daily pesticide concentration in terms of a seasonal wave, flow-related variability, long-term trend, and serially correlated errors. Methods are described for estimating the model parameters, generating conditional simulations of daily pesticide concentration given sparse (weekly or less frequent) and potentially highly censored observations, and estimating concentration extremes based on the conditional simulations. The model can be applied to datasets with as few as 3 years of record, as few as 30 total observations, and as few as 10 uncensored observations. The model was applied to atrazine, carbaryl, chlorpyrifos, and fipronil data for U.S. Geological Survey pesticide sampling sites with sufficient data for applying the model. A total of 112 sites were analyzed for atrazine, 38 for carbaryl, 34 for chlorpyrifos, and 33 for fipronil. The results are summarized in this report; and, R functions, described in this report and provided in an accompanying model archive, can be used to fit the model parameters and generate conditional simulations of daily concentrations for use in investigations involving pesticide exposure risk and uncertainty.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175159","collaboration":"National Water Quality Program","usgsCitation":"Vecchia, A.V., 2018, Model methodology for estimating pesticide concentration extremes based on sparse monitoring data: U.S. Geological Survey Scientific Investigations Report 2017–5159, 47 p., https://doi.org/10.3133/sir20175159.","productDescription":"Report: viii, 47 p.; Appendix; Data release","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090885","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":352536,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NV9H50","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Files to Support SEAWAVE-QEX Model for Simulating Concentrations of Selected Pesticides in the Continental United States, 1992–2012"},{"id":352529,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/science/mission-areas/water/national-water-quality-program?qt-programs_l2_landing_page=0#qt-programs_l2_landing_page","text":"National Water Quality Program"},{"id":352528,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5159/downloads/","text":"Model Archive","description":"SIR 2017–5159 Model Archive"},{"id":352526,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5159/coverthb.jpg"},{"id":352527,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5159/sir20175159.pdf","text":"Report","size":"2.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5159"}],"contact":"Director, Dakota Water Science Center, North Dakota Office
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
This report describes (1) work done between January 2015 and May 2017 as part of the U.S. Geological Survey (USGS) hexavalent chromium, Cr(VI), background study and (2) the summative-scale approach to be used to estimate the extent of anthropogenic (man-made) Cr(VI) and background Cr(VI) concentrations near the Pacific Gas and Electric Company (PG&E) natural gas compressor station in Hinkley, California. Most of the field work for the study was completed by May 2017. The summative-scale approach and calculation of Cr(VI) background were not well-defined at the time the USGS proposal for the background Cr(VI) study was prepared but have since been refined as a result of data collected as part of this study. The proposed summative scale consists of multiple items, formulated as questions to be answered at each sampled well. Questions that compose the summative scale were developed to address geologic, hydrologic, and geochemical constraints on Cr(VI) within the study area. Each question requires a binary (yes or no) answer. A score of 1 will be assigned for an answer that represents data consistent with anthropogenic Cr(VI); a score of –1 will be assigned for an answer that represents data inconsistent with anthropogenic Cr(VI). The areal extent of anthropogenic Cr(VI) estimated from the summative-scale analyses will be compared with the areal extent of anthropogenic Cr(VI) estimated on the basis of numerical groundwater flow model results, along with particle-tracking analyses. On the basis of these combined results, background Cr(VI) values will be estimated for “Mojave-type” deposits, and other deposits, in different parts of the study area outside the summative-scale mapped extent of anthropogenic Cr(VI).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181045","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board and the State Water Resources Control Board","usgsCitation":"Izbicki, J.A., and Groover, K., 2018, Natural and man-made hexavalent chromium, Cr(VI), in groundwater near a mapped plume, Hinkley, California—study progress as of May 2017, and a summative-scale approach to estimate background Cr(VI) concentrations: U.S. Geological Survey Open-File Report 2018–1045, 28 p., https://doi.org/10.3133/ofr20181045.","productDescription":"vi, 28 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-095489","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":352720,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1045/ofr20181045.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1045"},{"id":352719,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1045/coverthb.jpg"}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.2333,\n 34.8667\n ],\n [\n -117.0667,\n 34.8667\n ],\n [\n -117.0667,\n 35.0333\n ],\n [\n -117.2333,\n 35.0333\n ],\n [\n -117.2333,\n 34.8667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
To evaluate the effect of fish growth on mercury (Hg) and polychlorinated biphenyl (PCB) bioaccumulation, a non–steady‐state toxicokinetic model, combined with a Wisconsin bioenergetics model, was developed to simulate Hg and PCB bioaccumulation in bluegill (Lepomis macrochirus). The model was validated by comparing observed with predicted Hg and PCB 180 concentrations across 5 age classes from 5 different waterbodies across North America. The non–steady‐state model generated accurate predictions for Hg and PCB bioaccumulation in 3 of 5 waterbodies: Apsey Lake (ON, Canada), Sharbot Lake (ON, Canada), and Stonelick Lake (OH, USA). The poor performance of the model for the Detroit River (MI, USA/ON, Canada) and Lake Hartwell (GA/SC, USA), which are 2 well‐known contaminated sites with possibly high heterogeneity in spatial contamination, was attributed to changes in feeding behavior and/or prey contamination. Model simulations indicate that growth dilution is a major component of contaminant bioaccumulation patterns in fish, especially during early life stages, and was predicted to be more important for hydrophobic PCBs than for Hg. Simulations that considered tissue‐specific growth provided some improvement in model performance particularly for PCBs in fish populations that exhibited changes in their whole‐body lipid content with age. Higher variation in lipid growth compared with that of lean dry protein was also observed between different bluegill populations, which partially explains the greater variation in PCB bioaccumulation slopes compared with Hg across sampling sites.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.4114","usgsCitation":"Li, J., Haffner, G.D., Patterson, G., Walters, D., Burtnyk, M.D., and Drouillard, K.G., 2018, Importance of growth rate on mercury and polychlorinated biphenyl bioaccumulation in fish: Environmental Toxicology and Chemistry, v. 37, no. 6, p. 1655-1667, https://doi.org/10.1002/etc.4114.","productDescription":"13 p.","startPage":"1655","endPage":"1667","ipdsId":"IP-090841","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":352682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-19","publicationStatus":"PW","scienceBaseUri":"5afee6fae4b0da30c1bfc002","contributors":{"authors":[{"text":"Li, Jiajia","contributorId":203411,"corporation":false,"usgs":false,"family":"Li","given":"Jiajia","email":"","affiliations":[{"id":36613,"text":"U. Windsor","active":true,"usgs":false}],"preferred":false,"id":731430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haffner, G. Douglas","contributorId":203414,"corporation":false,"usgs":false,"family":"Haffner","given":"G.","email":"","middleInitial":"Douglas","affiliations":[{"id":36613,"text":"U. Windsor","active":true,"usgs":false}],"preferred":false,"id":731433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patterson, Gordon","contributorId":203412,"corporation":false,"usgs":false,"family":"Patterson","given":"Gordon","email":"","affiliations":[{"id":36614,"text":"Michigan Tech","active":true,"usgs":false}],"preferred":false,"id":731431,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, David M. 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":4444,"corporation":false,"usgs":true,"family":"Walters","given":"David M.","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":731429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burtnyk, Michael D.","contributorId":203413,"corporation":false,"usgs":false,"family":"Burtnyk","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":36615,"text":"CH2M Hill","active":true,"usgs":false}],"preferred":false,"id":731432,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drouillard, Ken G.","contributorId":127334,"corporation":false,"usgs":false,"family":"Drouillard","given":"Ken","email":"","middleInitial":"G.","affiliations":[{"id":6778,"text":"University of Windsor, Windsor, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":731434,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196147,"text":"sim3393 - 2018 - Delineation of the hydrogeologic framework of the Big Sioux aquifer near Sioux Falls, South Dakota, using airborne electromagnetic data","interactions":[],"lastModifiedDate":"2018-09-25T08:02:09","indexId":"sim3393","displayToPublicDate":"2018-03-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3393","title":"Delineation of the hydrogeologic framework of the Big Sioux aquifer near Sioux Falls, South Dakota, using airborne electromagnetic data","docAbstract":"The U.S. Geological Survey, in cooperation with the City of Sioux Falls, South Dakota, began developing a groundwater-flow model of the Big Sioux aquifer in 2014 that will enable the City to make more informed water management decisions, such as delineation of areas of the greatest specific yield, which is crucial for locating municipal wells. Innovative tools are being evaluated as part of this study that can improve the delineation of the hydrogeologic framework of the aquifer for use in development of a groundwater-flow model, and the approach could have transfer value for similar hydrogeologic settings. The first step in developing a groundwater-flow model is determining the hydrogeologic framework (vertical and horizontal extents of the aquifer), which typically is determined by interpreting geologic information from drillers’ logs and surficial geology maps. However, well and borehole data only provide hydrogeologic information for a single location; conversely, nearly continuous geophysical data are collected along flight lines using airborne electromagnetic (AEM) surveys. These electromagnetic data are collected every 3 meters along a flight line (on average) and subsequently can be related to hydrogeologic properties. AEM data, coupled with and constrained by well and borehole data, can substantially improve the accuracy of aquifer hydrogeologic framework delineations and result in better groundwater-flow models.
AEM data were acquired using the Resolve frequency-domain AEM system to map the Big Sioux aquifer in the region of the city of Sioux Falls. The survey acquired more than 870 line-kilometers of AEM data over a total area of about 145 square kilometers, primarily over the flood plain of the Big Sioux River between the cities of Dell Rapids and Sioux Falls. The U.S. Geological Survey inverted the survey data to generate resistivity-depth sections that were used in two-dimensional maps and in three-dimensional volumetric visualizations of the Earth resistivity distribution. Contact lines were drawn using a geographic information system to delineate interpreted geologic stratigraphy. The contact lines were converted to points and then interpolated into a raster surface. The methods used to develop elevation and depth maps of the hydrogeologic framework of the Big Sioux aquifer are described herein.
The final AEM interpreted aquifer thickness ranged from 0 to 31 meters with an average thickness of 12.8 meters. The estimated total volume of the aquifer was 1,060,000,000 cubic meters based on the assumption that the top of the aquifer is the land-surface elevation. A simple calculation of the volume (length times width times height) of a previous delineation of the aquifer estimated the aquifer volume at 378,000,000 cubic meters; thus, the estimation based on AEM data is more than twice the previous estimate. The depth to top of Sioux Quartzite, which ranged in depth from 0 to 90 meters, also was delineated from the AEM data.
Director, Dakota Water Science Center, South Dakota Office
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine (Pinus ponderosa), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently-available, geographically-widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently-devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/sysbio/syy017","usgsCitation":"Maguire, K.C., Shinneman, D.J., Potter, K.M., and Hipkins, V.D., 2018, Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change: Systematic Biology, v. 67, no. 6, p. 965-978, https://doi.org/10.1093/sysbio/syy017.","productDescription":"14 p.","startPage":"965","endPage":"978","ipdsId":"IP-088076","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":468902,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/sysbio/syy017","text":"Publisher Index Page"},{"id":352679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-14","publicationStatus":"PW","scienceBaseUri":"5afee6f9e4b0da30c1bfc000","contributors":{"authors":[{"text":"Maguire, Kaitlin C. 0000-0001-8193-2384","orcid":"https://orcid.org/0000-0001-8193-2384","contributorId":203419,"corporation":false,"usgs":true,"family":"Maguire","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":731442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","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":731443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Potter, Kevin M.","contributorId":167660,"corporation":false,"usgs":false,"family":"Potter","given":"Kevin","email":"","middleInitial":"M.","affiliations":[{"id":24794,"text":"Department of Forestry and Environmental Resources, North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":731444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hipkins, Valerie D.","contributorId":167661,"corporation":false,"usgs":false,"family":"Hipkins","given":"Valerie","email":"","middleInitial":"D.","affiliations":[{"id":24795,"text":"National Forest Genetics Laboratory, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":731445,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196115,"text":"70196115 - 2018 - Annual variation in polychlorinated biphenyl (PCB) exposure in tree swallow (Tachycineta bicolor) eggs and nestlings at Great Lakes Restoration Initiative (GLRI) study sites","interactions":[],"lastModifiedDate":"2022-04-04T20:43:36.748088","indexId":"70196115","displayToPublicDate":"2018-03-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Annual variation in polychlorinated biphenyl (PCB) exposure in tree swallow (Tachycineta bicolor) eggs and nestlings at Great Lakes Restoration Initiative (GLRI) study sites","title":"Annual variation in polychlorinated biphenyl (PCB) exposure in tree swallow (Tachycineta bicolor) eggs and nestlings at Great Lakes Restoration Initiative (GLRI) study sites","docAbstract":"Tree swallow (Tachycineta bicolor) eggs and nestlings were collected from 16 sites across the Great Lakes to quantify normal annual variation in total polychlorinated biphenyl (PCB) exposure and to validate the sample size choice in earlier work. A sample size of five eggs or five nestlings per site was adequate to quantify exposure to PCBs in tree swallows given the current exposure levels and variation. There was no difference in PCB exposure in two randomly selected sets of five eggs collected in the same year, but analyzed in different years. Additionally, there was only modest annual variation in exposure, with between 69% (nestlings) and 73% (eggs) of sites having no differences between years. There was a tendency, both statistically and qualitatively, for there to be less exposure in the second year compared to the first year.
","language":"English","publisher":"Springer","doi":"10.1007/s10661-018-6617-3","usgsCitation":"Custer, C.M., Custer, T.W., Dummer, P.M., Goldberg, D., and Franson, J.C., 2018, Annual variation in polychlorinated biphenyl (PCB) exposure in tree swallow (Tachycineta bicolor) eggs and nestlings at Great Lakes Restoration Initiative (GLRI) study sites: Environmental Monitoring and Assessment, v. 190, p. 1-7, https://doi.org/10.1007/s10661-018-6617-3.","productDescription":"Article 227; 7 p.","startPage":"1","endPage":"7","ipdsId":"IP-090193","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":352684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.21923828124999,\n 41.244772343082076\n ],\n [\n -78.7060546875,\n 41.244772343082076\n ],\n [\n -78.7060546875,\n 46.86019101567027\n ],\n [\n -92.21923828124999,\n 46.86019101567027\n ],\n [\n -92.21923828124999,\n 41.244772343082076\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"190","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-17","publicationStatus":"PW","scienceBaseUri":"5afee6fae4b0da30c1bfc004","contributors":{"authors":[{"text":"Custer, Christine M. 0000-0003-0500-1582 ccuster@usgs.gov","orcid":"https://orcid.org/0000-0003-0500-1582","contributorId":1143,"corporation":false,"usgs":true,"family":"Custer","given":"Christine","email":"ccuster@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":731421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Custer, Thomas W. 0000-0003-3170-6519 tcuster@usgs.gov","orcid":"https://orcid.org/0000-0003-3170-6519","contributorId":2835,"corporation":false,"usgs":true,"family":"Custer","given":"Thomas","email":"tcuster@usgs.gov","middleInitial":"W.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":731422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dummer, Paul M. 0000-0002-2055-9480 pdummer@usgs.gov","orcid":"https://orcid.org/0000-0002-2055-9480","contributorId":3015,"corporation":false,"usgs":true,"family":"Dummer","given":"Paul","email":"pdummer@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":731423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldberg, Diana R. 0000-0001-8540-8512","orcid":"https://orcid.org/0000-0001-8540-8512","contributorId":82252,"corporation":false,"usgs":true,"family":"Goldberg","given":"Diana R.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":731424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Franson, J. Christian 0000-0002-0251-4238 jfranson@usgs.gov","orcid":"https://orcid.org/0000-0002-0251-4238","contributorId":177499,"corporation":false,"usgs":true,"family":"Franson","given":"J.","email":"jfranson@usgs.gov","middleInitial":"Christian","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":731425,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195978,"text":"sir20185006 - 2018 - Nitrogen concentrations and loads for the Connecticut River at Middle Haddam, Connecticut, computed with the use of autosampling and continuous measurements of water quality for water years 2009 to 2014","interactions":[],"lastModifiedDate":"2018-03-21T15:01:13","indexId":"sir20185006","displayToPublicDate":"2018-03-20T16:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5006","title":"Nitrogen concentrations and loads for the Connecticut River at Middle Haddam, Connecticut, computed with the use of autosampling and continuous measurements of water quality for water years 2009 to 2014","docAbstract":"The daily and annual loads of nitrate plus nitrite and total nitrogen for the Connecticut River at Middle Haddam, Connecticut, were determined for water years 2009 to 2014. The analysis was done with a combination of methods, which included a predefined rating curve method for nitrate plus nitrite and total nitrogen for water years 2009 to 2011 and a custom rating curve method that included sensor measurements of nitrate plus nitrite nitrogen concentration and turbidity along with mean daily flow to determine total nitrogen loads for water years 2011 to 2014. Instantaneous concentrations of total nitrogen were estimated through the use of a regression model based on sensor measurements at 15-minute intervals of nitrate plus nitrite nitrogen and turbidity for water years 2011 to 2014.
Annual total nitrogen loads at the Connecticut River at Middle Haddam ranged from 12,900 to 19,200 metric tons, of which about 42 to 49 percent was in the form of nitrate plus nitrite. The mean 95-percent prediction intervals on daily total nitrogen load estimates were smaller from the custom model, which used sensor data, than those calculated by the predefined model.
Annual total nitrogen load estimates at the Connecticut River at Middle Haddam were compared with the upstream load estimates at the Connecticut River at Thompsonville, Conn. Annual gains in total nitrogen loads between the two stations ranged from 3,430 to 6,660 metric tons. These increases between the two stations were attributed to the effects of increased urbanization and to combined annual discharges of 1,540 to 2,090 metric tons of nitrogen from 24 wastewater treatment facilities in the drainage area between the two stations. The contribution of total nitrogen from wastewater discharge between the two stations had declined substantially before the beginning of this study and accounted for from 31 to 52 percent of the gain in nitrogen load between the Thompsonville and Middle Haddam sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185006","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Mullaney, J.R., Martin, J.W., and Morrison, J., 2018, Nitrogen concentrations and loads for the Connecticut River at Middle Haddam, Connecticut, computed with the use of autosampling and continuous measurements of water quality for water years 2009 to 2014: U.S. Geological Survey Scientific Investigations Report 2018–5006, 22 p., https://doi.org/10.3133/sir20185006.","productDescription":"Report: vii, 22 p.; Data release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091217","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":352399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5006/sir20185006.pdf","text":"Report","size":"4.79 MB ","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5006"},{"id":352631,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ31WT","text":"USGS data release","description":"USGS data release","linkHelpText":"Nitrogen Concentrations and Loads for the Connecticut River at Middle Haddam, Connecticut, Computed With the Use of Autosampling and Continuous Measurements of Water Quality for Water Years 2009 to 2014"},{"id":352409,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5006/coverthb.jpg"}],"country":"United States","state":"Connecticut","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 -72.94097900390625,\n 41.34691753986531\n ],\n [\n -72.18154907226562,\n 41.34691753986531\n ],\n [\n -72.18154907226562,\n 42.04011410708205\n ],\n [\n -72.94097900390625,\n 42.04011410708205\n ],\n [\n -72.94097900390625,\n 41.34691753986531\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
101 Pitkin Street
East Hartford, CT 06108
Assessment of the “Bird or Animal Deformities or Reproductive Problems” Beneficial Use Impairment (BUI) can be accomplished by (1) comparing tissue concentrations to established background and Lowest Observable Effect Level (LOEL) for reproductive effects, or (2) directly measuring reproductive success at Areas of Concern (AOCs) and statistically comparing those rates to minimally impacted reference locations (non-AOCs). Results from recent tree swallow (Tachycineta bicolor) publications were used to evaluate this BUI based on both approaches. For both endpoints, a 95-percent confidence interval (CI) was used to test for significant differences. Additional information on BUIs, AOCs, and the program in general can be found in the Great Lakes Water Quality Agreement (2012).
For the first metric, there are good background and reproductive effect threshold LOELs for tree swallow egg concentrations for polychlorinated biphenyls (PCBs), dioxins and furans (PCDD/Fs), and mercury, as well as, for some other organic and inorganic contaminants. For the second assessment, comparisons were made between AOC and non-AOC sites for reproductive success, which was measured as the daily probability of egg failure and the percentage of eggs laid that hatched. Multistate modeling was used to assess whether there was an association between the daily probability of egg failure and a suite of contaminants, including PCBs, but also whether there was an association with ecological variables, such as female age and date within season. Both of these ecological variables are known to affect hatching success in birds. The objective of this report is to synthesize the previously published information to assist in the assessment of the “Bird or Animal Deformities or Reproductive Problems” BUI at 16 sites within the 5 Wisconsin AOCs (table 1). The logic behind this interpretation is applicable to other AOCs as well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181032","usgsCitation":"Custer, C.M., Custer, T.W., and Dummer, P.M., 2018, Synthesis of tree swallow (Tachycineta bicolor) data for Beneficial Use Impairment (BUI) assessment at Wisconsin Areas of Concern: U.S. Geological Survey Open-File Report 2018–1032, 8 p., https://doi.org/10.3133/ofr20181032.","productDescription":"iv, 8 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092682","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":352653,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1032/ofr20181032.pdf","text":"Report","size":"111 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1032"},{"id":352652,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1032/coverthb.jpg"}],"country":"United States","state":"Michigan, Minnesota, Wisconsin","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Cross, WI 54603
The Edwin B. Forsythe National Wildlife Refuge (hereafter Forsythe refuge or the refuge) is situated along the central New Jersey coast and provides a mixture of freshwater and saltwater habitats for numerous bird, wildlife, and plant species. Little data and information were previously available regarding the freshwater dynamics that support the refuge’s ecosystems. In cooperation with the U.S. Fish and Wildlife Service, the U.S. Geological Survey conducted an assessment of the hydrologic resources and processes in the refuge and surrounding areas to provide baseline information for evaluating restoration projects and future changes in the hydrologic system associated with climate change and other anthropogenic stressors.
During spring 2015, water levels were measured at groundwater and surface-water sites in and near the Forsythe refuge. These water-level measurements, along with surface-water elevations obtained from digital elevation models, were used to construct water-table-elevation and depth-to-water maps of the refuge and surrounding areas. Water-table elevations in the refuge ranged from sea level to approximately 65 feet above sea level; in most of the refuge, the water-table elevation was within 3 feet of sea level. The water-table-elevation map indicates that the direction of shallow groundwater flow at the regional scale is generally from west to east (much of it from the northwest to the southeast), and groundwater moves downgradient from the uplands toward major groundwater discharge areas consisting of coastal streams and wetlands. The depth to water is estimated to be less than 2 feet for approximately 86 percent of the refuge, which coincides closely with the percentage of wetland area in the refuge. Depth to water in excess of 20 feet below land surface is limited to higher elevation areas of the refuge.
Streamflow data collected at continuous-record streamgages and partial-record stations within the Mullica-Toms Basin were summarized. Hydrograph separation of streamflow data for eight streamgages (2004–13) reveals that base flow accounts for 68–94 percent of streamflow in basins upstream from the refuge. The high base-flow inputs underscore the importance of groundwater as a source of freshwater that supports both the streams that flow into the refuge and the hydroecology of the contributing basins. Mean annual flow typically ranged from 1.7 to 2.1 cubic feet per second per square mile at the streamgages (2004–13) and between 1.2 and 2.3 cubic feet per second per square mile at the partial-record stations (1965–2015) but was notably greater or lower than these ranges at several stations.
Mean annual water budgets were estimated for multiple regions of the refuge for 2004–13 using data compiled from nearby meteorological stations and groundwater flows derived from previously calibrated groundwater-flow models. Precipitation, groundwater recharge, and evapotranspiration were estimated from available data; direct runoff was calculated as the residual component of the water balance. Groundwater recharge rates were greatest in the upland-dominated areas of the refuge with estimates of 14.4 to 18.9 inches per year, which are equivalent to 30 to 40 percent of precipitation. Groundwater recharge rates were nearly zero in the central coastal areas because these areas are major groundwater discharge zones, the water table is near land surface, the subsurface is close to saturation and cannot accept much recharge, and much of the area is underlain by thick marsh deposits likely with low permeability. Estimates of evapotranspiration varied from about 26 inches per year in the upland-dominated areas to more than 35 inches per year in the coastal wetlands, equivalent to 55–79 percent of mean annual precipitation, indicating that it is a major component of the hydrodynamics of the Forsythe refuge.
On the basis of output from previously calibrated groundwater-flow models, nearly all of the groundwater exiting the surficial aquifer system in the central coastal areas of the refuge is discharged to wetlands, which highlights the importance of groundwater discharge in supporting the ecosystems of the Forsythe refuge. In the central coastal areas, horizontal flow contributes more than 90 percent of the groundwater flow to the surficial system, indicating that the upbasin areas are a substantial source of water that ultimately discharges to streams and wetlands in the refuge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175088","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Wieben, C.M., and Chepiga, M.M., 2018, Hydrologic assessment of the Edwin B. Forsythe National Wildlife Refuge, New Jersey: U.S. Geological Survey Scientific Investigations Report 2017–5088, 38 p., https://doi.org/10.3133/sir20175088.\n","productDescription":"Report: viii, 38 p.; 2 Plates: 24.0 x 36.0 inches; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079840","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":352411,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5088/sir20175088.pdf","text":"Report","size":"25.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5088"},{"id":352410,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5088/coverthb.jpg"},{"id":352412,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JMN","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-table elevation contours and depth-to-water grid for the Edwin B. Forsythe National Wildlife Refuge, New Jersey, and vicinity, spring 2015"},{"id":352535,"rank":6,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5088/sir20175088_plate02.pdf","text":"Plate 2","size":"4.15 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Water-Table Elevation in and near the Southern Part of the Edwin B. Forsythe National Wildlife Refuge, New Jersey, Spring 2015"},{"id":352426,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20175135","text":"Scientific Investigations Report 2017–5135","linkHelpText":"- Hydrogeology of, Simulation of Groundwater Flow in, and Potential Effects of Sea-Level Rise on the Kirkwood-Cohansey Aquifer System in the Vicinity of Edwin B. Forsythe National Wildlife Refuge, New Jersey"},{"id":352534,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5088/sir20175088_plate01.pdf","text":"Plate 1 ","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Water-Table Elevation in and near the Northern Part of the Edwin B. Forsythe National Wildlife Refuge, New Jersey, Spring 2015"}],"country":"United States","state":"New Jersey","otherGeospatial":"Edwin B. Forsythe National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74,\n 39.4167\n ],\n [\n -74,\n 40.07807142745009\n ],\n [\n -74.5,\n 40.07807142745009\n ],\n [\n -74.5,\n 39.4167\n ],\n [\n -74,\n 39.4167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
The Edwin B. Forsythe National Wildlife Refuge encompasses more than 47,000 acres of New Jersey coastal habitats, including salt marshes, freshwater wetlands, tidal wetlands, barrier beaches, woodlands, and swamps. The refuge is along the Atlantic Flyway and provides breeding habitat for fish, migratory birds, and other wildlife species. The refuge area may be threatened by global climate change, including sea-level rise (SLR).
The Kirkwood-Cohansey aquifer system underlies the Edwin B. Forsythe National Wildlife Refuge. Groundwater is an important source of freshwater flow into the refuge, but information about the interaction of surface water and groundwater in the refuge area and the potential effects of SLR on the underlying aquifer system is limited. The U.S. Geological Survey (USGS), in cooperation with the U.S. Fish and Wildlife Service (USFWS), conducted a hydrologic assessment of the refuge in New Jersey and developed a groundwater flow model to improve understanding of the geohydrology of the refuge area and to serve as a tool to evaluate changes in groundwater-level altitudes that may result from a rise in sea level.
Groundwater flow simulations completed for this study include a calibrated baseline simulation that represents 2005–15 hydraulic conditions and three SLR scenarios―20, 40, and 60 centimeters (cm) (0.656, 1.312, and 1.968 feet, respectively). Results of the three SLR simulations indicate that the water table in the unconfined Kirkwood-Cohansey aquifer system in the refuge area will rise, resulting in increased discharge of fresh groundwater to freshwater wetlands and streams. As sea level rises, simulated groundwater discharge to the salt marsh, bay, and ocean is projected to decrease. Flow from the salt marsh, bay, and ocean to the overlying surface water is projected to increase as sea level rises.
The simulated movement of the freshwater-seawater interface as sea level rises depends on the hydraulic-head gradient. In the center of the Forsythe model area, topographic relief is 23 feet (ft) and the hydraulic-head gradient is 0.0033. In the center of the Forsythe model area, the simulated interface moved inland about 600 ft and downward about 15 ft from the baseline simulation to scenario 3 as a result of a SLR of 60 cm. In the southern part of the Forsythe model area, the topography is flatter (relief of 8 ft) and the hydraulic-head gradient is smaller (0.001). In the southern part of the Forsythe model study area, the simulated interface in this area is projected to move inland about 200 ft from the baseline simulation to scenario 3 and does not move downward.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175135","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Fiore, A.R., Voronin, L.M., and Wieben, C.M., 2018, Hydrogeology of, simulation of groundwater flow in, and potential effects of sea-level rise on the Kirkwood-Cohansey aquifer system in the vicinity of Edwin B. Forsythe National Wildlife Refuge, New Jersey: U.S. Geological Survey Scientific Investigations Report 2017-5135, 59 p., https://doi.org/10.3133/sir20175135.","productDescription":"Report: vii, 59 p.; Data releases","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074587","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":352424,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76W98JB","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-2005 model used to evaluate the potential effects of sea-level rise on the Kirkwood-Cohansey aquifer system in the vicinity of Edwin B. Forsythe National Wildlife Refuge, New Jersey"},{"id":352423,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KBD","text":"USGS data release","description":"USGS data release","linkHelpText":"Raw ground-penetrating radar data, Edwin B. Forsythe National Wildlife Refuge, New Jersey, 2014–15"},{"id":352422,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5135/sir20175135.pdf","text":"Report","size":"16.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5135"},{"id":352421,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5135/coverthb.jpg"},{"id":352425,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20175088","text":"Scientific Investigations Report 2017–5088","linkHelpText":"- Hydrologic Assessment of the Edwin B. Forsythe National Wildlife Refuge, New Jersey"}],"country":"United States","state":"New Jersey","otherGeospatial":"Edwin B. Forsythe National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.67,\n 39.33\n ],\n [\n -73.67,\n 39.33\n ],\n [\n -73.67,\n 40.09067983779908\n ],\n [\n -74.67,\n 40.09067983779908\n ],\n [\n -74.67,\n 39.33\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
The giant gartersnake (Thamnophis gigas) is a state and federally threatened species precinctive to California. The range of the giant gartersnake has contracted in the last century because its wetland habitat has been drained for agriculture and development. As a result of this habitat alteration, giant gartersnakes now largely persist in and near rice agriculture in the Sacramento Valley, because the system of canals that conveys water for rice growing approximates historical wetland habitat. Many aspects of the demography of giant gartersnakes are unknown, including how individuals grow throughout their life, how size influences reproduction, and how survival varies over time and among populations. We studied giant gartersnakes throughout the Sacramento Valley of California from 1995 to 2016 using capture-mark-recapture to study the growth, reproduction, and survival of this threatened species. We then use these data to construct an Integral Projection Model, and analyze this demographic model to understand which vital rates contribute most to the growth rate of giant gartersnake populations. We find that giant gartersnakes exhibit indeterminate growth; growth slows as individuals’ age. Fecundity, probability of reproduction, and survival all increase with size, although survival may decline for the largest female giant gartersnakes. The population growth rate of giant gartersnakes is most influenced by the survival and growth of large adult females, and the size at which 1 year old recruits enter the population. Our results indicate that management actions benefitting these influential demographic parameters will have the greatest positive effect on giant gartersnake population growth rates, and therefore population persistence. This study informs the conservation and management of giant gartersnakes and their habitat, and illustrates the effectiveness of hierarchical Bayesian models for the study of rare and elusive species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171164","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Rose, J.P., Ersan, J.S.M., Wylie, G.D., Casazza, M.L., and Halstead, B.J., 2018, Construction and analysis of a giant gartersnake (Thamnophis gigas) population projection model: U.S. Geological Survey Open-File Report 2017–1164, 98 p., https://doi.org/10.3133/ofr20171164.","productDescription":"viii, 98 p.","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-090465","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":352644,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1164/ofr20171164.pdf","text":"Report","size":"8.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1164"},{"id":352643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1164/coverthb.jpg"}],"contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This report provides an overview of the pilot study and description of the techniques developed for a future mitigation study of Pomacea maculata (giant applesnail) at the U.S. Fish and Wildlife Service Mandalay National Wildlife Refuge, Louisiana (MNWR). Egg mass suppression is a potential strategy for the mitigation of the invasive giant applesnail. In previous studies at Langan Municipal Park in Mobile, Alabama (LMP), and National Park Service Jean Lafitte National Park-Barataria Unit, Louisiana (JLNP), we determined that spraying food-grade oil (coconut oil or Pam™ spray) on egg masses significantly reduced egg hatching. At JLNP we also developed methods to estimate snail population size. The purpose of this pilot study was to adapt techniques developed for previous studies to the circumstances of MNWR in preparation for a larger experiment whereby we will test the effectiveness of egg mass suppression as an applesnail mitigation tool. We selected four canals that will be used as treatment and control sites for the experiment (two each). We established that an efficient way to destroy egg masses is to knock them down with a high-velocity stream of water pumped directly from the canal. The traps used at JLNP had to be modified to accommodate the greater range of water-level fluctuation at MNWR. One of the three marking methods used at JLNP was selected for use at MNWR.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181041","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Barataria-Terrebonne National Estuary Program","usgsCitation":"Carter, Jacoby, and Merino, Sergio, 2018, Pilot testing and protocol development of giant applesnail suppression at Mandalay National Wildlife Refuge, Louisiana—July–October 2017: U.S. Geological Survey Open-File Report 2018-1041, 17 p., https://doi.org/10.3133/ofr20181041.","productDescription":"vi, 17 p.","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-093234","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":352641,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1041/ofr20181041.pdf","text":"Report","size":"903 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1041"},{"id":352640,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1041/coverthb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mandalay National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.84182739257812,\n 29.486378867043253\n ],\n [\n -90.76766967773438,\n 29.486378867043253\n ],\n [\n -90.76766967773438,\n 29.559422089438876\n ],\n [\n -90.84182739257812,\n 29.559422089438876\n ],\n [\n -90.84182739257812,\n 29.486378867043253\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
700 Cajundome Blvd.
Lafayette, LA 70506
We investigated the fate of Sb and As downstream of the abandoned Su Suergiu mine (Sardinia, Italy) and surrounding areas. The mined area is a priority in the Sardinian remediation plan for contaminated sites due to the high concentrations of Sb and As in the mining-related wastes, which may impact the Flumendosa River that supplies water for agriculture and domestic uses. Hydrogeochemical surveys conducted from 2005 to 2015 produced time-series data and downstream profiles of water chemistry at 46 sites. Water was sampled at: springs and streams unaffected by mining; adits and streams in the mine area; drainage from the slag heaps; stream water downstream of the slag drainages; and the Flumendosa River downstream from the confluence of the contaminated waters. At specific sites, water sampling was repeated under different flow conditions, resulting in a total of 99 samples. The water samples were neutral to slightly alkaline. Elevated Sb (up to 30 mg L−1) and As (up to 16 mg L−1) concentrations were observed in water flowing from the slag materials from where the Sb ore was processed. These slag materials were the main Sb and As source at Su Suergiu. A strong base, Na-carbonate, from the foundry wastes, had a major influence on mobilizing Sb and As. Downstream contamination can be explained by considering that: (1) the predominant aqueous species, Sb(OH)6 − and HAsO4 −2, are not favored in sorption processes at the observed pH conditions; (2) precipitation of Sb- and As-bearing solid phases was not observed, which is consistent with modeling results indicating undersaturation; and (3) the main decrease in dissolved Sb and As concentrations was by dilution. Dissolved As concentrations in the Flumendosa River did not generally exceed the EU limit of 10 µg L−1, whereas dissolved Sb in the river downstream of the contamination source always exceeded the EU limit of 5 µg L−1. Recent actions aimed at retaining runoff from the slag heaps are apparently not sufficiently mitigating contamination in the Flumendosa River.
","language":"English","publisher":"Springer","doi":"10.1007/s10230-017-0479-8","usgsCitation":"Cidu, R., Dore, E., Biddau, R., and Nordstrom, D.K., 2018, Fate of antimony and arsenic in contaminated waters at the abandoned Su Suergiu mine (Sardinia, Italy): Mine Water and the Environment, v. 37, no. 1, p. 151-165, https://doi.org/10.1007/s10230-017-0479-8.","productDescription":"15 p.","startPage":"151","endPage":"165","ipdsId":"IP-071489","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":352624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","state":"Sardinia","otherGeospatial":"Su Suergiu mine","volume":"37","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-31","publicationStatus":"PW","scienceBaseUri":"5afee6fce4b0da30c1bfc020","contributors":{"authors":[{"text":"Cidu, Rosa","contributorId":194017,"corporation":false,"usgs":false,"family":"Cidu","given":"Rosa","affiliations":[{"id":36605,"text":"University of Cagliari, Cagliari, Sardinia","active":true,"usgs":false}],"preferred":false,"id":731269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dore, Elisabetta","contributorId":203363,"corporation":false,"usgs":false,"family":"Dore","given":"Elisabetta","email":"","affiliations":[{"id":36605,"text":"University of Cagliari, Cagliari, Sardinia","active":true,"usgs":false}],"preferred":false,"id":731271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biddau, Riccardo","contributorId":203362,"corporation":false,"usgs":false,"family":"Biddau","given":"Riccardo","email":"","affiliations":[{"id":36605,"text":"University of Cagliari, Cagliari, Sardinia","active":true,"usgs":false}],"preferred":false,"id":731270,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":731268,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196088,"text":"70196088 - 2018 - Bioremediation in fractured rock: 1. Modeling to inform design, monitoring, and expectations","interactions":[],"lastModifiedDate":"2018-03-17T17:45:14","indexId":"70196088","displayToPublicDate":"2018-03-17T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Bioremediation in fractured rock: 1. Modeling to inform design, monitoring, and expectations","docAbstract":"Field characterization of a trichloroethene (TCE) source area in fractured mudstones produced a detailed understanding of the geology, contaminant distribution in fractures and the rock matrix, and hydraulic and transport properties. Groundwater flow and chemical transport modeling that synthesized the field characterization information proved critical for designing bioremediation of the source area. The planned bioremediation involved injecting emulsified vegetable oil and bacteria to enhance the naturally occurring biodegradation of TCE. The flow and transport modeling showed that injection will spread amendments widely over a zone of lower‐permeability fractures, with long residence times expected because of small velocities after injection and sorption of emulsified vegetable oil onto solids. Amendments transported out of this zone will be diluted by groundwater flux from other areas, limiting bioremediation effectiveness downgradient. At nearby pumping wells, further dilution is expected to make bioremediation effects undetectable in the pumped water. The results emphasize that in fracture‐dominated flow regimes, the extent of injected amendments cannot be conceptualized using simple homogeneous models of groundwater flow commonly adopted to design injections in unconsolidated porous media (e.g., radial diverging or dipole flow regimes). Instead, it is important to synthesize site characterization information using a groundwater flow model that includes discrete features representing high‐ and low‐permeability fractures. This type of model accounts for the highly heterogeneous hydraulic conductivity and groundwater fluxes in fractured‐rock aquifers, and facilitates designing injection strategies that target specific volumes of the aquifer and maximize the distribution of amendments over these volumes.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12585","usgsCitation":"Tiedeman, C.R., Shapiro, A.M., Hsieh, P.A., Imbrigiotta, T.E., Goode, D.J., Lacombe, P., DeFlaun, M.F., Drew, S.R., Johnson, C.D., Williams, J., and Curtis, G.P., 2018, Bioremediation in fractured rock: 1. Modeling to inform design, monitoring, and expectations: Groundwater, v. 56, no. 2, p. 300-316, https://doi.org/10.1111/gwat.12585.","productDescription":"17 p.","startPage":"300","endPage":"316","ipdsId":"IP-088879","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":352623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-05","publicationStatus":"PW","scienceBaseUri":"5afee6fce4b0da30c1bfc01e","contributors":{"authors":[{"text":"Tiedeman, Claire R. 0000-0002-0128-3685 tiedeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0128-3685","contributorId":196777,"corporation":false,"usgs":true,"family":"Tiedeman","given":"Claire","email":"tiedeman@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":731272,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":731273,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":731274,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Imbrigiotta, Thomas E. 0000-0003-1716-4768 timbrig@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":152114,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas","email":"timbrig@usgs.gov","middleInitial":"E.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goode, Daniel J. 0000-0002-8527-2456 djgoode@usgs.gov","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":193394,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel","email":"djgoode@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":731276,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lacombe, Pierre 0000-0002-9596-7622 placombe@usgs.gov","orcid":"https://orcid.org/0000-0002-9596-7622","contributorId":152113,"corporation":false,"usgs":true,"family":"Lacombe","given":"Pierre","email":"placombe@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731277,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeFlaun, Mary F.","contributorId":203177,"corporation":false,"usgs":false,"family":"DeFlaun","given":"Mary","email":"","middleInitial":"F.","affiliations":[{"id":36571,"text":"Geosyntec Consultants","active":true,"usgs":false}],"preferred":false,"id":731278,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Drew, Scott R.","contributorId":203178,"corporation":false,"usgs":false,"family":"Drew","given":"Scott","email":"","middleInitial":"R.","affiliations":[{"id":36571,"text":"Geosyntec Consultants","active":true,"usgs":false}],"preferred":false,"id":731279,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Johnson, Carole D. 0000-0001-6941-1578 cjohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-6941-1578","contributorId":1891,"corporation":false,"usgs":true,"family":"Johnson","given":"Carole","email":"cjohnson@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":731280,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Williams, John H. 0000-0002-6054-6908 jhwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-6054-6908","contributorId":1553,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"jhwillia@usgs.gov","middleInitial":"H.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731281,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Curtis, Gary P. 0000-0003-3975-8882 gpcurtis@usgs.gov","orcid":"https://orcid.org/0000-0003-3975-8882","contributorId":2346,"corporation":false,"usgs":true,"family":"Curtis","given":"Gary","email":"gpcurtis@usgs.gov","middleInitial":"P.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":731282,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70196083,"text":"70196083 - 2018 - Calibration of a field-scale Soil and Water Assessment Tool (SWAT) model with field placement of best management practices in Alger Creek, Michigan","interactions":[],"lastModifiedDate":"2018-03-26T13:40:10","indexId":"70196083","displayToPublicDate":"2018-03-16T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3504,"text":"Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Calibration of a field-scale Soil and Water Assessment Tool (SWAT) model with field placement of best management practices in Alger Creek, Michigan","docAbstract":"Subwatersheds within the Great Lakes “Priority Watersheds” were targeted by the Great Lakes Restoration Initiative (GLRI) to determine the effectiveness of the various best management practices (BMPs) from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) Database. A Soil and Water Assessment Tool (SWAT) model is created for Alger Creek, a 50 km2 tributary watershed to the Saginaw River in Michigan. Monthly calibration yielded very good Nash–Sutcliffe efficiency (NSE) ratings for flow, sediment, total phosphorus (TP), dissolved reactive phosphorus (DRP), and total nitrogen (TN) (0.90, 0.79, 0.87, 0.88, and 0.77, respectively), and satisfactory NSE rating for nitrate (0.51). Two-year validation results in at least satisfactory NSE ratings for flow, sediment, TP, DRP, and TN (0.83, 0.54, 0.73, 0.53, and 0.60, respectively), and unsatisfactory NSE rating for nitrate (0.28). The model estimates the effect of BMPs at the field and watershed scales. At the field-scale, the most effective single practice at reducing sediment, TP, and DRP is no-tillage followed by cover crops (CC); CC are the most effective single practice at reducing nitrate. The most effective BMP combinations include filter strips, which can have a sizable effect on reducing sediment and phosphorus loads. At the watershed scale, model results indicate current NCP BMPs result in minimal sediment and nutrient reductions (<10%).
","language":"English","publisher":"MDPI","doi":"10.3390/su10030851","usgsCitation":"Merriman, K.R., Russell, A.M., Rachol, C.M., Daggupati, P., Srinivasan, R., Hayhurst, B.A., and Stuntebeck, T.D., 2018, Calibration of a field-scale Soil and Water Assessment Tool (SWAT) model with field placement of best management practices in Alger Creek, Michigan: Sustainability, v. 10, no. 3, p. 1-23, https://doi.org/10.3390/su10030851.","productDescription":"Article 851; 23 p.","startPage":"1","endPage":"23","ipdsId":"IP-092133","costCenters":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":468908,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/su10030851","text":"Publisher Index Page"},{"id":352617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Alger Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.9167,\n 42.8333\n ],\n [\n -83.75,\n 42.8333\n ],\n [\n -83.75,\n 42.95\n ],\n [\n -83.9167,\n 42.95\n ],\n [\n -83.9167,\n 42.8333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-16","publicationStatus":"PW","scienceBaseUri":"5afee6fce4b0da30c1bfc022","contributors":{"authors":[{"text":"Merriman, Katherine R. 0000-0002-1303-2410","orcid":"https://orcid.org/0000-0002-1303-2410","contributorId":203352,"corporation":false,"usgs":true,"family":"Merriman","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Amy M. 0000-0003-0582-0094 arussell@usgs.gov","orcid":"https://orcid.org/0000-0003-0582-0094","contributorId":200011,"corporation":false,"usgs":true,"family":"Russell","given":"Amy","email":"arussell@usgs.gov","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rachol, Cynthia M. 0000-0001-9984-3435","orcid":"https://orcid.org/0000-0001-9984-3435","contributorId":203353,"corporation":false,"usgs":true,"family":"Rachol","given":"Cynthia","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731246,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daggupati, Prasad 0000-0002-7044-3435","orcid":"https://orcid.org/0000-0002-7044-3435","contributorId":189193,"corporation":false,"usgs":false,"family":"Daggupati","given":"Prasad","email":"","affiliations":[],"preferred":false,"id":731247,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Srinivasan, Raghavan","contributorId":189191,"corporation":false,"usgs":false,"family":"Srinivasan","given":"Raghavan","affiliations":[],"preferred":false,"id":731248,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayhurst, Brett A. 0000-0002-1717-2015 bhayhurs@usgs.gov","orcid":"https://orcid.org/0000-0002-1717-2015","contributorId":3398,"corporation":false,"usgs":true,"family":"Hayhurst","given":"Brett","email":"bhayhurs@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stuntebeck, Todd D. 0000-0002-8405-7295 tdstunte@usgs.gov","orcid":"https://orcid.org/0000-0002-8405-7295","contributorId":902,"corporation":false,"usgs":true,"family":"Stuntebeck","given":"Todd","email":"tdstunte@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731249,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249425,"text":"70249425 - 2018 - Attribution analysis of the Ethiopian drought of 2015","interactions":[],"lastModifiedDate":"2023-10-06T14:09:55.757687","indexId":"70249425","displayToPublicDate":"2018-03-15T09:01:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2216,"text":"Journal of Climate","active":true,"publicationSubtype":{"id":10}},"title":"Attribution analysis of the Ethiopian drought of 2015","docAbstract":"In northern and central Ethiopia, 2015 was a very dry year. Rainfall was only from one-half to three-quarters of the usual amount, with both the “belg” (February–May) and “kiremt” rains (June–September) affected. The timing of the rains that did fall was also erratic. Many crops failed, causing food shortages for many millions of people. The role of climate change in the probability of a drought like this is investigated, focusing on the large-scale precipitation deficit in February–September 2015 in northern and central Ethiopia. Using a gridded analysis that combines station data with satellite observations, it is estimated that the return period of this drought was more than 60 years (lower bound 95% confidence interval), with a most likely value of several hundred years. No trend is detected in the observations, but the large natural variability and short time series means large trends could go undetected in the observations. Two out of three large climate model ensembles that simulated rainfall reasonably well show no trend while the third shows an increased probability of drought. Taking the model spread into account the drought still cannot be clearly attributed to anthropogenic climate change, with the 95% confidence interval ranging from a probability decrease between preindustrial and today of a factor of 0.3 and an increase of a factor of 5 for a drought like this one or worse. A soil moisture dataset also shows a nonsignificant drying trend. According to ENSO correlations in the observations, the strong 2015 El Niño did increase the severity of the drought.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JCLI-D-17-0274.1","usgsCitation":"Philip, S., Kew, S.F., van Oldenborgh, G.J., Otto, F., O’Keefe, S., Haustein, K., King, A.L., Zegeye, A., Eshetu, Z., Hailemariam, K., Singh, R., Jjemba, E., Funk, C., and Cullen, H., 2018, Attribution analysis of the Ethiopian drought of 2015: Journal of Climate, v. 31, no. 6, p. 2465-2486, https://doi.org/10.1175/JCLI-D-17-0274.1.","productDescription":"22 p.","startPage":"2465","endPage":"2486","ipdsId":"IP-091117","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468909,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://ora.ox.ac.uk/objects/uuid:f057b9f9-2a54-4a67-9c5f-492b38cdb84d","text":"External 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