One of the candidate essential biodiversity variable (EBV) groups described in the seminal paper by Pereira et al. (2014) concerns Ecosystem Structure. This EBV group is distinguished from another EBV group which encompasses aspects of Ecosystem Function. While the Ecosystem Function EBV treats ecosystem processes like nutrient cycling, primary production, trophic interactions, etc., the Ecosystem Structure EBV relates to the set of biophysical properties of ecosystems that create biophysical environmental context, confer biophysical structure, and occur geographically. The Ecosystem Extent and Fragmentation EBV is one of the EBVs in the Ecosystem Structure EBV group.
Ecosystems are understood to exist at multiple scales, from very large areas (macro-ecosystems) like the Arctic tundra, for example, to something as small as a tree in an Amazonian rain forest. As such, ecosystems occupy space and therefore can be mapped across any geography of interest, whether that area of interest be a site, a nation, a region, a continent, or the planet. One of the most obvious and seemingly straightforward EBVs is Ecosystem Extent and Fragmentation. Ecosystem extent refers to the location and geographic distribution of ecosystems across landscapes or in the oceans, while ecosystem fragmentation refers to the spatial pattern and connectivity of ecosystem occurrences on the landscape.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"A sourcebook of methods and procedures for monitoring essential biodiversity variables in tropical forests with remote sensing","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Global Observation of Forest Cover and Land Dynamics","usgsCitation":"Sayre, R., and Hansen, M., 2017, Ecosystem extent and fragmentation, 7 p.","productDescription":"7 p.","startPage":"60","endPage":"66","ipdsId":"IP-082059","costCenters":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"links":[{"id":349612,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":349611,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.gofcgold.wur.nl/sites/gofcgold-geobon_biodiversitysourcebook.php"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc3de4b06e28e9c23be9","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105 rsayre@usgs.gov","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":191629,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","email":"rsayre@usgs.gov","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":721739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Matt","contributorId":61330,"corporation":false,"usgs":true,"family":"Hansen","given":"Matt","email":"","affiliations":[],"preferred":false,"id":721740,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193676,"text":"70193676 - 2016 - Prediction of lake depth across a 17-state region in the United States","interactions":[],"lastModifiedDate":"2018-01-24T16:07:57","indexId":"70193676","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1999,"text":"Inland Waters","active":true,"publicationSubtype":{"id":10}},"title":"Prediction of lake depth across a 17-state region in the United States","docAbstract":"Lake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Zmax) observations. The model was used to predict depth for all lakes ≥4 ha (n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Zmax. Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance (R2 = 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/IW-6.3.957","usgsCitation":"Oliver, S., Soranno, P.A., Fergus, C.E., Wagner, T., Winslow, L., Scott, C.E., Webster, K.E., Downing, J., and Stanley, E.H., 2016, Prediction of lake depth across a 17-state region in the United States: Inland Waters, v. 6, no. 3, p. 314-324, https://doi.org/10.1080/IW-6.3.957.","productDescription":"11 p.","startPage":"314","endPage":"324","ipdsId":"IP-071256","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"6","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-02","publicationStatus":"PW","scienceBaseUri":"5a60fc5ae4b06e28e9c23da8","contributors":{"authors":[{"text":"Oliver, Samantha K.","contributorId":169273,"corporation":false,"usgs":false,"family":"Oliver","given":"Samantha K.","affiliations":[],"preferred":false,"id":721804,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":721805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fergus, C. Emi","contributorId":150608,"corporation":false,"usgs":false,"family":"Fergus","given":"C.","email":"","middleInitial":"Emi","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":721806,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719862,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Winslow, Luke A. lwinslow@usgs.gov","contributorId":139775,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke A.","email":"lwinslow@usgs.gov","affiliations":[],"preferred":false,"id":721807,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Caren E.","contributorId":172184,"corporation":false,"usgs":false,"family":"Scott","given":"Caren","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":721808,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Webster, Katherine E.","contributorId":147903,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":721809,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Downing, John A.","contributorId":70348,"corporation":false,"usgs":true,"family":"Downing","given":"John A.","affiliations":[],"preferred":false,"id":721810,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":721811,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70173974,"text":"ofr20161105 - 2016 - Regional water table (2014) in the Mojave River and Morongo Groundwater Basins, southwestern Mojave Desert, California","interactions":[],"lastModifiedDate":"2020-07-28T14:39:44.825439","indexId":"ofr20161105","displayToPublicDate":"2017-03-30T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1105","displayTitle":"Regional Water Table (2014) in the Mojave River and Morongo Groundwater Basins, Southwestern Mojave Desert, California","title":"Regional water table (2014) in the Mojave River and Morongo Groundwater Basins, southwestern Mojave Desert, California","docAbstract":"Data for static water-levels measured in about 610 wells during March-April 2014 by the U.S. Geological Survey (USGS), the Mojave Water Agency (MWA), and other local water districts were compiled to construct a regional water-table map. This map shows the elevation of the water table and general direction of groundwater movement in and around the Mojave River and Morongo groundwater basins. Water-level measurements recorded by the USGS and MWA staff were measured and compiled according to the procedures described in the Groundwater Technical Procedures of the U.S. Geological Survey (Cunningham and Schalk, 2011). Water-level data submitted by cooperating local water districts were collected by using procedures established by the corresponding agency, and compiled according to the procedures described in the Groundwater Technical Procedures of the U.S. Geological Survey (Cunningham and Schalk, 2011). All data were compared to historical data for quality-assurance purposes. Water-level contours from the 2012 water-level map (Teague and others, 2014) were used as a guide to interpret and shape the 2014 water-level contours in areas where 2014 water-level data were not available; these contours are shown as dashed (approximate) on the water-table map. In addition to being available on the interactive map, 2014 water-level data and contours are shown for the entire area of the Mojave River and Morongo groundwater basins on Plate 1. Water-level data for 2014 are accessible through the website by clicking the 2014 Sites button on the Data Downloads page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161105","collaboration":"Prepared in cooperation with the Mojave Water Agency","usgsCitation":"Teague, N.F., Dick, M.C., House, S.F., and Clark, D.A., 2016, Regional water table (2014) in the Mojave River and Morongo groundwater basins, southwestern Mojave Desert, California, 2016: U.S. Geological Survey Open-File Report 2016–1105 (ver. 3, July 2020), 1 sheet, scale 1:170,000, https://doi.org/10.3133/ofr20161105.","productDescription":"1 Sheet: 42.00 x 37.00 inches; Project Site; Version History","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074861","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":438469,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P848ZZ","text":"USGS data release","linkHelpText":"Regional Water Table (2014) in the Mojave River and Morongo Groundwater Basins, Southwestern Mojave Desert, California (ver. 1.2, September 2020)"},{"id":337647,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1105/cover/coverthb.jpg"},{"id":340194,"rank":2,"type":{"id":18,"text":"Project Site"},"url":"https://ca.water.usgs.gov/mojave/mojave-2014-water-levels.html","text":"Project Site"},{"id":376391,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2016/1105/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":376390,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1105/ofr20161105.pdf","text":"Sheet","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.5,\n 34.05\n ],\n [\n -117.5,\n 35.25\n ],\n [\n -116.0,\n 35.25\n ],\n [\n -116.0,\n 34.05\n ],\n [\n -117.5,\n 34.05\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted June 28, 2016; Version 2.0: March 30, 2017; Version 3.0: July 15, 2020","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
This report provides an overview of current (2016) U.S. Geological Survey policies and practices related to publishing data on ScienceBase, and an example interactive mapping application to display those data. ScienceBase is an integrated data sharing platform managed by the U.S. Geological Survey. This report describes resources that U.S. Geological Survey Scientists can use for writing data management plans, formatting data, and creating metadata, as well as for data and metadata review, uploading data and metadata to ScienceBase, and sharing metadata through the U.S. Geological Survey Science Data Catalog. Because data publishing policies and practices are evolving, scientists should consult the resources cited in this paper for definitive policy information.
An example is provided where, using the content of a published ScienceBase data release that is associated with an interpretive product, a simple user interface is constructed to demonstrate how the open source capabilities of the R programming language and environment can interact with the properties and objects of the ScienceBase item and be used to generate interactive maps.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161202","collaboration":"Prepared in cooperation with the Community for Data Integration","usgsCitation":"Chase, K.J., Bock, A.R., and Sando, Roy, 2017, Sharing our data—An overview of current (2016) USGS policies and practices for publishing data on ScienceBase and an example interactive mapping application: U.S. Geological Survey Open-File Report 2016–1202, 10 p., https://doi.org/10.3133/ofr20161202.","productDescription":"Report: v, 10 p. ","startPage":"1","endPage":"10","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-076724","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":332690,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1202/coverthb.jpg"},{"id":332691,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1202/ofr20161202.pdf","text":"Report","size":"2.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1202 Report PDF"}],"contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
http://wy-mt.water.usgs.gov/
Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea. We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1002/eap.1447","usgsCitation":"Shryock, D.F., Havrilla, C.A., DeFalco, L.A., Esque, T., Custer, N., and Wood, T.E., 2016, Landscape genetic approaches to guide native plant restoration in the Mojave Desert: Ecological Applications, v. 27, no. 2, p. 429-445, https://doi.org/10.1002/eap.1447.","productDescription":"17 p.","startPage":"429","endPage":"445","ipdsId":"IP-070517","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470320,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.1447","text":"Publisher Index Page"},{"id":332262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mojave Desert","volume":"27","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-30","publicationStatus":"PW","scienceBaseUri":"58590007e4b03639a6025e27","chorus":{"doi":"10.1002/eap.1447","url":"http://dx.doi.org/10.1002/eap.1447","publisher":"Wiley-Blackwell","authors":"Shryock Daniel F., Havrilla Caroline A., DeFalco Lesley A., Esque Todd C., Custer Nathan A., Wood Troy E.","journalName":"Ecological Applications","publicationDate":"1/30/2017","publiclyAccessibleDate":"1/30/2017"},"contributors":{"authors":[{"text":"Shryock, Daniel F. dshryock@usgs.gov","contributorId":5139,"corporation":false,"usgs":true,"family":"Shryock","given":"Daniel","email":"dshryock@usgs.gov","middleInitial":"F.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":656102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Havrilla, Caroline A. 0000-0003-3913-0980","orcid":"https://orcid.org/0000-0003-3913-0980","contributorId":146326,"corporation":false,"usgs":true,"family":"Havrilla","given":"Caroline","email":"","middleInitial":"A.","affiliations":[{"id":16669,"text":"U of CO, Boulder","active":true,"usgs":false},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":656104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeFalco, Lesley A. 0000-0002-7542-9261 ldefalco@usgs.gov","orcid":"https://orcid.org/0000-0002-7542-9261","contributorId":177536,"corporation":false,"usgs":true,"family":"DeFalco","given":"Lesley","email":"ldefalco@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":656105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esque, Todd C. 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":168763,"corporation":false,"usgs":true,"family":"Esque","given":"Todd C.","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":656103,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Custer, Nathan ncuster@usgs.gov","contributorId":5561,"corporation":false,"usgs":true,"family":"Custer","given":"Nathan","email":"ncuster@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":656106,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wood, Troy E. 0000-0002-1533-5714 twood@usgs.gov","orcid":"https://orcid.org/0000-0002-1533-5714","contributorId":4023,"corporation":false,"usgs":true,"family":"Wood","given":"Troy","email":"twood@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":656107,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70176666,"text":"sir20165134 - 2016 - Groundwater and surface-water interaction, water quality, and processes affecting loads of dissolved solids, selenium, and uranium in Fountain Creek, near Pueblo, Colorado, 2012–2014","interactions":[],"lastModifiedDate":"2026-02-23T18:19:23.800194","indexId":"sir20165134","displayToPublicDate":"2016-11-28T17:30:00","publicationYear":"2016","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":"2016-5134","displayTitle":"Groundwater and Surface-Water Interaction, Water Quality, and Processes Affecting Loads of Dissolved Solids, Selenium, and Uranium in Fountain Creek, near Pueblo, Colorado, 2012–2014","title":"Groundwater and surface-water interaction, water quality, and processes affecting loads of dissolved solids, selenium, and uranium in Fountain Creek, near Pueblo, Colorado, 2012–2014","docAbstract":"In 2012, the U.S. Geological Survey, in cooperation with the Arkansas River Basin Regional Resource Planning Group, initiated a study of groundwater and surface-water interaction, water quality, and loading of dissolved solids, selenium, and uranium to Fountain Creek near Pueblo, Colorado, to improve understanding of sources and processes affecting loading of these constituents to streams in the Arkansas River Basin. Fourteen monitoring wells were installed in a series of three transects across Fountain Creek near Pueblo, and temporary streamgages were established at each transect to facilitate data collection for the study. Groundwater and surface-water interaction was characterized by using hydrogeologic mapping, groundwater and stream-surface levels, groundwater and stream temperatures, vertical hydraulic-head gradients and ratios of oxygen and hydrogen isotopes in the hyporheic zone, and streamflow mass-balance measurements. Water quality was characterized by collecting periodic samples from groundwater, surface water, and the hyporheic zone for analysis of dissolved solids, selenium, uranium, and other selected constituents and by evaluating the oxidation-reduction condition for each groundwater sample under different hydrologic conditions throughout the study period. Groundwater loads to Fountain Creek and in-stream loads were computed for the study area, and processes affecting loads of dissolved solids, selenium, and uranium were evaluated on the basis of geology, geochemical conditions, land and water use, and evapoconcentration.
During the study period, the groundwater-flow system generally contributed flow to Fountain Creek and its hyporheic zone (as a single system) except for the reach between the north and middle transects. However, the direction of flow between the stream, the hyporheic zone, and the near-stream aquifer was variable in response to streamflow and stage. During periods of low streamflow, Fountain Creek generally gained flow from groundwater. However, during periods of high streamflow, the hydraulic gradient between groundwater and the stream temporarily reversed, causing the stream to lose flow to groundwater.
Concentrations of dissolved solids, selenium, and uranium in groundwater generally had greater spatial variability than surface water or hyporheic-zone samples, and constituent concentrations in groundwater generally were greater than in surface water. Constituent concentrations in the hyporheic zone typically were similar to or intermediate between concentrations in groundwater and surface water. Concentrations of dissolved solids, selenium, uranium, and other constituents in groundwater samples collected from wells located on the east side of the north monitoring well transect were substantially greater than for other groundwater, surface-water, and hyporheic-zone samples. With one exception, groundwater samples collected from wells on the east side of the north transect exhibited oxic to mixed (oxic-anoxic) conditions, whereas most other groundwater samples exhibited anoxic to suboxic conditions. Concentrations of dissolved solids, selenium, and uranium in surface water generally increased in a downstream direction along Fountain Creek from the north transect to the south transect and exhibited an inverse relation to streamflow with highest concentration occurring during periods of low streamflow and lowest concentrations occurring during periods of high streamflow.
Groundwater loads of dissolved solids, selenium, and uranium to Fountain Creek were small because of the small amount of groundwater flowing to the stream under typical low-streamflow conditions. In-stream loads of dissolved solids, selenium, and uranium in Fountain Creek varied by date, primarily in relation to streamflow at each transect and were much larger than computed constituent loads from groundwater. In-stream loads generally decreased with decreases in streamflow and increased as streamflow increased. In-stream loads of dissolved solids and selenium increased between the north and middle transects but generally decreased between the middle and south transects. By contrast, uranium loads generally decreased between the north and middle transects but increased between the middle and south transects. In-stream load differences between transects appear primarily to be related to differences in streamflow. However, because groundwater typically flows to Fountain Creek under low-flow conditions, and groundwater has greater concentrations of dissolved solids, selenium, and uranium than surface water in Fountain Creek, increases in loads between transects likely are affected by inflow of groundwater to the stream, which can account for a substantial proportion of the in-stream load difference between transects. When loads decreased between transects, the primary cause likely was decreased streamflow as a result of losses to groundwater and flow through the hyporheic zone. However, localized groundwater inflow likely attenuated the magnitude by which the in-stream loads decreased.
The combination of localized soluble geologic sources and oxic conditions likely is the primary reason for the occurrence of high concentrations of dissolved solids, selenium, and uranium in groundwater on the east side of the north monitoring well transect. To evaluate conditions potentially responsible for differences in water quality and redox conditions, physical characteristics such as depth to water, saturated thickness, screen depth below the water table, screen height above bedrock, and aquifer hydraulic conductivity were compared by using Wilcoxon rank-sum tests. Results indicated no significant difference between depth to water, screen height above bedrock, and hydraulic conductivity for groundwater samples collected from wells on the east side of the north transect and groundwater samples from all other wells. However, saturated thickness and screen depth below the water table both were significantly smaller for groundwater samples collected from wells on the east side of the north transect than for groundwater samples from other wells, indicating that these characteristics might be related to the elevated constituent concentrations found at that location. Similarly, saturated thickness and screen depth below the water table were significantly smaller for groundwater samples under oxic or mixed (oxic-anoxic) conditions than for those under anoxic to suboxic conditions.
The greater constituent concentrations at wells on the east side of the north transect also could, in part, be related to groundwater discharge from an unnamed alluvial drainage located directly upgradient from that location. Although the quantity and quality of water discharging from the drainage is not known, the drainage appears to collect water from a residential area located upgradient to the east of the wells, and groundwater could become concentrated in nitrate and other dissolved constituents before flowing through the drainage. High levels of nitrate, whether from anthropogenic or natural geologic sources, could promote more soluble forms of selenium and other constituents by affecting the redox condition of groundwater. Whether oxic conditions at wells on the east side of the north transect are the result of physical characteristics or of groundwater inflow from the alluvial drainage, the oxic conditions appear to cause increased dissolution of minerals from the shallow shale bedrock at that location. Because ratios of hydrogen and oxygen isotopes indicate evaporation likely has not had a substantial effect on groundwater, constituent concentrations at that location likely are not the result of evapoconcentration.
Director, USGS Colorado Water Science Center
Box 25046, Mail Stop 415
Denver, CO 80225
This book encapsulates and extends many seminal ideas presented at the eighth “Attending to Early Modern Women” conference held at the University of Wisconsin–Milwaukee in June 2012. Merry Wiesner-Hanks has done a masterful job editing these papers within a central theme of the interaction of spatial domains with gender-based phenomena. The fifteen chapters of this book are organized into four sections: “Framework,” discussing theoretical concepts; “Embodied Environments,” focusing on physicality; “Communities and Networks” of social patterns; and “Exchanges” across geographic space. Together, a global society shaped by gender and sexuality and intersected by race and class emerges.
","language":"English","publisher":"Renaissance Society of America","publisherLocation":"New York, NY","doi":"10.1086/689092","usgsCitation":"Varanka, D.E., 2016, Book review: Mapping gendered routes and spaces in the early modern world: Renaissance Quarterly, v. 69, no. 3, https://doi.org/10.1086/689092.","productDescription":"1 p.","startPage":"1092","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069367","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":339002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"69","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-20","publicationStatus":"PW","scienceBaseUri":"58e35f7fe4b09da67997eca9","contributors":{"authors":[{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":588401,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70177878,"text":"70177878 - 2016 - Dynamic distributions and population declines of Golden-winged Warblers","interactions":[],"lastModifiedDate":"2020-08-25T17:09:33.774891","indexId":"70177878","displayToPublicDate":"2016-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":5103,"text":"Studies in Avian Biology","printIssn":"0197-9922","active":true,"publicationSubtype":{"id":24}},"chapter":"1","title":"Dynamic distributions and population declines of Golden-winged Warblers","docAbstract":"With an estimated breeding population in 2010 of 383,000 pairs, the Golden-winged Warbler (Vermivora chrysoptera) is among the most vulnerable and steeply declining of North American passerines. This species also has exhibited among the most dynamic breeding distributions, with populations expanding and then contracting over the past 150 years in response to regional habitat changes, interactions with closely related Blue-winged Warblers (V. cyanoptera), and possibly climate change. Since 1966, the rangewide population has declined by >70% (-2.3% per year; latest North American Breeding Bird Survey data), with much steeper declines in the Appalachian Mountains bird conservation region (-8.3% per year, 98% overall decline). Despite apparently stable or increasing populations in the northwestern part of the range (Minnesota, Manitoba), population estimates for Golden-winged Warbler have continued to decline by 18% from the decade of the 1990s to the 2000s. Population modeling predicts a further decline to roughly 37,000 individuals by 2100, with the species likely to persist only in Manitoba, Minnesota, and possibly Ontario. To delineate the present-day distribution and to identify population concentrations that could serve as conservation focus areas, we compiled rangewide survey data collected in 2000-2006 in 21 states and 3 Canadian provinces, as part of the Golden-winged Warbler Atlas Project (GOWAP), supplemented by state and provincial Breeding Bird Atlas data and more recent observations in eBird. Based on >8,000 GOWAP surveys for Golden-winged and Blue-winged warblers and their hybrids, we mapped occurrence of phenotypically pure and mixed populations in a roughly 0.5-degree grid across the species’ ranges. Hybrids and mixed Golden-winged-Blue-winged populations occurred in a relatively narrow zone across Minnesota, Wisconsin, Michigan, southern Ontario, and northern New York. Phenotypically pure Golden-winged Warbler populations occurred north of this hybrid zone, but the future of northern populations in the Great Lakes states and Canada (where >80% of the species occurs at present) is highly uncertain because of continued northward expansion of Blue-winged Warblers and hybridization. A second, now-disjunct band of Golden-winged Warbler populations exists in the Appalachian Mountains from southeastern New York to northern Georgia, surrounded at lower elevations by Blue-winged Warblers. Important concentrations of Golden-winged Warblers persist in the Allegheny Mountains region of West Virginia, the Cumberland Mountains in Tennessee, Blue Ridge Mountains of western North Carolina, Allegheny Plateau and Pocono Mountains in Pennsylvania, and in the Hudson Highlands of southern New York. These high-elevation Appalachian populations have escaped contact with Blue-winged Warblers until very recently and represent important refugia for conservation and management; other Appalachian populations are rapidly declining. In addition, based on historical records and standardized surveys across the wintering grounds, we identified three regions of concentration: highlands and Caribbean slopes from Guatemala and Belize to northwestern Nicaragua; middle elevations (both slopes) in Costa Rica and western Panama; and in an arc of the northern Andes from central Colombia to northern Venezuela. It is possible that the winter range has been shifting towards the northwest in recent decades, paralleling shifts in the breeding distribution. Future conservation efforts for Golden-winged Warbler need to include close monitoring of the dynamic phenotypic and genetic distributional shifts, and may need to consider the “winged warbler” complex together as a highly adaptable evolutionary unit.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Golden-winged Warbler ecology, conservation, and habitat management (Studies in Avian Biology, volume 49)","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","publisherLocation":"Boca Raton, FL","isbn":"978-1-4822-4068-9","usgsCitation":"Rosenberg, K.V., Will, T., Buehler, D.A., Barker Swarthout, S., Thogmartin, W.E., Bennett, R.E., and Chandler, R., 2016, Dynamic distributions and population declines of Golden-winged Warblers, chap. 1 of Golden-winged Warbler ecology, conservation, and habitat management (Studies in Avian Biology, volume 49): Studies in Avian Biology, v. 49, p. 3-28.","productDescription":"26 p.","startPage":"3","endPage":"28","ipdsId":"IP-059605","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":330436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330361,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/11299/189700"}],"volume":"49","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f2e4b0f497e79a5a6b","contributors":{"authors":[{"text":"Rosenberg, Kenneth V.","contributorId":171463,"corporation":false,"usgs":false,"family":"Rosenberg","given":"Kenneth","email":"","middleInitial":"V.","affiliations":[{"id":27615,"text":"Cornell Lab of Ornithology, Conservation Science Program","active":true,"usgs":false}],"preferred":false,"id":651970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Will, Tom","contributorId":149777,"corporation":false,"usgs":false,"family":"Will","given":"Tom","email":"","affiliations":[{"id":17821,"text":"U.S. Fish and Wildlife Service, Division of Migratory Birds","active":true,"usgs":false}],"preferred":false,"id":651971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buehler, David A.","contributorId":169746,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":651972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barker Swarthout, Sara","contributorId":176239,"corporation":false,"usgs":false,"family":"Barker Swarthout","given":"Sara","email":"","affiliations":[{"id":34544,"text":"Cornell Lab of Ornithology, Cornell University","active":true,"usgs":false}],"preferred":false,"id":651973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":651969,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bennett, Ruth E.","contributorId":94622,"corporation":false,"usgs":false,"family":"Bennett","given":"Ruth","email":"","middleInitial":"E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":709867,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chandler, Richard rchandler@usgs.gov","contributorId":2511,"corporation":false,"usgs":true,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","affiliations":[{"id":13266,"text":"Warnell School of Forestry and Natural Resources, The University of Georgia","active":true,"usgs":false}],"preferred":false,"id":709868,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70176040,"text":"ofr20161141 - 2016 - U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2015 annual report","interactions":[],"lastModifiedDate":"2018-09-21T11:27:15","indexId":"ofr20161141","displayToPublicDate":"2016-09-28T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1141","title":"U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2015 annual report","docAbstract":"This is the eighth annual report highlighting U.S. Geological Survey (USGS) science and decision-support activities conducted for the Wyoming Landscape Conservation Initiative (WLCI). The activities address specific management needs identified by WLCI partner agencies. In 2015, USGS scientists continued 24 WLCI projects in 5 categories: (1) acquiring and analyzing resource-condition data to form a foundation for understanding and monitoring landscape conditions and projecting changes; (2) using new technologies to improve the scope and accuracy of landscape-scale monitoring and assessments, and applying them to monitor indicators of ecosystem conditions and the effectiveness of on-the-ground habitat projects; (3) conducting research to elucidate the mechanisms that drive wildlife and habitat responses to changing land uses; (4) managing and making accessible the large number of databases, maps, and other products being developed; and (5) coordinating efforts among WLCI partners, helping them to use USGS-developed decision-support tools, and integrating WLCI outcomes with future habitat enhancement and research projects. Of the 24 projects, 21 were ongoing, including those that entered new phases or more in-depth lines of inquiry, 2 were new, and 1 was completed.
A highlight of 2015 was the WLCI science conference sponsored by the USGS, Bureau of Land Management, and National Park Service in coordination with the Wyoming chapter of The Wildlife Society. Of 260 participants, 41 were USGS professionals representing 13 USGS science centers, field offices, and Cooperative Wildlife Research Units. Major themes of USGS presentations included using new technologies for developing more efficient research protocols for modeling and monitoring natural resources, researching effects of energy development and other land uses on wildlife species and habitats of concern, and modeling species distributions, population trends, habitat use, and effects of land-use changes. There was also a special session on the effectiveness of Wyoming’s Sage-Grouse Executive Order. Combined, USGS presentations provided WLCI partners with a wealth of information and conservation tools.
The project completed in 2015 yielded an index of important agricultural lands in the WLCI region. The index improves upon existing measures of agricultural productivity and provides planners and managers with additional values to consider when making decisions about land use and conservation actions. The two new projects include an analysis of satellite imagery to quantify sagebrush productivity and mortality, and an evaluation of how groundwater and small streams interact in the upper Green River Basin. Initiated in response to concern among WLCI partners that large areas of sagebrush appear to have died recently, the sagebrush study objectives are to assess effects of these mortality events on overall sagebrush ecosystem productivity, evaluate the feasibility of using satellite imagery to detect patterns in sagebrush mortality over time, and identify factors driving these mortality events. The groundwater-streamflow interaction study is being conducted by hydrologists and fish ecologists to better understand how groundwater-streamflow interactions are affected by energy-resource development and how native fish communities are affected by these factors. Expected outcomes of both new projects will provide WLCI partners with additional information and decision-support tools.
Highlights of ongoing science foundation activities included simulations of nine alternative build-out scenarios for oil and gas development and an associated online fact sheet that explains how the simulations were conducted, with an applied example for the Atlantic Rim. Also completed in 2015 was an update of the USGS online inventory of mineral resources data, and publication of a USGS uranium resource survey for the WLCI region. Combined, the outcomes of this work provide decisionmakers and managers with important baseline information for existing and (or) future planning and monitoring efforts.
Terrestrial monitoring activities in 2015 emphasized the use of satellite data in combination with other technologies and field data to monitor, assess, and (or) forecast distribution patterns and (or) trends in sagebrush ecosystems, seasonal and migration stopover habitats used by mule deer and elk, and semi-arid aspen woodlands. Several professional papers detailing new monitoring models and results have been published. Combined, this and related work will help managers understand distribution patterns and trends among priority habitats, identify areas in need of restoration or conservation, and monitor the effectiveness of habitat-management actions.
Aquatic monitoring activities entailed not only the new groundwater-streamflow interaction study already mentioned, but also continued monitoring with streamgages paired with nearby wells in the Green River Basin to assess groundwater effects on streamflow and surface water temperatures. A map that portrays groundwater levels and general direction of flow in the Green River Basin was published as well. Overall, outcomes of USGS hydrological research and monitoring will inform WLCI partners about water resources in the WLCI region and help to explain fish-community responses to energy-resource development.
In 2015, USGS terrestrial wildlife ecologists continued to make crucial strides towards better understanding wildlife species responses to energy-resource development and other land-use changes. This body of research includes six taxa that require or heavily depend on sagebrush habitats: sage-grouse, pygmy rabbits, 3 songbird species, and mule deer. Native fish communities are also being evaluated. Approaches include modeling and mapping wildlife species distributions, abundances, and trends; using satellite and other technologies to track wildlife seasonal movements; conducting successive phases of research that build on the knowledge gained through prior phases to reveal the specific factors or thresholds that drive population- or individual-level responses to changes; and conducting population viability analyses. Additionally, wildlife habitat association models for pygmy rabbit and sage-grouse were combined with the oil and gas build-out scenarios to project species responses to alternative energy development scenarios. Outcomes of the wildlife response research are helping decisionmakers and managers identify specific factors that contribute to species population trends, the potential for spatial overlap between important wildlife habitats and proposed energy-resource development, locations of priority habitats for restoration and conservation, and more.
Data and WLCI Web site management highlights of 2015 included not only ongoing software upgrades, but also an update of the datasets displayed in two of the online products developed for the WLCI effort: (1) a map of 15,532 oil and natural gas well pad scars and other features associated with oil and gas extraction, and (2) a map of oil and gas, oil shale, uranium, and solar energy production, both for southwestern Wyoming. In addition, a map viewer was developed for a previously published map of coal and wind production in relation to sage-grouse distribution and core management areas in southwestern Wyoming. Combined, these maps place valuable decision-support tools in the hands of WLCI partners.
The USGS coordination efforts on behalf of the WLCI in 2015 included significant work on planning and executing the WLCI science conference. They also included ongoing efforts to support Local Project Development Teams and the WLCI Coordination Team (CT) with developing conservation priorities and strategies, identifying priority areas for future conservation actions, supporting the evaluation and ranking of conservation projects, and evaluating the ways in which proposed habitat projects relate to WLCI priorities. In 2015, the USGS also assisted the WLCI CT with updating the WLCI Conservation Action Plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161141","usgsCitation":"Bowen, Z.H., Aldridge, C.L., Anderson, P.J., Assal, T.J., Bartos, T.T., Chalfoun, A.D., Chong, G.W., Dematatis, M.K., Eddy-Miller, C.A., Garman, S.L., Germaine, S.S., Homer, C.G., Huber, C.C., Kauffman, M.J., Manier, D.J., Melcher, C.P., Miller, K.A., Norkin, Tamar, Sanders, L.E., Walters, A.W., Wilson, A.B., and Wyckoff, T.B., 2016, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2015 annual report: U.S. Geological Survey Open-File Report 2016–1141, 59 p., https://dx.doi.org/10.3133/ofr20161141.","productDescription":"viii, 59 p.","onlineOnly":"Y","ipdsId":"IP-075182","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":329020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1141/coverthb.jpg"},{"id":329021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1141/ofr20161141.pdf","text":"Report","size":"36.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1141"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.07177734375,\n 43.40504748787035\n ],\n [\n -111.0498046875,\n 41\n ],\n [\n -105.99609375,\n 41\n ],\n [\n -105.99609375,\n 41.80407814427234\n ],\n [\n -105.35888671875,\n 41.82045509614034\n ],\n [\n -105.380859375,\n 42.47209690919285\n ],\n [\n -106.5673828125,\n 42.52069952914966\n ],\n [\n -107.20458984375,\n 42.48830197960227\n ],\n [\n -107.70996093749999,\n 42.53689200787315\n ],\n [\n -108.5009765625,\n 42.779275360241904\n ],\n [\n -108.80859375,\n 42.98857645832184\n ],\n [\n -109.22607421875,\n 43.229195113965005\n ],\n [\n -109.3798828125,\n 43.42100882994726\n ],\n [\n -109.79736328125,\n 43.5326204268101\n ],\n [\n -110.41259765625,\n 43.56447158721811\n ],\n [\n -111.07177734375,\n 43.40504748787035\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
An investigation into the magnitude and frequency of floods in Washington State computed the annual exceedance probability (AEP) statistics for 648 U.S. Geological Survey unregulated streamgages in and near the borders of Washington using the recorded annual peak flows through water year 2014. This is an updated report from a previous report published in 1998 that used annual peak flows through the water year 1996. New in this report, a regional skew coefficient was developed for the Pacific Northwest region that includes areas in Oregon, Washington, Idaho and western Montana within the Columbia River drainage basin south of the United States-Canada border, the coastal areas of Oregon and western Washington, and watersheds draining into Puget Sound, Washington. The skew coefficient is an important term in the Log Pearson Type III equation used to define the distribution of the log-transformed annual peaks. The Expected Moments Algorithm was used to fit historical and censored peak-flow data to the log Pearson Type III distribution. A Multiple Grubb-Beck test was employed to censor low outliers of annual peak flows to improve on the frequency distribution. This investigation also includes a section on observed trends in annual peak flows that showed significant trends (p-value < 0.05) in 21 of 83 long-term sites, but with small magnitude Kendall tau values suggesting a limited monotonic trend in the time series of annual peaks. Most of the sites with a significant trend in western Washington were positive and all the sites with significant trends (three sites) in eastern Washington were negative.
Multivariate regression analysis with measured basin characteristics and the AEP statistics at long-term, unregulated, and un-urbanized (defined as drainage basins with less than 5 percent impervious land cover for this investigation) streamgages within Washington and some in Idaho and Oregon that are near the Washington border was used to develop equations to estimate AEP statistics at ungaged basins. Washington was divided into four regions to improve the accuracy of the regression equations; a set of equations for eight selected AEPs and for each region were constructed. Selected AEP statistics included the annual peak flows that equaled or exceeded 50, 20, 10, 4, 2, 1, 0.5 and 0.2 percent of the time equivalent to peak flows for peaks with a 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively. Annual precipitation and drainage area were the significant basin characteristics in the regression equations for all four regression regions in Washington and forest cover was significant for the two regression regions in eastern Washington. Average standard error of prediction for the regional regression equations ranged from 70.19 to 125.72 percent for Regression Regions 1 and 2 on the eastern side of the Cascade Mountains and from 43.22 to 58.04 percent for Regression Regions 3 and 4 on the western side of the Cascade Mountains. The pseudo coefficient of determination (where a value of 100 signifies a perfect regression model) ranged from 68.39 to 90.68 for Regression Regions 1 and 2, and 92.35 to 95.44 for Regions 3 and 4.
The calculated AEP statistics for the streamgages and the regional regression equations are expected to be incorporated into StreamStats after the publication of this report. StreamStats is the interactive Web-based map tool created by the U.S. Geological Survey to allow the user to choose a streamgage and obtain published statistics or choose ungaged locations where the program automatically applies the regional regression equations and computes the estimates of the AEP statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165118","collaboration":"Prepared in cooperation with the Washington State Department of Transportation and the Washington State Department of Ecology","usgsCitation":"Mastin, M.C., Konrad, C.P., Veilleux, A.G., and Tecca, A.E., 2016, Magnitude, frequency, and trends of floods at gaged and ungaged sites in Washington, based on data through water year 2014 (ver 1.2, November 2017): U.S. Geological Survey Scientific Investigations Report 2016–5118, 70 p., https://dx.doi.org/10.3133/sir20165118.","productDescription":"Report: vi, 69 p.; 3 Tables","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075256","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":329272,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5118/versionHist.txt","size":"3 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\"}}]}","edition":"Version 1.0: Originally post September 20, 2016; Version 1.1: October 4, 2016; Version 1.2: November 2017","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The removal of dams has recently increased over historical levels due to aging infrastructure, changing societal needs, and modern safety standards rendering some dams obsolete. Where possibilities for river restoration, or improved safety, exceed the benefits of retaining a dam, removal is more often being considered as a viable option. Yet, as this is a relatively new development in the history of river management, science is just beginning to guide our understanding of the physical and ecological implications of dam removal. Ultimately, the “lessons learned” from previous scientific studies on the outcomes dam removal could inform future scientific understanding of ecosystem outcomes, as well as aid in decision-making by stakeholders. We created a database visualization tool, the Dam Removal Information Portal (DRIP), to display map-based, interactive information about the scientific studies associated with dam removals. Serving both as a bibliographic source as well as a link to other existing databases like the National Hydrography Dataset, the derived National Dam Removal Science Database serves as the foundation for a Web-based application that synthesizes the existing scientific studies associated with dam removals. Thus, using the DRIP application, users can explore information about completed dam removal projects (for example, their location, height, and date removed), as well as discover sources and details of associated of scientific studies. As such, DRIP is intended to be a dynamic collection of scientific information related to dams that have been removed in the United States and elsewhere. This report describes the architecture and concepts of this “metaknowledge” database and the DRIP visualization tool.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161132","usgsCitation":"Duda, J.J., Wieferich, D.J., Bristol, R.S., Bellmore, J.R., Hutchison, V.B., Vittum, K.M., Craig, Laura, and Warrick, J.A., 2016, Dam Removal Information Portal (DRIP)—A map-based resource linking scientific studies and associated geospatial information about dam removals: U.S. Geological Survey Open-File Report 2016-1132, 14 p., https://dx.doi.org/10.3133/ofr20161132.","productDescription":"iv, 14 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-076705","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":326867,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1132/coverthb.jpg"},{"id":326868,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1132/ofr20161132.pdf","text":"Report","size":"441 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1132"}],"contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
Climate change coupled with an intensifying wildfire regime is becoming an important driver of permafrost loss and ecosystem change in the northern boreal forest. There is a growing need to understand the effects of fire on the spatial distribution of permafrost and its associated ecological consequences. We focus on the effects of fire a decade after disturbance in a rocky upland landscape in the interior Alaskan boreal forest. Our main objectives were to (1) map near-surface permafrost distribution and drainage classes and (2) analyze the controls over landscape-scale patterns of post-fire permafrost degradation. Relationships among remote sensing variables and field-based data on soil properties (temperature, moisture, organic layer thickness) and vegetation (plant community composition) were analyzed using correlation, regression, and ordination analyses. The remote sensing data we considered included spectral indices from optical datasets (Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI)), the principal components of a time series of radar backscatter (Advanced Land Observing Satellite—Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR)), and topographic variables from a Light Detection and Ranging (LiDAR)-derived digital elevation model (DEM). We found strong empirical relationships between the normalized difference infrared index (NDII) and post-fire vegetation, soil moisture, and soil temperature, enabling us to indirectly map permafrost status and drainage class using regression-based models. The thickness of the insulating surface organic layer after fire, a measure of burn severity, was an important control over the extent of permafrost degradation. According to our classifications, 90% of the area considered to have experienced high severity burn (using the difference normalized burn ratio (dNBR)) lacked permafrost after fire. Permafrost thaw, in turn, likely increased drainage and resulted in drier surface soils. Burn severity also influenced plant community composition, which was tightly linked to soil temperature and moisture. Overall, interactions between burn severity, topography, and vegetation appear to control the distribution of near-surface permafrost and associated drainage conditions after disturbance.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8080654","usgsCitation":"Brown, D.R., Jorgenson, M., Kielland, K., Verbyla, D.L., Prakash, A., and Koch, J.C., 2016, Landscape effects of wildfire on permafrost distribution in interior Alaska derived from remote sensing: Remote Sensing, v. 8, no. 8, p. 1-22, https://doi.org/10.3390/rs8080654.","productDescription":"Article 654; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-077121","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":470694,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8080654","text":"Publisher Index Page"},{"id":337471,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"8","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-12","publicationStatus":"PW","scienceBaseUri":"58c7afa1e4b0849ce9795ea2","contributors":{"authors":[{"text":"Brown, Dana R. N.","contributorId":140386,"corporation":false,"usgs":false,"family":"Brown","given":"Dana","email":"","middleInitial":"R. N.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":684126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, M. Torre","contributorId":127675,"corporation":false,"usgs":false,"family":"Jorgenson","given":"M. Torre","affiliations":[],"preferred":false,"id":684127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kielland, Knut","contributorId":189214,"corporation":false,"usgs":false,"family":"Kielland","given":"Knut","email":"","affiliations":[],"preferred":false,"id":684128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verbyla, David L.","contributorId":84611,"corporation":false,"usgs":true,"family":"Verbyla","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":684129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prakash, Anupma","contributorId":189216,"corporation":false,"usgs":false,"family":"Prakash","given":"Anupma","email":"","affiliations":[{"id":13662,"text":"Geophysical Institute, University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":684130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":684125,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174072,"text":"70174072 - 2016 - The Maryland Coastal Plain Aquifer Information System: A GIS-based tool for assessing groundwater resources","interactions":[],"lastModifiedDate":"2016-06-27T14:56:51","indexId":"70174072","displayToPublicDate":"2016-06-27T13:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3459,"text":"Special Paper of the Geological Society of America","active":true,"publicationSubtype":{"id":10}},"title":"The Maryland Coastal Plain Aquifer Information System: A GIS-based tool for assessing groundwater resources","docAbstract":"Groundwater is the source of drinking water for ∼1.4 million people in the Coastal Plain Province of Maryland (USA). In addition, groundwater is essential for commercial, industrial, and agricultural uses. Approximately 0.757 × 109 L d–1 (200 million gallons/d) were withdrawn in 2010. As a result of decades of withdrawals from the coastal plain confined aquifers, groundwater levels have declined by as much as 70 m (230 ft) from estimated prepumping levels. Other issues posing challenges to long-term groundwater sustainability include degraded water quality from both man-made and natural sources, reduced stream base flow, land subsidence, and changing recharge patterns (drought) caused by climate change. In Maryland, groundwater supply is managed primarily by the Maryland Department of the Environment, which seeks to balance reasonable use of the resource with long-term sustainability. The chief goal of groundwater management in Maryland is to ensure safe and adequate supplies for all current and future users through the implementation of appropriate usage, planning, and conservation policies. To assist in that effort, the geographic information system (GIS)–based Maryland Coastal Plain Aquifer Information System was developed as a tool to help water managers access and visualize groundwater data for use in the evaluation of groundwater allocation and use permits. The system, contained within an ESRI ArcMap desktop environment, includes both interpreted and basic data for 16 aquifers and 14 confining units. Data map layers include aquifer and confining unit layer surfaces, aquifer extents, borehole information, hydraulic properties, time-series groundwater-level data, well records, and geophysical and lithologic logs. The aquifer and confining unit layer surfaces were generated specifically for the GIS system. The system also contains select groundwater-quality data and map layers that quantify groundwater and surface-water withdrawals. The aquifer information system can serve as a pre- and postprocessing environment for groundwater-flow models for use in water-supply planning, development, and management. The system also can be expanded to include features that evaluate constraints to groundwater development, such as insufficient available drawdown, degraded groundwater quality, insufficient aquifer yields, and well-field interference. Ultimately, the aquifer information system is intended to function as an interactive Web-based utility that provides a broad array of information related to groundwater resources in Maryland’s coastal plain to a wide-ranging audience, including well drillers, consultants, academia, and the general public.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2016.2520(15)","usgsCitation":"Andreasen, D., Nardi, M.R., Staley, A., Achmad, G., and Grace, J.W., 2016, The Maryland Coastal Plain Aquifer Information System: A GIS-based tool for assessing groundwater resources: Special Paper of the Geological Society of America, v. 520, p. 159-170, https://doi.org/10.1130/2016.2520(15).","productDescription":"12 p.","startPage":"159","endPage":"170","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068540","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":324417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"520","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57724023e4b07657d1a793b0","contributors":{"authors":[{"text":"Andreasen, David C.","contributorId":59003,"corporation":false,"usgs":true,"family":"Andreasen","given":"David C.","affiliations":[],"preferred":false,"id":640806,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nardi, Mark R. 0000-0002-7310-8050 mrnardi@usgs.gov","orcid":"https://orcid.org/0000-0002-7310-8050","contributorId":1859,"corporation":false,"usgs":true,"family":"Nardi","given":"Mark","email":"mrnardi@usgs.gov","middleInitial":"R.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":640805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staley, Andrew W.","contributorId":43319,"corporation":false,"usgs":true,"family":"Staley","given":"Andrew W.","affiliations":[],"preferred":false,"id":640807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Achmad, Grufron","contributorId":172464,"corporation":false,"usgs":false,"family":"Achmad","given":"Grufron","email":"","affiliations":[{"id":25435,"text":"Maryland Geological Survey","active":true,"usgs":false}],"preferred":false,"id":640808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grace, John W.","contributorId":172465,"corporation":false,"usgs":false,"family":"Grace","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":27050,"text":"Maryland Department of the Environment","active":true,"usgs":false}],"preferred":false,"id":640809,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70173702,"text":"70173702 - 2016 - Seeing the forest through the trees: Considering roost-site selection at multiple spatial scales","interactions":[],"lastModifiedDate":"2016-06-07T12:52:20","indexId":"70173702","displayToPublicDate":"2016-06-07T13:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Seeing the forest through the trees: Considering roost-site selection at multiple spatial scales","docAbstract":"Conservation of bat species is one of the most daunting wildlife conservation challenges in North America, requiring detailed knowledge about their ecology to guide conservation efforts. Outside of the hibernating season, bats in temperate forest environments spend their diurnal time in day-roosts. In addition to simple shelter, summer roost availability is as critical as maternity sites and maintaining social group contact. To date, a major focus of bat conservation has concentrated on conserving individual roost sites, with comparatively less focus on the role that broader habitat conditions contribute towards roost-site selection. We evaluated roost-site selection by a northern population of federally-endangered Indiana bats (Myotis sodalis) at Fort Drum Military Installation in New York, USA at three different spatial scales: landscape, forest stand, and individual tree level. During 2007–2011, we radiotracked 33 Indiana bats (10 males, 23 females) and located 348 roosting events in 116 unique roost trees. At the landscape scale, bat roost-site selection was positively associated with northern mixed forest, increased slope, and greater distance from human development. At the stand scale, we observed subtle differences in roost site selection based on sex and season, but roost selection was generally positively associated with larger stands with a higher basal area, larger tree diameter, and a greater sugar maple (Acer saccharum) component. We observed no distinct trends of roosts being near high-quality foraging areas of water and forest edges. At the tree scale, roosts were typically in American elm (Ulmus americana) or sugar maple of large diameter (>30 cm) of moderate decay with loose bark. Collectively, our results highlight the importance of considering day roost needs simultaneously across multiple spatial scales. Size and decay class of individual roosts are key ecological attributes for the Indiana bat, however, larger-scale stand structural components that are products of past and current land use interacting with environmental aspects such as landform also are important factors influencing roost-tree selection patterns.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0150011","usgsCitation":"Jachowski, D.S., Rota, C., Dobony, C.A., Ford, W.M., and Edwards, J.W., 2016, Seeing the forest through the trees: Considering roost-site selection at multiple spatial scales: PLoS ONE, v. 11, no. 3, e0150011; 19 p., https://doi.org/10.1371/journal.pone.0150011.","productDescription":"e0150011; 19 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064338","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470905,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0150011","text":"Publisher Index Page"},{"id":323107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-30","publicationStatus":"PW","scienceBaseUri":"5757e220e4b04f417c2426b0","contributors":{"authors":[{"text":"Jachowski, David S.","contributorId":82966,"corporation":false,"usgs":true,"family":"Jachowski","given":"David","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":637531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rota, Christopher T.","contributorId":92547,"corporation":false,"usgs":true,"family":"Rota","given":"Christopher T.","affiliations":[],"preferred":false,"id":637532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dobony, Christopher A.","contributorId":171455,"corporation":false,"usgs":false,"family":"Dobony","given":"Christopher","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":637533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark wford@usgs.gov","contributorId":169828,"corporation":false,"usgs":false,"family":"Ford","given":"W.","email":"wford@usgs.gov","middleInitial":"Mark","affiliations":[],"preferred":false,"id":637528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edwards, John W.","contributorId":169827,"corporation":false,"usgs":false,"family":"Edwards","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":637534,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182809,"text":"70182809 - 2016 - Extending Alaska's plate boundary: tectonic tremor generated by Yakutat subduction","interactions":[],"lastModifiedDate":"2017-03-01T14:05:34","indexId":"70182809","displayToPublicDate":"2016-06-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Extending Alaska's plate boundary: tectonic tremor generated by Yakutat subduction","docAbstract":"The tectonics of the eastern end of the Alaska-Aleutian subduction zone are complicated by the inclusion of the Yakutat microplate, which is colliding into and subducting beneath continental North America at near-Pacific-plate rates. The interaction among these plates at depth is not well understood, and further east, even less is known about the plate boundary or the source of Wrangell volcanism. The drop-off in Wadati-Benioff zone (WBZ) seismicity could signal the end of the plate boundary, the start of aseismic subduction, or a tear in the downgoing plate. Further compounding the issue is the possible presence of the Wrangell slab, which is faintly outlined by an anemic, eastward-dipping WBZ beneath the Wrangell volcanoes. In this study, I performed a search for tectonic tremor to map slow, plate-boundary slip in south-central Alaska. I identified ∼11,000 tremor epicenters, which continue 85 km east of the inferred Pacific plate edge marked by WBZ seismicity. The tremor zone coincides with the edges of the downgoing Yakutat terrane, and tremors transition from periodic to continuous behavior as they near the aseismic Wrangell slab. I interpret tremor to mark slow, semicontinuous slip occurring at the interface between the Yakutat and North America plates. The slow slip region lengthens the megathrust interface beyond the WBZ and may provide evidence for a connection between the Yakutat slab and the aseismic Wrangell slab.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G37817.1","usgsCitation":"Wech, A.G., 2016, Extending Alaska's plate boundary: tectonic tremor generated by Yakutat subduction: Geology, v. 44, no. 7, p. 587-590, https://doi.org/10.1130/G37817.1.","productDescription":"4 p.","startPage":"587","endPage":"590","ipdsId":"IP-074024","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":336769,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-01","publicationStatus":"PW","scienceBaseUri":"58b7eba8e4b01ccd5500bb21","contributors":{"authors":[{"text":"Wech, Aaron G. 0000-0003-4983-1991 awech@usgs.gov","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":5344,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","email":"awech@usgs.gov","middleInitial":"G.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":673843,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70171326,"text":"70171326 - 2016 - Presence of rapidly degrading permafrost plateaus in south-central Alaska","interactions":[],"lastModifiedDate":"2018-03-26T14:39:07","indexId":"70171326","displayToPublicDate":"2016-05-23T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Presence of rapidly degrading permafrost plateaus in south-central Alaska","docAbstract":"Permafrost presence is determined by a complex interaction of climatic, topographic, and ecological conditions operating over long time scales. In particular, vegetation and organic layer characteristics may act to protect permafrost in regions with a mean annual air temperature (MAAT) above 0 °C. In this study, we document the presence of residual permafrost plateaus in the western Kenai Peninsula lowlands of south-central Alaska, a region with a MAAT of 1.5 ± 1 °C (1981–2010). Continuous ground temperature measurements between 16 September 2012 and 15 September 2015, using calibrated thermistor strings, documented the presence of warm permafrost (−0.04 to −0.08 °C). Field measurements (probing) on several plateau features during the fall of 2015 showed that the depth to the permafrost table averaged 1.48 m but at some locations was as shallow as 0.53 m. Late winter surveys (augering, coring, and GPR) in 2016 showed that the average seasonally frozen ground thickness was 0.45 m, overlying a talik above the permafrost table. Measured permafrost thickness ranged from 0.33 to > 6.90 m. Manual interpretation of historic aerial photography acquired in 1950 indicates that residual permafrost plateaus covered 920 ha as mapped across portions of four wetland complexes encompassing 4810 ha. However, between 1950 and ca. 2010, permafrost plateau extent decreased by 60.0 %, with lateral feature degradation accounting for 85.0 % of the reduction in area. Permafrost loss on the Kenai Peninsula is likely associated with a warming climate, wildfires that remove the protective forest and organic layer cover, groundwater flow at depth, and lateral heat transfer from wetland surface waters in the summer. Better understanding the resilience and vulnerability of ecosystem-protected permafrost is critical for mapping and predicting future permafrost extent and degradation across all permafrost regions that are currently warming. Further work should focus on reconstructing permafrost history in south-central Alaska as well as additional contemporary observations of these ecosystem-protected permafrost sites south of the regions with relatively stable permafrost.
","language":"English","publisher":"European Geosciences Union","publisherLocation":"Katlenberg-Lindau, Germany","doi":"10.5194/tc-2016-91","usgsCitation":"Jones, B.M., Baughman, C., Romanovsky, V.E., Parsekian, A.D., Babcock, E., Stephani, E., Jones, M.C., Grosse, G., and Berg, E.E., 2016, Presence of rapidly degrading permafrost plateaus in south-central Alaska: The Cryosphere, v. 10, p. 2673-2692, https://doi.org/10.5194/tc-2016-91.","productDescription":"20 p.","startPage":"2673","endPage":"2692","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074467","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":470969,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-2016-91","text":"Publisher Index Page"},{"id":321881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kenai Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153,\n 60\n ],\n [\n -153,\n 61\n ],\n [\n -150,\n 61\n ],\n [\n -150,\n 60\n ],\n [\n -153,\n 60\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"574eb5dde4b0ee97d51a8411","contributors":{"authors":[{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":630560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":630561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanovsky, Vladimir E.","contributorId":169658,"corporation":false,"usgs":false,"family":"Romanovsky","given":"Vladimir","email":"","middleInitial":"E.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":630562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parsekian, Andrew D.","contributorId":23829,"corporation":false,"usgs":false,"family":"Parsekian","given":"Andrew","email":"","middleInitial":"D.","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":630563,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Babcock, Esther 0000-0001-7665-7795 ebabcock@usgs.gov","orcid":"https://orcid.org/0000-0001-7665-7795","contributorId":169659,"corporation":false,"usgs":true,"family":"Babcock","given":"Esther","email":"ebabcock@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":630564,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephani, Eva","contributorId":176912,"corporation":false,"usgs":false,"family":"Stephani","given":"Eva","affiliations":[],"preferred":false,"id":653958,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":630565,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grosse, Guido","contributorId":146182,"corporation":false,"usgs":false,"family":"Grosse","given":"Guido","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":630566,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Berg, Edward E","contributorId":169660,"corporation":false,"usgs":false,"family":"Berg","given":"Edward","email":"","middleInitial":"E","affiliations":[{"id":25568,"text":"USFWS retired","active":true,"usgs":false}],"preferred":false,"id":630567,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70170966,"text":"70170966 - 2016 - Modeled effects of soil acidification on long-term ecological and economic outcomes for managed forests in the Adirondack region (USA)","interactions":[],"lastModifiedDate":"2016-05-13T13:39:46","indexId":"70170966","displayToPublicDate":"2016-05-13T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Modeled effects of soil acidification on long-term ecological and economic outcomes for managed forests in the Adirondack region (USA)","docAbstract":"Sugar maple (Acer saccharum) is among the most ecologically and economically important tree species in North America, and its growth and regeneration is often the focus of silvicultural practices in northern hardwood forests. A key stressor for sugar maple (SM) is acid rain, which depletes base cations from poorly-buffered forest soils and has been associated with much lower SM vigor, growth, and recruitment. However, the potential interactions between forest management and soil acidification – and their implications for the sustainability of SM and its economic and cultural benefits – have not been investigated. In this study, we simulated the development of 50 extant SM stands in the western Adirondack region of NY (USA) for 100 years under different soil chemical conditions and silvicultural prescriptions. We found that interactions between management prescription and soil base saturation will strongly shape the ability to maintain SM in managed forests. Below 12% base saturation, SM did not regenerate sufficiently after harvest and was replaced mainly by red maple (Acer rubrum) and American beech (Fagus grandifolia). Loss of SM on acid-impaired sites was predicted regardless of whether the shelterwood or diameter-limit prescriptions were used. On soils with sufficient base saturation, models predicted that SM will regenerate after harvest and be sustained for future rotations. We then estimated how these different post-harvest outcomes, mediated by acid impairment of forest soils, would affect the potential monetary value of ecosystem services provided by SM forests. Model simulations indicated that a management strategy focused on syrup production – although not feasible across the vast areas where acid impairment has occurred – may generate the greatest economic return. Although pollution from acid rain is declining, its long-term legacy in forest soils will shape future options for sustainable forestry and ecosystem stewardship in the northern hardwood forests of North America.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.scitotenv.2016.04.008","collaboration":"New York State Energy Research and Development Authority; USGS","usgsCitation":"Caputo, J., Beier, C.M., Sullivan, T.J., and Lawrence, G.B., 2016, Modeled effects of soil acidification on long-term ecological and economic outcomes for managed forests in the Adirondack region (USA): Science of the Total Environment, v. 565, p. 401-411, https://doi.org/10.1016/j.scitotenv.2016.04.008.","productDescription":"11 p.","startPage":"401","endPage":"411","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073095","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":471002,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.04.008","text":"Publisher Index 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glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":629267,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178653,"text":"70178653 - 2016 - @KarlTheFog has been mapped!","interactions":[],"lastModifiedDate":"2017-04-28T10:19:25","indexId":"70178653","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5024,"text":"BayGEO Journal","active":true,"publicationSubtype":{"id":10}},"title":"@KarlTheFog has been mapped!","docAbstract":"Within the world of mapping, clouds are a pesky interference to be removed from satellite remote sensed imagery. However, to many of us, that is a waste of pixels. Cloud maps are becoming increasingly valuable in the quest to understand land cover change and surface processes. In coastal California, the dynamic summertime interactions between air masses, the ocean, and topography result in blankets of fog and low clouds flowing into low lying areas of the San Francisco Bay Area. The low clouds and fog advected from the Pacific bring moisture and shade to coastal ecosystems. This acts to reduce temperatures and evapotranspiration stress during the otherwise arid Mediterranean climate season, in turn impacting vegetation distribution, irrigation needs, and urban energy consumption.
","language":"English","publisher":"BayGeo","usgsCitation":"Torregrosa, A.A., 2016, @KarlTheFog has been mapped!: BayGEO Journal, v. 9, no. 1, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-075388","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":331423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331424,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://journal.baygeo.org/karlthefog/"}],"volume":"9","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"584144e0e4b04fc80e5073b0","contributors":{"authors":[{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654724,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70169005,"text":"sir20165029 - 2016 - Flood-inundation maps for a 9.1-mile reach of the Coast Fork Willamette River near Creswell and Goshen, Lane County, Oregon","interactions":[],"lastModifiedDate":"2016-04-13T15:20:35","indexId":"sir20165029","displayToPublicDate":"2016-04-13T12:40:00","publicationYear":"2016","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":"2016-5029","title":"Flood-inundation maps for a 9.1-mile reach of the Coast Fork Willamette River near Creswell and Goshen, Lane County, Oregon","docAbstract":"Digital flood-inundation maps for a 9.1-mile reach of the Coast Fork Willamette River near Creswell and Goshen, Oregon, were developed by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers (USACE). The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected stages at the USGS streamgage at Coast Fork Willamette River near Goshen, Oregon (14157500), at State Highway 58. Current stage at the streamgage for estimating near-real-time areas of inundation may be obtained at http://waterdata.usgs.gov/or/nwis/uv/?site_no=14157500&PARAmeter_cd=00065,00060. In addition, the National Weather Service (NWS) forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation.
In this study, areas of inundation were provided by USACE. The inundated areas were developed from flood profiles simulated by a one-dimensional unsteady step‑backwater hydraulic model. The profiles were checked by the USACE using documented high-water marks from a January 2006 flood. The model was compared and quality assured using several other methods. The hydraulic model was then used to determine eight water-surface profiles at various flood stages referenced to the streamgage datum and ranging from 11.8 to 19.8 ft, approximately 2.6 ft above the highest recorded stage at the streamgage (17.17 ft) since 1950. The intervals between stages are variable and based on annual exceedance probability discharges, some of which approximate NWS action stages.
The areas of inundation and water depth grids provided to USGS by USACE were used to create interactive flood‑inundation maps. The availability of these maps with current stage from USGS streamgage and forecasted stream stages from the NWS provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures as well as for post flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165029","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Hess, G.W., and Haluska, T.L., 2016, Flood-inundation maps for a 9.1-mile reach of the Coast Fork Willamette River near Creswell and Goshen, Lane County, Oregon: U.S. Geological Survey Scientific Investigations Report 2016–5029, 8 p., https://dx.doi.org/10.3133/sir20165029.","productDescription":"Report: vi, 8 p.; Metadata","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053101","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":319983,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5029/sir20165029.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5029 Report PDF"},{"id":319984,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5029/sir20165029_metadata.html"},{"id":319982,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5029/coverthb.jpg"}],"country":"United States","state":"Oregon","county":"Lane County","city":"Creswell, Goshen","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.98370361328124,\n 44.00318741021592\n ],\n [\n -122.99022674560545,\n 43.9942964557587\n ],\n [\n -122.96688079833984,\n 43.987133329129215\n ],\n [\n -122.99571990966798,\n 43.95649503643676\n ],\n [\n -122.99606323242188,\n 43.914959878503154\n ],\n [\n -122.98542022705078,\n 43.914959878503154\n ],\n [\n -122.97683715820312,\n 43.94339481559037\n ],\n [\n -122.9813003540039,\n 43.95328204198018\n ],\n [\n -122.9541778564453,\n 43.990838502564706\n ],\n [\n -122.97958374023438,\n 43.9965193192732\n ],\n [\n -122.9754638671875,\n 44.00219959217852\n ],\n [\n -122.98370361328124,\n 44.00318741021592\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
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2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
The magnitude (M) 7.0 Haiti earthquake of January 12, 2010, triggered landslides throughout much of Haiti on the island of Hispaniola in the Caribbean Sea. The epicenter of the quake was located at 18.44°N., 72.57°W. at a depth of 13 kilometers (km) approximately 25 km southwest of the capital, Port-au-Prince. Although estimates vary widely, the most reliable surveys of casualties indicate that the earthquake caused 158,679 fatalities and more than 300,000 injuries. The U.S. Geological Survey compared publicly available satellite imagery acquired both before and after the earthquake and mapped 23,567 landslides that were triggered by the strong shaking. Our mapping from aerial photography and satellite imagery was augmented by field observations.
Most of the landslides triggered by the earthquake were south of the Léogâne fault on the footwall and were fairly shallow falls and slides in weathered limestone (2–5 meters [m] thick) and volcanic rock and soil (generally <1 m thick). Landslides extended from the north to the south coasts of the southwestern peninsula (southwest of Port-au-Prince) and almost 60 km to the east and west of the epicenter. The highest concentration of landslides was on the steep limestone slopes of incised river valleys, but large numbers of landslides also occurred on gentler slopes in weathered volcanic rocks. Although some high landslide concentrations did occur near areas of maximum fault slip, the overall distribution of landslides appears to involve complex interactions between geology, topography, and strong shaking with limited spatial correlation between fault slip and landslides.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3353","usgsCitation":"Harp, E.L., Jibson, R.W., and Schmitt, R.G., 2016, Map of landslides triggered by the January 12, 2010, Haiti earthquake: U.S. Geological Survey Scientific Investigations Map 3353, 15 p., 1 sheet, scale 1:150,000, https://dx.doi.org/10.3133/sim3353.","productDescription":"Pamphlet: iv, 15 p.; 2 Maps: 36.0 x 29.0 inches; Read Me; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068322","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":319849,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3353/sim3353.pdf","text":"Pamphlet","size":"22.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3533 Pamphlet"},{"id":319848,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3353/coverthb2.jpg"},{"id":319858,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3353/sim3353_map_geo.pdf","text":"Georeferenced Map","size":"85.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3533 Georeferenced Map"},{"id":319873,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3353/sim3353_LS_inventory.met","text":"Metadata","size":"12.0 kB","description":"SIM 3533 Metadata (met)"},{"id":319872,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3353/sim3353_shapefile.zip","text":"Shapefile","size":"11.8 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3533 Shapefile"},{"id":319856,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3353/sim3353_readme.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3533 ReadMe file"},{"id":319857,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3353/sim3353_map.pdf","text":"Map","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3533 Map"},{"id":319867,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3353/sim3353_Haiti_Landslide_inventory.gdb.zip","text":"Geodatabase","size":"6.16 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3533 Geodatabase"},{"id":319875,"rank":9,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3353/sim3353_LS_inventory.xml","text":"Metadata (xml)","size":"11.0 kB","description":"SIM 3533 Metadata (xml)"}],"country":"Haiti","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.1,\n 18.1\n ],\n [\n -73.1,\n 18.65\n ],\n [\n -71.9,\n 18.65\n ],\n [\n -71.9,\n 18.1\n ],\n [\n -73.1,\n 18.1\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Geologic Hazards Science Center
Box 25046, Mail Stop 966
Denver, CO 80225
http://geohazards.cr.usgs.gov/
","tableOfContents":"Effective and responsible management of water resources relies on a thorough understanding of the quantity and quality of available water; however, streamgages cannot be installed at every location where streamflow information is needed. Therefore, methods for estimating streamflow at ungaged stream locations need to be developed. This report presents a statewide study to develop methods to estimate the structure of historical daily streamflow at ungaged stream locations in Minnesota. Historical daily mean streamflow at ungaged locations in Minnesota can be estimated by transferring streamflow data at streamgages to the ungaged location using the QPPQ method. The QPPQ method uses flow-duration curves at an index streamgage, relying on the assumption that exceedance probabilities are equivalent between the index streamgage and the ungaged location, and estimates the flow at the ungaged location using the estimated flow-duration curve. Flow-duration curves at ungaged locations can be estimated using recently developed regression equations that have been incorporated into StreamStats (http://streamstats.usgs.gov/), which is a U.S. Geological Survey Web-based interactive mapping tool that can be used to obtain streamflow statistics, drainage-basin characteristics, and other information for user-selected locations on streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155181","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency","usgsCitation":"Lorenz, D.L., and Ziegeweid, J.R., 2016, Methods to estimate historical daily streamflow for ungaged stream locations in Minnesota: U.S. Geological Survey Scientific Investigations Report 2015–5181, 18 p., https://dx.doi.org/10.3133/sir20155181.","productDescription":"iv, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068751","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":318797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5181/coverthb.jpg"},{"id":318798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5181/sir20155181.pdf","text":"Report","size":"2.91 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\"}}]}","contact":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
A 15-year estimated Trophic State Index (eTSI) for Michigan inland lakes is available, and it spans seven datasets, each representing 1 to 3 years of data from 1999 to 2013. On average, 3,000 inland lake eTSI values are represented in each of the datasets by a process that relates field-measured Secchi-disk transparency (SDT) to Landsat satellite imagery to provide eTSI values for unsampled inland lakes. The correlation between eTSI values and field-measured Trophic State Index (TSI) values from SDT was strong as shown by R2 values from 0.71 to 0.83. Mean eTSI values ranged from 42.7 to 46.8 units, which when converted to estimated SDT (eSDT) ranged from 8.9 to 12.5 feet for the datasets. Most eTSI values for Michigan inland lakes are in the mesotrophic TSI class. The Environmental Protection Agency (EPA) Level III Ecoregions were used to illustrate and compare the spatial distribution of eTSI classes for Michigan inland lakes. Lakes in the Northern Lakes and Forests, North Central Hardwood Forests, and Southern Michigan/Northern Indiana Drift Plains ecoregions are predominantly in the mesotrophic TSI class. The Huron/Erie Lake Plains and Eastern Corn Belt Plains ecoregions, had predominantly eutrophic class lakes and also the highest percent of hypereutrophic lakes than other ecoregions in the State. Data from multiple sampling programs—including data collected by volunteers with the Cooperative Lakes Monitoring Program (CLMP) through the Michigan Department of Environmental Quality (MDEQ), and the 2007 National Lakes Assessment (NLA)—were compiled to compare the distribution of lake TSI classes between each program. The seven eTSI datasets are available for viewing and download with eSDT from the Michigan Lake Water Clarity Interactive Map Viewer at http://mi.water.usgs.gov/projects/RemoteSensing/index.html.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165023","collaboration":"Prepared in cooperation with the Michigan Department of Environmental Quality","usgsCitation":"Fuller, L.M., and Jodoin, R.S., 2016, Estimation of a Trophic State Index for selected inland lakes in Michigan, 1999–2013: U.S. Geological Survey Scientific Investigations Report 2016–5023, 16 p., https://dx.doi.org/10.3133/sir20165023.","productDescription":"vii, 16 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067016","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":318806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5023/sir20165023.pdf","text":"Report","size":"1.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"Director, Michigan Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911-5991
http://mi.water.usgs.gov/
VS30, the time-averaged shear-wave velocity (VS) to a depth of 30 meters, is a key index adopted by the earthquake engineering community to account for seismic site conditions. VS30 is typically based on geophysical measurements of VS derived from invasive and noninvasive techniques at sites of interest. Owing to cost considerations, as well as logistical and environmental concerns, VS30 data are sparse or not readily available for most areas. Where data are available, VS30 values are often assembled in assorted formats that are accessible from disparate and (or) impermanent Web sites. To help remedy this situation, we compiled VS30 measurements obtained by studies funded by the U.S. Geological Survey (USGS) and other governmental agencies. Thus far, we have compiled VS30 values for 2,997 sites in the United States, along with metadata for each measurement from government-sponsored reports, Web sites, and scientific and engineering journals. Most of the data in our VS30 compilation originated from publications directly reporting the work of field investigators. A small subset (less than 20 percent) of VS30 values was previously compiled by the USGS and other research institutions. Whenever possible, VS30 originating from these earlier compilations were crosschecked against published reports. Both downhole and surface-based VS30 estimates are represented in our VS30 compilation. Most of the VS30 data are for sites in the western contiguous United States (2,141 sites), whereas 786 VS30 values are for sites in the Central and Eastern United States; 70 values are for sites in other parts of the United States, including Alaska (15 sites), Hawaii (30 sites), and Puerto Rico (25 sites). An interactive map is hosted on the primary USGS Web site for accessing VS30 data (https://earthquake.usgs.gov/data/vs30/us/).
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Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
http://earthquake.usgs.gov/
The Enbridge Line 6B pipeline release of diluted bitumen into the Kalamazoo River downstream of Marshall, Michigan, U.S.A., in July 2010 was one of the largest oil spills into freshwater in North American history. A portion of the oil interacted with river sediment and submerged requiring the development and implementation of new approaches for detection and recovery of oil mixed with river sediment. Hydrodynamic and sediment transport modeling became an integral part of containment and recovery operations for decision support about the potential fate and migration of submerged oil and oiled sediment. Three models were developed for the U.S. Environmental Protection Agency to cover a range of spatial scales of interest to onsite operations. Two-dimensional (2D) hydrodynamic and sediment transport models from the Environmental Fluid Dynamics Code and the sediment bed model SEDZLJ1 were used to simulate potential resuspension, migration, and deposition of submerged oil and oiled sediment along a 38-mile reach of the Kalamazoo River affected by the oil from Marshall to Kalamazoo. An algorithm was added to SEDZLJ to represent three additional particle size classes of oilparticle aggregates (OPAs) with a range of sizes, specific gravities, and settling velocities. Field and laboratory experiments and flume tests were done to support the numerical modeling of OPAs. A three-dimensional hydrodynamic model was developed to simulate hydrodynamics and OPA tracking through Morrow Lake, the most downstream impoundment. This model incorporated wind and dam operations into high and low flow, lake drawdown, and containment simulations. Finally, a 2D unstructured grid model, HydroSed2D, was used to simulate flows and sediment transport along 1- to 2-mile segments of the Kalamazoo River around islands and through side channels and backwater areas that are particularly prone to submerged oil deposition.
Integrated models could be developed quickly due to the availability of information and services combined with spill response operations that included: bathymetry and topography data, fieldbased geomorphic mapping of submerged oil, and discharge measured at U.S. Geological Survey streamflow gauges. Modeling results were included in a multiple-lines-of-evidence approach that was used by the Federal On-Scene Coordinator and operations staff for decision-making related to assessment and recovery of submerged oil, as well as net environmental benefit analysis. Similar modeling approaches will likely be useful for future oil spills in riverine environments.
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