Although most species of sea ducks are poorly studied, much is known about the population dynamics of the American race of Somateria mollissma dresseri (Common Eider). Although Common Eiders typically have high adult survival and low recruitment rates, their populations in Maine have declined since the early 1990s. Wildlife managers hypothesized this decline was due to reduced adult survival; therefore, they decreased daily bag limits in Maine in 1999 and 2009 to increase local populations. The goals of this project were to assess (a) whether survival rates of adult females captured while nesting varied between historical estimates (1943–1993) and recent estimates (2000–2012), (b) whether survival rates increased from 2000–2009 after the initial harvest restrictions were implemented in 1999, and (c) determine if estimates of survival rates of adult males and females captured while molting differed from estimates of adult females captured while nesting. We used mark–recapture models to estimate survival rates of Common Eiders we banded in Maine (nesting females [n = 2340] from 2000 to 2012; molting males [ n = 4366] and molting females [n = 4952] from 2000 to 2009). We found no difference in survival of nesting females based on historical (mean ± SE = 0.9003 ± 0.0841) and recent estimates (0.90 ± 0.015). Although we observed annual fluctuations in survival, survival rates did not increase following the implementation of harvest restrictions. Mean annual survival rates were similarly high for molting females (0.894 ± 0.0205) and nesting females, but lower for molting males (0.855 ± 0.0128). Lower survival rates of adult males may reflect the preference by US hunters to selectively harvest adult males. Overall mean recovery rates of banded birds were low (females: 0.037 ± 0.00043, males: 0.0226 ± 0.0006). We hypothesize that current harvest rates may be influencing decreased survival of adult males to some extent.
","language":"English","publisher":"BioOne","doi":"10.1656/045.026.0320","usgsCitation":"Allen, R.B., McAuley, D., and Zimmerman, G., 2019, Adult survival of common eiders in Maine: Northeastern Naturalist, v. 26, no. 3, p. 656-671, https://doi.org/10.1656/045.026.0320.","productDescription":"6 p.","startPage":"656","endPage":"671","ipdsId":"IP-090678","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":367601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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B.","contributorId":219165,"corporation":false,"usgs":false,"family":"Allen","given":"R.","email":"","middleInitial":"B.","affiliations":[{"id":39965,"text":"Maine Department of Inland Fisheries and Wildlife","active":true,"usgs":false}],"preferred":false,"id":771466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAuley, Daniel 0000-0003-3674-6392 dmcauley@usgs.gov","orcid":"https://orcid.org/0000-0003-3674-6392","contributorId":215182,"corporation":false,"usgs":true,"family":"McAuley","given":"Daniel","email":"dmcauley@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":771465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zimmerman, G.","contributorId":219166,"corporation":false,"usgs":false,"family":"Zimmerman","given":"G.","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":771467,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70207459,"text":"70207459 - 2019 - Benthic algal (Periphyton) growth rates in response to nitrogen and phosphorus: Parameter estimation for water quality models","interactions":[],"lastModifiedDate":"2019-12-19T16:50:18","indexId":"70207459","displayToPublicDate":"2019-09-03T16:46:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Benthic algal (Periphyton) growth rates in response to nitrogen and phosphorus: Parameter estimation for water quality models","docAbstract":"Nitrogen (N) and phosphorus (P) are important pollutants that can stimulate nuisance blooms of algae. Water-quality models (e.g., WASP, CE-QUAL-R1, CE-QUAL-ICM, QUAL2k) are valuable and widely used management tools for algal accrual because of excess nutrients in the presence of other limiting factors. These models utilize the Monod and Droop equations to associate algal growth rate with dissolved nutrient concentration and intra-cellular nutrient concentration. Having accurate parameter values is essential to model performance; however, published values for model parameterization are limited, particularly for benthic (periphyton) algae. We conducted a 10-day mesocosm experiment and measured diatom-dominated periphyton biomass accrual through time as chlorophyll a (chl a) and ash-free dry mass (AFDM) in response to additions of N (range 5-12,390 µg NO3-N/L) and P (range 0.89-59.51 µg SRP/L). Resulting half saturation coefficients and growth rates are similar to other published values, but minimum intra-cellular nutrient concentration (quota, Qmin) are higher than those previously reported. Saturation concentration for N ranged from 150 to 2450 µg NO3-N/L based on chl a and from 8.5 to 60 µg NO3-N/L when based on AFDM. Similarly, the saturation concentration for P ranged from 12 to 29 µg-P/L based on chl a, and from 2.5 to 6.1 µg-P/L based on AFDM. These saturation concentrations provide an upper limit for streams where diatom growth can be expected to respond to nutrient levels and a benchmark for reducing nutrient concentrations to a point where benthic algal growth will be limited.","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12797","usgsCitation":"Schmidt, T., Konrad, C., Miller, J.L., Whitlock, S.D., and Stricker, C.A., 2019, Benthic algal (Periphyton) growth rates in response to nitrogen and phosphorus: Parameter estimation for water quality models: Journal of the American Water Resources Association, v. 55, no. 6, p. 1479-1491, https://doi.org/10.1111/1752-1688.12797.","productDescription":"13 p.","startPage":"1479","endPage":"1491","ipdsId":"IP-102549","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":459923,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7029675","text":"External Repository"},{"id":370525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":778127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konrad, Christopher 0000-0002-7354-547X","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":217886,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778128,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Janet L.","contributorId":218842,"corporation":false,"usgs":false,"family":"Miller","given":"Janet","email":"","middleInitial":"L.","affiliations":[{"id":39922,"text":"No affilcation","active":true,"usgs":false}],"preferred":false,"id":778129,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitlock, Stephen D.","contributorId":218841,"corporation":false,"usgs":false,"family":"Whitlock","given":"Stephen","email":"","middleInitial":"D.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":778130,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":778131,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204416,"text":"sir20195070 - 2019 - Stratigraphic analysis of Corte Madera Creek flood control channel deposits","interactions":[],"lastModifiedDate":"2019-09-03T16:51:36","indexId":"sir20195070","displayToPublicDate":"2019-09-03T14:15:55","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5070","displayTitle":"Stratigraphic Analysis of Corte Madera Creek Flood Control Channel Deposits","title":"Stratigraphic analysis of Corte Madera Creek flood control channel deposits","docAbstract":"Sedimentation in a channel can reduce flood conveyance capability and potentially place nearby property and life at risk from flooding. In 1998, Marin County Public Works dredged the concrete-lined segment of Corte Madera Creek, which drains a hilly and largely urbanized watershed that terminates in San Francisco Bay, California. From then through 2015, approximately 4,100 cubic meters of sand and gravel infilled the concrete-lined segment. Determining when and under what conditions this material was deposited informs dredging operations for the Corte Madera Creek Flood Control Project and increases understanding of sediment delivery timing and mechanisms from this and other San Francisco Bay tributaries.
Two hypothesized scenarios were investigated: (1) complete flushing during high flows and re-deposition of channel fill afterward and (2) more steady, gradual channel infilling. Stratigraphic analysis of eight sediment cores collected from the flood-control channel deposits in August 2017 was used to identify the most likely scenario. In addition, sediment elevation profiles, grain-size data, and a one-dimensional hydrodynamic model were used to assess the potential for longitudinal-channel scour and deposition following the wet winter of water year 2017 in the intertidal reach of the concrete channel in Corte Madera Creek.
Results indicated the channel is undergoing gradual infilling. Storm flows of water year 2017 did not completely scour the concrete channel fill. Sediment cores, stratigraphic analysis, and sediment elevation profiles indicated 0.23 meter of scour at the downstream end of the concrete-lined section and that roughly 0.5 meter of channel fill remained in the channel. The hydrodynamic model demonstrated that sediment deposition in the concrete channel is expected to start downstream from the point where the channel bed reaches mean lower low-water level. High flows can carry most of the sediment through this segment of channel, depositing the bed-material load downstream from the transition to a wide channel, where velocity and bed shear stress decrease abruptly.
Although the storm flows of 2017 did not completely scour the channel fill, subsequent material deposited in the channel could be transported downstream from the concrete channel if the sediment elevation profile is in equilibrium with present (2019) mean sea level. A calibrated, coupled hydrodynamic-sediment transport model could be used to test the present equilibrium between sediment elevation profiles and mean sea level, such that additional sediment build-up in the concrete channel is remobilized during subsequent wet-season flows and deposited downstream from the concrete-lined segment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195070","collaboration":"Prepared in cooperation with Marin County Flood Control District","usgsCitation":"Livsey, D., Work, P., and Downing-Kunz, M., 2019, Stratigraphic analysis of Corte Madera Creek flood control channel deposits: U.S. Geological Survey Scientific Investigation Report 2019–5070, 28 p., https://doi.org/10.3133/sir20195070.","productDescription":"vi, 28 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-102889","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":367137,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5070/sir20195070.pdf","text":"Report","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5070"},{"id":367136,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5070/coverthb.jpg"}],"country":"United States","state":"California","county":"Marin County","otherGeospatial":"Corte Madera Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.55360603332518,\n 37.95983152006781\n ],\n [\n -122.55401372909544,\n 37.95940856550367\n ],\n [\n -122.55317687988281,\n 37.95847805688854\n ],\n [\n -122.55341291427611,\n 37.957395268386534\n ],\n [\n -122.5513744354248,\n 37.95446827582245\n ],\n [\n -122.54940032958984,\n 37.95382533697663\n ],\n [\n -122.54691123962402,\n 37.95343618704664\n ],\n [\n -122.54618167877197,\n 37.95331774970222\n ],\n [\n -122.54616022109984,\n 37.95120276497281\n ],\n [\n -122.54598855972289,\n 37.95069515957716\n ],\n [\n -122.54487276077272,\n 37.95086436176544\n ],\n [\n -122.54515171051024,\n 37.95292859708327\n ],\n [\n -122.5455379486084,\n 37.9540960487555\n ],\n [\n -122.54740476608275,\n 37.95441751769712\n ],\n [\n -122.55006551742552,\n 37.95548343096359\n ],\n [\n -122.55236148834227,\n 37.957632129735394\n ],\n [\n -122.55236148834227,\n 37.9587487515198\n ],\n [\n -122.55360603332518,\n 37.95983152006781\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Eutrophication problems in the Great Lakes are caused by excessive nutrient inputs (primarily phosphorus, P, and nitrogen, N) from various sources throughout its basin. In developing protection and restoration plans, it is important to know where and from what sources the nutrients originate. As part of a binational effort, Midcontinent SPARROW (SPAtially Referenced Regression On Watershed attributes) models were developed and used to estimate P and N loading from throughout the entire basin based on nutrient inputs similar to 2002; previous SPARROW models only estimated U.S. contributions. The new models have a higher resolution (~2‐km2 catchments) enabling improved descriptions of where nutrients originate and the sources at various spatial scales. The models were developed using harmonized geospatial datasets describing the stream network, nutrient sources, and environmental characteristics affecting P and N delivery. The models were calibrated using loads from sites estimated with ratio estimator and regression techniques and additional statistical approaches to reduce spatial correlation in the residuals and have all monitoring sites equally influence model development. SPARROW results, along with interlake transfers and direct atmospheric inputs, were used to quantify the entire P and N input to each lake and describe the importance of each nutrient source. Model results can be used to compare loading and yields from various tributaries and jurisdictions.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12792","usgsCitation":"Robertson, D.M., Saad, D., Benoy, G.A., Vouk, I., Schwarz, G.E., and Laitta, M.T., 2019, Phosphorus and nitrogen transport in the binational Great Lakes Basin estimated using SPARROW watershed models: Journal of the American Water Resources Association, v. 55, no. 6, p. 1401-1424, https://doi.org/10.1111/1752-1688.12792.","productDescription":"24 p.","startPage":"1401","endPage":"1424","ipdsId":"IP-099596","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":459925,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12792","text":"Publisher Index Page"},{"id":369886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.8125,\n 41.27780646738183\n ],\n [\n -75.8056640625,\n 41.27780646738183\n ],\n [\n -75.8056640625,\n 48.980216985374994\n ],\n [\n -92.8125,\n 48.980216985374994\n ],\n [\n -92.8125,\n 41.27780646738183\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"6","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":204668,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saad, David A. 0000-0001-6559-6181","orcid":"https://orcid.org/0000-0001-6559-6181","contributorId":217251,"corporation":false,"usgs":true,"family":"Saad","given":"David A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benoy, Glenn A. 0000-0001-6530-7220","orcid":"https://orcid.org/0000-0001-6530-7220","contributorId":172405,"corporation":false,"usgs":false,"family":"Benoy","given":"Glenn","email":"","middleInitial":"A.","affiliations":[{"id":13361,"text":"International Joint Commission, Washington DC","active":true,"usgs":false}],"preferred":false,"id":776561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vouk, Ivana 0000-0002-9134-6933","orcid":"https://orcid.org/0000-0002-9134-6933","contributorId":211795,"corporation":false,"usgs":false,"family":"Vouk","given":"Ivana","email":"","affiliations":[{"id":38321,"text":"National Research Council Canada","active":true,"usgs":false}],"preferred":false,"id":776562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - 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Protecting and restoring important floodplain ecosystems requires understanding how organisms use these habitats and respond to altered environmental conditions. We developed Bayesian models to evaluate occupancy of six amphibian species across 103 off‐channel aquatic habitats in the Chehalis River floodplain, Washington State, USA. The basin has been altered by changes in land use, reduced river–wetland connections, and the establishment of non‐native American bullfrogs (Rana catesbeiana = Lithobates catesbeianus) and centrarchid fishes, all of which we hypothesized could influence native amphibian occupancy. Despite potential threats, the floodplain habitats had relatively high rates of native amphibian occupancy, particularly when compared to studies from non‐floodplain habitats within the species’ native ranges. The biggest challenge for native amphibians appears to be non‐native centrarchid fishes, which strongly reduced occupancy of two native amphibians: the northern red‐legged frog (Rana aurora) and the northwestern salamander (Ambystoma gracile). Emergent vegetative cover increased occupancy probability for all five native amphibian species, indicating that plant management may offer a strategy to counter the negative effect of centrarchids by providing refuge from predation. We found that temporary and permanent hydroperiod sites supported different species; hence, both should be conserved on the landscape. Lastly, human‐created and natural ponds had similar amphibian occupancy patterns, suggesting that pond construction offers a viable strategy for adding habitats to the floodplain landscape. Overall, floodplain ponds and wetlands provide important amphibian habitat, and we offer management strategies that will bolster amphibian occupancy in an altered floodplain landscape.
","language":"English","publisher":"ESA","doi":"10.1002/ecs2.2853","usgsCitation":"Holgerson, M., Duarte, A., Hayes, M., Adams, M.J., Tyson, J.A., Douville, K., and Strecker, A., 2019, Floodplains provide important amphibian habitat despite multiple ecological threats: Ecosphere, v. 10, no. 9, e02853, 18 p., https://doi.org/10.1002/ecs2.2853.","productDescription":"e02853, 18 p.","ipdsId":"IP-106837","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":459938,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2853","text":"Publisher Index Page"},{"id":367248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Chehalis River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.17297363281251,\n 47.09069560264967\n ],\n [\n -124.17297363281251,\n 46.9465122958623\n ],\n [\n -124.07958984375001,\n 46.77184961467733\n ],\n [\n -123.33251953125,\n 46.77749276376827\n ],\n [\n -123.43414306640625,\n 46.60982785835103\n ],\n [\n -122.9754638671875,\n 46.219752144776876\n ],\n [\n -122.25585937500001,\n 46.543749602738565\n ],\n [\n -123.5687255859375,\n 47.344406158662125\n ],\n [\n -124.17297363281251,\n 47.09069560264967\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Holgerson, Meredith","contributorId":218790,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":770278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":28492,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":770279,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Marc","contributorId":218791,"corporation":false,"usgs":false,"family":"Hayes","given":"Marc","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":770280,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Michael J. 0000-0001-8844-042X","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":211916,"corporation":false,"usgs":true,"family":"Adams","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":770282,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tyson, Julie A.","contributorId":218792,"corporation":false,"usgs":false,"family":"Tyson","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":770281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douville, Keith","contributorId":218793,"corporation":false,"usgs":false,"family":"Douville","given":"Keith","email":"","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":770283,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Strecker, Angela","contributorId":218794,"corporation":false,"usgs":false,"family":"Strecker","given":"Angela","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":770284,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70205841,"text":"70205841 - 2019 - Evaluating the factors responsible for post-fire water quality response in forests of the western USA","interactions":[],"lastModifiedDate":"2019-10-28T10:32:13","indexId":"70205841","displayToPublicDate":"2019-09-03T07:34:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the factors responsible for post-fire water quality response in forests of the western USA","docAbstract":"Wildfires commonly increase nutrient, carbon, sediment, and metal inputs to streams yet the factors responsible for the type, magnitude and duration of water quality effects are poorly understood. Prior work by the current authors found increased nitrogen, phosphorus and cation exports were common the first five post-fire years from a synthesis of 159 wildfires across the western United States. In the current study, an analysis is undertaken to determine factors that best explain post-fire streamwater responses observed in those watersheds. Increased post-fire total nitrogen and phosphorus loading were proportional to the catchment extent of moderate and high burn severity. Post-fire dissolved metal concentrations increased in catchments with < 2% pre-fire soil organic matter. In contrast, in catchments with > 2% soil organic matter, post-fire dissolved metal concentrations decreased compared to pre-fire conditions. Where post-fire normalized difference vegetation index (NDVI), a remote sensing indicator of live green vegetation, was low, total metal concentrations increased by 25% on average and by > 100% in some cases. When pre-fire soil field capacity exceeded 17%, there was a 750% median increase in total metals export to streams. Overall, the current analysis identified burn severity, post-fire vegetation cover, and several soil properties as the key variables explaining extended post-fire water quality response across a broad range of conditions found in the western US.","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WF18191","usgsCitation":"Rust, A.J., Saxe, S., McCray, J.E., Rhoades, C.C., and Hogue, T.S., 2019, Evaluating the factors responsible for post-fire water quality response in forests of the western USA: International Journal of Wildland Fire, v. 28, no. 10, p. 769-784, https://doi.org/10.1071/WF18191.","productDescription":"16 p.","startPage":"769","endPage":"784","ipdsId":"IP-109789","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":368081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.053249,41.001406],[-102.124972,41.002338],[-102.051292,40.749591],[-102.04192,37.035083],[-102.979613,36.998549],[-103.002247,36.911587],[-103.064423,32.000518],[-106.565142,32.000736],[-106.577244,31.810406],[-106.750547,31.783706],[-108.208394,31.783599],[-108.208573,31.333395],[-111.000643,31.332177],[-114.813613,32.494277],[-114.722746,32.713071],[-117.118868,32.534706],[-117.50565,33.334063],[-118.088896,33.729817],[-118.428407,33.774715],[-118.519514,34.027509],[-119.159554,34.119653],[-119.616862,34.420995],[-120.441975,34.451512],[-120.608355,34.556656],[-120.644311,35.139616],[-120.873046,35.225688],[-120.884757,35.430196],[-121.851967,36.277831],[-121.932508,36.559935],[-121.788278,36.803994],[-121.880167,36.950151],[-122.140578,36.97495],[-122.419113,37.24147],[-122.511983,37.77113],[-122.425942,37.810979],[-122.168449,37.504143],[-122.144396,37.581866],[-122.385908,37.908136],[-122.301804,38.105142],[-122.484411,38.11496],[-122.492474,37.82484],[-122.972378,38.020247],[-123.103706,38.415541],[-123.725367,38.917438],[-123.851714,39.832041],[-124.373599,40.392923],[-124.063076,41.439579],[-124.536073,42.814175],[-124.150267,43.91085],[-123.962887,45.280218],[-123.996766,46.20399],[-123.548194,46.248245],[-124.029924,46.308312],[-124.06842,46.601397],[-123.97083,46.47537],[-123.84621,46.716795],[-124.022413,46.708973],[-124.108078,46.836388],[-123.86018,46.948556],[-124.138035,46.970959],[-124.425195,47.738434],[-124.672427,47.964414],[-124.727022,48.371101],[-123.981032,48.164761],[-122.748911,48.117026],[-122.637425,47.889945],[-123.15598,47.355745],[-122.527593,47.905882],[-122.578211,47.254804],[-122.725738,47.33047],[-122.691771,47.141958],[-122.796646,47.341654],[-122.863732,47.270221],[-122.67813,47.103866],[-122.364168,47.335953],[-122.429841,47.658919],[-122.230046,47.970917],[-122.425572,48.232887],[-122.358375,48.056133],[-122.512031,48.133931],[-122.424102,48.334346],[-122.689121,48.476849],[-122.425271,48.599522],[-122.796887,48.975026],[-104.048736,48.999877],[-104.053249,41.001406]]],[[[-119.789798,34.05726],[-119.5667,34.053452],[-119.795938,33.962929],[-119.916216,34.058351],[-119.789798,34.05726]]],[[[-118.524531,32.895488],[-118.573522,32.969183],[-118.369984,32.839273],[-118.524531,32.895488]]],[[[-118.500212,33.449592],[-118.32446,33.348782],[-118.593969,33.467198],[-118.500212,33.449592]]],[[[-122.519535,48.288314],[-122.66921,48.240614],[-122.400628,48.036563],[-122.419274,47.912125],[-122.744612,48.20965],[-122.664928,48.374823],[-122.519535,48.288314]]],[[[-122.800217,48.60169],[-122.883759,48.418793],[-123.173061,48.579086],[-122.949116,48.693398],[-122.743049,48.661991],[-122.800217,48.60169]]]]},\"properties\":{\"name\":\"Arizona\",\"nation\":\"USA \"}}]}","volume":"28","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rust, Ashley J.","contributorId":219575,"corporation":false,"usgs":false,"family":"Rust","given":"Ashley","email":"","middleInitial":"J.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":772578,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saxe, Samuel","contributorId":219574,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":772577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCray, John E.","contributorId":169186,"corporation":false,"usgs":false,"family":"McCray","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":772579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rhoades, Charles C.","contributorId":219576,"corporation":false,"usgs":false,"family":"Rhoades","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":40027,"text":"United States Forest Service","active":true,"usgs":false}],"preferred":false,"id":772580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":772581,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206689,"text":"70206689 - 2019 - PaCTS 1.0: A crowdsourced reporting standard for paleoclimate data","interactions":[],"lastModifiedDate":"2019-12-03T10:05:49","indexId":"70206689","displayToPublicDate":"2019-09-03T06:36:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5790,"text":"Paleoceanography and Paleoclimatology","active":true,"publicationSubtype":{"id":10}},"title":"PaCTS 1.0: A crowdsourced reporting standard for paleoclimate data","docAbstract":"The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on that data being standardized. Accordingly, the lack of community-sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the re-use value of paleoclimate datasets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, this standard elicitation process involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive-specific properties and distinguished reporting standards for new vs. legacy datasets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate datasets. Since such goals are at odds with present practices, we discuss a transparent path towards implementing or revising these recommendations in the near future, using both bottom-up and top-down approaches.","language":"English","publisher":"Wiley","doi":"10.1029/2019PA003632","usgsCitation":"Kehrwald, N.M., Khider, D., Emile-Geay, J., McKay, N., Gili, Y., Garijo, D., Ratnakar, V., Brewer, P., Csank, A., Dassie, E., Delong, K., Felix, T., Gray, W., Jonkers, L., Kahle, M., Kaufman, D.S., Richey, J.N., Schmittner, A., Sutherland, E.K., Alonso-Garcia, M., Sebastian, B., Bothe, O., Bunn, A., Chevalier, M., Francus, P., Frappier, A., Goring, S., Martrat, B., McGregor, H.V., Allen, K.J., Arnaud, F., Axford, Y.L., Barrows, T.T., Bazin, L., Birch, P., Bradley, E., Bregy, J., Capron, E., Cartapanis, O., Chiang, H., Cobb, K., Debret, M., Dommain, R., Du, J., Dyez, K., Emerick, S., Erb, M., Falster, G., Finsinger, W., Fortier, D., Gauthier, N., George, S., Grimm, E., Hertzberg, J., Hibbert, F., Hillman, A., Hobbs, W., Huber, M., Hughes, A.L., Jaccard, S., Jiaoyang, R., Kienast, M., Konecky, B., Le Roux, G., Lyubchich, V., Novello, V., Olaka, L., Partin, J.W., Pearce, C., Phipps, S.J., Pignol, C., Pietrowska, N., Poli, M., Prokopenko, A., Schwanck, F., Stepanek, C., Swann, G.E., Telford, R., Thomas, E.R., Thomas, Z., Truebe, S., von Gunten, L., Waite, A., Weitzel, N., Wilhelm, B., Williams, J.B., Winstrup, M., Zhao, N., and Zhou, Y., 2019, PaCTS 1.0: A crowdsourced reporting standard for paleoclimate data: Paleoceanography and Paleoclimatology, v. 34, no. 10, p. 1570-1596, https://doi.org/10.1029/2019PA003632.","productDescription":"27 p.","startPage":"1570","endPage":"1596","ipdsId":"IP-108314","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":459946,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019pa003632","text":"Publisher Index 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This effect can have significant implications for paleoclimate analyses. In particular, using a “fixed-length” definition of months (i.e., defined by a fixed number of days), as opposed to a “fixed-angular” definition (i.e., defined by a fixed number of degrees of the Earth's orbit), leads to comparisons of data from different positions along the Earth's orbit when comparing paleo with modern simulations. This effect can impart characteristic spatial patterns or signals in comparisons of time-slice simulations that otherwise might be interpreted in terms of specific paleoclimatic mechanisms, and we provide examples for 6, 97, 116, and 127 ka. The calendar effect is exacerbated in transient climate simulations in which, in addition to spatial or map-pattern effects, it can influence the apparent timing of extrema in individual time series and the characterization of phase relationships among series. We outline an approach for adjusting paleo simulations that have been summarized using a modern fixed-length definition of months and that can also be used for summarizing and comparing data archived as daily data. We describe the implementation of this approach in a set of Fortran 90 programs and modules (PaleoCalAdjust v1.0).
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-12-3889-2019","usgsCitation":"Bartlein, P.J., and Shafer, S., 2019, Paleo calendar-effect adjustments in time-slice and transient climate-model simulations (PaleoCalAdjust v1.0): Impact and strategies for data analysis: Geoscientific Model Development, v. 12, p. 3889-3913, https://doi.org/10.5194/gmd-12-3889-2019.","productDescription":"25 p.","startPage":"3889","endPage":"3913","ipdsId":"IP-101124","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":459948,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-12-3889-2019","text":"Publisher Index Page"},{"id":397667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Bartlein, Patrick J. 0000-0001-7657-5685","orcid":"https://orcid.org/0000-0001-7657-5685","contributorId":211587,"corporation":false,"usgs":false,"family":"Bartlein","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":33397,"text":"U of Oregon","active":true,"usgs":false}],"preferred":false,"id":838926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shafer, Sarah 0000-0003-3739-2637 sshafer@usgs.gov","orcid":"https://orcid.org/0000-0003-3739-2637","contributorId":149866,"corporation":false,"usgs":true,"family":"Shafer","given":"Sarah","email":"sshafer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215341,"text":"70215341 - 2019 - Updating estimates of low-streamflow statistics to account for possible trends","interactions":[],"lastModifiedDate":"2020-10-15T18:52:38.595974","indexId":"70215341","displayToPublicDate":"2019-09-02T13:46:08","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7162,"text":"Hydrologic Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"Updating estimates of low-streamflow statistics to account for possible trends","docAbstract":"Accurate estimators of streamflow statistics are critical to the design, planning, and management of water resources. Given increasing evidence of trends in low-streamflow, new approaches to estimating low-streamflow statistics are needed. Here we investigate simple approaches to select a recent subset of the low-flow record to update the commonly used statistic of 7Q10, the annual minimum 7-day streamflow exceeded in 9 out of 10 years on average. Informed by low-streamflow records at 174 US Geological Survey streamgages, Monte Carlo simulation experiments evaluate competing approaches. We find that a strategy which estimates 7Q10 using the most recent 30 years of record when a trend is detected, reduces error and bias in 7Q10 estimators compared to use of the full record. This simple rule-based approach has potential as the basis for a framework for updating frequency-based statistics in the context of possible trends.
Soil and plant responses to climate change can be quantified in controlled settings. However, the complexity of climate projections often leads researchers to evaluate ecosystem response based on general trends, rather than specific climate model outputs. Climate projections capture spatial and temporal climate extremes and variability that are lost when using mean climate trends. In addition, application of climate projections in experimental settings remains limited. Our objective was to develop a framework to incorporate statistically downscaled climate model projections into the design of temperature and precipitation treatments for ecological experiments. To demonstrate the utility of experimental treatments derived from climate projections, we used wetlands in the Great Plains as a model ecosystem for evaluating plant and soil responses. Spatial and temporal projections were selected to capture variability and intensity of projected future conditions for exemplary purposes. To illustrate climate projection application for ecological experiments, we developed temperature and precipitation treatments based on moderate-emissions scenario climate outputs (i.e., RCP4.5–650 ppm CO2 equivalent). Our temperature treatments captured weekly trends that represented cool, average, and warm temperature predictions, and our daily precipitation treatments mimicked various seasonal precipitation trends and extreme events projected for the late 21st century. Treatments were applied to two short-term controlled experiments evaluating (1) plant germination (temperature treatment applied in growth chamber) and (2) soil nitrogen cycling (precipitation treatment applied in greenhouse) responses to projected future conditions in the Great Plains. Our approach provides flexibility for selecting appropriate and precise climate model outputs to design experimental treatments. Using these techniques, ecologists can better incorporate variation in climate model projections for experimentally evaluating ecosystem responses to future climate conditions, reduce uncertainty in predictive ecological models, and apply predicted outcomes when making management and policy decisions.
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Missouri","active":true,"usgs":false}],"preferred":false,"id":832685,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203458,"text":"70203458 - 2019 - Absence of magnetite microlites, geochemistry of magnetite veins and replacements in IOA deposits, SE Missouri, USA: Relations to intermediate intrusions","interactions":[],"lastModifiedDate":"2019-12-03T12:22:43","indexId":"70203458","displayToPublicDate":"2019-08-31T12:19:04","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Absence of magnetite microlites, geochemistry of magnetite veins and replacements in IOA deposits, SE Missouri, USA: Relations to intermediate intrusions","docAbstract":"The paragenesis, textures, and chemical compositions of magnetite in two mafic to intermediate intrusions and four IOA deposits in SE Missouri were studied to discriminate between igneous and hydrothermal sources. In this study, we found that replacement magnetite with mineral inclusion-rich cores yields erroneously high Ti, Al, Si, Mg, and Mn contents as determined by EMP and LA-ICP-MS due to rutile and silicate inclusions. Thus, identification of high-Ti microlites on the basis of inclusion-rich cores with high Ti contents is an analytical artefact. Since the high-Ti magnetite microlite flotation model is critically dependent on this type of analysis, it may be invalid. The presence of coarse-grained high-Ti vein magnetite with ilmenite lamellae enveloped by replacement magnetite with inclusion-rich cores in ore zones suggests that the veins were high-temperature conduits for low-temperature replacement ores. The trace element compositions of vein and replacement magnetite suggest that iron was sourced from mafic to intermediate intrusions. These results support a magmatic-hydrothermal origin for IOA systems in SE Missouri.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Life with Ore Deposits on Earth – 15th SGA Biennial Meeting 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":" 15th SGA Biennial Meeting 2019","conferenceDate":"August 27-30, 2019","conferenceLocation":"Glasgow, Scotland","language":"English","publisher":"Society for Geology Applied to Mineral Deposits (SGA)","usgsCitation":"Meighan, C.J., Hofstra, A.H., Adams, D., Marsh, E.E., Lowers, H.A., and Koenig, A., 2019, Absence of magnetite microlites, geochemistry of magnetite veins and replacements in IOA deposits, SE Missouri, USA: Relations to intermediate intrusions, in Life with Ore Deposits on Earth – 15th SGA Biennial Meeting 2019, v. 1, Glasgow, Scotland, August 27-30, 2019, p. 396-399.","productDescription":"4 p.","startPage":"396","endPage":"399","ipdsId":"IP-106901","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":369877,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":369876,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sga2019glasgow.com/abstract"}],"country":"United States","state":"Missouri","otherGeospatial":"St. Francois Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.9949493408203,\n 37.37997911184045\n ],\n [\n -90.33370971679688,\n 37.37997911184045\n ],\n [\n -90.33370971679688,\n 37.72021976910832\n ],\n [\n -90.9949493408203,\n 37.72021976910832\n ],\n [\n -90.9949493408203,\n 37.37997911184045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meighan, Corey J. 0000-0002-5668-1621 cmeighan@usgs.gov","orcid":"https://orcid.org/0000-0002-5668-1621","contributorId":5892,"corporation":false,"usgs":true,"family":"Meighan","given":"Corey","email":"cmeighan@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hofstra, Albert H. 0000-0002-2450-1593 ahofstra@usgs.gov","orcid":"https://orcid.org/0000-0002-2450-1593","contributorId":1302,"corporation":false,"usgs":true,"family":"Hofstra","given":"Albert","email":"ahofstra@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, David 0000-0003-2679-2344 dadams@usgs.gov","orcid":"https://orcid.org/0000-0003-2679-2344","contributorId":199358,"corporation":false,"usgs":true,"family":"Adams","given":"David","email":"dadams@usgs.gov","affiliations":[{"id":169,"text":"Central Mineral Resources Team","active":false,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":762766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marsh, Erin E. 0000-0001-5245-9532 emarsh@usgs.gov","orcid":"https://orcid.org/0000-0001-5245-9532","contributorId":1250,"corporation":false,"usgs":true,"family":"Marsh","given":"Erin","email":"emarsh@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762768,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koenig, Alan 0000-0002-5230-0924","orcid":"https://orcid.org/0000-0002-5230-0924","contributorId":206119,"corporation":false,"usgs":true,"family":"Koenig","given":"Alan","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762769,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70205696,"text":"70205696 - 2019 - The effect of resolution on terrain feature extraction","interactions":[],"lastModifiedDate":"2019-10-08T07:06:17","indexId":"70205696","displayToPublicDate":"2019-08-30T12:48:21","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The effect of resolution on terrain feature extraction","docAbstract":"Recent increase in the production of high-resolution digital elevation models (DEMs) from lidar data has led to interest in their use for terrain mapping. Although the impact of different resolutions has been studied relative to terrain characteristics like roughness, slope and curvature, its relationship to the extraction of terrain features remains unclear. To address this question, this study tests the impact of four resolutions on the capture of glacial cirques from DEMs. Mean curvature was derived from one arc-second, one-third arc-second, one-ninth arc-second and half meter DEMs representing a cirque-covered mountainous region southwest of Lake Tahoe, California. Using a GEOBIA workflow, ridge objects were identified, and three scales - via the multi-resolution scale parameter (SP) - of objects bordering the ridges were classified as cirque objects. The resulting classifications were compared to reference cirques digitized at a scale of ~1:10,000. Results show that the one-third arc-second DEM produces the set of cirque objects most closely resembling the reference cirques. The one-ninth arc-second DEM afforded the second-best classification. These results emphasize the importance in carefully choosing resolution relative to the features extracted, rather than using the highest resolution data available. In the case of GEOBIA workflows, the choice of scale parameter is equally important.","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Geomorphometry 2018","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceDate":"August 13-17, 2018","language":"English","publisher":"PeerJ","doi":"10.7287/peerj.preprints.27072v1","usgsCitation":"Arundel, S., Li, W., and Zhou, X., 2019, The effect of resolution on terrain feature extraction, in Geomorphometry 2018, August 13-17, 2018, 4 p., https://doi.org/10.7287/peerj.preprints.27072v1.","productDescription":"4 p.","ipdsId":"IP-094644","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":460303,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.7287/peerj.preprints.27072v1","text":"External Repository"},{"id":368033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada mountain range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.23574829101562,\n 38.82981255949193\n ],\n [\n -120.02632141113283,\n 38.82981255949193\n ],\n [\n -120.02632141113283,\n 39.0047782882536\n ],\n [\n -120.23574829101562,\n 39.0047782882536\n ],\n [\n -120.23574829101562,\n 38.82981255949193\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":772079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Wenwen 0000-0003-2237-9499","orcid":"https://orcid.org/0000-0003-2237-9499","contributorId":219356,"corporation":false,"usgs":false,"family":"Li","given":"Wenwen","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":772080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Xiran","contributorId":219357,"corporation":false,"usgs":false,"family":"Zhou","given":"Xiran","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":772081,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208760,"text":"70208760 - 2019 - Sequestration and transformation in chemically enhanced treatment wetlands: DOC, DBPPs and Nutrients","interactions":[],"lastModifiedDate":"2020-02-28T06:43:57","indexId":"70208760","displayToPublicDate":"2019-08-30T06:42:13","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2255,"text":"Journal of Environmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Sequestration and transformation in chemically enhanced treatment wetlands: DOC, DBPPs and Nutrients","docAbstract":"We examined the effectiveness of chemically enhanced treatment wetlands (CETWs), wetlands that received water treated with coagulants, to remove dissolved organic carbon (DOC), disinfection byproduct precursors (DBPPs), nutrients and metals from agricultural drain water. Wetlands consisted of controls with no coagulant addition, ferric sulfate dosed and polyaluminum chloride dosed treatments. CETWs were more effective in removal of DOC, DBPPs, phosphate, dissolved organic nitrogen and metals than control wetlands. Coagulation treated wetlands removed 245 – 349 g/m2yr DOC, whereas control wetlands produced 51 g/m2yr. Wetland passage released DOC in the controls and treatments; this release was directly correlated to temperature and suggested thermally dependent mechanism(s) were partly responsible. A first-order plug flow reactor kinetic model that considered hydraulic retention time (HRT), temperature and concentration was tested for wetland DOC processing. Models indicate that operating CETWs at high coagulant dosing and low temperature can result in lowest DOC release with additional release suppression. Operating at the lowest HRT to meet discharge targets help overcome wetland processes that increase DOC release and provide the smallest footprint needed for treatment.","language":"English","publisher":"ASCE","doi":"10.1061/(ASCE)EE.1943-7870.0001536","usgsCitation":"Bachand, P.A., Bachand, S.M., Kraus, T.E., Stern, D., Ling Liang, Y., and Horwath, W.R., 2019, Sequestration and transformation in chemically enhanced treatment wetlands: DOC, DBPPs and Nutrients: Journal of Environmental Engineering, v. 145, no. 8, 04019044, 16 p., https://doi.org/10.1061/(ASCE)EE.1943-7870.0001536.","productDescription":"04019044, 16 p.","ipdsId":"IP-097091","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":467330,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/(asce)ee.1943-7870.0001536","text":"Publisher Index Page"},{"id":372722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"145","issue":"8","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bachand, Philip A. M. 0000-0002-6757-2404","orcid":"https://orcid.org/0000-0002-6757-2404","contributorId":207558,"corporation":false,"usgs":false,"family":"Bachand","given":"Philip","email":"","middleInitial":"A. M.","affiliations":[{"id":12526,"text":"Bachand & Associates","active":true,"usgs":false}],"preferred":false,"id":783301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bachand, Sandra M. 0000-0001-5235-9726","orcid":"https://orcid.org/0000-0001-5235-9726","contributorId":207557,"corporation":false,"usgs":false,"family":"Bachand","given":"Sandra","email":"","middleInitial":"M.","affiliations":[{"id":12526,"text":"Bachand & Associates","active":true,"usgs":false}],"preferred":false,"id":783302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stern, Dylan 0000-0001-5676-8711","orcid":"https://orcid.org/0000-0001-5676-8711","contributorId":215742,"corporation":false,"usgs":false,"family":"Stern","given":"Dylan","email":"","affiliations":[{"id":39311,"text":"Delta Stewardship Program, Aquatic Science Program","active":true,"usgs":false}],"preferred":false,"id":783303,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ling Liang, Yan 0000-0001-5999-3148","orcid":"https://orcid.org/0000-0001-5999-3148","contributorId":207555,"corporation":false,"usgs":false,"family":"Ling Liang","given":"Yan","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":783304,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Horwath, William R. 0000-0003-3707-0697","orcid":"https://orcid.org/0000-0003-3707-0697","contributorId":207560,"corporation":false,"usgs":false,"family":"Horwath","given":"William","email":"","middleInitial":"R.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":783305,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70206389,"text":"70206389 - 2019 - Surface rupture and distributed deformation revealed by optical satellite imagery: The intraplate 2016 Mw 6.0 Petermann Ranges earthquake, Australia","interactions":[],"lastModifiedDate":"2019-12-23T08:40:19","indexId":"70206389","displayToPublicDate":"2019-08-29T14:19:21","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Surface rupture and distributed deformation revealed by optical satellite imagery: The intraplate 2016 Mw 6.0 Petermann Ranges earthquake, Australia","docAbstract":"High-resolution optical satellite imagery is used to quantify vertical surface deformation associated with the intraplate 20 May 2016 Mw 6.0 Petermann Ranges earthquake, Northern Territory, Australia. The 21 ╓ 1 km long NW-trending rupture resulted from reverse motion on a northeast-dipping fault. Vertical surface offsets of up to 0.7 ╓ 0.1 m distributed across a 0.5-to-1 km wide deformation zone are measured using the Iterative Closest Point (ICP) algorithm to compare pre- and post-earthquake digital elevation models (DEMs) derived from Worldview imagery. The results are validated by comparison with field-based observations and interferometric synthetic aperture radar (InSAR). The pattern of surface uplift is consistent with distributed shear above the propagating tip of a reverse fault, leading to both an emergent fault and folding proximal to the rupture. This study demonstrates the potential for quantifying modest (<1 m) vertical deformation on a reverse fault using optical satellite imagery.","language":"English","publisher":"AGU","doi":"10.1029/2019GL084926","collaboration":"Geoscience Australia, Symonston, ACT, Australia; University of Iowa, Iowa City, IA, USA; University of Melbourne, Melbourne, Australia","usgsCitation":"Gold, R.D., Clark, D., Barnhart, W., King, T., Quigley, M., and Briggs, R.W., 2019, Surface rupture and distributed deformation revealed by optical satellite imagery: The intraplate 2016 Mw 6.0 Petermann Ranges earthquake, Australia: Geophysical Research Letters, v. 46, no. 17-18, p. 10394-10403, https://doi.org/10.1029/2019GL084926.","productDescription":"10 p.","startPage":"10394","endPage":"10403","ipdsId":"IP-110619","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":467331,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl084926","text":"Publisher Index Page"},{"id":368840,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 125.33203125,\n -26.509904531413916\n ],\n [\n 129.111328125,\n -26.509904531413916\n ],\n [\n 129.111328125,\n -21.739091217718574\n ],\n [\n 125.33203125,\n -21.739091217718574\n ],\n [\n 125.33203125,\n -26.509904531413916\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"17-18","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":774360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Dan","contributorId":175111,"corporation":false,"usgs":false,"family":"Clark","given":"Dan","email":"","affiliations":[],"preferred":false,"id":774361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, William D. 0000-0003-0498-1697","orcid":"https://orcid.org/0000-0003-0498-1697","contributorId":192730,"corporation":false,"usgs":false,"family":"Barnhart","given":"William D.","affiliations":[],"preferred":false,"id":774362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Tamarah","contributorId":220153,"corporation":false,"usgs":false,"family":"King","given":"Tamarah","email":"","affiliations":[{"id":40134,"text":"University of Melbourne, Melbourne, Australia","active":true,"usgs":false}],"preferred":false,"id":774363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quigley, Mark","contributorId":220154,"corporation":false,"usgs":false,"family":"Quigley","given":"Mark","email":"","affiliations":[{"id":40134,"text":"University of Melbourne, Melbourne, Australia","active":true,"usgs":false}],"preferred":false,"id":774364,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":139002,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":774365,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204522,"text":"sir20195072 - 2019 - Arsenic, antimony, mercury, and water temperature in streams near Stibnite mining area, central Idaho, 2011–17","interactions":[],"lastModifiedDate":"2019-08-28T10:27:00","indexId":"sir20195072","displayToPublicDate":"2019-08-27T13:23:40","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5072","displayTitle":"Arsenic, Antimony, Mercury, and Water Temperature in Streams near Stibnite Mining Area, Central Idaho, 2011–17","title":"Arsenic, antimony, mercury, and water temperature in streams near Stibnite mining area, central Idaho, 2011–17","docAbstract":"Mineralization and historical mining of stibnite (antimony sulfide), tungsten, gold, silver, and mercury in the headwaters of the East Fork of the South Fork Salmon River (EFSFSR) near the former town of Stibnite in central Idaho resulted in water-quality impairments related to mercury, antimony, and arsenic. Additionally, mining-related disturbances and wildfires have resulted in a lack of riparian shade in some areas, likely impacting water temperatures. In 2011, the U.S. Geological Survey, in cooperation with Midas Gold Corporation and the Idaho Department of Lands, began a study to characterize the spatial and temporal occurrence of trace metals to the EFSFSR. Five sites on the EFSFSR and its tributaries (Meadow and Sugar Creeks) were sampled about six times annually during 2011–17, during a range of streamflow conditions, for a total of 36–40 samples per location. Continuous water temperature, specific conductance, and streamflow also were measured at each site. The purpose of this report is to update previously reported information related to arsenic, antimony, mercury, and water temperature.\n\nConcentrations of dissolved arsenic and antimony generally increased from upstream to downstream in the EFSFSR. At the upstream site, upstream of the Meadow Creek confluence, dissolved arsenic and antimony concentrations averaged 8.86 and 0.93 micrograms per liter (μg/L), respectively. Downstream, upstream from the Sugar Creek confluence, average dissolved concentrations increased to 56.5 and 27.9 μg/L, respectively. All samples from the downstream EFSFSR site exceeded the human-health based criterion for both dissolved arsenic (10 µg/L) and dissolved antimony (5.6 µg/L). The chronic aquatic life criterion for dissolved arsenic (150 μg/L) was not exceeded (the maximum sample concentration was 108 μg/L), and aquatic life criteria for antimony have not been established. The highest concentrations of both dissolved arsenic and dissolved antimony occurred during low-flow periods (July–March), suggesting the constituents are present in groundwater. In contrast, total mercury concentrations were highest during high-flow periods (April–June) and were particulate-associated, suggesting that mercury is present in surface materials. At Sugar Creek, where the highest total mercury concentrations were measured, 97 percent of samples exceeded the chronic aquatic life criterion (0.012 μg/L) and 11 percent exceeded the acute criterion (2.1 μg/L). At all sites, summertime water temperatures frequently exceeded criteria related to salmonid spawning.\n\nSurrogate models previously developed to estimate continuous concentrations of arsenic, antimony, and mercury were reevaluated and updated, and the importance of explanatory variables on constituent concentrations is discussed. Results from this study can help guide future remediation locations and strategies, and provide a baseline against which future changes can be measured.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195072","collaboration":"Prepared in cooperation with the Idaho Department of Lands and Midas Gold Idaho, Inc.","usgsCitation":"Baldwin, A.K., and Etheridge, A.B., 2019, Arsenic, antimony, mercury, and water temperature in streams near Stibnite mining area, central Idaho, 2011–17: U.S. Geological Survey Scientific Investigations Report 2019-5072, 20 p., plus appendix, https://doi.org/10.3133/sir20195072.","productDescription":"Report: vi, 20 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-093353","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":366989,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5072/coverthb.jpg"},{"id":366990,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5072/sir20195072.pdf","text":"Report","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5072"},{"id":366991,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5072/sir20195072_appendix.pdf","text":"Appendix","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5072 Appendix","linkHelpText":" — Surrogate Regression Model Archive Summaries."}],"country":"United States","state":"Idaho","otherGeospatial":"Stibnite Mining Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.67985534667969,\n 44.793530904744074\n ],\n [\n -115.14564514160158,\n 44.793530904744074\n ],\n [\n -115.14564514160158,\n 45.15541134861056\n ],\n [\n -115.67985534667969,\n 45.15541134861056\n ],\n [\n -115.67985534667969,\n 44.793530904744074\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
A field study was conducted to estimate survival of juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon, during 2018. The study consisted of releasing three groups of genetically-marked fish into the reservoir, and sampling them monthly. Juveniles were released during April 10–13 (116,708 fish), May 15–18 (31,911 fish), and June 19–20 (11,758 fish). Reservoir sampling began in May and occurred monthly through October, consisting of 5-day events where juvenile Chinook salmon were collected using electrofishing, shoreline traps, and gill nets. Data were analyzed using a staggered release-recovery model and a parentage-based tagging (PBT) N-mixture model. The staggered release-recovery model provided survival estimates from three periods: mid-April to mid-May (SSRRM1); mid-May to mid-June (SSRRM2); and mid-April to mid-June (SSRRM12). Multiple estimates of survival were possible for each period using different combinations of recovery data from the three groups of fish that were released. Survival probability estimates for SSRRM1 ranged from 0.98520 to 0.98954; estimates for SSRRM2 ranged from 0.09338 to 0.62142; and the estimate for cumulative survival from mid-April to mid-June (SSRRM12) were 0.75211. We suspect that issues with release groups in May (R2) and June (R3) led to biased survival results using the staggered release-recovery model. The PBT N-mixture model provided survival estimates from six periods: mid-April to mid-May (SNMIX1); mid-May to mid-June (SNMIX2), mid-June to mid-July (SNMIX3), mid-July to mid-August (SNMIX4), mid-August to mid-September (SNMIX5); and mid-September to mid-October (SNMIX6). Survival estimates from the PBT N-mixture model were lowest for SNMIX6 (0.41620) and highest for SNMIX1 (0.79587). These results differed from those in 2017 when monthly survival increased across months. This suggests that one or more factors could have affected juvenile Chinook salmon survival in Lookout Point Reservoir. One possible factor could be copepods (which were highly prevalent on juvenile Chinook salmon during summer 2018), but environmental factors such as reserveroir elevation, discharge at Lookout Point Dam, and fish distributions within the reservoir differed between study years. Two PBT N-mixture models provided cumulative survival estimates from mid-April to mid-October. Estimates from the two models were 0.061 and 0.039, which suggests that survival of subyearling Chinook salmon in Lookout Point Reservoir was very low in 2018. Additional research is recommended to better understand inter-annual variability of subyearling Chinook salmon in the reservoir and to gain insights into factors that affect their survival.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191097","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and Oregon State University","usgsCitation":"Kock, T.J., Perry, R.W., Hansen, G.S., Haner, P.V., Pope, A.C., Plumb, J.M., Cogliati, K.M., and Hansen, A.C., 2019, Juvenile Chinook salmon (Oncorhynchus tshawytscha) survival in Lookout Point Reservoir, Oregon, 2018: U.S. Geological Survey Open-File Report 2019–1097, 41 p., https://doi.org/10.3133/ofr20191097.","productDescription":"vi, 41 p.","onlineOnly":"Y","ipdsId":"IP-108747","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":366974,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1097/coverthb.jpg"},{"id":366975,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1097/ofr20191097.pdf","text":"Report","size":"6.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1097"}],"country":"United States","state":"Oregon","otherGeospatial":"Lookout Point Reservoir, Middle Fork Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.8165054321289,\n 43.80009302166679\n ],\n [\n -122.55970001220705,\n 43.80009302166679\n ],\n [\n -122.55970001220705,\n 43.93919224634882\n ],\n [\n -122.8165054321289,\n 43.93919224634882\n ],\n [\n -122.8165054321289,\n 43.80009302166679\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Fresh volcanic eruption deposits tend to be loose, bare, and readily resuspended by wind. Major resuspension events in Patagonia, Iceland, and Alaska have lofted ash clouds with potential to impact aircraft, infrastructure, and downwind communities. However, poor constraints on this resuspension process limit our ability to model this phenomenon. Here, we present laboratory experiments measuring threshold shear velocities and emission rates of resuspended ash under different environmental conditions, including relative humidity of 25–75% and simulated rainfall with subsequent drying. Eruption deposits were replicated using ash collected from two major eruptions: the 18 May 1980 eruption of Mount St. Helens and the 1912 eruption of Novarupta, in Alaska's Valley of Ten Thousand Smokes. Samples were conditioned in a laboratory chamber and prepared with bulk deposit densities of 1,300–1,500 kg/m3. A control sample of dune sand was included for comparison. The deposits were subjected to different wind speeds using a modified PI‐SWERL® instrument. Under a constant relative humidity of 50% and shear velocities 0.4–0.8 m/s, PM10 emission by resuspension ranged from 10 to >100 mg·m−2·s−1. Addition of liquid water equivalent to 5 mm of rainfall had little lasting effect on Mount St. Helens wind erosion potential, while the Valley of Ten Thousand Smokes deposits exhibited lower emissions for at least 12 days. The results indicate that particle resuspension due to wind erosion from ash deposits potentially exceeds that of most desert surfaces and approaches some of the highest emissions ever measured.
Hyperalkaline (pH greater than 12) ponds and groundwater exist at Big Marsh near Lake Calumet, Chicago, Illinois, a site used by the steel industry during the mid-1900s to deposit steel- and iron-making waste, in particular, slag. The hyperalkaline ponds may pose a hazard to human health and the environment. The U.S. Geological Survey (USGS), in cooperation with the Environmental Protection Agency (EPA) and in collaboration with the City of Chicago’s Park District, completed a study to evaluate the hydrologic balance, water quality, and chemical-mass balance of hyperalkaline ponds at Big Marsh and geochemical modeling used to evaluate remediation options for water quality at the site based on data collected in 2016–17.
Synoptic measurements of surface-water and groundwater elevations were used to determine flow directions and to enable a preliminary estimate of the hydrologic balance for the ponds. Water-quality samples also were collected and analyzed for selected constituents including major anions and cations, nutrients, metals, and trace elements. The results of the water-quality analyses were used to develop a geochemical model to evaluate concentrations, factors affecting pH, and the state of equilibrium between surface waters and atmospheric carbon dioxide. The geochemical model was used to evaluate remediation scenarios using riprap, spillways, or active aeration. The results indicate that active aeration will decrease the pH to near 7.5 in about 8 hours, the fastest rate of the scenarios. Passive aeration, such as riprap or spillways, also can be effective at decreasing the pH in about 45 hours, but spatial obstacles limit their implementation. Seasonal variations in temperature also affect the rate of equilibration, where colder temperatures may have a lower pH than warmer temperatures and may affect the timing and frequency of remediation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195078","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Brownfields Program, and in collaboration with the City of Chicago’s Park District","usgsCitation":"Gahala, A.M., Seal, R.R., and Piatak, N.M., 2019, Hydrologic balance, water quality, chemical-mass balance, and geochemical modeling of hyperalkaline ponds at Big Marsh, Chicago, Illinois, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5078, 31 p., https://doi.org/10.3133/sir20195078.","productDescription":"Report: vi, 31 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-091826","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":366917,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5078/sir20195078.pdf","text":"SIR 2019–5078","size":"3.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5078"},{"id":366918,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VUAQ35","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Water level data from single-well (slug) tests at a monitoring well in Big Marsh, Chicago, Illinois"},{"id":366916,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5078/coverthb.jpg"}],"country":"United States","state":"Illinois","county":"Cook 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Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Santa Rosa is no stranger to earthquakes. This northern California city was damaged several times in the late 19th and early 20th centuries by shaking from earthquakes, culminating in the devastating earthquake of 1906, whose rupture passed 20 miles to the west of the city on the San Andreas Fault. Then in 1969, Santa Rosa was again strongly shaken and buildings were damaged by a pair of nearby, moderate-sized earthquakes on the Rodgers Creek Fault. Since then, scientists have learned how the underlying geology increases shaking damage in Santa Rosa, have mapped where the Rodgers Creek Fault runs beneath the city, and have discovered that this fault is capable of much larger earthquakes. Following the 1969 earthquakes, Santa Rosa rose to the challenge of improving seismic safety; however, continued progress is needed to increase seismic resilience and reduce the impact of future earthquakes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193035","usgsCitation":"Hecker, S., McPhee, D.K., Langenheim, V.E., and Watt, J.T., 2019, Santa Rosa's past and future earthquakes: U.S. Geological Survey Fact Sheet 2019–3035, 4 p., https://doi.org/10.3133/fs20193035. ","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-102642","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":366908,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3035/fs20193035.pdf","text":"Report","size":"5.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2019-3035"},{"id":366907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3035/coverthb.jpg"}],"country":"United States","state":"California","city":"Santa Rosa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.12927246093751,\n 38.11727165830543\n ],\n [\n -122.26409912109375,\n 38.11727165830543\n ],\n [\n -122.26409912109375,\n 38.603993275591684\n ],\n [\n -123.12927246093751,\n 38.603993275591684\n ],\n [\n -123.12927246093751,\n 38.11727165830543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, California 94025
The invasion of non-native fishes has caused a great detriment to many of our native fishes. Since the introduction of invasive carps, such as Silver, Bighead, Common and Grass Carp, managers and researcher have been struggling to remove these species while also hypothesizing the detriment of further invasion. This study developed a food web model of four locations on the Mississippi River and used those models to assess the impacts of two scenarios: carp removal and carp invasion. In the Middle Mississippi River where these invasive carps are already present, the models found that it would take a sustained exploitation of up to 30% of initial biomass over an extended period to remove Grass Carp and up to 90% removal of initial biomass to remove Silver and Bighead Carp. In the locations where Silver, Bighead, and Grass Carp are not yet established (i.e., Pools 4,8, and 13) the invasion of these species could cause declines from 10 to 30% in initial biomass of native fishes as well as already established nonnative invasive species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fooweb.2019.e00120","usgsCitation":"Kramer, N.W., Phelps, Q.E., Pierce, C., and Colvin, M., 2019, A food web modeling assessment of Asian Carp impacts in the Middle and Upper Mississippi River, USA: Food Webs, v. 21, e00120, 9 p., https://doi.org/10.1016/j.fooweb.2019.e00120.","productDescription":"e00120, 9 p.","ipdsId":"IP-103332","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467338,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1330&context=nrem_pubs","text":"External Repository"},{"id":388582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Middle and Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.5770263671875,\n 37.09900294387622\n ],\n [\n -89.35455322265625,\n 37.09900294387622\n ],\n [\n -89.35455322265625,\n 37.57070524233116\n ],\n [\n -89.5770263671875,\n 37.57070524233116\n ],\n [\n -89.5770263671875,\n 37.09900294387622\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.03659057617188,\n 44.31058342934815\n ],\n [\n -91.91024780273438,\n 44.31058342934815\n ],\n [\n -91.91024780273438,\n 44.414654778606874\n ],\n [\n -92.03659057617188,\n 44.414654778606874\n ],\n [\n -92.03659057617188,\n 44.31058342934815\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.2802505493164,\n 43.57516780951105\n ],\n [\n -91.2081527709961,\n 43.57516780951105\n ],\n [\n -91.2081527709961,\n 43.74108839068835\n ],\n [\n -91.2802505493164,\n 43.74108839068835\n ],\n [\n -91.2802505493164,\n 43.57516780951105\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.2581787109375,\n 41.83478149415483\n ],\n [\n -90.076904296875,\n 41.83478149415483\n ],\n [\n -90.076904296875,\n 42.157295553651664\n ],\n [\n -90.2581787109375,\n 42.157295553651664\n ],\n [\n -90.2581787109375,\n 41.83478149415483\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kramer, Nicholas W.","contributorId":264840,"corporation":false,"usgs":false,"family":"Kramer","given":"Nicholas","email":"","middleInitial":"W.","affiliations":[{"id":17621,"text":"Southeast Missouri State University","active":true,"usgs":false}],"preferred":false,"id":822082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phelps, Quinton E.","contributorId":264841,"corporation":false,"usgs":false,"family":"Phelps","given":"Quinton","email":"","middleInitial":"E.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":822083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pierce, Clay 0000-0001-5088-5431 cpierce@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-5431","contributorId":150492,"corporation":false,"usgs":true,"family":"Pierce","given":"Clay","email":"cpierce@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822081,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colvin, Michael E.","contributorId":264842,"corporation":false,"usgs":false,"family":"Colvin","given":"Michael E.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":822084,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223496,"text":"70223496 - 2019 - Invertebrate prey contributions to juvenile Coho Salmon diet from riparian habitats along three Alaska streams: Implications for environmental change","interactions":[],"lastModifiedDate":"2021-08-31T13:39:20.889311","indexId":"70223496","displayToPublicDate":"2019-08-26T08:30:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2299,"text":"Journal of Freshwater Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Invertebrate prey contributions to juvenile Coho Salmon diet from riparian habitats along three Alaska streams: Implications for environmental change","docAbstract":"Stream fish rely on a mix of terrestrial and aquatic prey sources. While the importance of terrestrial invertebrates as a food source for stream fish is well documented, the role of aquatic insects that emerge from the stream as winged adult insects (aquatic winged adults) and return to the stream as prey is less understood. In this study we determined the proportion of total diet for stream-rearing juvenile Coho Salmon (Oncorhynchus kisutch) that is derived from terrestrial and aquatic winged adult invertebrates which enter the stream from riparian habitats and consider how those cross-ecosystem prey contributions vary based on riparian habitat type. Study reaches were identified in three streams within the Kenai River watershed of Alaska that were representative of habitats found throughout the region and riparian vegetation was classified into grass/sedge, shrub and tree types using LiDAR. Juvenile Coho Salmon stomach contents were sampled seasonally in study reaches over a two-year period and ingested invertebrates were identified by taxa, life stage and origin. Our results showed that aquatic winged adult prey contributions to juvenile salmon diet were significantly lower in the grass/sedge study reach, and cross-ecosystem invertebrate prey represented a significantly higher proportion of juvenile salmon diet in the tree study reach. Invertebrate prey in the grass/sedge reach were composed primarily of the larval life stage of aquatic winged adults. These results suggest that change in riparian vegetation from tree/shrub to grass/sedge along Kenai streams as projected by regional climate change models, or that results from anthropogenic modification, will likely lead to lower availability of cross-ecosystem prey for stream fish. Management of riparian buffers along streams to preserve or increase occurrence of trees and shrubs is likely to help mitigate impacts of those possible changes.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2019.1642243","usgsCitation":"Grunblatt, J., Meyer, B., and Wipfli, M.S., 2019, Invertebrate prey contributions to juvenile Coho Salmon diet from riparian habitats along three Alaska streams: Implications for environmental change: Journal of Freshwater Ecology, v. 34, no. 1, p. 617-631, https://doi.org/10.1080/02705060.2019.1642243.","productDescription":"16 p.","startPage":"617","endPage":"631","ipdsId":"IP-103789","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467339,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2019.1642243","text":"Publisher Index Page"},{"id":388688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kenai watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.369384765625,\n 59.77852198502987\n ],\n [\n -148.919677734375,\n 59.77852198502987\n ],\n [\n -148.919677734375,\n 61.312451574838214\n ],\n [\n -152.369384765625,\n 61.312451574838214\n ],\n [\n -152.369384765625,\n 59.77852198502987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Grunblatt, Jess","contributorId":264907,"corporation":false,"usgs":false,"family":"Grunblatt","given":"Jess","affiliations":[{"id":54579,"text":"uak","active":true,"usgs":false}],"preferred":false,"id":822179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Benjamin E.","contributorId":264908,"corporation":false,"usgs":false,"family":"Meyer","given":"Benjamin E.","affiliations":[{"id":54579,"text":"uak","active":true,"usgs":false}],"preferred":false,"id":822180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":822178,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204839,"text":"sir20195067 - 2019 - Flood-inundation maps for a 23-mile reach of the Medina River at Bandera, Texas, 2018","interactions":[],"lastModifiedDate":"2019-08-26T05:37:05","indexId":"sir20195067","displayToPublicDate":"2019-08-26T05:36:50","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5067","displayTitle":"Flood-Inundation Maps for a 23-Mile Reach of the Medina River at Bandera, Texas, 2018","title":"Flood-inundation maps for a 23-mile reach of the Medina River at Bandera, Texas, 2018","docAbstract":"In 2018, the U.S. Geological Survey (USGS), in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, studied floods through the period of record to create a library of flood-inundation maps for the Medina River at Bandera, Texas. Digital flood-inundation maps for a 23-mile reach of the Medina River at and near Bandera, from the confluence with Winans Creek to English Crossing Road, were developed. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to a range of different gage heights (gage height is commonly referred to as “stage,” or the water-surface elevation at a streamflow-gaging station) at USGS streamflow-gaging station 08178880 Medina River at Bandera, Tex. (hereinafter referred to as the “Bandera station”). Water-surface profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The stage-discharge (streamflow) relation effective in 2018 was used to calibrate the model, and stages from four recent flood events were used to independently validate the model. The calibrated hydraulic model was then used to compute 29 water-surface profiles for stages at 1-foot (ft) increments referenced to the station datum and ranging from 10 ft (near bankfull) to 38 ft, which exceeds the major flood stage of the National Weather Service Advanced Hydrologic Prediction Service of 24 ft. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.4-ft vertical accuracy and 1.6-ft horizontal resolution) to delineate the area flooded for stages ranging from 10 to 38 ft.
The digital flood-inundation maps are delivered through the USGS Flood Inundation Mapper application that presents map libraries and provides detailed information on flood-inundation extents and stages for modeled sites. The flood-inundation maps developed in this study, in conjunction with the real-time stage data from the Bandera station, are intended to help guide the public in taking individual safety precautions and provide emergency management personnel with a tool to efficiently manage emergency flood operations and post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195067","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., and Engel, F.L., 2019, Flood-inundation maps for a 23-mile reach of the Medina River at Bandera, Texas, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5067, 15 p., https://doi.org/10.3133/sir20195067.","productDescription":"Report: viii, 15 p.; Fact Sheet: 2 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-104084","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":366755,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/fs20193043","text":"FS 2019–3043","size":"895 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3043","linkHelpText":" Flood Warning Toolset for the Medina River in Bandera County, Texas"},{"id":366756,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WYD6LS","text":"USGS data release ","linkHelpText":"Geospatial and survey data for flood-inundation maps in a 23-mile reach of the Medina River at Bandera, Texas, 2018"},{"id":366666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5067/coverthb.jpg"},{"id":366667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5067/sir20195067.pdf","text":"Report","size":"3.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5067"}],"contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Floods are the most common natural disaster in the United States. The Medina River in Bandera County, Texas, is in the Edwards Plateau, where high-intensity rain rates and steep terrain frequently contribute to severe flash flooding capable of causing loss of life and property. For example, the July 5, 2002, flood claimed a total of 12 lives in the central Texas area. The estimated peak discharge during this flood at U.S. Geological Survey (USGS) streamflow-gaging station 08178880 Medina River at Bandera, Tex., was 159,000 cubic feet per second (corresponding to a stage or gage height of 38.91 feet), causing significant flooding in Bandera near Mud Creek and farther downstream.
In 2018, the USGS, in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, developed a flood early-warning toolset to enhance the communication of flood risk and provide emergency management with additional information to improve flood response and mitigation. This toolset consists of a continuous streamflow-gage monitoring network, a well-calibrated hydraulic model of the Medina River, and a flood-inundation mapper application for the study area. A library of flood-inundation maps tied to the National Weather Service river stage forecast capability is included with the toolset.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193043","usgsCitation":"Engel, F.L., and Choi, N., 2019, Flood warning toolset for the Medina River in Bandera County, Texas: U.S. Geological Survey Fact Sheet 2019–3043, 2 p., https://doi.org/10.3133/fs20193043. ","productDescription":"Report: 2 p.; Companion Files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-110193","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":366754,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195067","text":"SIR 2019–5067","size":"3.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5067","linkHelpText":" Flood-Inundation Maps for a 23-Mile Reach of the Medina River at Bandera, Texas, 2018"},{"id":366753,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3043/fs20193043.pdf","text":"Report","size":"895 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3043"},{"id":366752,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3043/coverthb.jpg"}],"country":"United States","state":"Texas","county":"Bandera County ","otherGeospatial":"Medina River","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-98.9253,29.7842],[-98.7869,29.7168],[-98.8056,29.6968],[-98.9213,29.5665],[-98.9245,29.562],[-98.9282,29.5593],[-98.9318,29.5588],[-98.9429,29.5585],[-98.9513,29.5581],[-98.9607,29.5578],[-98.9633,29.5578],[-98.9676,29.5546],[-98.9712,29.5533],[-98.9765,29.5547],[-98.978,29.5556],[-98.9811,29.5589],[-98.9832,29.5625],[-98.9837,29.5671],[-98.9836,29.5717],[-98.9819,29.5804],[-98.9818,29.5909],[-98.9801,29.5983],[-98.9779,29.606],[-98.9789,29.6102],[-98.9794,29.6129],[-98.982,29.6148],[-98.9909,29.6185],[-99.0103,29.6187],[-99.4132,29.6253],[-99.6033,29.6257],[-99.6031,29.9068],[-99.2839,29.905],[-99.1766,29.8946],[-98.9253,29.7842]]]},\"properties\":{\"name\":\"Bandera\",\"state\":\"TX\"}}]}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11172005","usgsCitation":"Fu, Y., He, H.S., Hawbaker, T., Henne, P., Zhu, Z., and Larsen, D.R., 2019, Evaluating k-nearest neighbor (kNN) imputation models for species-level aboveground forest biomass mapping in northeast China: Remote Sensing, v. 17, no. 11, p. 1-20, https://doi.org/10.3390/rs11172005.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-109980","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":467340,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11172005","text":"Publisher Index Page"},{"id":437358,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MOB5E3","text":"USGS data release","linkHelpText":"Data release for: Evaluating k-nearest neighbor (kNN) imputation models for species-level aboveground forest biomass mapping in northeast China"},{"id":367198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 124.8046875,\n 40.3130432088809\n ],\n [\n 129.90234375,\n 43.32517767999296\n ],\n [\n 131.484375,\n 42.293564192170095\n ],\n [\n 135.17578125,\n 48.45835188280866\n ],\n [\n 130.95703125,\n 47.87214396888731\n ],\n [\n 124.27734374999999,\n 53.54030739150022\n ],\n [\n 120.41015624999999,\n 52.696361078274485\n ],\n [\n 118.47656249999999,\n 49.61070993807422\n ],\n [\n 116.54296874999999,\n 49.724479188712984\n ],\n [\n 115.83984375,\n 47.87214396888731\n ],\n [\n 119.00390625,\n 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