Truncated distributions of pelagophilic fishes have been observed across the Great Plains of North America, with water use and landscape fragmentation implicated as contributing factors. Developing conservation strategies for these species is hindered by the existence of multiple competing flow regime hypotheses related to species persistence. Our primary study objective was to compare the predicted distributions of one pelagophil, the Arkansas River Shiner Notropis girardi, constructed using different flow regime metrics. Further, we investigated different approaches for improving temporal transferability of the species distribution model (SDM). We compared four hypotheses: mean annual flow (a baseline), the 75th percentile of daily flow, the number of zero-flow days, and the number of days above 55th percentile flows, to examine the relative importance of flows during the spawning period. Building on an earlier SDM, we added covariates that quantified wells in each catchment, point source discharges, and non-native species presence to a structured variable framework. We assessed the effects on model transferability and fit by reducing multicollinearity using Spearman’s rank correlations, variance inflation factors, and principal component analysis, as well as altering the regularization coefficient (β) within MaxEnt. The 75th percentile of daily flow was the most important flow metric related to structuring the species distribution. The number of wells and point source discharges were also highly ranked. At the default level of β, model transferability was improved using all methods to reduce collinearity; however, at higher levels of β, the correlation method performed best. Using β = 5 provided the best model transferability, while retaining the majority of variables that contributed 95% to the model. This study provides a workflow for improving model transferability and also presents water-management options that may be considered to improve the conservation status of pelagophils.
High-resolution airborne magnetic and gravity gradiometry data provide the geophysical framework for evaluating the exploration potential of hidden iron oxide deposits in Mesoproterozoic basement rocks of southeast Missouri. The data are used to calculate mineral prospectivity for iron oxide-apatite (IOA) ± rare earth element (REE) and iron oxide-copper-gold (IOCG) deposits. Results delineate the geophysical footprints of all known iron oxide deposits and reveal several previously unrecognized prospective areas. The airborne data are also inverted to three-dimensional density and magnetic susceptibility models over four concealed deposits at Pea Ridge (IOA ± REE), Boss (IOCG), Kratz Spring (IOA), and Bourbon (IOCG). The Pea Ridge susceptibility model shows a magnetic source that is vertically extensive and traceable to a depth of greater than 2 km. A smaller density source, located within the shallow Precambrian basement, is partly coincident with the magnetic source at Pea Ridge. In contrast, the Boss models show a large (625-m-wide), vertically extensive, and coincident dense and magnetic stock with shallower adjacent lobes that extend more than 2,600 m across the shallow Precambrian paleosurface. The Kratz Spring deposit appears to be a smaller volume of iron oxides and is characterized by lower density and less magnetic rock compared to the other iron deposits. A prospective area identified south of the Kratz Spring deposit shows the largest volume of coincident dense and nonmagnetic rock in the subsurface, and is interpreted as prospective for a hematite-dominant lithology that extends from the top of the Precambrian to depths exceeding 2 km. The Bourbon deposit displays a large bowl-shaped volume of coincident high density and high-magnetic susceptibility rock, and a geometry that suggests the iron mineralization is vertically restricted to the upper parts of the Precambrian basement. In order to underpin the evaluation of the prospectivity and three-dimensional models, an extensive statistical summary of density and apparent magnetic susceptibility measurements is presented that includes data on several hundred samples taken from the deposits, altered wall rocks, and unaltered country rocks.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.111.8.1859","usgsCitation":"McCafferty, A.E., Phillips, J., and Driscoll, R.L., 2016, Magnetic and gravity gradiometry framework for Mesoproterozoic iron oxide-apatite and iron oxide-copper-gold deposits, southeast Missouri, USA: Economic Geology, v. 111, no. 8, https://doi.org/10.2113/econgeo.111.8.1859.","productDescription":"24 p.","startPage":"1882","ipdsId":"IP-069306","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":438504,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5XM4","text":"USGS data release","linkHelpText":"Helicopter magnetic and gravity gradiometry survey over the Pea Ridge iron mine and surrounding area, southeast Missouri, 2014"},{"id":331203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.65869140625,\n 35.88905007936091\n ],\n [\n -92.65869140625,\n 38.788345355085625\n ],\n [\n -89.05517578125,\n 38.788345355085625\n ],\n [\n -89.05517578125,\n 35.88905007936091\n ],\n [\n -92.65869140625,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"111","issue":"8","edition":"1859","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"58356728e4b0070c0abfb6d2","contributors":{"authors":[{"text":"McCafferty, Anne E. 0000-0001-5574-9201 anne@usgs.gov","orcid":"https://orcid.org/0000-0001-5574-9201","contributorId":1120,"corporation":false,"usgs":true,"family":"McCafferty","given":"Anne","email":"anne@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":654215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Jeffrey 0000-0002-6459-2821 jeff@usgs.gov","orcid":"https://orcid.org/0000-0002-6459-2821","contributorId":127453,"corporation":false,"usgs":true,"family":"Phillips","given":"Jeffrey","email":"jeff@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":654216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Driscoll, Rhonda L. 0000-0001-7725-8956 rdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0001-7725-8956","contributorId":745,"corporation":false,"usgs":true,"family":"Driscoll","given":"Rhonda","email":"rdriscoll@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":654217,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188572,"text":"70188572 - 2016 - Constraining the relative importance of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery time scales","interactions":[],"lastModifiedDate":"2017-06-16T09:30:12","indexId":"70188572","displayToPublicDate":"2016-11-22T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Constraining the relative importance of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery time scales","docAbstract":"Mountain watersheds recently burned by wildfire often experience greater amounts of runoff and increased rates of sediment transport relative to similar unburned areas. Given the sedimentation and debris flow threats caused by increases in erosion, more work is needed to better understand the physical mechanisms responsible for the observed increase in sediment transport in burned environments and the time scale over which a heightened geomorphic response can be expected. In this study, we quantified the relative importance of different hillslope erosion mechanisms during two postwildfire rainstorms at a drainage basin in Southern California by combining terrestrial laser scanner-derived maps of topographic change, field measurements, and numerical modeling of overland flow and sediment transport. Numerous debris flows were initiated by runoff at our study area during a long-duration storm of relatively modest intensity. Despite the presence of a well-developed rill network, numerical model results suggest that the majority of eroded hillslope sediment during this long-duration rainstorm was transported by raindrop-induced sediment transport processes, highlighting the importance of raindrop-driven processes in supplying channels with potential debris flow material. We also used the numerical model to explore relationships between postwildfire storm characteristics, vegetation cover, soil infiltration capacity, and the total volume of eroded sediment from a synthetic hillslope for different end-member erosion regimes. This study adds to our understanding of sediment transport in steep, postwildfire landscapes and shows how data from field monitoring can be combined with numerical modeling of sediment transport to isolate the processes leading to increased erosion in burned areas.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JF003867","usgsCitation":"McGuire, L., Kean, J.W., Staley, D.M., Rengers, F.K., and Wasklewicz, T.A., 2016, Constraining the relative importance of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery time scales: Journal of Geophysical Research, v. 121, no. 11, p. 2211-2237, https://doi.org/10.1002/2016JF003867.","productDescription":"27 p.","startPage":"2211","endPage":"2237","ipdsId":"IP-077491","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470407,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jf003867","text":"Publisher Index Page"},{"id":342596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Arroyo Seco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.383333,\n 34.441667\n ],\n [\n -117.875,\n 34.441667\n ],\n [\n -117.875,\n 34.2\n ],\n [\n -118.383333,\n 34.2\n ],\n [\n -118.383333,\n 34.441667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-22","publicationStatus":"PW","scienceBaseUri":"5944ee16e4b062508e333607","contributors":{"authors":[{"text":"McGuire, Luke lmcguire@usgs.gov","contributorId":167018,"corporation":false,"usgs":true,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":698465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wasklewicz, Thad A.","contributorId":39275,"corporation":false,"usgs":true,"family":"Wasklewicz","given":"Thad","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":698397,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178466,"text":"70178466 - 2016 - Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays","interactions":[],"lastModifiedDate":"2018-08-07T12:05:31","indexId":"70178466","displayToPublicDate":"2016-11-21T15:10:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1290,"text":"Comparative Biochemistry and Physiology, Part D: Genomics and Proteomics","active":true,"publicationSubtype":{"id":10}},"title":"Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays","docAbstract":"Environmental studies increasingly identify the presence of both contaminants of emerging concern (CECs) and legacy contaminants in aquatic environments; however, the biological effects of these compounds on resident fishes remain largely unknown. High throughput methodologies were employed to establish partial transcriptomes for three wild-caught, non-model fish species; smallmouth bass (Micropterus dolomieu), white sucker (Catostomus commersonii) and brown bullhead (Ameiurus nebulosus). Sequences from these transcriptome databases were utilized in the development of a custom nCounter CodeSet that allowed for direct multiplexed measurement of 50 transcript abundance endpoints in liver tissue. Sequence information was also utilized in the development of quantitative real-time PCR (qPCR) primers. Cross-species hybridization allowed the smallmouth bass nCounter CodeSet to be used for quantitative transcript abundance analysis of an additional non-model species, largemouth bass (Micropterus salmoides). We validated the nCounter analysis data system with qPCR for a subset of genes and confirmed concordant results. Changes in transcript abundance biomarkers between sexes and seasons were evaluated to provide baseline data on transcript modulation for each species of interest.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cbd.2016.07.001","usgsCitation":"Hahn, C.M., Iwanowicz, L., Cornman, R.S., Mazik, P.M., and Blazer, V., 2016, Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays: Comparative Biochemistry and Physiology, Part D: Genomics and Proteomics, v. 20, p. 27-40, https://doi.org/10.1016/j.cbd.2016.07.001.","productDescription":"14 p.","startPage":"27","endPage":"40","ipdsId":"IP-071925","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":331166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583415aae4b0070c0abed80e","contributors":{"authors":[{"text":"Hahn, Cassidy M. cmhahn@usgs.gov","contributorId":5321,"corporation":false,"usgs":true,"family":"Hahn","given":"Cassidy","email":"cmhahn@usgs.gov","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":654096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iwanowicz, Luke R. liwanowicz@usgs.gov","contributorId":386,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","email":"liwanowicz@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":654097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":654098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":654099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":149414,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":654095,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178481,"text":"70178481 - 2016 - Challenge to the model of lake charr evolution: Shallow- and deep-water morphs exist within a small postglacial lake","interactions":[],"lastModifiedDate":"2016-11-21T11:35:07","indexId":"70178481","displayToPublicDate":"2016-11-21T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1019,"text":"Biological Journal of the Linnean Society","active":true,"publicationSubtype":{"id":10}},"title":"Challenge to the model of lake charr evolution: Shallow- and deep-water morphs exist within a small postglacial lake","docAbstract":"All examples of lake charr (Salvelinus namaycush) diversity occur within the largest, deepest lakes of North America (i.e. > 2000 km2). We report here Rush Lake (1.3 km2) as the first example of a small lake with two lake charr morphs (lean and huronicus). Morphology, diet, life history, and genetics were examined to demonstrate the existence of morphs and determine the potential influence of evolutionary processes that led to their formation or maintenance. Results showed that the huronicus morph, caught in deep-water, had a deeper body, smaller head and jaws, higher eye position, greater buoyancy, and deeper peduncle than the shallow-water lean morph. Huronicus grew slower to a smaller adult size, and had an older mean age than the lean morph. Genetic comparisons showed low genetic divergence between morphs, indicating incomplete reproductive isolation. Phenotypic plasticity and differences in habitat use between deep and shallow waters associated with variation in foraging opportunities seems to have been sufficient to maintain the two morphs, demonstrating their important roles in resource polymorphism. Rush Lake expands previous explanations for lake charr intraspecific diversity, from large to small lakes and from reproductive isolation to the presence of gene flow associated with strong ecological drivers.
","language":"English","publisher":"Wiley","doi":"10.1111/bij.12913","usgsCitation":"Chavarie, L., Muir, A., Zimmerman, M.S., Baillie, S.M., Hansen, M.J., Nate, N.A., Yule, D.L., Middel, T., Bentzen, P., and Krueger, C., 2016, Challenge to the model of lake charr evolution: Shallow- and deep-water morphs exist within a small postglacial lake: Biological Journal of the Linnean Society, https://doi.org/10.1111/bij.12913.","ipdsId":"IP-078858","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":462033,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/bij.12913","text":"Publisher Index Page"},{"id":331153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"583415ace4b0070c0abed814","contributors":{"authors":[{"text":"Chavarie, Louise","contributorId":156227,"corporation":false,"usgs":false,"family":"Chavarie","given":"Louise","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":654136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muir, Andrew M.","contributorId":103933,"corporation":false,"usgs":false,"family":"Muir","given":"Andrew M.","affiliations":[],"preferred":false,"id":654137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zimmerman, Mara S.","contributorId":152687,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Mara","email":"","middleInitial":"S.","affiliations":[{"id":13269,"text":"Washington Department of Fish & Wildlife","active":true,"usgs":false}],"preferred":false,"id":654138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baillie, Shauna M.","contributorId":176176,"corporation":false,"usgs":false,"family":"Baillie","given":"Shauna","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":654139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, Michael J. 0000-0001-8522-3876 michaelhansen@usgs.gov","orcid":"https://orcid.org/0000-0001-8522-3876","contributorId":5006,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","email":"michaelhansen@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":654140,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nate, Nancy A.","contributorId":26626,"corporation":false,"usgs":true,"family":"Nate","given":"Nancy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":654141,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yule, Daniel L. dyule@usgs.gov","contributorId":139525,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","email":"dyule@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":654142,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Middel, Trevor","contributorId":176991,"corporation":false,"usgs":false,"family":"Middel","given":"Trevor","affiliations":[],"preferred":false,"id":654143,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bentzen, Paul","contributorId":176178,"corporation":false,"usgs":false,"family":"Bentzen","given":"Paul","email":"","affiliations":[],"preferred":false,"id":654144,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Krueger, Charles C.","contributorId":73131,"corporation":false,"usgs":true,"family":"Krueger","given":"Charles C.","affiliations":[],"preferred":false,"id":654145,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70178474,"text":"70178474 - 2016 - Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis","interactions":[],"lastModifiedDate":"2017-01-17T19:03:21","indexId":"70178474","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis","docAbstract":"This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributing to the grassland model and 15 flux towers for the cropland model. The models yielded weekly mean daily grassland and cropland NEP maps of the U.S. Great Plains at 250 m resolution for 2000–2008. The grassland and cropland NEP maps were spatially summarized and statistically compared. The results of this study indicate that grassland and cropland ecosystems generally performed as weak net carbon (C) sinks, absorbing more C from the atmosphere than they released from 2000 to 2008. Grasslands demonstrated higher carbon sink potential (139 g C·m−2·year−1) than non-irrigated croplands. A closer look into the weekly time series reveals the C fluctuation through time and space for each land cover type.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8110944","usgsCitation":"Wylie, B.K., Howard, D., Dahal, D., Gilmanov, T., Ji, L., Zhang, L., and Smith, K., 2016, Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis: Remote Sensing, v. 8, no. 11, p. 1-28, https://doi.org/10.3390/rs8110944.","productDescription":"Article 944; 28 p.","startPage":"1","endPage":"28","ipdsId":"IP-057161","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":462035,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110944","text":"Publisher Index Page"},{"id":331161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Plains","volume":"8","issue":"11","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"583415ace4b0070c0abed816","contributors":{"authors":[{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":56946,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":654161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":654162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gilmanov, Tagir","contributorId":6351,"corporation":false,"usgs":true,"family":"Gilmanov","given":"Tagir","affiliations":[],"preferred":false,"id":654163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Li","contributorId":98139,"corporation":false,"usgs":true,"family":"Zhang","given":"Li","affiliations":[],"preferred":false,"id":654165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":654166,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178470,"text":"70178470 - 2016 - Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","interactions":[],"lastModifiedDate":"2018-09-13T14:45:17","indexId":"70178470","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","docAbstract":"Reducing uncertainty in data inputs at relevant spatial scales can improve tidal marsh forecasting models, and their usefulness in coastal climate change adaptation decisions. The Marsh Equilibrium Model (MEM), a one-dimensional mechanistic elevation model, incorporates feedbacks of organic and inorganic inputs to project elevations under sea-level rise scenarios. We tested the feasibility of deriving two key MEM inputs—average annual suspended sediment concentration (SSC) and aboveground peak biomass—from remote sensing data in order to apply MEM across a broader geographic region. We analyzed the precision and representativeness (spatial distribution) of these remote sensing inputs to improve understanding of our study region, a brackish tidal marsh in San Francisco Bay, and to test the applicable spatial extent for coastal modeling. We compared biomass and SSC models derived from Landsat 8, DigitalGlobe WorldView-2, and hyperspectral airborne imagery. Landsat 8-derived inputs were evaluated in a MEM sensitivity analysis. Biomass models were comparable although peak biomass from Landsat 8 best matched field-measured values. The Portable Remote Imaging Spectrometer SSC model was most accurate, although a Landsat 8 time series provided annual average SSC estimates. Landsat 8-measured peak biomass values were randomly distributed, and annual average SSC (30 mg/L) was well represented in the main channels (IQR: 29–32 mg/L), illustrating the suitability of these inputs across the model domain. Trend response surface analysis identified significant diversion between field and remote sensing-based model runs at 60 yr due to model sensitivity at the marsh edge (80–140 cm NAVD88), although at 100 yr, elevation forecasts differed less than 10 cm across 97% of the marsh surface (150–200 cm NAVD88). Results demonstrate the utility of Landsat 8 for landscape-scale tidal marsh elevation projections due to its comparable performance with the other sensors, temporal frequency, and cost. Integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially. Improving information for coastal modeling will support planning for ecosystem services, including habitat, carbon storage, and flood protection.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1582","usgsCitation":"Byrd, K.B., Windham-Myers, L., Leeuw, T., Downing, B.D., Morris, J.T., and Ferner, M.C., 2016, Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model: Ecosphere, v. 7, no. 11, e01582; 27 p., https://doi.org/10.1002/ecs2.1582.","productDescription":"e01582; 27 p.","ipdsId":"IP-073438","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470411,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1582","text":"Publisher Index Page"},{"id":438505,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76M34Z1","text":"USGS data release","linkHelpText":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model"},{"id":331164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335610,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F76M34Z1","text":"Data release for journal article titled, \"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model\""}],"country":"United States","state":"California","otherGeospatial":"Rush Ranch Open Space Preserve, Suisun Slough, Suisun Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05501556396483,\n 38.17802085110361\n ],\n [\n -122.05501556396483,\n 38.212288054388175\n ],\n [\n -121.99802398681642,\n 38.212288054388175\n ],\n [\n -121.99802398681642,\n 38.17802085110361\n ],\n [\n -122.05501556396483,\n 38.17802085110361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"583415ade4b0070c0abed81a","chorus":{"doi":"10.1002/ecs2.1582","url":"http://dx.doi.org/10.1002/ecs2.1582","publisher":"Wiley-Blackwell","authors":"Byrd Kristin B., Windham-Myers Lisamarie, Leeuw Thomas, Downing Bryan, Morris James T., Ferner Matthew C.","journalName":"Ecosphere","publicationDate":"11/2016","auditedOn":"11/29/2016"},"contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":654114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leeuw, Thomas","contributorId":176970,"corporation":false,"usgs":false,"family":"Leeuw","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":654115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Downing, Bryan D. 0000-0002-2007-5304 bdowning@usgs.gov","orcid":"https://orcid.org/0000-0002-2007-5304","contributorId":1449,"corporation":false,"usgs":true,"family":"Downing","given":"Bryan","email":"bdowning@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":654116,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morris, James T.","contributorId":29118,"corporation":false,"usgs":true,"family":"Morris","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":654117,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferner, Matthew C.","contributorId":176972,"corporation":false,"usgs":false,"family":"Ferner","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":654118,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178402,"text":"70178402 - 2016 - Survival of translocated sharp-tailed grouse: Temporal threshold and age effects","interactions":[],"lastModifiedDate":"2016-11-17T12:23:11","indexId":"70178402","displayToPublicDate":"2016-11-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"Survival of translocated sharp-tailed grouse: Temporal threshold and age effects","docAbstract":"Context: The Columbian sharp-tailed grouse (Tympanuchus phasianellus columbianus) is a subspecies of conservation concern in the western United States, currently occupying ≤10% of its historic range. Land and management agencies are employing translocation techniques to restore Columbian sharp-tailed grouse (CSTG) populations. However, establishing self-sustaining populations by translocating grouse often is unsuccessful, owing, in part, to low survivorship of translocated grouse following release.
Aims: We measured and modelled patterns of CSTG mortality for 150 days following translocation into historic range, to better understand patterns and causes of success or failure in conservation efforts to re-establish grouse populations.
Methods: We conducted two independent multi-year translocations and evaluated individual and temporal factors associated with CSTG survival up to 150 days following their release. Both translocations were reintroduction attempts in Nevada, USA, to establish viable populations of CSTG into their historic range.
Key results: We observed a clear temporal threshold in survival probability, with CSTG mortality substantially higher during the first 50 days following release than during the subsequent 100 days. Additionally, translocated yearling grouse exhibited higher overall survival (0.669 ± 0.062) than did adults (0.420 ± 0.052) across the 150-day period and higher survival than adults both before and after the 50-day temporal threshold.
Conclusions: Translocated CSTG are especially vulnerable to mortality for 50 days following release, whereas translocated yearling grouse are more resistant to mortality than are adult grouse. On the basis of the likelihood of survival, yearling CSTG are better candidates for population restoration through translocation than are adult grouse.
Implications: Management actions that ameliorate mortality factors for 50 days following translocation and translocations that employ yearling grouse will increase the likelihood of population establishment.
","language":"English","publisher":"Commonwealth Scientific and Industrial Research Organisation","publisherLocation":"East Melbourne, Austrailia","doi":"10.1071/WR15158","usgsCitation":"Mathews, S.R., Coates, P.S., and Delehanty, D., 2016, Survival of translocated sharp-tailed grouse: Temporal threshold and age effects: Wildlife Research, v. 43, no. 3, p. 220-227, https://doi.org/10.1071/WR15158.","productDescription":"8 p.","startPage":"220","endPage":"227","ipdsId":"IP-075276","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470413,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr15158","text":"Publisher Index Page"},{"id":331095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.21240234375001,\n 36.01356058518153\n ],\n [\n -120.21240234375001,\n 47.78363463526376\n ],\n [\n -108.80859375,\n 47.78363463526376\n ],\n [\n -108.80859375,\n 36.01356058518153\n ],\n [\n -120.21240234375001,\n 36.01356058518153\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582ecfede4b04d580bd4352a","contributors":{"authors":[{"text":"Mathews, Steven R. 0000-0002-3165-9460 smathews@usgs.gov","orcid":"https://orcid.org/0000-0002-3165-9460","contributorId":176922,"corporation":false,"usgs":true,"family":"Mathews","given":"Steven","email":"smathews@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":653983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":653982,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Delehanty, David J.","contributorId":86683,"corporation":false,"usgs":true,"family":"Delehanty","given":"David J.","affiliations":[],"preferred":false,"id":653984,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175252,"text":"sir20165093 - 2016 - Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013","interactions":[],"lastModifiedDate":"2016-11-17T16:24:46","indexId":"sir20165093","displayToPublicDate":"2016-11-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5093","title":"Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013","docAbstract":"Despite widespread and ongoing implementation of conservation practices throughout the Chesapeake Bay watershed, water quality continues to be degraded by excess sediment and nutrient inputs. While the Chesapeake Bay Program has developed and maintains a large-scale and long-term monitoring network to detect improvements in water quality throughout the watershed, fewer resources have been allocated for monitoring smaller watersheds, even though water-quality improvements that may result from the implementation of conservation practices are likely to be first detected at smaller watershed scales.
In 2010, the U.S. Geological Survey partnered with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture to initiate water-quality monitoring in four selected small watersheds that were targeted for increased implementation of conservation practices. Smith Creek watershed is an agricultural watershed in the Shenandoah Valley of Virginia that is dominated by cattle and poultry production, and the Upper Chester River watershed is an agricultural watershed on the Eastern Shore of Maryland that is dominated by row-cropping activities. The Conewago Creek watershed is an agricultural watershed in southeastern Pennsylvania that is characterized by mixed agricultural activities. The fourth watershed, Difficult Run, is a suburban watershed in northern Virginia that is dominated by medium density residential development. The objective of this study was to investigate spatial and temporal variations in water chemistry and suspended sediment in these four relatively small watersheds that represent a range of land-use patterns and underlying geology to (1) characterize current water-quality conditions in these watersheds, and (2) identify the dominant sources, sinks, and transport processes in each watershed.
The general study design involved two components. The first included intensive routine water-quality monitoring at an existing streamgage within each study area (including continuous water-quality monitoring as well as discrete water-quality sampling) to develop a detailed understanding of the temporal and hydrologic variability in stream chemistry and sediment transport in each watershed. The second component involved extensive water-quality monitoring at various sites throughout each watershed to develop a detailed understanding of spatial patterns. Both components were used to improve understanding of sources and transport processes affecting stream chemistry, including nutrients and suspended sediments, and their implications for detecting long-term trends related to best management practices. This report summarizes the results of monitoring that was performed from April 2010 through September 2013.
Summaries for each of the four small watersheds are presented below. Each watershed has a more descriptive and detailed section in the report, but these summaries may be particularly useful for some watershed managers and stakeholders desiring slightly less technical detail.
Smith Creek is a 105.39-mi2 watershed within the Shenandoah Valley that drains to the North Fork Shenandoah River. The long-term Smith Creek base-flow index is 72.3 percent, indicating that on average, approximately 72 percent of Smith Creek flow was base flow, which suggests that Smith Creek streamflow is dominated by groundwater discharge rather than stormwater runoff. A series of cluster and principal components analyses demonstrated that the majority of the variability in Smith Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed for turbidity, suspended sediments, total nitrogen, ammonium, orthophosphate, iron, total phosphorus, and the ratio of calcium to magnesium. Statistically significant inverse correlations with flow were observed for specific conductance, magnesium, δ15N of nitrate, pH, bicarbonate, calcium, and δ18O of nitrate. Of particular note, flow and nitrate were not statistically significantly correlated, likely because of the relatively complex concentration-discharge relationship observed in continuous and discrete datasets. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, turbidity, orthophosphate, total phosphorus, suspended-sediment concentration, and silica were higher during the warm season, but pH, dissolved oxygen, and sulfate were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loads in Smith Creek using the continuous water-quality monitors. The mean Smith Creek in-stream sediment load was approximately 6,900 tons per year, with nearly 90 percent of the sediment load over the 3-year study period contributed during the eight largest storm events during that period. The Smith Creek total phosphorus load was approximately 21,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods, although a substantial phosphorus load still occurs during base-flow conditions. The Smith Creek total nitrogen load was approximately 400,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions (as was the case for sediment and total phosphorus) and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Smith Creek watershed revealed how the complex geology and hydrology interacted to result in variable water chemistry. During relatively dry and low base-flow periods, much of the discharge in Smith Creek was contributed by a single dominant spring—Lacey Spring. During wetter base-flow periods, the flows in Smith Creek were largely generated by a mixture of headwater springs and forested mountain tributaries with very different geochemical composition. The headwater springs generally issued from limestone bedrock and were characterized as having relatively high nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The undeveloped, high-gradient, forested mountain sites were generally characterized by low ionic strength waters with low nutrient concentrations. Nitrate isotope data from the limestone springs generally were consistent with manure-derived nitrogen sources (such as cattle and poultry), although the possibility of other mixed sources cannot be excluded. Nitrate isotope data from the undeveloped, high-gradient forested mountain sites were more consistent with nitrogen from undisturbed soils, atmospheric deposition, or nitrogen fixation. Regardless of the nitrogen source, oxygen isotope data indicate that the nitrate was largely a result of nitrification. Land-use data indicate that manure sources of nitrogen dominated watershed nitrogen inputs. Phosphorus sources were less well studied. The presence of a single point-source discharge near the town of New Market contributed the majority of the phosphorus to Smith Creek under base-flow conditions, but nonpoint sources of phosphorus dominated the loading to Smith Creek during stormflow periods.
Implementation of conservation practices increased in the Smith Creek watershed during the study period, and even though a broad range of practice types was implemented, the most common practices included stream fencing (for cattle exclusion), the development of nutrient management plans, conservation crop rotation, and the planting of cover crops. While the implementation of these conservation practices is encouraging, results indicate small increases in nitrate concentrations at the streamgage over the last 29 years, concurrent with small decreases in nitrate fluxes. It will likely be years before the cumulative effect of these practices can be detected in the Smith Creek water quality, and the magnitude of the effect of these conservation practices detected in Smith Creek will depend largely on whether nutrient loading (of manure and commercial fertilizer) is reduced over time.
The Upper Chester River watershed includes the 36-square-mile (mi2) watershed area around several nontidal tributaries that drain into the tidal Chester River. The streamgage is on Chesterville Branch, the largest nontidal tributary (approximately 6.12 mi2) and is the site for continuous water-quality monitoring for this project. The base-flow index at Chesterville Branch is about 72 percent and indicates that, as in most of the Coastal Plain, groundwater is the greatest contributor to streamflow. As such, more than 90 percent of the nitrogen in the stream is in the form of nitrate from groundwater. Continuous and discrete data collected at Chesterville Branch show the effects of streamflow and season on water quality. Significantly positive correlations with flow were observed for ammonium, dissolved and total phosphorus, sediment, and turbidity as runoff carried these constituents from the land surface into Chesterville Branch. Other constituents that increased significantly with flow include potassium, sulfate, iron, and manganese, which are likely contributed from near-stream areas and ponds with high organic-matter content. Total nitrogen, pH, and specific conductance, along with chemical constituents associated with groundwater inputs including nitrate, calcium, ratio of calcium to magnesium, silica, bicarbonate, and sodium, were negatively correlated with flow because concentrations of these constituents were diluted by runoff.
Seasonal differences in water chemistry, which are most likely related to increased biologic effects on the uptake and release of chemicals in the stream and near-stream areas, also were observed. Water temperature, orthophosphate, δ15N of nitrate, bicarbonate, sodium, and the ratio of sodium to chloride were higher during the warm season, and dissolved oxygen, total nitrogen, nitrate, magnesium, sulfate, and manganese were higher during the cool season.
Surrogate-regression models developed by using continuous water-quality data showed that the annual sediment load for the 2013 water year was about 2,600 tons, with more than 90 percent of this sediment contributed during two storms. The total phosphorus load in 2013 was about 13,000 pounds with more than 90 percent contributed during the same two storms as sediment. The load of total nitrogen, 140,000 pounds, accumulated steadily throughout the 2013 water year as nitrate in groundwater continuously discharged into the stream. The same two large storms that contributed 90 percent of the suspended-sediment and total phosphorus load only contributed about 20 percent of the annual total nitrogen load.
Extensive water-quality monitoring of stream base flow throughout the Upper Chester River watershed identified how differences in land use and hydrogeology affected water chemistry. In parts of the watershed with well-drained soil and thick sandy aquifer sediments, concentrations of nitrate and other chemicals associated with fertilizer and lime application increased in streams as agricultural land use increased. More than 90 percent of the nitrogen in streams from these areas was in the form of nitrate, and concentrations ranged from about 5 milligrams per liter (mg/L) to 8 mg/L as nitrogen in the two largest tributaries. Stream nitrate concentrations were about 1 mg/L as nitrogen where soils were more poorly drained, the surficial aquifer sediments were thinner, and forests and wetlands were more widespread than agriculture. Nitrate isotope data were consistent with inorganic fertilizers ± atmospheric deposition and N2 fixation as sources of nitrogen, and with nitrification as the dominant nitrate-forming process. Nitrate reduction was indicated by elevated δ15N and δ18O values in some samples from streams draining watersheds with poorly drained soils. An analysis of land-use data and SPARROW modeling input data attributed almost 90 percent of the nitrogen sources in the Upper Chester River watershed to inorganic fertilizer and fixation of atmospheric nitrogen by legumes, which is in agreement with the isotopic characteristics of nitrate in this watershed. Local sources of manure are limited in this area. Total phosphorus concentrations during base flow ranged from below detection to about 0.2 mg/L. Stream phosphorus concentrations during base flow were generally lower than those measured during storms because most phosphorus transport likely occurs as phosphorus attached to sediment particles during runoff. Because manure is not widely used in this area, the major source of phosphorus is likely fertilizer.
The implementation of conservation practices in the Upper Chester River watershed increased substantially during the study period, with a total implementation of 1,194 U.S. Department of Agriculture-compliant practices. The most frequently used practices were oriented towards nutrient and sediment control, including cover crops, nutrient management planning, conservation crop rotation, conservation tillage, and irrigation management. The current Chesapeake Bay model for this area predicts that implementation of best management practices should result in a 13-percent decrease in overall delivery of nitrogen to the Upper Chester River. Because most nitrogen travels through the groundwater system for years to decades before being discharged to streams, the time period of monitoring was not sufficient to see the effects of these practices on water quality. The magnitude of the effect that may eventually be detected will depend on the degree to which nitrate leaching into the groundwater system is reduced over time. Loadings of phosphorus and sediment are primarily transported during large runoff events and are difficult to control and analyze for trends because of their timing and episodic nature.
Conewago Creek has two primary monitoring locations—one near the middle of the 47-mi2 watershed and the other near the outlet just upstream of the Susquehanna River. The base-flow index was 47.3 percent for 2012–2013, indicating that on average, approximately 53 percent of the streamflow in Conewago Creek exited the watershed as surface flow, which suggests that the stormwater runoff was somewhat greater than groundwater discharge (base flow). A series of cluster and principal components analyses demonstrated that the majority of the variability in the Conewago Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed at both monitoring sites for ammonium, total phosphorus, orthophosphate, iron, and manganese; additionally, at the upstream monitoring station, total nitrogen demonstrated a statistically significant positive correlation with flow. Statistically significant inverse correlations with flow were observed at both sites for water temperature, specific conductance (at the downstream site only), sulfate, chloride, calcium, and magnesium. Statistically significant seasonal patterns were observed for several water-quality constituents. Water temperature, phosphorus (upstream site only), and orthophosphate were higher during the warm season, and nitrate and total nitrogen (upstream site only) were higher during the cool season.
Surrogate regression models were developed to compute sediment and nutrient load in Conewago Creek by using the continuous water-quality monitors and water-quality samples. Conewago Creek sediment load was approximately 9,900 tons in 2012 and approximately 18,900 tons in 2013, with nearly 80 percent of the sediment load in 2013 contributed by the three largest storm events. Annual total nitrogen loads could not be estimated due to poor model performance. The addition of continued monitoring or a continuously recording nitrate sensor could improve estimates of total nitrogen loads. During 2012 and 2013, phosphorus loads in Conewago Creek were approximately 50,000 pounds in each year.
Combining data from one high-flow synoptic sampling with the data from routine sampling revealed how the geology and hydrology interact to result in variable water chemistry throughout the Conewago Creek watershed. The areas above the upstream gage in the headwaters are generally underlain by forested non-carbonate bedrock and are characterized by relatively low nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The more developed, agricultural areas below the upstream site were generally characterized by higher ionic strength waters with higher nutrient and metal concentrations. An analysis of land-use data and SPAtially Referenced Regressions On Watershed (SPARROW) modeling data indicates that manure sources of nitrogen dominate the input of nitrogen to the watershed.
Implementation of conservation practices increased in the Conewago Creek watershed during the study period, and while a broad range of practice types were implemented, the most common practices included residue and tillage management, cover crops, nutrient management, terracing, and stream fencing (for animal exclusion or bank restoration). While the implementation of these conservation practices is encouraging, the cumulative effects of these practices probably will not be detected in Conewago Creek water quality for several years. The magnitude of the effects of these conservation practices on water quality in Conewago Creek will depend largely on the extent to which nutrient loading (septic, manure, and commercial fertilizer) and sediment-producing activities are reduced over time.
The Difficult Run watershed is a 57.82-mi2 watershed that drains to the Potomac River. The long-term Difficult Run base-flow index (from 1936 to 2010) was 57.9, indicating that approximately 58 percent of streamflow exited the watershed as base flow and 42 percent as stormflow; however, with continued development and urbanization of the watershed, the base-flow index has decreased to 50 percent during the last 20 years. This base-flow index was less than those of the other watersheds evaluated in this study, likely because the Difficult Run watershed largely is underlain by crystalline piedmont metamorphic rocks and has a greater proportion of impervious urban land cover. A series of cluster and principal components analyses indicated that most of the variability in Difficult Run water quality could be attributed to hydrologic variability and seasonality. Statistically significant positive correlations with flow were observed for turbidity, dissolved oxygen, suspended sediments, ammonium, orthophosphate, iron, and total phosphorus. Statistically significant inverse correlations with flow were observed for water temperature, pH, specific conductance, bicarbonate, calcium, magnesium, nitrate, δ15N of nitrate, and silica. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, ammonium, orthophosphate, and δ15N of nitrate were higher during the warm season, and dissolved oxygen, nitrate, and manganese were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loading rates. The Difficult Run sediment load was approximately 8,000 tons per year, with greater than 95 percent of the sediment load in the 2013 water year contributed by the seven largest storm events. The total phosphorus load in Difficult Run was approximately 14,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods. The total nitrogen load in Difficult Run is estimated to have been approximately 140,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions than that of phosphorus and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Difficult Run watershed revealed relatively uniform generation of flow per unit of watershed area, as well as spatial variation in water quality that is strongly related to land-use activities. Elevated nitrate concentrations were observed in a subset of monitoring sites that are inversely correlated with population density and positively correlated to the septic system density within each subwatershed. The majority of the elevated nitrate concentrations for these sites are hypothesized to be caused by nitrate leaching from septic systems, more so than homeowner fertilizer usage among these subwatersheds that have lower population densities than other parts of the watershed. Nitrate isotope data, temporal patterns in the water-quality data, mass-balance computations, and a separate land-use analysis all generally indicate that leachate from septic systems was the likely source of the elevated nitrate. Another group of water-quality sites have relatively low nitrogen concentrations, are located in areas that are served by city sewer lines, and have experienced stream restoration activities. A final group of sites drained the areas with the highest imperviousness and had strongly elevated specific conductance, chloride, and sodium, which were likely caused by a combination of road salting and other anthropogenic sources draining these urbanized areas in the watershed. A fourth group of sites represents a mixture of water sources and had water quality similar to that at the Difficult Run streamgage. Analysis of the nitrate isotope data generally indicates a broad range of composition indicative of mixed natural and anthropogenic nitrogen sources. Implementation of conservation practices increased in the Difficult Run watershed during the study period, and while a broad range of practice types was implemented, the most common practices included stream restoration. While the implementation of these conservation practices is encouraging, the cumulative effect of these practices probably will not be detected in Difficult Run water quality for several years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165093","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Hyer, K.E., Denver, J.M., Langland, M.J., Webber, J.S., Böhlke, J.K., Hively, W.D., and Clune, J.W., 2016, Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013: U.S. Geological Survey Scientific Investigations Report 2016–5093, 211 p., https://dx.doi.org/10.3133/sir20165093.","productDescription":"Report: xix, 211 p.","startPage":"1","endPage":"211","numberOfPages":"236","onlineOnly":"N","ipdsId":"IP-067371","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":330861,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5093/coverthb.jpg"},{"id":330862,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5093/sir20165093.pdf","text":"Report","size":"30.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5093"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia","otherGeospatial":"Conewago Creek watershed, Difficult Run watershed, Smith Creek watershed, Upper Chester River watershed","geographicExtents":"{\n\"id\": \"2434359\",\n\"crs\": {\n\"type\": \"name\",\n\"properties\": {\n\"name\": \"urn:ogc:def:crs:OGC:1.3:CRS84\"\n}\n},\n\"type\": \"Feature\",\n\"geometry\": {\n\"coordinates\": 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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Digital flood-inundation maps for a 4.9-mile reach of the Yellow River at Plymouth, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 05516500, Yellow River at Plymouth, Ind. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/in/nwis/uv?site_no=05516500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system (http:/water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many sites that are often collocated with USGS streamgages, including the Yellow River at Plymouth, Ind. NWS AHPS-forecast peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood and forecasts of flood hydrographs at this site.
For this study, flood profiles were computed for the Yellow River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the current stage-discharge relations at the Yellow River streamgage, in combination with the flood-insurance study for Marshall County (issued in 2011). The calibrated hydraulic model was then used to determine eight water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The 1-percent annual exceedance probability flood profile elevation (flood elevation with recurrence intervals within 100 years) is within the calibrated water-surface elevations for comparison. The simulated water-surface profiles were then used with a geographic information system (GIS) digital elevation model (DEM, derived from Light Detection and Ranging [lidar]) in order to delineate the area flooded at each water level.
The availability of these maps, along with Internet information regarding current stage from the USGS streamgage 05516500, Yellow River at Plymouth, Ind., and forecast stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165117","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Menke, C.D., Bunch, A.R., and Kim, M.H., 2016, Flood-inundation maps for the Yellow River at Plymouth, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5117, 9 p., https://dx.doi.org/10.3133/sir20165117.","productDescription":"Report: vi, 9 p.; Metadata: 2 files; Read Me; Spatial Data: 2 files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078607","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":331047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5117/coverthb.jpg"},{"id":331048,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5117"},{"id":331049,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117_dep_grd.metadata","text":"Metadata Depth Grids","size":"16.5 KB"},{"id":331050,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117_shapefile.metadata","text":"Metadata Shapefiles","size":"16.7 KB"},{"id":331051,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2016/5117/00Readme.txt","text":"Readme","size":"8.18 KB","linkFileType":{"id":2,"text":"txt"}},{"id":331052,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5117/gis_data/depth_grids.zip","text":"Depth Grids","size":"4.37 MB","linkFileType":{"id":6,"text":"zip"}},{"id":331053,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5117/gis_data/shapefile.zip","text":"Shape File","size":"807 KB","linkFileType":{"id":6,"text":"zip"}}],"country":"United States","state":"Indiana","city":"Plymouth","otherGeospatial":"Yellow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.4,\n 41.3\n ],\n [\n -86.4,\n 41.5\n ],\n [\n -86.2,\n 41.5\n ],\n [\n -86.2,\n 41.3\n ],\n [\n -86.4,\n 41.3 ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278
http://in.water.usgs.gov/
http://ky.water.usgs.gov/
We present two‐dimensional electrical resistivity models of two 40 km magnetotelluric (MT) profiles across the Frome Embayment to the east of the northern Flinders Ranges, South Australia. The lower crust shows low resistivity of 10 Ω m at around 30 km depth. The middle crust is dominated by resistive (>1000 Ω m) basement rocks underlying the Flinders Ranges. Adjacent to the ranges, conductive lower crust is connected to vertical zones of higher conductivity extending to just below the brittle‐ductile transition at ∼10 km depth. The conductive zones narrow in the brittle upper crust and dip at roughly 45° beneath the surface. Zones of enhanced conductivity coincide with higher strain due to topographic loading and sparse seismicity. We propose that fluids are generated through neotectonic metamorphic devolatilization. Low‐resistivity zones display areas of fluid pathways along either preexisting faults or an effect of crustal compression leading to metamorphic fluid generation. The lower crustal conductors are responding to long‐wavelength flexure‐induced strain, while the upper crustal conductors are responding to short wavelength faulting in the brittle regime. MT is a useful tool for imaging crustal strain in response to far‐field stresses in an intraplate setting and provides important constraints for geodynamic modeling and crustal rheology.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL071351","usgsCitation":"Thiel, S., Soeffky, P., Krieger, L., Regenauer-Lieb, K., Peacock, J., and Heinson, G., 2016, Conductivity response to intraplate deformation: Evidence for metamorphic devolatilization and crustal‐scale fluid focusing: Geophysical Research Letters, v. 43, no. 21, p. 11,236-11,244, https://doi.org/10.1002/2016GL071351.","productDescription":"9 p.","startPage":"11,236","endPage":"11,244","ipdsId":"IP-081053","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":360475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","state":"South Australia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 138.75,\n -30.75\n ],\n [\n 140,\n -30.75\n ],\n [\n 140,\n -29.75\n ],\n [\n 138.75,\n -29.75\n ],\n [\n 138.75,\n -30.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"21","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-15","publicationStatus":"PW","scienceBaseUri":"5c1a1534e4b0708288c23544","contributors":{"authors":[{"text":"Thiel, Stephan","contributorId":169326,"corporation":false,"usgs":false,"family":"Thiel","given":"Stephan","email":"","affiliations":[{"id":25477,"text":"Geological Survey of South Australia","active":true,"usgs":false}],"preferred":false,"id":754449,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soeffky, Paul","contributorId":211594,"corporation":false,"usgs":false,"family":"Soeffky","given":"Paul","email":"","affiliations":[{"id":36897,"text":"University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":754450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krieger, Lars","contributorId":140053,"corporation":false,"usgs":false,"family":"Krieger","given":"Lars","email":"","affiliations":[{"id":13368,"text":"University of Adelaide, Australia","active":true,"usgs":false}],"preferred":false,"id":754451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Regenauer-Lieb, Klaus","contributorId":211595,"corporation":false,"usgs":false,"family":"Regenauer-Lieb","given":"Klaus","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":754452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":754448,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heinson, Graham","contributorId":211596,"corporation":false,"usgs":false,"family":"Heinson","given":"Graham","email":"","affiliations":[{"id":36897,"text":"University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":754453,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178392,"text":"70178392 - 2016 - Probability of acoustic transmitter detections by receiver lines in Lake Huron: results of multi-year field tests and simulations","interactions":[],"lastModifiedDate":"2016-11-16T10:28:12","indexId":"70178392","displayToPublicDate":"2016-11-16T11:20:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Probability of acoustic transmitter detections by receiver lines in Lake Huron: results of multi-year field tests and simulations","docAbstract":"Advances in acoustic telemetry technology have led to an improved understanding of the spatial ecology of many freshwater and marine fish species. Understanding the performance of acoustic receivers is necessary to distinguish between tagged fish that may have been present but not detected and from those fish that were absent from the area. In this study, two stationary acoustic transmitters were deployed 250 m apart within each of four acoustic receiver lines each containing at least 10 receivers (i.e., eight acoustic transmitters) located in Saginaw Bay and central Lake Huron for nearly 2 years to determine whether the probability of detecting an acoustic transmission varied as a function of time (i.e., season), location, and distance between acoustic transmitter and receiver. Distances between acoustic transmitters and receivers ranged from 200 m to >10 km in each line. The daily observed probability of detecting an acoustic transmission was used in simulation models to estimate the probability of detecting a moving acoustic transmitter on a line of receivers.
The probability of detecting an acoustic transmitter on a receiver 1000 m away differed by month for different receiver lines in Lake Huron and Saginaw Bay but was similar for paired acoustic transmitters deployed 250 m apart within the same line. Mean probability of detecting an acoustic transmitter at 1000 m calculated over the study period varied among acoustic transmitters 250 m apart within a line and differed among receiver lines in Lake Huron and Saginaw Bay. The simulated probability of detecting a moving acoustic transmitter on a receiver line was characterized by short periods of time with decreased detection. Although increased receiver spacing and higher fish movement rates decreased simulated detection probability, the location of the simulated receiver line in Lake Huron had the strongest effect on simulated detection probability.
Performance of receiver lines in Lake Huron varied across a range of spatiotemporal scales and was inconsistent among receiver lines. Our simulations indicated that if 69 kHz acoustic transmitters operating at 158 dB in 10–30 m of freshwater were being used, then receivers should be placed 1000 m apart to ensure that all fish moving at 1 m s−1 or less will be detected 90% of days over a 2-year period. Whereas these results can be used as general guidelines for designing new studies, the irregular variation in acoustic transmitter detection probabilities we observed among receiver line locations in Lake Huron makes designing receiver lines in similar systems challenging and emphasizes the need to conduct post hoc analyses of acoustic transmitter detection probabilities.
The Potomac River is a large source of N to Chesapeake Bay, where reducing nutrient loads is a focus of efforts to improve trophic status. Better understanding of NO3– loss, reflected in part by diel variation in NO3– concentrations, may refine model predictions of N loads to the Bay. We analyzed 2 y of high-frequency NO3– sensor data in the Potomac to quantify seasonal variation in the magnitude and timing of diel NO3– loss. Diel patterns were evident, especially during low flow, despite broad seasonal and flow-driven variation in NO3– concentrations. Diel variation was ~0.01 mg N/L in winter and 0.02 to 0.03 mg N/L in summer with intermediate values in spring and autumn, equivalent to <1% of the daily mean NO3– concentration in winter and ~2 to 4% in summer. Maximum diel NO3– values generally occurred in mid- to late morning, with more repeatable patterns in summer and wider variation in autumn and winter. Diel NO3– loss reduced loads by 0.7% in winter and 3% in summer. These losses were less than estimates of total in-stream NO3– load loss across the basin that averaged 33% of the annual groundwater contribution to the river. Water temperature and discharge had stronger relationships to the daily magnitude of diel NO3– variation than did photosynthetically active radiation. Estimated diel areal NO3– loss rates were generally >1000 mg N m–2 d–1, greater than most published values because measurements in this large river integrate over a greater depth/unit stream bottom area than do those from smaller rivers. These diel NO3– patterns are consistent with the influence of photoautotrophic uptake and related denitrification, but we cannot attribute these patterns to assimilation alone because the magnitude and timing of diel dynamics were affected to an unknown extent by processes, such as evapotranspiration, transient storage, and hydrodynamic dispersion. Improvements to diel loss estimates will require additional high-frequency measures, such as dissolved O2, dissolved organic N, and NH4+, and deployment of 2 measurement stations.
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/688777","usgsCitation":"Burns, D.A., Miller, M.P., Pellerin, B., and Capel, P.D., 2016, Patterns of diel variation in nitrate concentrations in the Potomac River: Freshwater Science, v. 35, no. 4, p. 1117-1132, https://doi.org/10.1086/688777.","productDescription":"16 p.","startPage":"1117","endPage":"1132","ipdsId":"IP-070616","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":438507,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7HT2MD4","text":"USGS data release","linkHelpText":"Water Quality and Hydrologic Data (2011-13) for Freshwater Science Paper titled, "Patterns of Diel Variation in Nitrate Concentrations in the Potomac River""},{"id":331066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Potomac River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.6893310546875,\n 38.052416771864834\n ],\n [\n -79.6893310546875,\n 39.8928799002948\n ],\n [\n -77.080078125,\n 39.8928799002948\n ],\n [\n -77.080078125,\n 38.052416771864834\n ],\n [\n -79.6893310546875,\n 38.052416771864834\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582dd8e8e4b04d580bd3fa85","contributors":{"authors":[{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":3919,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pellerin, Brian A. 0000-0003-3712-7884 bpeller@usgs.gov","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":147077,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":653934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":653935,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178387,"text":"70178387 - 2016 - Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change","interactions":[],"lastModifiedDate":"2017-01-03T16:05:22","indexId":"70178387","displayToPublicDate":"2016-11-16T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change","docAbstract":"Mean water depth and range of interannual water-level fluctuations over wet-dry cycles in precipitation are major drivers of vegetation zone formation in North American prairie potholes. We used harmonic hydrological models, which require only mean interannual water depth and amplitude of water-level fluctuations over a wet–dry cycle, to examine how the vegetation zones in a pothole would respond to small changes in water depth and/or amplitude of water-level fluctuations. Field data from wetlands in Saskatchewan, North Dakota, and South Dakota were used to parameterize harmonic models for four pothole classes. Six scenarios in which small negative or positive changes in either mean water depth, amplitude of interannual fluctuations, or both, were modeled to predict if they would affect the number of zones in each wetland class. The results indicated that, in some cases, even small changes in mean water depth when coupled with a small change in amplitude of water-level fluctuations can shift a prairie pothole wetland from one class to another. Our results suggest that climate change could alter the relative proportion of different wetland classes in the prairie pothole region.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0850-8","usgsCitation":"van der Valk, A., and Mushet, D.M., 2016, Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change: Wetlands, v. 36, no. 2, p. 397-406, https://doi.org/10.1007/s13157-016-0850-8.","productDescription":"10 p.","startPage":"397","endPage":"406","ipdsId":"IP-072077","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470416,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1303&context=eeob_ag_pubs","text":"External Repository"},{"id":331061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"582dd8e9e4b04d580bd3fa87","contributors":{"authors":[{"text":"van der Valk, Arnold","contributorId":145612,"corporation":false,"usgs":false,"family":"van der Valk","given":"Arnold","affiliations":[{"id":15296,"text":"Iowa State University, Ames, IA, USA","active":true,"usgs":false}],"preferred":false,"id":653912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653911,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178380,"text":"70178380 - 2016 - Bioenergy production and forest landscape change in the southeastern United States","interactions":[],"lastModifiedDate":"2018-12-20T11:53:27","indexId":"70178380","displayToPublicDate":"2016-11-16T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1718,"text":"GCB Bioenergy","active":true,"publicationSubtype":{"id":10}},"title":"Bioenergy production and forest landscape change in the southeastern United States","docAbstract":"Production of woody biomass for bioenergy, whether wood pellets or liquid biofuels, has the potential to cause substantial landscape change and concomitant effects on forest ecosystems, but the landscape effects of alternative production scenarios have not been fully assessed. We simulated landscape change from 2010 to 2050 under five scenarios of woody biomass production for wood pellets and liquid biofuels in North Carolina, in the southeastern United States, a region that is a substantial producer of wood biomass for bioenergy and contains high biodiversity. Modeled scenarios varied biomass feedstocks, incorporating harvest of ‘conventional’ forests, which include naturally regenerating as well as planted forests that exist on the landscape even without bioenergy production, as well as purpose-grown woody crops grown on marginal lands. Results reveal trade-offs among scenarios in terms of overall forest area and the characteristics of the remaining forest in 2050. Meeting demand for biomass from conventional forests resulted in more total forest land compared with a baseline, business-as-usual scenario. However, the remaining forest was composed of more intensively managed forest and less of the bottomland hardwood and longleaf pine habitats that support biodiversity. Converting marginal forest to purpose-grown crops reduced forest area, but the remaining forest contained more of the critical habitats for biodiversity. Conversion of marginal agricultural lands to purpose-grown crops resulted in smaller differences from the baseline scenario in terms of forest area and the characteristics of remaining forest habitats. Each scenario affected the dominant type of land-use change in some regions, especially in the coastal plain that harbors high levels of biodiversity. Our results demonstrate the complex landscape effects of alternative bioenergy scenarios, highlight that the regions most likely to be affected by bioenergy production are also critical for biodiversity, and point to the challenges associated with evaluating bioenergy sustainability.
","language":"English","publisher":"Wiley","doi":"10.1111/gcbb.12386","usgsCitation":"Costanza, J.K., Abt, R.C., McKerrow, A., and Collazo, J., 2016, Bioenergy production and forest landscape change in the southeastern United States: GCB Bioenergy, v. 9, no. 5, p. 924-939, https://doi.org/10.1111/gcbb.12386.","productDescription":"16 p.","startPage":"924","endPage":"939","ipdsId":"IP-075800","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":38315,"text":"GAP Analysis Project","active":true,"usgs":true}],"links":[{"id":470417,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcbb.12386","text":"Publisher Index Page"},{"id":331063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582dd8e9e4b04d580bd3fa89","chorus":{"doi":"10.1111/gcbb.12386","url":"http://dx.doi.org/10.1111/gcbb.12386","publisher":"Wiley-Blackwell","authors":"Costanza Jennifer K., Abt Robert C., McKerrow Alexa J., Collazo Jaime A.","journalName":"GCB Bioenergy","publicationDate":"8/1/2016","publiclyAccessibleDate":"8/1/2016"},"contributors":{"authors":[{"text":"Costanza, Jennifer K.","contributorId":176907,"corporation":false,"usgs":false,"family":"Costanza","given":"Jennifer","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":653929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abt, Robert C.","contributorId":174475,"corporation":false,"usgs":false,"family":"Abt","given":"Robert","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":653930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":653931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":653877,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184236,"text":"70184236 - 2016 - Tradeoffs between physical captures and PIT tag antenna array detections: A case study for the Lower Colorado River Basin population of humpback chub (Gila cypha)","interactions":[],"lastModifiedDate":"2017-03-06T10:38:54","indexId":"70184236","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Tradeoffs between physical captures and PIT tag antenna array detections: A case study for the Lower Colorado River Basin population of humpback chub (Gila cypha)","docAbstract":"A key component of many monitoring programs for special status species involves capture and handling of individuals as part of capture-recapture efforts for tracking population health and demography. Minimizing negative impacts from sampling, such as through reduced handling, aids prevention of negative impacts on species from monitoring efforts. Using simulation analyses, we found that long-term population monitoring techniques, requiring physical capture (i.e. hoop-net sampling), can be reduced and supplemented with passive detections (i.e. PIT tag antenna array detections) without negatively affecting estimates of adult humpback chub (HBC; Gila cypha) survival (S) and skipped spawning probabilities (γ' = spawner transitions to a skipped spawner, γ′ = skipped spawner remains a skipped spawner). Based on our findings of the array’s in situ detection efficiency (0.42), estimability of such demographic parameters would improve over hoop-netting alone. In addition, the array provides insight into HBC population dynamics and movement patterns outside of traditional sampling periods. However, given current timing of sampling efforts, spawner abundance estimates were negatively biased when hoop-netting was reduced, suggesting not all spawning HBC are present during the current sampling events. Despite this, our findings demonstrate that PIT tag antenna arrays, even with moderate potential detectability, may allow for reduced handling of special status species while also offering potentially more efficient monitoring strategies, especially if ideal timing of sampling can be determined.
","language":"English","publisher":"Elsevier","publisherLocation":"New York","doi":"10.1016/j.fishres.2016.06.014","usgsCitation":"Pearson, K.N., Kendall, W., Winkelman, D.L., and Persons, W.R., 2016, Tradeoffs between physical captures and PIT tag antenna array detections: A case study for the Lower Colorado River Basin population of humpback chub (Gila cypha): Fisheries Research, v. 183, p. 263-274, https://doi.org/10.1016/j.fishres.2016.06.014.","productDescription":"12 p.","startPage":"263","endPage":"274","ipdsId":"IP-062594","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":336852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Little Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.825,\n 36.23\n ],\n [\n -111.71,\n 36.23\n ],\n [\n -111.71,\n 36.13\n ],\n [\n -111.825,\n 36.13\n ],\n [\n -111.825,\n 36.23\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"183","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58be8338e4b014cc3a3a99dd","contributors":{"authors":[{"text":"Pearson, Kristen Nicole","contributorId":171538,"corporation":false,"usgs":false,"family":"Pearson","given":"Kristen","email":"","middleInitial":"Nicole","affiliations":[],"preferred":false,"id":680765,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891 wkendall@usgs.gov","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":166709,"corporation":false,"usgs":true,"family":"Kendall","given":"William L.","email":"wkendall@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":680686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":680766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Persons, William R. wpersons@usgs.gov","contributorId":4028,"corporation":false,"usgs":true,"family":"Persons","given":"William","email":"wpersons@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":680767,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193968,"text":"70193968 - 2016 - River rating complexity","interactions":[],"lastModifiedDate":"2025-01-29T15:54:06.16656","indexId":"70193968","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"River rating complexity","docAbstract":"Accuracy of streamflow data depends on the veracity of the rating model used to derive a continuous time series of discharge from the surrogate variables that can readily be collected autonomously at a streamgage. Ratings are typically represented as a simple monotonic increasing function (simple rating), meaning the discharge is a function of stage alone, however this is never truly the case unless the flow is completely uniform at all stages and in transitions from one stage to the next. For example, at some streamflow-monitoring sites the discharge on the rising limb of the hydrograph is discernably larger than the discharge at the same stage on the falling limb of the hydrograph. This is the so-called “loop rating curve” (loop rating). In many cases, these loops are quite small and variation between rising- and falling-limb discharge measurements made at the same stage are well within the accuracy of the measurements. However, certain hydraulic conditions can produce a loop that is large enough to preclude use of a monotonic rating. A detailed data campaign for the Mississippi River at St. Louis, Missouri during a multi-peaked flood over a 56-day period in 2015 demonstrates the rating complexity at this location. The shifting-control method used to deal with complexity at this site matched all measurements within 8%.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River flow 2016","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Proceedings of the International Conference on Fluvial Hydraulics (River flow 2016)","conferenceDate":"July 11-14, 2016","conferenceLocation":"St. Louis, MO","language":"English","publisher":"CRC Press","usgsCitation":"Holmes, R.R., 2016, River rating complexity, in River flow 2016, St. Louis, MO, July 11-14, 2016, p. 679-686.","productDescription":"8 p.","startPage":"679","endPage":"686","ipdsId":"IP-071265","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":348967,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":348966,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.crcpress.com/River-Flow-2016-Iowa-City-USA-July-11-14-2016/Constantinescu-Garcia-Hanes/p/book/9781138029132","linkFileType":{"id":5,"text":"html"}},{"id":350997,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ja/70193968/70193968.pdf","text":"USGS open-access version of article","size":"507 kB","linkFileType":{"id":1,"text":"pdf"},"description":"USGS open-access version of article"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc9be4b06e28e9c24040","contributors":{"authors":[{"text":"Holmes, Robert R. Jr. 0000-0002-5060-3999 bholmes@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-3999","contributorId":156293,"corporation":false,"usgs":true,"family":"Holmes","given":"Robert","suffix":"Jr.","email":"bholmes@usgs.gov","middleInitial":"R.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":721769,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70190038,"text":"70190038 - 2016 - A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection","interactions":[],"lastModifiedDate":"2017-08-06T16:19:26","indexId":"70190038","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection","title":"A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection","docAbstract":"The largest recorded earthquake in Kansas occurred northeast of Milan on 12 November 2014 (Mw 4.9) in a region previously devoid of significant seismic activity. Applying multistation processing to data from local stations, we are able to detail the rupture process and rupture geometry of the mainshock, identify the causative fault plane, and delineate the expansion and extent of the subsequent seismic activity. The earthquake followed rapid increases of fluid injection by multiple wastewater injection wells in the vicinity of the fault. The source parameters and behavior of the Milan earthquake and foreshock–aftershock sequence are similar to characteristics of other earthquakes induced by wastewater injection into permeable formations overlying crystalline basement. This earthquake also provides an opportunity to test the empirical relation that uses felt area to estimate moment magnitude for historical earthquakes for Kansas.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160100","usgsCitation":"Choy, G., Rubinstein, J.L., Yeck, W.L., McNamara, D.E., Mueller, C., and Boyd, O.S., 2016, A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection: Seismological Research Letters, v. 87, no. 6, p. 1433-1441, https://doi.org/10.1785/0220160100.","productDescription":"9 p.","startPage":"1433","endPage":"1441","ipdsId":"IP-076956","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":344606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-14","publicationStatus":"PW","scienceBaseUri":"59882a94e4b05ba66e9ffdd8","contributors":{"authors":[{"text":"Choy, George choy@usgs.gov","contributorId":2161,"corporation":false,"usgs":true,"family":"Choy","given":"George","email":"choy@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rubinstein, Justin L. 0000-0003-1274-6785 jrubinstein@usgs.gov","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":2404,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","email":"jrubinstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":707278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707277,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mueller, Charles 0000-0002-1868-9710 cmueller@usgs.gov","orcid":"https://orcid.org/0000-0002-1868-9710","contributorId":140380,"corporation":false,"usgs":true,"family":"Mueller","given":"Charles","email":"cmueller@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707280,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":707281,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178266,"text":"sir20165133 - 2016 - Quantifying seepage using heat as a tracer in selected irrigation canals, Walker River Basin, Nevada, 2012 and 2013","interactions":[],"lastModifiedDate":"2025-05-14T18:37:27.795967","indexId":"sir20165133","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5133","title":"Quantifying seepage using heat as a tracer in selected irrigation canals, Walker River Basin, Nevada, 2012 and 2013","docAbstract":"The Walker River is an important source of water for western Nevada. The river provides water for agriculture and recharge to local aquifers used by several communities. Farmers began diverting water from the Walker River in the 1860s to support growing agricultural development. Over time, the reduced inflows into Walker Lake from upstream reservoirs and diversions have resulted in 170 feet of lake level decline and increased dissolved-solids concentrations to levels that threaten aquatic ecosystems, including survival of Lahonton cutthroat trout, a native species listed in the Endangered Species Act. Investigations of the water-budget components in the Walker River Basin have revealed uncertainty in the recharge to aquifers from irrigation canals. To address this need, the U.S. Geological Survey conducted an extensive field study from March 2012 through October 2013 to quantify seepage losses in selected canals in the Smith Valley, Mason Valley, and Walker Lake Valley irrigation areas.
The seepage rates estimated for the 2012 and 2013 irrigation seasons in the Smith Valley transect sites (Saroni and Plymouth canals) ranged between 0.01 to 2.5 feet per day (ft/d) (0.01 to 0.68 cubic feet per second per mile [ft3/s-mi]). From 2012 to 2013, the average number of days the canals had flowing water decreased from 190 to 125 due to drier climate and lack of water available for diversion from the Walker River. The nearly 50-percent reductions in volumetric loss rates between 2012 and 2013 were associated with less than average diversions into canals from the Walker River and reductions in infiltration rates following routine canal maintenance.
Models developed for the Saroni canal in 2012 were recalibrated in 2013 to evaluate changes in seepage as a result of siltation. Just prior to the 2012 irrigation season, nearly the entire length of the canal was cleared of vegetation and debris to improve flow conveyance. In 2013, following the first year of maintenance, a 90-percent reduction in seepage was observed at one of the transect sites. The removal of sediment-clogged layers during canal maintenance may have more profound effects on seepage rates beyond what was observed at the transect sites. The seepage rates for the Saroni canal in 2012 ranged from 0.02 to 1.6 ft/d (0.03 to 0.4 ft3/s-mi). The total seepage loss in the Saroni canal for the 2012 and 2013 irrigation seasons was estimated to be 1,100 and 590 acre-feet (acre-ft), respectively.
Seepage rates on the Plymouth canal in Smith Valley in 2012 were among the lowest, ranging from 0.01 to 0.2 ft/d (0.01 to 0.1 ft3/s-mi). In 2013, the seepage rate on the Plymouth canal was similar to 2012; however, the volumetric loss was reduced by 50 percent due to the 50-percent reduction in number of canal flow days. Lower rates of seepage on the Plymouth canal for the 2012 and 2013 irrigation seasons were estimated to be 210 and 130 acre-ft, respectively.
The seepage rates estimated for the 2012 and 2013 irrigation seasons in the Mason Valley transect sites (Fox, Mickey, and Campbell ditches) ranged from 0.1 to 3.3 ft/d (0.2 to 1.3 ft3/s-mi). The influence of water-table declines on seepage was observed at the Mickey and Campbell ditches. In 2012, the estimated seepage on the Mickey ditch was 1.6 ft/d during a period when the water-table altitude was at or above the canal altitude. Following extensive declines in the water table, the hydraulic gradient increased between the canal and the shallow aquifer, thereby increasing the seepage rates to 3.2 ft/d in 2013. During the period of hydraulic disconnection, seepage rates increased to 9.5 ft/d during intermittent periods of canal flow. For the Mickey ditch, the seepage loss in 2013 was 1.5 times the rate estimated in 2012 despite the canal having 45 days less flow. Similarly, the Campbell ditch seepage loss increased slightly from 660 to 700 acre-ft, a factor of 1.1, with 49 days less flow. The seepage loss for the Fox ditch did not exhibit significant year to year variability. The annual seepage loss estimated for 2012 and 2013 in the Fox ditch was 2,100 and 2,200 acre-ft, respectively.
The seepage rates estimated for the 2013 irrigation season in the Walker Lake Valley transect sites (Schurz Lateral Canals 1A and 2A, and Canal 2) ranged from 0.7 to 0.9 ft/d (0.4 to 1.3 ft3/s-mi). In Walker Lake Valley, diversions into Lateral Canals 1A and 2A during the 2013 irrigation season were highly intermittent, a characteristic common of lateral diversions. The annual estimated seepage loss in Walker Lake Valley ranged between 50 and 725 acre-ft among the transect sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165133","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Naranjo, R.C., and Smith, D.W., 2016, Quantifying seepage using heat as a tracer in selected irrigation canals, Walker River Basin, Nevada, 2012 and 2013: U.S. Geological Survey Scientific Investigations Report 2016-5133, 169 p.,\nhttps://dx.doi.org/10.3133/sir20165133.","productDescription":"Report: viii, 169 p.; 2 Appendixes","onlineOnly":"Y","ipdsId":"IP-066495","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":331031,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5133/sir20165133_appendix_6a.xlsx","text":"Appendix 6A","size":"16.4 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5133 Appendix 6A"},{"id":331030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5133/sir20165133.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5133"},{"id":331032,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5133/sir20165133_appendix_6b.xlsx","text":"Appendix 6B","size":"13.9 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5133 Appendix 6B"},{"id":331029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5133/coverthb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Walker River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.75,\n 38\n ],\n [\n -119.75,\n 39.25\n ],\n [\n -118.25,\n 39.25\n ],\n [\n -118.25,\n 38\n ],\n [\n -119.75,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, NV 89701
http://nv.water.usgs.gov/
The integrity of wetlands is of global concern. A common approach to evaluating ecological integrity involves bioassessment procedures that quantify the degree to which communities deviate from historical norms. While helpful, bioassessment provides little information about how altered conditions connect to community response. More detailed information is needed for conservation and restoration. We have illustrated an approach to addressing this challenge using structural equation modeling (SEM) and long-term monitoring data from Rocky Mountain National Park (RMNP). Wetlands in RMNP are threatened by a complex history of anthropogenic disturbance including direct alteration of hydrologic regimes; elimination of elk, wolves, and grizzly bears; reintroduction of elk (absent their primary predators); and the extirpation of beaver. More recently, nonnative moose were introduced to the region and have expanded into the park. Bioassessment suggests that up to half of the park's wetlands are not in reference condition. We developed and evaluated a general hypothesis about how human alterations influence wetland integrity and then develop a specific model using RMNP wetlands. Bioassessment revealed three bioindicators that appear to be highly sensitive to human disturbance (HD): (1) conservatism, (2) degree of invasion, and (3) cover of native forbs. SEM analyses suggest several ways human activities have impacted wetland integrity and the landscape of RMNP. First, degradation is highest where the combined effects of all types of direct HD have been the greatest (i.e., there is a general, overall effect). Second, specific HDs appear to create a “mixed-bag” of complex indirect effects, including reduced invasion and increased conservatism, but also reduced native forb cover. Some of these effects are associated with alterations to hydrologic regimes, while others are associated with altered shrub production. Third, landscape features created by historical beaver activity continue to influence wetland integrity years after beavers have abandoned sites via persistent landforms and reduced biomass of tall shrubs. Our model provides a system-level perspective on wetland integrity and provides a context for future evaluations and investigations. It also suggests scientifically supported natural resource management strategies that can assist in the National Park Service mission of maintaining or, when indicated, restoring ecological integrity “unimpaired for future generations.”
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1548","usgsCitation":"Schweiger, E.W., Grace, J.B., Cooper, D., Bobowski, B., and Britten, M., 2016, Using structural equation modeling to link human activities to wetland ecological integrity: Ecosphere, v. 7, no. 11, p. 1-30, https://doi.org/10.1002/ecs2.1548.","productDescription":"e01548; 30 p.","startPage":"1","endPage":"30","ipdsId":"IP-074267","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470418,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1548","text":"Publisher Index Page"},{"id":331006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.93292236328124,\n 40.15998434802335\n ],\n [\n -105.93292236328124,\n 40.69625781921317\n ],\n [\n -105.4302978515625,\n 40.69625781921317\n ],\n [\n -105.4302978515625,\n 40.15998434802335\n ],\n [\n -105.93292236328124,\n 40.15998434802335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-10","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bf8","chorus":{"doi":"10.1002/ecs2.1548","url":"http://dx.doi.org/10.1002/ecs2.1548","publisher":"Wiley-Blackwell","authors":"Schweiger E. William, Grace James B., Cooper David, Bobowski Ben, Britten Mike","journalName":"Ecosphere","publicationDate":"11/2016"},"contributors":{"authors":[{"text":"Schweiger, E. William","contributorId":53635,"corporation":false,"usgs":true,"family":"Schweiger","given":"E.","email":"","middleInitial":"William","affiliations":[],"preferred":false,"id":653814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":653815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, David","contributorId":176856,"corporation":false,"usgs":false,"family":"Cooper","given":"David","affiliations":[],"preferred":false,"id":653816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bobowski, Ben","contributorId":176857,"corporation":false,"usgs":false,"family":"Bobowski","given":"Ben","email":"","affiliations":[],"preferred":false,"id":653817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britten, Mike","contributorId":176858,"corporation":false,"usgs":false,"family":"Britten","given":"Mike","email":"","affiliations":[],"preferred":false,"id":653818,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182812,"text":"70182812 - 2016 - Forecasting inundation from debris flows that grow during travel, with application to the Oregon Coast Range, USA","interactions":[],"lastModifiedDate":"2017-03-01T10:36:16","indexId":"70182812","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting inundation from debris flows that grow during travel, with application to the Oregon Coast Range, USA","docAbstract":"Many debris flows increase in volume as they travel downstream, enhancing their mobility and hazard. Volumetric growth can result from diverse physical processes, such as channel sediment entrainment, stream bank collapse, adjacent landsliding, hillslope erosion and rilling, and coalescence of multiple debris flows; incorporating these varied phenomena into physics-based debris-flow models is challenging. As an alternative, we embedded effects of debris-flow growth into an empirical/statistical approach to forecast potential inundation areas within digital landscapes in a GIS framework. Our approach used an empirical debris-growth function to account for the effects of growth phenomena. We applied this methodology to a debris-flow-prone area in the Oregon Coast Range, USA, where detailed mapping revealed areas of erosion and deposition along paths of debris flows that occurred during a large storm in 1996. Erosion was predominant in stream channels with slopes > 5°. Using pre- and post-event aerial photography, we derived upslope contributing area and channel-length growth factors. Our method reproduced the observed inundation patterns produced by individual debris flows; it also generated reproducible, objective potential inundation maps for entire drainage networks. These maps better matched observations than those using previous methods that focus on proximal or distal regions of a drainage network.
","language":"English","publisher":"Elsevier ","doi":"10.1016/j.geomorph.2016.07.039","usgsCitation":"Reid, M.E., Coe, J.A., and Brien, D., 2016, Forecasting inundation from debris flows that grow during travel, with application to the Oregon Coast Range, USA: Geomorphology, v. 273, p. 396-411, https://doi.org/10.1016/j.geomorph.2016.07.039.","productDescription":"16 p. ","startPage":"396","endPage":"411","ipdsId":"IP-071225","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470422,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2016.07.039","text":"Publisher Index Page"},{"id":336725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"273","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba5e4b01ccd5500baf1","contributors":{"authors":[{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":673853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":673854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brien, Dianne dbrien@usgs.gov","contributorId":176271,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":673855,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178356,"text":"70178356 - 2016 - An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data","interactions":[],"lastModifiedDate":"2017-01-17T19:03:37","indexId":"70178356","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data","docAbstract":"Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8110943","usgsCitation":"Gu, Y., Wylie, B.K., Boyte, S.P., Picotte, J.J., Howard, D., Smith, K., and Nelson, K., 2016, An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data: Remote Sensing, v. 8, p. 1-13, https://doi.org/10.3390/rs8110943.","productDescription":"Article 943; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-079805","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470423,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110943","text":"Publisher Index Page"},{"id":331008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bfa","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653756,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653757,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176610,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":653758,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":653760,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653759,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178340,"text":"70178340 - 2016 - Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration","interactions":[],"lastModifiedDate":"2016-11-14T12:17:49","indexId":"70178340","displayToPublicDate":"2016-11-14T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration","docAbstract":"The Delaware River Basin (DRB) encompasses approximately 0.4 % of the area of the United States (U.S.), but supplies water to 5 % of the population. We studied three forested tributaries to quantify the potential climate-driven change in hydrologic budget for two 25-year time periods centered on 2030 and 2060, focusing on sensitivity to the method of estimating potential evapotranspiration (PET) change. Hydrology was simulated using the Water Availability Tool for Environmental Resources (Williamson et al. 2015). Climate-change scenarios for four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) and two Representative Concentration Pathways (RCPs) were used to derive monthly change factors for temperature (T), precipitation (PPT), and PET according to the energy-based method of Priestley and Taylor (1972). Hydrologic simulations indicate a general increase in annual (especially winter) streamflow (Q) as early as 2030 across the DRB, with a larger increase by 2060. This increase in Q is the result of (1) higher winter PPT, which outweighs an annual actual evapotranspiration (AET) increase and (2) (for winter) a major shift away from storage of PPT as snow pack. However, when PET change is evaluated instead using the simpler T-based method of Hamon (1963), the increases in Q are small or even negative. In fact, the change of Q depends as much on PET method as on time period or RCP. This large sensitivity and associated uncertainty underscore the importance of exercising caution in the selection of a PET method for use in climate-change analyses.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-016-1782-2","usgsCitation":"Williamson, T., Nystrom, E.A., and Milly, P., 2016, Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration: Climatic Change, v. 139, no. 2, p. 215-228, https://doi.org/10.1007/s10584-016-1782-2.","productDescription":"14 p.","startPage":"215","endPage":"228","ipdsId":"IP-072262","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":330971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-16","publicationStatus":"PW","scienceBaseUri":"582adb44e4b0c253bdfff09a","chorus":{"doi":"10.1007/s10584-016-1782-2","url":"http://dx.doi.org/10.1007/s10584-016-1782-2","publisher":"Springer Nature","authors":"Williamson Tanja N., Nystrom Elizabeth A., Milly Paul C. D.","journalName":"Climatic Change","publicationDate":"9/16/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"9/16/2016"},"contributors":{"authors":[{"text":"Williamson, Tanja N. tnwillia@usgs.gov","contributorId":148942,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja N.","email":"tnwillia@usgs.gov","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":false,"id":653642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nystrom, Elizabeth A. 0000-0002-0886-3439 nystrom@usgs.gov","orcid":"https://orcid.org/0000-0002-0886-3439","contributorId":1072,"corporation":false,"usgs":true,"family":"Nystrom","given":"Elizabeth","email":"nystrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milly, Paul C.D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":2119,"corporation":false,"usgs":true,"family":"Milly","given":"Paul C.D.","email":"cmilly@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":653644,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178338,"text":"70178338 - 2016 - Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability","interactions":[],"lastModifiedDate":"2016-11-14T12:27:13","indexId":"70178338","displayToPublicDate":"2016-11-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability","docAbstract":"Understanding and managing dynamic coastal landscapes for beach-dependent species requires biological and geological data across the range of relevant environments and habitats. It is difficult to acquire such information; data often have limited focus due to resource constraints, are collected by non-specialists, or lack observational uniformity. We developed an open-source smartphone application called iPlover that addresses these difficulties in collecting biogeomorphic information at piping plover (Charadrius melodus) nest sites on coastal beaches. This paper describes iPlover development and evaluates data quality and utility following two years of collection (n = 1799 data points over 1500 km of coast between Maine and North Carolina, USA). We found strong agreement between field user and expert assessments and high model skill when data were used for habitat suitability prediction. Methods used here to develop and deploy a distributed data collection system have broad applicability to interdisciplinary environmental monitoring and modeling.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0164979","usgsCitation":"Thieler, E.R., Zeigler, S.L., Winslow, L., Hines, M., Read, J.S., and Walker, J.I., 2016, Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability: PLoS ONE, v. 11, no. 11, e0164979; 22 p., https://doi.org/10.1371/journal.pone.0164979.","productDescription":"e0164979; 22 p.","ipdsId":"IP-077649","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470425,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0164979","text":"Publisher Index Page"},{"id":330973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"11","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582adb45e4b0c253bdfff0a5","contributors":{"authors":[{"text":"Thieler, E. Robert 0000-0003-4311-9717 rthieler@usgs.gov","orcid":"https://orcid.org/0000-0003-4311-9717","contributorId":2488,"corporation":false,"usgs":true,"family":"Thieler","given":"E.","email":"rthieler@usgs.gov","middleInitial":"Robert","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeigler, Sara L. 0000-0002-5472-769X szeigler@usgs.gov","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":169601,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","email":"szeigler@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":653641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winslow, Luke 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":168947,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, Megan 0000-0002-9845-4849 mhines@usgs.gov","orcid":"https://orcid.org/0000-0002-9845-4849","contributorId":4783,"corporation":false,"usgs":true,"family":"Hines","given":"Megan","email":"mhines@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":653638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walker, Jordan I. 0000-0003-2226-3373 jiwalker@usgs.gov","orcid":"https://orcid.org/0000-0003-2226-3373","contributorId":4608,"corporation":false,"usgs":true,"family":"Walker","given":"Jordan","email":"jiwalker@usgs.gov","middleInitial":"I.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653640,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178726,"text":"70178726 - 2016 - Competitive exclusion over broad spatial extents is a slow process: Evidence and implications for species distribution modeling","interactions":[],"lastModifiedDate":"2017-02-02T11:01:52","indexId":"70178726","displayToPublicDate":"2016-11-11T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Competitive exclusion over broad spatial extents is a slow process: Evidence and implications for species distribution modeling","docAbstract":"There is considerable debate about the role of competition in shaping species distributions over broad spatial extents. This debate has practical implications because predicting changes in species' geographic ranges in response to ongoing environmental change would be simpler if competition could be ignored. While this debate has been the subject of many reviews, recent literature has not addressed the rates of relevant processes. This omission is surprising in that ecologists hypothesized decades ago that regional competitive exclusion is a slow process. The goal of this review is to reassess the debate under the hypothesis that competitive exclusion over broad spatial extents is a slow process.
Available evidence, including simulations presented for the first time here, suggests that competitive exclusion over broad spatial extents occurs slowly over temporal extents of many decades to millennia. Ecologists arguing against an important role for competition frequently study modern patterns and/or range dynamics over periods of decades, while much of the evidence for competition shaping geographic ranges at broad spatial extents comes from paleoecological studies over time scales of centuries or longer. If competition is slow, as evidence suggests, the geographic distributions of some, perhaps many species, would continue to change over time scales of decades to millennia, even if environmental conditions did not continue to change. If the distributions of competing species are at equilibrium it is possible to predict species distributions based on observed species–environment relationships. However, disequilibrium is widespread as a result of competition and many other processes. Studies whose goal is accurate predictions over intermediate time scales (decades to centuries) should focus on factors associated with range expansion (colonization) and loss (local extinction), as opposed to current patterns. In general, understanding of modern range dynamics would be enhanced by considering the rates of relevant processes.
","language":"English","publisher":"Blackwell Publishers ","doi":"10.1111/ecog.02836","usgsCitation":"Yackulic, C.B., 2016, Competitive exclusion over broad spatial extents is a slow process: Evidence and implications for species distribution modeling: Ecography, v. 40, no. 2, p. 305-313, https://doi.org/10.1111/ecog.02836.","productDescription":"9 p.","startPage":"305","endPage":"313","ipdsId":"IP-073460","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470426,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.02836","text":"Publisher Index Page"},{"id":331661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"58492df2e4b06d80b7b093a2","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":654997,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}