{"pageNumber":"304","pageRowStart":"7575","pageSize":"25","recordCount":41074,"records":[{"id":70237952,"text":"70237952 - 2020 - Conjoint use of hydraulic head and groundwater age data to detect hydrogeologic barriers","interactions":[],"lastModifiedDate":"2022-11-01T14:06:22.857819","indexId":"70237952","displayToPublicDate":"2020-01-11T08:57:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Conjoint use of hydraulic head and groundwater age data to detect hydrogeologic barriers","docAbstract":"Hydraulic head and groundwater age data are effective in building understanding of groundwater systems. Yet their joint role in detecting and characterising low-permeability geological structures, i.e. hydrogeologic barriers such as faults and dykes, has not been widely studied. Here, numerical flow and transport models, using MODFLOW-NWT and MT3D-USGS, were developed with different hydrogeologic barrier configurations in a hypothetical aquifer. Computed hydraulic head and groundwater age distributions were compared to those without a barrier. The conjoint use of these datasets helps in detecting vertically-oriented barriers. Two forms of recharge were compared: (1) applied across the entire aquifer surface (uniform), and (2) applied to the upstream part of the aquifer (upgradient). The hydraulic head distribution is significantly impacted by a barrier that penetrates the aquifer’s full vertical thickness. This barrier also perturbs the groundwater age distribution when upgradient recharge prevails; however, with uniform recharge, groundwater age is not successful in detecting the barrier. When a barrier is buried, such as by younger sediment, hydraulic head data also do not clearly identify the barrier. Groundwater age data could, on the other hand, prove to be useful if sampled at depth-specific intervals. These results are important for the detection and characterisation of hydrogeologic barriers, which may play a significant role in the compartmentalisation of groundwater flow, spring dynamics, and drawdown and recovery associated with groundwater extraction.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-019-02095-9","usgsCitation":"Marshall, S.K., Cook, P., Konikow, L.F., Simmons, C., and Dogramaci, S., 2020, Conjoint use of hydraulic head and groundwater age data to detect hydrogeologic barriers: Hydrogeology Journal, v. 28, p. 1003-1019, https://doi.org/10.1007/s10040-019-02095-9.","productDescription":"17 p.","startPage":"1003","endPage":"1019","ipdsId":"IP-109151","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":408987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","noUsgsAuthors":false,"publicationDate":"2020-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Sarah K.","contributorId":298728,"corporation":false,"usgs":false,"family":"Marshall","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":40595,"text":"Flinders University","active":true,"usgs":false}],"preferred":false,"id":856337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, Peter G.","contributorId":298729,"corporation":false,"usgs":false,"family":"Cook","given":"Peter G.","affiliations":[{"id":40595,"text":"Flinders University","active":true,"usgs":false}],"preferred":false,"id":856338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Konikow, Leonard F. 0000-0002-0940-3856 lkonikow@usgs.gov","orcid":"https://orcid.org/0000-0002-0940-3856","contributorId":158,"corporation":false,"usgs":true,"family":"Konikow","given":"Leonard","email":"lkonikow@usgs.gov","middleInitial":"F.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simmons, Craig T.","contributorId":298730,"corporation":false,"usgs":false,"family":"Simmons","given":"Craig T.","affiliations":[{"id":40595,"text":"Flinders University","active":true,"usgs":false}],"preferred":false,"id":856340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dogramaci, Shawan","contributorId":298731,"corporation":false,"usgs":false,"family":"Dogramaci","given":"Shawan","email":"","affiliations":[{"id":64684,"text":"Rio Tinto Iron Ore Co.","active":true,"usgs":false}],"preferred":false,"id":856341,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70208357,"text":"70208357 - 2020 - Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA)","interactions":[],"lastModifiedDate":"2020-02-05T16:05:31","indexId":"70208357","displayToPublicDate":"2020-01-10T15:56:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA)","docAbstract":"Transitional areas between ecosystem types are often active biogeochemically due to resource limitation changes. Lotic-to-lentic transitions in freshwaters appear active biogeochemically, but few studies have directly measured nutrient processing rates to assess whether processing within the rivermouth is important for load estimates or the local communities. We measured oxic fluxes of inorganic nitrogen and phosphorus and dissolved organic carbon (DOC) from sediments in two rivermouths of Green Bay (Lake Michigan, USA). Soluble reactive phosphorus (SRP) flux was positive in most cases (overall mean 1.74 mg SRP m− 2 day− 1), as was ammonium (NH4) flux (40.6 mg NH4 m− 2 day− 1). Partial least square regression (PLSR) indicated a latent variable associated with both sediment [loosely bound phosphorus (P), iron bound P, organic content] and water column properties [temperature, DOC:dissolved inorganic nitrogen (DIN) and DOC:SRP ratios (negatively)] that was moderately associated with variation in SRP flux. PLSR analysis also indicated several sediment characteristics were moderately related to NH4 flux, especially organic content, density (negative), and porosity. Flux of nitrates/nitrites (NOX) and DOC were positively associated with the water column concentrations of NOX and DOC and qualitative estimates of the labile, non-humic types of DOC. In early summer, water column NOX and DOC concentrations were high and labile DOC may have fueled denitrification, resulting in net flux into sediments of both NOX and DOC. By late summer, water column NOX and DOC were very low and both these constituents were fluxing out of sediments into the water column. Based on our estimates for the entire period from April through September, rivermouth sediments were a net source of SRP and DIN, with a DIN:SRP ratio of ~ 44 and a NH4:NOX > 1. We estimated that the sediments in the Fox rivermouth probably contributed a small proportion of the total Fox River load during the growing season 2016 (< 5%), but at times may have contributed as much as 14% of the daily load. Despite the small size of the Fox rivermouth (< 0.5% of the watershed area), these results indicate that at times sediments can contribute substantially to the overall delivery of nitrogen and phosphorus to the nearshore zone.
","language":"English","publisher":"Springer Nature Switzerland AG","doi":"10.1007/s10533-020-00635-0","usgsCitation":"Larson, J.H., James, W.F., Fitzpatrick, F.A., Frost, P.C., Evans, M.A., Reneau, P., and Xenopoulos, M.A., 2020, Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA): Biogeochemistry, v. 147, p. 179-197, https://doi.org/10.1007/s10533-020-00635-0.","productDescription":"19 p.","startPage":"179","endPage":"197","ipdsId":"IP-101349","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437171,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LVTWS8","text":"USGS data release","linkHelpText":"Data from 92 sediment incubation experiments using sediments collected from the Fox and Duck rivermouths (adjacent to Green Bay, Lake Michigan; 2016 data)"},{"id":437170,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P995SMVW","text":"USGS data release","linkHelpText":"\tR Code to analyze data from sediment incubation experiments (Fox and Duck Rivermouths; 2016)"},{"id":372096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Green Bay","otherGeospatial":"Duck Creek, Fox River, Green Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.077392578125,\n 44.44162421758805\n ],\n [\n -87.99121856689453,\n 44.44162421758805\n ],\n [\n -87.99121856689453,\n 44.57873024377564\n ],\n [\n -88.077392578125,\n 44.57873024377564\n ],\n [\n -88.077392578125,\n 44.44162421758805\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"147","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":781554,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"James, William F.","contributorId":213265,"corporation":false,"usgs":false,"family":"James","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":38729,"text":"University of Wisconsin-Stout","active":true,"usgs":false}],"preferred":false,"id":781555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":196543,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","email":"fafitzpa@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":781556,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frost, Paul C.","contributorId":138628,"corporation":false,"usgs":false,"family":"Frost","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON CA","active":true,"usgs":false}],"preferred":false,"id":781557,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":781558,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reneau, Paul C.","contributorId":222219,"corporation":false,"usgs":false,"family":"Reneau","given":"Paul C.","affiliations":[{"id":40507,"text":"former employee, Wisconsin Water Science Center","active":true,"usgs":false}],"preferred":false,"id":781559,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xenopoulos, Marguerite A.","contributorId":138629,"corporation":false,"usgs":false,"family":"Xenopoulos","given":"Marguerite","email":"","middleInitial":"A.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON CA","active":true,"usgs":false}],"preferred":false,"id":781560,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70249354,"text":"70249354 - 2020 - Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires","interactions":[],"lastModifiedDate":"2023-10-05T00:14:25.937585","indexId":"70249354","displayToPublicDate":"2020-01-10T12:32:34","publicationYear":"2020","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":"Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires","docAbstract":"Fire Radiative Power (FRP) is related to fire combustion rates and is used to quantify the atmospheric emissions of greenhouse gases and aerosols. FRP over gas flares and wildfires can be retrieved remotely using satellites that observe in shortwave infrared (SWIR) to middle infrared (MIR) wavelengths. Heritage techniques to retrieve FRP developed for wildland fires using the MIR 4 μm radiances have been adapted for the hotter burning gas flares using the SWIR 2 μm observations. Effects of atmosphere, including smoke and aerosols, are assumed to be minimal in these algorithms because of the use of longer than visual wavelengths. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS) and Landsat 8 observations acquired before and during emergency oil and gas flaring in eastern Saudi Arabia to show that dark, sooty smoke affects both 4 μm and 2 μm observations. While the 2 μm observations used to retrieve gas FRP may be reliable during clear atmospheric conditions, performance is severely impacted by dark smoke. Global remote sensing-based inventories of wildfire and gas flaring need to consider the possibility that soot and dark smoke can potentially lead to an underestimation of FRP over fires.
","language":"English","publisher":"MPDI","doi":"10.3390/rs12020238","usgsCitation":"Kumar, S.S., Hult, J.E., Picotte, J., and Peterson, B., 2020, Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires: Remote Sensing, v. 12, no. 2, 238, 9 p., https://doi.org/10.3390/rs12020238.","productDescription":"238, 9 p.","ipdsId":"IP-113025","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458161,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12020238","text":"Publisher Index Page"},{"id":421611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Saudi Arabia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[42.77933,16.34789],[42.64957,16.77464],[42.34799,17.07581],[42.27089,17.47472],[41.75438,17.83305],[41.22139,18.6716],[40.93934,19.48649],[40.24765,20.17463],[39.80168,20.33886],[39.1394,21.2919],[39.0237,21.98688],[39.06633,22.57966],[38.49277,23.68845],[38.02386,24.07869],[37.48363,24.28549],[37.15482,24.85848],[37.20949,25.08454],[36.93163,25.60296],[36.6396,25.82623],[36.24914,26.57014],[35.64018,27.37652],[35.13019,28.06335],[34.63234,28.05855],[34.78778,28.60743],[34.83222,28.95748],[34.95604,29.35655],[36.06894,29.19749],[36.50121,29.50525],[36.74053,29.86528],[37.50358,30.00378],[37.66812,30.33867],[37.99885,30.5085],[37.00217,31.50841],[39.00489,32.01022],[39.19547,32.16101],[40.39999,31.88999],[41.88998,31.19001],[44.7095,29.17889],[46.56871,29.09903],[47.45982,29.00252],[47.70885,28.52606],[48.41609,28.552],[48.80759,27.68963],[49.29955,27.46122],[49.47091,27.11],[50.15242,26.68966],[50.21294,26.27703],[50.1133,25.94397],[50.23986,25.60805],[50.52739,25.32781],[50.66056,24.9999],[50.81011,24.75474],[51.11242,24.55633],[51.38961,24.62739],[51.57952,24.2455],[51.61771,24.01422],[52.00073,23.00115],[55.0068,22.49695],[55.20834,22.70833],[55.66666,22],[54.99998,19.99999],[52.00001,19],[49.11667,18.61667],[48.18334,18.16667],[47.46669,17.11668],[47,16.95],[46.74999,17.28334],[46.36666,17.23332],[45.4,17.33334],[45.21665,17.43333],[44.06261,17.41036],[43.79152,17.31998],[43.38079,17.57999],[43.1158,17.08844],[43.21838,16.66689],[42.77933,16.34789]]]},\"properties\":{\"name\":\"Saudi Arabia\"}}]}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Kumar, Sanath S. 0000-0003-4067-4926","orcid":"https://orcid.org/0000-0003-4067-4926","contributorId":330540,"corporation":false,"usgs":true,"family":"Kumar","given":"Sanath","email":"","middleInitial":"S.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hult, John Edward 0000-0001-8895-3727","orcid":"https://orcid.org/0000-0001-8895-3727","contributorId":330551,"corporation":false,"usgs":true,"family":"Hult","given":"John","email":"","middleInitial":"Edward","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885283,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Picotte, Joshua J. 0000-0002-4021-4623","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":202800,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885284,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Birgit 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":192353,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885285,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70209414,"text":"70209414 - 2020 - Calcite precipitation in Lake Powell reduces alkalinity and total salt loading to the Lower Colorado River Basin","interactions":[],"lastModifiedDate":"2020-08-04T13:59:38.294865","indexId":"70209414","displayToPublicDate":"2020-01-10T08:25:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Calcite precipitation in Lake Powell reduces alkalinity and total salt loading to the Lower Colorado River Basin","docAbstract":"Reservoirs can retain and transform carbon, nitrogen, phosphorus, and silica, but less is known about their effects on other biogeochemically relevant solutes. The salinization of freshwater ecosystems is a growing concern in many regions, and the role of reservoirs in salinity transport is an important research frontier. Here, we examine how a large desert southwest reservoir, Lake Powell, has altered the downstream transport of total dissolved solids (TDSs) as well as the dominant cations and anions comprising the TDS pool (![\"urn:x-wiley:00243590:media:lno11399:lno11399-math-0001\"](\"https://aslopubs.onlinelibrary.wiley.com/cms/asset/e804c3ff-bfd7-48f1-aae4-42cd05a557b1/lno11399-math-0001.png\")
, ![\"urn:x-wiley:00243590:media:lno11399:lno11399-math-0002\"](\"https://aslopubs.onlinelibrary.wiley.com/cms/asset/6502c6e0-db3d-4fd2-b57f-0736ce6bea4a/lno11399-math-0002.png\")
, and Ca2+). Average downstream TDS concentrations have declined significantly since river impoundment and seasonal fluctuations in TDS concentrations have become more modulated, but year to year variation in TDS concentrations has remained similar. While some of the reductions in TDS concentration can be attributed to watershed management, we find that Lake Powell retains about 10% of the TDS loaded to the system (1991 Mg TDS d−1). Much of this retention is occurring in the forms of calcium and bicarbonate, likely via calcite precipitation, and is equivalent to an average burial of 522 mg C m−2 d−1, thus reducing the alkalinity of downstream water. Flow‐weighted modeling suggests that, in the absence of Lake Powell, downstream salinity limits would be surpassed at the outflow to Lake Powell 41% of the time (vs. 0% of the time currently). Understanding the dominant mechanisms regulating solute transport through the reservoir is important given the relevance for downstream drinking water and irrigation concerns, biogeochemical cycling, and the high potential for reduced flows in the future.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.11399","usgsCitation":"Deemer, B., Stets, E.G., and Yackulic, C.B., 2020, Calcite precipitation in Lake Powell reduces alkalinity and total salt loading to the Lower Colorado River Basin: Limnology and Oceanography, v. 65, no. 7, p. 1439-1455, https://doi.org/10.1002/lno.11399.","productDescription":"17 p.","startPage":"1439","endPage":"1455","ipdsId":"IP-112663","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":437173,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A9P44R","text":"USGS data release","linkHelpText":"Calcium, magnesium and total dissolved solids data as well as modeled salinity and mass balance estimates for Lake Powell, 1952-2017"},{"id":373749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Lower Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.4228515625,\n 36.87962060502676\n ],\n [\n -109.3798828125,\n 35.02999636902566\n ],\n [\n -104.765625,\n 35.639441068973944\n ],\n [\n -104.19433593749999,\n 37.996162679728116\n ],\n [\n -104.4580078125,\n 40.74725696280421\n ],\n [\n -107.5341796875,\n 43.42100882994726\n ],\n [\n -110.56640625,\n 43.739352079154706\n ],\n [\n -112.54394531249999,\n 43.58039085560784\n ],\n [\n -113.115234375,\n 41.672911819602085\n ],\n [\n -112.412109375,\n 40.3130432088809\n ],\n [\n -112.1484375,\n 39.13006024213511\n ],\n [\n -112.8955078125,\n 37.61423141542417\n ],\n [\n -113.4228515625,\n 36.87962060502676\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":786378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stets, Edward G. 0000-0001-5375-0196 estets@usgs.gov","orcid":"https://orcid.org/0000-0001-5375-0196","contributorId":194490,"corporation":false,"usgs":true,"family":"Stets","given":"Edward","email":"estets@usgs.gov","middleInitial":"G.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":786379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":786380,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70208310,"text":"70208310 - 2020 - How often can Earthquake Early Warning systems alert sites with high intensity ground motion?","interactions":[],"lastModifiedDate":"2020-02-04T07:34:49","indexId":"70208310","displayToPublicDate":"2020-01-10T07:33:55","publicationYear":"2020","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":"How often can Earthquake Early Warning systems alert sites with high intensity ground motion?","docAbstract":"Although numerous Earthquake Early Warning (EEW) algorithms have been developed we still lack a detailed understanding of how often and under what circumstances useful ground motion alerts can be provided to end-users. Here we analyze the alerting performance of the PLUM, EPIC and FinDer algorithms by running them retrospectively on the seismic strong motion data of the 219 earthquakes in Japan since 1996 that exceeded Modified Mercalli Intensity (MMI) of 4.5 on at least 10 sites (Mw 4.5-9.1). Our analysis suggests that, irrespective of the algorithm, EEW end-users should be prepared that EEW can often but not always provide useful ground motion alerts. A majority of sites with moderate-strong ground motion (MMI 5-6) can generally get at least a few seconds of warning time from all algorithms. If such shaking is caused by a shallow crustal event, around 50% of such sites receive alerts with warning times >5 s. Many sites with severe-extreme ground motion (MMI >=8) can be alerted successfully in the case of very large offshore earthquakes, but less than 20% can be alerted ahead of time if such shaking is caused by a shallow crustal event. Our results provide detailed quantitative insight into the expected alerting performance for EEW algorithms under realistic conditions. The main caveat is that the largest shallow crustal event in our data set has Mw7.0, i.e. the data set does not contain very large strike slip events.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB017718","usgsCitation":"Meier, M., Kodera, Y., Bose, M., Chung, A.I., Hoshiba, M., Cochran, E.S., Minson, S.E., Hauksson, E., and Heaton, T., 2020, How often can Earthquake Early Warning systems alert sites with high intensity ground motion?: Journal of Geophysical Research, v. 125, e2019JB017718, 17 p., https://doi.org/10.1029/2019JB017718.","productDescription":"e2019JB017718, 17 p.","ipdsId":"IP-107685","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":458167,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019jb017718","text":"Publisher Index Page"},{"id":371988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Meier, M.-A.","contributorId":222138,"corporation":false,"usgs":false,"family":"Meier","given":"M.-A.","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":781351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kodera, Y.","contributorId":216381,"corporation":false,"usgs":false,"family":"Kodera","given":"Y.","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":781352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bose, M.","contributorId":222139,"corporation":false,"usgs":false,"family":"Bose","given":"M.","email":"","affiliations":[{"id":40494,"text":"ETH-Zurich","active":true,"usgs":false}],"preferred":false,"id":781353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chung, A. I.","contributorId":39293,"corporation":false,"usgs":false,"family":"Chung","given":"A.","email":"","middleInitial":"I.","affiliations":[{"id":7033,"text":"School of Earth Sciences, Stanford University","active":true,"usgs":false}],"preferred":false,"id":781354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoshiba, M.","contributorId":222140,"corporation":false,"usgs":false,"family":"Hoshiba","given":"M.","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":781355,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":781350,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":781356,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hauksson, E.","contributorId":196003,"corporation":false,"usgs":false,"family":"Hauksson","given":"E.","affiliations":[],"preferred":false,"id":781357,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Heaton, T.","contributorId":222141,"corporation":false,"usgs":false,"family":"Heaton","given":"T.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":781358,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70227742,"text":"70227742 - 2020 - Combined influence of intrinsic and environmental factors in shaping productivity in a small pelagic gull, the black-legged kittiwake Rissa tridactyla","interactions":[],"lastModifiedDate":"2022-01-28T16:01:47.756094","indexId":"70227742","displayToPublicDate":"2020-01-09T09:57:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Combined influence of intrinsic and environmental factors in shaping productivity in a small pelagic gull, the black-legged kittiwake Rissa tridactyla","title":"Combined influence of intrinsic and environmental factors in shaping productivity in a small pelagic gull, the black-legged kittiwake Rissa tridactyla","docAbstract":"While we have a good understanding in many systems of the effects of single variable changes on organisms, we understand far less about how variables act in concert to affect living systems, where interactions among variables can lead to unanticipated results. We used mixed-effect models to evaluate the effects of multiple variables that we expected to play a role in the early reproductive stages of a North Pacific seabird, the black-legged kittiwake Rissa tridactyla, during 1992-2008 using data collected on known-aged individuals. Our work revealed the potential for contrasting stressor effects across successive stages of reproduction. Bird age, timing of egg laying, and winter ENSO conditions best explained individual laying success, such that laying success was greater when parents were older, the average winter ENSO index was positive (as occurs during El Niño episodes), and the median laying date for the colony was earlier. Age and salmon run timing (a proxy for predator presence at the colony) best explained hatching success, such that hatching success was greater when parents were older and when salmon runs were early. Identifying such differential effects of multiple stressors across consecutive reproductive stages can greatly enhance our ability to interpret trends and manage populations in the face of changes currently occurring in living systems.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13162","usgsCitation":"McKnight, A., Irons, D., Loftin, C., McKinney, S., and Olsen, B., 2020, Combined influence of intrinsic and environmental factors in shaping productivity in a small pelagic gull, the black-legged kittiwake Rissa tridactyla: Marine Ecology Progress Series, v. 633, p. 207-223, https://doi.org/10.3354/meps13162.","productDescription":"17 p.","startPage":"207","endPage":"223","ipdsId":"IP-088508","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"633","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKnight, Aly","contributorId":272505,"corporation":false,"usgs":false,"family":"McKnight","given":"Aly","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":832006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irons, David B.","contributorId":272506,"corporation":false,"usgs":false,"family":"Irons","given":"David B.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":832007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKinney, Shawn T.","contributorId":272507,"corporation":false,"usgs":false,"family":"McKinney","given":"Shawn T.","affiliations":[],"preferred":false,"id":832008,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olsen, Brian J.","contributorId":272508,"corporation":false,"usgs":false,"family":"Olsen","given":"Brian J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":832009,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207989,"text":"70207989 - 2020 - Challenges for leveraging citizen science to support statistically robust monitoring programs","interactions":[],"lastModifiedDate":"2020-01-23T06:36:56","indexId":"70207989","displayToPublicDate":"2020-01-09T06:35:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Challenges for leveraging citizen science to support statistically robust monitoring programs","docAbstract":"Large samples and long time series are often needed for effective broad-scale monitoring of status and trends in wild populations. Obtaining those sample sizes can be more feasible when volunteers contribute to the dataset, but volunteer-selected sites are not always representative of a population. Previous work to account for biased site selection has relied on knowledge of covariates to explain differences between site types, but such knowledge is often unavailable. For cases where relevant covariates have not been defined, we used a simulation study to identify the consequences of including non-probabilistically selected sites (NP sites) in addition to sites selected from a probability-based design (P sites), test modeling frameworks that might correct for biases, and evaluate whether those frameworks could allow NP sites to reduce the sampling requirement for P sites and potentially reduce costs of monitoring. We informed the simulation with pilot data from surveys of monarch butterflies and their obligate larval host plant, milkweed. We found strong biases in NP sites versus P sites in density and trends of monarchs and milkweed. Modeling frameworks that accounted for site type with a group effect or that strongly downweighted NP sites successfully produced unbiased estimates. However, sampling more NP sites typically did not improve accuracy or precision, and adding NP sites sometimes required also adding P sites to prevent biases. Further work on novel modeling frameworks would be useful to allow citizen-science data to contribute useful information to conservation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108411","usgsCitation":"Weiser, E.L., Diffendorfer, J., Lopez-Hoffman, L., Semmens, D., and Thogmartin, W.E., 2020, Challenges for leveraging citizen science to support statistically robust monitoring programs: Biological Conservation, v. 242, 108411, 10 p., https://doi.org/10.1016/j.biocon.2020.108411.","productDescription":"108411, 10 p.","ipdsId":"IP-112580","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":458175,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2020.108411","text":"Publisher Index Page"},{"id":371491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"242","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":780046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":780047,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lopez-Hoffman, Laura","contributorId":149127,"corporation":false,"usgs":false,"family":"Lopez-Hoffman","given":"Laura","affiliations":[{"id":17654,"text":"School of Natural Resources & the Environment and Udall Center for Studies in Public Policy, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":780048,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":780049,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":780050,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207244,"text":"sir20195141 - 2020 - Water-balance techniques for determining available soil-water storage for selected sandy and clay soil study sites in Cass County, North Dakota, 2016–17","interactions":[],"lastModifiedDate":"2022-04-25T20:16:09.086144","indexId":"sir20195141","displayToPublicDate":"2020-01-08T16:45:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5141","displayTitle":"Water-Balance Techniques for Determining Available Soil-Water Storage for Selected Sandy and Clay Soil Study Sites in Cass County, North Dakota, 2016–17","title":"Water-balance techniques for determining available soil-water storage for selected sandy and clay soil study sites in Cass County, North Dakota, 2016–17","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service, collected field and remotely sensed data on precipitation, evapotranspiration (ET), and soil-water content to determine available soil-water storage (AWS) at six study sites on sandy and clay soils in Cass County, North Dakota. Data were collected at all the study sites from May 1–October 31, 2016, and from May 1–October 24, 2017. Estimated daily AWS was determined using daily meteorological and potential evapotranspiration (PET) data obtained from various climate stations, and estimated monthly AWS was determined using monthly meteorological and PET data and monthly ET data determined using the Operational Simplified Surface Energy Balance model. AWS during 2016 and 2017 was determined at daily and monthly time steps because of data availability and to assess results using varying time steps. Comparisons of measured and estimated daily values of AWS at the Brewer Lake site indicated poor agreement during May–October 2016 and May–October 2017. Comparisons of measured and estimated daily values of AWS at the Embden East and Embden West sites indicated poor and fair agreement respectively. At the Lynchburg Crop and Lynchburg Grass sites, comparisons of measured and estimated daily values of AWS indicated fair and good relations, respectively, even with the possible effects of soil cracks. Mean estimated values of daily runoff plus soil percolation for the four sandy soil sites indicated that a maximum of about 19 percent of the estimated runoff plus soil percolation could be considered runoff and that the remaining 81 percent could be considered soil percolation, and for the two clay soil sites about 13 percent of the runoff plus soil percolation could have been considered runoff and about 87 percent could have been considered soil percolation. Results indicated little difference between using monthly PET or monthly ET in water-balance equations to estimate monthly AWS for the grouped sandy soil sites, and only slightly better results were obtained using monthly PET than monthly ET to estimate monthly AWS for the grouped clay soil study sites. Overall, the monthly water-balance models did not perform as well as the daily water-balance models for determining AWS at the six study sites. Additional data collection from a longer-period study and adjustments to the models may improve results from the monthly water-balance techniques.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195141","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service","usgsCitation":"Vining, K.C., 2020, Water-balance techniques for determining available soil-water storage for selected sandy and clay soil study sites in Cass County, North Dakota, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5141, 39 p., https://doi.org/10.3133/sir20195141.","productDescription":"Report: vii, 39 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-098347","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":399616,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109584.htm"},{"id":371052,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GG2ILI","text":"USGS data 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Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
","tableOfContents":"- Abstract
- Introduction
- Methods
- Results of Water-Balance Techniques and Available Soil-Water Storage Analyses
- Limitations
- Summary
- References Cited
","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-01-08","noUsgsAuthors":false,"publicationDate":"2020-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Vining, Kevin C. 0000-0001-5738-3872","orcid":"https://orcid.org/0000-0001-5738-3872","contributorId":221225,"corporation":false,"usgs":true,"family":"Vining","given":"Kevin","email":"","middleInitial":"C.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777428,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70210164,"text":"70210164 - 2020 - Fluxes of agricultural nitrogen and metolachlor metabolites are highly correlated in a first order stream in Maryland, USA","interactions":[],"lastModifiedDate":"2020-05-19T14:55:14.610714","indexId":"70210164","displayToPublicDate":"2020-01-08T09:49:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Fluxes of agricultural nitrogen and metolachlor metabolites are highly correlated in a first order stream in Maryland, USA","docAbstract":"Nitrogen pollution in watersheds containing significant\ncropland area is generally problematic. Conservation practices intended\nto reduce nitrate-N (NO3--N) export from watersheds are being implemented\nby many regions without necessary tools to assess effectiveness of these\nabatement tools. A commonly used herbicide metolachlor degrades in the\nvadose zone of croplands to form two metabolites (metolachlor ethane\nsulfonic acid (MESA) and metolachlor oxanilic acid (MOXA)) which are both\nhighly soluble in soils. Study of metabolite fates in a first order\nwatershed provided evidence that transport of these metabolites to stream\nwater is highly correlated to transport of the agricultural NO3--N that\nalso forms in the cropland vadose zone. Linear models describing the\nrelationships of stream flux of MESA and MOXA to NO3--N flux generated\ngoodness of fit values of 0.93 and 0.82 respectively. These findings\nsupport a conclusion that both MESA and MOXA act as excellent transport\nanalogs of NO3- and become strongly correlated to agricultural NO3--N\nleaching from the cropland vadose zone. Moreover, their use as\nconservative tracers in agricultural watersheds can provide valuable\ninformation concerning movement and fate of agricultural nitrogen at\nwatershed scales of observation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.136590","usgsCitation":"Rice, C., Hively, W.D., McCarty, G.W., and Hapeman, C., 2020, Fluxes of agricultural nitrogen and metolachlor metabolites are highly correlated in a first order stream in Maryland, USA: Science of the Total Environment, v. 716, 136590, 7 p., https://doi.org/10.1016/j.scitotenv.2020.136590.","productDescription":"136590, 7 p.","ipdsId":"IP-113701","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":458180,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.136590","text":"Publisher Index 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Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":210993,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":789365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCarty, Gregory W.","contributorId":192367,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":789366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hapeman, Cathleen","contributorId":224745,"corporation":false,"usgs":false,"family":"Hapeman","given":"Cathleen","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":789367,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70209229,"text":"70209229 - 2020 - Characterization of the genetic structure of four sucker species in the Klamath River. Final Report","interactions":[],"lastModifiedDate":"2020-03-26T06:42:12","indexId":"70209229","displayToPublicDate":"2020-01-08T06:47:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Characterization of the genetic structure of four sucker species in the Klamath River. Final Report","docAbstract":"Four species of suckers (family Catostomidae) inhabit the Klamath River Basin of Oregon and California: Lost River suckers (LRS; Deltistes luxatus), shortnose suckers (SNS; Chasmistes brevirostris), Klamath largescale suckers (KLS; Catostomus snyderi), and Klamath smallscale suckers (KSS; Catostomus rimiculus). All but Klamath smallscale suckers are endemic and restricted to the Klamath River Basin where they occur sympatrically in large lakes and reservoirs, including the Lost River and Klamath Lake subbasins (Figure 1; USFWS 2012).\nPopulation declines, primarily due to loss or degradation of spawning, rearing, and adult habitat, have resulted in Lost River and shortnose suckers being listed as endangered throughout their entire range under the U.S. Endangered Species Act (USFWS 1988).\n\nContinued population declines coupled with failed adult recruitment prompted the USFWS to initiate an assisted rearing program in 2015 as a part of their recovery strategy (Childress et al. 2019). The program was designed to maintain as much genetic diversity as possible while improving recruitment by averting high early life stage mortality (Day et al. 2017). However, while assisted rearing efforts are targeted towards endangered LRS and SNS, species differentiation of larval and juvenile suckers is problematic in the Klamath River Basin. This, in turn, complicates the management of these species as well as the population modeling used to evaluate recovery efforts. Maintaining as much as possible of the genetic resources, or “evolutionary legacy” of a species is a goal common to conservation and endangered species recovery strategies. Inappropriate assumptions regarding species’ evolutionary lineages, and genetic characteristics may lead to the mismanagement of an endangered species through a failure to recognize and appropriately manage species boundaries and genetic population structure.\n\nDespite a considerable amount of research, the partitioning of genetic diversity within and among the four species of suckers in the Klamath River Basin remains unclear. Previously developed genetic markers are effective at differentiating some species, but fail to effectively differentiate all four species of suckers in the basin (Tranah et al. 2001; Wagman 2003; Tranah and May 2006; Hoy and Ostberg 2015; Dowling et al. 2016). Peer-reviewed publications describing the morphological characteristics of (Markle et al. 2005) and genetic relationships\n \namong (Dowling et al. 2016; Tranah and May 2006) Klamath River Basin suckers have not resolved uncertainties regarding the systematic relationships among the four currently recognized taxa. Specifically, genetic and morphological data generally support LRS and KSS as being distinct entities, but genetic evidence does not support a distinction between KLS and SNS. All three publications above refer to unpublished information regarding ecological differences between KLS and SNS as evidence to support the existence of two entities. However, the authors also acknowledge that overlap in morphological characters (Markle et al. 2005) and a lack of genetic differentiation (Dowling et al. 2016; Tranah and May 2006) between KLS and SNS raises the question of their specific identity. This is particularly problematic in the Lost River subbasin, where overlap in morphological characters between KLS and SNS is greatest.\n\nIn our opinion, the basis of the strong genetic similarity between KLS and SNS has not been resolved. Morphological characters mostly support the existence of two distinct species, while genetic characters do not (i.e., genetic divergence between KLS and SNS is less than divergence among populations of each species; (Smith et al. 2015). Some have suggested that introgressive hybridization may have resulted in a lack of genetic differentiation between KLS and SNS and a breakdown of monophyletic species (Dowling et al. 2016; Tranah and May 2006), and that this hybridization may be an important process in","language":"English","publisher":"U.S. Fish and Wildlife Service","collaboration":"Bureau of Reclamation","usgsCitation":"Smith, M., Von Bargen, J., Smith, C.A., Miller, M.A., Rasmussen, J., and Hewitt, D.A., 2020, Characterization of the genetic structure of four sucker species in the Klamath River. Final Report, 32 p.","productDescription":"32 p.","ipdsId":"IP-115877","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373497,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373490,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/aftc/Reports.cfm"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.695068359375,\n 40.88029480552824\n ],\n [\n -120.9375,\n 40.88029480552824\n ],\n [\n -120.9375,\n 42.83569550641452\n ],\n [\n -123.695068359375,\n 42.83569550641452\n ],\n [\n -123.695068359375,\n 40.88029480552824\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Matt","contributorId":223557,"corporation":false,"usgs":false,"family":"Smith","given":"Matt","email":"","affiliations":[{"id":40741,"text":"USFWS, Abernathy Fish Technology Center, Longview, WA","active":true,"usgs":false}],"preferred":false,"id":785470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Von Bargen, Jennifer","contributorId":223558,"corporation":false,"usgs":false,"family":"Von Bargen","given":"Jennifer","email":"","affiliations":[{"id":40741,"text":"USFWS, Abernathy Fish Technology Center, Longview, WA","active":true,"usgs":false}],"preferred":false,"id":785471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Christian A.","contributorId":200768,"corporation":false,"usgs":false,"family":"Smith","given":"Christian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":785472,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Michael A.","contributorId":85920,"corporation":false,"usgs":false,"family":"Miller","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":785473,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rasmussen, Josh","contributorId":223559,"corporation":false,"usgs":false,"family":"Rasmussen","given":"Josh","email":"","affiliations":[{"id":40742,"text":"USFWS, Klamath Falls Fish and Wildlife Office, Klamath Falls, OR","active":true,"usgs":false}],"preferred":false,"id":785474,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hewitt, David A. 0000-0002-5387-0275 dhewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-5387-0275","contributorId":3767,"corporation":false,"usgs":false,"family":"Hewitt","given":"David","email":"dhewitt@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":785475,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70208607,"text":"70208607 - 2020 - Spatiotemporal variability of modeled watershed scale surface-depression storage and runoff for the conterminous United States","interactions":[],"lastModifiedDate":"2020-02-21T11:50:49","indexId":"70208607","displayToPublicDate":"2020-01-08T06:45:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal variability of modeled watershed scale surface-depression storage and runoff for the conterminous United States","docAbstract":"This study uses the explores the viability of a proxy model calibration strategy through assessment of the spatiotemporal variability of surface-depression storage and runoff generated with the U.S. Geological Survey’s National Hydrologic Model (NHM) infrastructure for hydrologic response units (HRUs; n=109,951) across the conterminous United States (CONUS). Simulated values for each HRU of daily surface-depression storage (treated as a decimal fraction of total possible volume) and monthly normalized runoff (0 to 1) values were calculated using Spearman’s rho at monthly and annual aggregations. Locations where values are correlated show where previously-developed proxy calibration strategies are likely to be effective. In addition, differences in the correlation for monthly and annual time scale aggregations show which time scale drives surface-depression storage processes in the NHM. Results show overall long-term (annual) correlation is more common than short-term (monthly) correlation over the CONUS; however, summary statistics for eighty-six ecoregions show five with higher ranges of monthly relative to annual Spearman’s rank coefficient values. This landscape-scale analysis shows simulations aggregated to an annual time scale are generally more dominant for the CONUS; however, simulations aggregated to monthly, short-term time scales are more dominant in focused areas where surface-depression storage processes are investigated.","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12826","usgsCitation":"Driscoll, J.M., Hay, L., Vanderhoof, M.K., and Viger, R.J., 2020, Spatiotemporal variability of modeled watershed scale surface-depression storage and runoff for the conterminous United States: Journal of the American Water Resources Association, v. 56, no. 1, p. 16-29, https://doi.org/10.1111/1752-1688.12826.","productDescription":"14 p.","startPage":"16","endPage":"29","ipdsId":"IP-093569","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction 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Branch","active":true,"usgs":true}],"preferred":true,"id":782704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":782706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Viger, Roland J. 0000-0003-2520-714X rviger@usgs.gov","orcid":"https://orcid.org/0000-0003-2520-714X","contributorId":168799,"corporation":false,"usgs":true,"family":"Viger","given":"Roland","email":"rviger@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction 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,{"id":70208745,"text":"70208745 - 2020 - Resolving small-scale forest snow patterns using an energy-balance snow model with a 1-layer canopy","interactions":[],"lastModifiedDate":"2020-02-28T06:31:32","indexId":"70208745","displayToPublicDate":"2020-01-08T06:28:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Resolving small-scale forest snow patterns using an energy-balance snow model with a 1-layer canopy","docAbstract":"Modelling spatiotemporal dynamics of snow in forests is challenging, as involved processes are strongly dependent on small-scale canopy properties. In this study, we explore how local canopy structure information can be integrated in a medium-complexity energy-balance snow model to replicate observed snow patterns at very high spatial resolutions. Snow depth distributions simulated with the Flexible Snow Model (FSM2) were tested against extensive experimental data acquired in discontinuous subalpine forest stands in Eastern Switzerland over three winters. While the default canopy implementation in FSM2 fails to capture the observed snow depth variability, performance is considerably improved when local canopy cover fraction and hemispherical sky view fraction are additionally accounted for (30% reduction in RMSE). However, realistic snow depth distribution patterns throughout the season are only achieved if effective temperatures of near and distant canopy elements are discerned, and if a mechanism to mimic preferential deposition of snow in canopy gaps is included. We demonstrate that by diversifying the canopy structure input in order to reflect respective portions of the canopy relevant to different processes, even a simple model based on widely used process parametrizations and canopy metrics can be applied for high-resolution simulations of the sub-canopy snow cover with just a few modifications. The presented approaches could be implemented in commonly used land surface models, allowing upscaling experiments and development of sub-grid parametrizations without necessitating complex high-resolution models.","language":"English","publisher":"Wiley","doi":"10.1029/2019WR026129","usgsCitation":"Mazzotti, G., Essery, R., Moeser, C.D., and Jonas, T., 2020, Resolving small-scale forest snow patterns using an energy-balance snow model with a 1-layer canopy: Water Resources Research, v. 56, no. 1, e2019WR026129, 22 p., https://doi.org/10.1029/2019WR026129.","productDescription":"e2019WR026129, 22 p.","ipdsId":"IP-112070","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":458191,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.research.ed.ac.uk/en/publications/941ef274-b54b-48d0-8263-ae1287bd8584","text":"External Repository"},{"id":372718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Switzerland","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[9.59423,47.52506],[9.63293,47.3476],[9.47997,47.10281],[9.93245,46.92073],[10.4427,46.89355],[10.36338,46.48357],[9.92284,46.3149],[9.18288,46.44021],[8.96631,46.03693],[8.48995,46.00515],[8.31663,46.16364],[7.75599,45.82449],[7.27385,45.77695],[6.84359,45.99115],[6.5001,46.42967],[6.02261,46.27299],[6.03739,46.72578],[6.76871,47.28771],[6.73657,47.5418],[7.1922,47.44977],[7.46676,47.62058],[8.3173,47.61358],[8.52261,47.83083],[9.59423,47.52506]]]},\"properties\":{\"name\":\"Switzerland\"}}]}","volume":"56","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Mazzotti, Giulia","contributorId":222821,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Giulia","email":"","affiliations":[{"id":40604,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland and Laboratory of Hydraulics, Hydrology and Glaciology, ETHZ, Zurich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":783256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Essery, Richard","contributorId":222822,"corporation":false,"usgs":false,"family":"Essery","given":"Richard","email":"","affiliations":[{"id":40605,"text":"School of Geosciences, University of Edinburgh, Edinburgh, UK","active":true,"usgs":false}],"preferred":false,"id":783257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783255,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jonas, Tobias","contributorId":222823,"corporation":false,"usgs":false,"family":"Jonas","given":"Tobias","email":"","affiliations":[{"id":40606,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":783258,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70215289,"text":"70215289 - 2020 - Biotic interactions help explain variation in elevational range limits of birds among Bornean mountains","interactions":[],"lastModifiedDate":"2020-10-14T22:40:56.836813","indexId":"70215289","displayToPublicDate":"2020-01-07T17:31:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Biotic interactions help explain variation in elevational range limits of birds among Bornean mountains","docAbstract":"Aim
Physiological tolerances and biotic interactions along habitat gradients are thought to influence species occurrence. Distributional differences caused by such forces are particularly noticeable on tropical mountains, where high species turnover along elevational gradients occurs over relatively short distances and elevational distributions of particular species can shift among mountains. Such shifts are interpreted as evidence of the importance of spatial variation in interspecific competition and habitat or climatic gradients. To assess the relative importance of competition and compression of habitat and climatic zones in setting range limits, we examined differences in elevational ranges of forest bird species among four Bornean mountains with distinct features.
Location
Bornean mountains Kinabalu, Mulu, Pueh and Topap Oso.
Taxon
Rain forest bird communities along elevational gradients.
Methods
We surveyed the elevational ranges of rain forest birds on four mountains in Borneo to test which environmental variables—habitat zone compression or presence of likely competitors—best predicted differences in elevational ranges of species among mountains. For this purpose, we used two complementary tests: a comparison of elevational range limits between pairs of mountains, and linear mixed models with naïve occupancy as the response variable.
Results
We found that lowland species occur higher in elevation on two small mountains compared to Mt. Mulu. This result is inconsistent with the expectation that distributions of habitats are elevationally compressed on small mountains, but is consistent with the hypothesis that a reduction in competition (likely diffuse) on short mountains, which largely lack montane specialist species, allows lowland species to occur higher in elevation. The relative influence of competition changes with elevation, and the correlation between lower range limits of montane species and the distribution of their competitors was weaker than in lowland species.
Main conclusions
These findings provide support for the importance of biotic interactions in setting elevational range limits of tropical bird species, although abiotic gradients explain the majority of distribution patterns. Thus, models predicting range shifts under climate change scenarios must include not only climatic variables, as is currently most common, but also information on potentially resulting changes in species interactions, especially for lowland species.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.13784","usgsCitation":"Burner, R., Boyce, A., Bernasconi, D., Styring, A.R., Shakya, S.B., Boer, C., Rahman, M.A., Martin, T.E., and Sheldon, F.H., 2020, Biotic interactions help explain variation in elevational range limits of birds among Bornean mountains: Journal of Biogeography, v. 47, no. 3, p. 760-771, https://doi.org/10.1111/jbi.13784.","productDescription":"12 p.","startPage":"760","endPage":"771","ipdsId":"IP-107210","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":458196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jbi.13784","text":"Publisher Index Page"},{"id":379392,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia, Malaysia","otherGeospatial":"Borneo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 119.17968749999999,\n 5.659718554577286\n ],\n [\n 116.89453125,\n 7.493196470122287\n ],\n [\n 113.5546875,\n 4.609278084409835\n ],\n [\n 111.09374999999999,\n 2.7235830833483856\n ],\n [\n 109.1162109375,\n 1.9771465537125772\n ],\n [\n 108.5888671875,\n 0.17578097424708533\n ],\n [\n 110.0830078125,\n -3.2063329870791315\n ],\n [\n 112.939453125,\n -3.90809888189411\n ],\n [\n 115.224609375,\n -4.258768357307995\n ],\n [\n 116.3232421875,\n -4.039617826768424\n ],\n [\n 118.95996093749999,\n 1.142502403706165\n ],\n [\n 117.7734375,\n 3.2940822283128175\n ],\n [\n 119.61914062499999,\n 5.134714634014467\n ],\n [\n 119.17968749999999,\n 5.659718554577286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Burner, Ryan C. 0000-0002-7314-9506","orcid":"https://orcid.org/0000-0002-7314-9506","contributorId":243138,"corporation":false,"usgs":false,"family":"Burner","given":"Ryan C.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":801602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyce, Andy J.","contributorId":243139,"corporation":false,"usgs":false,"family":"Boyce","given":"Andy J.","affiliations":[{"id":48645,"text":"umt","active":true,"usgs":false}],"preferred":false,"id":801603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bernasconi, David","contributorId":243140,"corporation":false,"usgs":false,"family":"Bernasconi","given":"David","email":"","affiliations":[{"id":16154,"text":"LSU","active":true,"usgs":false}],"preferred":false,"id":801604,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Styring, Alison R.","contributorId":243175,"corporation":false,"usgs":false,"family":"Styring","given":"Alison","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":801653,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shakya, Subir B.","contributorId":243141,"corporation":false,"usgs":false,"family":"Shakya","given":"Subir","email":"","middleInitial":"B.","affiliations":[{"id":16154,"text":"LSU","active":true,"usgs":false}],"preferred":false,"id":801605,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boer, Chandradewana","contributorId":243142,"corporation":false,"usgs":false,"family":"Boer","given":"Chandradewana","email":"","affiliations":[{"id":48646,"text":"u m","active":true,"usgs":false}],"preferred":false,"id":801606,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rahman, Mustafa Abdul","contributorId":243143,"corporation":false,"usgs":false,"family":"Rahman","given":"Mustafa","email":"","middleInitial":"Abdul","affiliations":[{"id":48647,"text":"college sabah","active":true,"usgs":false}],"preferred":false,"id":801607,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":801608,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sheldon, Frederick H.","contributorId":243144,"corporation":false,"usgs":false,"family":"Sheldon","given":"Frederick","email":"","middleInitial":"H.","affiliations":[{"id":16154,"text":"LSU","active":true,"usgs":false}],"preferred":false,"id":801609,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70208125,"text":"70208125 - 2020 - Habitat of the endangered salt marsh harvest mouse (Reithrodontomys raviventris) in San Francisco Bay","interactions":[],"lastModifiedDate":"2020-02-06T11:44:25","indexId":"70208125","displayToPublicDate":"2020-01-07T16:40:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Habitat of the endangered salt marsh harvest mouse (Reithrodontomys raviventris) in San Francisco Bay","title":"Habitat of the endangered salt marsh harvest mouse (Reithrodontomys raviventris) in San Francisco Bay","docAbstract":"Understanding habitat associations is vital for conservation of at‐risk marsh‐endemic wildlife species, particularly those under threat from sea level rise. We modeled environmental and habitat associations of the marsh‐endemic, Federally endangered salt marsh harvest mouse (Reithrodontomys raviventris, RERA) and co‐occurrence with eight associated small mammal species from annual trap data, 1998–2014, in six estuarine marshes in North San Francisco Bay, California. Covariates included microhabitat metrics of elevation and vegetation species and cover; and landscape metrics of latitude–longitude, distance to anthropogenic features, and habitat patch size. The dominant cover was pickleweed (Salicornia pacifica) with 86% mean cover and 37 cm mean height, and bare ground with about 10% mean cover. We tested 38 variants of Bayesian network (BN) models to determine covariates that best account for presence of RERA and of all nine small mammal species. Best models had lowest complexity and highest classification accuracy. Among RERA presence models, three best BN models used covariates of latitude–longitude, distance to paved roads, and habitat patch size, with 0% error of false presence, 20% error of false nonpresence, and 20% overall error. The all‐species presence models suggested that within the pickleweed marsh environment, RERA are mostly habitat generalists. Accounting for presence of other species did not improve prediction of RERA. Habitat attributes compared between RERA and the next most frequently captured species, California vole (Microtus californicus), suggested substantial habitat overlap, with RERA habitat being somewhat higher in marsh elevation, greater in percent cover of the dominant plant species, closer to urban areas, further from agricultural areas, and, perhaps most significant, larger in continuous size of marsh patch. Findings will inform conservation management of the marsh environment for RERA by identifying best microhabitat elements, landscape attributes, and adverse interspecific interactions.
","language":"English","publisher":"Wiley-Blackwell","doi":"10.1002/ece3.5860","usgsCitation":"Marcot, B.G., Woo, I., Thorne, K., Freeman, C.M., and Guntenspergen, G.R., 2020, Habitat of the endangered salt marsh harvest mouse (Reithrodontomys raviventris) in San Francisco Bay: Ecology and Evolution, v. 0, no. 2, p. 662-677, https://doi.org/10.1002/ece3.5860.","productDescription":"16 p.","startPage":"662","endPage":"677","ipdsId":"IP-101159","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458198,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5860","text":"Publisher Index Page"},{"id":437176,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96Q5D2T","text":"USGS data release","linkHelpText":"Small mammal surveys from northern San Francisco Bay: 1998-2014"},{"id":371662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.7117919921875,\n 37.82497195707114\n ],\n [\n -121.98669433593749,\n 37.82497195707114\n ],\n [\n -121.98669433593749,\n 38.190704293996504\n ],\n [\n -122.7117919921875,\n 38.190704293996504\n ],\n [\n -122.7117919921875,\n 37.82497195707114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"0","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Marcot, Bruce G.","contributorId":152612,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":18944,"text":"Pacific Northwest Research Station, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":780617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Chase M. 0000-0003-4211-6709 cfreeman@usgs.gov","orcid":"https://orcid.org/0000-0003-4211-6709","contributorId":150052,"corporation":false,"usgs":true,"family":"Freeman","given":"Chase","email":"cfreeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780619,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":780620,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70205181,"text":"ofr20191089 - 2020 - Remnant hardwood forest mapping within the Upper Mississippi River floodplain","interactions":[],"lastModifiedDate":"2022-04-21T18:41:17.216501","indexId":"ofr20191089","displayToPublicDate":"2020-01-07T13:45:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1089","displayTitle":"Remnant Hardwood Forest Mapping within the Upper Mississippi River Floodplain","title":"Remnant hardwood forest mapping within the Upper Mississippi River floodplain","docAbstract":"Executive Summary
The primary objective of the project was to locate previously unknown stands of mast-producing hardwood forest trees in the Upper Mississippi River floodplain using existing information. We located and mapped 399 previously unknown hardwood forest stands within the Mississippi River floodplain area of navigation pools 9, 10, and 11. Using color infrared images in combination with true-color imagery was useful for identifying hardwood forest stands. We recommend our result be refined by visiting the forest stands we identified to evaluate our classification rate and determine which stands are regenerating. In combination with regeneration information, our results can help better inform flood inundation modeling, which will help improve the efficacy of restoration design. Although we had some success using the best available information, to obtain more relevant observations, we recommend acquiring color infrared aerial imagery during the late fall season if providing detailed mapping of forest stands is a management priority. Imagery of this type collected in the fall, when trees may be distinguished by their differing senescence, has the potential to uniquely identify individual species or perhaps even individual trees. Gaining a better understanding of forest diversity and developing conservation strategies to preserve that diversity is timely because remaining aging trees, established before lock-and-dam installation on the Mississippi River, are nearing the end of their life expectancy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191089","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Hanson, J.L., King, R., Hoy, E.E., 2019, Remnant hardwood forest mapping within the Upper Mississippi River floodplain: U.S. Geological Survey Open-File Report 2019–1089, 10 p., https://doi.org/10.3133/ofr20191089.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-102264","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":399413,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109582.htm"},{"id":370698,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1089/ofr20191089.pdf","text":"Report","size":"4.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1089"},{"id":370697,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1089/coverthb.jpg"},{"id":370699,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TD9WNW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"FWS McGregor District Mast Hardwood Floodplain Forest Community"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","otherGeospatial":"Upper Mississippi floodplain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.42822265625,\n 43.67581809328341\n ],\n [\n -91.5380859375,\n 43.6599240747891\n ],\n [\n -91.49414062499999,\n 43.48481212891603\n ],\n [\n -91.60400390625,\n 43.100982876188546\n ],\n [\n -91.2744140625,\n 42.65012181368022\n ],\n [\n -90.81298828125,\n 42.52069952914966\n ],\n [\n -90.37353515625,\n 42.52069952914966\n ],\n [\n -90.68115234375,\n 42.97250158602597\n ],\n [\n -90.72509765625,\n 43.30919109985686\n ],\n [\n -91.01074218749999,\n 43.61221676817573\n ],\n [\n -91.42822265625,\n 43.67581809328341\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
","tableOfContents":"- Acknowledgments
- Executive Summary
- Introduction
- Study Area
- Methods
- Discussion and Conclusions
- Summary
- References Cited
","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2020-01-07","noUsgsAuthors":false,"publicationDate":"2020-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Hanson, Jenny L. 0000-0001-8353-6908 jhanson@usgs.gov","orcid":"https://orcid.org/0000-0001-8353-6908","contributorId":461,"corporation":false,"usgs":true,"family":"Hanson","given":"Jenny","email":"jhanson@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":770243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Rich 0000-0001-5462-5303","orcid":"https://orcid.org/0000-0001-5462-5303","contributorId":146426,"corporation":false,"usgs":false,"family":"King","given":"Rich","email":"","affiliations":[],"preferred":false,"id":778775,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoy, Erin E. 0000-0002-2853-3242 ehoy@usgs.gov","orcid":"https://orcid.org/0000-0002-2853-3242","contributorId":4523,"corporation":false,"usgs":true,"family":"Hoy","given":"Erin","email":"ehoy@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":770245,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70207998,"text":"70207998 - 2020 - Zircon-hosted melt inclusion record of silicic magmatism in the Mesoproterozoic St. Francois Mountains terrane, Missouri: Origin of the Pea Ridge iron oxide-apatite rare earth element deposit and implications for regional crustal pathways of mineralization","interactions":[],"lastModifiedDate":"2020-01-23T06:25:56","indexId":"70207998","displayToPublicDate":"2020-01-07T06:23:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Zircon-hosted melt inclusion record of silicic magmatism in the Mesoproterozoic St. Francois Mountains terrane, Missouri: Origin of the Pea Ridge iron oxide-apatite rare earth element deposit and implications for regional crustal pathways of mineralization","docAbstract":"Voluminous silicic magmatism was coeval with iron ore mineralization in the St.\nFrancois Mountains terrane in southeast Missouri, part of the broader Mesoproterozoic\nGranite-Rhyolite province along the eastern margin of Laurentia. Some of the iron\ndeposits contain extraordinary endowments of critical elements, such as the Pea Ridge\niron oxide-apatite (IOA) deposit, which has an average grade of ~12 wt% rare earth\noxides in breccia pipes that flank the ore body. To assess the role of silicic magmatism in\nthe genesis of the Pea Ridge deposit, we present a high-spatial resolution study of zirconhosted\nmelt inclusions from rhyolitic ash-flow tuffs. Melt inclusion data are combined\nwith textural, geochemical, and geochronological analyses of zircon hosts to elucidate the\nmagmatic-hydrothermal evolution of the Pea Ridge system. Two contemporaneous silicic\nigneous centers in the St. Francois Mountains terrane, Bourbon and Eminence, were\nstudied for comparison. Pea Ridge melt inclusions are trachydacitic to rhyolitic (~63-79\nwt% SiO2, ~5.6-11.7 wt% Na2O+K2O) with very high Cl in the least-evolved and most\nalkaline melt inclusions (~2,000-5,000 ppm Cl). Rare earth elements (REE) in melt\ninclusions have identical chondrite-normalized patterns to the mineralized breccia pipes,\nbut with systematically lower absolute concentrations. Haplogranite ternary pressures\nrange from ~0.5-10 kbar, with an average of ~2-3 kbar (7-12 km depth), and liquidus\ntemperatures are ~850-950 °C, with an average of ~920 °C. Silicate and phosphate\nmineral inclusions have compositions that overlap minerals from the iron ore body and\nbreccia pipes, recording a transition from igneous to hydrothermal zircon growth.\nIgneous iron oxide inclusions have compositions that indicate Pea Ridge magmas were\nreduced to moderately oxidized (log fO2 of -0.8 to -1.84 NNO). Zircons from two Pea\nRidge samples have 207Pb/206Pb concordia ages of 1456 ± 9 Ma and 1467 ± 13 Ma that\noverlap published ages for the breccia pipes and iron ore zones of the Pea Ridge deposit.\nA population of texturally and chemically disrupted zircons have discordant domains that\ncorrespond to high Fe, U, and REE concentrations, consistent with the unique\ngeochemical attributes of the IOA-REE ore body. Inherited cores in Pea Ridge and\nBourbon zircons have concordant 207Pb/206Pb dates of 1550-1618 Ma, providing direct\nevidence of cratonic basement beneath these centers. Oxygen isotope data for inherited\nand autocrystic igneous zircons span from mantle to crustal values (18Ozircon=5.5-7.9‰).\nOur data are consistent with a model in which metasomatized mantle components were\nmixed with cratonic and accreted crustal material in a back-arc or rifted segment of a\nvolcanic arc, with ore fluids derived from Cl-rich melts to transport Fe and REE in a\nlong-lived (tens of Myr), pulsed, magmatic-hydrothermal system. Bourbon, which also\npossesses IOA mineralization, shares key petrologic similarities with the Pea Ridge\nsystem, whereas Eminence, which is not mineralized, has disparate geochemical and\nisotopic signatures that indicate it formed in a different crustal setting. The location of\nPea Ridge and Bourbon along a cratonic margin may have been important in focusing\nsilicic melts and mineralization in the upper crust, serving as a guide for future\nexploration efforts.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2019.12.032","usgsCitation":"Watts, K., and Mercer, C.N., 2020, Zircon-hosted melt inclusion record of silicic magmatism in the Mesoproterozoic St. Francois Mountains terrane, Missouri: Origin of the Pea Ridge iron oxide-apatite rare earth element deposit and implications for regional crustal pathways of mineralization: Geochimica et Cosmochimica Acta, v. 272, p. 54-77, https://doi.org/10.1016/j.gca.2019.12.032.","productDescription":"24 p.","startPage":"54","endPage":"77","ipdsId":"IP-111591","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":458206,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2019.12.032","text":"Publisher Index Page"},{"id":437178,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TFVXR5","text":"USGS data release","linkHelpText":"Geochemistry, geochronology, and isotope geochemistry data for zircons and zircon-hosted melt and mineral inclusions in the St. Francois Mountains terrane, Missouri"},{"id":371486,"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 -92.2412109375,\n 36.58024660149866\n ],\n [\n -89.967041015625,\n 36.58024660149866\n ],\n [\n -89.967041015625,\n 38.34165619279595\n ],\n [\n -92.2412109375,\n 38.34165619279595\n ],\n [\n -92.2412109375,\n 36.58024660149866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"272","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Watts, Kathryn E. 0000-0002-6110-7499","orcid":"https://orcid.org/0000-0002-6110-7499","contributorId":204344,"corporation":false,"usgs":true,"family":"Watts","given":"Kathryn E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":780089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mercer, Celestine N. 0000-0001-8359-4147 cmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-8359-4147","contributorId":4006,"corporation":false,"usgs":true,"family":"Mercer","given":"Celestine","email":"cmercer@usgs.gov","middleInitial":"N.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":780090,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70239440,"text":"70239440 - 2020 - Introduction to this special section: Geothermal energy","interactions":[],"lastModifiedDate":"2023-01-13T12:49:12.91956","indexId":"70239440","displayToPublicDate":"2020-01-07T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3568,"text":"The Leading Edge","active":true,"publicationSubtype":{"id":10}},"title":"Introduction to this special section: Geothermal energy","docAbstract":"Geothermal energy is a global renewable resource that has the potential to provide a significant portion of baseload energy in many regions. In the United States, it has the potential to provide 8.5% of the electric generation capacity by the middle of the century. In general, geothermal systems require heat, permeability, and water to be viable for energy generation. However, with current technologies, only heat is strictly necessary in a native system. Engineered geothermal systems (EGS) introduce water into the subsurface at elevated pressures and reduced temperatures and enhance permeability through hydraulic and/or shear fracturing. Additionally, although moderate- to high-temperature resources currently dominate geothermal energy production, low-temperature resources have been utilized for direct-use cases. When well balanced and maintained, geothermal resources can produce significant amounts of heat and achieve long-term sustainability on the order of an estimated tens to hundreds of years.
Federal agencies need credible scientific information to determine the production and value of ecosystem services in an efficient and timely manner. The U.S. Geological Survey addresses this scientific information need through the Sustaining Environmental Capital Initiative project. The project has relied on U.S. Geological Survey expertise related to water, fisheries, advanced modeling, and economics and other social sciences to conduct eight case studies across a range of environment types, including water-based environments, deserts, sagebrush ecosystems, floodplains, and forests. The Sustaining Environmental Capital Initiative also supported the development and expansion of four tools with the intent of adding content and usability for partners’ decision-making needs. The tools are the Natural Value Resource Center, Benefit Transfer Toolkit, Riverine Environmental Flow Decision Support System, and Artificial Intelligence for Ecosystem Services modeling platform.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191117","usgsCitation":"Huber, C., Meldrum, J.R., Schuster, R.M., Ancona, Z.H., Bagstad, K.J., Beck, S.M., Carlisle, D.M., Claggett, P.R., Franco, F., Galbraith, H.S., Haefele, M., Hoelting, K.R., Hogan, D.M., Hopkins, K.G., Kern, T., Lawrence, C.B., Lischka, S., Loomis, J.B., Mueller, J.M., Noe, G.B., Pindilli, E.J., Quay, B., Semmens, D.J., Sinclair, W., Spooner, D.E., Voigt, B., and St. John White, B., 2020, Sustaining Environmental Capital Initiative summary report: U.S. Geological Survey Open-File Report 2019–1117, 23 p., https://doi.org/10.3133/ofr20191117.","productDescription":"v, 23 p.","onlineOnly":"Y","ipdsId":"IP-099772","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":370958,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1117/ofr20191117.pdf","text":"Report","size":"348 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1117"},{"id":370957,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1117/coverthb.jpg"}],"contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
","tableOfContents":"- Acknowledgments
- Abstract
- Introduction and Motivation
- Ecosystem Services Background and Methods
- Case Studies
- Tool and Model Development
- Ongoing Activities, Future Research Needs, and Organizational Structure
- References Cited
- Appendix 1. Sustaining Environmental Capital Initiative Research Products
","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-01-06","noUsgsAuthors":false,"publicationDate":"2020-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Huber, Christopher 0000-0001-8446-8134 chuber@usgs.gov","orcid":"https://orcid.org/0000-0001-8446-8134","contributorId":127600,"corporation":false,"usgs":true,"family":"Huber","given":"Christopher","email":"chuber@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":778796,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":773179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schuster, Rudy 0000-0003-2353-8500 schusterr@usgs.gov","orcid":"https://orcid.org/0000-0003-2353-8500","contributorId":3119,"corporation":false,"usgs":true,"family":"Schuster","given":"Rudy","email":"schusterr@usgs.gov","affiliations":[],"preferred":true,"id":773180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773182,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beck, Scott M. 0000-0003-4060-9403","orcid":"https://orcid.org/0000-0003-4060-9403","contributorId":219791,"corporation":false,"usgs":true,"family":"Beck","given":"Scott","email":"","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":773183,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":773184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Claggett, Peter R. 0000-0002-5335-2857 pclaggett@usgs.gov","orcid":"https://orcid.org/0000-0002-5335-2857","contributorId":176287,"corporation":false,"usgs":true,"family":"Claggett","given":"Peter","email":"pclaggett@usgs.gov","middleInitial":"R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":773185,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Franco, Fabiano 0000-0002-4849-3057","orcid":"https://orcid.org/0000-0002-4849-3057","contributorId":208367,"corporation":false,"usgs":true,"family":"Franco","given":"Fabiano","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":773186,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Galbraith, Heather S. 0000-0003-3704-3517 hgalbraith@usgs.gov","orcid":"https://orcid.org/0000-0003-3704-3517","contributorId":4519,"corporation":false,"usgs":true,"family":"Galbraith","given":"Heather","email":"hgalbraith@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":778831,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Haefele, Michelle","contributorId":211855,"corporation":false,"usgs":false,"family":"Haefele","given":"Michelle","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":773188,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hoelting, Kristin R","contributorId":219792,"corporation":false,"usgs":false,"family":"Hoelting","given":"Kristin","email":"","middleInitial":"R","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":773189,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hogan, Dianna M. 0000-0003-1492-4514 dhogan@usgs.gov","orcid":"https://orcid.org/0000-0003-1492-4514","contributorId":131137,"corporation":false,"usgs":true,"family":"Hogan","given":"Dianna","email":"dhogan@usgs.gov","middleInitial":"M.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":773190,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":773191,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Kern, Tim 0000-0002-4725-8266 kernt@usgs.gov","orcid":"https://orcid.org/0000-0002-4725-8266","contributorId":219793,"corporation":false,"usgs":true,"family":"Kern","given":"Tim","email":"kernt@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":773192,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Lawrence, Collin B. 0000-0001-9224-5774","orcid":"https://orcid.org/0000-0001-9224-5774","contributorId":212089,"corporation":false,"usgs":true,"family":"Lawrence","given":"Collin","email":"","middleInitial":"B.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":773193,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Lischka, Stacy 0000-0002-9075-939X","orcid":"https://orcid.org/0000-0002-9075-939X","contributorId":219794,"corporation":false,"usgs":false,"family":"Lischka","given":"Stacy","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":773194,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Loomis, John B.","contributorId":197268,"corporation":false,"usgs":false,"family":"Loomis","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":773195,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Mueller, Julie M.","contributorId":219795,"corporation":false,"usgs":false,"family":"Mueller","given":"Julie","email":"","middleInitial":"M.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":773196,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":773197,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Pindilli, Emily 0000-0002-5101-1266 epindilli@usgs.gov","orcid":"https://orcid.org/0000-0002-5101-1266","contributorId":140262,"corporation":false,"usgs":true,"family":"Pindilli","given":"Emily","email":"epindilli@usgs.gov","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":773198,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Quay, Brian 0000-0002-3948-4327","orcid":"https://orcid.org/0000-0002-3948-4327","contributorId":198369,"corporation":false,"usgs":true,"family":"Quay","given":"Brian","email":"","affiliations":[{"id":27836,"text":"Chinese Center for Disease Control and Prevention","active":true,"usgs":false}],"preferred":true,"id":773199,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773200,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Sinclair, Wilson","contributorId":219796,"corporation":false,"usgs":false,"family":"Sinclair","given":"Wilson","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":773201,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Spooner, Daniel E. 0000-0002-5408-4364 dspooner@usgs.gov","orcid":"https://orcid.org/0000-0002-5408-4364","contributorId":4603,"corporation":false,"usgs":true,"family":"Spooner","given":"Daniel","email":"dspooner@usgs.gov","middleInitial":"E.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":778832,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Voigt, Brian","contributorId":208483,"corporation":false,"usgs":false,"family":"Voigt","given":"Brian","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":773203,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"St. John White, Barabara","contributorId":219798,"corporation":false,"usgs":false,"family":"St. John White","given":"Barabara","email":"","affiliations":[{"id":40072,"text":"Previously affiliated with 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,{"id":70208924,"text":"70208924 - 2020 - Inundation exposure assessment for Majuro Atoll, Republic of the Marshall Islands using a high-accuracy digital elevation model","interactions":[],"lastModifiedDate":"2021-06-14T19:51:01.344547","indexId":"70208924","displayToPublicDate":"2020-01-06T10:59:28","publicationYear":"2020","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":"Inundation exposure assessment for Majuro Atoll, Republic of the Marshall Islands using a high-accuracy digital elevation model","docAbstract":"Majuro Atoll in the central Pacific has high coastal vulnerability due to low-lying islands, rising sea level, high wave events, eroding shorelines, a dense population center, and limited freshwater resources. Land elevation is the primary geophysical variable that determines exposure to inundation in coastal settings. Accordingly, coastal elevation data (with accuracy information) are critical for assessments of inundation exposure. Previous research has demonstrated the importance of using high-accuracy elevation data and rigorously accounting for uncertainty in inundation assessments. A quantitative analysis of inundation exposure was conducted for Majuro Atoll, including accounting for the cumulative vertical uncertainty from the input digital elevation model (DEM) and datum transformation. The project employed a recently produced and validated DEM derived from structure-from-motion processing of very-high-resolution aerial imagery. Areas subject to marine inundation (direct hydrologic connection to the ocean) and low-lying lands (disconnected hydrologically from the ocean) were mapped and characterized for three inundation levels using deterministic and probabilistic methods. At the highest water level modeled (3.75 ft, or 1.143 m), more than 34% of the atoll study area is likely to be exposed to inundation (68% chance or greater), while more than 20% of the atoll is extremely likely to be exposed (95% chance or greater). The study demonstrates the substantial value of a high-accuracy DEM for assessing inundation exposure of low-relief islands and the enhanced information from accounting for vertical uncertainty.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12010154","usgsCitation":"Gesch, D.B., Palaseanu-Lovejoy, M., Danielson, J.J., Fletcher, C., Kottermair, M., Barbee, M., and Jalandoni, A., 2020, Inundation exposure assessment for Majuro Atoll, Republic of the Marshall Islands using a high-accuracy digital elevation model: Remote Sensing, v. 12, no. 1, Article: 154, 20 p.; Data Release, https://doi.org/10.3390/rs12010154.","productDescription":"Article: 154, 20 p.; Data Release","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458218,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12010154","text":"Publisher Index Page"},{"id":372951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373335,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5ba9511ee4b08583a5ca09fe","text":"USGS data release","description":"USGS data release","linkHelpText":"Inundation exposure assessment for Majuro Atoll, Republic of the Marshall Islands"}],"country":"Republic of the Marshall Islands","otherGeospatial":"Majuro Atoll","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 170.98777770996094,\n 6.976183516197836\n ],\n [\n 171.43203735351562,\n 6.976183516197836\n ],\n [\n 171.43203735351562,\n 7.198319490613516\n ],\n [\n 170.98777770996094,\n 7.198319490613516\n ],\n [\n 170.98777770996094,\n 6.976183516197836\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":784036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":784037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","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":784038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fletcher, Charles","contributorId":192304,"corporation":false,"usgs":false,"family":"Fletcher","given":"Charles","affiliations":[],"preferred":false,"id":784039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kottermair, Maria","contributorId":119958,"corporation":false,"usgs":true,"family":"Kottermair","given":"Maria","email":"","affiliations":[],"preferred":false,"id":784040,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barbee, Matthew 0000-0002-8929-7255","orcid":"https://orcid.org/0000-0002-8929-7255","contributorId":196651,"corporation":false,"usgs":false,"family":"Barbee","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":784041,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jalandoni, Andrea 0000-0002-4821-7183","orcid":"https://orcid.org/0000-0002-4821-7183","contributorId":196653,"corporation":false,"usgs":false,"family":"Jalandoni","given":"Andrea","email":"","affiliations":[],"preferred":false,"id":784042,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70211340,"text":"70211340 - 2020 - Using conceptual models to relate multiparameter satellite data to subsurface volcanic processes in Latin America","interactions":[],"lastModifiedDate":"2020-09-01T13:54:44.456524","indexId":"70211340","displayToPublicDate":"2020-01-05T10:07:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Using conceptual models to relate multiparameter satellite data to subsurface volcanic processes in Latin America","docAbstract":"Satellite data have been extensively used to identify volcanic behavior. However, the physical subsurface processes causing any individual manifestation of activity can be ambiguous. We propose a classification scheme for the cause of unrest that simultaneously considers three multiparameter satellite observations. The scheme is based on characteristics of the volcanic system (open, closed, and eruptive) and unrest mechanisms (intrusion, evolution, and withdrawal) occurring at shallow depths in the volcanic system. We applied these models to satellite observations acquired at 47 of the most active volcanoes in Latin America. Of the volcanoes studied, 44 had a robust enough dataset for classification and were clustered into 4 groups and 10 subgroups with common behavioral characteristics. By identifying that these volcanoes can be clustered into a number of groupings significantly less than the number of volcanoes, we have demonstrated that commonalities in behavior patterns exist among diverse volcanic systems. Identifying volcanoes with similar characteristics underpins the use of past observations at one volcano to forecast activity at another and diverges from typical volcanic groupings, which are focused on geologic parameters (i.e., composition, volcano type, and tectonic setting). Based on satellite data alone, we have identified preeruptive intrusion prior to 15 eruptions at 12 different volcanoes, magma evolution prior to 18 eruptions at 13 volcanoes, and magma withdrawal at 3 eruptions and 3 volcanoes. Improvements to the spatial and temporal resolution are needed to make these relations robust. This classification scheme provides a framework for future automated clustering of volcanoes.
A common idea in the discussion of soil carbon processes is that litter decomposition rates and soil carbon stocks are inversely related. To test this overall hypothesis, simultaneous studies were conducted of the relationship of environmental gradients to leaf and wood decomposition, buried cloth decomposition and percent soil organic matter in Taxodium distichum swamps across the Mississippi River Alluvial Valley (MRAV) and northern Gulf of Mexico (GOM) of the US. Decomposition of leaf tissue was 6.2 to 10.9 times faster than wood tissue. Both precipitation and flooding gradients were negatively related to leaf and wood litter decomposition rates based on models developed using Stepwise General Model Selection (MRAV vs. GOM, respectively). Cotton cloth should not be used as a proxy for plant litter without prior testing because cloth responded differently than plant litter to regional environmental gradients in T. distichum swamps. The overall hypothesis was supported in the MRAV because environments with higher precipitation (climate normal) had lower rates of decomposition and higher percent soil organic matter. In the MRAV, higher levels of percent soil organic matter were related to increased 30-year climate normals (30 year averages of precipitation and air temperature comprising southward increasing PrinComp1). Soil organic carbon % in inland vs. coastal T. distichum forests of the MRAV were comparable (range = 1.5% to 26.9% vs. 9.8 to 31.5%, respectively). GOM swamps had lower rates of litter decomposition in more flooded environments. Woody T. distichum detritus had a half-life of up to 300 years in the MRAV, which points to its likely role in the maintenance of inland “teal” soil organic carbon. This unique study can contribute to the discussion of approaches to maintain environments conducive to soil carbon stock maximization.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0226998","usgsCitation":"Middleton, B.A., 2020, Trends of litter decomposition and soil organic matter stocks across forested swamp environments of the southeastern US: PLoS ONE, v. 15, no. 1, e0226998, 23 p., https://doi.org/10.1371/journal.pone.0226998.","productDescription":"e0226998, 23 p.","ipdsId":"IP-085013","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":458237,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0226998","text":"Publisher Index Page"},{"id":371401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Florida, Illinois, Louisiana, Mississippi, Missouri, Texas","otherGeospatial":"Mississippi River Alluvial Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.24218749999999,\n 37.78808138412046\n ],\n [\n -90.17578124999999,\n 37.055177106660814\n ],\n [\n -91.7578125,\n 34.52466147177172\n ],\n [\n -92.5048828125,\n 30.977609093348686\n ],\n [\n -90.2197265625,\n 28.65203063036226\n ],\n [\n -88.9013671875,\n 29.036960648558267\n ],\n [\n -89.20898437499999,\n 29.84064389983441\n ],\n [\n -91.01074218749999,\n 31.203404950917395\n ],\n [\n -88.06640625,\n 37.055177106660814\n ],\n [\n -88.24218749999999,\n 37.78808138412046\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.15234375,\n 29.38217507514529\n ],\n [\n -93.8232421875,\n 29.38217507514529\n ],\n [\n -93.8232421875,\n 31.240985378021307\n ],\n [\n -96.15234375,\n 31.240985378021307\n ],\n [\n -96.15234375,\n 29.38217507514529\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.814453125,\n 29.458731185355344\n ],\n [\n -83.583984375,\n 29.458731185355344\n ],\n [\n -83.583984375,\n 30.56226095049944\n ],\n [\n -84.814453125,\n 30.56226095049944\n ],\n [\n -84.814453125,\n 29.458731185355344\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Middleton, Beth A. 0000-0002-1220-2326 middletonb@usgs.gov","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":2029,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","email":"middletonb@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":779850,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70208716,"text":"70208716 - 2020 - Formation and prevention of pipe scale from acid mine drainage at Iron Mountain and Leviathan Mines, California, USA","interactions":[],"lastModifiedDate":"2020-02-25T15:17:36","indexId":"70208716","displayToPublicDate":"2020-01-03T15:14:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Formation and prevention of pipe scale from acid mine drainage at Iron Mountain and Leviathan Mines, California, USA","docAbstract":"Pipelines carrying acid mine drainage (AMD) to treatment plants commonly form pipe scale, an Fe(III)-rich precipitate that forms inside the pipelines and requires periodic and costly cleanout and maintenance. Pipelines at Iron Mountain Mine (IMM) and Leviathan Mine (LM) in California carry acidic water from mine sources to a treatment plant and have developed pipe scale. Samples of scale and AMD were collected from both mine sites for mineralogical, microbiological, and chemical analysis. The scale mineralogy was primarily schwertmannite with minor amounts of poorly crystalline goethite. Although the bulk composition of the scale was similar along the length of the pipeline at IMM, the number of iron-oxidizing bacteria and concentrations of associated trace elements decreased along the flow-path inside the pipeline. Laboratory batch experiments with unfiltered AMD from IMM and LM showed that Fe(II) oxidation was driven by microbial activity when the pH was <5. A remediation strategy of decreasing the pH to <2.2 was tested through geochemical modeling and laboratory experiments. These experiments indicated that scale formation could be prevented by decreasing the pH, which could be achieved at IMM by mixing source waters. However, the presence of Fe(III)-rich scale in a pipeline buffers the pH to higher values that may affect the efficacy of this remedial approach.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2020.104521","usgsCitation":"Campbell, K.M., Alpers, C.N., and Nordstrom, D.K., 2020, Formation and prevention of pipe scale from acid mine drainage at Iron Mountain and Leviathan Mines, California, USA: Applied Geochemistry, v. 115, 104521, 14 p. , https://doi.org/10.1016/j.apgeochem.2020.104521.","productDescription":"104521, 14 p. ","ipdsId":"IP-105776","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":458240,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2020.104521","text":"Publisher Index Page"},{"id":372639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Iron Mountain and Leviathan Mines","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.10455322265625,\n 40.065460682065535\n ],\n [\n -122.53875732421875,\n 40.065460682065535\n ],\n [\n -122.53875732421875,\n 40.6723059714534\n ],\n [\n -123.10455322265625,\n 40.6723059714534\n ],\n [\n -123.10455322265625,\n 40.065460682065535\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.16296386718749,\n 38.03078569382294\n ],\n [\n -118.9215087890625,\n 38.03078569382294\n ],\n [\n -118.9215087890625,\n 38.6897975322717\n ],\n [\n -120.16296386718749,\n 38.6897975322717\n ],\n [\n -120.16296386718749,\n 38.03078569382294\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"115","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Campbell, Kate M. 0000-0002-8715-5544 kcampbell@usgs.gov","orcid":"https://orcid.org/0000-0002-8715-5544","contributorId":1441,"corporation":false,"usgs":true,"family":"Campbell","given":"Kate","email":"kcampbell@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":783148,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":783150,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70211922,"text":"70211922 - 2020 - Estimating bedload from suspended load and water discharge in sand bed rivers","interactions":[],"lastModifiedDate":"2020-08-11T20:13:57.981854","indexId":"70211922","displayToPublicDate":"2020-01-03T15:10:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Estimating bedload from suspended load and water discharge in sand bed rivers","docAbstract":"Estimates of fluvial sediment discharge from in situ instruments are an important component of large‐scale sediment budgets that track long‐term geomorphic change. Suspended sediment load can be reliably estimated using acoustic or physical sampling techniques; however, bedload is difficult to measure directly and can consequently be one of the largest sources of uncertainty in estimates of total load. We propose a physically informed predictive empirical model for bedload sand flux as a function of variables that are measured using existing acoustic or physical sampling techniques. This model depends on the assumption that concentration and grain size in suspension are in equilibrium with reach‐averaged boundary conditions. Bayesian inference is used to fit model parameters to data from eight sand‐bed rivers and to simulate bedload flux over the available gage record at one site on the Colorado River in Grand Canyon National Park. We find that the cumulative bedload flux during the 9 year period from 2008 to 2016 was 5% of the cumulative suspended sand load; however, instantaneous bedload flux ranged from as little as 1% of instantaneous suspended sand load to as much as 75% of instantaneous suspended sand load due to fluctuations in flow strength and sediment supply. Changes in bedload flux at a constant discharge are indicative of short‐term sediment supply enrichment and depletion. Long‐term average bedload flux cannot be expected to remain constant in the future as the river adjusts to changes in sediment runoff and the dam‐regulated discharge regime.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR025883","usgsCitation":"Ashley, T., McElroy, B., Buscombe, D., Grams, P.E., and Kaplinski, M., 2020, Estimating bedload from suspended load and water discharge in sand bed rivers: Water Resources Research, v. 56, no. 2, e2019WR025883, 25 p., https://doi.org/10.1029/2019WR025883.","productDescription":"e2019WR025883, 25 p.","ipdsId":"IP-108262","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":458242,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/essoar.10503756.1","text":"External Repository"},{"id":377386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.005126953125,\n 35.71083783530009\n ],\n [\n -111.37390136718749,\n 35.71083783530009\n ],\n [\n -111.37390136718749,\n 36.92793899776678\n ],\n [\n -114.005126953125,\n 36.92793899776678\n ],\n [\n -114.005126953125,\n 35.71083783530009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Ashley, T.C.","contributorId":238017,"corporation":false,"usgs":false,"family":"Ashley","given":"T.C.","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":795824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McElroy, B.","contributorId":23797,"corporation":false,"usgs":true,"family":"McElroy","given":"B.","email":"","affiliations":[],"preferred":false,"id":795825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buscombe, D.","contributorId":44020,"corporation":false,"usgs":true,"family":"Buscombe","given":"D.","email":"","affiliations":[],"preferred":false,"id":795826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grams, Paul E. 0000-0002-0873-0708","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":216115,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":795827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kaplinski, M.","contributorId":31576,"corporation":false,"usgs":true,"family":"Kaplinski","given":"M.","email":"","affiliations":[],"preferred":false,"id":795828,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70264996,"text":"70264996 - 2020 - A model for the growth and development of wave-dominated deltas fed by small mountainous rivers: Insights from the Elwha River delta, Washington","interactions":[],"lastModifiedDate":"2025-03-27T15:25:17.624368","indexId":"70264996","displayToPublicDate":"2020-01-03T10:20:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3369,"text":"Sedimentology","active":true,"publicationSubtype":{"id":10}},"title":"A model for the growth and development of wave-dominated deltas fed by small mountainous rivers: Insights from the Elwha River delta, Washington","docAbstract":"Observations from ground-penetrating radar, sediment cores, elevation surveys and aerial imagery are used to understand the development of the Elwha River delta in north-western Washington, USA, which prograded as a result of two dam removals in late 2011. Swash-bar, foreshore and swale depositional elements are recognized within ground-penetrating radar profiles and sediment cores. A model for the growth and development of small mountainous river wave-dominated deltas is proposed based on observation of both the fluvial and deltaic settings. If enough sediment is available in the fluvial system, mouth-bars form after higher than average river discharge events, creating a large platform seaward of the subaqueous delta plain. Swash-bars form concurrently or within a month of mouth-bar deposition as a result of wave action. Fair-weather waves drive swash-bar migration landward and in the direction of littoral drift. The signature of swash-bar welding to the shoreline is landward-dipping reflections, as a result of overwash processes and slipface migration. However, most swash-bars are eroded by the river mouth, as only 10 of the 37 swash-bars that formed between August 2011 and July 2016 survived within the Elwha River delta. The swash-bars that do survive either amalgamate onto the shoreline or an earlier deposited swash-bar, forming a single larger barrier at the delta front. In asymmetrical deltas, the signature of swash-bar welding is more likely to be preserved on the downdrift side of the delta, where formation is more likely and accommodation behind newer swash-bars preserves older deposits. On small mountainous river deltas, welded swash-bars may be more indicative of a large sediment pulse to the system, rather than large hydrological events.
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