We assess monthly temperature and precipitation data produced by four statistically based techniques that were used to downscale general circulation models (GCMs) in the Climate Model Intercomparison Program Phase 5 (CMIP5) (Taylor et al., 2012). We drive a simple water-balance model with the downscaled data to demonstrate the effect of the methods on the cold season hydrology of three, snow dominated regions in the western U.S. Independent of substantial variation among the GCM simulations over the regions (maximum range of ~3.5 °C and 50% change in precipitation), the four methods produce disparate high resolution representations of the magnitude and spatial patterns of future temperature and precipitation simulated by the models that range for up to ~3 °C and 30% change in precipitation that propagate into the hydrologic simulations. Temperature-dependent snowfall, accumulation, and melt in the model are sensitive to how atmospheric lapse rates are applied in the gridded observations that are used to remove the bias in raw GCM temperatures. By the end of the century the same downscaling method (Bias Corrected Spatial Disaggregation) yields a loss of cold-season snowpack of 34% over the Greater Yellowstone Area under a constant lapse rate ( 6.5°C km-1), whereas spatially variable lapse rates nearly double the loss to 66%, highlighting the roll of both lapse rates and high elevation stations in the bias correction dataset. The two newest downscaling methods (Multivariate Adaptive Constructed Analogs and Localized Constructed Analogs) preserve the magnitude of change simulated GCMs better than the other methods and the produce comparable hydrologic projections. Because the downscaled data from the methods vary spatially and by GCM, the downscaled data should be evaluated carefully as part of the process of using downscaled climate products to drive hydrological models over the area of interest.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023458","usgsCitation":"Alder, J.R., and Hostetler, S.W., 2019, The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data: Water Resources Research, v. 55, no. 3, p. 2279-2300, https://doi.org/10.1029/2018WR023458.","productDescription":"22 p.","startPage":"2279","endPage":"2300","ipdsId":"IP-097120","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437482,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9EB1C","text":"USGS data release","linkHelpText":"Data Release for The dependence of hydroclimate projections in snow-dominated regions of the western U.S. on the choice of statistically downscaled climate data"},{"id":363240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana,Nevada, New Mexico, Oregon, Washington, Wyoming","otherGeospatial":"Columbia River Basin, Greater Yellowstone Area, Sierra Nevada, Upper Colorado Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.33300781249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 34.66935854524543\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":761576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":761577,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203422,"text":"70203422 - 2019 - Improving estimates of coral reef construction and erosion with in-situ measurements","interactions":[],"lastModifiedDate":"2019-09-16T12:15:11","indexId":"70203422","displayToPublicDate":"2019-04-25T12:37:50","publicationYear":"2019","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":"Improving estimates of coral reef construction and erosion with in-situ measurements","docAbstract":"The decline in living coral since the 1970s has conspicuously slowed reef construction on a global scale, but the related process of reef erosion is less visible and not often quantified. Here we present new data on the constructional and deconstructional side of the carbonate-budget equation in the Florida Keys, U.S.A. We documented Orbicella spp. calcification rates at four offshore reefs and quantified decadal-scale rates of Orbicella-reef erosion at a mid-shore patch reef. Using Orbicella coral heads fitted with permanent markers in 1998, we measured reef-elevation loss at 28 stations over 17.3 years to estimate a mean erosion rate of -5.5 (± 3.2, SD) mm yr-1. This loss equates to an erosion rate of -8.2 (± 4.8, SD) kg m-2 yr-1 on dead Orbicella colonies, or -6.6 kg m-2 yr-1 when adjusted reef-wide. Calculating net carbonate production using a census-based approach on the same patch reef in 2017, we estimated a reef-wide bioerosion rate of -1.9 (± 2.0, SD) kg m-2 yr-1, and a net carbonate production rate of 0.5 (± 0.3, SD) kg m-2 yr-1. Substituting the erosion rate we estimated with the markers would suggest that net carbonate production at this patch reef was lower and negative, -4.2 kg m-2 yr-1. This divergence could be a function of high erosion rates measured on the tops of Orbicella colonies, which may be preferentially targeted by parrotfish. Nonetheless, our study suggests the need for new field data to improve estimates of reef-structure persistence as coral reefs continue to degrade.","language":"English","publisher":"ASLO","doi":"10.1002/lno.11184","usgsCitation":"Kuffner, I.B., Toth, L., Hudson, J.H., Goodwin, W.B., Stathakopoulos, A., Bartlett, L., and Whitcher, E.M., 2019, Improving estimates of coral reef construction and erosion with in-situ measurements: Limnology and Oceanography, v. 64, no. 5, p. 2283-2294, https://doi.org/10.1002/lno.11184.","productDescription":"12 p.","startPage":"2283","endPage":"2294","ipdsId":"IP-101455","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467672,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.11184","text":"Publisher Index Page"},{"id":437483,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92NVINW","text":"USGS data release","linkHelpText":"Experimental Data on Construction and Erosion of Orbicella Coral Reefs in the Florida Keys, U.S.A."},{"id":363776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.72430419921875,\n 24.084081797317943\n ],\n [\n -80.01068115234375,\n 24.084081797317943\n ],\n [\n -80.01068115234375,\n 26.165298896316042\n ],\n [\n -82.72430419921875,\n 26.165298896316042\n ],\n [\n -82.72430419921875,\n 24.084081797317943\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"64","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hudson, J. Harold","contributorId":214860,"corporation":false,"usgs":false,"family":"Hudson","given":"J.","email":"","middleInitial":"Harold","affiliations":[{"id":39127,"text":"Reef Tech, Inc.","active":true,"usgs":false}],"preferred":false,"id":762634,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goodwin, William B.","contributorId":214861,"corporation":false,"usgs":false,"family":"Goodwin","given":"William","email":"","middleInitial":"B.","affiliations":[{"id":39128,"text":"NOAA Florida Keys National Marine Sanctuary,","active":true,"usgs":false}],"preferred":false,"id":762635,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762636,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bartlett, Lucy 0000-0001-6603-7090","orcid":"https://orcid.org/0000-0001-6603-7090","contributorId":214863,"corporation":false,"usgs":true,"family":"Bartlett","given":"Lucy","email":"","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762637,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Whitcher, Elizabeth M.","contributorId":214862,"corporation":false,"usgs":false,"family":"Whitcher","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[{"id":17748,"text":"Florida Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":762638,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70194290,"text":"sir20175118 - 2019 - Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States","interactions":[],"lastModifiedDate":"2025-05-15T13:21:20.301081","indexId":"sir20175118","displayToPublicDate":"2019-04-25T11:25:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5118","displayTitle":"Geochemical and Mineralogical Maps, with Interpretation, for Soils of the Conterminous United States","title":"Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States","docAbstract":"Between 2007 and 2013, the U.S. Geological Survey conducted a low-density (1 site per 1,600 square kilometers, 4,857 sites) geochemical and mineralogical survey of soils in the conterminous United States. The sampling protocol for the national-scale survey included, at each site, a sample from a depth of 0 to 5 centimeters, a composite of the soil A horizon, and a deeper sample from the soil C horizon or, if the top of the C horizon was at a depth greater than 1 meter, a sample from a depth of approximately 80–100 centimeters. The <2-millimeter fraction of each sample was analyzed for a suite of 45 major and trace elements by methods that yield the total or near-total elemental concentration. The major mineralogical components in the samples from the soil A and C horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. This report presents all the maps and statistical information for each determined element and mineral along with an interpretive section discussing the possible processes that caused the observed national-scale geochemical and mineralogical patterns. Most often, the geochemical and mineralogical patterns reflect the composition of the underlying soil parent material with some modifications caused by leaching of the more mobile elements (for example, calcium and sodium) in the humid areas of the country.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175118","usgsCitation":"Smith, D.B., Solano, Federico, Woodruff, L.G., Cannon, W.F., and Ellefsen, K.J., 2019, Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States: U.S. Geological Survey Scientific Investigations Report 2017-5118, https://doi.org/10.3133/sir20175118. 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U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
The Gulf of Mexico is home to a large proportion of the wintering population of the threatened piping plover (Charadrius melodus), but little is known about the bird's ecology in this region. In Louisiana, the majority of nonbreeding piping plovers are found on the state's rapidly eroding barrier islands. Between August 2013 and May 2014, surveys were conducted to assess the abundance and habitat use of piping plovers, as well as to characterize their invertebrate prey base, on Whiskey and Trinity islands. Seventy-eight percent of piping plovers observed were foraging, 18% roosting, and 4% engaged in other ambulatory activities. Intertidal habitat, such as foreshore beach and tidal flats, was used by 87% of foraging and 96% of roosting piping plovers. Though available, backshore beach, interior sand flats, and dunes were rarely used. The invertebrate community was dominated by haustoriid amphipods (87.5% of individuals collected), followed by bivalves (9.3%) and polychaetes (2.7%). Seasonal patterns and between-island differences were observed in all three invertebrate taxa, but these effects differed between beach habitat and the gulfside and bayside of prominent sand spits. Moisture had a positive effect on amphipod abundance and polychaete presence. There was no association between invertebrate and plover abundance, and prey abundance did not differ between sample sites where piping plovers were observed foraging and random sites. The low abundances of birds and prey, coupled with high variation among samples, are challenges for establishing baseline datasets to evaluate the consequences of coastal restoration activities.
","language":"English","publisher":"BioOne","doi":"10.2112/JCOASTRES-D-17-00147.1","usgsCitation":"Schulz, J.L., and Leberg, P., 2019, Factors affecting prey availability and habitat usage of nonbreeding piping plovers (Charadrius melodus) in coastal Louisiana: Journal of Coastal Research, v. 35, no. 4, p. 861-871, https://doi.org/10.2112/JCOASTRES-D-17-00147.1.","productDescription":"11 p.","startPage":"861","endPage":"871","ipdsId":"IP-089847","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":437485,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RJ4GP7","text":"USGS data release","linkHelpText":"Factors affecting prey availability and habitat use of nonbreeding piping plovers (Charadrius melodus) in coastal Louisiana"},{"id":365573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","county":"Terrebonne Parish","otherGeospatial":"Isles Dernières Barrier Island Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.021728515625,\n 28.954080659357132\n ],\n [\n -90.42022705078125,\n 28.954080659357132\n ],\n [\n -90.42022705078125,\n 29.28160772298835\n ],\n [\n -91.021728515625,\n 29.28160772298835\n ],\n [\n -91.021728515625,\n 28.954080659357132\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schulz, Jessica L. 0000-0002-8311-9423 jschulz@usgs.gov","orcid":"https://orcid.org/0000-0002-8311-9423","contributorId":200299,"corporation":false,"usgs":true,"family":"Schulz","given":"Jessica","email":"jschulz@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":766166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leberg, Paul","contributorId":216903,"corporation":false,"usgs":false,"family":"Leberg","given":"Paul","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":766167,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203386,"text":"70203386 - 2019 - Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization","interactions":[],"lastModifiedDate":"2019-06-18T12:02:06","indexId":"70203386","displayToPublicDate":"2019-04-25T09:33:18","publicationYear":"2019","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":"Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization","docAbstract":"Chronic exposure to geogenic arsenic via drinking water is a worldwide health concern. However, effects of well installation and operation on arsenic concentrations and mobilization are not well understood. This knowledge gap impacts both reliable detection of arsenic in drinking water and effective public health recommendations to reduce exposure to arsenic. This study examines changes in arsenic and redox geochemistry over one year following installation of 254 new domestic water wells in three regions of the north-central USA that commonly have elevated arsenic concentrations. Our regions' geologic settings share some important characteristics with other high-arsenic aquifers: igneous bedrock aquifers; or late Pleistocene-age glacial sand and gravel aquifers interbedded with aquitards. Over the study, arsenic concentrations increased by 16% or more in 25% of wells in glacial aquifer regions, and the redox conditions changed towards more reducing. In wells in the bedrock region, there was no significant change in arsenic concentrations, and redox conditions changed towards more oxidizing. Our findings illustrate the importance of understanding short- to moderate-term impacts of well installation and operation on arsenic and aqueous chemistry, as it relates to human exposure. Our study informs water quality sampling requirements, which currently do not consider the implications sampling timing with respect to well installation. Evaluating arsenic concentrations in samples from new wells in the context of general regional pH and redox conditions can provide information regarding the degree of disequilibrium created by well drilling. Our analysis approach may be transferable and scalable to similar aquifer settings across the globe.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.04.362","usgsCitation":"Erickson, M., Malenda, H.F., Berquist, E.C., and Ayotte, J.D., 2019, Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization: Science of the Total Environment, v. 678, p. 681-691, https://doi.org/10.1016/j.scitotenv.2019.04.362.","productDescription":"11 p.","startPage":"681","endPage":"691","ipdsId":"IP-090484","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467673,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.04.362","text":"Publisher Index Page"},{"id":363660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70203211,"text":"70203211 - 2019 - Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland","interactions":[],"lastModifiedDate":"2019-09-18T15:24:01","indexId":"70203211","displayToPublicDate":"2019-04-25T08:23:14","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland","docAbstract":"Rapid warming in northern ecosystems over the past four decades has resulted in earlier spring, increased precipitation, and altered timing of plant–animal interactions, such as herbivory. Advanced spring phenology can lead to longer growing seasons and increased carbon (C) uptake. Greater precipitation coincides with greater cloud cover possibly suppressing photosynthesis. Timing of herbivory relative to spring phenology influences plant biomass. None of these changes are mutually exclusive and their interactions could lead to unexpected consequences for Arctic ecosystem function. We examined the influence of advanced spring phenology, cloud cover, and timing of grazing on C exchange in the Yukon–Kuskokwim Delta of western Alaska for three years. We combined advancement of the growing season using passive-warming open-top chambers (OTC) with controlled timing of goose grazing (early, typical, and late season) and removal of grazing. We also monitored natural variation in incident sunlight to examine the C exchange consequences of these interacting forcings. We monitored net ecosystem exchange of C (NEE) hourly using an autochamber system. Data were used to construct daily light curves for each experimental plot and sunlight data coupled with a clear-sky model was used to quantify daily and seasonal NEE over a range of incident sunlight conditions. Cloudy days resulted in the largest suppression of NEE, reducing C uptake by approximately 2 g C m−2 d−1 regardless of the timing of the season or timing of grazing. Delaying grazing enhanced C uptake by approximately 3 g C m−2 d−1. Advancing spring phenology reduced C uptake by approximately 1.5 g C m−2 d−1, but only when plots were directly warmed by the OTCs; spring advancement did not have a long-term influence on NEE. Consequently, the two strongest drivers of NEE, cloud cover and grazing, can have opposing effects and thus future growing season NEE will depend on the magnitude of change in timing of grazing and incident sunlight.
","language":"English","publisher":"IOPscience","doi":"10.1088/1748-9326/ab1c91","usgsCitation":"Leffler, J., Beard, K.H., Kelsey, K.C., Choi, R.T., Schmutz, J.A., and Welker, J., 2019, Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland: Environmental Research Letters, v. 14, no. 8, 084030, 11 p., https://doi.org/10.1088/1748-9326/ab1c91.","productDescription":"084030, 11 p.","ipdsId":"IP-103167","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":467674,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab1c91","text":"Publisher Index Page"},{"id":363281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -140.625,\n 69.83962194067463\n ],\n [\n -157.58789062499997,\n 72.28906720017675\n ],\n [\n -166.2890625,\n 69.80930869552193\n ],\n [\n -168.310546875,\n 66.23145747862573\n ],\n [\n -167.607421875,\n 61.270232790000634\n ],\n [\n -158.90625,\n 55.1286490684888\n ],\n [\n -151.171875,\n 56.218923189166624\n ],\n [\n -147.91992187499997,\n 59.5343180010956\n ],\n [\n -143.525390625,\n 59.44507509904714\n ],\n [\n -134.6484375,\n 53.12040528310657\n ],\n [\n -129.814453125,\n 50.84757295365389\n ],\n [\n -129.28710937499997,\n 53.38332836757156\n ],\n [\n -128.32031249999997,\n 55.62799595426723\n ],\n [\n -134.736328125,\n 59.93300042374631\n ],\n [\n -139.482421875,\n 60.673178565817715\n ],\n [\n -140.625,\n 69.83962194067463\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Leffler, Josh","contributorId":215123,"corporation":false,"usgs":false,"family":"Leffler","given":"Josh","email":"","affiliations":[{"id":39181,"text":"South Dakota State","active":true,"usgs":false}],"preferred":false,"id":761682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, Karen H.","contributorId":205934,"corporation":false,"usgs":false,"family":"Beard","given":"Karen","email":"","middleInitial":"H.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":761683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelsey, Katharine C.","contributorId":195397,"corporation":false,"usgs":false,"family":"Kelsey","given":"Katharine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":761684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choi, Ryan T.","contributorId":205936,"corporation":false,"usgs":false,"family":"Choi","given":"Ryan","email":"","middleInitial":"T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":761686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":761681,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Welker, Jeffrey","contributorId":214926,"corporation":false,"usgs":false,"family":"Welker","given":"Jeffrey","affiliations":[{"id":37194,"text":"University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":761685,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236837,"text":"70236837 - 2019 - S2HM of buildings in USA","interactions":[],"lastModifiedDate":"2022-10-06T15:53:46.029855","indexId":"70236837","displayToPublicDate":"2019-04-25T08:10:19","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"displayTitle":"S2HM of buildings in USA","title":"S2HM of buildings in USA","docAbstract":"The evolution of seismic structural-health monitoring (S2HM) of buildings in the USA is described in this chapter, emphasizing real-time monitoring. 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Using real-time data-driven computation of drift ratios as the parametric indicator of structural deformation and damage to a structure could be of great value to minimize potential judgmental errors in such assessments. Recorded sensor data are an indication of performance, and performance-based design standards stipulate that the amplitude of relative displacement of a building’s roof (with respect to its base) indicates performance. Establishing sound criteria for performance is the most important issue for S2HM process, and since 2000 (in the USA), using real-time computed drift ratios and acceptable threshold criteria form the basis for almost all applications in S2HM.
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0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":854083,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852330,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70203185,"text":"70203185 - 2019 - Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”","interactions":[],"lastModifiedDate":"2019-04-25T06:29:42","indexId":"70203185","displayToPublicDate":"2019-04-25T06:23:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”","docAbstract":"Over the last decade, studies at the Shale Hills Critical Zone Observatory (Shale Hills) have greatly expanded knowledge of weathering in previously understudied, shale-mantled terrains, as well as Earth's Critical Zone as a whole. Among the many discoveries made was the importance of redistribution and losses of micron-sized particles during development of shale-derived soils. A geochemical fingerprint of this process for Al and Fe was illustrated quantitatively by Jin et al. (2010). Subsequent papers, too numerous to list in a Comment, built upon this new recognition by evaluating the spatial and temporal aspects element mobilization. Recently, Kim et al. (2018) examined the composition of suspended, generally micron-sized particles in the Shale Hills stream, along with the dissolved load, across seasons and ranges of discharge.
One prominent conclusion from Kim et al. (2018) is that Zr is essentially immobile at Shale Hills. Such a broad conclusion is in direct contradiction with one from Bern and Yesavage (2018) that Zr has been mobilized from soils at Shale Hills, and the losses relative to soil parent material are significant (median 41%). The point is important, because assuming Zr immobility is necessary to index gains and losses of other elements using the open-chemical-system transport function (τ). Both papers draw upon patterns and calculations using elemental concentration data from Shale Hills and attempt to construct conceptual frameworks to explain the results. Here, the argument is made that the understanding of substantial Zr mobility from soils at Shale Hills described by Bern and Yesavage (2018) is more accurate. Additionally, issues with adaptations of the standard τ equations used in Kim et al. (2018) and some previous papers are also addressed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.02.014","usgsCitation":"Bern, C.R., and Yesavage, T., 2019, Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”: Earth and Planetary Science Letters, v. 514, p. 166-168, https://doi.org/10.1016/j.epsl.2019.02.014.","productDescription":"3 p.","startPage":"166","endPage":"168","ipdsId":"IP-102182","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":363221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Shale Hills Critical Zone Observatory ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.17596435546875,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.4323142901375\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"514","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bern, Carleton R. 0000-0002-8980-1781 cbern@usgs.gov","orcid":"https://orcid.org/0000-0002-8980-1781","contributorId":201152,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton","email":"cbern@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yesavage, Tiffany 0000-0001-9433-763X","orcid":"https://orcid.org/0000-0001-9433-763X","contributorId":215057,"corporation":false,"usgs":false,"family":"Yesavage","given":"Tiffany","email":"","affiliations":[{"id":39167,"text":"USGS Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":false}],"preferred":false,"id":761538,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203164,"text":"70203164 - 2019 - Calcrete uranium deposits in the Southern High Plains, USA","interactions":[],"lastModifiedDate":"2019-04-25T05:57:51","indexId":"70203164","displayToPublicDate":"2019-04-25T05:53:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Calcrete uranium deposits in the Southern High Plains, USA","docAbstract":"The Southern High Plains (SHP) is a new and emerging U.S. uranium province. Here, uranyl vanadates form deposits in Pliocene to Pleistocene sandstone, dolomite, and limestone. Fifteen calcrete uranium occurrences are identified; two of these, the Buzzard Draw and Sulfur Springs Draw deposits, have combined in-place resources estimated at about 4 million pounds of U3O8. Ore minerals carnotite and finchite are hosted in dolomite at the Sulfur Springs Draw deposit, with accessory fluorite, celestine, smectite/illite, autunite, and strontium carbonate. Host carbonate at the Sulfur Springs Draw deposit is ∼190 ka and mineralization mobilized as recently as 3.8 ka. Ash collected near the deposit is 631 ka and erupted from the Yellowstone caldera complex. The Triassic Dockum Group that contains sandstone-hosted uranium deposits throughout the region and underlies the SHP is a potential source for uranium and vanadium. Regional uplift and dissection reintroduced oxygenated groundwater into the Dockum Group, mobilizing uranium. Additional uranium may have been contributed to groundwater by weathering of volcanic ash in Pliocene and Pleistocene host rocks. The locations of the uranium occurrences are mostly in modern drainage systems in the southeast portion of the SHP. Modelling of modern groundwater in the SHP carried out in a parallel study shows that a single fluid could form carnotite through evaporation, and that fluids of the requisite composition are more prevalent in the southern portion of the SHP. The southeastern portion of the SHP hosts more uranium occurrences due to a variety of factors including (1) upward transport of groundwater and connectivity between source and host rock, (2) higher uranium and vanadium content of groundwater, (3) higher rates of groundwater recharge in this region to drive the mineralizing system, and (4) shallower groundwater facilitating surface evaporation. Ongoing erosion of host rocks challenges preservation of deposits and may limit their size.
Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias. This work was aimed to (1) synthesize and compare WRTDS estimation bias for constituent concentrations and fluxes for rivers and streams in the Chesapeake Bay watershed (including headwater sites) and (2) identify controlling factors from five broad categories (watershed size, sampling practice, concentration and discharge conditions, land use, and geology). Five major constituents were considered, namely, suspended sediment (SS), total phosphorus (TP), total nitrogen (TN), orthophosphate (PO4), and nitrate-plus-nitrite (NOx). For both concentration and flux, estimation bias follows the general order of SS > TP > PO4 > TN ≈ NOx. Median TN and NOx bias statistics were near zero, with an equal distribution of small positive and negative bias. TP, PO4, and SS each showed a median positive bias across sites of <18% for flux and <7% for concentration. Particulate constituents, especially SS, tend to have larger bias at sites with smaller sampling frequencies, shorter sampling record lengths, and smaller watershed sizes. Results of multivariate models showed that both flux and concentration biases are most affected by concentration and discharge variabilities and the length of concentration record. In comparison, flux bias of particulate constituents is more affected by flow variability, whereas flux bias of dissolved constituents is more affected by concentration variability. Moreover, analysis using classification and regression trees provided additional information on how the factors affected flux bias: when all site-constituent combinations are considered, large flux biases are more likely associated with sites that have large concentration and discharge variabilities, small lengths of concentration record, and small sampling frequencies. These results may be useful for identifying sites with large biases, modifying monitoring practice at existing sites to reduce those biases, and choosing new monitoring locations in the Chesapeake watershed and beyond.
The Little Colorado River in Arizona, U.S.A. has undergone substantial geomorphic change since the early 1900s. We analyzed hydrologic and geomorphic data at different spatial and temporal scales to determine the type, magnitude, and rate of geomorphic change that has occurred since the early 20th century. Since the 1920s, there have been 4 alternating periods of high and low total-annual flow. Peak-flow magnitude, however, has progressively declined. In some reaches, the channel has narrowed between 72 and 88% since the 1930s. Increases in sinuosity in wide alluvial valleys have resulted in reductions in channel slope by ~21 to 32%; channel bed aggradation up to 1.4 m has also occurred in some reaches. Newly developed floodplains have been colonized by dense stands of vegetation that appear to have stabilized these surfaces. Large, long duration floods may cause some channel widening, and meander migration, however, these floods are infrequent, and narrowing resumes shortly thereafter. Channel narrowing, increases in sinuosity, decreases in slope, and increases in vegetative roughness appear to have caused biogeomorphic feedbacks, thereby exacerbating sediment deposition, and disrupting flood conveyance. In recent decades, there has been an increase in the travel time of floods up to ~100% compared to floods of the 1940s and 1950s, and this has likely led to increased flood attenuation, contributing to decreases in peak-flow magnitude. The progressive increase in water development in parts of the basin has also likely played some role in the progressive declines in peak flow over the duration of the study.
","language":"English","publisher":"The Geological Society of America","doi":"10.1130/B35047.1","usgsCitation":"Dean, D.J., and Topping, D.J., 2019, Geomorphic change and biogeomorphic feedbacks in a dryland river: The Little Colorado River, Arizona, USA: GSA Bulletin, Repository Item: 2019158; 23 p., https://doi.org/10.1130/B35047.1.","productDescription":"Repository Item: 2019158; 23 p.","ipdsId":"IP-099021","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437486,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XPWIBM","text":"USGS data release","linkHelpText":"Geomorphic Change Data for the Little Colorado River, Arizona, USA"},{"id":363278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Little Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.412353515625,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 35.54116627999815\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":215067,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761569,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761570,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203195,"text":"70203195 - 2019 - Modeling barrier island habitats using landscape position information","interactions":[],"lastModifiedDate":"2019-08-19T16:53:07","indexId":"70203195","displayToPublicDate":"2019-04-24T16:23:07","publicationYear":"2019","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":"Modeling barrier island habitats using landscape position information","docAbstract":"Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.","language":"English","publisher":"MDPI","doi":"10.3390/rs11080976","usgsCitation":"Enwright, N., Lei Wang, Wang, H., Osland, M., Feher, L., Borchert, S., and Day, R., 2019, Modeling barrier island habitats using landscape position information: Remote Sensing, v. 11, no. 8, Article 976; 24 p., https://doi.org/10.3390/rs11080976.","productDescription":"Article 976; 24 p.","ipdsId":"IP-105601","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460395,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11080976","text":"Publisher Index Page"},{"id":437488,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90MACYS","text":"USGS data release","linkHelpText":"Modeling barrier island habitats using landscape position information for Dauphin Island, Alabama"},{"id":363276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":215077,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lei Wang","contributorId":215078,"corporation":false,"usgs":false,"family":"Lei Wang","affiliations":[{"id":39170,"text":"Department of Geography and Anthropology, Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":761586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Hongqing 0000-0002-2977-7732 wangh@usgs.gov","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":215079,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","email":"wangh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":215080,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feher, Laura 0000-0002-5983-6190","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":215081,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Borchert, Sinéad M. 0000-0002-6665-7115","orcid":"https://orcid.org/0000-0002-6665-7115","contributorId":193278,"corporation":false,"usgs":false,"family":"Borchert","given":"Sinéad M.","affiliations":[],"preferred":false,"id":761590,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":215082,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761591,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202128,"text":"ofr20191010 - 2019 - Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas","interactions":[],"lastModifiedDate":"2019-04-26T15:38:27","indexId":"ofr20191010","displayToPublicDate":"2019-04-24T14:35:00","publicationYear":"2019","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":"2019-1010","displayTitle":"Geochemistry and Mineralogy of Soils Collected in the Lower Rio Grande Valley, Texas","title":"Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas","docAbstract":"Presented in this report are the chemical and mineralogical results of a soil study conducted in the lower Rio Grande valley, Texas. Samples were collected from soils formed on Holocene alluvial flood-plain and distributary channel deposits of the Rio Grande, flood plain and meander-belt deposits of the Pliocene Goliad Formation, and the Pleistocene Lissie and Beaumont Formations. The lower Rio Grande valley is located on the old distributary delta of the Rio Grande. The watersheds on the U.S. side of the delta no longer drain into the Rio Grande but are part of a complex system of irrigation channels and wastewater drains that flow into the lower Laguna Madre. The results of the study have been used to map concealed geologic units and identify potential mosquito breeding habitat.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191010","collaboration":" ","usgsCitation":"Whitney, H.A., Solano, F., and Hubbard, B.E., 2019, Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas: U.S. Geological Survey Open-File Report 2019–1010, 92 p., https://doi.org/10.3133/ofr20191010.","productDescription":"Report: v, 92 p.; 6 Tables","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062701","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":363123,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1010/coverthb.jpg"},{"id":363124,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1010"},{"id":363125,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table01.xlsx","text":"Table 1","size":"70.9 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Geochemical analyses of soil samples collected in 2003–04, by element and method of analysis, lower Rio Grande valley, Texas\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":363126,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table02.xlsx","text":"Table 2","size":"64.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Geochemical analyses of soil samples collected in 2007, by element and method of analysis, lower Rio Grande valley, Texas"},{"id":363127,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table03.xlsx","text":"Table 3","size":"18.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Univariate statistics and percentiles of analytical results for soil samples collected in 2003 and 2004, lower Rio Grande valley, Texas"},{"id":363128,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table04.xlsx","text":"Table 4","size":"19.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Univariate statistics and percentiles of analytical results for soil samples collected in 2007, lower Rio Grande valley, Texas"},{"id":363129,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table05.xlsx","text":"Table 5","size":"31.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Mineralogy of all soil samples collected in 2003, 2004, and 2007, lower Rio Grande valley, Texas"},{"id":363130,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table06.xlsx","text":"Table 6","size":"16.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Summary statistics of mineral content of soils by geologic formation (Page and others, 2005) as determined by x‐ray diffraction"}],"country":"United States","state":"Texas","otherGeospatial":"Rio Grande Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.1845703125,\n 25.686087780724858\n ],\n [\n -97.1136474609375,\n 25.686087780724858\n ],\n [\n -97.1136474609375,\n 26.76277822801415\n ],\n [\n -99.1845703125,\n 26.76277822801415\n ],\n [\n -99.1845703125,\n 25.686087780724858\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Mineral and Energy Resources Center
U.S. Geological Survey
MS 954 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
The Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic responses and streamflow. The U.S. Geological Survey (USGS), in cooperation with the New Mexico Department of Homeland Security and Emergency Management, developed procedures to apply the PRMS model to simulate the effects of fire on hydrologic responses.
A PRMS model was built of the upper Rio Hondo Basin from the headwaters to approximately 19 miles downstream from the USGS streamgage Rio Hondo above Chavez Canyon near Hondo, New Mexico, by using 24 hydrologic response units (HRUs), or hydrologically similar subareas, from the National Hydrologic Model. A quasi-graphical user interface was created to easily query and analyze published PRMS sensitivity-analysis data. Simulation of mean daily streamflow was most sensitive to parameters related to snowmelt or infiltration throughout the upper Rio Hondo Basin. In the basin’s eastern and northern HRUs, flashiness and timing of streamflow were most sensitive to interflow; in many western-basin HRUs (higher elevations), flashiness of streamflow was most sensitive to soil moisture parameters, and timing of streamflow was most sensitive to infiltration and evapotranspiration parameters.
The PRMS model was calibrated for the fire-affected North Fork Eagle Creek subwatershed by comparing modeled to observed daily streamflow for the nonfrozen (May through October) period for a prefire and postfire time period. The prefire model was calibrated for the period 2007–12 before the 2012 fire, and the postfire model was calibrated for a 2-year (2014–15) period after the fire. Model parameterization combined manual adjustment of 8 parameters on the basis of prior knowledge and automated adjustment of the most sensitive parameters by using the Let Us Calibrate interface. A gridded, daily precipitation dataset that captured the spatial heterogeneity across the study watershed was used as the precipitation input for calibration. Model performance was assessed as satisfactory by using standard statistical measures for prefire and postfire periods.
The calibrated model was run by using data from a single precipitation gage to better represent the effect of localized, extreme storms on postfire hydrologic response. The calibrated models for prefire and postfire conditions simulated streamflows with greater consistency than the uncalibrated model for the corresponding (prefire or postfire) period of hydrographic record. The effect of fire on streamflow was found to be primarily a shift from streamflow dominated by base flow prior to fire to streamflow dominated by surface runoff after fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195022","collaboration":"Prepared in cooperation with the New Mexico Department of Homeland Security and Emergency Management","usgsCitation":"Douglas-Mankin, K.R., and Moeser, C.D., 2019, Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5022, 25 p., https://doi.org/10.3133/sir20195022.","productDescription":"Report: vi, 25 p.; Data Release","numberOfPages":"36","ipdsId":"IP-094970","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":363146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KD1X7Q","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model input and output for prefire and postfire hydrologic simulations in the Upper Rio Hondo Basin, New Mexico using the Precipitation-Runoff Modeling System (PRMS)"},{"id":363157,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5022/coverthb2.jpg"},{"id":363145,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5022/sir20195022.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5022"}],"country":"United States","state":"New Mexico","county":"Lincoln County, Otero County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.83610534667969,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.33741240611175\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113
A lagoon-wide, point-sampling survey of eelgrass (Zostera marina) abundance was conducted in Izembek Lagoon, Alaska, August 7–16, 2018, the ninth year of annual surveys (2007–11, 2015–18). Mean predicted aboveground biomass of eelgrass across 116 sampled points was 238 grams per square meter (g m-2) (95 percent confidence interval: 203–278 g m-2) in 2018, an increase of 240 percent from the previous year’s low estimate of 97 g m-2 (95 percent confidence interval: 78–120 g m-2). The increase marked the third year since 2015 where eelgrass biomass was above the long-term mean (158 g m-2). Eelgrass biomass was stable over the 9 years of this survey. A separate (transect) survey for eelgrass abundance at Grant Point-Old Boat Launch showed annual trends in eelgrass biomass similar to the lagoon-wide survey, but over a slightly longer time (2007–18). The estimates of above-average eelgrass biomass in Izembek Lagoon were likely influenced by relatively warm air temperatures and little or no ice in winter (air temperatures 2.7 degrees Celsius greater than the 12-year mean) and average (cool) air temperatures during the growing season (April–August) in 2018.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191042","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., and Amundson, C.L., 2019, Monitoring annual trends in abundance of eelgrass (Zostera marina) at Izembek National Wildlife Refuge, Alaska, 2018: U.S. Geological Survey Open-File Report 2019-1042, 8 p., https://doi.org/10.3133/ofr20191042.","productDescription":"iv, 8 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-105984","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437489,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13EG9KS","text":"USGS data release","linkHelpText":"Eelgrass Biomass Model"},{"id":363193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1042/ofr20191042.pdf","text":"Report","size":"532 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1042"},{"id":363192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1042/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":" Izembek National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.42987060546875,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.02802211299252\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
This report provides a synthesis of geologic mapping and geochronologic research along the South Platte River between the town of Masters and the city of Fort Morgan, northeastern Colorado. This work was undertaken to better understand landscape development along this part of the river corridor. The focus is on times of rapid change within the fluvial system that had a marked effect on the landscape. The study area is susceptible to drought, which destabilizes vegetation and makes the landscape vulnerable to eolian activity. This is reflected in a landscape that is largely covered by eolian sand and lesser amounts of loess. Past glaciation of the river’s headwaters had a major influence on river discharge and sediment supply, as have major flood events particularly on unglaciated tributaries heading on the piedmont.
In the mapping area, fluvial deposits of the South Platte River system span the Pliocene and early Pleistocene(?) deposits of Nussbaum Alluvium to present-day deposits of the active channel and floodplain. Results of the study indicate that along this stretch of the South Platte River, the early Pleistocene and first half of the middle Pleistocene were times of net incision, periodically interrupted by episodes of aggradation that resulted in deposition of alluvium that has been correlated to Rocky Flats Alluvium, Verdos Alluvium, and Slocum Alluvium. Net incision between depositional events formed a series of poorly preserved terrace deposits along the valley sides that are now largely covered by eolian deposits. Sometime after about 380 thousand years, the river cut a deep paleovalley into Upper Cretaceous Pierre Shale that was then filled with a thick sequence of inferred Louviers Alluvium (coeval with Bull Lake glaciation). Net aggradation continued during the late Pleistocene, resulting in burial of the Louviers paleovalley with a thick sequence of mainstream and sidestream Broadway Alluvium (coeval with Pinedale glaciation). Subsequent incision during the late Pleistocene–Holocene transition formed the Kersey (Broadway) terrace, whose riser forms a prominent bluff on the south side of the river valley. This episode of incision spanned a very short period and was followed by renewed aggradation that deposited the next-lower terrace alluvium (Kuner terrace alluvium). The Kuner terrace level was probably abandoned sometime around the beginning of the middle Holocene. Low terraces on the valley floor indicate that the river has been primarily cutting and backfilling laterally rather than incising during the late Holocene.
Synthesis of geologic mapping and chronologic data generated in this study indicate that the South Platte River in northeastern Colorado likely was highly sensitive to rapidly changing environmental conditions or crossed threshold conditions that triggered rapid geomorphic response during major climate changes associated with the late Pleistocene–Holocene transition. Historical times have been another period marked by rapid incision, reflected by gully incision and headward erosion in tributary valleys draining the north side of the South Platte River. This historical erosion could be related at least in part to extensive construction of irrigation ditches and reservoirs in the late 1800s–early 1900s, which altered drainage paths and groundwater flow and could have amplified natural factors such as climate change or intrinsic geomorphic instabilities within the system.
Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS 980
Denver, CO 80225
Mammoth Mountain, California, has exhibited unrest over the past ~30 years, characterized by seismicity over a broad range of depths, elevated 3He/4He ratios in fumarolic gas, and large-scale diffuse CO2 emissions. This activity has been attributed to magmatic intrusion, but minimal ground deformation and the presence of a shallow crustal gas reservoir beneath Mammoth Mountain pose a challenge for estimating magma supply rate. Here, we use the record of fumarolic 3He/4He ratios and CO2 emissions to estimate that of the ~5.2 Mt of CO2 released from Mammoth Mountain between 1989 and 2016, 1.6 Mt was associated with active intrusion and degassing of ~0.05–0.07 km3 of basaltic magma. Intrusion at an average rate of ~0.002–0.003 km3/year into a postulated zone of partial melt at ~15-km depth could occur without detection by local Global Navigation Satellite System stations.
","language":"English","publisher":"Wiley","doi":"10.1029/2019GL082487","usgsCitation":"Lewicki, J.L., Evans, W.C., Montgomery-Brown, E.K., Mangan, M.T., King, J., and Hunt, A., 2019, Rate of magma supply beneath Mammoth Mountain, California based on helium isotopes and CO2 emissions: Geophysical Research Letters, v. 46, no. 9, p. 4636-4644, https://doi.org/10.1029/2019GL082487.","productDescription":"9 p.","startPage":"4636","endPage":"4644","ipdsId":"IP-105520","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":437490,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FGZ0ED","text":"USGS data release","linkHelpText":"Fumarole gas geochemistry and tree-ring radiocarbon data at Mammoth Mountain, California (1989-2016)"},{"id":387401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mammoth Mountain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.07772064208983,\n 37.61232767789535\n ],\n [\n -118.97832870483398,\n 37.61232767789535\n ],\n [\n -118.97832870483398,\n 37.66289614387081\n ],\n [\n -119.07772064208983,\n 37.66289614387081\n ],\n [\n -119.07772064208983,\n 37.61232767789535\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"9","noUsgsAuthors":false,"publicationDate":"2019-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Lewicki, Jennifer L. 0000-0003-1994-9104 jlewicki@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-9104","contributorId":5071,"corporation":false,"usgs":true,"family":"Lewicki","given":"Jennifer","email":"jlewicki@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":819778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, William C. 0000-0001-5942-3102 wcevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5942-3102","contributorId":2353,"corporation":false,"usgs":true,"family":"Evans","given":"William","email":"wcevans@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":819779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montgomery-Brown, Emily K. 0000-0001-6787-2055","orcid":"https://orcid.org/0000-0001-6787-2055","contributorId":214074,"corporation":false,"usgs":true,"family":"Montgomery-Brown","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":819780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangan, Margaret T. 0000-0002-5273-8053 mmangan@usgs.gov","orcid":"https://orcid.org/0000-0002-5273-8053","contributorId":3343,"corporation":false,"usgs":true,"family":"Mangan","given":"Margaret","email":"mmangan@usgs.gov","middleInitial":"T.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":819781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, John","contributorId":243582,"corporation":false,"usgs":false,"family":"King","given":"John","affiliations":[{"id":48739,"text":"Lon Pine Research","active":true,"usgs":false}],"preferred":false,"id":819782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hunt, Andrew G. 0000-0002-3810-8610","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":206197,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":819783,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215491,"text":"70215491 - 2019 - Fault slip associated with the 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake and aftershock sequence","interactions":[],"lastModifiedDate":"2021-01-22T19:21:30.773896","indexId":"70215491","displayToPublicDate":"2019-04-23T13:21:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Fault slip associated with the 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake and aftershock sequence","docAbstract":"The 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake is one of the largest earthquakes in southern Idaho since the 1983 M 6.9 Borah Peak earthquake. It was followed by a vigorous aftershock sequence for nearly two weeks that included five events above M 4.5. The coseismic and early postseismic deformation was measured with both Interferometric Synthetic Aperture Radar and Global Positioning System (GPS), yielding up to 3 cm subsidence southwest of the mainshock epicenter and horizontal motions of